Reviewerly/exHarmony huggingface dataset:
An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.
Social Media Used by Arable Crop Farmers for Communicating Climate Change Adaptation Strategies in Imo State, Nigeria

The study examined social media use by arable crop farmers for climate change adaptation communication in Imo State, Nigeria. Multistage sampling was used to collect data with the aid of structured questionnaire from 285 randomly selected arable crop farmers in the study area. Results showed that arable crop farmers use social media to seek knowledge about climate change (𝑥̅=2.71), disseminate information to friends (𝑥̅=2.67), and seek climate change adaptation strategies (𝑥̅=2.59) and post-climate change adaptation strategies to farmer groups (𝑥̅=2.72). However, the proportion of arable crop farmers’ use of social media types was low. Sex, educational level and age influenced utilization of social media for climate change adaptation communication. Farmers should be encouraged to raise concern, make posts and engage in discussions on social media so as to attract and raise awareness of issues of concern among them, other stakeholders and the public. Government (ADP, Ministry of Agriculture), international organizations (FAO and CTA) and non-governmental organizations should organize training on use of smart phones, social media and information andcommunication technologies for farmers, and farmer groups.&#x0D; Keywords: crop farmers, social media, climate change, adaptation strategies
Application of Chinese Traditional Musical Instruments

Traditional Musical Instruments are a part of Chinese traditional culture, forming a series of intangible cultural heritage of related music. This project focuses on various traditional Musical Instruments in the traditional Chinese concert scene, providing background knowledge and audio files of relevant Musical Instruments. The project will involve digital formats such as audio, video, 3D, other images and virtual reality. The project integrates the information of intangible cultural heritage in various forms of media, and with the help of communication and social platforms, breaks the limitation of specific time and place, and makes it the technical condition of modern communication and quality should be in a new platform, which can better protect and develop traditional Musical Instruments.
Flatness Prediction of Cold Rolled Strip Based on EM-TELM

Flatness of cold rolled strip is an extremely important indicator of quality, and flatness control is the key technology of the modern high-accuracy rolling mill. The establishment of an efficient and accurate flatness prediction model is conducive to improving the flatness accuracy and realizing the effective control of flatness. Inspired by the error minimization principle, error minimized extreme learning machine with two hidden layers (EM-TELM) used to automatically determine the optimum hidden nodes is proposed in the paper, which is applied to establish the flatness prediction model of cold rolled strip. EM-TELM uses the block matrices to solve the output matrix of the second hidden layer. EM-TELM randomly adds one or a group of hidden nodes to the current network every time. During the increasing process of the network structure, the weights matrix connecting the hidden layer and the output layer are updated incrementally. Since EM-TELM is a no analytic method, it can be used in a kind of prediction problem for complex and difficult modeling systems. The experimental results indicated that the accuracy of EM-TELM is higher than that of EM-ELM, and EM-TELM reduces the computational complexity and training time compared to TELM which recalculates the parameters between different hidden layers when the network structure changes.
CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.
Emilia: Catching Iago in Legacy Code

There has been interest in mechanisms that enable the secure use of legacy code to implement trusted code in a Trusted Execution Environment (TEE), such as Intel SGX.However, because legacy code generally assumes the presence of an operating system, this naturally raises the spectre of Iago attacks on the legacy code.We observe that not all legacy code is vulnerable to Iago attacks and that legacy code must use return values from system calls in an unsafe way to have Iago vulnerabilities.Based on this observation, we develop Emilia, which automatically detects Iago vulnerabilities in legacy applications by fuzzing applications using system call return values.We use Emilia to discover 51 Iago vulnerabilities in 17 applications, and find that Iago vulnerabilities are widespread and common.We conduct an in-depth analysis of the vulnerabilities we found and conclude that while common, the majority (82.4%) can be mitigated with simple, stateless checks in the system call forwarding layer, while the rest are best fixed by finding and patching them in the legacy code.Finally, we study and evaluate different trade-offs in the design of Emilia.
Cross‐modal semantic correlation learning by Bi‐CNN network

Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi-dimensional kernel to extract multi-level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within-modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large-scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods.
Optimization of Curricula of Engineering and Pedagogical Specialties Based on the Construction of a Model for Structuring Interdisciplinary Relations

