Reviewerly/exHarmony huggingface dataset:
Full-Glow: Fully Conditional Glow for More Realistic Image Generation

Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative model with collected real data, and then augmenting the collected real dataset with synthetic images from the model, generated with control of the scene layout and ground truth labeling. In this paper we propose Full-Glow, a fully conditional Glow-based architecture for generating plausible and realistic images of novel street scenes given a semantic segmentation map indicating the scene layout. Benchmark comparisons show our model to outperform recent works in terms of the semantic segmentation performance of a pretrained PSPNet. This indicates that images from our model are, to a higher degree than from other models, similar to real images of the same kinds of scenes and objects, making them suitable as training data for a visual semantic segmentation or object recognition system.
Local dampening: differential privacy for non-numeric queries via local sensitivity

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the output of numeric queries (e.g., the Laplace mechanism and smooth sensitivity mechanism). Those mechanisms guarantee differential privacy by adding noise to the true query's output. The amount of noise added is calibrated by the notions of global sensitivity and local sensitivity of the query that measure the impact of the addition or removal of an individual on the query's output. Mechanisms that use local sensitivity add less noise and, consequently, have a more accurate answer. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query's output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We provide theoretical comparisons to the exponential mechanism and show under which conditions the local dampening mechanism is more accurate than the exponential mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to three diverse problems: (i) percentile selection problem. We report the p-th element in the database; (ii) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (iii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset. Experimental evaluation shows that we can reduce the error for percentile selection application up to $$73\%$$ , reduce the use of privacy budget by 2 to 4 orders of magnitude for influential node analysis application, and increase accuracy up to $$12\%$$ for decision tree induction when compared to global sensitivity-based approaches. Finally, to illustrate the scalability of our local dampening mechanism, we empirically evaluate its runtime performance for the influential node analysis problem and show a sub-quadratic behavior.
Reflective Hyperconnectivity of Social Networks Virtual Space as a Factor in the Design of Distant Learning Environment

In this article, topics related to the urgent transition to distance education (DE), through decisive measures taken to protect Russian university community against consequences of COVID-19 epidemic are covered. Based on semi-structured interviews with administrators, faculty, IT specialists and students, it is argued that in the force majeure circumstances the university offers for DE a standard model of one-size-fits-all curriculum though modified through information and communication technologies. The use of the Internet in a localized online space is limited to delivering courses without considering needs of individual learners, their autonomy, preferences and prior knowledge of the subject. The observation method was used by both participant and non-participant. An experiment on the teachers’ self-assessment proves that many still view the university as a source of offline information for students. The article purports to discuss ways to promote DE by the concepts of postindustrial educational space connectivism, hyperconnectivity of social networks, teachers’ reflective practice, digital nativity of teachers and students in mastering information and communication technologies. It is claimed that real transition of a Russian university to DE cannot be achieved without internet-provided increased transactional distance in the dialogue between the teacher and students mediated by technology and reflective approach.
Self-adaptive bacterial foraging algorithm based on estimation of distribution

Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy.
Application of Blockchain in Academic Credit Bank System

The essence of blockchain is decentralized and distributed ledger/database technology. Blockchain has the advantages of decentralization, Safety and tamper-proof, traceability, openness and transparency and anonymity. Blockchain technology can effectively deal with the various problems and challenges faced by traditional academic credit bank. In the application scenario of learning achievements deposit, the blockchain technology can avoid import errors or tampering of learning achievements and increase the authority and credibility of the academic credit bank. Automatically executed smart contracts can improve the efficiency of depositing learning achievements into the academic credit bank, reduce labor work and save time. In the application scenario of job application and recruitment, through blockchain, job applicants and employers HR no longer need to re-confirm the certification authority from the certificate issuing institution to achieve the purpose of data confirmation and data verification, which improves the efficiency of job application and recruitment. In the application scenario of academic credit transfer, the use of smart contracts + Oracle for academic credit transfer can effectively integrate students’ academic competence information spread across multiple data sources, and improve the efficiency and authority of the academic credit bank system. A three-step strategy can be used to build a blockchain on the academic credit bank system, followed by private chain, alliance chain and public chain. In this paper the account-based blockchain model is preferred rather than the transaction-based blockchain model to be used in the academic credit bank system.
Augmented Reality applications in Cultural Heritage: Technology or Content?

New technologies have changed the way we live, work, have fun and mobile phones are now one of the essential tools of work, communication and entertainment. Augmented Reality technology and the rapid spreading of mobile devices in everyday life are shaping new conditions in the culture field as well. Many studies focus on key questions of usability and technical issues without going into further analysis. In order to understand users/visitors needs and expectations as well as the role of technology in depth, the current study will try to answer an important question "what does impress users more, technology or content?". It is an important and a difficult question to be answered due to the diverse user's profiles, the AR applications and sometimes the indistinct boundaries between the concepts of “like” and “impress”. The current research incorporates an AR app of cultural routes that could be found in every city/site and includes participants representing all ages and the associated categories (technology, theme, general) in order to approach the question in a balanced way. A qualitative research and results showed that more than the half select “both technology and content”, fewer users select only “content” and even less select only “technology”. Therefore, the AR technology continues to have a positive effect on users, content plays a significant key role but the way content and technology are combined emerges as the most important factor. Also, it is highlighted, that as users become more familiar with technology, they are becoming more demanding and more selective in using technology, making the creation of an AR app and generally the technology use more difficult to cover their increasing needs. In addition to, users' view on the use of technology should not be ignored in order to avoid a negative attitude towards technology and more emphasis should be given on content and on applications that really have something innovative to add both to users and to cultural assets value.
Improvement of Classification of Shark Behaviors using K-Nearest Neighbors

