Reviewerly/exHarmony huggingface dataset:
Between communication and violence

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Dynamic power control for rational cryptocurrency mining

In blockchain and cryptocurrency, miners participate in a proof-of-work-based distributed consensus protocol to find and generate a valid block, process transactions, and earn the corresponding reward. Because cryptocurrency is designed to adapt to the dynamic miner network size, a miner's participation affects the block difficulty which sets the expected amount of work to find a valid block. We study the dependency between the mining power control and the block difficulty and study a rational miner utilizing such dependency to dynamically control its mining power over a longer horizon than just the impending block. More specifically, we introduce I-O Mining strategy where a miner takes advantage of the block difficulty adjustment rule and toggles between mining with full power and power off between the difficulty adjustments. In I-O Mining, the miner influences the block difficulty and mines only when the difficulty is low, gaming and violating the design integrity of the mining protocol for its profit gain. We analyze the I-O Mining's incentive/profit gain over the static-mining strategies and its negative impact on the rest of the blockchain mining network in the block/transaction scalability. Our results show that I-O Mining becomes even more effective and profitable as there are greater competitions for mining and the reward and the cost difference becomes smaller, which are the trends in cryptocurrencies.
User Profiling Using Sequential Mining Over Web Elements

Most of today's sensitive systems offer receiving information and performing actions remotely through the company's website by using authentication mechanisms such as username and password, one-time tokens, etc. However, these methods are not immune against credentials theft. Authenticated sessions can be subjected to session hijacking through stolen cookies, man in the middle (MitM) attacks, social Engineering and more. Behavioral biometrics models within the website can help continuously verify user's identity throughout the session and prevent a wide variety of attacks. Such methods usually focus on sampling the raw movements and extracting meaningful features for profiling such as velocity, curvature, distance, etc. However, traditional methods usually avoid the elements that are being traversed by the user and do not use the created sequence of elements the user usually go through, which can be easily extracted in web applications. Therefore, we introduce a novel approach for pointing device (e.g. mouse, finger, touchpad, trackball) sequential verification in web applications. This approach can be used in addition to traditional feature-based movement techniques and enhance them. Our approach is based on Hidden Markov Model and Linear Regression to profile a user based on web elements sequences constructed from the user's pointing device movements. We demonstrate the competitive advantage via encouraging results on anonymized dataset collected by IBM from accounts of 2500 web application users.
Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks

Convolutional and recurrent neural networks (RNNs) have been widely used to achieve state-of-the-art performance on classification tasks. However, it has also been noted that these networks can be manipulated adversarially with relative ease, by carefully crafted additive perturbations to the input. Though several experimentally established prior works exist on crafting and defending against attacks, it is also desirable to have rigorous theoretical analyses to illuminate conditions under which such adversarial inputs exist. This article provides both the theory and supporting experiments for real-time attacks. The focus is specifically on recurrent architectures and inspiration is drawn from dynamical systems’ theory to naturally cast this as a control problem, allowing dynamic computation of adversarial perturbations at each timestep of the input sequence, thus resembling a feedback controller. Illustrative examples are provided to supplement the theoretical discussions.
Reversible Logic Implementation of Image Denoising for Grayscale Images

Reversible logic has emerged as a solution for the limit set by power consumption of devices on reduction of transistor size. This paper aims at introducing reversible logic based implementation for image processing units. A image denoising module is implemented using reversible logic gates. Gray equivalent of an RGB input is processed using a mean filter to remove the noise present in the images. Mean filter with spatial window size of 2×2 and 4×4 is implemented. The verification of functionality is carried out on Kintex 7 using Verilog. The architectures are analysed in terms of power consumption and hardware utilised. The reversible logic implementation is done and the efficacy is measured in scales of gate count(GC), ancilla inputs(AI), quantum cost(QC) and garbage output(GO). The proposed work can act as the initial blocks in image compression algorithm implementations in reversible logic.
An In-Memory Physics Environment as a World Model for Robot Motion Planning

A 2D replica of real-world terrain was created in a physics simulation environment, allowing a robot to "imagine" a simulated version of itself navigating the terrain. The physics of the environment simulates the movement of robot parts and its interaction with objects, thus avoiding the need for explicitly programming various calculations. Since the complexity of motion increases with each degree of freedom of the robot's joints, the utility of uniform randomness was also investigated as an alternative to computational intelligence algorithms for exploring the fitness landscape. Such techniques potentially simplify the algorithmic complexity of programming multi-jointed robots and could adapt by dynamically adjusting simulation parameters, on encountering environments with varied gravity, viscosity or traction.
Cooperative-Coevolution-CMA-ES with Two-Stage Grouping

