Reviewerly/exHarmony huggingface dataset:
Transferable Interpolated Adversarial Attack with Random-Layer Mixup

Deep neural networks have shown their vulnerabilities to adversarial examples crafted by adding imperceptible perturbations to original examples. Despite showing powerful attack strength under the white-box setting, most existing adversarial attack methods can only mislead the black-box model with low attack success rates. In response, a class of image transformation-based attacks has been proposed. Its main idea is to apply transformations to adversarial examples during attack iterations and improve the transferability on the black-box model. However, a major limitation of these transformation-based attacks is that they only apply transformations to input images, while ignoring transformations’ usages in hidden representations. Based on our observation that mixup in hidden space can help attack methods achieve higher transferability than in input space, we propose the Random-Layer Mixup Attack Method (RLMAM). Our method interpolates the adversarial examples with clean examples in both input space and hidden space. The interpolated adversarial representations induced by our random-layer mixup can improve representations’ diversity in both two spaces and alleviate adversarial examples’ overfitting phenomenon on the white-box model. Furthermore, we incorporate RLMAM with our enhanced momentum method. Experimental results on ImageNet and CIFAR-10 datasets demonstrate that our RLMAM outperforms other state-of-the-art black-box attacks.
Putting It All Together: An Email Campaign

The objective of this example email campaign is to see whether the buying habits of customers are affected by the type of model used in advertising, with an A/B-style test format. The campaign randomly splits up the creative content delivered in an email campaign. One version of the email shows pictures of models wearing a dress. The other version of the email does not have models; it just shows the same dress on a mannequin.
Progressive Clustering: An Unsupervised Approach Towards Continual Knowledge Acquisition of Incremental Data

In this paper, we propose a categorization strategy to handle the incremental nature of data by identifying concepts of drift in the data stream. In the world of digitalization, the total amount of data created, captured, copied, and consumed is increasing rapidly, reaching a few zettabytes. Various fields of data mining and machine learning applications involve clustering as their principal component, considering the non-incremental nature of the data. However, many real-world machine learning algorithms need to adapt to this ever-growing global data sphere to continually learn new patterns. In addition, the model needs to be acquainted with the continuous change in the distribution of the input data. Towards this, we propose a clustering algorithm termed as Progressive Clustering to foresee the phenomenon of increase in data and sustain it until the pattern of the data changes considerably. We demonstrate the results of our clustering algorithm by simulating various instances of the incremental nature of the data in the form of a data stream. We demonstrate the results of our proposed methodology on benchmark MNIST and Fashion-MNIST datasets and evaluate our strategy using appropriate quantitative metrics.
Emotional and Physiological Responses to the Roll Motion Effect in 4D Movies

4D effects have been introduced to increase movie audience immersion. Nevertheless, it is difficult to find research studies that investigated systematically the audience’s responses to the motion effects that account for a large portion of the 4D effects. This study investigated how the motion effect of rolling an audience chair affects emotions and physiological responses. The participants, with attached sensors, experienced movie clips with or without the motion effect and responded to questionnaires about their emotions and immersion. The results showed that the motion effect amplified arousal and immersion. Changes in the blood volume pulse and skin conductance level due to motion effects were also found. It was concluded that the roll motion effect can be effectively used to arouse and immerse the audience. In addition, significant correlations were found between emotional and physiological responses.
Distributed Data Register Technology as the Main Component of Economic Decentralization

The paper shows that the widespread use of advanced information and communication technologies presupposes the transition of different industries and economies to the digital area, directly reflected in management methods at the macro and commercial entities, making the transition to a decentralized management system. In this regard, the paper illustrates that the digital economy reveals new opportunities. However, the proposed approach to the study allowed noting that the digital economy has several subtleties associated with insufficient knowledge, its flexibility, and various problems of technical implementation. The research’s main task is to reveal the features and principles of the formation of one of the significant components of the digital economy, the distributed ledger technology (blockchain). As a decentralized data registry, the paper notes that blockchain technology is today the most relevant and discussed topic of digital economy development. The technology strengths are analyzed: cost reduction, high-security level, and transaction transparency. The main mechanisms of contradictions, implementation, and application of blockchain technology are highlighted in detail. Finally, the author’s approach to eliminating the identified contradictions is provided. The studies in this paper demonstrate that one of the main problems is the features of the modeling process, both machine and mathematical. For instance, this technology requires powerful computing equipment and high-performance ones for servicing and solving security problems. It is noted that the most promising approach to increase the speed and productivity of computing facilities is parallel computing. Distributed modeling allows processing the problems which solution demands huge processing time to study complex mathematical models.
Eliciting Preferences with Partial Information in Multi-issue Negotiations: An Analysis of the FITradeoff-Based Negotiation Protocol

