Reviewerly/exHarmony huggingface dataset:
Opportunities to Use Google Cloud Services in High School Computer Science Classes

The article examines the issue of usage of contemporary technologies in education. The authors describe some of the functions of GoogleDocs cloud services and how to implement them at various stages of computer science lessons.
A Novel Approach for Pattern String Matching in Intrusion Detection System

Abstract String matching algorithms are critical for web usage devices which screen the packets and moves on the basis of load. Intrusion detection or prevention systems, web cleansing, anti-virus and junk preventive systems all have a necessity for effective algorithms when managing string matching scenarios. Some of the algorithms viz. Aho-Corasick, WuManber, Rabin-Karp, Commentz-Walter, Bit parallel etc. entails lot of space and time. AhoCorasick algorithm is built on the basis of finite state machines. On the shadow of KnuttMorris-Pratt, Commentz Walter developed an algorithm which in turn uses finite state machines. Bit parallel algorithms like shift-or takes the help of varied computer words ie. the registers in CPU, to compare the efforts. Rabin Karp implements hashing for discovering matching pattern strings in a given text. Wu-Manber looks text in chunks than of every character coalescing the work of Aho-Corasick and Boyer-Moore. Our algorithm reduces character comparisons and consumption of memory on the basis of graph evolution structure and search procedure by means of dynamic linked list. Theoretic study and trial results demonstrates that the projected work is much enhanced in terms of space and time consumption, when compared with previously known pattern-matching algorithms.
A model for tonal progressions of seventh chords

In this paper, we propose a model for idealized diatonic and chromatic voice leadings between seventh chords and study its computational aspects. The model provides a mathematical formalization of the concept of tonal seventh chord realizations including augmented sixth chords. Certain contrapuntal aspects, such as preparation and resolution of certain dissonant intervals, are taken into account. Possible applications of the model are discussed using a graph-theoretic approach. In particular, we present an algorithm for generating concrete voicings from sequences of seventh-chord symbols.
Improving the Effectiveness of Professional Education Based on Innovative Methods and New Technologies in the Framework of an International Project «ImProfEdu»

The article is devoted to the problems of improving professional education in the service sector in Russia and Finland. The international project ImProfEdu, implemented within the framework of the South-East Finland—Russia cross-border cooperation program 2014–2020, funded by the European Union, the Russian Federation and the Republic of Finland, is dedicated to solving these problems. The ImProfEdu project is aimed at improving professional education focused on the needs of the labor market in Russia and Finland using digital technologies, creating the necessary conditions for students and University graduates to develop motivation for training, advanced training and professional retraining for subsequent employment. The article presents the relevance of the project, its mission, goals and objectives. The General problem of improving the quality of professional education in the article is divided into four sub-problems: improving the effectiveness of distance training, increasing the motivation of students, improving the quality of distance training, ensuring the availability of training for all population segments.
Extraction of Space Domain Entity and Relation via Word Vector Representation and Clustering Method

Abstract Knowledge graph has shown great value in search engine, natural language Q&amp;A, recommendation system and other application scenarios in recent years. The basic elements of a knowledge graph are entities and relations therein, so how to automatically extract entities and relations from natural language texts becomes a key issue in knowledge graph construction. In this paper, we propose an unsupervised method to extract space domain entities and relations with the goal of building a space knowledge graph. Firstly, a neural network model is used to extract implicit semantic features of domain words represented by dense vectors from original space domain corpus, and then new entities are discovered by clustering in vector space through a small number of labeled data. By concatenating space domain-specific word vectors and general domain word vectors, universal vector representations of entities are obtained, which include general features and domain features as well. On this basis, semantic vectors of relations between entities are calculated, and more new entity relations can be extracted from the corpus by using semantic vectors of relations. Compared with supervised method, the entity and relation extraction method proposed in this paper only needs a small amount of labeled data, thus is quite suitable for the construction of knowledge graph in space domain where labeled data is rather rare and expensive.
Effective Multi-Label Classification Using Data Preprocessing