The paper discusses various methods for the Educational Data Mining (EDM), including curricula, based on interdisciplinary teaching. Their advantages and disadvantages are being discussed. The problem of interdisciplinary teaching in engineering and pedagogical education has its own specifics, which is due to the fact that, in essence, this education is complex interdisciplinary, and the training of future teachers of vocational institutions has been implemented at the border of pedagogical and engineering sciences. The authors propose a method for optimizing the curricula of engineering and pedagogical specialties based on the algorithm of phased construction model for structuring interdisciplinary relations. This method is based on the graph theory. The result of the method has been presented as a graph of interdisciplinary relations in tier-parallel form. The construction of the graph and solving the problem of interdisciplinary integration has been implemented using software developed by the authors, the description of which is given in the paper. The paper also discusses the results of optimization.
Design of an optical Toffoli gate for reversible logic operation using silicon photonic integrated circuits

Reversible logic is an emerging research area, which can achieve the one-to-one mapping function to reduce the power dissipation has made these circuits lossless circuits. In this letter, we propose an on-chip optical Toffoli gate for reversible logic operation using silicon micro-ring resonators (MRRs). The possibility of using Silicon-on-insulator (SOI) based MRR as thermo-optic switch is described through thermo-optic effect. Three electrical input signals (logic operands) are applied across the micro-heaters above MRRs to determine the switching states of MRRs, and the reversible logic operation results are directed to the output ports in the form of light, respectively. For proof of concept, the scattering matrix method is employed to establish numerical model of the device. Simulation results confirming described method are presented in this paper. The proposed design is optimized in terms of optical cost and the delay and garbage output.
Flow-aware synthesis: A generic motion model for video frame interpolation

Abstract A popular and challenging task in video research, frame interpolation aims to increase the frame rate of video. Most existing methods employ a fixed motion model, e.g., linear, quadratic, or cubic, to estimate the intermediate warping field. However, such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations. Instead, we present an adaptive flow prediction module to better approximate the complex motions in video. Furthermore, interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions. To enable multi-frame interpolation, we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module. Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing state-of-the-art methods on popular public datasets.
Distributed detection of sequential anomalies in univariate time series

Abstract The automated detection of sequential anomalies in time series is an essential task for many applications, such as the monitoring of technical systems, fraud detection in high-frequency trading, or the early detection of disease symptoms. All these applications require the detection to find all sequential anomalies possibly fast on potentially very large time series. In other words, the detection needs to be effective, efficient and scalable w.r.t. the input size. Series2Graph is an effective solution based on graph embeddings that are robust against re-occurring anomalies and can discover sequential anomalies of arbitrary length and works without training data. Yet, Series2Graph is no t scalable due to its single-threaded approach; it cannot, in particular, process arbitrarily large sequences due to the memory constraints of a single machine. In this paper, we propose our distributed anomaly detection system, short DADS, which is an efficient and scalable adaptation of Series2Graph. Based on the actor programming model, DADS distributes the input time sequence, intermediate state and the computation to all processors of a cluster in a way that minimizes communication costs and synchronization barriers. Our evaluation shows that DADS is orders of magnitude faster than S2G, scales almost linearly with the number of processors in the cluster and can process much larger input sequences due to its scale-out property.
Design and implementation of trusted sensing framework for IoT environment

Even though Internet of things (IoT) sensing services are introduced in a wide range of areas, it is not applicable to mission-critical services due to the lack of the trustworthiness of the IoT sensing data. To address this problem, we propose TruSense, a novel trusted sensing framework for the IoT environment that covers end-to-end implementation from an IoT device to a cloud service. The TruSense framework includes a small sensing board, a communication protocol, and a cloud service for the trusted sensing in the IoT environment. To show our framework's feasibility, we design an ARM TrustZone-based IoT sensing board and implement an application and a trusted sensing service running in the Secure world. We also implement a cloud service for the trusted sensing in Google app engine, which is one of the widely used cloud services.
Effects of image compression on face image manipulation detection: A case study on facial retouching

Numerous methods have been introduced to reliably detect digital face image manipulations in the past. Of late, the generalisability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biometric application scenarios. Severe compression might erase digital traces of face image manipulation and hence hamper a reliable detection thereof. In this work, the effects of image compression on the face image manipulation detection are analysed. In particular, a case study on facial retouching detection under the influence of image compression is presented. To this end, ICAO-compliant subsets of two public face databases are used to automatically create a database containing more than 9000 retouched reference images together with unconstrained probe images. Subsequently, reference images are compressed applying JPEG and JPEG 2000 at compression levels recommended for face image storage in electronic travel documents. Novel detection algorithms utilising texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario. Results obtained from challenging cross-database experiments in which the analysed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilising deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.
Survey of Twitter Viewpoint on Application of Drugs by VADER Sentiment Analysis among Distinct Countries

