Miracle-dz/UniFAR-FTUs huggingface dataset:
Review for Team's Behavior Recognition

Recognition and understanding of team's behavior which is planned and high-cooperative and multi-player (agent) participate is an important field of computer vision research. In this paper, we in detail analyze and summarize the existing research achievements in the area of the team's behavior recognition from four aspects of target tracking and detection, representation of behavior, construction of classification and behavior recognition. The problems waiting to be solved and some research trends in the field of team's behavior recognition in the future are also analyzed in this paper. It may provide some novel insights and literature references for the researcher of recognition of multi-objective behavior in the future.
The Designed Gamification Application Architecture and Elements for a C# Programming Course

Teaching computer programming develops the capabilities of students required of a professional software developer. Difficulty experienced by students in programming is not unique to CSU IT students but spans across most universities in Cagayan. Failures in programming course have most often pooled in the inability of the students to understand the programming concepts. In this study, K-means clustering algorithm produced 2 clusters which are the 0 and 1; 0 cluster with 60 instances is considered good proficiency level while cluster 1 with 43 instances has a poor proficiency level. The designed architecture considered the level of proficiency of students, and thus, more time and gamification are given to cluster 1 compared to the other. The architecture is already implemented in an online distance learning platform which is the Moodle platform for the duration of five months.
How Can I Agree with You Better?

This work investigates how to automatically create wordplay during small talk, and use wordplay to make the conversation more engaging. In small talk, conversational partners often agree with each other. We hypothesize that using wordplay in the responses will make the speaker seem more engaged and the dialogue exchange more entertaining. An empirical study was conducted and the hypotheses are largely supported.
The Effects of Anthropomorphism and Non-verbal Social Behaviour in Virtual Assistants

The adoption of virtual assistants is growing at a rapid pace. However, these assistants are not optimised to simulate key social aspects of human conversational environments. Humans are intellectually biased toward social activity when facing anthropomorphic agents or when presented with subtle social cues. In this paper, we test whether humans respond the same way to assistants in guided tasks, when in different forms of embodiment and social behaviour. In a within-subject study (N=30), we asked subjects to engage in dialogue with a smart speaker and a social robot. We observed shifting of interactive behaviour, as shown in behavioural and subjective measures. Our findings indicate that it is not always favourable for agents to be anthropomorphised or to communicate with nonverbal cues. We found a trade-off between task performance and perceived sociability when controlling for anthropomorphism and social behaviour.
Deep Patch Matching For Hand Vein Recognition

Hand vein recognition for personal identification has attracted considerable attention from scholars due to the uniqueness of vein pattern and nearly impossible forgery. However, recognition can be difficult to obtain when remarkable discrepancies exist in vein images caused by inconstant hand poses during imaging. This study proposes deep patch matching methodology (DPM) for hand vein recognition to solve significant differences in vein images. The proposed method calculates the similarity map of each circle patch based on vessel-enhanced filters using convolution. All similarity maps are further aggregated to calculate image similarity using max-pooling. The proposed algorithm is evaluated using public NCUT part A database. The recognition rate of the proposed algorithm is 99.71%. Experimental results reveal that the proposed algorithm obtains the highest accuracy among existing methods.
As the Tweet, so the Reply?: Gender Bias in Digital Communication with Politicians

This study investigates gender bias in political interactions on digital platforms by considering how politicians present themselves on Twitter and how they are approached by others. Incorporating social identity theory, we use dictionary analyses to detect biases in individual tweets connected to the German federal elections in 2017. Besides sentiment analysis, we introduce a new measure of personal- vs. job-related content in text data, that is validated with structural topic models. Our results indicate that politicians' communication on Twitter is driven by party identity rather than gender. However, we find systematic gender differences in tweets directed at politicians: female politicians are significantly more likely to be reduced to their gender rather than to their profession compared to male politicians.
Managing Agent's Impression Based on User's Engagement Detection

When interacting with others, we form an impression that can be declined along the two psychological dimensions of warmth and competence. By managing them, high level of engagement in an interaction can be maintained and reinforced. Our aim is to develop a virtual agent that can form and maintain a positive impression on the user that can help in improving the quality of the interaction and the user's experience. In this paper, we present an interactive system in which a virtual agent adopts a dynamic communication strategy during the interaction with a user, aiming at forming and maintaining a positive impression of warmth and competence. The agent continuously analyzes user's non-verbal signals to determine user's engagement level and adapts its communication strategy accordingly. We present a study in which we manipulate the communication strategy of the agent and we measure user's experience and user's perception of the agent's warmth and competence.
Road traffic demand estimation and traffic signal control

