Reviewerly/exHarmony huggingface dataset:
Towards Artificial Social Intelligence: Inherent Features, Individual Differences, Mental Models, and Theory of Mind

We discuss the potential for near-future artificial social intelligences (ASIs) to exhibit functional artificial theory of mind in a video-game based teaming scenario. We also describe the impact of individual differences on mental model formation and mental state development. We focus on the possibility for an ASI to develop profiles of human agents by eliciting or observing a range of information which may influence their counterpart's behavior during a novel task. We conclude with a discussion of the implications of this approach for integrating methods from the cognitive and social sciences in development of ASI.
Enhancing the Cybersecurity Education Curricula Through Quantum Computation

Governmental and corporate networks are increasingly at risk, malicious acts are escalating abruptly, and organized crime and terrorist groups are expanding and improving their cyber capabilities. Individuals and countries have initiated attacks against others, including the public and private sectors. Hiring qualified cybersecurity experts is currently considered to be the highest priority in the United States. Quantum computation is a revolutionary technology for Cybersecurity education. The traditional digital computers run on encoded data according to the standard binary system 0 or 1. With the emerging of quantum computation, quantum computers run qubits or quantum bits instead of binary data 0 and 1. Quantum computation is vital for Cybersecurity education. The overarching goal of this chapter is to present a cybersecurity educational model with a focus on quantum computation. The given education model is designed according to the National Institute of Standard and Technology (NIST), and the industry’s expectations regarding the knowledge, training, and skills that cybersecurity experts should possess.
Self-Aware Effective Identification and Response to Viral Cyber Threats

Artificial intelligence (AI) techniques can significantly improve cyber security operations if tasks and responsibilities are effectively shared between human and machine. AI techniques excel in some situational understanding tasks; for instance, classifying intrusions. However, existing AI systems are often overconfident in their classification: this reduces the trust of human analysts. Furthermore, sophisticated intrusions span across long time periods to reduce their footprint, and each decision to respond to a (suspected) attack can have unintended side effects. In this position paper we show how advanced AI systems handling uncertainty and encompassing expert knowledge can lessen the burden on human analysts. In detail: (1) Effective interaction with the analyst is a key issue for the success of an intelligence support system. This involves two issues: a clear and unambiguous system-analyst communication, only possible if both share the same domain ontology and conceptual framework, and effective interaction, allowing the analyst to query the system for justifications of the reasoning path followed and the results obtained. (2) Uncertainty-aware machine learning and reasoning is an effective method for anomaly detection, which can provide human operators with alternative interpretations of data with an accurate assessment of their confidence. This can contribute to reducing misunderstandings and building trust. (3) An event-processing algorithm including both a neural and a symbolic layer can help identify attacks spanning long intervals of time, that would remain undetected via a pure neural approach. (4) Such a symbolic layer is crucial for the human operator to estimate the appropriateness of possible responses to a suspected attack by considering both the probability that an attack is actually occurring and the impact (intended and unintended) of a given response.
Face Gesture Recognition Using Deep-Learning Models

This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe & CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe & CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.
Blindly Follow: SITS CRT and FHE for DCLSMPC of DUFSM (Extended Abstract)

A Statistical Information Theoretic Secure (SITS) system utilizing the Chinese Remainder Theorem (CRT), coupled with Fully Homomorphic Encryption (FHE) for Distributed Communication-less Secure Multiparty Computation (DCLSMPC) of any Distributed Unknown Finite State Machine (DUFSM) is presented. Namely, secret shares of the input(s) and output(s) are passed to/from the computing parties, while there is no communication between them throughout the computation. We propose a novel approach of transition table representation and polynomial representation for arithmetic circuits evaluation, joined with a CRT secret sharing scheme and FHE to achieve SITS communication-less within computational secure execution of DUFSM. We address the severe limitation of FHE implementation over a single server to cope with a malicious or Byzantine server. We use several distributed memory-efficient solutions that are significantly better than the majority vote in replicated state machines, where each participant maintains an FHE replica. A Distributed Unknown Finite State Machine (DUFSM) is achieved when the transition table is secret shared or when the (possible zero value) coefficients of the polynomial are secret shared, implying communication-less SMPC of an unknown finite state machine.
A Comparison Between Methods to Cluster Mixed-Type Data: Gaussian Mixtures Versus Gower Distance