Who does not know that Twitter is an august social networking podium now? Here the folks around the globe are able to establish their viewpoints. Every day, almost 500 million tweets are established in twitter, and this volume contains 8TB data. The data that we derive from twitter is very much significant if we illustrate it, because we are able to derive significant news in the mode of sentiment analysis. From Twitter data, we get to know about the information and remarks of the augmentation of various product, novel fashion etc. Exploration of emotions, viewpoints, subjectivity and motive from a normal message or tweet for the application of drugs for the therapy of COVID-19, is the prime objective of this sentiment analysis. Now, comes clustering. It’s a method by which one can detect homogeneous substances, combine together and create a class or cluster. There was an initiative from of going on with a research from our end and the result of it showed that the implementation of clustering is able to get infirm and solid positive or negative tweets while getting clustered with outcomes of distinct dictionaries and present robust scaffolding on our prediction. The research analyzes the polarity calculation, applying VADER sentiment analysis for the application of drug for the therapy of COVID-19.
Sentiment Analysis, Tweet Analysis and Visualization on Big Data Using Apache Spark and Hadoop

Abstract Big Data lets companies generate new growth opportunities and whole new types of industries that can continually change and mushroom data from the market that can merge and evaluate. In today’s world, Apache Spark and Hadoop are the most popular and effective tools used to conduct big data analysis. This paper aims to perform two types of analysis-Sentiment Analysis of tweets and Tweet Analysis. For conducting sentiment analysis, we used a dataset created by Stanford University which contains 4 fields – ID of the tweet, Sentiment of the tweet, Sentiment Source and the Tweet itself. Two machine learning algorithms performance are compared these are Logistic Regression and Random Forest Classifier and observe which algorithm provides better accuracy and why. A data visualization tool -Tableau is used to view the sentiments of tweets by filtering the text of tweets by words and hashtags. For tweet analysis, we use Python and a library called Tweepy to download tweets from the Twitter API and perform analysis using another data visualization tool called Power BI. We conduct analysis on the basis of three parameters – user followers count, retweet count and favourite count. Finally, we draw a comparison on the performance and user-friendly nature of both data visualization tools – Power BI and Tableau.
Analysis of Students’ Oculomotor Activity Observed when Reading from the PC Screen

Introduction. This paper analyzes the problem of formation of educational Internet resources. Despite numerous works concerning the importance of using information technologies in the education, the issue of its rational organization remains understudied. The purpose of the article is to analyze what patterns are observed when students read educational information from the screen, and how these patterns can be used to create educational Internet resources. Materials and Methods. To study the interaction of students with educational information on the screen, the authors used the method of oculography. The results were processed using the BeGaze and IBM SPSS Statistics 22 programs. In total 100 respondents participated in the experiment, 50 of which were trained in the natural sciences, 50 – in the humanities. Results. It is determined that the presence of media text influences the oculomotor activity of students: students commit fewer fixations and saccades, the average duration of saccades increases, the value of the Scanpath Length indicator decreases. It was revealed that the orientation of training forms strategies for working with information on the screen: students of natural sciences read texts longer, blink less often; they have a higher Scanpath Length value. When forming educational Internet resources, the developer should take into account the students’ specialisms with reference to e-material. It should also be borne in mind that the presence of images on educational Internet pages is perceived by students as a simplifying factor for completing the cognitive task of reading, but students practically do not pay attention to the images themselves. This may be attributed to the specificity of the learning process, when their main focus is on written texts acquisition. Discussion and Conclusion. The results of the study open a new direction in the study of the problem of rational organization of the student's learning process using technical devices and may be of interest to developers of educational Internet resources, as well as practicing teachers.
Production of Indicators for Evaluation of Digital Transformation of Modern University

The article describes the main threats and opportunities for the use of digital technology by universities. We considered the features of the functioning of universities in the digital economy. In the study, we applied the method of a balanced scorecard, which allows integrating estimates of diverse activities to assess the overall result of the university’s socio-economic system in the development of digital technologies. Achieved results. An original balanced system of indicators of the digital transformation of the university has been formed. The study will improve the effectiveness of tactical and strategic management of a university due to the authors' recommendations for the formation of indicators for assessing and regulating the digitalization processes at the university.
News Recommendation Systems in the Era of Information Overload

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.