Reviewerly/exHarmony huggingface dataset:
Distributed scalable abstract reasoning based on dl-lite

In recent years, researchs have shown that the ontology of a large number of data sets can be inferred effectively by abstract techniques. Distributed environment can further improve the performance of abstract reasoning. Firstly, abstract technology refers to a technology that divides ontology into equivalence classes and uses reasoning tools to reason abstract ontology. Then,The newly generated entities and relationships are added to the original ontology data set through materialization techniques. Finally, in order to ensure the consistency of ontology, the consistency check of ontology is needed later. We use the benchmark to obtain preliminary experimental results. The final results show that this abstract method can deal with a large number of ontologies effectively. Compared with the original reasoning method, the abstract technique proposed in this paper can effectively improve the reasoning efficiency and reduce the memory consumption time.
Combining discriminant embedding and transfer learning for visual tracking

Visual tracking can be viewed as a discrimination problem to distinguish the target object from the background. However, it is difficult to get efficient training samples and there is usually a strong similarity between the foreground and the background in reality, which makes it challenging to discriminate the target object from the background. In this paper, we present a tracking method based on the combination of discriminant embedding and transfer learning to tackle the challenge. For one thing, we use the graph embedding method to characterize the relationship between the foreground samples and the background samples, for another we exploit the knowledge of the tracking results in the previous frame to track the next frame. We then learn a subspace by jointly optimizing discriminant embedding and transfer learning into a unified framework. The classifier is constructed on the learned subspace to discriminate the target from the background. Experiments on several video benchmarks demonstrate the effectiveness of our approach.
Background reconstruction via low rank tensor factorization

This paper introduces a new method for background reconstruction. Background reconstruction from video sequences captured by a static camera can be regarded as a low rank factorization problem. Background is the low dimensional subspace restored from the higher dimensional visual data, and foreground is treated as sparse noise of unknown distribution. The existing algorithm could not deal with noise of unknown distribution effectively. Due to the limitation of the matrix decomposition which would lost space structure information, we process video data directly as higher order tensor based on low rank tensor factorization (LRTF). We put forward a new model of foreground by using Mixture of Gaussians (MoG) and Markov Random Field (MRF). Extensive experiments show that our method can effectively construct the background.
Work in Progress: One Approach to Software Engineering Project Selection for Small Student Populations

Abstract Software Engineering is a discipline that by its nature expects students to engage in group work. Our Software Engineering program culminates with a senior project course that is intended to provide students with an opportunity to see a software project through one or more increments that can be delivered to the customer. In addition, it is desirable for students to gain real world experience working in a group on a software project for a real customer. Ideally, groups will consist of 3 to 5 students in the senior project course, however, since our program is small, we may have fewer than 3 students enrolled in the course during any given offering. The goal of this work is to find a sustainable method of providing students with opportunities to work on group projects for outside entities, even when the enrollment in the capstone course precludes forming groups of the desired size. Our initial investigation is evaluating the effectiveness of utilizing socialcoder.org as a source for student projects. socialcoder.org is a website that seeks to match volunteer developers with projects for non-profit organizations. By utilizing socialcoder.org, we provide an opportunity for students to work in a team that includes individuals from outside the institution. Students also have an opportunity to work with a real customer and engage in a service learning project consistent with the university's mission. This work examines two trials. The first trial had a single student participant and was quite successful. The second trial had 4 students participate, but had mixed results.
AGM Meets Abstract Argumentation: Contraction for Dung Frameworks

The aim of the paper is to combine two of the most important areas of knowledge representation, namely belief revision and argumentation. We present a first study of AGM-style contraction for abstract argumentation frameworks (AFs). Contraction deals with removing former beliefs from a given knowledge base. Our presented approach is based on a reformulation of the original AGM postulates. In contrast to the AGM setup, where propositional logic is used, we build upon the recently developed Dung-logics. These logics have been introduced to tackle the somehow inverse problem, namely adding new beliefs. Importantly, they satisfy the characterization property that ordinary equivalence in Dung logics coincides with strong equivalence for the respective argumentation semantics. Although using the same setup we prove a negative result regarding the unrestricted existence of contraction operators. This means, an analog to the Harper Identity, which allows to construct a contraction operator from a given revision operator, is not available. However, dropping the somewhat controversial recovery postulate leads to the existence of reasonable operators.
Chamberlin State-Variable Filter Structure in FPGA for Musical Applications

