Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Secure Data Storage scheme based on Adaptive ANN and Homomorphic Re-Encryption Algorithm for Cloud

Intrusion detection systems (IDSs) protect the communication networks and computer systems by supervising data traffic and finding malicious activities that represent uncommon network or system behavior. From data instances that correspond to the normal behavior of the network, the IDS learn the data model of normality which is known as inliers. Also, they detect the malicious activities that are capable of detecting intruders and generating the alarms when any illegitimate activity occurs in a cloud environment. As the cloud users are growing rapidly, the security and privacy concern also increase. As the users’ data are being managed by the third party, data protection has become a primary issue. So as to handle the mentioned issue, an IDS has been presented to find any malicious activity on a cloud environment that is capable of detecting intruders and sends a warning when any illegitimate activity occurs. For detecting normal and malicious data, the Adaptive artificial neural network (AANN) has been utilized in the proposed scheme. For optimally designing the training structure of presented AANN based IDS, the grasshopper optimization algorithm (GOA) has been utilized in the novel approach. This further enhances the intrusion detection. Then the homomorphic reencryption technique has been utilized for improving the security further. Further, a third party auditing (TPA) has been performed for checking the data periodically. Finally, the data have been stored in the cloud. Using the cloud simulator, the presented scheme has been implemented in the working platform of Java software.
A Proposal of Metrics for Software Requirements Elicitation in the Context of a Small-Sized Software Enterprise

The requirements elicitation process aims to obtain a preliminary version of the requirements before their specification. However, due the nature of this process, it is very difficult to know if the elicited requirements have quality. Furthermore, there are some metrics that allow requirements engineers to measure some aspects of the elicitation process and its final product (i.e., a preliminary version of the requirements, also called pre-requirements), however there is no way of measuring and controlling the elicited pre-requirements. With the aim of understanding, evaluating, and improving the requirements elicitation process, the existing metrics for this process have been analyzed and, as a consequence, eight metrics using the GQM paradigm are proposed. Additionally, a preliminary validation of these metrics has been performed in the context of a small-sized software enterprise. The obtained results are also presented.
Predicting the 2019 M7 Strong Earthquake in California

The U.S. Geological Survey has estimated a 99% chance of a magnitude 6.7 earthquake in California in the next 30 years from 2008.In this paper, Fourier transform, mode analysis and commensurability analysis are used to study the earthquake cycle in California and predict the future strong earthquake.The analysis shows that the strong earthquake in California is most likely to occur in 2019, and the prediction was sent to the relevant institutions of United States in 2018.In July 2019 an M7.1 earthquake occurred in California, which proved our prediction to be correct.
Players Detection using U-Net based Fully Convolutional Network

People detection in image and video is challenging problem that has great importance in different applications such as surveillance systems, autonomous driving systems, sports video analysis etc. Player detection task, as a subproblem of people detection, is one of the fundamental steps in football video analysis. In this paper we propose a method for player detection based on a fully convolutional neural network. Novelty in our approach is usage of U-Net architecture for generation of player probability map. U-Net consists of contracting path that has typical convolutional neural network architecture and extracting path where upsampled feature maps are combined with features from contracting path to obtain more precise segmentation. Next step is thresholding of player probability map followed by connected component analysis that gives player bounding boxes. Experimental results show promising performance on football field images including distant views, motion blur, complex background etc.
Binary Sequences Derived from Monomial Permutation Polynomials over GF(2$$^{p}$$)

In this paper, we propose a class of binary sequences induced by monomial permutation polynomials over \(\mathrm {GF}(2^{p})\) and study the period property and the shift-equivalence of these binary sequences. In particularly, we give a necessary and sufficient condition for such a sequence to have maximal period. Moreover, we also give a necessary and sufficient condition for two such sequences to be shift equivalent.
Epimorphism testing with virtually Abelian targets

We show that the epimorphism problem is solvable for targets that are virtually cyclic or a product of an Abelian group and a finite group.
A Quantitative Comparison of Image Classification Models under Adversarial Attacks and Defenses

