Tootfinder

Modeling reputation-based behavioral biases in school choice
Jon Kleinberg, Sigal Oren, Emily Ryu, \'Eva Tardos
https://arxiv.org/abs/2403.04616 https:…

Modeling reputation-based behavioral biases in school choice
A fundamental component in the theoretical school choice literature is the problem a student faces in deciding which schools to apply to. Recent models have considered a set of schools of different selectiveness and a student who is unsure of their strength and can apply to at most $k$ schools. Such models assume that the student cares solely about maximizing the quality of the school that they attend, but experience suggests that students' decisions are also influenced by a set of behavioral b…

Behavioral Manifolds: Representing the Landscape of Grasp Affordances in the Relative Pose Space
Michael Zechmair, Yannick Morel
https://arxiv.org/abs/2405.04188

Behavioral Manifolds: Representing the Landscape of Grasp Affordances in the Relative Pose Space
The use of machine learning to investigate grasp affordances has received extensive attention over the past several decades. The existing literature provides a robust basis to build upon, though a number of aspects may be improved. Results commonly work in terms of grasp configuration, with little consideration for the manner in which the grasp may be (re-)produced from a reachability and trajectory planning perspective. In addition, the majority of existing learning approaches focus of produci…

Behavioral analysis in immersive learning environments: A systematic literature review and research agenda
Yu Liu, Kang Yue, Yue Liu
https://arxiv.org/abs/2405.03442

Behavioral analysis in immersive learning environments: A systematic literature review and research agenda
The rapid growth of immersive technologies in educational areas has increased research interest in analyzing the specific behavioral patterns of learners in immersive learning environments. Considering the fact that research on the technical affordances of immersive technologies and the pedagogical affordances of behavioral analysis remains fragmented, this study first contributes by developing a conceptual framework that amalgamates learning requirements, specification, evaluation, and iterati…

Subregions of the fusiform gyrus are differentially involved in the attentional mechanism supporting visual mental imagery in depression https://link.springer.com/article/10.1007/s11682-024-00885-8 "Impaired visual mental imagery is an important symptom of d…

Subregions of the fusiform gyrus are differentially involved in the attentional mechanism supporting visual mental imagery in depression - Brain Imaging and Behavior
Background Impaired visual mental imagery is an important symptom of depression and has gradually become an intervention target for cognitive behavioral therapy. Methods Our study involved a total of 25 healthy controls (HC) and 23 individuals with moderate depressive symptoms (MD). This study explored the attentional mechanism supporting visual mental imagery impairments in depression using the Vividness of Visual Imagery Questionnaire (VVIQ), attentional network test (ANT), and resting-state …

"One of the things that makes Liskov such an influential author is that she believes that academic software engineering needs to be applicable to practice to be of any value."
#InternationalWomensDay
https://

Barbara Liskov
Many developers will have heard of Barbara Liskov, through her appearance in Robert C. Martin's SOLID list of design principles. The abstract of her 1994 paper with Jeanette Wing, A Behavioral Notion of Subtyping, makes the principle sound easy in, well, in principle.

“Why Do I Keep Getting COVID-19 But Those Around Me Don’t? - TIME “ https://apple.news/AbVT17vntRg2wVQgJH-_Eqg
It makes sense that imperfect behavioral prevention becomes less relevant as the virus has become more infectious, and immunity factors (genetic and dynamic) become…

Why Do I Keep Getting COVID-19 But Those Around Me Don’t? — TIME
A new study explores how much control you really have over getting infected, and how much you don't.

Behavioral analysis in immersive learning environments: A systematic literature review and research agenda
Yu Liu, Kang Yue, Yue Liu
https://arxiv.org/abs/2405.03442

Behavioral analysis in immersive learning environments: A systematic literature review and research agenda
The rapid growth of immersive technologies in educational areas has increased research interest in analyzing the specific behavioral patterns of learners in immersive learning environments. Considering the fact that research on the technical affordances of immersive technologies and the pedagogical affordances of behavioral analysis remains fragmented, this study first contributes by developing a conceptual framework that amalgamates learning requirements, specification, evaluation, and iterati…

This https://arxiv.org/abs/2405.01015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Network reconstruction via the minimum description length principle
A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a statistically justifiable number of edges. The status quo in this context is based on $L_{1}$ regularization combined with cross-validation. As we demonstrate, besides its high computational cost, this commonplace approach unnecessarily ties the promo…

This https://arxiv.org/abs/2310.15937 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
Networks of dynamical systems play an important role in various domains and have motivated many studies on the control and analysis of linear dynamical networks. For linear network models considered in these studies, it is typically pre-determined what signal channels are inputs and what are outputs. These models do not capture the practical need to incorporate different experimental situations, where different selections of input and output channels are applied to the same network. Moreover, a…

This https://arxiv.org/abs/2402.01705 has been replaced.
link: https://scholar.google.com/scholar?q=a

Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions of representational harms to discern what is included and what is not. This analysis motivates our expansion beyond behavioral definitions to encompass harms to cognitive and affective states. The paper outlines high-level requirements for measurement: identifying the necessary expertise to implement this approach an…