This paper describes the hardware implementation of the Chamberlin filter and how its structure can be mapped on Xilinx FPGA (Field Programmable Gate Array) for musical applications. Furthermore, it shows the comparison of the performance of the Chamberlin filter with other types of digital filter for different audio inputs. Finally, the structure of the Chamberlin Filter has been implemented in C code to check if high-level synthesis leads to further improvements in system optimization.
Selection of Optimal Reasoning path by Bayesian Switching mechanism in the Brain Sensory System

Adopting the brain functions of Switching mechanism in synapse, Brain Sensory system with Bayesian Switching mechanism was proposed for developing intelligent system. It is specially designed to have the function of selection that can choose the optimal reasoning path from the generated Thinking paths. In experiments, the proposed system is applied to the reasoning problem and tested with sample data.
EXECUTION AFTER REDIRECTION VULNERABILITIES IN WEB-APPLICATIONS

The article presents the main provisions of the construction of web applications. The mechanism of the application protocol HTTP in the development of web applications. The description of the operation of HTTP status codes and their processing in the user’s browser is given. Describes how to use status codes in the development of web applications. An example of working with a redirection code and header is given. A definition and characterization of execution vulnerabilities after redirection is proposed. Examples of applications vulnerable to run after redirection to server programming languages PHP and Ruby on Rails are given. Describes the problem of typical solutions to neutralize the vulnerability of execution after redirection. A way of writing the source code of the application, taking into account the possibility of execution vulnerabilities after redirection, is proposed. Provides recommendations for writing code.
Image Augmentation Techniques for Road Sign Detection in Automotive Vision System

Well-prepared image data set for any automatic object detector is a crucial step in training a convolutional neural network. A vast collection of diverse images with corresponding labels is the ultimate goal for any researcher. Data acquisition, however, is the most time-consuming and tedious task in building an object detector. Therefore, various methods of generating new data from existing one have been developed and used. When facing the task of building an object detector for automotive purposes, a question appeared whether all commonly used augmentation methods are suitable in this context. This led to an investigation of different techniques as well as proposing and testing a new one, which is simple in principle but gives very promising results.
Person re-identification for 365-day video surveillance based on stride convolutional neural network

Person re-identification (ReID) is an important task in video surveillance and can be applied in various practical applications. The traditional methods and deep learning model cannot satisfy the real-world challenges of environmental complexity and scene dynamics, especially under fixed scene. What’s more, most of the existing datasets are outdoor and has a single style, which is not good for indoor person re-identification. Focusing on these problems, the paper improves a Stride Convolutional Neural Network (S-CNN) to process indoor images based on multi-features fusion. The deep model is established in which the identity information, stride information and other information are learned to handle more challenging indoor images. Then a metric learning method (Joint Bayesian) is employed based on the deep model. Finally, the entire classifier is retrained with supervised learning. The experiment is tested on the OUC365 dataset created by us which is captured for 365 days including all seasons style. Compared with other state-of-the-art methods, the performance of the proposed method yields best results
Nonblind Image Deblurring Based on Bi-Composition Decomposition by Local Smoothness and Nonlocal Self-Similarity Priors

Image deblurring is a classical inverse problem in image processing and computer vision. The vital task is to construct the proper image prior model to obtain the high-quality restored image with salient edges and rich details. A new nonblind image deblurring method by combining local smoothness and nonlocal self-similarity of natural images in the regularization framework is proposed. First, the observed image is decomposed into two components: structure component and detail component by a global gradient extraction scheme. Second, the four-directional anisotropic total variation regularization satisfying the local smoothness property is adopted for the structure component, and a new nonlocal statistical modeling for self-similarity is used for the detail component, respectively. At last, the split Bregman-based iteration algorithm and four-directional fast gradient projection algorithm are introduced to optimize the proposed L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized problem. The extensive experiments demonstrate the efficiency and viability of the proposed method for preserving salient edges and texture details while alleviating the artifacts.
Long lasting effects of awareness training methods on reducing overall cyber security risk

