Reviewerly/exHarmony huggingface dataset:
Fault diagnosis architecture for SKINNY family of block ciphers

Cryptographic hardware and software applications are prone to various attacks either from the environments or from the attacker to gain the secret key. Resource-constrained devices use lightweight cryptographic algorithms to achieve a high level of security. It's always a trade-off between efficient resource utilization and level of security. Out of different attacks, in recent years, fault injection attacks is well matured. It becomes imperative to choose the best and efficient fault diagnosis schemes for lightweight cryptography. In this paper, we propose novel Concurrent error detection (CED), i.e., recomputing with inverted operands (REIO) method for SKINNY Family of Block Ciphers to increase the reliability. The proposed fault detection technique for SKINNY round-based pipelined architecture is adapted. The result shows that the throughput overhead of the SKINNY remains within 2.5% variance for the proposed novel fault detection with a pipelined technique, a maximum of 10% area overhead. We have implemented the proposed fault detection scheme using Xilinx FPGA. Best to our knowledge, there is no CED based fault detection technique proposed in the literature for the SKINNY family of block ciphers. The implementation results show that the proposed scheme is more effective and well suited for resource-constrained environments.
Cogito ergo quid? The Effect of Cognitive Style in a Transparent Mobile Music Recommender System

An increasing body of research indicates that transparency in recommender systems affects trust of users. Additionally, a vast amount of studies already showed that personality impacts the way users perceive a recommender system. However, only recently, research has begun to investigate the effects of cognitive style on the perception of recommender systems. Furthermore, it is still unclear whether this cognitive style also affects the interaction strategies of users, and whether the reason why and when users want transparency is affected by this cognitive style. Additionally, despite the ubiquitous presence of recommender systems on mobile environments, no study has investigated the effect of transparency for mobile music recommender systems. In this paper, we report the results of a within-subject study (N=25) on a mobile music recommender system where we investigated the effect of cognitive styles on three different aspects: the interaction strategies with the different applications, the reasons why and when users want transparency and the effect of transparency on the trust of users. The results show that users with a rational thinking style put more effort in seeking the best recommendations and that they want scrutable explanations to adjust the recommendation. In contrast, intuitive thinkers only need explanations when they search for a very specific kind of music.
RGB2AO: Ambient Occlusion Generation from RGB Images

Abstract We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non‐directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometry‐aware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.
Minority Sub-Region Estimation-Based Oversampling for Imbalance Learning

Class imbalance problem that characterized with the skew distribution towards the majority arises as one challenge in recent years. Many oversampling techniques have been proposed to cope with this problem and some of them combine the oversampling procedure with the clustering algorithm which guaranteeing new synthetic samples being generated in clusters. However far-away samples but with the same minority sub-region are generally clustered into different groups owing to the characteristic of clustering algorithm itself. Therefore, the following oversampling procedure is mostly carried in incomplete minority sub-regions that synthetic samples not well cover the integral minority region. And to our best knowledge, none of existing algorithm is designed to directly estimate minority sub-regions for class imbalance problem. Thus, one new grouping algorithm, named Direction Distribution-based Minority Sub-region Estimation (DDMSE), is first proposed. The new algorithm exploits the intuitive observation, that the minority with the same sub-region almost distribute within the same direction when compared to other majority, to estimate minority sub-regions that tactfully ignoring negative impacts brought by the distance factor like in clustering algorithms. Finally, new synthetic samples are generated in those minority sub-regions. And experimental results on real-world datasets show the comparable performance with other state-of-the-art oversampling methods.
Sandboxes and Testnets as “Trading Zones” for Blockchain Governance

