Reviewerly/exHarmony huggingface dataset:
Gbit/s Throughput Under 6.3-W Lossless Hyperspectral Image Compression on Parallel Embedded Devices

The consultative committee for space data system (CCSDS)-123 is a standard for lossless compression of multispectral and hyperspectral images with applications in on-board power-constrained systems, such as satellites and military drones. This letter explores the low-power heterogeneous architecture of the Nvidia Jetson TX2 by proposing a parallel solution to the CCSDS-123 compressor on embedded systems, reducing development effort compared with the production of dedicated circuits, while maintaining low energy consumption. This solution parallelizes the predictor on a low-power graphics processing unit (GPU) while the encoders exploit the heterogeneous multiple cores of the CPUs and GPU concurrently. We report more than 16.6 Gb/s for the predictor and 1.4-Gb/s for the whole system, requiring less than 6.3 W and providing an efficiency of 245.6 Mb/s/W.
A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.
Improved Calibration of Numerical Integration Error in Sigma-Point Filters

The sigma-point filters, such as the unscented Kalman filter, are popular alternatives to the ubiquitous extended Kalman filter. The classical quadrature rules used in the sigma-point filters are motivated via polynomial approximation of the integrand; however, in the applied context, these assumptions cannot always be justified. As a result, a quadrature error can introduce bias into estimated moments, for which there is no compensatory mechanism in the classical sigma-point filters. This can lead in turn to estimates and predictions that are poorly calibrated. In this article, we investigate the Bayes-Sard quadrature method in the context of sigma-point filters, which enables uncertainty due to quadrature error to be formalized within a probabilistic model. Our first contribution is to derive the well-known classical quadratures as special cases of the Bayes-Sard quadrature method. Based on this, a general-purpose moment transform is developed and utilized in the design of a novel sigma-point filter, which explicitly accounts for the additional uncertainty due to quadrature error.
Paraphrasing Identification Techniques in English and Arabic Texts

Rewriting sentences using different words leading to the same meaning of the original sentence is called paraphrasing while paraphrasing identification task is the task of detecting the sentence that is paraphrased from another. This research provides a literature survey of researches that have studied and proposed methods for paraphrasing identification of English and Arabic texts. Accordingly, the impact of paraphrasing on NLP applications and paraphrasing benchmarks are also studied. The comparative study on the available paraphrasing identification techniques shows that the best precision is provided with WordNet based techniques while the best accuracy is provided by deep learning with statistical features of English texts. In the case of Arabic, the best precision is provided by the distributed word vector representations with Convolutional Neural Network.
Immersive WYSIWYG (What You See is What You Get) Volume Visualization

Extended to immersive environment, volume visualization has the analytical superiority in spatial immersion, user engagement, multidimensional awareness and other aspects. But in a highly immersive virtual environment, traditional single-channel precise interactive methods cannot be applied directly to the immersive environment. Inspired by how users typically interact with everyday objects, a novel non-contact gesture interaction method base on What You See is What You Get(WYSIWYG)for volume rendering results is proposed in this paper. Just likes grab interaction in real scene, a full set of tools have been developed to enable direct volume rendering manipulation of color, saturation, contrast, brightness, and other optical properties by gestural motions in our method. Simultaneously, in order to improve the interactive experience in immersive environment, the evaluation model of motion comfort is introduced to design the interactive hand gestures, the cursor model is defined to estimating the gesture state combined with context gestural motions. Finally, the test platform is established with Oculus Rift + Leap Motion to verify the functionality and effectiveness of our method in improving the visual cognitive ability for volume visualization.
Study on Abstractive Text Summarization Techniques

As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve the problems. It is available in a structured or unstructured format. Therefore, in order to get corresponding data as per the application’s purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers makes it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that it should not lose its context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research.
An Empirical Investigation on Software Practices in Growth Phase Startups

