Reviewerly/exHarmony huggingface dataset:
ConArgLib: an argumentation library with support to search strategies and parallel search

We present ConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based abstract Argumentation. The library is based on ConArg, which exploits Constraint Programming and, in particular, Gecode, a toolkit for developing constraint-based systems and applications. Given a semantics, such problems consist, for example, in enumerating all the extensions, and checking the credulous or sceptical acceptance of an argument passed as parameter. The goal is to let programmers use the library to quickly develop programs on top of it, as, for instance, implementing decision-making procedures based on the strongest arguments, or comparing two frameworks by looking at the differences between their (e.g., stable) semantics. The library features the possibility to use different branching strategies, which we all test and compare on a set of frameworks taken from the International Competition on Computational Models of Argumentation (ICCMA17). Moreover, for some of the tasks, it is possible to perform a parallel search using several workers at the same time: we test the speed-up between using from 1 to 16 threads on a set of ICCMA17 frameworks.
Unsupervised 3D Reconstruction and Grouping of Rigid and Non-Rigid Categories

In this paper we present an approach to jointly recover camera pose, 3D shape, and object and deformation type grouping, from incomplete 2D annotations in a multi-instance collection of RGB images. Our approach is able to handle indistinctly both rigid and non-rigid categories. This advances existing work, which only addresses the problem for one single object or, they assume the groups to be known a priori when multiple instances are handled. In order to address this broader version of the problem, we encode object deformation by means of multiple unions of subspaces, that is able to span from small rigid motion to complex deformations. The model parameters are learned via Augmented Lagrange Multipliers, in a completely unsupervised manner that does not require any training data at all. Extensive experimental evaluation is provided in a wide variety of synthetic and real scenarios, including rigid and non-rigid categories with small and large deformations. We obtain state-of-the-art solutions in terms of 3D reconstruction accuracy, while also providing grouping results that allow splitting the input images into object instances and their associated type of deformation.
Mechanisms for Hiding Sensitive Genotypes With Information-Theoretic Privacy

Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.
Military Message Passing using Consortium Blockchain Technology

Blockchain is a emerging technology with decentralized infrastructure. This technology is used in almost all the domains including finance, energy, cloud computing, messaging and many more. Blockchain plays a important role in military message passing which ensures security of messages by avoiding changes made to data that is stored in blocks. Consortium blockchain requires the permission where each and every user are authorized users. The main issue is Distributed Consensus in the current system of PoW algorithm. So here the consortium blockchain technology is proposed on “MESSAGE PASSING TECHNOLOGY” in military messaging system called HPBC. This algorithm will works well on asynchronous network which in turn gives the results in high performance and analysis evaluation.
Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.
Automatic Speaker Recognition System based on Optimised Machine Learning Algorithms

Speaker recognition is a technique that automatically identifies a speaker from a recording of their voice. Speaker recognition technologies are taking a new trend due to the progress in artificial intelligence and machine learning and have been widely used in many domains. Continuing research in the field of speaker recognition has now spanned over 50 years. In over half a century, a great deal of progress has been made towards improving the accuracy of the system's decisions, through the use of more successful machine learning algorithms. This paper presents the development of automatic speaker recognition system based on optimised machine learning algorithms. The algorithms are optimised for better and improved performance. Four classifier models, namely, Support Vector Machines, K-Nearest Neighbors, Random Forest, Logistic Regression, and Artificial Neural Networks are trained and compared. The system resulted with Artificial Neural Networks obtaining the state-of-the-art accuracy of 96.03% outperforming KNN, SVM, RF and LR classifiers.
Moving Targets: Addressing Concept Drift in Supervised Models for Hacker Communication Detection

In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface web) have not shown a decrease in accuracy for the same period of time. The problem is alleviated by retraining the model with new instances (and applying weights) in order to reduce the effects of concept drift. While our results indicated that performance degradation due to concept drift is avoided by 50% relabelling, which is challenging in real-world scenarios, our work paves the way to more targeted concept drift solutions to reduce the re-training tasks.
Inverse Problems, Constraint Satisfaction, Reversible Logic, Invertible Logic and Grover Quantum Oracles for Practical Problems

It is well-known that the “Unsorted Database” quantum algorithm by Grover gives quadratic speedup to several important combinatorial and enumerative problems, such as: SAT, Graph Coloring, Maximum Cliques, Travelling Salesman and many others. Recently, quantum programming languages such as Quipper start to be used to design, verify and simulate practical quantum algorithms for important problems in Quantum Machine Learning. So far, however, no methodologies have been created to program Grover Oracles for particular classes of problems. In contrast, such methodologies have been already created for classical Constraint Satisfaction Problems. The goal of this invited talk is to show results of some initial research towards creating systematic methodologies to program quantum computers that solve search problems in Artificial Intelligence, Logic Design and Machine Learning. Our methods are based on unified oracle blocks for such problem representations as set partition algebra, cube calculus and optimal mappings. For instance, several important problems in CAD and Machine Learning can be solved using only two basic operations on set partitions; Π1 ≤ Π2 and Π1 · Π2. Moreover, building oracles is the fundamental concept in the new approach to solve CSP proposed here and based on Invertible Logic introduced recently by Supriyo Datta and his team.
Perspective-2-Point Solution in the Problem of Indirectly Measuring the Distance to a Wagon