In this paper, we present a comparison of the performance of two state-of-the-art model architectures under Adversarial attacks. These are attacks that are designed to trick trained machine learning models. The models compared in this paper perform commendable on the popular image classification dataset CIFAR-10. To generate these adversarial examples for the attack, we are using two strategies, the first one being a very popular attack based on the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> metric. And the other one is a relatively new technique that covers fundamentally different types of adversarial examples generated using the Wasserstein distance. We will also be applying two adversarial defenses, preprocessing the input and adversarial training. The comparative results show that even these new state-of-the-art techniques are susceptible to adversarial attacks. Also, we concluded that more studies on adversarial defences are required and current defence techniques must be adopted in real-world applications.
Convergent teaching strategies towards the development of specific competencies in the discipline of Science, the primary stage of education

Today, more than ever, we are aware of the essential role of Information and Communication Technologies (ICT) in extreme situations. These have facilitated (more or less) our access to further education in the harsh conditions of the „Covid-19” pandemic. Through information technologies, it has been possible to achieve distance learning, the emphasis being on the constructivist model of learning, the active involvement of the student, the involvement of parents, the cooperation of all educational actors. Changing teaching strategies to achieve a qualitative educational process is a priority and requires sustained effort from all communities to develop the connection between learning and life. This article addresses issues regarding the application of active-participatory teaching strategies, information technologies for distance learning, ensuring the realization of the concept of life-long learning, based on technology. Examples of good practice are also presented.
Designing Boater Advocacy: A Lean UX Mobile App Project to Increase Emergency Response Accountability

Based on an ongoing UX research project into the development of a grant-funded mobile safety application for recreational boaters, this paper details a workflow for aligning user advocacy with organizational accountability. Essentially, boating safety regulations have not been updated since the 1970s due to successful lobbying by boating manufacturers. Meanwhile, numerous boating safety concerns and associated incidents go unreported each year. User research into this context has taught us that recreational boaters most want a mobile application that will help them enjoy their boating trips while remaining safe. State agencies most want to locate boaters who are in distress without having to launch costly, and often ineffective, searches over large areas. Based on a review of best practices from amassed literature, as well as our own analysis of user interviews with 141 stakeholders obtained during an NSF grant devoted to this purpose, we will discuss a Lean UX workflow for mobile application development that balances user goals with organizational accountability.
Focus on Blockchain: Introducation, Benefits and Future Application Trends

With the popularity of cryptocurrencies such as Bitcoin and Blockchain platforms such as Ethereum, Blockchain has attracted significant attention in many aspects. Blockchain has the characteristics of decentralization, high security, high transparency, high autonomy and non-tampering, which can play a great role in constructing programmable cryptocurrency system and large-scale financial system. Through the analysis of the Blockchain, this paper expounds on the technology of Blockchain, analyzes the existing problems of Blockchain, and introduces the development trend and application scenarios of Blockchain in the future.
SynXLM-R: Syntax-Enhanced XLM-R in Translation Quality Estimation

Quality estimation (QE) is an NLP downstream task that aims to determine the quality of translations without given references. Existing QE models mainly draw on neural-based or pre-trained models, overlooking syntactic information. In this paper, we propose a syntax-enhanced model based on GAT and XLM-R. Our model (SynXLM-R) introduces GAT to extract graph-structured features derived from dependency parsing. As for semantics, we utilize XLM-R to obtain the representations of the input sentences. Our model achieves higher correlations (Pearson's r) with human judgments than previous models both on mid-resource (Ro-En) and high-resource (En-De, En-Zh) language pairs in WMT20 QE sentence-level tasks. Subsequent analysis shows that different parsers result in different performances of our model. Furthermore, our model achieves the best result when the number of attention head is three.
Comparative Analysis on Frequent Itemset Mining Algorithms in Vertically Partitioned Cloud Data

Frequent itemset mining and Association Rule Mining are the extensively utilized data analysis techniques for a transactional database concerned with a trade-off. Data owners wish to acquire knowledge in these data analysis techniques to protect their sensitive data from additional data proprietor and third parties. This work emphasizes on privacy-preserving frequent itemset mining on a vertically partitioned database. An efficient homomorphic encryption scheme is designed to assure data privacy. Cryptography is a part of encryption that is used to guard information against third parties. In this work, frequent itemset mining algorithms such as Eclat, apriori, and FP-Growth are taken for analysis in terms of computation time and scalability of data. The analysis result shows that apriori algorithm is less time-consuming to generate rule in the cloud, irrespective of the number of transactions.
Case Weighted Similarity Measure Optimization Method Based On Black Hole Cuckoo Search Algorithm