This https://arxiv.org/abs/2405.03562 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ID-centric Pre-training for Recommendation
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of pre-trained language model (PLM), some pioneer works adopt PLM for pre-trained recommendation, where modality information (e.g., text) is considered universal across domains via PLM. Unfortunately, the behavioral information in ID embeddings is still…

This https://arxiv.org/abs/2404.14665 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Illuminating the Unseen: A Framework for Designing and Mitigating Context-induced Harms in Behavioral Sensing
With the advanced ability to capture longitudinal sensed data and model human behavior, behavioral sensing technologies are progressing toward numerous wellbeing applications. However, the widespread use of top-down design approaches, often based on assumptions made by technology builders about user goals, needs, and preferences, can result in a lack of context sensitivity. Such oversights may lead to technologies that do not fully support the diverse needs of users and may even introduce poten…

Keeping Behavioral Programs Alive: Specifying and Executing Liveness Requirements
Tom Yaacov, Achiya Elyasaf, Gera Weiss
https://arxiv.org/abs/2404.01858 h…

Keeping Behavioral Programs Alive: Specifying and Executing Liveness Requirements
One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system requirement, including negative specifications such as "don't do X after Y." The existing BP protocol, however, allows only the enforcement of…

New on the arxiv: “Network reconstruction via the minimum description length principle"
#network

in den 60ern gab es experimente mit mäusen bei denen den mäusen unbegrenzt viel nahrung und nistmaterial bereitgestellt wurde um zu schauen wie sich das auf die populationsdynamik auswirkt
frage mich ob sowas immer noch gemacht wird würde mich gerne bewerben
https://en.wikipedia.org/wiki/Behavior

This https://arxiv.org/abs/2208.09061 has been replaced.
link: https://scholar.google.com/scholar?q=a

Mouse Dynamics Behavioral Biometrics: A Survey
Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dy…

A Conceptual Design of In-Game Real and Virtual Currency Tracker
Dennis Barzanoff, Amna Asif
https://arxiv.org/abs/2404.03951 https://

A Conceptual Design of In-Game Real and Virtual Currency Tracker
The gaming industry is earning huge revenues from incorporating virtual currencies into the game design experience. Even if it is a useful approach for the game industry to boost up their earnings, the unidirectional and bidirectional in-game virtual currencies can invoke inadequate gaming behaviors and additions among players. The market lacks gaming and customer protection regulations to avoid the financial, behavioral, and psychological exploitation of users. Therefore, it is needed to devel…

Towards Improving Learning from Demonstration Algorithms via MCMC Methods
Hanwen Qi, Edward Sun, Harry Zhang
https://arxiv.org/abs/2405.02243 https://

Towards Improving Learning from Demonstration Algorithms via MCMC Methods
Behavioral cloning, or more broadly, learning from demonstrations (LfD) is a priomising direction for robot policy learning in complex scenarios. Albeit being straightforward to implement and data-efficient, behavioral cloning has its own drawbacks, limiting its efficacy in real robot setups. In this work, we take one step towards improving learning from demonstration algorithms by leveraging implicit energy-based policy models. Results suggest that in selected complex robot policy learning sce…

This https://arxiv.org/abs/2312.02445 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

LLaRA: Large Language-Recommendation Assistant
Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has sparked interest in leveraging them for sequential recommendation, viewing it as language modeling. Previous studies represent items within LLMs' input prompts as either ID indices or textual metadata. However, these approaches often fail to either encapsulate comprehensive world knowledge or exhibit sufficient behavioral …

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
Siqi Feng, Rui Yao, Stephane Hess, Ricardo A. Daziano, Timothy Brathwaite, Joan Walker, Shenhao Wang
https://arxiv.org/abs/2404.14701

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as novel metrics to evaluate the monotonicity of individual demand functions (a.k.a. law of demand), and further designs a constrained optimization framework with six gradient regularizers to enhance DNNs' behavioral regularity. The proposed framework is …

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

This https://arxiv.org/abs/2405.01477 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

"Sometimes You Just Gotta Risk it for the Biscuit": A Portrait of Student Risk-Taking
Understanding how individuals, including students, make decisions involving risk is a fundamental aspect of behavioral research. Despite the ubiquity of risk in various aspects of life, limited empirical work has explored student risk-taking behavior in computing education. This study aims to partially replicate prior research on risk-taking behavior in software engineers while focusing on students, shedding light on the factors that affect their risk-taking choices. In our work, students were …

Pelago, formerly Quit Genius, which provides virtual therapy sessions for treating addictions, raised a $58M Series C, bringing its total funding to $151M (Kate Park/TechCrunch)
https://techcrunch.com/2024/03/28/yc-b…

YC-backed Pelago, a virtual clinic for addiction treatment, raises $58M Series C | TechCrunch
Pelago Health,  Y Combinator-backed telehealth company that uses cognitive behavioral therapy (CBT) to help treat people with tobacco, alcohol and opioid

Reward Bound for Behavioral Guarantee of Model-based Planning Agents
Zhiyu An, Xianzhong Ding, Wan Du
https://arxiv.org/abs/2402.13419 https://

