A Serious Game-Derived Index for Detecting Children With Heterogeneous Developmental Disabilities: Randomized Controlled Trial

BACKGROUND: Developmental disabilities are a set of heterogeneous delays or difficulties in one or more areas of neuropsychological development. Considering that childhood is an essential stage of brain development and developmental delays lead to personal or social burdens, the early detection of childhood developmental disabilities is important. However, early screening for developmental disabilities has been a challenge because of the fear of positive results, expensive tests, differences in diagnosis depending on examiners' abilities, and difficulty in diagnosis arising from the need for long-term follow-up observation. OBJECTIVE: This study aimed to assess the feasibility of using a serious game-derived index to identify heterogeneous developmental disabilities. This study also examines the correlation between the game-derived index and existing neuropsychological test results. METHODS: The randomized controlled trial involved 48 children with either normal development or developmental disabilities. In this clinical trial, we used 19 features (6 from the Korean-Wechsler Preschool and Primary Scale of Intelligence, 8 from the Psychoeducational Profile Revised, 2 from the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition, and 3 from the Pediatric Evaluation of Disability Inventory) from neuropsychological tests and 9 (7 game scores, path accuracy, and completion rate) from the serious game, DoBrain. The following analysis was conducted based on participants' baseline information and neuropsychological test and game-derived index data for one week: (1) we compared the baseline information between the normal development and developmental disabilities groups; (2) then we measured the correlation between the game-derived index and the neuropsychological test scores for each group; and (3) we built a classifier based on the game-derived index with a Gaussian process method and then compared the area under the curve (AUC) with a model based on neuropsychological test results. RESULTS: A total of 16 children (normal development=9; developmental disabilities=7) were analyzed after selection. Their developmental abilities were assessed before they started to play the serious games, and statistically significant differences were found in both groups. Specifically, the normal development group was more developed than the developmental disabilities group in terms of social function, gross motor function, full-scale IQ, and visual motor imitation, in that order. Similarly, the normal development group obtained a higher score on the game-derived index than the developmental disabilities group. In the correlation analysis between the game-derived index and the neuropsychological tests, the normal development group showed greater correlation with more variables than the developmental disabilities group. The game-derived index-based model had an AUC=0.9, a similar detection value as the neuropsychological test-based model's AUC=0.86. CONCLUSIONS: A game-derived index based on serious games can detect children with heterogenous developmental disabilities. This suggests that serious games can be used as a potential screening tool for developmental disabilities. TRIAL REGISTRATION: Clinical Research Information Service KCT0003247; https://cris.nih.go.kr/cris/en/search/search_result_st01 .jsp?seq=12365.ope

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

The Naming Game in Social Networks: Community Formation and Consensus Engineering

We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat. Mech.: Theory Exp. P06014] in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.Comment: The original publication is available at http://www.springerlink.com/content/70370l311m1u0ng3

On the security of machine learning in malware C & C detection:a survey

One of the main challenges in security today is defending against malware attacks. As trends and anecdotal evidence show, preventing these attacks, regardless of their indiscriminate or targeted nature, has proven difficult: intrusions happen and devices get compromised, even at security-conscious organizations. As a consequence, an alternative line of work has focused on detecting and disrupting the individual steps that follow an initial compromise and are essential for the successful progression of the attack. In particular, several approaches and techniques have been proposed to identify the command and control (C&C) channel that a compromised system establishes to communicate with its controller. A major oversight of many of these detection techniques is the design's resilience to evasion attempts by the well-motivated attacker. C&C detection techniques make widespread use of a machine learning (ML) component. Therefore, to analyze the evasion resilience of these detection techniques, we first systematize works in the field of C&C detection and then, using existing models from the literature, go on to systematize attacks against the ML components used in these approaches

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Deception Detection in Group Video Conversations using Dynamic Interaction Networks