This paper proposes an optimization method based on the Black Hole Cuckoo Search Algorithm (BH-CS) to improve the accuracy of the weighted similarity measure in the case-based reasoning (CBR) model. First, take the root mean square error of the case-based reasoning prediction model as the fitness function. Secondly, use the Levy flight of the Cuckoo Search algorithm to update the feature weights in the weighted similarity measure method and evaluate the optimal weights from them. Then, randomly generate new feature weights with some probability. Finally, use the Black Hole algorithm to optimize feature weights further to obtain optimal weights and optimize the case similarity measure. The optimization method was tested using UCI standard data set. The results show that the BH-CS algorithm has an advantage over other algorithms in improving the accuracy of case similarity measures and can effectively improve the prediction accuracy of the CBR model.
A Generative Approach to Visualizing Satellite Data

We propose EVOKE, a model based on progressive Generative Adversarial Networks, that dynamically reconstructs high-resolution imagery during zoom-in operations using in-memory historical low-resolution images and is space-efficient to facilitate memory-residency at the clients.
Assisting News Media Editors with Cohesive Visual Storylines

Creating a cohesive, high-quality, relevant, media story is a challenge that news media editors face on a daily basis. This challenge is aggravated by the flood of highly-relevant information that is constantly pouring onto the newsroom. To assist news media editors in this daunting task, this paper proposes a framework to organize news content into cohesive, high-quality, relevant visual storylines. First, we formalize, in a nonsubjective manner, the concept of visual story transition. Leveraging it, we propose four graph based methods of storyline creation, aiming for global story cohesiveness. These where created and implemented to take full advantage of existing graph algorithms, ensuring their correctness and good computational performance. They leverage a strong ensemble-based estimator which was trained to predict story transition quality based on both the semantic and visual features present in the pair of images under scrutiny. A user study covered a total of 28 curated stories about sports and cultural events. Experiments showed that (i) visual transitions in storylines can be learned with a quality above 90%, and (ii) the proposed graph methods can produce cohesive storylines with a quality in the range of 88% to 96%.
Grey Wolf Optimizer for Data Envelopment Analysis

Grey Wolf Optimizer (GWO) is a robust algorithm used for solving optimization problems. GWO is a population-oriented algorithm and simulates the social governorship and hunting nature of grey wolves. Data envelopment analysis (DEA) is a data-oriented decision support technique that helps decision-makers select the best potential solutions among a group of candidate solutions. In the current study, we propose GWO algorithm for solving DEA problems. Moreover, we present a real-life application of the proposed approach to the education sector.
A New Multi-source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment

Dempster-Shafer theory (DST) can effectively distinguish between imprecise information and unknown information, which is widely used in information fusion. However, when the evidence highly contradicts each other, it may lead to counter-intuitive results. In addition, the existing information fusion methods do not take the negation of BPA into consideration, which can be improved. In this paper, we propose a new information fusion method by taking into account not only the information in basic probability assignment (BPA) but also the information contained in the negation of BPA. In the method, the belief divergence measure is not only used to calculate the difference between BPA and its negative BPA to reflect the information volume carried by its initial BPA, but also to calculate the difference between BPA and other BPA to consider the discrepancy between evidence. The efficiency of the method is verified by case studies.
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images

Existing reference (RF)-based super-resolution (SR) models try to improve perceptual quality in SR under the assumption of the availability of high-resolution RF images paired with low-resolution (LR) inputs at testing. As the RF images should be similar in terms of content, colors, contrast, etc. to the test image, this hinders the applicability in a real scenario. Other approaches to increase the perceptual quality of images, including perceptual loss and adversarial losses, tend to dramatically decrease fidelity to the ground-truth through significant decreases in PSNR/SSIM. Addressing both issues, we propose a simple yet universal approach to improve the perceptual quality of the HR prediction from a pre-trained SR network on a given LR input by further fine-tuning the SR network on a subset of images from the training dataset with similar patterns of activation as the initial HR prediction, with respect to the filters of a feature extractor. In particular, we show the effects of fine-tuning on these images in terms of the perceptual quality and PSNR/SSIM values. Contrary to perceptually driven approaches, we demonstrate that the fine-tuned net-work produces a HR prediction with both greater perceptual quality and minimal changes to the PSNR/SSIM with respect to the initial HR prediction. Further, we present novel numerical experiments concerning the filters of SR networks, where we show through filter correlation, that the filters of the fine-tuned network from our method are closer to "ideal" filters, than those of the baseline network or a network fine-tuned on random images.
On the problem of finding the optimal set of computer device protection tools