The Cooperative Coevolution (CC) framework is the state of the art for solving large scale global optimization (LSGO) problems. A particular challenge in using CC lies in the decomposition of variables and resource allocation. In this work, the decomposition phase of the framework is performed in two stages to address both variable interaction and efficient resource allocation. The algorithm starts with differential analysis followed by differential grouping. The differential analysis allows efficient resource allocation while the differential grouping will detect variable interactions. The differential grouping will act on a small number of variables and will not consume as much computational budget as a single-stage grouping. While not all variable interactions will be detected, separable variables will be recognized hence specialized solvers for separable problems can be employed on these subproblems. In this work, the twostage grouping CC (TSCC) is paired with a hybrid algorithm where sep-CMA-ES is used to solve the separable subproblem and CMA-ES is used to solve the non-separable subproblems, the algorithm is referred as TSCC-CMAES. A comparison between TSCC and CC with groups based on either differential analysis or differential grouping is carried out. In general, TSCC could outperform the two single-stage grouping methods. Additionally, the TSCC-CMAES shows a competitive advantage on a number of more complex problems against state-of-the-art algorithms and it is shown that the effect of the population size and group size is crucial in achieving these results.
Fooling It - Student Attacks on Automatic Short Answer Grading

Modern machine learning approaches have been shown to be vulnerable to adversarial attacks in many fields. This is a critical weakness, especially for models that are expected to function in an adversarial environment, such as automatic grading models in exams. However, as most of these attacks are either limited in their success rate, their applicability in diverse scenarios or require mathematical expertise of the attacker, the question arises to which extent students themselves are even capable of fooling state-of-the-art grading models. This work aims to investigate this question for the short answer question format. For this purpose, we tasked students of an educational technologies university course with probing the state-of-the-art automatic short answer grading model for weaknesses. Of the fourteen active participants, only one reported the model to be sufficiently free of deficits. The following weaknesses were identified by the students: a disregard for negation, no plagiarism detection, correct answers not being predicted as such and oversensitivity to small linguistic changes in answers, triggers, and keywords.
Robust Sparse Regularization: Defending Adversarial Attacks Via Regularized Sparse Network

Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the countermeasures against adversarial attacks, increasing the model capacity for DNN robustness enhancement was discussed and reported as an effective approach by many recent works. In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack. For obtaining a simultaneously robust and compact DNN model, we propose a multi-objective training method called Robust Sparse Regularization (RSR), through the fusion of various regularization techniques, including channel-wise noise injection, lasso weight penalty, and adversarial training. We conduct extensive experiments to show the effectiveness of RSR against popular white-box (i.e., PGD and FGSM) and black-box attacks. Thanks to RSR, 85 % weight connections of ResNet-18 can be pruned while still achieving 0.68 % and 8.72 % improvement in clean- and perturbed-data accuracy respectively on CIFAR-10 dataset, in comparison to its PGD adversarial training baseline.
MaxSAT-Based Postprocessing for Treedepth

Treedepth is an increasingly popular graph invariant. Many NP-hard combinatorial problems can be solved efficiently on graphs of bounded treedepth. Since the exact computation of treedepth is itself NP-hard, recent research has focused on the development of heuristics that compute good upper bounds on the treedepth. In this paper, we introduce a novel MaxSAT-based approach for improving a heuristically obtained treedepth decomposition. At the core of our approach is an efficient MaxSAT encoding of a weighted generalization of treedepth arising naturally due to subtree contractions. The encoding is applied locally to the given treedepth decomposition to reduce its depth, in conjunction with the collapsing of subtrees. We show the local improvement method’s correctness and provide an extensive experimental evaluation with some encouraging results.
Inter Frame Video Forgery Detection using Normalized Multi Scale One Level Subtraction