This paper aims to analyze the performance of the preferences elicitation protocol for multi-issue negotiations based on the FITradeoff multicriteria method through simulation experiments. The negotiation protocol is conducted based on a dynamic set of packages, considering partial information about negotiators preferences. Efficiency of the agreement package can also be verified through a pareto optimality analysis. Simulation experiments involving negotiators were performed, supported by a web-based Electronic Negotiation System (ENS), through which the whole process is operationalized. The results of the experiments show that the FITradeoff-based negotiation protocol enables the parties to achieve an agreement without spending much cognitive effort in the preferences elicitation process. Moreover, in most cases, an agreement package can be faster due to the convergence mechanism derived from the dynamic set approach.
Reverse Engineering

AbstractReverse engineering is a technical process that involves the reverse analysis and study of a target product to derive design elements such as processing flow, organizational structure, and functional specifications of the product to produce a product with similar but not identical functions. In CTF, reverse engineering generally refers to software reverse engineering, that is, analyzing compiled executable files, studying program behavior and algorithms, and recover the flag based on the analyze.
Development of a Method to Visualize the Approximate Synchronization Ratio of a Teaching Material Clickstream in Class

This study proposes a method for a large number of university students to analyze the synchronization ratio in opening teaching materials in classes using Moodle learning management system (LMS) and online teaching materials. Furthermore, this research analyzes the results in terms of both educational technology and ergonomics and discusses the improvement in the interaction between teachers and learners. Learners’ reactions are influenced not only by the teacher's instructions, but also by various factors such as the quality of the content, the method of presenting the teaching materials, the performance of the software, and the performance of the monitor screen. Therefore, using the proposed method, the analysis was limited to the clickstream of the teaching material accumulated in the LMS. The time-series cross-section displays the synchronization ratio of teaching materials in the class in graphs and tables at intervals of between 1 and 5 minutes. Moreover, since the synchronization ratio is based on the time-series cross-section for each learner, one can observe the synchronized learners, the learners who open the teaching materials late, and the learners who never open the teaching materials.
Data Synthesis with Dual-Stage Sample Grouping for Electromyography Signals

The correlation-based data synthesis (CDS) model can improve the accuracy of machine learning models via dataset enrichment. However, the performance of the classical CDS model is unsatisfactory when applied to electromyography (EMG) signals because the signal is stochastic and nonstationary. To overcome this problem, this study proposes a new CDS model integrated with dual-stage sample grouping: intra-class clustering followed by intra-cluster random selection. This sample grouping method not only enables the CDS model to create signals similar to the original EMG signal but also increases the diversity of the synthesized dataset. The synthesized sample quality was verified using the sample probability distribution, the Jensen-Shannon divergence, and tdistributed stochastic neighbor embedding plots. Furthermore, the classification accuracy of various machine learning models increased when the synthesized dataset was used for training. Specifically, accuracy improvements of 6.29%, 5.09%, 9.26%, and 3.69% were observed for the multi-layer perceptron, support vector machine (SVM) with linear kernel, SVM with radial basis function kernel, and k -nearest neighbor models, respectively. As the EMG signals considerably vary over subjects, the classifiers need to be optimized for individual subjects. The proposed model is useful to create personalized classifiers with a small number of original samples.
Blockchain Technology: A Smart Technology for Demand Response in Smart Grids

Demand response is a potential solution for efficient control of smart grids with renewable sources integration. It enables all the consumers to adjust their demand profiles on the request from a centralized operator called as independent system operator (ISO). In the recent past, there has been increase in decentralized approach because of the disadvantages posed by the centralized approach. Data breach and security lapse are the major issues that are involved in a centralized approach. Hence, a novel system of control using blockchain technology has been researched for smart control, which involves smart contracts, distribution ledgers, and also peer-to-peer network communications. This paper gives a glimpse of this latest technology and how this can be useful in implementing demand response effectively in smart grids.
Orthogonal Procrustes Based Anomaly Detection and Error Prediction for Vehicle Bills of Materials