Context: Software startups are software-intensive early-stage companies with high growth rates. We notice little evidence in the literature concerning engineering practices when startups transition to the growth phase. Aim: Our goal is to evaluate how software startups embrace software engineering practices. Methodology: We conduct a survey guided by semi-structured interviews as an initial step, to be followed by field questionnaires as part of a future exploratory study. We use open coding to identify patterns leading to themes we use to state our hypotheses. To identify our samples, we use purposive sampling. Results: Specifically, we analyze seven startup cases during the first qualitative phase. We obtain five anti-patterns (no-documentation, no-agile, no-code intellectual property protection, cowboy programming, no-automated testing) and corresponding patterns (readable code, ad-hoc project management, private code repositories, paired and individual programming, ad-hoc testing) in adopting software engineering practices. We state 10 corresponding hypotheses we intend to corroborate by surveying a more significant number of software startups. Contribution: This study, throughout its recommendations, provides an initial road map for software startups in the growth phase, allowing future researchers and practitioners to make educated recommendations.
Reducing Wrong Labels for Distantly Supervised Relation Extraction With Reinforcement Learning

Relation extraction (RE) aims to mine semantic relations between entity pairs from plain texts, which plays an important role in various natural language processing (NLP) tasks.However, the existing methods in distant supervision (DS) are sensitive to bags and fail to handle sentence-level relation prediction.In particular, few methods focus on the sentence-level label denoising.In this paper, the sentence-level label denoising model based on reinforcement learning (RL) and the express-only-one assumption is proposed for distantly supervised RE.First, unlike removing the noisy sentences in previous studies, this paper designs Deep Q Network (DQN), a value-based RL algorithm, as a label denoiser to select the most reliable labels from the multiple relations that sentences are labeled.Second, the relation extractor applies the typical neural network model to predict relations between the data before and after the label denoiser cleans.The rewards in label denoiser are measured by the differences of prediction scores.Finally, the two modules between label denoiser and relation extractor are trained jointly to obtain correct labels and improve the extraction performance at the sentence level.The experimental results show that the proposed denoiser can deal with the noise labels of data effectively and the proposed model outperforms previous state-of-the-art baselines on both the Riedel dataset and human-annotated dataset.
Theory of Mind based Communication for Human Agent Cooperation

For human agent cooperation, reasoning about the partner is necessary to enable an efficient interaction. To provide helpful information, it is important not only to account for environmental uncertainties or dangers but also to maintain a sophisticated understanding of each other's mental state, a theory of mind. Sharing every piece of information is not a good idea, as some may be irrelevant at time or already known, leading to distraction and annoyance. Instead, an agent will have to estimate the novelty and relevance of information for the receiver, to trade off the cost of communication against potential benefits. We propose the concept of theory of mind based communication as principled formulation to ground an agents cooperative communication on an understanding of the receiver's mental states to support her awareness and action selection. Therefore we formulate the problem of whether, when and what information to share as a sequential decision process with the human belief as central source of uncertainty. The agent's communication decision is obtained online during interaction by combining a second level Bayesian inference of human belief with planning under uncertainty, evaluating the influence of communication on the human belief and her future decisions. We discuss the resulting behavior on an illustrative communication scenario with different uncertain state aspects that an observing agent can communicate to the actor.
The correlation and volatility between bitcoin and the blockchain index

Purpose The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the blockchain index is necessary. Design/methodology/approach This paper uses the Granger causality test to explore the correlation between bitcoin and the blockchain index. Furthermore, their volatility is analyzed by a GARCH-class model. Findings The results show that no significant correlation exists between bitcoin and the blockchain index; external shocks aggravate the volatility of bitcoin and the blockchain index, and the volatility has a certain degree of sustainability; and blockchain index has obvious leverage, namely, its decline has a stronger impact. Originality/value The volatility of bitcoin and the blockchain index is crucial for investors.
Cross-Chain Blockchain Networks, Compatibility Standards, and Interoperability Standards