The aim of this paper is to enhance the performance of K-Nearest Neighbors (K-NN) used to classify data collected from an Acceleration Data Logger (ADL) into four shark behaviors, namely, Resting, Swimming, Feeding, and Non-Directed Motion (NDM). It is shown that using Ensemble Averaging (EA) to improve Signal-to-Noise Ratio (SNR), data resizing to reduce unbalanced samples distribution among behaviors, and other signal processing techniques enhance K-NN F1 Scores.
A new Mathematical Modell for COVID-19: A Fuzzy Cognitive Map Approach for Coronavirus Diseases

The novel Coronavirus outbreak late in 2019 and early 2020, known today as COVID-19 or SARS-CoV-2. is with us.The WHO has accepted COVID-19 as a pandemic disease.The outbreak of COVID-19 has gained ground in many countries, leading towards a global health emergency.Increased national and international measures are being taken to contain the outbreak leading to total "lockdown" of many countries directly affecting urban economies on a multi-lateral level..This is a perspective paper, written from a classical engineering point of view only four months after detecting the COVID-19 pandemic.All known studies for COVID-19 are done based on statistical models.These statistical approaches depend solely on correlation factors.The factor of causality has not been considered due to the luck of sufficient mathematical models based on causality.Correlation does not imply causality while causality always implies correlation.The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, for the first time, to investigate the whole spectrum of COVID-19.An FCM model is proposed and referred as the classical FCM methods.Early theoretical simulation studies using a COVID-19 FCM are very promising.Simulations were performed and results were compared with the classical FCM approach.Useful conclusions and future research directions are provided
Deep mining of open source software bug repositories

Large scale software projects adopt bug tracking systems such as Bugzilla and Jira to manage the bugs’ fixes and store their information. Mining bug repositories is essential to automate some maintenance phase activities like bug triage. Recently, embedding and deep learning models have emerged as a breakthrough for text classification applications. This paper proposes utilizing word embedding and deep learning models for mining bug repositories, in order to identify severity classes of newly reported bugs. Embedding models were utilized to represent bug reports, due to their ability to acquire semantic relations among words and sentences. Five effective deep learning models (CNN, LSTM, GRU, hybrid CNN-LSTM/GRU) were trained to classify the bug reports to their severity classes. We attempted these models as each of them has different capabilities; CNN can extract key features of an input and reduce the feature space size. While, LSTM and GRU can represent sequential words (sentences) effectively. Experimental results on two open large-scale bug repositories, Eclipse and Mozilla, demonstrated that CNN model is the superior. However, it was found that the micro-average classification performance of the CNN could be achieved by some traditional classifiers, e.g. SVM and KNN, trained using the embedding vectors instead of the bag-of-words-model.
POSTER: A Brief Overview of Biometrics in Cybersecurity: A Comparative Analysis

Protecting privacy and security are some of the major challenges in today's society. Herein, we point out various forms of biometric security systems and how they work. This research focuses on the banking industry, which uses biometric identification technologies as a security strategy. Last, we compare several biometric features based on various factors.
Image glossiness from curvelet features using SVM-based classification

Distinguishing artwork from digital photographs is a simple task for a human observer. However, imparting this ability to a machine requires quantification of the quality of naturalness. In particular, the quality of `glossiness' of a scene in a digital photograph as opposed to that in artwork or a painting of the same scene, is a challenging parameter to quantify. In this paper, a classification-based approach is proposed to quantify this glossiness of an image. The authors propose and validate the hypothesis that features extracted from the curvelet transform contain information regarding glossiness of an image. By training a support vector machine (SVM) classifier using curvelet coefficient features of images of varying naturalness, a framework to predict no-reference glossiness index for any general query image is proposed. The reliability of the proposed metric was then gauged by obtaining its correlation with subjective scores on naturalness provided by human observers. Results exhibit noteworthy performance of this metric in representing image glossiness, and could be further improved by using a larger training database and advanced classifiers.
One-array Differential Evolution Algorithm with a Novel Replacement Strategy for Numerical Optimization

Differential Evolution (DE) algorithm is an efficient metaheuristic algorithm in solving complex real-world optimization problems. DE algorithm benefits from two populations for updating individuals, while it might cause memory problems in practice during solving large-scale optimization problems; especially when they are used in an embedded system. One strategy to tackle this problem is utilizing a one-array scheme which benefits from only one population, leading to a half-space memory. This paper proposes a novel DE algorithm based on one-array DE and a random replacement strategy; it adds an additional competition to the selection operator to make better use of the new individual that it might be potentially noteworthy. The positive feature of the introduced replacement strategy is that it does not need any extra computational budget. Also, due to employing one-array strategy, the proposed scheme has a lower memory complexity. Our experiments on CEC-2017 benchmark function with dimensions 30, 50, and 100 clearly illustrate the effectiveness of the proposed DE algorithm.
Construction of a syllabus adhering to the teaching of software testing using agile practices

