Reviewerly/exHarmony huggingface dataset:
Robust Digital Watermarking Algorithm Based on DCT-SVD and QR Code

Digital watermarking technology is an important technology for copyright protection. In practical applications, the robustness of digital watermarking technology is more demanding. In order to resist attacks and improve invisibility, a new data watermarking scheme is proposed. First, watermark local feature regions will be determined by an improved method which contains two steps: texture complexity roughly location and SIFT precisely location. Second, DCT transformation on these regions is applicated and a set of low frequency coefficients of chosen regions are selected to construct a DCT coefficient matrix. Last, SVD decomposition is performed on DCT coefficient matrix and a QR code carrying watermark information is embedded by the additive rule. The watermark detection algorithm is the reverse process of embedding. The experimental results show that the algorithm has good robustness to common attacks.
An Android Application for Automatic Content Summarization of News Articles Using Multilayer Perceptron

With the advent of digital news, there is an overload of information from various news sites and blogs, on a wide variety of topics. People generally, neither have the time, nor the patience to go through a number of long news articles from various sites to get a gist of what is happening around them. This brings out the need for content summarization. But manual summarization is highly inefficient and practically infeasible. Therefore an automated approach to text summarization must be adopted. This paper is an attempt to implement and build Android Application for Automatic Content Summarization of News Articles Using Multilayer Perceptron, based on extraction. We have employed a feature based machine learning approach multilayer perceptron method, treating this as a classification problem, where for every sentence, we predict the probability of that sentence belonging to the summary. We have collected news articles and generated their extractive summaries using Newspaper module in python. We have considered a list of features that include sentence length and position, sentence similarity with title, presence of numerical values and proper nouns etc., Three different classifiers were used to train, test and compare used dataset, namely Logistic Regression, Artificial Neural Networks (multilayer perceptron) and Gaussian Naive Bayes, with average accuracies of 88.80%, 89.50% and 87.90% respectively. We also developed an android application to act as the interface between user and the summarizer. The user can either get the summary of an article which he/she gives as input, or get the summaries of news articles fetched in real time from various news sites.
Educational STEM designing of virtual and real devices on the Arduino platform

The article discusses possible forms of educational STEM projects in the field of electronics and device control using Arduino controllers. As you know, the implementation of such STEM projects can be carried out not only using various electronic constructors, but also using virtual modeling environments. The knowledge obtained during modeling in virtual environments makes it possible to increase the efficiency of face-to-face practical training with a real constructor, and to improve the quality of students’ knowledge. The use of virtual modeling environments in combination with the use of real constructors provides links between distance and full-time learning. A real constructors can be used simultaneously by both the teacher and the student, jointly practicing the features of solving practical problems. The article provides examples of using a virtual environment for preliminary prototyping of circuits available in the documentation for electronic constructors, to familiarize students with the basics of designing and assembling electronic circuits using the surface mounting method and on a breadboard, as well as programming controllers on the Arduino platform that control electronic devices. This approach allows students to accelerate the assimilation of various interdisciplinary knowledge in the field of natural sciences using STEM design.
Design and Implementation of a Low-cost AES Coprocessor Based on eSTT-MRAM IP

In order to insure the security of message transmission between resource-constrained Internet of Things (IoT) devices, a low-cost hardware implementation of Advanced Encryption Standard (AES) coprocessor based on eSTT-MRAM (embedded spin-torque transfer magnetic random access memory) is presented in this paper. By reusing the round unit, the resource overhead of the design is reduced. In order to optimize the total area, a compact SubBytes module without look-up tables is presented by mapping the arithmetic operations on GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) to isomorphic field GF((2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> )2). The promising second-generation memory STT-MRAM is selected to store the key for each round (roundkey) to verify its coordination with security coprocessors. Simulation and synthesis results show that the proposed coprocessor can complete one encryption in 92/110/128 (AES-128/192/256) cycles consuming only 13.07K gates and 28µW at 13.56MHz and 1.2V.
Food traceability system based on blockchain