In this paper, we compare through a simulation study two approaches to cluster mixed-type data, where some variables are continuous and some others ordinal. The first is model-based, according to which the variables are assumed to follow a Gaussian mixture model, where, as regards the ordinal variables, it is only partially observed. In order to overcome computational issues, the parameter estimation is carried out through an EM-like algorithm maximizing a composite log-likelihood based on low-dimensional margins. In the second approach, the Gower distance matrix is computed, then the PAM algorithm is used for clustering.
Multimodal video-text matching using a deep bifurcation network and joint embedding of visual and textual features

Video-text matching is of high importance in the field of machine vision and artificial intelligence. The main challenging issue in video-text matching is the projection of the video and textual features into a common semantic space, which is called video-text joint embedding. The proper functionality of video-text joint embedding depends on two important factors: the effectiveness of the extracted information for video-text matching and the suitability of network structure for the projection of the extracted features into a common space. Generally, existing approaches do not leverage all the audio and visual information of a video for video-text matching. This study proposes a new approach for video-text matching by extracting a comprehensive set of visual and textual features and projecting them into a common semantic space using an effective structure. We use a new deep network with two textual and video branches that extracts several informative high-level visual and textual features and maps them into a shared space. In the video branch, we extract several descriptors comprising appearance-based, concept-based, and action-based features as well as an audio encoder to extract sound features in the video clip. We also utilize several feature extraction approaches in the textual branch for the effective transformation of an input sentence to a common semantic space. Furthermore, an image description database is used to pre-train and initialize network weights. We evaluated the proposed architecture with two popular video description datasets and compared the results with the results of several state-of-the-art approaches. The comparison results showed the effectiveness of the proposed algorithm.
RL-GRIT: Reinforcement Learning for Grammar Inference

When working to understand usage of a data format, examples of the data format are often more representative than the format’s specification. For example, two different applications might use very different JSON representations, or two PDF-writing applications might make use of very different areas of the PDF specification to realize the same rendered content. The complexity arising from these distinct origins can lead to large, difficult-to-understand attack surfaces, presenting a security concern when considering both exfiltration and data schizophrenia. Grammar inference can aid in describing the practical language generator behind examples of a data format. However, most grammar inference research focuses on natural language, not data formats, and fails to support crucial features such as type recursion. We propose a novel set of mechanisms for grammar inference, RL-GRIT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , and apply them to understanding de facto data formats. After reviewing existing grammar inference solutions, it was determined that a new, more flexible scaffold could be found in Reinforcement Learning (RL). Within this work, we lay out the many algorithmic changes required to adapt RL from its traditional, sequential-time environment to the highly interdependent environment of parsing. The result is an algorithm which can demonstrably learn recursive control structures in simple data formats, and can extract meaningful structure from fragments of the PDF format. Whereas prior work in grammar inference focused on either regular languages or constituency parsing, we show that RL can be used to surpass the expressiveness of both classes, and offers a clear path to learning context-sensitive languages. The proposed algorithm can serve as a building block for understanding the ecosystems of de facto data formats. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> RL-GRIT may be pronounced as “Real Grit.”
What Makes for Hierarchical Vision Transformer?

Recent studies indicate that hierarchical Vision Transformer (ViT) with a macro architecture of interleaved non-overlapped window-based self-attention & shifted-window operation can achieve state-of-the-art performance in various visual recognition tasks, and challenges the ubiquitous convolutional neural networks (CNNs) using densely slid kernels. In most recently proposed hierarchical ViTs, self-attention is the de-facto standard for spatial information aggregation. In this paper, we question whether self-attention is the only choice for hierarchical ViT to attain strong performance, and study the effects of different kinds of cross-window communication methods. To this end, we replace self-attention layers with embarrassingly simple linear mapping layers, and the resulting proof-of-concept architecture termed TransLinear can achieve very strong performance in ImageNet-[Formula: see text] image recognition. Moreover, we find that TransLinear is able to leverage the ImageNet pre-trained weights and demonstrates competitive transfer learning properties on downstream dense prediction tasks such as object detection and instance segmentation. We also experiment with other alternatives to self-attention for content aggregation inside each non-overlapped window under different cross-window communication approaches. Our results reveal that the macro architecture, other than specific aggregation layers or cross-window communication mechanisms, is more responsible for hierarchical ViT's strong performance and is the real challenger to the ubiquitous CNN's dense sliding window paradigm.
Multiframe weak target track-before-detect based on pseudo-spectrum in mixed coordinates