This Research to Practice full paper presents an investigation into the construction of a software testing syllabus, using agile practices as support to make it adherent to teaching by academic skills. In most Brazilian universities, software testing is approached mainly as subtopics of the subjects of Software Engineering or Software Quality; this little contact with the topic can become a discouraging problem for higher education students seeking knowledge about this important area. For the development of the syllabus, we had as reference the competence and syllabus guidelines of the Ministry of Education of Brazil, which is the regulator of the syllabus guidelines for undergraduate and graduate courses in computing in Brazil. In conjunction with these guidelines, other assets in the area were aligned, such as the academic syllabus of the Brazilian Computer Society and the ACM / IEEE, in conjunction with the SWEBOK test-oriented knowledge areas, the Test Maturity Model Integration process areas and guided by agile practices, principles and techniques found in the literature. After this stage, the construction of teaching units for teaching of software testing was carried out. These teaching units were evaluated as part of the software testing teaching model by means a focus group, composed of three doctors who are also specialists in the teaching of software engineering, with experience in teaching and research. Thus, this work also provides a set of skills that we consider favorable to the teaching of software testing, considering several academic syllabuses analyzed. Academic program managers and teachers can use the knowledge generated in this article to facilitate the construction of syllabuses or subjects related to tests, learning objects and academic syllabus. Our findings can also be learning facilitators for competency-based teaching and learning experiences for testing professionals.
DNet: An Efficient Privacy-Preserving Distributed Learning Framework for Healthcare Systems

Medical data held in silos by institutions, makes it challenging to predict new trends and gain insights, as, sharing individual data leaks user privacy and is restricted by law. Meanwhile, the Federated Learning framework [] would solve this problem by facilitating on-device training while preserving privacy. However, the presence of a central server has its inherent problems, including a single point of failure and trust. Moreover, data may be prone to inference attacks. This paper presents a Distributed Net algorithm called DNet to address these issues posing its own set of challenges in terms of high communication latency, performance, and efficiency. Four different networks have been discussed and compared for computation, latency, and precision. Empirical analysis has been performed over Chest X-ray Images and COVID-19 dataset. The theoretical analysis proves our claim that the algorithm has a lower communication latency and provides an upper bound.
Emotion Enhancement for Facial Images Using GAN

Labeled images play an important role for training convolutional neural networks (CNN). In particular, training CNNs for facial emotion classification, the publicly available datasets suffer from noisy labels and inter-class imbalance problem. In this paper, we adopt a Generative Adversarial Network (GAN) to alleviate both noisy labeling and inter-class imbalance problems. Specifically, the noisy labelled images are identified by cross-checking the classified results with two fine-tuned CNNs and their facial emotions are strengthened by a GAN. Also, some of the neutral emotion images are transformed into minor emotion classes to solve the imbalance problem.
A taxonomy of service discovery approaches in IoT

Nowadays, Internet of things (IoT) is becoming the trademark technology for connecting real-world physical entities to provide specific services and enable the creation of a smart world. Due to the growing number of connected objects and the lack of standardization that meet IoT requirements, discovering and selecting the desirable service is becoming a challenging issue. In the literature, many works have been put forward to address service discovery in IoT. In this paper, we propose a complete classification of these works to provide a more elaborated taxonomy that includes several aspects and criteria. Then, we compare some of the most recent approaches according to several performance criteria, followed with a discussion to set up the future research directions.
QoS-aware Automatic Service Composition Based on Service Execution Timeline with Multi-objective Optimization

With the evolution of web technologies, various services become available in the pervasive network environment. Combining atomic services via the input and output dependency according to functional requirements with the multiple nonfunctional Quality-of-Service (QoS) guarantees has become a widely considered optimization problem. The conventional multi-objective service composition relying on manually predefined service chains fails to ensure global optimality. Although the automatic service composition successfully expands the search space, the searching graph which it relies on causes computationally expensive and fails to handle multiple objectives. Therefore, this paper proposes a novel efficient multi-objective automatic service composition approach. Particularly, it introduces a service execution timeline model to decompose the composition problem into several sub-problems to reduce computational complexity. Further, it employs an evolutionary process to explore the search space and determine the approximately Pareto front of the composition solutions. The experimental results on the benchmarks show that our approach could achieve a better trade-off between the computation cost and ensuring a better QoS compared with two recently proposed automatic composition approaches.
The Probabilistic Description Logic

Abstract Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension of the propositionally closed DL . We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical .
Multiple Input-Multiple Output Quantum Radar

We consider a quantum radar, using as sources for entangled photons devices with spontaneous parametric downconversion, with a given number of channels. This conception is similar to a MIMO radar and we exploit the detection theory for this case to show a neat improvement in the receiver operating characteristics (ROC) curves with increasing number of emitting sources. This shows significant improvement with respect to recent proposals.