Reviewerly/exHarmony huggingface dataset:
The Legal Basis of Countering Cyber-Terrorism in Russia and in Other Countries from the Standpoint of Its Social and Political Dimension

The authors discuss the legal specifics of counteracting cyber-terrorism in Russian and in foreign law at the modern stage of the development of information space. They show that such principles of telecommunication technologies as transparency and accessibility are used by terrorist organizations for criminal purposes. The analysis of foreign legislation shows that legislations of most countries do not have a special crime of cyber-terrorism. At the same time, the use of IT in disseminating terrorist ideology is included in these legislations as an aggravating circumstance. The authors conclude that Russian legal scholars do not currently have a common opinion on adding a new special crime of cyber-terrorism to the Criminal Code of the Russian Federation. They argue that such amendment of Russian criminal law is not necessary. The authors also single out different features of cyber-terrorism and present its classification that includes a hybrid type and cyber-terrorism proper. In the former case, the Internet is used for terrorist activities: propaganda, recruitment of supporters, their training, radicalization of society, collecting funds and data, connections, planning of actual terrorist attacks. In the latter case, there are direct attacks against cyber-infrastructure with political, religious and ideological purposes. The authors conclude that the dangers of cyber-terrorism as a threat to the whole infrastructure of society are overstated because ordinary cyber-crimes inflict a much greater damage on the economy of any country. However, as these crimes are not coordinated from one center and because of the ideology of extremism, this type of crime cannot strike a social fear comparable with the fears that could be inspired by references to the activities of terrorist organizations. It is shown that in the USA and in Western Europe cyber-terrorism is greatly politically charged, and the society is encouraged to evaluate the threat of cyber-terrorism though the prism of the level of security of each member of this society. Besides, the society is indoctrinated to believe that it is necessary to carry out cyber-attacks against those countries that might pose a threat. In this connection, the authors conclude that a high level of attention to the problem of cyber-terrorism has a prominent ideological and value component.
Identification the Models of Atmospheric Pollution by Nitrogen Dioxide Based on the Artificial Bee Colony Algorithm with Modified Operators for Determining of Profitable Food Sources

In the article the method of structural identification of interval discrete dynamic models (IDDM) of processes based on the results of observations is considered. The method of structural identification is used, if the resulting experimental data is inaccurate (ie. is presented in the form of interval values of the simulated characteristic due to the limited amplitude of measurement errors). The results of detailed analysis of the method have shown the following: algebraic expressions for the implementation of definite operators are insufficiently substantiated. In particular, it is an operator for determining of the profitable food sources which in terms of behavioral models of bee colony determines the probability of choosing by an onlooker bee a particular food source. In the paper, two ways to improve the computational scheme of the method is proposed. In particular, by introducing the new operators for determinaton the intensity of investigation the "better" structures of IDDM. The comparison of efficiency of these operators on an example of building the discrete dynamic models of nitrogen dioxide dynamic due to air pollution by harmful emissions of motor vehicles in a control point (Chekhova-Za Rudkoyu Streets crossroads, Ternopil city, Ukraine) is carried out. The results of the series of research have showed the following: the using of the proposed in the paper operators for the determining the profitable food sources, has reduced the computational complexity of the known method of structural identification by an average of 8%.
Sentiment Mining Model for Opinionated Afaan Oromo Texts

Opinions are personal judgment on entity. This is not only true for individuals but also true for organizations. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. The process of sentiment mining involves categorizing an opinionated document into predefined categories such as positive, negative or neutral based on the sentiment terms that appear within the opinionated document. For this study text document corpus is prepared by the researcher encompassing different movies ‘reviews and Various techniques of text pre-processing including tokenization, normalization, stop word removal and stemming are used for this system(sentiment mining model for opinionated afaan Oromo texts). The experiment shows that the performance is on the average 0.849(84.9%) precision and 0.887(88.7%) recall. The challenging tasks in the study are handling synonymy and inability of the stemmer algorithm to all word variants, and ambiguity of words in the language. The performance the system can be increased if stemming algorithm is improved, standard test corpus is used, and thesaurus is used to handle polysemy and synonymy words in the language.
Effective Iterative Program Synthesis with Knowledge Searched from Internet

This paper presents the ongoing work of studying the iterative program synthesis based on knowledge searched from the Internet, which can fairly reduce the scale of program space and improve the efficiency of synthesis. First, we implement a tool named Args(api Recommendation via General Search) to obtain the API knowledge from the Internet. Second, we propose an iterative method that incrementally constructs the program space to quickly approach the target program. The initial experimental result shows the effectiveness of our work.
An Efficient Sieve Algorithm with Evolutionary Technique