Industrial Bill of Materials (BOM) suffer from an surging complexity and cause errors in production which have detrimental effects on a product’s profitability. Currently, BOM anomalies have to be identified manually and errors have to be detected in the same way. This preprint describes a combination of data analysis and Machine Learning methods, such as hierarchical and agglomerative clustering, an isolation forest algorithm, association mining and a multi-output Artificial Neural Network, all based on a deterministic distance measure for an industrial BOMs. Solving the orthogonal Procrustes problem for complex, multi-level matrices, a distance measure for real world industrial BOMs was derived. A multi-output MLP was used in order to predict error probabilities with a time- reference. Our results show how to detect anomalies and predict errors in a complex, multi-level BOM based on historical, labelled data. While other authors focus on the mere comparison of BOMs, we aimed at a holistic approach, combining descriptive and predictive methods in order to forecast where in a BOM and at what time of BOM creation process errors occur. The resulting, prescriptive system was tested using real world data and has shown to effectively predict where and when BOM errors are probable to occur. Consequently, the prescriptive system is superior to prior, purely predictive systems, can help to decrease errors and thereby decreases product development time and cost in real world companies.
Text Analytics

In the age of big data, organizations and businesses have had to manage and make sense of data generated by a wide variety of systems, processes and transactions. The data contained in traditional relational databases is rather small compared to various sensor or social media data.
Research on Evaluation Method of Network Vulnerability in Power Monitoring System

With the development of power monitoring system, the continuous occurrence of network attacks has made people deeply aware of the importance of the power monitoring system network security. How to understand and evaluate the security of the network has become the focus of the power monitoring system network. In response to this problem, this article proposes a vulnerability assessment method. The purpose is to find out the hidden security risks in the network system and the path that may be invaded by the intruder, so that the problem can be found before the attack occurs. Subsequent to make up for the problems found to improve the security of the network system, this article is based on the attack graph and Bayesian network to realize the vulnerability assessment of the power monitoring system network, through the complex network to find the vulnerabilities in the network system can be from the network topology From the structural point of view, the attack graph is constructed from the network topology through the attack graph generation algorithm to determine the vulnerability and reachability of each node, and to correspond to the Bayesian network. The method is based on the Bayesian network. The network conducts an overall assessment. Experimental analysis shows that the vulnerability assessment method in this paper can effectively reflect the overall vulnerability of the power monitoring system network.
A Comparison Study About Parameter Optimization Using Swarm Algorithms

Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to automatically adjust the models’ parameters. These models were built around two machine learning-based algorithms, an extreme learning machine neural network and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets indicated that optimized algorithms models could better the performance and robustness of the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool to analyze different contexts of data.
CONSTRUCT Queries Performance on a Spark-Based Big RDF Triplestore

Despite their potential, CONSTRUCT queries have gained little attraction so far among data practitioners, vendors and researchers. In this paper, we first exhibit performance bottlenecks of existing triplestores for supporting CONSTRUCT queries over large knowledge graphs. Then, we describe a novel Spark-based architecture for big triplestores, called TESS, that we have designed and implemented to overcome the above limitations by using parallel computing. TESS ensures ACID properties that are required for a sound and complete implementation of CONSTRUCT-based forward-chaining rules reasoning.
Marine Ship Identification Algorithm Based on Object Detection and Fine-Grained Recognition

To solve the problem of low accuracy, reliability, and generalization ability of traditional ship recognition methods, a fusion classifier for ship detection and recognition based on deep learning is presented. Accurate positioning and identification of offshore ships in visible images can be achieved by building data sets, object detection, fine-grained recognition, and other technical means. Experiments show that the recognition accuracy of this method can reach 84.7% on the built ship image test set, and the recognition efficiency is high. The validity of this method is verified.
Design and Implementation of Internet of Things Big Data in Computer Application Basic Course Examination System

With the development of computer technology and network technology, the application of computer in the field of education is more and more widely.In modern teaching, many subjects are gradually using computer as a tool for examination, that is, paperless examination.So as to save human and material resources, but also improve the efficiency of the examination.The automatic examination system of computer basic course is a CAI software developed by using object-oriented programming technology under Windows environment.The purpose of this paper is to combine the needs of the author's work, based on the current situation of the examination, combined with the characteristics of the course, develop an examination management system with moderate scale, moderate difficulty, strong operability, good portability, full coverage of knowledge points, and good security, so as to solve the paperless examination problem of basic computer application course.
Novel Approach for Character Recognition Using Chi-Square