FinTech regulatory sandboxes and testnets use cases offer a hybrid model for integrating blockchain technologies with governance, connecting code with regulations, on-chain infrastructure with off-chain institutions. The hybrid models are an alternative to the reduction of governance to consensus mechanisms in the present libertarian but also anarcho-capitalist and communitarian blockchain projects. Inspired by the concepts of “innovation through dissonance” in the so-called “trading zones,” we claim that the regulatory sandboxes can integrate all four regulatory forces (law, social norms, market, and technical infrastructure) rather than only two (FinTech insistence on markets and technology). This evaluation criterium for sandboxes was defined and tested with a simulated ledger (testnet) for exploring near-future scenarios of blockchain governance. In 2019, we conducted five workshops with 35 participants using templates of smart contracts to decide upon regulations of novel services that use satellite data to trigger automatic transactions (change of ownership). In the workshop and following questionnaire, the participants expressed need for a better integration of their natural language, regulations, and code without prioritizing any regulatory force or domain (market, culture, technology, or law), but supporting what we describe as a playful “regulation through dissonance.”
QuickN: Practical and Secure Nearest Neighbor Search on Encrypted Large-Scale Data

In this article, we propose a scheme, named QuickN, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted clouds. Specifically, we modify the search algorithm of nearest neighbors in tree structures (e.g., R-trees), such that the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. Moreover, we propose an optimized algorithm on top of our modified search algorithm, where it can significantly save communication overheads of a client without introducing any additional information leakage. We devise an approximate algorithm to <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor search to improve search efficiency by taking a tradeoff in the completeness of search results. In addition, we also demonstrate our design only leaks minimal privacy against advanced inference attacks. Our experimental results on Amazon EC2 show that our algorithms are extremely practical over massive datasets.
Towards Queryable User Profiles: Introducing Conversational Agents in a Platform for Holistic User Modeling

In this article we introduce the concept of queryable user profile, that is to say, a representation of the user that can be queried through natural language requests.
Bounds on Probability of Detection Error in Quantum-Enhanced Noise Radar

Several methods for exploiting quantum effects in radar have been proposed, and some have been shown theoretically to outperform any classical radar scheme. Here, a model is presented of quantum-enhanced noise radar enabling a similar analysis. This quantum radar scheme has a potential advantage in terms of ease of implementation insofar as it requires no quantum memory. A significant feature of the model introduced is the inclusion of quantum noise consistent with the Heisenberg uncertainty principle applied to simultaneous determination of field quadratures. The model enables direct comparison to other quantum and classical radar schemes. A bound on the probability of an error in target detection is shown to match that of the optimal classical-state scheme. The detection error is found to be typically higher than for ideal quantum illumination, but orders of magnitude lower than for the most similar classical noise radar scheme.
Topic discovery method based on topic model combined with hierarchical clustering

Topic discovery is increasingly used today in the rapid development of the Internet, especially in the field of Weibo. However, due to the short content of Weibo, the traditional LDA topic model has some effects on the topic discovery of Weibo. An LDA model combined with an improved BIRCH hierarchical clustering algorithm was used for topic discovery, and short text extensions were made for microblogs with short text content. Compared with the combined effect of K-means and BIRCH clustering algorithms, the results of topic discovery using BC-BIRCH clustering algorithm are better.
Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions

Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks, whose kernels are typically defined on a fixed grid and focus on all input pixels independently. However, this is in contrast with the sparse nature of handwritten pages, in which only pixels representing the ink of the writing are useful for the recognition task. Furthermore, the standard convolution operator is not explicitly designed to take into account the great variability in shape, scale, and orientation of handwritten characters. To overcome these limitations, we investigate the use of deformable convolutions for handwriting recognition. The kernel of this type of convolution deforms according to the content of the neighborhood, and can therefore be more adaptable to geometric variations and other deformations of the text. Experiments conducted on the IAM and RIMES datasets demonstrate that the use of deformable convolutions is a promising direction for the design of novel architectures for handwritten text recognition.
Review of Counter-UAV Solutions Based on the Detection of Remote Control Communication

One of the problems that we face today is the illegal use of drones. Monitoring the RF transmissions has proved to be a reliable technique for detecting and identifying such devices. Although there are some solutions on the market for detection and jamming, they are not always successful. The aim of this paper is to analyze the typical communications used by drone manufacturers, which usually implement their own algorithms, and to propose a more robust solution that would consider the range of characteristics that these protocols have. A wide-band energy detection with adaptive threshold is used for extracting specifications of some remote control communications that use spread spectrum modulation.
Multilevel motion planning: A fiber bundle formulation