At present, the traceability of food information in China is the traceability of a specific product, which lacks universality. Therefore, in order to trace the food information used in major activities, this paper proposes a one-code-one-item food information traceability system. Call Alibaba Cloud's commodity bar code query API to obtain the original information of the product; use the food information traceability software designed in this article to generate an exclusive code for each product to achieve precise identification of one-code-one-item; collect information on subsequent "transaction" links of product warehousing, transportation, testing and processing to ensure the integrity of food traceability information; use AES encryption method to encrypt food traceability information; utilize the distributed fault tolerance, immutability and privacy protection features of blockchain technology to store the encryption key on the chain to avoid illegal tampering and forgery of information and ensure data security.
Some Aspects of Statistical and Scientific Data Visualization

The purpose of the article is to research, analysis and consider the general problems and perspectives for the use of existing methods of statistical and analytical information visualization.The research methods are methods of semantic analysis of the basic concepts of the considered subject area (visualization of statistical and scientific data). The article discusses approaches to information visualization.The novelty of the research is the analysis of the mathematical and statistical apparatus that can be used to visualize statistical and scientific data and describe some aspects of visualization.Conclusions. The research conducted in the article concludes that the paper explores existing views on data visualization, which is a representation of some data in a graphical form and can be used for marketing activities, as well as types of infographic objects and criteria for its creation.Taking into account the results of the analysis, the authors decided to develop a software product for the visualization of statistical and scientific data, determined its mathematical justification.
Research on Optimal Layout of the Mobile Project Department based on Internet technology

Abstract Diversified mobile project department layout has a positive impact on the formation of a guiding design of scheme. In this paper, the parametric analysis of design elements are carried out. And the random of generations and optimization of the schemes are simulated by Grasshopper and Excel. Firstly, the initial parameter input options are set. After the size of each partition is limited, the random generation is carried out, and the overall layout shape of each partition is recommended forming a variety of design schemes. Finally, the schemes are screened by combining the constraint of conditions, and the scheme is optimized by using the Galapagos genetic algorithm in Grasshopper [1] to generate the final scheme.
A Methodology to Handle Heterogeneous Data Generated in Online Social Networks

Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing the Twitter text data. Using this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.
EMOTIONAL INTELLIGENCE: IT’S RELATIONSHIP TO MATHEMATICS ACHIEVEMENT OF SENIOR HIGH SCHOOL

The main purpose of this study was to determine the relationship between emotional intelligence and Mathematics achievement of the senior high school students of Don Mariano Marcos National High School. Respondents were Grade 11 students with a total number of one hundred sixteen (116) students. This study applied descriptive Correlational design. The Bar-On Emotional Quotient Inventory: Short (BarOn EQ-i: S) was used to determine emotional intelligence of the respondents and Mathematics achievement was measured by their third quarter grade in Statistics and Probability.The study revealed that the emotional intelligence of the respondents was average, which indicates that they have an adequate emotional and social capacity. Furthermore, their emotional intelligence varies when grouped according to Strand. On the other hand, negligible difference was observed when they were grouped in terms of their socio-economic status, age and sex. There exist a weak relationship between emotional intelligence and Mathematics achievement of the respondents. Lastly, the study revealed that Mathematics achievement is affected by emotional intelligence of the respondents. Senior high school students should be abreast of their emotional intelligence they possess and as much as possible, so they can find ways and means to increase their emotional intelligence. In addition, curriculum developers must include emotional intelligence in the competencies to be developed in the senior high school curriculum. Keywords:  Emotional Intelligence, Relationship, Mathematics, Achievement, SHS.
GPS-Based Behavioral Authentication Utilizing Distance Coherence