A transport network is a complex non-stationary open environment with multiple heterogeneous stochastic agents such as intersection controllers and road users. Changes in such a network may occur due to random demand fluctuations, supply degradation, or actions of agents. Thus, it makes the task of adaptive Traffic Light Control (TLC) extremely challenging. This paper is our attempt to analyze the complexity of TLC on a single isolated intersection by considering it from the perspective of supervised learning. We explicitly split the problem into two parts: demand estimation and traffic light control itself. For each task we train offline a model (Neural Network) on synthesized data. Our experiments show that even Shallow Neural Network (SNN) models can estimate and control traffic well on a single intersection. The only bottleneck of this method resides in the process of data generation and annotation - the number of simulations needed for data generation polynomially grows with the increase of number of intersections. This might prevent the current approach from being applied to transport networks with multiple intersections.
Concepts Clarification and Differentiation between Smartphone Addiction and Compulsive Internet Use Based on Diagnostic Investigation on Two Scales

Smartphone addiction, Internet addiction, online gaming addiction, communication addiction disorder and social network addiction are widely discussed topics nowadays. However, there is lack of distinction between different types of these technology-based addictions. The aim of this study is to investigate whether two commonly used instruments measure two different concepts as they intend to. Therefore, we made a diagnostic analysis of the Smartphone Addiction Scale (SAS-SV) for adolescents and the Compulsive Internet Use Scale (CIUS). We surveyed 96 adolescents aged between 14 and 15 years through online questionnaire. The results of the statistical analysis show that the two scales are highly correlated (r=.98, p≤.001). By using Confirmatory Factor Analysis (CFA) we stepwise reduced both instruments to their main "loading" cores to get insights in the phenomena they measure. The reduced scales stayed highly correlated. However, a combined model based on both reduced scales implies very good model fit and high reliability. We argue that both instruments measure almost the same phenomena and propose an alternative scale based on the reduction of the SAS-SV and CIUS scales -- the Compulsory Mobile Internet Use Scale (CMIU). Further validation of this scale is needed. The main limitation of the study is its relatively small "convenience" sample. Further limitations and implications are discussed.
Concept of Keystone Species in Web Systems: Identifying Small Yet Influential Online Bulletin Board Threads

Research is being conducted to understand social and innovative behavior in human interactions on the Web as a biological ecosystem. Keystone species in a biological ecosystem are defined as a set of species that significantly impacts the ecosystem if they are removed from the system, irrespective of its small biomass. Identifying keystone species is an important problem as they play an important role in maintaining diversity and stability in ecosystems. A human community in the web system also possesses keystone species. They can be influential users or contents on the web systems, even though their commitments to the web are relatively small. We use data from an online bulletin board, and identify keystone threads (= "species") that have a large impact if they are removed or become unpopular, despite their small population size. Here, the removal of threads can be regarded as a state in which there is no attention or actions by users on the thread. The multivariate Hawkes process is used to measure the degree of influence among all threads and calculate the overall activity level on the online bulletin board. Our analysis confirms that keystone threads do exist in the system. Apparently, the number of keystone species increases along with the service maturation. The keystone concept in online services proposed in this study gives a new viewpoint for their stable operation.
Guiding the Behavior Design of Virtual Assistants

In recent years there has been an explosive proliferation of virtual assistants such as Amazon's Alexa or Google's Home Assistant. These virtual assistants are becoming increasingly sophisticated and able to manage many aspects of our lives. Through bundled sales campaigns, consumers are encouraged to install multiple devices throughout the house and to share their use across the household. Unfortunately, competing devices do not behave consistently with each other. Furthermore, the increased popularity of third party behavior extensions (e.g. Alexa Skills or Google Home Actions) has the potential to add to this behavioral discontinuity. Ambiguities in multi-user behavior combined with disparate agent and extension behavior can lead to consumer confusion when the agent does not behave as expected. This report documents ongoing research intended to identify users' inherent assumptions about a smart agent's behavior through user studies. The results can be applied to inform design guidelines that lead to a more consistent user experience, with agents clarifying their behavior in situations identified as ambiguous.
The Rise of Germany's AfD: A Social Media Analysis