Reward Bound for Behavioral Guarantee of Model-based Planning Agents
Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industry. Obtaining behavioral guarantees for these agents remains an important problem. In this work, we focus on guaranteeing a model-based planning agent reaches a goal state within a specific future time step. We show that there exists a lower bound for the reward at the goal state, such that if the said reward is below …

Kamala Harris Touts Secret Service Program Encouraging High School Spying #surveillance

Towards behavioral consistency in heterogeneous modeling scenarios
Tim Kr\"auter
https://arxiv.org/abs/2404.12941 https://arxiv.…

Towards behavioral consistency in heterogeneous modeling scenarios
Behavioral models play an essential role in Model-driven engineering (MDE). Keeping inter-related behavioral models consistent is critical to use them successfully in MDE. However, consistency checking for behavioral models, especially in a heterogeneous scenario, is limited. We propose a methodology to integrate heterogeneous behavioral models to achieve consistency checking in broader scenarios. It is based on aligning the respective behavioral metamodels by defining possible inter-model rela…

Robust inference for linear regression models with possibly skewed error distribution
Amarnath Nandy, Ayanendranath Basu, Abhik Ghosh
https://arxiv.org/abs/2404.03404

Robust inference for linear regression models with possibly skewed error distribution
Traditional methods for linear regression generally assume that the underlying error distribution, equivalently the distribution of the responses, is normal. Yet, sometimes real life response data may exhibit a skewed pattern, and assuming normality would not give reliable results in such cases. This is often observed in cases of some biomedical, behavioral, socio-economic and other variables. In this paper, we propose to use the class of skew normal (SN) distributions, which also includes the …

Analyzing Musical Characteristics of National Anthems in Relation to Global Indices
S M Rakib Hasan, Aakar Dhakal, Ms. Ayesha Siddiqua, Mohammad Mominur Rahman, Md Maidul Islam, Mohammed Arfat Raihan Chowdhury, S M Masfequier Rahman Swapno, SM Nuruzzaman Nobel
https://arxiv.org/abs/2404.03606

Analyzing Musical Characteristics of National Anthems in Relation to Global Indices
Music plays a huge part in shaping peoples' psychology and behavioral patterns. This paper investigates the connection between national anthems and different global indices with computational music analysis and statistical correlation analysis. We analyze national anthem musical data to determine whether certain musical characteristics are associated with peace, happiness, suicide rate, crime rate, etc. To achieve this, we collect national anthems from 169 countries and use computational music …

This https://arxiv.org/abs/2104.01392 has been replaced.
link: https://scholar.google.com/scholar?q=a

Place Bisimilarity is Decidable, Indeed!
Place bisimilarity $\sim_p$ is a behavioral equivalence for finite Petri nets, originally proposed in \cite{ABS91}, that, differently from all the other behavioral relations proposed so far, is not defined over the markings of a finite net, rather over its places, which are finitely many. Place bisimilarity $\sim_p$ was claimed decidable in \cite{ABS91}, but its decidability was not really proved. We show that it is possible to decide $\sim_p$ with a simple algorithm, which essentially scans al…

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets
Jessica Dafflon, Dustin Moraczewski, Eric Earl, Dylan M. Nielson, Gabriel Loewinger, Patrick McClure, Adam G. Thomas, Francisco Pereira
https://arxiv.org/abs/2405.00255

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets
One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their correspon…

Learning or Self-aligning? Rethinking Instruction Fine-tuning
Mengjie Ren, Boxi Cao, Hongyu Lin, Liu Cao, Xianpei Han, Ke Zeng, Guanglu Wan, Xunliang Cai, Le Sun
https://arxiv.org/abs/2402.18243

Learning or Self-aligning? Rethinking Instruction Fine-tuning
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingl…

ID-centric Pre-training for Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Zhanhui Kang, Yongjun Xu
https://arxiv.org/abs/2405.03562

ID-centric Pre-training for Recommendation
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of pre-trained language model (PLM), some pioneer works adopt PLM for pre-trained recommendation, where modality information (e.g., text) is considered universal across domains via PLM. Unfortunately, the behavioral information in ID embeddings is still…

Good stuff by @… on the limits of benchmark testing in AI, and the dangers of overrelying on them and overestimating their value.
https://sigmoid.social/@…

The evolution of pluralistic ignorance
Alessandra F. L\"utz, Lucas Wardil
https://arxiv.org/abs/2403.00195 https://arxiv.org/pdf…

The evolution of pluralistic ignorance
Pluralistic ignorance is a social-psychological phenomenon characterized by individuals privately maintaining beliefs or opinions that diverge from what they perceive to be the prevailing norm within a group or society. Typical models are based on opinion dynamics involving both private and public states, where agents' binary states undergo changes influenced by social pressure. However, these models overlook a crucial aspect of pluralistic ignorance: if the absence of behavioral expression mat…

AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering
Wenxiang Zhao, Juntao Wu, Zhaoyi Meng
https://arxiv.org/abs/2404.18816

AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering
Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions. However, numerous feature engineering based methods suffer from a limitation in mining behavioral semantic information, thus impeding the accuracy and efficiency of Android malware detection. Besides, the majority of existing feature engineering based methods are weakly interpre…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Can a Funny Chatbot Make a Difference? Infusing Humor into Conversational Agent for Behavioral Intervention
Xin Sun, Isabelle Teljeur, Zhuying Li, Jos A. Bosch
https://arxiv.org/abs/2403.00365

Can a Funny Chatbot Make a Difference? Infusing Humor into Conversational Agent for Behavioral Intervention
Regular physical activity is crucial for reducing the risk of non-communicable disease (NCD). With NCDs on the rise globally, there is an urgent need for effective health interventions, with chatbots emerging as a viable and cost-effective option because of limited healthcare accessibility. Although health professionals often utilize behavior change techniques (BCTs) to boost physical activity levels and enhance client engagement and motivation by affiliative humor, the efficacy of humor in cha…

Unveiling Behavioral Transparency of Protocols Communicated by IoT Networked Assets (Full Version)
Savindu Wannigama (Department of Computer Engineering, University of Peradeniya, Sri Lanka), Arunan Sivanathan (School of EE&T, UNSW Sydney, Australia), Ayyoob Hamza (School of EE&T, UNSW Sydney, Australia), Hassan Habibi Gharakheili (School of EE&T, UNSW Sydney, Australia)

Unveiling Behavioral Transparency of Protocols Communicated by IoT Networked Assets (Full Version)
Behavioral transparency for Internet-of-Things (IoT) networked assets involves two distinct yet interconnected tasks: (a) characterizing device types by discerning the patterns exhibited in their network traffic, and (b) assessing vulnerabilities they introduce to the network. While identifying communication protocols, particularly at the application layer, plays a vital role in effective network management, current methods are, at best, ad-hoc. Accurate protocol identification and attribute ex…

Intelligent Message Behavioral Identification System
Yuvaraju Chinnam, Bosubabu Sambana
https://arxiv.org/abs/2404.10795 https://arxi…

Intelligent Message Behavioral Identification System
On social media platforms, the act of predicting reposting is seen as a challenging issue related to Short Message Services (SMS). This study examines the issue of predicting picture reposting in SMS and forecasts users' behavior in sharing photographs on Twitter. Several research vary. The paper introduces a network called Image Retweet Modeling (IRM) that models heterogeneous image retransmission. It considers the user's previous reposting of the image tweet, the next contact in the SMS, and …

Flock2: A model for orientation-based social flocking
Rama Carl Hoetzlein
https://arxiv.org/abs/2404.17804 https://arxiv.org/pdf/2404…

Flock2: A model for orientation-based social flocking
The aerial flocking of birds, or murmurations, have fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneously. To understand these behaviors a new model is presented for orientation-based social flocking. Previous methods model inter-bird dyna…

CogBench: a large language model walks into a psychology lab
Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz
https://arxiv.org/abs/2402.18225 htt…

CogBench: a large language model walks into a psychology lab
Large language models (LLMs) have significantly advanced the field of artificial intelligence. Yet, evaluating them comprehensively remains challenging. We argue that this is partly due to the predominant focus on performance metrics in most benchmarks. This paper introduces CogBench, a benchmark that includes ten behavioral metrics derived from seven cognitive psychology experiments. This novel approach offers a toolkit for phenotyping LLMs' behavior. We apply CogBench to 35 LLMs, yielding a r…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Composing Codensity Bisimulations
Mayuko Kori, Kazuki Watanabe, Jurriaan Rot, Shin-ya Katsumata
https://arxiv.org/abs/2404.08308 https://

Composing Codensity Bisimulations
Proving compositionality of behavioral equivalence on state-based systems with respect to algebraic operations is a classical and widely studied problem. We study a categorical formulation of this problem, where operations on state-based systems modeled as coalgebras can be elegantly captured through distributive laws between functors. To prove compositionality, it then suffices to show that this distributive law lifts from sets to relations, giving an explanation of how behavioral equivalence …

MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
Haicheng Liao, Zhenning Li, Chengyue Wang, Huanming Shen, Bonan Wang, Dongping Liao, Guofa Li, Chengzhong Xu
https://arxiv.org/abs/2405.01266

MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical trajectory data combined with a novel dynamic geometric graph-based behavior-aware module. At its core, an adaptive structure-aware interactive graph convolutional network captures both positional and behavioral features of road users, preserving spatia…

This https://arxiv.org/abs/2312.03429 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Behavioral Authentication for Security and Safety
The issues of both system security and safety can be dissected integrally from the perspective of behavioral \emph{appropriateness}. That is, a system is secure or safe can be judged by whether the behavior of certain agent(s) is \emph{appropriate} or not. Specifically, a so-called \emph{appropriate behavior} involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behaviora…

This https://arxiv.org/abs/2402.07462 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse?
The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm that uses hormetic analysis to regulate the behavioral patterns of AI. Behavioral hormesis is a phenomenon where low frequencies of a beha…

"Sometimes You Just Gotta Risk it for the Biscuit": A Portrait of Student Risk-Taking
Juho Leinonen, Paul Denny
https://arxiv.org/abs/2405.01477 …