Current user authentication systems are based on PIN code, password, or biometrics traits, which can have some limitations in usage and security. Lifestyle authentication has become a new research approach in which the promising idea is to use the location history since it is relatively unique. Even when people live in the same area or have occasional travel, it does not vary from day to day. For Global Positioning System (GPS) data, previous work used the longitude, latitude, and timestamp as the classification features. In this paper, we investigate a new approach utilizing distance coherence, which can be extracted from the GPS itself without the need to require other information. We applied three ensemble classifications, including RandomForest, ExtraTrees, and Bagging algorithms. The experimental result showed that our approach could achieve 99.42%, 99.12%, and 99.25% of accuracy, respectively.
Boolean reasoning in a higher-order superposition prover

We present a pragmatic approach to extending a Boolean-free higher-order superposition calculus to support Boolean reasoning. Our approach extends inference rules that have been used only in a firstorder setting, uses some well-known rules previously implemented in higher-order provers, as well as new rules. We have implemented the approach in the Zipperposition theorem prover. The evaluation shows highly competitive performance of our approach and clear improvement over previous techniques.
FASTMATCH: Accelerating the Inference of BERT-based Text Matching

Recently, pre-trained language models such as BERT have shown state-of-the-art accuracies in text matching.When being applied to IR (or QA), the BERT-based matching models need to online calculate the representations and interactions for all query-candidate pairs.The high inference cost has prohibited the deployments of BERT-based matching models in many practical applications.To address this issue, we propose a novel BERT-based text matching model, in which the representations and the interactions are decoupled.Then, the representations of the candidates can be calculated and stored offline, and directly retrieved during the online matching phase.To conduct the interactions and generate final matching scores, a lightweight attention network is designed.Experiments based on several large scale text matching datasets show that the proposed model, called FASTMATCH, can achieve up to 100X speed-up to BERT and RoBERTa at the online matching phase, while keeping more up to 98.7% of the performance.
Technological partnership of industrial enterprises with higher education organizations

The article presents a study of technological partnership with the participation of industrial enterprises. Special attention is paid to the partnership in the technical and technological sphere between industrial entities and higher education organizations. To conduct the research there were used scientific methods of constructing concepts, analyzing regulatory documents and statistical materials. The main results of the study are to clarify the concept of technological partnership and its relationship with other related concepts, including "technological exchange", "technological cooperation" and "technological clustering". The principles of implementing technological partnership, including the principles of openness, partnership relations, and mutual benefit, are summarized. As part of the analytical part of the study, the main normative and legal documents describing the conditions and incentives for the formation of cooperative relations between industry and subjects of science and education are highlighted. Data on the current performance of economic entities within the framework of the considered documents are presented. The analysis of statistical information showed the interest of industrial enterprises and universities in the "project" format of interaction, which is more active at a higher technological level of both subjects. It is noted that since 2011, the share of industrial enterprises participating in joint projects with higher education organizations has increased. Using the example of Peter the Great St. Petersburg Polytechnic University, it is shown that the multidirectional nature of the university's research and its clear focus on modern global trends in technological development is a key factor in building close cooperation "organization of higher education - industrial enterprise".
Modeling and Investigation of the Movement of the User of Augmented Reality Service

In recent years, the use of augmented reality (AR) applications in various industries and spheres of human life has increased significantly. With the appearance of new AR devices and the development of technology, the mobility of users has increased and the possibilities of using AR have expanded. This leads to the need to develop new AR user traffic models taking into account the features of its movement. The paper presents the results of an investigation of the movement of the user of augmented reality services. Augmented reality user movement models have been developed and a detailed analysis of the influence of user behavior on AR traffic parameters and the quality of experience of AR services has been carried out. The generalized augmented reality user movement model is based on experimental data and can be used in the future for modeling and implementation of AR services.
Social Pooling with Edge Convolutions on Local Connectivity Graphs for Human Trajectory Prediction in Crowded Scenes