Social Engineering holds one of the most critical threats to public and private organizations. In this paper we focus on phishing threats by measuring the positive impact that awareness methods may provide to them in a long-term period to companies and public bodies. The assessment criterion uses two phishing attacks in a period of 18 weeks. The phishing attack comprises a hook mail containing a link to a credentials harvesting website. Users’ reaction and user agent fingerprints are used in order to calculate a risk score for each victim. By applying chi square – tests it was found that there is a statistically significant score improvement for participants that were trained via the awareness methods. Furthermore, a risk analysis is conducted to identify, quantify and prioritize potential risks that could negatively affect the end-user’s operations. The main idea concerning this proposed technique is the fact that the assessment methods can assist the employees to develop skills and abilities in order to use the digital world safely, avoiding phishing attacks. The risk analysis findings indicate that the awareness approach has significant improvement in long term lasting risk reduction. The study was conducted as part of the European Horizon 2020 DOGANA project which aims to deploy effective mitigation strategies and lead to reduce the risk created by modern Social Engineering 2.0 attack techniques. The results obtained in this paper corroborate the results obtained by the EU funded project SAINT from the econometric analysis and modeling of the cybercrime and cyber security markets.
Radar applications of quantum squeezing

Recent experimental results have demonstrated gains in sensing capability using novel possibilities offered by quantum mechanics. In particular, a prototype radar which uses quantum techniques to enhance detection ability has been built in a laboratory, showing that quantum radars at RF frequencies are feasible. This prototype is called a quantum two-mode squeezing radar (QTMS radar). In this paper, we use the QTMS radar as a springboard to review the concept of quantum squeezing. We find that Josephson parametric amplifiers (JPAs), one of which was used in the QTMS radar prototype, can be employed to produce two-mode and one-mode squeezed states at RF frequencies. We then briefly discuss some of the possible applications of such states to the field of radar engineering.
Super-Resolution Reconstruction Using Deep Learning: Should We Go Deeper?

Super-resolution reconstruction (SRR) is aimed at increasing image spatial resolution from multiple images presenting the same scene or from a single image based on the learned relation between low and high resolution. Emergence of deep learning allowed for improving single-image SRR significantly in the last few years, and a variety of deep convolutional neural networks of different depth and complexity were proposed for this purpose. However, although there are usually some comparisons reported in the papers introducing new deep models for SRR, such experimental studies are somehow limited. First, the networks are often trained using different training data, and/or prepared in a different way. Second, the validation is performed for artificially-degraded images, which does not correspond to the real-world conditions. In this paper, we report the results of our extensive experimental study to compare several state-of-the-art SRR techniques which exploit deep neural networks. We train all the networks using the same training setup and validate them using several datasets of different nature, including real-life scenarios. This allows us to draw interesting conclusions that may be helpful for selecting the most appropriate deep architecture for a given SRR scenario, as well as for creating new SRR solutions.
Chipertext stream construction by using super total labeling $(a,d)-{P}_{2}\unicode{x22B3}H$ -antimagic of comb product graph

Development of ciphertext can be developed through labeling concept. Graphical labeling is a mapping of the elements of the graph (vertex or edge) to a set of positive integers. Mapping is called a functional function if each element on the graph is numbered with different positive integers. The process in ciphertext development can be called a cryptosystem. Ciphertext aims to make the message delivered by the message sender unreadable by the person who was not intended as the recipient of the message. There are several ways to change the secret message, one of which is Cipher Block Chaining (CBC) method. In this system, plaintext or P is divided into blocks and sequential keys K. One of the science fields of discrete mathematics that can be applied to the development of ciphertext is graph theory, with the topic of labeling of the graph. In this article, we discussed about the development of ciphertext which is developed from the labeling of comb product graph, namely .
Simulation with skeletons of applications using dimemas

Large computer systems, like those in the TOP 500 ranking, comprise about hundreds of thousands cores. Simulating application execution in these systems is very complex and costly. This article explores the option of using application skeletons, together with an analytic simulator, to study the performance of these large systems. With this aim, the Dimemas simulator has been enhanced with the capability of simulating application skeletons. This enhancement allows simulating the skeleton of Lulesh, an application with 90k processes in a single day. In addition, it also generates traces, which is of great value to validate skeletons and simulations.
A Conceptual Artist Programming for Social Change