Usage of feature similarity is expected when the nearest neighbors are to be explored. Examples in multi-label datasets are associated with multiple labels. Hence, the use of label dissimilarity accompanied by feature similarity may reveal better neighbors. Information extracted from such neighbors is explored by devised MLFLD and MLFLD-MAXP algorithms. Among three distance metrics used for computation of label dissimilarity, Hamming distance has shown the most improved performance and hence used for further evaluation. The performance of implemented algorithms is compared with the state-of-the-art MLkNN algorithm. They showed an improvement for some datasets only. This chapter introduces parameters MLE and skew. MLE, skew, along with outlier parameter help to analyze multi-label and imbalanced nature of datasets. Investigation of datasets for various parameters and experimentation explored the need for data preprocessing for removing outliers. It revealed an improvement in the performance of implemented algorithms for all measures, and effectiveness is empirically validated.
Underexposed Image Enhancement via Unsupervised Feature Attention Network

In order to solve the problem that deep learning method needs a lot of paired data sets in image enhancement, this paper proposes unsupervised feature attention network (UFANet), which uses a new illumination estimation that combines pixel estimation and channel estimation to guide the network to decompose underexposed images. In addition, a feature attention residual network is trained to decompose under-exposed images into illumination and reflectance. Through a set of carefully designed non reference loss functions, which implicitly enhance the quality and drive the learning of the network, we train UFANet without any paired images. A large number of experiments on various benchmarks have proved the advantages of our method over the latest methods in terms of quality and quantity. Compared to the state-of-the-art methods, our method only needs to be trained on 350 underexposed images. All the above advantages make our UFANet attractive in practical applications.
Demo: Using Smartphone and Optical Mouse to Enhance Web Interaction

This demo shows example applications of the MousePath system-a novel approach to leverage optical mouse to enhance the web interaction on general PCs. MousePath works as a lightweight and ubiquitous bridge to convey information between the co-located smartphone and desktop web pages. MousePath consists of two major entities, the MousePath transmitter and the MousePath receiver. The MousePath transmitter is an app running on the user's smartphone. The MousePath receiver is a web script offered by the webserver and running in the web browser on the PC. MousePath works by putting the optical mouse on top of the smartphone's screen, and its transmission is then started and instantly finished without association and login frictions. Its core mechanism is encoding data into the movement of the smartphone's display content and using the optical mouse of the computer to sense the movement for decoding. This demo illustrates MousePath in two practical scenarios: general message sharing, e.g., sharing a URL/password from smartphone to PC's web, and hardware interface sharing, e.g., making smartphone sensor accessible for PC's web.
Summary and Final Remarks

This chapter, in Sect. 5.1, summarizes the original contributions of the author, presented in Chaps. 3 and 4, with regard to the neuro-fuzzy networks described in Chap. 2. In addition, directions of future research are outlined in Sect. 5.2. A list of main novelties, first of order referring to Chap. 4, is included in Sect. 5.3.
Investigating Pupils’ Cognitive Engagement in Augmented Reality-Supported Second Language Learning Classrooms

Augmented Reality (AR) is one of the promising technologies that has been used in the educational field. It helps to increase learners’ motivation, establish links with real-life experiences, and create contextual awareness. Yet current research in AR for education is still in its infancy and there are few studies regarding the integration of AR in language learning classroom. This study is part of an on-going pedagogical innovation project on AR-enhanced creating and sharing activities for pupils’ Chinese character learning. The study concentrates on examining the effectiveness of the designed AR activities by focusing on students’ cognitive engagement, in terms of the ICAP framework which helps to assess cognitive engagement with behavioural metric. A total of 53 grade two students from a government primary school in Singapore participated in this study. The findings of the study provide insights into designing and assessing AR-enabled activities in language classrooms.
Research on Maintainability Simulation of Complex System

Using the SQL and Visual Basic language as the implement tools, this paper designs and realizes the maintenance visualization intelligent simulation environment (MVISE). Based on the maintenance flow chart, this paper analyzes the function, application time and structure of MVSL. It can simulate the complex network structure model conveniently, quickly and accurately, and provide the simulation output data which is suitable for the user. It will be able to play a very good role in the automation of maintainability design. It adopts a unified model structure which is convenient for users to study and use, reflects the real needs of users and presents a new design idea and method for maintainability simulation.
Interpret The Predictions Of Deep Networks Via Re-Label Distillation