High-dimensional motion planning problems can often be solved significantly faster by using multilevel abstractions. While there are various ways to formally capture multilevel abstractions, we formulate them in terms of fiber bundles. Fiber bundles essentially describe lower-dimensional projections of the state space using local product spaces, which allows us to concisely describe and derive novel algorithms in terms of bundle restrictions and bundle sections. Given such a structure and a corresponding admissible constraint function, we develop highly efficient and asymptotically optimal sampling-based motion planning methods for high-dimensional state spaces. Those methods exploit the structure of fiber bundles through the use of bundle primitives. Those primitives are used to create novel bundle planners, the rapidly-exploring quotient space trees (QRRT*), and the quotient space roadmap planner (QMP*). Both planners are shown to be probabilistically complete and almost-surely asymptotically optimal. To evaluate our bundle planners, we compare them against classical sampling-based planners on benchmarks of four low-dimensional scenarios, and eight high-dimensional scenarios, ranging from 21 to 100 degrees of freedom, including multiple robots and nonholonomic constraints. Our findings show improvements up to two to six orders of magnitude and underline the efficiency of multilevel motion planners and the benefit of exploiting multilevel abstractions using the terminology of fiber bundles.
Distributed IoT Attestation via Blockchain

We propose a novel attestation architecture for the Internet of Things (IoT). Our distributed attestation network (DAN) utilizes blockchain technology to store and share device information. We present the design of this new attestation architecture as well as a prototype system chosen to emulate an IoT deployment with a network of Raspberry Pi, Infineon TPMs, and a Hyperledger Fabric blockchain.
Perceptual Validation of Nonlinear Postural Predictors of Visually Induced Motion Sickness

Virtual reality (VR) technology has become increasingly prevalent in our society and has been used for a myriad of applications ranging from psychotherapy to training members of the military. However, one issue that arises from the use of VR is motion sickness, thus making predictors and indicators of motion sickness desirable. To date, a number of indicators of motion sickness have been derived based on nonlinear characteristics of human motion recorded using motion capture systems. While it is known that nonlinear measures can be used to predict motion sickness, it is not known whether people are perceptually sensitive to these particular nonlinear parameters. The aims of this study included establishing whether individuals consistently sort phase plots of sick and well individuals' postural motion without being explicitly told to do so; determining what nonlinear movement parameters could be used to represent these judgments; and assessing the stability of nonlinear measures found to be successful at predicting motion sickness by Smart et al. (2014). Through two methods of analysis (perceptual and quantitative), this research demonstrated that participants can indeed sort the graphic depictions of sick and well participants' postural motion and seem to be perceptually sensitive to nonlinear parameters (normalized path length, path length, elliptical area) that are known to be predictive of motion sickness.
Factors promoting the use of virtual worlds in educational settings

Abstract This paper focuses on student attitudes about using a virtual world (VW) learning platform. VWs allow students to meet via avatars and experience events that simulate real social experiences. It can also enhance student motivation, learning and collaboration. However, studies have reported that many students and teachers find VW difficult to use, and thus, do not adopt it. The current study sought to examine preservice teachers' experiences in VW in a multiple‐college course. The aims of the study were to focus on the possible difficulties and supporting factors affecting the present use of VW and identify factors affecting the future use of VW. Ninety‐nine Jewish and Arab students from eight teachers' colleges in Israel who participated in a VW learning experience completed an online questionnaire, which included items relating to their experience with computer games, difficulties and level of satisfaction with the course. Computer‐game experience, cultural background, technical difficulties and satisfaction with class management predicted the intention to use VW in the future. Possible practical and theoretical implications are discussed.
The Construction of Teaching Quality Evaluation System of Modern Apprenticeship Based on Big Data

Abstract The apprenticeship system with teachers and apprentices has a long history in China, and has made great contributions to China’s economic development, the inheritance of advanced technology and the progress of human society. Essentially, modern apprenticeship and traditional apprenticeships is consistent, namely have teacher, student and teacher to student’s education and instruction, emphasize “in high school, learn to do”, but its formation and the basis of the meaning and value is different from that of traditional apprenticeship, the main body of apprenticeships, forms, as well as the relationship between teachers and students, are changed.
Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing

The use of machine learning algorithms to enrich agent-based models has increased over the past years. This integration adds value when combining the advantages of the data-driven approach and the possibilities to explore future situations and human interventions. However, this integrating is still in its infant stage. Full integration of learning algorithms and agent-based models is often technically challenging and can make the behavioural rules of the agents less transparent. Experiments are needed in which different integration strategies are compared using the same agent-based model to determine when each of these approaches is most effective. In this paper, we present a comparison of two versions of the same cholera model. In the initial version, agent behaviour was driven directly by a learning algorithm. In our experiments, we replace this strategy by applying a learning algorithm directly on the data and implement the outcomes as behaviour rules in the model. The results showed that when the integration aims to create agents that show characteristics that are data-driven, deriving rules based on these data is a good alternative. In addition, a key element in this strategy is the dataset. A large dataset representing the behaviour of different types of agents over the complete time period is needed.
Image Deblurring of Camera Under Display by Deep Learning

A deep learning method is provided in this paper to deblur images captured behind OLED screen named Camera Under Display (CUD). Because of optical diffraction of OLED pixel pattern, images captured by front camera under the display panel are unfavorably blurred. This paper proposed a modified GAN training network to deblur these images, with result showing perceptually clearer image with better restored high spatial frequency information.
Designing Chatbots as Community-Owned Agents

This work investigates how social agents can be designed to create a sense of ownership over them within a group of users. Social agents, such as conversational agents and chatbots, currently interact with people in impersonal, isolated, and often one-on-one interactions: one user and one agent. This is likely to change as agents become more socially sophisticated and integrated in social fabrics. Previous research has indicated that understanding who owns an agent can assist in creating expectations and understanding who an agent is accountable to within a group. We present findings from a three week case-study in which we implemented a chatbot that was successful in creating a sense of collective ownership within a community. We discuss the design choices that led to this outcome and implications for social agent design.
An Application of Random Walk on Fake Account Detection Problem: A Hybrid Approach

In the world we are living now, it's very difficult to find a people who access to Internet regularly but own no social network account. Social networks play a significant and day by day, more important role. People use Facebook or Twitter for communicating, news reading, information sharing, and advertising. However, these social networks lead to some issues. One of which is the need for a defense mechanism against fake accounts. To separate fake accounts from authentic ones is obviously a non trivial task. In this paper, we propose an empirical ranking scheme, comprising of both graph-based and feature-based approaches to aid the detection of fake Facebook profiles. Utilizing Support Vector Machine (SVM) [4] and Sybil- Walk [8], the model achieved high accuracy over the set of ten thousands Vietnamese Facebook accounts.
Sublinear Time Force Computation for Big Complex Network Visualization

Abstract In this paper, we present a new framework for sublinear time force computation for visualization of big complex graphs. Our algorithm is based on the sampling of vertices for computing repulsion forces and edge sparsification for attraction force computation. More specifically, for vertex sampling, we present three types of sampling algorithms, including random sampling, geometric sampling, and combinatorial sampling, to reduce the repulsion force computation to sublinear in the number of vertices. We utilize a spectral sparsification approach to reduce the number of attraction force computations to sublinear in the number of edges for dense graphs. We also present a smart initialization method based on radial tree drawing of the BFS spanning tree rooted at the center. Experiments show that our new sublinear time force computation algorithms run quite fast, while producing good visualization of large and complex networks, with significant improvements in quality metrics such as shape‐based and edge crossing metrics.
Measuring the Effectiveness of Software Code Review Comments

Code reviewing becomes a more popular technique to find out early defects in source code. Nowadays practitioners are going for peer reviewing their codes by their co-developers to make the source code clean. Working on a distributed or dispersed team, code review is mandatory to check the patches to merge. Code reviewing can also be a form of validating functional and non-functional requirements. Sometimes reviewers do not put structured comments, which becomes a bottle neck to developers for solving the findings or suggestions commented by the reviewers. For making the code review participation more effective, structured and efficient review comments is mandatory. Mining the repositories of five commercialized projects, we have extracted 15223 review comments and labelled them. We have used 8 different machine learning and deep learning classifiers to train our model. Among those Stochastic Gradient Descent (SGD) technique achieves higher accuracy of 80.32%. This study will help the practitioners to build up structured and effective code review culture among global software developers.