Rachel AraAra, Rachel describes herself as a conceptual and data artistData artist , whose work brings to light the trials and tribulations of women in a male dominated world. Through her art, she summons disparate elements from past to present, physical and digital, recalling histories of feminismFeminism and memories of women in an alien world. We see red neonNeon lights and red-light districts, Florentine nuns in the shuttered silence of cloisters weaving cloth in codes. We are experiencing the artworks of Rachel Ara that conjure vivid feminist images and convey powerful messages about women’s states of being and consciousness, and consider issues of gender and sexuality, a theme that runs through her work. By comparison, the 2017 exhibition at the Tate BritainTate Britain , Queer British Art, 1861–1967, put on view mostly familiar works of women by male artists, and reframed them as works by queer artists living at a time when that identity seemed camouflaged. As gender and sexual identity have come to the fore, queer art and artists take more visible forms of gender expression. In this new and emerging approach, Ara takes a lead position for conveying, and putting before us, a female perspective where the artist and her art are one. Using the sophisticated means of production that she has mastered, from computing, technology, systems and data analytics, all part of her palette of artistic expression, her art takes its place at the intersection of the digital and physical worlds. She is not married to one mode of making art, as she considers what is possible and what is practical, but in either case, it is her passion for art as an expression of ideas that provoke the viewer to think critically about issues of contemporary life. You might say that Ara is a “postdigital”Postdigital artist who delves deeply into these issues, and speaks with an authentic voice uniquely hers, and this is reflected in her aestheticAesthetics sense of hands-on highly skilled methods of working that meld computing and programmingProgramming , woodworking and technology, for making art and making a difference.
A New Advantage Sharing Inspired Particle Swarm Optimization Algorithm

Particle swarm optimization algorithm is a widely used computational method for optimizing a problem. This algorithm has been applied to many applications due to its easy implementation and few particles required. However, thereKong, Lingping is a big problem with the PSO algorithm, all the virtual particles converged to a point which may or may not be the optimum. In the paper, we propose an improved version of PSO by introducing the ideaSnášel, Václav of advantage sharing and pre-learning walk mode. The advantage sharing means that the good particles share their advantage attributes to the evolving ones. The pre-learning walk mode notices one particle if it should continue to move or not which uses the feedback of the last movement. Two more algorithms are simulated as the comparison methods to test Benchmark function. The experimental results show that our proposed scheme can converge to a better optimum than the comparison algorithms.
Improving Semantic Style Transfer Using Guided Gram Matrices

Style transfer is a computer vision task that attempts to transfer the style of an artistic image to a content image. Thanks to the advance in Deep Convolutional Neural Networks, exciting style transfer results has been achieved, but traditional algorithms do not fully understand semantic information. Those algorithms are not aware of which regions in the style image have to be transferred to which regions in the content image. A common failure case is style transfer involving landscape images. After stylization, the textures and colors of the land are often found in incoherent places such as in the river or in the sky. In this work, we investigate semantic style transfer for content images with more than 2 semantic regions. We combine guided Gram matrices with gradient capping and multi-scale representations. Our approach simplifies the parameter tuning problem, improves the style transfer results and is faster than current semantic methods.
F-measure maximizing logistic regression

Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, wherein the majority classes dominate the minority classes, all class labels are estimated as “majority class.” In this study, we use an F-measure optimization method to improve the performance of logistic regression applied to imbalanced data. Although many F-measure optimization methods adopt a ratio of the estimators to approximate the F-measure, the ratio of the estimators tends to exhibit more bias than when the ratio is directly approximated. Therefore, we employ an approximate F-measure to estimate the relative density ratio. In addition, we define and approximate a relative F-measure. We present an algorithm for a logistic regression weighted approximation relative to the F-measure. The results of an experiment using real world data demonstrate that our proposed algorithm can efficiently improve the performance of logistic regression applied to imbalanced data.
Formation of Professional Culture of Youth in the Digital Economy: Foreign and Domestic Experience

The article is devoted to the analysis of the professional culture of youth. In the beginning, the author presents a description of the concept of professional culture as inherent in the human creative potential, his abilities, talents, level of achievement of social goals and ideals, as well as their implementation in practice. Further, the author defines the difference between professional selection aimed at identifying the most capable people and screening out other candidates and professional orientation, allowing the young person to independently and responsibly choose their future occupation. Also, the author analysed the impact of digital tools in the process of training young professionals for the upcoming activities, while indicating both positive and negative consequences of their use and compared the ways of formation of professional culture in the United States, Finland, China. At the same time, the author paid much attention to the study of the historical experience of instilling a professional culture of the Soviet youth and pointed out characteristics of the pedological and activity stages of development of professional culture in the Soviet Union. In conclusion, the author reveals the features of the development of a professional culture of young people in modern Russia and concludes that it is necessary to introduce young people to the existing traditions and values in the interests of the formation of new intellectual and political elites.