"Sometimes You Just Gotta Risk it for the Biscuit": A Portrait of Student Risk-Taking
Understanding how individuals, including students, make decisions involving risk is a fundamental aspect of behavioral research. Despite the ubiquity of risk in various aspects of life, limited empirical work has explored student risk-taking behavior in computing education. This study aims to partially replicate prior research on risk-taking behavior in software engineers while focusing on students, shedding light on the factors that affect their risk-taking choices. In our work, students were …

Strategic Behavior and AI Training Data
Christian Peukert, Florian Abeillon, J\'er\'emie Haese, Franziska Kaiser, Alexander Staub
https://arxiv.org/abs/2404.18445

Strategic Behavior and AI Training Data
Human-created works represent critical data inputs to artificial intelligence (AI). Strategic behavior can play a major role for AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create new works at all. We examine creators' behavioral change when their works become training data for AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform with about 6 million high-quality photos and ill…

This https://arxiv.org/abs/2306.17260 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation
The use of smart devices (e.g., smartphones, smartwatches) and other wearables for context sensing and delivery of digital interventions to improve health outcomes has grown significantly in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on mobile health (mHealth) intervention effectiveness where each individual is repeatedly randomized to receive treatments over numerous time points. Individual characterist…

Illuminating the Unseen: A Framework for Designing and Mitigating Context-induced Harms in Behavioral Sensing
Han Zhang, Vedant Das Swain, Leijie Wang, Nan Gao, Yilun Sheng, Xuhai Xu, Flora D. Salim, Koustuv Saha, Anind K. Dey, Jennifer Mankoff
https://arxiv.org/abs/2404.14665

Illuminating the Unseen: A Framework for Designing and Mitigating Context-induced Harms in Behavioral Sensing
With the advanced ability to capture longitudinal sensed data and model human behavior, behavioral sensing technologies are progressing toward numerous wellbeing applications. However, the widespread use of top-down design approaches, often based on assumptions made by technology builders about user goals, needs, and preferences, can result in a lack of context sensitivity. Such oversights may lead to technologies that do not fully support the diverse needs of users and may even introduce poten…

Replicating Electoral Success
Kiran Tomlinson, Tanvi Namjoshi, Johan Ugander, Jon Kleinberg
https://arxiv.org/abs/2402.17109 https://…

Replicating Electoral Success
A core tension in the study of plurality elections is the clash between the classic Hotelling-Downs model, which predicts that two office-seeking candidates should position themselves at the median voter's policy, and the empirical observation that real-world democracies often have two major parties with divergent policies. Motivated by this tension and drawing from bounded rationality, we introduce a dynamic model of candidate positioning based on a simple behavioral heuristic: candidates imit…

This https://arxiv.org/abs/2305.18841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis
Autism spectrum disorder is one of the leading neurodevelopmental disorders in our world, present in over 1% of the population and rapidly increasing in prevalence, yet the condition lacks a robust, objective, and efficient diagnostic. Clinical diagnostic criteria rely on subjective behavioral assessments, which are prone to misdiagnosis as they face limitations in terms of their heterogeneity, specificity, and biases. This study proposes a novel convolutional neural network-based classificatio…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

This https://arxiv.org/abs/2301.11050 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Minerva: A File-Based Ransomware Detector
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigation. Behavioral-based ransomware detection approaches have garnered considerable attention recently. These behavioral detectors typically rely on process-based behavioral profiles to identify malicious behaviors. However, with an increasing body of literature highlighting the vul…

A Change of Scenery: Transformative Insights from Retrospective VR Embodied Perspective-Taking of Conflict With a Close Other
Seraphina Yong, Leo Cui, Evan Suma Rosenberg, Svetlana Yarosh
https://arxiv.org/abs/2404.02277

A Change of Scenery: Transformative Insights from Retrospective VR Embodied Perspective-Taking of Conflict With a Close Other
Close relationships are irreplaceable social resources, yet prone to high-risk conflict. Building on findings from the fields of HCI, virtual reality, and behavioral therapy, we evaluate the unexplored potential of retrospective VR-embodied perspective-taking to fundamentally influence conflict resolution in close others. We develop a biographically-accurate Retrospective Embodied Perspective-Taking system (REPT) and conduct a mixed-methods evaluation of its influence on close others' reflectio…

Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks
Mingyu Cai, Karankumar Patel, Soshi Iba, Songpo Li
https://arxiv.org/abs/2403.19770

Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks
In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents an intention estimation technique at hierarchical levels i.e., low-level actions and high-level tasks, by incorporating multi-scale hierarc…

A Prototypical Expert-Driven Approach Towards Capability-Based Monitoring of Automated Driving Systems
Richard Schubert, Cedrik Kaufmann, Marcus Nolte, Markus Maurer
https://arxiv.org/abs/2404.16502

A Prototypical Expert-Driven Approach Towards Capability-Based Monitoring of Automated Driving Systems
Supervising the safe operation of automated vehicles is a key requirement in order to unleash their full potential in future transportation systems. In particular, previous publications have argued that SAE Level 4 vehicles should be aware of their capabilities at runtime to make appropriate behavioral decisions. In this paper, we present a framework that enables the implementation of an online capability monitor. We derive a graphical system model that captures the relationships between the qu…