Traditional multiframe Track-Before-Detect (TBD) may incur adverse integration loss resulting from model mismatch in sensor coordinates. Its suboptimal integration strategy may cause target envelope degradation. To address these issues, a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly. The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates. The algorithm performance is improved since the model mismatch is eliminated, and the pseudo-spectrum based integration facilitates well maintained target envelope. The detailed multiframe integration procedure and theoretical target integrated envelope are derived. Secondly, to cope with the unknown target velocity, a velocity filter bank based on pseudo-spectrum in mixed coordinates is proposed. The effect of velocity mismatch on algorithm performance is analyzed and an efficient method for filter bank design is presented. Thirdly, a parameter estimation method using characteristics of integrated envelope is presented for improved target polar position and Cartesian velocity estimation. Finally, numerical results are provided to demonstrate the effectiveness of the proposed method.
Location-Based Mobile Augmented Reality Systems: A Systematic Review

The advancement of virtual reality has sharpened the concept of augmented reality (AR) to new dimension of perceptions of seeing, hearing and immersing in a real world. The evolution of mobile devices has pioneered AR as a state-of-the-art technology in the last decade giving rise to more and more location-based mobile AR (LBMAR) systems. However, notably there are very limited review studies that have focused on investigating factors such as: growth, types, characteristics, features, sensors, application domains and their respective challenges. This study presents a systematic review on location-based mobile augmented reality (LBMAR) system. A total of 35 studies published between the years 2013 and 2018 in top six most popular indexed databases are reviewed. The systematic review has been conducted using Kitchenham method, and the analysis of the findings was carried out using the PRISMA method. This chapter provides a major review of the current state of LBMAR system and outlines the research issues that require more investigation.
The Full Gamut of an Attack: An Empirical Analysis of OAuth CSRF in the Wild

OAuth 2.0 is a popular and industry-standard protocol. To date, different attack classes and relevant countermeasures have been proposed. However, despite the presence of guidelines and best practices, the current implementations are still vulnerable and error-prone. In this research, we focus on OAuth Cross-Site Request Forgery (OCSRF) as an overlooked attack scenario. We studied one of the most recurrent types of OCSRF attacks by proposing several novel attack strategies based on different status of the victim browser. In order to validate them, we designed a repeatable methodology and conducted a large-scale analysis on 314 high-ranked sites to assess the prevalence of OCSRF vulnerabilities. Our automated crawler discovered about 36% of targeted sites are still vulnerable and detected about 20% more well-hidden vulnerable sites utilizing the novel attack strategies. Although our experiment revealed a significant increase in the number of OCSRF protection compared to the past scale analyses, over one-fourth are still vulnerable to at least one proposed attack strategy.
A New Key Generation Technique based on Neural Networks for Lightweight Block Ciphers

In recent years, small computing devices used in wireless sensors, radio frequency identification (RFID) tags, Internet of Things (IoT) are increasing rapidly.However, the resources and capabilities of these devices are limited.Conventional encryption ciphers are computationally expensive and not suitable for lightweight devices.Hence, research in lightweight ciphers is important.In this paper, a new key scheduling technique based on neural network (NN) is introduced for lightweight block ciphers.The proposed NN approach is based on a multilayer feedforward neural network with a single hidden layer with the concept of nonlinear activation function to satisfy the Shannon confusion properties.It is shown here that NN consisting of 4 input, 4 hidden, and 4 output neurons is the best in key scheduling process.With this architecture, 5 unique keys are generated from 64 bit input data.Nonlinear bit shuffling is applied to create enough diffusion.The 4-4-4 NN approach generates the se-cure keys with an avalanche effect of more than 50 percent and consumes less power and memory, thus ensuring better performance than that of the existing algorithms.In our experiments, the memory usage and execution cycle of the NN key scheduling technique are evaluated on the fair evaluation of lightweight cryptographic systems (FELICS) tool that runs on the Linux operating system.The proposed NN approach is also implemented using MATLAB 2021a to test the key sensitivity by the histogram and correlation graphs of several encrypted and decrypted images.Results also show that compared to the existing algorithms, the proposed NNcipher algorithm has lower number of execution cycles and hence less power consumption.
Camera Obscura: Exploiting in-camera processing for image counter forensics