Protecting a computer device is an important task that requires software implementation, because not every user can choose the right set of security features for their device, and there are not many applications that can help a user choose the right set of security features. In this paper, the authors consider the following tools for finding the optimal strategy for protecting a computer device: the method of Savage, Hurwitz, Bayes, Monte Carlo, Thompson Sampling algorithm. Their features and advantages are highlighted. They are based on a software application that will allow users to calculate the optimal combination of tools designed to protect their computers, depending on the wishes of users and the characteristics of their devices. Such an application can be used by system administrators to select the optimal protection of the entire computer fleet. The authors conducted a series of experiments and comparative analysis of the simulation results. The average search time for an optimal strategy for protecting a computer device is estimated.
Square peg, round hole: A case study on using Visual Question &amp; Answering in Games

The discussion about what can Artificial Intelligence (AI) contribute to games has been running for a long time, however, recent advances in AI show promise of providing new kind of experiences for players and new tools for game developers. In contrast with the traditional Finite State Machine for interaction and response, we consider the scenario of Visual Question & Answering (VQA) - the automatic answering of a textual question about an image. VQA is a tool that can enrich possible answers by combining both visual and textual information. It is also trivial to extrapolate to a game setting without going away from the training domain. In this Work In Progress, we present two original prototypes designed to explore the potential of VQA in games and discuss preliminary findings originated through a Wizard of Oz (WOz) pilot study using VQA to investigate how people interact with such an AI algorithm.
Violence Detection With Two-Stream Neural Network Based on C3D

In recent years, violence detection has gradually turned into an important research area in computer vision, and have proposed many models with high accuracy. However, the unsatisfactory generalization ability of these methods over different datasets. In this paper, the authors propose a violence detection method based on C3D two-stream network for spatiotemporal features. Firstly, the authors preprocess the video data of RGB stream and optical stream respectively. Secondly, the authors feed the data into two C3D networks to extract features from the RGB flow and the optical flow respectively. Third, the authors fuse the features extracted by the two networks to obtain a final prediction result. To testify the performance of the proposed model, four different datasets (two public datasets and two self-built datasets) are selected in this paper. The experimental results show that our model has good generalization ability compared to state-of-the-art methods, since it not only has good ability on large-scale datasets, but also performs well on small-scale datasets.
Revisit Two Memoryless State-Recovery Cryptanalysis Methods on A5/1

At ASIACRYPT 2019, Zhang proposes a near collision attack on A5/1. He claims that such an attack method can recover the 64-bit A5/1 state with a time complexity around \(2^{32}\) cipher ticks and requires negligible memory complexities. Soon after its proposal, Zhang’s near collision attack is severely challenged by Derbez et al. who claim that Zhang’s attack cannot have a time complexity lower than Golic’s memoryless guess-and-determine attack dating back to EUROCRYPT 1997. In this paper, we study both the guess-and-determine and the near collision attacks for recovering A5/1 states with negligible memory complexities. In order to make a fair comparison, we recover the state \(\boldsymbol{s}^0\) using both methods. We propose a new guessing technique that can construct linear equation filters in a more efficient manner. When evaluating time complexities, we take the filtering strength of the linear equation systems into account making the complexities more convincing. According to our detailed analysis, the new guess-and-determine attack can recover the state \(\boldsymbol{s}^0\) with a time complexity of \(2^{43.91}\) simple operations. The time complexity for the near collision attack is \(2^{50.57}\) simple operations.
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning

Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in the data. Classical regressors usually fulfill only a subset of these properties. In this work, we extend seminal work on Bayesian nonparametric mixtures and derive an efficient variational Bayes inference technique for infinite mixtures of probabilistic local polynomial models with well-calibrated certainty quantification. We highlight the model&#x2019;s power in combining data-driven complexity adaptation, fast prediction, and the ability to deal with discontinuous functions and heteroscedastic noise. We benchmark this technique on a range of large real-world inverse dynamics datasets, showing that the infinite mixture formulation is competitive with classical Local Learning methods and regularizes model complexity by adapting the number of components based on data and without relying on heuristics. Moreover, to showcase the practicality of the approach, we use the learned models for online inverse dynamics control of a Barrett-WAM manipulator, significantly improving the trajectory tracking performance.