Due to the ease of accessibility to image and video processing software, it has become easier to tamper images and videos without leaving any traces. Such malicious tampering, which cannot be grasped by human eye may lead to undesirable social as well as legal problems. Tampered videos may be used to provide false proof in court or mislead the public about the truth in news reports. This paper aims to propose a novel method called Normalized Multi Scale One Level Subtraction (NMOLS) to detect forgery. In our proposed method video sequence is divided into frames and then pixel grey values of each frame computed, after this all these frames are go through proposed approach. Our system will detect insertion and deletion forgery and it will also detect which type of forgery is done on the video. The proposed method has been assessed utilizing a sizably voluminous dataset. Precision and recall rate of insertion forgery detection is 99% and for deletion forgery detection precision and recall rates are 93% and 96% respectively.
Pyramid: A Hierarchical Approach to Scaling Down Population Size in Genetic Algorithms

We present Pyramid, a Hierarchical Genetic Algorithm that decomposes problems by first tackling simpler versions of them, before automatically scaling up to more difficult versions while also reducing the population size. Pyramid takes its name from the architectural phenomenon of the Mayan pyramid in Chichen-Itza, which, although constructed from the bottom up, operates in a top down manner through interactions with the sun. This gives a two stage approach: initially we create our pyramid of experiments, with the most complex fitness functions at the bottom, and with increasingly more simplified/decomposed version as we move up through the pyramid. Runs start at the top of the pyramid and populations descend through it, decreasing in size, being exposed to increasingly complex fitness functions, until, at the bottom layer, we have small populations with the original full fitness function. We conduct experiments comparing the performance of Pyramid for a suite of difficult unimodal and multimodal functions. The experimental results show that in three of four cases, Pyramid achieves the same or better fitness scores as standard algorithms, with substantially fewer evaluations, and that in two cases it actually performs statistically significantly better.
Decision making for two learning agents acting like human agents : A proof of concept for the application of a Learning Classifier Systems

The paper investigates the suitability of a Learning Classifier System (LCS) implementation for mimicking human decision making in agent based social simulations incorporating network effects. Model behavior is studied for three distinct scenario settings. We provide proof of concept for the adequacy of LCS to tackle the task at hand. Specifically, it is found that the LCS provides the agents within the simulation model with the ability to learn and to react to environmental changes while accounting for bounded rational decision making and the presence of imperfect information, as well as network effects. Moreover, it can be shown that the LCS-agents exhibit a habit like behavioural pattern.
Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine

Constrained multi-objective optimization is frequently encountered from the point of view of practical problem solving. The difficulty of constrained multi-objective optimization is how to offer guarantee of finding feasible optimal solutions within a specified number of iterations. To address the issue, this paper proposes an innovative optimization framework with objective space mapping to decision space for constrained multiobjective optimization and a novel multi-objective optimization algorithms are proposed based on this framework. Extreme learning machine implements prediction of decision variables from modified objective values with distance measure and adaptive penalty. This algorithm employs the framework of artificial bee colony to divide this optimization process into two phases: the employed bees and the onlooker bees. In the phase of employed bees, multi-objective strategy employs fast non-dominant sort and crowded distance to push the population toward Pareto front. In the phase of onlooker bees, multi-objective strategy employs Tchebycheff approach to enhance the population diversity. The experimental results on a series of benchmark problems suggest that our proposed algorithm is quite effective, in comparison to other state-of-the-art constrained multi-objective optimizers.
Research on TFIDF Algorithm Based on Weighting of Distribution Factors

Abstract The current TFIDF (Term Frequency and Inverted Document Frequency) algorithm cannot effectively reflect the relationship between the importance of a word and its distribution. This paper proposes a Class Variance-Term Frequency and Inverted Document Frequency algorithm. This algorithm improves the TFIDF algorithm based on three distribution factors: category, inter-class and variance. In order to measure the optimization effect of this method, three algorithms were compared using the original algorithm, improved algorithm and TFIDF algorithm based on dual parallel calculation model. Experiments show that the improved algorithm has significantly improved recall, accuracy, and F metric values, comparing with the original algorithm, and has improved compared with the TFIDF algorithm based on dual parallel calculation model. Therefore, the improved algorithm can well adapt to the demand for feature word extraction and has better text classification performance.
Blockchains and Stealth Tactics for Teaching Security

Presents the introductory editorial for this issue of the publication.
Fuzzing-based hard-label black-box attacks against machine learning models