In 2017, a far-right party entered the German parliament for the first time in over half a century. The Alternative für Deutschland (AfD) became the third largest party in the government. Its campaign focused on Euroscepticism and a nativist stance against immigration. The AfD used all available social media channels to spread this message. This paper seeks to understand the AfD's social media strategy over the last years on the full gamut of social media platforms and to verify the effectiveness of the party's online messaging strategy. For this purpose, we collected data related to Germany's main political parties from Facebook, Twitter, YouTube, and Instagram. This data was subjected to a unified multi-platform analysis, which relies on four measures: party engagement, user engagement, message spread, and acceptance. This analysis proves the AfD's superior online popularity relative to the rest of Germany's political parties. The evidence also indicates that automated accounts contributed to this online superiority. Finally, we demonstrate that as part of its social media strategy, the AfD avoided discussion of its economic proposals and instead focused on pushing its anti-immigration agenda to gain popularity.
The Effectiveness of Social Influence Tactics when Used by a Virtual Agent

Research in social science distinguishes between two types of social influence: informational and normative. Informational social influence is driven by the desire to evaluate ambiguous situations correctly, whereas normative social influence is driven by the desire to be liked and gain social acceptance from another person. Although we know from research that humans can effectively use either of these techniques to persuade other humans, scholars have yet to examine the relative effectiveness of informational versus normative social influence when used by virtual agents. We report a study in which users interact with a system that persuades them either using informational or normative social influence. Furthermore, to compare agents to human interlocutors, users are told that the system is either tele-operated by a human (avatar) or fully-automated (agent). Using this design, we are able to compare the effectiveness of virtual agents (vs humans) in employing informational versus normative social influence. Participants interacted with the system, which employed a Wizard-of-Oz operated virtual agent that tried to persuade the user to agree with its rankings on a "survival task. " Controlling for initial divergence in rankings between user and the agent, there was a significant main effect such that informational social influence resulted in greater influence than normative influence. However, this was qualified by an interaction that approached significance; users were, if anything, more persuaded by informational influence when they believe the agent was AI (compared to a human), whereas there was no difference between the agent and avatar in the normative influence condition.
Generating Facial Expression Data: Computational and Experimental Evidence

It is crucial that naturally-looking Embodied Conversational Agents (ECAs) display various verbal and non-verbal behaviors, including facial expressions. The generation of credible facial expressions has been approached by means of different methods, yet remains difficult because of the availability of naturalistic data. To infuse more variability into the facial expressions of ECAs, we proposed a model that considered temporal dynamic of facial behaviors as a countable-state Markov process. Once trained, the model was able to output new sequences of facial expressions from an existing dataset containing facial videos with Action Unit (AU) encodings. The approach was validated by having computer software and humans identify facial emotion from video. Half of the videos employed newly generated sequences of facial expressions using the model while the other half contained sequences selected directly from the original dataset. We found no statistically significant evidence that the newly generated facial expression sequences could be differentiated from the original ones, demonstrating that the model was able to generate new facial expression data that were indistinguishable from the original data. Our proposed approach could be used to expand the amount of labelled facial expression data in order to create new training sets for machine learning methods.
Expressive Virtual Human: Impact of expressive wrinkles and pupillary size on emotion recognition

Improving the expressiveness of virtual humans is essential for qualitative interactions and development of an emotional bond. It is certainly indicated for all applications using the user's cognitive processes, such as applications dedicated to training or health. Our study aims to contribute to the design of an expressive virtual human, by identifying and adapting visual factors promoting transcription of emotions. In this paper, we investigate the effect of expressive wrinkles and variation of pupil size. We propose to compare the recognition of basic emotions on a real human and on an expressive virtual human. The virtual human was subject to two different factors: expressive wrinkles and/or pupil size. Our results indicate that emotion recognition rates on the virtual agent are high. Moreover, expressive wrinkles affect emotion recognition. The effect of pupillary size is less significant. However, both are recommended to design an expressive virtual human.
ATOM: a system for building customized program analysis tools

ATOM (Analysis Tools with OM) is a single framework for building a wide range of customized program analysis tools. It provides the common infrastructure present in all code-instrumenting tools; this is the difficult and time-consuming part. The user simply defines the tool-specific details in instrumentation and analysis routines. Building a basic block counting tool like Pixie with ATOM requires only a page of code. ATOM, using OM link-time technology, organizes the final executable such that the application program and user's analysis routines run in the same address space. Information is directly passed from the application program to the analysis routines through simple procedure calls instead of inter-process communication or files on disk. ATOM takes care that analysis routines do not interfere with the program's execution, and precise information about the program is presented to the analysis routines at all times. ATOM uses no simulation or interpretation. ATOM has been implemented on the Alpha AXP under OSF/1. It is efficient and has been used to build a diverse set of tools for basic block counting, profiling, dynamic memory recording, instruction and data cache simulation, pipeline simulation, evaluating branch prediction, and instruction scheduling.
How do Leaders Perceive Stress and Followership from Nonverbal Behaviors Displayed by Virtual Followers?