The article considers an algorithm for solving the problem of indirectly measuring the distance to a wagon by reducing it to solving the Perspective-2-Point problem. It is shown that on a straight section of a railway track at distances up to 50 meters, an absolute error of measuring the distance to a locomotive or wagon is no more than 1‥2 meters.
Feature optimization and hybrid classification for malicious web page detection

Summary The security threats from malicious web pages have become a hot topic for cyber security. One goal pursued by current research is to identify malicious web pages quickly, accurately, and efficiently. Considering the high detection costs and potential dimensionality curse of malicious webpage detection, in this article, we proposes a detection framework based on feature optimization and hybrid classification. It provides three properties: more new malicious webpage features, information gain‐based feature selection method, and integrating multiple machine learning method. A comprehensive experimental evaluation demonstrates that the proposed framework has remarkable advantages in aspects of detection accuracy and detection performance.
Engaging Place with Mixed Realities: Sharing Multisensory Experiences of Place Through Community-Generated Digital Content and Multimodal Interaction

This paper discusses the motivation and potential methodologies for the use of mixed reality and multimodal interaction technologies to engage communities and members of the public with participation in the active creation and use of urban data. This research has been conducted within the context of a wider research program investigating the use of data dashboard technologies and open data to more effectively communicate information to urban authorities and citizens and enable more evidence-based decision making. These technologies have drawn criticism for promoting objectifying, data-driven approaches to urban governance that have proven insensitive to the specificity of place and the contexts of citizens' daily lives. Within the digital and spatial humanities, there has been growing interest in 'deep mapping' as a means for recovering the sense of place and the nuances of everyday life through the incorporation of spatial narratives and multimedia in their mapping practices. This paper considers the ways in which mixed realities can contribute to these efforts, and in particular the unique affordances of virtual reality for evoking an embodied sense of presence that contributes to the communication of a sense of place via rich multisensory experiences. The paper concludes with the discussion of a pilot study conducted with members of the public. This demonstrates the ways in which virtual environments can be created in ways that maintain contextual and affective links to the places they represent as a result of involvement in 'hands-on' activity of mapping through urban sensing and the capture of place-based media.
Illicit Drug Purchases via Social Media Among American Young People

Illicit drugs are sold online. Besides cryptomarkets, young people today are also using social media to buy and sell different drugs. The aim of this nationwide study was to investigate the phenomenon of buying drugs from social media among American young people. Relatively few studies have investigated young people buying drugs online and, therefore, it is important to know more about the psychological and social risk factors of this behavior. The participants of the study were 15–25-year-olds from the U.S. (M = 20.05; 50.17% female). Buying drugs online was utilized as an outcome variable. The covariates included measures of impulsivity and delay of gratification, sense of belonging to online communities, online homophily, friends sharing risk material online, psychological distress, and measures for addictive behaviors including hazardous drinking, problem gambling, and compulsive Internet use. Results showed that buying drugs online is still a relatively rare phenomenon, but many of those buying drugs online used social media services to do so. Buying drugs online was associated with higher impulsivity and lower measures of delay discounting indicating self-control problems. Online buyers also had multiple problems with mental wellbeing, as they reported more psychological distress, problem gambling, and compulsive Internet use than those drug users who had not bought drugs online. The existence and comorbidity of these problems suggest that drug availability online might worsen their situation. As impulsive decisions are especially easy to make on social media, more focus should be placed on youth behavior on mainstream social media services.
Efficient Context-Sensitive CFI Enforcement Through a Hardware Monitor

Recent works on Control-Flow Integrity (CFI) have mainly focused on Context-Sensitive CFI policies to provide higher security guarantees. They utilize a debugging hardware feature in modern Intel CPUs, Processor Trace (PT), to efficiently collect runtime contextual information. These PT-based CFI mechanisms offload the processing of the collected PT trace and CFI enforcement onto idle cores. However, a processor does not always have idle cores due to the commonly-used multi-threaded applications such as web browsers. In fact, dedicating one or more cores for CFI enforcement reduces the number of available cores for running user programs. Our evaluation with a state-of-the-art CFI mechanism ( $$\mu $$ CFI) shows that the performance overhead of a CFI mechanism can substantially increase (up to 652% on a single-core processor) when there is no idle core for CFI enforcement. To improve the performance of $$\mu $$ CFI, we propose to leverage a hardware monitor that unlike PT does not incur trace processing overhead. We show that the hardware monitor can be used to efficiently collect program traces (<1% overhead) in their original forms and apply $$\mu $$ CFI. We prototype the hardware-monitor based $$\mu $$ CFI on a single-core RISC-V processor. Our analysis show that hardware-monitor based $$\mu $$ CFI incurs, on average, 43% (up to 277%) performance overhead.
Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed compensator (PDC) TS fuzzy model to characterize the global behaviour of the nonlinear system and synthesize a feasible control framework using a velocity compensation scheme. The nonlinear dynamics of the ball on plate system is obtained using the Euler-Lagrangian energy based approach. To identify the moving objects in the video stream, a background subtraction algorithm using thresholding technique is formulated. Moreover, the stability analysis of the TS fuzzy control is reduced to linear matrix inequality (LMI) problem and solved using the Lyapunov direct method. The potential benefits of the proposed control structure for real time test cases are experimentally assessed using hardware in loop (HIL) testing on a ball on plate system. Experimental results substantiate that the TS fuzzy scheme can significantly improve not only the tracking performance but also the robustness of the closed loop system.
An effective binary artificial bee colony algorithm for maximum set k-covering problem