This chapter explains the role and how interoperability standards and compatibility standards will enable the development of cross-chain blockchain networks. Blockchain currently lacks standardization. There are different initiatives aimed at facilitating interoperability between different blockchains. But if there are no uniform interoperability and compatibility standards, that will cause a problem for the development of cross-chain blockchain networks. In the EU, there is an initiative to facilitate a continental cross-chain blockchain network. Interoperability and compatibility standards are an integral aspect of the initiative. This chapter uses the case of the EU as a basis for explanation on how blockchain service providers can work together to develop these standards.
Role of ontology in software testing

Software testing is a multifaceted and essential method to achieve the quality of product. Its significance has been increasing and well accepted, so there is an emergent concern in improving the attainment of this practice. In this perspective, Knowledge Management (KM) emerged as an key sustaining method for improving the software testing process. However, managing relevant testing knowledge according to the influencing factors requires an effective way to represent and to correlate to a huge number of testing aspects. To address this concern, we have analyzed an impact of ontology on software testing across influenced asects of software testing. Ontology provides a standard conceptualization of the software testing domain that can construct a knowledge base, establishing a common vocabulary for knowledge owners about the testing domain, structured testing knowledge databases, identification of testing knowledge objects, and making it easier to search for relevant information. In this paper, we present factors affecting software testing direct or indirect influenced aspects. Moreover, we discuss how ontology helps in alleviating the software testing.
A meta-model of cybersecurity curriculums: Assessing cybersecurity curricular frameworks for business schools

Business schools can meet the increasingly multidisciplinary requirements of training cybersecurity professionals. But in developing curriculums, they face different cybersecurity curricular frameworks with diverse emphases. Using a meta-framework consisting of a 2x3 model and a 2x2 model, this study examines ten cybersecurity curricular frameworks and 380 associated topics and areas. It finds that most curricular frameworks examined tend to have more technical topics and are less IS/business-related. Among them, CAE-CDE is the most technically-oriented curricular framework, while CISSP has more topics that are cybersecurity-related and nontechnical. The proposed meta-model can enable business schools to evaluate cybersecurity curricular frameworks based on their own priorities and resources.
A Bunch of Broken Schemes: A Simple yet Powerful Linear Approach to Analyzing Security of Attribute-Based Encryption

Verifying security of advanced cryptographic primitives such as attribute-based encryption (ABE) is often difficult. In this work, we show how to break eleven schemes: two single-authority and nine multi-authority (MA) ABE schemes. Notably, we break DAC-MACS, a highly-cited multi-authority scheme, published at TIFS. This suggests that, indeed, verifying security of complex schemes is complicated, and may require simpler tools. The multi-authority attacks also illustrate that mistakes are made in transforming single-authority schemes into multi-authority ones. To simplify verifying security, we systematize our methods to a linear approach to analyzing generic security of ABE. Our approach is not only useful in analyzing existing schemes, but can also be applied during the design and reviewing of new schemes. As such, it can prevent the employment of insecure (MA-)ABE schemes in the future.
Multi-Source Transfer Network for Cross Domain Person Re-Identification

Unsupervised person re-identification has been improved significantly by the development of cross domain person re-identification models, which apply useful knowledge in source data to completely unlabeled target data.However, existing cross domain re-identification models still remain a major limitation that they are all based on single-source and single-target setting.The only one source domain may remain a tremendous gap between target, generating negative effect for the model training in target domain.To overcome this drawback, this paper proposes a Multi-Source Transfer Network to learn a shared target-biased feature space between multi-source and target domains, which achieves transfer learning in feature-level, pixel-level, and task-level by the proposed target-biased multi-source transfer learning module, relativistic adversarial learning module, and task-gap bridging module, respectively.Through leveraging the domain gaps in feature-level, pixel-level, and task-level, this network can synthetically learn a discriminative model from multiple source domains to effectively conduct re-identification in target domain.Furthermore, this paper conducts extensive experiments on three widely-recognized person re-identification datasets, and the proposed network achieves rank-1 accuracies of 80.9% and 74.6% on DukeMTMC-reID and Market-1501 datasets, respectively.The results demonstrate the contribution of the proposed method, compared with state-of-the-art methods, including hand-crated feature, clustering and transfer learning based methods.
Detection of Cross-Site Scripting Attacks using Dynamic Analysis and Fuzzy Inference System