Managing a medical team in emergency situations requires not only technical but also non-technical skills. Leaders must train to manage different types of subordinates, and how these subordinates will respond to orders and stressful events. Before designing virtual training environments for these leaders, it is necessary to understand how leaders perceive the nonverbal behaviors of virtual characters playing the role of subordinates. In this article, we describe a study we conducted to explore how leaders categorize virtual subordinates from the non-verbal expressions they display (i.e., facial expressions, torso orientation, gaze direction). We analyze how these multimodal behaviors impact the perception of follower style (proactive vs. passive, insubordination), interpersonal attitudes (dominance vs. submission) and stress. Our results suggest that leaders categorize virtual subordinates via nonverbal behaviors that are also perceived as signs of stress and interpersonal attitudes.
The Design and Implementation of a Dynamic IP defense System Accelerated by Vector Packet Processing

IP address dynamic protection techniques will introduce additional overhead. Therefore, the performance of normal network transmission decreases. In order to deal with this problem, we design and implement a dynamic IP address defense system accelerated by Vector Packet Processing (VPP). Using VPP's flexibility and extensibility, we change the packet's IP in the gateway. We derive the dynamic IP strategy profiles based on the optimization analysis, and design the fast and slow forwarding logic based on VPP's graph processing to reduce the overhead. The experimental results show that the system can hide the real IP address and enhance the system's data processing ability effectively. It can also control the potential detection attack hit rate below 5%, which significantly improved compared with existing technologies.
Tree based data fusion approach for mining temporal patterns

Discovering time profiled temporal patterns from time stamped transaction datasets is addressed in our previous research works which includes proposing new support estimation techniques, similarity measures for computing similarity between temporal patterns. This paper proposes a novel approach for discovering temporal pattern by introducing the concept of data fusion w.r. t the temporal pattern tree. The tree is generated for each timeslot and then the trees obtained for individual timeslots are merged or fused to get the overall tree for the entire dataset. The concept of tree based data fusion helps to prune elements efficiently and well ahead during pattern mining process. A pruning function is also introduced in this paper to prune invalid temporal patterns.
In What Mood Are You Today?: An Analysis of Crowd Workers' Mood, Performance and Engagement

The mood of individuals in the workplace has been well-studied due to its influence on task performance, and work engagement. However, the effect of mood has not been studied in detail in the context of microtask crowdsourcing. In this paper, we investigate the influence of one's mood, a fundamental psychosomatic dimension of a worker's behaviour, on their interaction with tasks, task performance and perceived engagement. To this end, we conducted two comprehensive studies; (i) a survey exploring the perception of crowd workers regarding the role of mood in shaping their work, and (ii) an experimental study to measure and analyze the actual impact of workers' moods in information findings microtasks. We found evidence of the impact of mood on a worker's perceived engagement through the feeling of reward or accomplishment, and we argue as to why the same impact is not perceived in the evaluation of task performance. Our findings have broad implications on the design and workflow of crowdsourcing systems.
Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion Processing

The present study sought to employ audio-visual video clip stimuli to explore the electroencephalography-based (EEG) correlation between various emotional states. To that end, seven short video clips were shown to ten volunteer participants without health conditions whilst emotional EEG data were captured. The method of independent component analysis and wavelets (AICA-WT) was adopted for screening the extracted data. The correlation indices were computed based on spectral features employing the relative powers (RP) of delta (ΔRP), theta (θRP), alpha (αRP), beta (βRP), and gamma (γRP). The next step was calculation of Pearson's correlation between the of the neutral state and the of the six fundamental emotional states (i.e. anger, anxiety, disgust, happiness, sadness and surprise) of every EEG channel for different brain areas (i.e. frontal, temporal, parietal and occipital scalp). According to the findings obtained, the correlation of brain activity and emotional states among the brain areas observable in healthy EEG data can be investigated based on the relevant indices afforded by the new denoising method alongside EEG-based correlation analysis of the RP.