Given a row set M and a column set C, where each column in C covers several rows of M, the maximum set k-covering problem (MKCP) is to select k columns from C, such hat the number of rows covered by the selected columns is maximized. It can be formulated as linear integer programming, and has several real world applications. Several heuristic approaches have been previously presented for solving the MKCP. However, the obtained solution quality is not stable, and the solution time increases very quickly as the size of the instance increases. This work proposes a hybrid binary artificial bee colony algorithm (HBABC) to solve the MKCP. First, based on the characteristic of MKCP, HBABC redesigns a food source updating method. The new updating method uses the previously found solutions to guide the search. Second, to improve the exploitation ability of the HBABC, a tabu based simulated annealing (TBSA) is proposed. Moveover, we employ a bucket sorting technique to speed up the TBSA. Finally, the computational results on 75 benchmark instances demonstrate that the HBABC competes favorably with other algorithms. Specifically, our algorithm improves the best known solutions on about 20 percent of the tested instances. In addition, the HBABC performs better than two binary artificial bee colony algorithms on the tested instances.
Low-resolution Human Identification in thermal imagery

Object identification from an image is a tedious task in image processing. In past years many methodologies have been proposed to solve these kinds of problems. The detection of humans is paramount for applications like Advanced Driving Assistance Systems (ADAS) or Intruder Surveillance Systems or survivor detection in search and rescue efforts. Presently available image processing methods are taking a large amount of time to process the images and present an approach to identify a human object from an image which has less brightness or low resolution. Object detection system based on unsupervised learning method “K-means clustering” followed by a deep learning approach on thermal images. The system follows a two-step approach of generating anchor boxes using the clustering method and then using those anchor boxes to predict proper boundary boxes by using the deep learning method “tiny Yolo_v3”. This proposed system can suitable for real-time object detection in small-applications (less-than 50MB), embedded applications and it can achieve a much higher rate of processing when compared to traditional approaches.
Forensic Event Analysis: From Seemingly Unrelated Data to Understanding

We discuss the problem of restructuring visual data from different heterogeneous sources to analyze an event of interest. We present X-coherence: a pipeline seeking to organize and represent pieces of data, tying them coherently with the real world and with one another. We also outline research challenges while seeking X-coherence.
Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning

This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.
Analysis of Pruned Neural Networks (MobileNetV2-YOLO v2) for Underwater Object Detection

Underwater object detection involves the activity of multiple object identification within a dynamic and noisy environment. Such task is challenging due to the inconsistency of moving shapes underwater (i.e. goldfish) within a very dynamic surrounding (e.g. bubbles, miscellaneous objects). The application of pre-trained deep learning classifiers (e.g. AlexNet, ResNet, GoogLeNet and so on) as the backbone of several object detection algorithms (e.g. YOLO, Faster-RCNN and so on) have gained popularity in recent years, however, there is a lack of attention on the systematic study to reduce the size of the pre-trained neural networks hence speeding up the object detection process in the real-world application. In this work, we investigate the effect of reducing the size of the pre-trained MobileNetV2 as the backbone of the YOLOv2 object detection framework to construct a fast, accurate and small neural network model to perform goldfish breed identification in real-time.
A Framework of Input Devices to Support Designing Composite Wearable Computers

Composite wearable computers combine multiple wearable devices to form a cohesive whole. Designing these complex systems and integrating devices to effectively leverage their affordances is nontrivial. To inform the design of composite wearable computers, we undertook a grounded theory analysis of 84 wearable input devices drawing from 197 data sources, including technical specifications, research papers, and instructional videos. The resulting prescriptive design framework consists of four axes: type of interactivity, associated output modalities, mobility, and body location. This framework informs a composition-based approach to the design of wearable computers, enabling designers to identify which devices fill particular user needs and design constraints. Using this framework, designers can understand the relationship between the wearable, the user, and the environment, identify limitations in available wearable devices, and gain insights into how to address design challenges developers will likely encounter.