Human trajectory prediction is a quite challenging task mainly due to numerous social interactions and plausible paths in complex crowed scenarios and varying environments. Data-driven machine learning approaches based on Recurrent Neural Networks (RNNs) have, recently, achieved significant results in modelling human-human interactions in a scene. However, information-sharing pooling modules across RNN Encoders which operate on a local spatial context fail to model long-term scene level correlations, while other that adopt a more global approach are restricted to a rather simplistic formulations due to high computational costs. In this work, we introduce a novel pooling mechanism designed to perform trajectory pooling on a higher semantic level. We have developed a novel multi-layer network architecture based on a new Edge Convolutional operator acting on irregular data which is able to generalize local human-human interactions on a semantic social context. To assess the performance of the proposed social pooling with edge convolutions, we have integrated it into a state-of-the-art trajectory prediction framework based on Generative Adversarial Networks (GANs). Our module managed to overall outperform, by a significant margin, several state-of-the-art pooling modules in real-world challenging benchmark datasets.
A Low-complexity Neural BP Decoder with Network Pruning

Existing deep learning-based channel decoders, called neural decoders, suffer from demands on an excessively high computational complexity and large memory resource. In this work, we will show that a low-complexity neural belief propagation (BP) decoder can be constructed by utilizing the network pruning technique. In particular, it will be shown that by removing unimportant edges in a neural BP decoder, a significant complexity gain can be achieved. When the decoding complexity is fixed, the proposed decoder highly achieves a notable performance improvement as compared to the existing neural BP decoder, which will be demonstrated with performance evaluations. In addition, we conduct a preliminary study investigating the structure of pruned edges, which we believe provides some clues of a general design framework of practical neural BP decoders.
Privacy Preserving Netting Protocol for Inter-bank Payments

Banks transfer money and securities instantaneously on a gross basis by utilizing Real-Time Gross Settlement (RTGS) systems. Central banks are in charge of managing and operating RTGS systems, and they require each local inter-bank payment instruction to be processed by them. Accordingly, contemporary RTGS systems face many challenges including unconditional trust and privileges given to the central banks, and the inherent single point of failure vulnerability associated with centralized systems. To address these challenges, some financial institutions are starting to embrace blockchain technology. Project Jasper and Project Ubin are two successful preliminary attempts. However, these projects do not preserve participants’ privacy, and more importantly, they lack liquidity saving mechanisms to resolve the frequently occurring gridlock state. An efficient way to resolve gridlock is to settle payment instructions on a netting basis. In this paper, we propose a decentralized netting protocol that ensures the correctness of the netting result, while hiding the transferred amounts and preserving recipients’ privacy. We evaluate the protocol’s performance on Ethereum and show that the proposed protocol achieves its desired security properties while being feasible to deploy in practice.
NWP Models Combined with Ensemble Models for Hourly Estimation of Global Solar Irradiance

The paper presents a 8-months-long dataset containing meteorological variables day of the year, sunrise and sunset time, maximum and minimum temperature, along with numerical weather forecasting model (NWP) dataset factors such as hourly solar irradiance, precipitation, humidity, wind direction, wind speed to train machine learning (ML) models and ensemble models for hourly global solar radiation prediction for City of lakes Bhopal, in the Central region of India. Bhopal has a very diverse climate. During the dry seasons of winter and summer, the sky is clear, and the sun shines brightly for most part of the day leading to solar radiation in a finite range every season. Thus, the climate of this city depicts the climate of the country and is suitable as solar radiation-based case study for further analysis of renewable energy source. The ensemble models were developed using Python scilearn kit code and trained over the dataset, and neural network model of machine learning is implemented through Keras library. The experimental setup identified two strong correlated factors wind direction and day of the year that affects the radiation the most, and results were obtained in the form of smaller values of root mean square error (RMSE), mean bias error(MBE) and r2_score by varying kernel initializer and optimizer, to demonstrate good accuracy of the model. When compared with mathematical model for the given case study of the city, the experimental neural network model in the paper produced better results. Combination of NWP model and ensemble model is found to be fruitful for solar radiance prediction.
Getting Familiar with Python

Python is an open source programming language created by a Dutch programmer named Guido van Rossum. Named after the British comedy group Monty Python, Python is a high-level, interpreted, open source language and is one of the most sought-after and rapidly growing programming languages in the world today. It is also the language of preference for data science and machine learning.
Event Coreference Resolution with Non-Local Information

We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.