One of the most amazing outcomes of image processing being optical character recognition (OCR) play a key role in various applications such as automated processing of documents, auto-evaluation of answer sheets. The multilingual character recognition is very challenging area in OCR, because of the complex shape of various characters. In this paper, we propose an extended shadow feature extraction technique along with shadow features which has the capability to extract the complex structure of most of the multilingual characters and these features can ease in the functioning of the multilingual OCR. We tend to extract the shadow features and have proposed extended shadow (ES) features from four directions of the input character namely, east, west, north, and south. We have also made brief use of the crossing count features with multiplication factor using four types of scanning lines namely, horizontal, vertical, left, and right diagonal scan lines. Chi-square test is used to recognize the character that has been input. The proposed method is evaluated using MediaLab License Plate (LP) benchmark data set and proprietary multilingual data set consisting of characters from Devanagari and English and thereby has achieved encouraging results.
The Institute of Scientific Philosophy in the Creative Biography of Aleksei F. Losev

The article reconstructs the historical and philosophical cooperation between Alexei F. Losev and the Institute of Scientific Philosophy in the early 1920s; on the basis of archival materials (Archive of the RAS, Archive of the Institute of Philosophy of the RAS, Archive of the State Academy of Art Sciences, State Archive of the Russian Federation, Central State Archive of Moscow) and per­sonal correspondence, we reconstruct the history of Alexey F. Losev’s crea­tive communication with the staff of the Institute (G.G. Shpet, I.N. Diakov, N.I. Ginkin, S.L. Frank, and others) and attempts to employ the philosopher as senior fellow researcher in 1921 and 1922. Special attention is paid to A.F. Lo­sev’s connections in the 1930s with the Institute of Philosophy of the Communist Academy, and then with the Institute of Philosophy of the Academy of Sciences of the USSR, and his participation in publishing projects initiated by the Insti­tute: preparation of the works of Nicholas of Cusa in 1937, creation of a new textbook of logic in 1941–1946, and the Encyclopedia of Philosophy in 1960–1970s. The question of the philosopher’s interaction with the staff of the Insti­tute of Philosophy of the Academy of Sciences of the USSR (V.V. Bibikhin, L.A. Gogotishvili, A.V. Gulyga, D.V. Dzhokhadze, M.F. Ovsyannikov, A.G. Spir­kin, A.A. Stolyarov, P.S. Trofimov) is touched upon, and the contribution of the Institute to the perpetuation of A.F. Losev’s memory in the 1990s and 2000s is underlined.
Design of Spoken English Speech Mobile Learning Platform Relying on Speech Identification Method

As science and technology continue to evolve, smartphones have been extensively used, and the learning through mobile terminals is also preferred by people. Nowadays, college students can learn English, especially spoken English, more conveniently by using smart devices. Based on the English learning habits of college students, a special training and self-learning application platform for spoken English is designed in this paper based on Android mobile platform.
Application of Image Defogging Algorithm Based on Deep Learning

Affected by atmospheric particles such as fog, haze, sand and dust, the images taken outdoors will appear gray and white. The dark channel priori defogging algorithm relies too much on prior information, which makes the calculation of the transmission map inaccurate. In order to solve these problems, In this paper, the image defogging is improved based on deep learning. The algorithm first distinguishes whether the image is foggy based on the VGG16 model, and then obtains the depth map of the foggy image based on the convolutional neural network, three different scales of feature extraction, feature learning and feature fusion are designed to improve the accuracy of the depth map. Then the depth map is converted into a transmission map. Finally, a clear image is obtained according to the atmospheric scattering model. The defogging experiment shows that the algorithm in the article has a good defogging effect on outdoor fog images, can obtain better color visual fidelity, and has strong applicability.
ELMO Embedding for Sarcasm Detection

Sarcasm is the new form of message or text widely used on social media and micro-blogging sites and commonly used to convey messages or information in a hidden way. Sarcasm can be used for a variety of purposes, such as criticism or ridicule. However, this is difficult even for human recognition as the meaning conveyed is not simple. Hence, sarcasm recognition is helpful for analyzing sentiment of data extracted from various sites. Sentiment analysis is identifying and summarizing the attitudes and opinions of Internet users toward a particular topic. In this chapter, we emphasize the use of deep contextualized word representation through pre-trained ELMo models that can model complex characteristics of word use (e.g., Syntax and Semantic). Our model achieves an accuracy of 74.15%. In general, we give our preference to ELMo embeddings for the task of sarcasm detection.