For the last two decades Image Forensics has been providing an arsenal of forensic tools to detect tampered images. At the same time, anti-forensics technologies kept evolving to mislead forensic detectors. Such attacks are generally designed to affect a single forensic trace (e.g. JPEG compression, sensor patter noise, histogram statistics) without considering that the image alteration can negatively affect other traces, thus making harder to mimic multiple statistics of natural images. Nevertheless, performing of several attacks can ruin the image quality. In this paper we introduce Camera Obscura, a method that, given a tampered picture, generates a pristine image with native camera metadata, JPEG structure, Quantization Tables, Preview, Thumbnails, their corresponding Quantization Tables, single compression statistics and any in-camera (even proprietary) processing. The attack is performed by exploiting and extending Magic Lantern, an alternative camera firmware, to perform in-camera image buffer substitution. As opposed to available image anti-forensic techniques, Camera Obscura accurately reproduces multiple image statistics in one attack with a limited effect on global image quality. With these characteristics, it can be applied for many different applications such as injecting reference image statistics into a computer generated image, exchange the supposed source of a given image or hide any signal-based editing operation.
Provenance-Based Security Audits and Its Application to COVID-19 Contact Tracing Apps

Software repositories contain information about source code, software development processes, and team interactions. We combine the provenance of development processes with code security analysis results to provide fast feedback on the software's design and security issues. Results from queries of the provenance graph drives the security analysis, which are conducted on certain events—such as commits or pull requests by external contributors. We evaluate our method on Open Source projects that are developed under time pressure and use Germany's COVID-19 contact tracing app 'Corona-Warn-App' as a case study.
A community-initiated website development project: promoting a San community campsite initiative

Digital services have not been truly available for marginalised rural communities, especially in the Global South. Rural communities often lack technical infrastructures, awareness about available services and the necessary skills to use them to their full potential. In this paper, we present a collaborative web-site development project initiated by a rural San community in Namibia. The website intends to support a current community livelihood activity of setting up a campground next to their village. Embedded in a community-based research approach we have deployed co-design methods and techniques to establish a consensus-based business description, including target market, activities, pricing and promotion strategies. Co-design techniques enabled the community to examine and refine their conceptualisation of their business endeavour as well as anticipate target audiences and operational scenarios. A basic website has been created including the agreed upon content. Further efforts include a wider dissemination of the information as well as possible inclusion in global broker websites. We conclude with a reflection on the co-design process providing a researcher and a community perspective.
Prioritizing code documentation effort: Can we do it simpler but better?

. Due to time or economic pressures, code developers are often unable to write documents for all modules in a project. Recently, a supervised artificial neural network (ANN) approach is proposed to prioritize documentation effort “to ensure that sections of code important to program comprehension are thoroughly explained”. . However, as a supervised approach, there is a need to use labeled training data to train the prediction model, which may not easy to obtain in practice. Furthermore, it is unclear whether the ANN approach is generalizable, as it is only evaluated on several small data sets collected from API libraries. . In this paper, we propose an unsupervised approach based on improved PageRank to prioritize documentation effort. This approach identifies “important” modules only based on the dependence relationships between modules in a project. As a result, the PageRank approach does not need any training data to build the prediction model. . In order to evaluate the effectiveness of the PageRank approach, we use six additional large data sets collected from two larger libraries and four applications to conduct the experiment. The experimental results show that the PageRank approach is superior to the state-of-the-art ANN approach. . Due to the simplicity and effectiveness, we advocate that the PageRank approach should be used as an easy-to-implement baseline in future research on documentation effort prioritization, and any newly proposed approach should be compared with it to demonstrate its effectiveness.
Blockchain Technology in Healthcare: A Scientific and Technological Driving Force

Blockchain is a technology to enable decentralized collaboration among un-trusted entities. Academia and industry are rushing to uncover its potential for their field of interest. Due to the novelty of the technology and its diverse applications, there are some ambiguities in approaches and trends. In this paper, we analyze the scientific publications and patents from the past five years to identify the trends for blockchain integration with healthcare. For this purpose, we have adopted a quantitative (clustering) and qualitative (theme extraction) approach to discover themes and temporal dynamics in academia and industry. Our results shed light on the potential challenges and vision for future works.