Many prevalent problems of web applications are induced by injected codes, which pose great security threats. Vulnerabilities found in web applications are commonly typically exploited to perpetrate attacks. With cross-site scripting (XSS), attackers can infuse malevolent contents into website pages, in this way gaining access privileges to sensitive page content of the user such as, session cookies, user's data or credentials and several other information often kept up by the browser on behalf of the users. This paper presents a hybrid mechanism for detecting XSS attacks using Dynamic Analysis and Fuzzy Inference. The approach scans the website for possible points of injection before generating an attack vector launched via an HTTP request to a web application. The analysis of the HTTP response predicts the presence of an attack vector. The detection capability of the system is evaluated using some active world web applications and the results show a high rate of detection.
Simulation-based receiver selective opening CCA secure PKE from standard computational assumptions

In the situation where there are one sender and multiple receivers and the sender transmits ciphertexts of correlated plaintexts, a receiver selective opening (RSO) attack for a public key encryption (PKE) scheme considers adversaries that can corrupt some of the receivers and get their secret keys and plaintexts. Security against RSO attacks for a PKE scheme ensures confidentiality of ciphertexts of uncorrupted receivers. Simulation-based RSO security against chosen ciphertext attacks (SIM-RSO-CCA) is the strongest security notion in all RSO attack scenarios. Jia, Lu, and Li (2016) [19] proposed the first SIM-RSO-CCA secure PKE scheme. However, their scheme used indistinguishability obfuscation, which is not known to be constructed from any standard computational assumption. In this paper, we give two contributions for constructing SIM-RSO-CCA secure PKE from standard computational assumptions. Firstly, we propose a generic construction of SIM-RSO-CCA secure PKE using an IND-CPA secure PKE scheme and a non-interactive zero-knowledge proof system satisfying one-time simulation soundness. Secondly, we propose an efficient and concrete construction of SIM-RSO-CCA secure PKE based on the decisional Diffie-Hellman (DDH) assumption. Moreover, we give a method for efficiently expanding the plaintext space of the DDH-based construction. By applying this method to the construction, we obtain the first DDH-based SIM-RSO-CCA secure PKE scheme supporting a super-polynomially large plaintext space with compact ciphertexts.
Cognitive Identity Management: Synthetic Data, Risk and Trust

Synthetic, or artificial data is used in security applications such as protection of sensitive information, prediction of rare events, and training neural networks. Risk and trust are assessed specifically for a given kind of synthetic data and particular application. In this paper, we consider a more complicated scenario, - biometric-enabled cognitive cognitive biometric-enabled identity management, in which multiple kinds of synthetic data are used in addition to authentic data. For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model extreme situations. This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction.
Multi-Level Joint Feature Learning for Person Re-Identification

In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient or robust to for scenarios with large differences. In this paper, we propose a Multi-level Feature Fusion model that combines both global features and local features of images through deep learning networks to generate more discriminative pedestrian descriptors. Specifically, we extract local features from different depths of network by the Part-based Multi-level Net to fuse low-to-high level local features of pedestrian images. Global-Local Branches are used to extract the local features and global features at the highest level. The experiments have proved that our deep learning model based on multi-level feature fusion works well in person re-identification. The overall results outperform the state of the art with considerable margins on three widely-used datasets. For instance, we achieve 96% Rank-1 accuracy on the Market-1501 dataset and 76.1% mAP on the DukeMTMC-reID dataset, outperforming the existing works by a large margin (more than 6%).