Sieve algorithm plays an important role in solving the shortest vector problem, which is one of the most representative hard lattice problems. In this paper, we propose an efficient sieve algorithm with evolutionary techniques. By combining Gauss Sieve algorithm with global selection, we propose an efficient sieve algorithm. Experimental results show that our algorithm performs better than FPLLL's Gauss Sieve algorithm in solving the shortest vector problem. Furthermore, we explore how mutation behaves in accelerating sieve algorithm.
“FRIENDLY CHAT”- A chat application with multi-headed classification models for identifying abusive levels and their comparative study

Abstract An application that tracks the use of offensive and vulgar language on chats on social media networks in order to prevent users from becoming disrespectful may play a role in changing how users interact. "Friendly chat" is a play on a community messaging program, which allows users to address a range of topics that are totally unregulated. Once things start heating up a user may become abusive towards other users, the application can disperse the situation quickly by implementing protective measures to stop the abusive user in his tracks. Abuse is however not the only type of "toxic" material to which a user is exposed when communicating with other users. Hatred of identification, intimidation, and other forms of obscenity may also become a part of the discussion. To identify a post as toxic, we need to refer to a reference, using the "toxic comment" dataset consisting of a large number of Wikipedia comments which have been rated by human critics who classify the comment to fall under one or more of the above-mentioned categories. Integrate this model in the real time data that we receive from “Friendly Chat” to classify a user into one or more of these categories. Use techniques of classification and machine learning such as Naïve Bayes, LSTM and Binary relevance and chain classifiers models. Our application’s capable of detecting abusive users.
Identification of Domain-Specific Senses Based on Word Embedding Learning

This paper focuses domain-specific senses and presents a method for detecting predominant sense for each domain. The method is based on the similarity of senses which is obtained by word embedding learning. To detect domain-specific senses, we applied Markov Random Walk (MRW) model, which is a ranking algorithm that has been successfully used in the Web-link analysis. The approach decides the importance of a vertex within a graph based on global information drawn recursively from the entire graph. We applied the technique to rank senses for each domain. The method was tested on WordNet 3.1 and the Reuters corpus. The results obtained by comparing manual annotation of domain-specific senses showed the effectiveness of the method.
Perspective-based inspection to improve user experience aspects in SCRUM website's development

A company needs a website representing the products or services offered to consumers with an attractive appearance. Along with the development of product or service variations, companies need to make updates to attract target consumers. The company aims to simplify the customer acquisition process through an attractive user interface and user experience. This aim is the goal of this study by applying aspects of the user experience and user interface. At the time of this research, the company was in the website redesign phase using the SCRUM method. This research tries to stay synergistic with the processes running in the company by applying the UX (user experience) method, which is in line with the SCRUM flow. The UX method in question is Perspective-based Inspection. The design strategy taken is to implement color changes to increase brand awareness from the customer's point of view. Furthermore, this study categorizes the insights from customers into the aspects of UX Honeycomb. The next step is applying Small Up Front Analysis (SUFA) to explore system requirements, then create a conceptual design by dividing three perspectives, namely ecology perspective, interaction perspective, and emotional perspective. Next, this study establishes a user story and applying the Department of Health and Human Services (HHS) guidelines into the high fidelity prototype. After all the processes are complete, the company publishes the redesign results online. This study increased the value of the user experience on usable, useful, and findable values.
Breast Cancer Recognition Algorithm Based on Convolution Neural Network

Most of the preliminary attempts of deep learning in medical images focus on replacing natural images with medical images into convolutional neural networks. In doing so, however, the particularity of medical images and the basic differences between the two types of images are ignored. This difference makes it impossible to directly use the network architecture developed for natural images. This paper therefore uses medical data sets for migration learning. Moreover, the reason why deep learning is difficult to apply in medicine is that it can easily lead to medical disputes because of its unexplainability. In this paper, the deep learning model is explained and implemented by using the theory of fuzzy logic. This paper tests the accuracy and stability of the original model and the new model in classification prediction. Our results show that the model implemented by fuzzy logic improves the accuracy, and makes the prediction more stable as well.
Issues concerning the language standardisation: Theoretical view