This https://arxiv.org/abs/2309.10837 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integration
Opioids are an effective analgesic for acute and chronic pain, but also carry a considerable risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly. Estimating OUD risk prior to prescription could improve the efficacy of treatment regimens, monitoring programs, and intervention strategies, but risk estimation is typically based on self-reported data or questionnaires. We develop an experimental design and co…

This https://arxiv.org/abs/2306.11376 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Coevolution of cognition and cooperation in structured populations under reinforcement learning
We study the evolution of behavior under reinforcement learning in a Prisoner's Dilemma where agents interact in a regular network and can learn about whether they play one-shot or repeatedly by incurring a cost of deliberation. With respect to other behavioral rules used in the literature, (i) we confirm the existence of a threshold value of the probability of repeated interaction, switching the emergent behavior from intuitive defector to dual-process cooperator; (ii) we find a different role…

This https://arxiv.org/abs/2309.02142 has been replaced.
link: https://scholar.google.com/scholar?q=a

Who are the users of ChatGPT? Implications for the digital divide from web tracking data
A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral (web tracking) and survey data of N=1068 German citizens to investigate differences in ChatGPT activity (usage, visits and duration) in a web tracked sample encompassing a period t…

This https://arxiv.org/abs/2401.09491 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Memory, Space, and Planning: Multiscale Predictive Representations
Memory is inherently entangled with prediction and planning. Flexible behavior in biological and artificial agents depends on the interplay of learning from the past and predicting the future in ever-changing environments. This chapter reviews computational, behavioral, and neural evidence suggesting these processes rely on learning the relational structure of experiences, known as cognitive maps, and draws two key takeaways. First, that these memory structures are organized as multiscale, comp…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Sound Concurrent Traces for Online Monitoring Technical Report
Chukri Soueidi, Ylies Falcone
https://arxiv.org/abs/2402.18391 https://

Sound Concurrent Traces for Online Monitoring Technical Report
Monitoring concurrent programs typically rely on collecting traces to abstract program executions. However, existing approaches targeting general behavioral properties are either not tailored for online monitoring, are no longer maintained, or implement naive instrumentation that often leads to unsound verdicts. We first define the notion of when a trace is representative of a concurrent execution. We then present a non-blocking vector clock algorithm to collect sound concurrent traces on the f…

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies
Jos\'e Alberto Ben\'itez-Andrades, Alejandro Rodr\'iguez-Gonz\'alez, Carmen Benavides, Leticia S\'anchez-Valde\'on, Isa\'ias Garc\'ia
https://arxiv.org/abs/2402.12390…

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies
Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different ty…

This https://arxiv.org/abs/2311.07117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Olfactory learning alters navigation strategies and behavioral variability in C. elegans
Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in respon…

Biometrics Employing Neural Network
Sajjad Bhuiyan
https://arxiv.org/abs/2404.16840 https://arxiv.org/pdf/2404.16840

Biometrics Employing Neural Network
Biometrics involves using unique human traits, both physical and behavioral, for the digital identification of individuals to provide access to systems, devices, or information. Within the field of computer science, it acts as a method for identifying and verifying individuals and controlling access. While the conventional method for personal authentication involves passwords, the vulnerability arises when passwords are compromised, allowing unauthorized access to sensitive actions. Biometric a…

Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Zeyu Hu, Yuzhi Xiao, Tao Huang, Xuanrong Huo
https://arxiv.org/abs/2404.18214

Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and prediction of users' ordered historical interactive behaviors. However, along with the growth of the user volume and the increasingly rich behavioral information, how to understand and disentangle the user's interactive multi-intention effectively also poses challenges to behavior prediction and sequential recommendation. In l…

ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification
Sehee Lim, Yejin Kim, Chi-Hyun Choi, Jy-yong Sohn, Byung-Hoon Kim
https://arxiv.org/abs/2403.14255

ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions from the interviewee's utterances can be an essential part of psychotherapy, especially for cognitive behavioral therapy. In this paper, we propose ERD, which improves LLM-based cognitive distortion classification performance with the aid of additional modules of (1) extracting the parts related to cognitive distortion,…

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
Farzan Kaviani, Ivan Markovsky, Hamid R. Ossareh
https://arxiv.org/abs/2404.15098

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
We revisit the problem of predicting the output of an LTI system directly using offline input-output data (and without the use of a parametric model) in the behavioral setting. Existing works calculate the output predictions by projecting the recent samples of the input and output signals onto the column span of a Hankel matrix consisting of the offline input-output data. However, if the offline data is corrupted by noise, the output prediction is no longer exact. While some prior works propose…

This https://arxiv.org/abs/2105.15106 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey of Knowledge Tracing: Models, Variants, and Applications
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge Tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In recent years, a substantial number of studies have concentrated on this rapidly growing field, significantly contributing to its advancements. In this s…

TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
Meng Yan, Haibin Huang, Ying Liu, Juan Zhao, Xiyue Gao, Cai Xu, Ziyu Guan, Wei Zhao
https://arxiv.org/abs/2404.17238

TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as reviews, images, etc. This content often contains inevitable noise. Some studies attempt to reduce noise interference by suppressing cross-modal inconsistent information. However, they could potentially constrain the capturing of personalized user p…

Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR
A. Stewart Fotheringham, Chen-Lun Kao, Hanchen Yu, Sarah Bardin, Taylor Oshan, Ziqi Li, Mehak Sachdeva, Wei Luo
https://arxiv.org/abs/2404.16209

Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR
Local spatial models such as Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) serve as instrumental tools to capture intrinsic contextual effects through the estimates of the local intercepts and behavioral contextual effects through estimates of the local slope parameters. GWR and MGWR provide simple implementation yet powerful frameworks that could be extended to various disciplines that handle spatial data. This bibliography aims to serve as a…

This https://arxiv.org/abs/2310.10858 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Designing Shared Information Displays for Agents of Varying Strategic Sophistication
Data-driven predictions are often perceived as inaccurate in hindsight due to behavioral responses. In this study, we explore the role of interface design choices in shaping individuals' decision-making processes in response to predictions presented on a shared information display in a strategic setting. We introduce a novel staged experimental design to investigate the effects of design features, such as visualizations of prediction uncertainty and error, within a repeated congestion game. In …

This https://arxiv.org/abs/2309.09101 has been replaced.
link: https://scholar.google.com/scholar?q=a

Behavioral-based circular formation control for robot swarms
This paper focuses on coordinating a robot swarm orbiting a convex path without collisions among the individuals. The individual robots lack braking capabilities and can only adjust their courses while maintaining their constant but different speeds. Instead of controlling the spatial relations between the robots, our formation control algorithm aims to deploy a dense robot swarm that mimics the behavior of tornado schooling fish. To achieve this objective safely, we employ a combination of a s…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Mu\~noz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, Danish Contractor
https://arxiv.org/abs/2402.059…

On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses. Nota…

This https://arxiv.org/abs/2310.17639 has been replaced.
link: https://scholar.google.com/scholar?q=a

In-Context Learning Dynamics with Random Binary Sequences
Large language models (LLMs) trained on huge corpora of text datasets demonstrate intriguing capabilities, achieving state-of-the-art performance on tasks they were not explicitly trained for. The precise nature of LLM capabilities is often mysterious, and different prompts can elicit different capabilities through in-context learning. We propose a framework that enables us to analyze in-context learning dynamics to understand latent concepts underlying LLMs' behavioral patterns. This provides …

Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
Shunpan Liang, Junjie Zhao, Chen Li, Yu Lei
https://arxiv.org/abs/2404.11993

Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
Multi-behavioral recommendation optimizes user experiences by providing users with more accurate choices based on their diverse behaviors, such as view, add to cart, and purchase. Current studies on multi-behavioral recommendation mainly explore the connections and differences between multi-behaviors from an implicit perspective. Specifically, they directly model those relations using black-box neural networks. In fact, users' interactions with items under different behaviors are driven by dist…

This https://arxiv.org/abs/2309.09101 has been replaced.
link: https://scholar.google.com/scholar?q=a

Behavioral-based circular formation control for robot swarms
This paper focuses on coordinating a robot swarm orbiting a convex path without collisions among the individuals. The individual robots lack braking capabilities and can only adjust their courses while maintaining their constant but different speeds. Instead of controlling the spatial relations between the robots, our formation control algorithm aims to deploy a dense robot swarm that mimics the behavior of tornado schooling fish. To achieve this objective safely, we employ a combination of a s…

Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
Jinhao Liang, Kaidi Yang, Chaopeng Tan, Jinxiang Wang, Guodong Yin
https://arxiv.org/abs/2404.14713

Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity…

On the Usability of Next-Generation Authentication: A Study on Eye Movement and Brainwave-based Mechanisms
Matin Fallahi, Patricia Arias Cabarcos, Thorsten Strufe
https://arxiv.org/abs/2402.15388

On the Usability of Next-Generation Authentication: A Study on Eye Movement and Brainwave-based Mechanisms
Passwords remain a widely-used authentication mechanism, despite their well-known security and usability limitations. To improve on this situation, next-generation authentication mechanisms, based on behavioral biometric factors such as eye movement and brainwave have emerged. However, their usability remains relatively under-explored. To fill this gap, we conducted an empirical user study (n=32 participants) to evaluate three brain-based and three eye-based authentication mechanisms, using bot…

This https://arxiv.org/abs/2403.17219 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies
Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML) models. However, the need to continuously self-report adds a significant burden on the participants, often resulting in attrition, missing labels, or insincere responses. In this work, we introduce…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

RealDex: Towards Human-like Grasping for Robotic Dexterous Hand
Yumeng Liu, Yaxun Yang, Youzhuo Wang, Xiaofei Wu, Jiamin Wang, Yichen Yao, S\"oren Schwertfeger, Sibei Yang, Wenping Wang, Jingyi Yu, Xuming He, Yuexin Ma
https://arxiv.org/abs/2402.13853