Machine learning models are vulnerable to adversarial examples. We study the most realistic hard-label black-box attacks in this paper. The main limitation of the existing attacks is that they need a large number of model queries, making them inefficient and even infeasible in practice. Inspired by the very successful fuzz testing approach in traditional software engineering and computer security domains, we propose fuzzing-based hard-label black-box attacks against machine learning models. We design an AdvFuzzer to explore multiple paths between a source image and a guidance image, and design a LocalFuzzer to explore the nearby space around a given input for identifying potential adversarial examples. We demonstrate that our fuzzing attacks are feasible and effective in generating successful adversarial examples with significantly reduced number of model queries and L 0 distance. More interestingly, given a successful example generated by either our or other attacks, LocalFuzzer can immediately generate more successful adversarial examples even with smaller L 2 distance from the source example.
Determinants of Foreign Investment and International Aid for Meeting the Sustainable Development Goals in Africa: A Visual Cognitive Review of the Literature

The sustainable development goals (SDGs) have recently framed sustainable development priorities in both developed and developing countries. External funding in the form of foreign direct investment (FDI) and official development assistance (ODA) is necessary for achieving practically all of the SDGs and their underlying targets. However, it is not always clear how this funding is allocated between different SDG priorities in Africa, and the factors that influence the ability to attract and effectively use FDI and ODA. We conduct a literature review and an analysis of financial flow data to identify (a) the SDGs mainly targeted by ODA in Africa; (b) the research priorities related to the SDGs in Africa and (c) the factors that attract or drive away FDI and ODA in Africa. Overall, there is a clear mismatch between academic research priorities related to SGDs and actual financial flows for SDGs in Africa. This presents potential risks for poor allocation of resources across the multiple sustainability domains represented by the SDGs. Various economic, institutional and political factors influence how FDIs and ODA are attracted and effectively used in Africa. Based on the literature, we identify a series of approaches to overcoming the obstacles to attracting FDIs and ODA. We group these proposals into five categories, namely: (a) capacity building, (b) liberalization and deregulation, (c) regulation and incentives, (d) partnerships and (e) regional integration.
Community-Grouping Based Particle Swarm optimisation Algorithm for Feature Selection

As a frequently-used dimensionality reduction technique in machine learning, feature selection has attracted interests in the last decade. Since feature selection is essentially a combinatorial optimization problem, how to search the valuable feature subset is a challenging optimization task. Particle swarm optimization (PSO) algorithm and its variations have shown their competitiveness in solving feature selection problem. However, they have been proven to be easily trapped into the local optimal in high-dimensional space due to their intrinsic characteristic of quick convergence. To this end, an effective binary particle swarm optimization algorithm, named CBPSOFS, is proposed for feature selection, where a community-grouping based adaptive updating strategy is designed to avoid trapping into the local optimum and enhance the performance of PSO algorithm in feature selection. To be specific, the correlationship among features is used to construct the feature network, where multiple feature groups are obtained by dividing the achieved feature network. Considering that a community usually contains multiple similar features, the proposed adaptive updating strategy utilizes these feature groups to make the similar features not be included in the same particle so as to maintain the diversity of the population in the evolution. In addition, an information gain based initialization strategy and a history information based resetting strategy are also developed to improve the quality of obtained feature subset. Experimental results on several real world datasets have demonstrated the effectiveness of CBPSOFS in feature selection when compared with the several state-of-the-art baselines.
A Concept-Based Approach for Generating Better Topics for Web Search Results

As the web is accessible to a vast population around the globe, web users today pose a large number of queries, with dynamic, vague and unclear intentions, using the web search tools, as a consequence of which organizing search results have become an all the more challenging task. Further, because of such web queries, it is difficult for web search tools to comprehend the exact user context, and thus they retrieve an extensive volume of results, a significant portion of which are unnecessary for the user. One of the answers to this problem is a strategy called search result clustering (SRC), which bunches the search results and presents them to users with many options for the query. In this work, we have proposed an approach that initially classifies the related topics and lays them out in the form of concepts, and then building search results clusters by designating each to the relevant topic and finally, providing relevant labels for these topics. We examine the effectiveness of our approach by measuring it against two most popular non-commercial methods in this field, specifically Lingo and STC, with two standard datasets, ODP and Ambient, and a newly developed dataset, Ex-Ambient, which is a rigorously extended version of the Ambient Dataset. We performed analysis on both qualitative and quantitative dimensions. We define a qualitative dimension as the expressiveness of the cluster label generated, while quantitative dimension regards the correctness of the document assigned to the cluster. The experimental results presented by the proposed method were encouraging in contrast with Lingo and STC for all the datasets and both the dimensions.