Monitoring and evaluating changes in standard languages, especially in Serbian, in the twenty-first century are the focus of this work. In order to determine the situation in the current norm, it is necessary to consider the Serbian language within the context of contemporary European languages. Challenges arising in the process of standardisation of the Serbian language (such as the language democratisation stemming from the democratisation of society, modernisation, rapid establishment of a gender-sensitive language and insisting on its use, social network terms, etc.), but likewise the needs that such a language should cover, are common with those in other European languages. The principal aim is to shed light on changes in the traditional Serbian language generating a parallel norm, which we refer to as ʻneo-standard', and whose formation is predominantly influenced by the media. The second objective is to exactly and thoroughly determine the reasons for the emergence of neo-standard, which, as previous research have shown, are common in all European languages.
Measuring the Effect of Elementary Descriptive Attributes on News Recommender Systems

Information overload and information obscuring are two most recent challenges in finding relevant information from the Internet. Moreover, specifically in the online news industry, using sensationalist and eye-catching headlines for click-baiting has become a common practice. Finding relevant information is becoming harder than ever before. A solution to overcome the challenge is by utilizing a news recommender system. In most of the news recommender systems, a certain number of data attributes is required to produce appropriate decisions. Unfortunately, this is not always the case, especially when users are not required to register to a news portal. In this case, valuable information that can be used to make decisions, such as users' preferences, visit or reading histories, will not be available. Therefore, in this paper, we take advantage of the elementary descriptive attributes of news articles, namely titles and keywords. We compare how these attributes affect the decision results, namely the recommended related news. We collected news articles from a news portal, generated two sets of related news using different compositions of descriptive attributes, and compared them to the originally defined set. Our findings indicate that the combination of titles and keywords produces highly relevant news which achieved a mean rating value close to 3 (on a scale of 1 to 5). Whereas the original recommended related news only has a mean rating value around 1. The combination produces better results compared to the original recommended related news which is highly affected by news categories. Our findings also revealed that the presence of specific entities (such as a person, location) in the titles has a significant impact on the outcome. This work has a wide spectrum of potential applications in the future, for example for automatic news aggregation, combating spam, reading context understanding, and so on.
ISG: Interworking System for Semantic data representation in Graph DB

Semantic technology gives meaning to numerous raw data collected through the various IoT platform of Smart City and creates a relationship between data so that all data can be organically connected. SPARQL is used as a query language for retrieving such semantic data, but the demand for graph database that can be traversed between data is increasing due to the speed of search and limitations of complex relationship search. However, due to the difference in the resource structure and the expression range between the existing semantic data and data in graph database, data could be lost, or unnecessary data is generated in the data conversion process. Therefore, in this paper, we propose an interworking system that enables retrieved semantic data from a smart city using SPARQL to be expressed in a graph database without data loss, thereby make possible complex relationship inquiry and graph traversal.
Detection of Human Emotion in Text

The diagnosis of human emotions and the analysis of human emotions are a topic at the moment; the derivation of emotions and feelings from the written text remains a major challenge in this area of research. We do the job of an emotional annotation to find a category of mood states, perhaps the emotional intensity and the words or word expressing emotions in the text. An overview of emotional intelligence research with emphasis on emotional agents has been covering areas like emotional agents, modelling artificial agent’s environment, different forms of learning, emotional intelligence in decisions support processes, etc. Machine-learning approaches and knowledge-based approaches are the two main approaches for this task (Agrawal and An in 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (2012), [1]). The performance of these two approaches is quite good but machine-learning approaches always outperform the knowledge-based approaches in all area.
A semi-supervised approach to growing classification trees

A classification tree is grown by repeated partitioning of the dataset based on a predefined split criterion. The node split in the growth process depends only on the class ratio of the data chunk that gets split in every internal node of the tree. In a classification tree learning task, when the class ratio of the unlabeled part of the dataset is available, it becomes feasible to use the unlabeled data alongside the labeled data to train the tree in a semi-supervised style. Our motivation is to facilitate the usage of the abundantly available unlabeled data for building classification trees, as it is laborious and expensive to acquire labels. In this paper, we propose a semi-supervised approach to growing classification trees, where we adapted the Maximum Mean Discrepancy (MMD) method for estimating the class ratio at every node split. In our experimentation using several binary and multiclass classification datasets, we observed that our semi-supervised approach to growing a classification tree is statistically better than traditional decision tree algorithms in 31 of 40 datasets.
Lightweight Progressive Residual Clique Network for Image Super-Resolution