Detecting groups of people who are jointly deceptive in video conversations is crucial in settings such as meetings, sales pitches, and negotiations. Past work on deception in videos focuses on detecting a single deceiver and uses facial or visual features only. In this paper, we propose the concept of Face-to-Face Dynamic Interaction Networks (FFDINs) to model the interpersonal interactions within a group of people. The use of FFDINs enables us to leverage network relations in detecting group deception in video conversations for the first time. We use a dataset of 185 videos from a deception-based game called Resistance. We first characterize the behavior of individual, pairs, and groups of deceptive participants and compare them to non-deceptive participants. Our analysis reveals that pairs of deceivers tend to avoid mutual interaction and focus their attention on non-deceivers. In contrast, non-deceivers interact with everyone equally. We propose Negative Dynamic Interaction Networks to capture the notion of missing interactions. We create the DeceptionRank algorithm to detect deceivers from NDINs extracted from videos that are just one minute long. We show that our method outperforms recent state-of-the-art computer vision, graph embedding, and ensemble methods by at least 20.9% AUROC in identifying deception from videos.Comment: The paper is published at ICWSM 2021. Dataset link: https://snap.stanford.edu/data/comm-f2f-Resistance.htm

Scalable Automation of Online Network Attack Characterization

Cyber attacks to enterprise networks and critical infrastructures are becoming more prevalent and diverse. Timely recognition of attack strategies and behaviors will assist analysts or resilient network defense systems in deploying effective means in anticipation of future threats. An attack can be characterized by the sequences of observed events that are relevant to critical assets. Earlier work has developed a semi-supervised learning framework to process large-scale events and extract attack behaviors. While the framework is designed to support online processing, the implementation requires extension and restructuring to support scalable automation of sustainable online network attack characterization. This work builds upon the semi-supervised Bayesian classification framework, and aims at providing a modular and scalable system that supports a variety of features to describe attacks, ranging from packet level information to metadata produced by sensors, such as Snort and Bro. The system will continuously process data streams, generating newly learned models, as well as record critical information of aged behavior models. These behavior models will reflect the attack strategies that are relevant to the critical assets, enhancing the situational awareness and enabling predictive and resilient network defense. The accuracy of the models is demonstrated through comparisons to network topologies and scenarios provided from the source of the dataset utilized. These scenarios often encapsulate multiple complex network attack behaviors allowing for more realistic representations of network traffic over time and better test cases for experimentation

Exploring the potential usefulness of binary space partitions in architectural representations

There have been recent advances developed within the computer gaming industry that have made real-time first-person perspective spatial experiences feasible on the personal computer. Principally through the use of binary space partition tree structures, developers of threedimensional gaming environments are able to convey to computer users a convincing sense of movement through space. The technology behind these advances may be termed as a particularization of Virtual Reality. This paper will outline research intended to determine the possible usefulness of binary space partitions in the fields of architectural education and practice. The feasibility of this technology was studied by directly observing original experimentation in practical application, which was conducted primarily in the Imaging Laboratory at the New Jersey School of Architecture. In addition, this paper references existing theories and experiencebased expositions on the application of computer technology to architectural design and representation, with particular regard to the use of generalized virtual reality

Attention and Social Cognition in Virtual Reality:The effect of engagement mode and character eye-gaze

Technical developments in virtual humans are manifest in modern character design. Specifically, eye gaze offers a significant aspect of such design. There is need to consider the contribution of participant control of engagement. In the current study, we manipulated participants’ engagement with an interactive virtual reality narrative called Coffee without Words. Participants sat over coffee opposite a character in a virtual café, where they waited for their bus to be repaired. We manipulated character eye-contact with the participant. For half the participants in each condition, the character made no eye-contact for the duration of the story. For the other half, the character responded to participant eye-gaze by making and holding eye contact in return. To explore how participant engagement interacted with this manipulation, half the participants in each condition were instructed to appraise their experience as an artefact (i.e., drawing attention to technical features), while the other half were introduced to the fictional character, the narrative, and the setting as though they were real. This study allowed us to explore the contributions of character features (interactivity through eye-gaze) and cognition (attention/engagement) to the participants’ perception of realism, feelings of presence, time duration, and the extent to which they engaged with the character and represented their mental states (Theory of Mind). Importantly it does so using a highly controlled yet ecologically valid virtual experience