Interpreting the predictions of a black-box deep network can facilitate the reliability of its deployment. In this work, we propose a re-label distillation approach to learn a direct map from the input to the prediction in a self-supervision manner. The image is projected into a VAE subspace to generate some synthetic images by randomly perturbing its latent vector. Then, these synthetic images can be annotated into one of two classes by identifying whether their labels shift. After that, using the labels annotated by the deep network as teacher, a linear student model is trained to approximate the annotations by mapping these synthetic images to the classes. In this manner, these re-labeled synthetic images can well describe the local classification mechanism of the deep network, and the learned student can provide a more intuitive explanation towards the predictions. Extensive experiments verify the effectiveness of our approach qualitatively and quantitatively.
Acoustic target recognition algorithm based on particle swarm neural network

Abstract In order to improve the automatic recognition rate of acoustic targets, this paper conducts research on acoustic target recognition algorithms based on particle swarm neural network. Firstly, the mathematical description of the particle swarm optimization algorithm is described, and the initial parameters and algorithm flow of the particle swarm optimization algorithm in the experiments in this paper are given. Second, the design includes the central processor, power supply, signal conditioner, filter, trigger circuit, and state. Acoustic target recognition prototypes of display circuit, memory, target type indication circuit, serial port, crystal circuit, microphone and hardware interface circuit, etc. Finally, using the collected acoustic signals of tanks and helicopters, a semi-physical simulation experiment was designed to carry out target recognition. Experimental research and experimental results verify the effectiveness and stability of the acoustic target recognition system in this paper.
A Secure Wavelet based Watermarking Approach using Psycho-Visual Redundancy in Image

The principal requirements of any digital watermarking algorithm are imperceptibility, robustness, security and capacity of the watermark [2], [9]. Imperceptibility and robustness are inherently conflicting in nature. Over numerous trials of digital watermarking of multimedia, it has been found that embedding a watermark with high strength also decreases its obscurity and makes the watermark more prone to detection. On the other hand, a watermark embedded with a low strength improves the imperceptibility but causes degradation of robustness against signal processing, geometric and feature distortive attacks. This paper proposes a secure approach for watermarking using the Image Partitioning approach, which exploits the psycho-visual redundancy in the image to embed the watermark. Using the chaotic scrambling based Arnold Transform, watermark is first scrambled and then embedded in the sub-band of the image obtained using the Discrete Wavelet Transform and characterized by the lowest frequency. This improves the security as well as enhances the imperceptibility of the watermark, without significant deviation in visual appearance of the watermarked image from the cover image. Further, frequency domain embedding leads to better robustness and stability. The method is used to perform watermarking of grayscale images and performs well when subjected to common distortions, geometric, order-statistic filtering and signal processing attacks. This was determined by calculating the Peak Signal to Noise Ratio of the watermarked cover image and the retrieved watermarks and the Normalized Cross-Correlation of the embedded and extracted watermarks.
A Private Statistic Query Scheme for Encrypted Electronic Medical Record System

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.
Graph Processing System for Network Science

Big graphs processing is a complex task that requires computational power as well as special purpose software. This happens due to the irregular structure of the graphs because it is not known in advance the way how the graph will be stored in memory. Also, since the size of graphs simulating real-world objects can achieve millions of nodes and edges (and even more), its efficient processing can be complicated by long search operations for navigating in the graph. Another obstacle to efficient development of graph algorithms is the non-deterministic structure of many such algorithms. With all of the above, this paper is devoted to a high-level description of a system that allows to develop graph algorithms efficiently and increase productivity of software developer. This system can also be used to build efficient data structures for storing graphs, since it allows to separate the development of algorithms for processing graphs from the issues of their storage on a computing system. The developed system is intended for use in research areas related to the processing of large graphs with complex algorithms, for example, in network science.
Video Summarisation using Shot Boundary Detection and TF-IDF Vectorization

The colossal development of technology has brought about expanded intricacy of time and information management. Many specialists have created strategies for dealing with this gigantic measure of information or longer recordings successfully. Therefore, summarizing the videos has become one of the important factors to save time and energy. This paper presents the summarisation of a video using shot boundary detection and TF-IDF vectorization. These video skimming methods divide the video into chunks based on the change in key frames. The proposed method is used to deal with discursive videos and take up important points from a video. This can help to save time of the viewer by providing the overall summary of the video consisting of only important points. The results of the original video and final summarized video are presented for clear depiction. It can be observed that summarized video is produced by holding back the key highlights of the video.
Action Category and Phase Consistency Regularization for High-Quality Temporal Action Proposal Generation