RealDex: Towards Human-like Grasping for Robotic Dexterous Hand
In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data. Utilizing a teleoperation system, we seamlessly synchronize human-robot hand poses in real time. This collection of human-like motions is crucial for training dexterous hands to mimic human movements more naturally and precisely. RealDex holds immense promise in advancing humanoid robot for automa…

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Feiran Huang, Zhenghang Yang, Junyi Jiang, Yuanchen Bei, Yijie Zhang, Hao Chen
https://arxiv.org/abs/2402.09176

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Recommending cold items is a long-standing challenge for collaborative filtering models because these cold items lack historical user interactions to model their collaborative features. The gap between the content of cold items and their behavior patterns makes it difficult to generate accurate behavioral embeddings for cold items. Existing cold-start models use mapping functions to generate fake behavioral embeddings based on the content feature of cold items. However, these generated embeddin…

SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies
Akshat Choube, Vedant Das Swain, Varun Mishra
https://arxiv.org/abs/2403.17219

SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies
Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML) models. However, the need to continuously self-report adds a significant burden on the participants, often resulting in attrition, missing labels, or insincere responses. In this work, we introduce…

Specification Mining for Smart Contracts with Trace Slicing and Predicate Abstraction
Ye Liu, Yi Li, Cyrille Artho, Yixuan Liu
https://arxiv.org/abs/2403.13279

Specification Mining for Smart Contracts with Trace Slicing and Predicate Abstraction
Smart contracts are computer programs running on blockchains to implement Decentralized Applications.The absence of contract specifications hinders routine tasks, such as contract understanding and testing. Inthis work, we propose a specification mining approach to infer contract specifications from past transactionhistories. Our approach derives high-level behavioral automata of function invocations, accompanied byprogram invariants statistically inferred from the transaction histories. We imp…

This https://arxiv.org/abs/2402.09494 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Can AI and humans genuinely communicate?
Can AI and humans genuinely communicate? In this article, after giving some background and motivating my proposal (sections 1 to 3), I explore a way to answer this question that I call the "mental-behavioral methodology" (sections 4 and 5). This methodology follows the following three steps: First, spell out what mental capacities are sufficient for human communication (as opposed to communication more generally). Second, spell out the experimental paradigms required to test whether a behavior …

Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
https://arxiv.org/abs/2403.16371

Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly engaging with online platforms, vast amounts of lifelong user behavioral sequences have been generated. However, existing sequential recommender models often struggle to handle such lifelong sequences. The primary challenges stem from computational complexity and the ability to capture long-range dependencies within the…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Specification Mining for Smart Contracts with Trace Slicing and Predicate Abstraction
Ye Liu, Yi Li, Cyrille Artho, Yixuan Liu
https://arxiv.org/abs/2403.13279

Specification Mining for Smart Contracts with Trace Slicing and Predicate Abstraction
Smart contracts are computer programs running on blockchains to implement Decentralized Applications.The absence of contract specifications hinders routine tasks, such as contract understanding and testing. Inthis work, we propose a specification mining approach to infer contract specifications from past transactionhistories. Our approach derives high-level behavioral automata of function invocations, accompanied byprogram invariants statistically inferred from the transaction histories. We imp…

Authenticity in Authorship: The Writer's Integrity Framework for Verifying Human-Generated Text
Sanad Aburass, Maha Abu Rumman
https://arxiv.org/abs/2404.10781

Authenticity in Authorship: The Writer's Integrity Framework for Verifying Human-Generated Text
The "Writer's Integrity" framework introduces a paradigm shift in maintaining the sanctity of human-generated text in the realms of academia, research, and publishing. This innovative system circumvents the shortcomings of current AI detection tools by monitoring the writing process, rather than the product, capturing the distinct behavioral footprint of human authorship. Here, we offer a comprehensive examination of the framework, its development, and empirical results. We highlight its potent…

Robotic Exploration using Generalized Behavioral Entropy
Aamodh Suresh, Carlos Nieto-Granda, Sonia Martinez
https://arxiv.org/abs/2402.10161 https://

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
Jiajie Su, Chaochao Chen, Zibin Lin, Xi Li, Weiming Liu, Xiaolin Zheng
https://arxiv.org/abs/2402.14473

Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aims to leverage time-evolving heterogeneous behavioral dependencies for better exploring users' potential intents on the target behavior. Sol…

Automatic Recommendations for Evolving Relational Databases Schema
Anne Etien, Nicolas Anquetil
https://arxiv.org/abs/2404.08525 https://

Automatic Recommendations for Evolving Relational Databases Schema
Relational databases play a central role in many information systems. Their schema contains structural (e.g. tables and columns) and behavioral (e.g. stored procedures or views) entity descriptions. Then, just like for ``normal'' software, changes in legislation, offered functionalities, or functional contexts, impose to evolve databases and their schemas. But in some scenarios, it is not so easy to deconstruct a wished evolution of the schema into a precise sequence of operations. Changing a d…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction
Zeya Chen, Ruth Schmidt
https://arxiv.org/abs/2402.09683 https://