Deeper and wider convolutional neural networks (CNN) hava been widely applied to the single image super-resolution (SR) task for its appealing performance. However, enormous parametric memory footprint hinders its real-time application on mobile devices, especially in the energy-sensitive environment. In this work, we take both the reconstruction performance and efficiency into consideration and propose a lightweight progressive residual clique network (PRCN) for image SR. PRCN is built on the two-stage residual channel separation block (RCSB) and long-skip connections. First, we divide the input into four channel groups to differently learn texture details, immediately followed by a primary fusion to establish cross-channel correspondence in the first stage. Then we perform a further fusion on the outputs of the first stage to constitute a clique for the refinement in the second stage. Meanwhile, we employ SENet to improve the outputs of the second stage with the separate features of the first stage. This design not only enforces the correlation across channels, but also allows fewer densely connected blocks. Experimental results on public datasets show that PRCN outperforms state-of-the-art methods in terms of performance and complexity.
Multi-scale ship target detection in remote sensing images

In this paper, a new method is proposed for multi-scale ship target detection and recognition in remote sensing image, which uses the change receptive field design of neural network to realize multi-scale target detection. The network structure is composed of three branches, and the expansion convolution is used to realize the effective perception of different size targets. In the training stage, the three branches share the same value parameters, and the training samples are divided into three sizes: large, medium and small, and the three branches are trained respectively; during the testing stage, the target detection is completed on the three branches at the same time, and finally the detection results are synthesized (non-maximally suppressed, NMS) to obtain the final detection results. Through the experiments on the Google Earth remote sensing image data set collected by ourselves, it is proved that the method is effective in multi-scale ship target detection in remote sensing image, and the performance is improved compared with SSD, FPN and other classical algorithms.
Selection of Network Feature Attribute Based On Classification Discrimination And Correlation Analysis

Thereare many parameter attributes for anomaly detection of traditional network. It limits the detection efficiency. The selection of feature parameters is limited by the balance of samples. For these problems, this paper studies and designs a selection algorithm of network feature attributes based on classification discrimination and correlation analysis, defines the classification discrimination of feature, and a quantitative selection strategy is established. The test analysis is developed through the Moore data set from Cambridge university. The experimental results show that without reducing the detection accuracy, the algorithm proposed in this paper can improve the detection performance of abnormal samples, and effectively reduce the time of detection modeling.
Auto-recognition Pedestrians Research Based on HOG Feature and SVM Classifier for Vehicle Images

In order to estimate the potential hazards and adopt strategies for preventing the accidents, this paper is about the research of auto-recognition pedestrians. Using the captured images of the vehicle recorder, based on the HOG feature processing technology and SVM classifier, the automatic detection and recognition of pedestrians are realized. In the past, the cost of pedestrian identification system is too high and its universality is poor. In this research, optimization methods of reasonable sample allocation and difficult training are used to make the training model more general and effective.
Extending CCSDS 123.0-B-1 for Lossless 4D Image Compression

A 4-dimensional (4D) image can be viewed as a stack of volumetric images over channels of observation depth or temporal frames. This data contains rich information at the cost of high demands for storage and transmission resources due to its large volume. In this paper, we present a lossless 4D image compression algorithm by extending CCSDS-123.0-B-1 standard. Instead of separately compressing the volumetric image at each channel of 4D images, the proposed algorithm efficiently exploits redundancy across the fourth dimension of data. Experiments conducted on two types of 4D images demonstrate the effectiveness of the proposed lossless compression method.
On Image Processing System for Robot Control using DSK 6713 DSP Kit

Real-time image processing techniques are used in many automation applications like in autonomous vehicle (AV) and robot control. In this paper, an image processing system for real-time video signal processing is studied, using DSK 6713 kit from Texas Instruments. The DSK kit is used to acquire frames from a camera module through HPI port and store them into external SDRAM memory. The DSP is used to extract the frames from memory through EMIF bus, and to run complex processing algorithms on 1D and 2D signals, like spectral analysis and artificial intelligence. Studying the system performances, three possible bottlenecks were identified and analyzed regarding image processing algorithms.
Absolute Stability for a Class of Takagi-Sugeno Fuzzy Control Systems

In this paper, we presented a sufficient condition on the frequency domain for the absolutely stable analysis of the Takagi-Sugeno (T-S) fuzzy control system. For modeling the plant and controller we used transfer functions instead of the state description and showed that the overall T-S fuzzy plant is a polytope of transfer functions. Besides, by applying local proportional controllers, the bounds of proportional parameter of the overall fuzzy controller can be computed. Hence the robust absolutely stability criterions with graphical interpretation for closed-loop T-S fuzzy control system were derived.