Temporal action detection is a fundamental yet challenging task in video content analysis. The performance of existing methods still remains far from satisfactory as the mAP reduces dramatically at high tIoU threshold. With the goal of predicting the starting and ending points more precisely, this work first introduces the action category label into the temporal proposal generation stage of the training process. Specifically, with the category information, we proposed two extra constrains, i.e, action based constraint and action-class agnostic constraints. The former aims at minimizing the discrepancy inside the same action category while the latter forces the feature of the samples aggregates in the same phase. Comprehensive experiments are conducted on the THUMOS’14 benchmark. A remarkable improvement of average recall is attained especially when the number of proposals is small. And our approach achieves 29.0% mAP at a strict tIoU@0.7.
DOBNET: Dynamic Object Boundary-Refinement Network for Real-Time Instance Segmentation

Mainstream real-time instance segmentation methods always predict masks in the ’detect-then-segment’ way and ignore the object boundaries, leading to resource wasting and indistinct masks. To overcome these drawbacks, we propose a Dynamic Object Boundary-refinement Network (DOBNet) to predict masks in the principle of SOLO [1]. In this method, we first adapt the OctConv [2] as the generator to produce two parallel dynamic convolutions for mask and boundary features, respectively. The Boundary Refinement Module then helps fuse the features from the two convolutions and thereby refine the final predictions with boundary information. Hence, our method attains a precise segmentation while maintaining real-time speed. More specifically, the architecture achieved 37.9 AP on the COCO test-dev2017 dataset with a speed of 31.8 FPS, as is shown in Table 1. The results are more accurate than the existing real-time method.
Image Analysis with Machine Learning Algorithms to Assist Breast Cancer Treatment

Real-time imaging technology has the potential to be applied to many complex surgical procedures such as those used in treating people with breast cancer. Key delaying factors for the successful development of real-time surgical imaging solutions include long execution time due to poor medical infrastructure and inaccuracy in processing mammogram images. In this work, we introduce a novel imaging technique that identifies malignant cells and supports breast cancer surgical procedures by analyzing mammograms in real-time with excellent accuracy. According to this method, hidden attributes of a target breast image are extracted and the extracted pixel values are analyzed using machine learning (ML) tools to determine if there are malignant cells. A malignant image is divided into contours and the rate of change in pixel value is calculated to pinpoint the regions of interest (ROIs) for a surgical procedure. Experimental results using 1500 known mammograms show that the imaging mechanism has the potential to identify benign and malignant cells with more than 99% accuracy. Experimental results also show that the rate of change in pixel values can be used to determine the ROIs with more than 98% accuracy.
Reflection of PISA research education in the post-soviet space

We consider, within the framework of the theory of the human-technological revolution, the role of knowledge, technology and education in the transition from the industrial to the post-industrial phase of the development of civilization. The world is at a bifurcation point and a change in attitude towards education and knowledge can determine the trajectory of the world as a whole and of individual civilizations. We analyze how attitudes toward education have changed in the course of historical development. The cultural and educational level of the population at the present stage is becoming the most important strategic resource that determines the country’s place in the world division of labor. We show the need to build educational strategies and policies based on macro indicators characterizing the education system. We pay great attention to the analysis of the results of the PISA study for Russia, Belarus, post-Soviet countries. New industrialization and breakthroughs require a change in attitudes towards knowledge, educational strategy, and politics. We are discussing possible directions for modernizing the educational strategy of the Union State.
Deep Optics: Learning Cameras and Optical Computing Systems

Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we review recent hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can facilitate application-domain-specific cameras or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems.
Workflow Deployment on Federated Clouds using Steiner Tree

Cloud computing is the most arising terminology in today's world due to its pay-as-per-your-requirements model. Customers pay only for services that come under their current requirements, which gives customer's a big advantage. In a federated cloud one or multiple cloud facility suppliers partition or share their resources, servers to fulfill the customers demand. This minimizes price, gives more security, optimal consumption of facilities, and improves excellence and performance. Here, Steiner Tree is used in the workflow deployment of the federated cloud. This work presents a fresh technique that resolves the concern of workflow solicitations setting out over associated clouds fulfilling the trustworthiness, safety, and budgetary price prerequisite. Also demonstrates the trade-off between trustworthiness, budget price and optimization difficulty is an NP-hard problem.