Reviewerly/exHarmony huggingface dataset:
A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.
A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

With the development of information technology for modern military confrontations, radar emitter fingerprint identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Owing to the confidentiality of military systems, most of the existing studies use simulation data for radar emitter fingerprint identification experiments and analysis. However, most of the existing modeling methods focus on the mechanism analysis of the nonlinear fingerprint characteristics of a single independent component. Its main disadvantage is that it can only represent the nonlinear fingerprint characteristics of some components in the radar emitter system but cannot fully reflect the nonlinear fingerprint characteristics of the whole radar emitter system. In this paper, a nonlinear fingerprint-level radar simulation modeling method is proposed. In contrast to the previous single component modeling method, the systematic nonlinear characteristic modeling method of this model can provide individual radar signal data under different modulation modes and working parameters, and provide experimental conditions for data support and theoretical analysis of radar emitter fingerprint identification.
Processing Quantum Signals Carried by Electrical Currents

A general theory is presented for processing, analyzing, and representing quantum electrical currents, directly at the level of electronic wavefunctions, establishing the ground for the development of electron-based quantum technologies.
Video Sketches as a Means of Introducing Blended Learning Approach in Teaching Foreign Languages at Technical Universities

The topicality of the article is determined by the widespread use of blended learning approaches in higher education, especially in studying foreign languages. With the limited number of classroom hours proposed for teaching foreign languages at technical universities, it became very relevant to introduce various alternative out-of-class monitoring methods. The aim of the research is to investigate the effectiveness of introducing video sketches as the way to improve the process of foreign language studying at technical universities. The authors assume that recording video monologues could facilitate students’ speaking abilities, reduce the level of their anxiety compared to delivering monologues in class and could stimulate the effective use of classroom hours for teaching foreign languages. To prove this hypothesis the authors of the study conducted an experiment in Peter the Great St. Petersburg Polytechnic University during the autumn term of 2019-2020 academic year with the total number of 232 participants randomly chosen from 1-course students of technical specialties with different levels of English language proficiency. The possibility to record a monologue instead of delivering it face-to-face was considered as the IV (independent variable) of the experiment. Such research method as an open-close questionnaire was used afterwards to identify the students’ satisfaction with the proposed alternative as well as the analysis of the exam results at the end of the course. These results as the part of the students’ academic performance represented the DV (dependent variable) of the experiment. The obtained data showed that students of the experimental group passed their exam significantly better than the students of the control group. Thus, the present survey proved that students of the experimental group would prefer to record video sketches instead of delivering monologues face to face and that making video monologues improved their fluency and helped overcome some psychological barriers. On the basis of the research and the results obtained during the experiment, the authors conclude that the application of video monologues in the process of teaching foreign languages is highly promising.
Real-Time Analysis of Basketball Sports Data Based on Deep Learning

This paper focuses on the theme of the application of deep learning in the field of basketball sports, using research methods such as literature research, video analysis, comparative research, and mathematical statistics to explore deep learning in real-time analysis of basketball sports data. The basketball posture action recognition and analysis system proposed for basketball movement is composed of two parts serially. The first part is based on the bottom-up posture estimation method to locate the joint points and is used to extract the posture sequence of the target in the video. The second part is the analysis and research of the action recognition algorithm based on the convolution of the space-time graph. According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. The method can actively correct technical movements, train players to form muscle memory, and improve their abilities. Through the research of this article, it provides a theoretical basis for promoting the application of deep learning in the field of basketball and also provides a theoretical reference for the wider application of deep learning in the field of sports. At the same time, the designed real-time analysis system of basketball data also provides more actual reference values for coaches and athletes.
Exploiting degeneracy in belief propagation decoding of quantum codes

Abstract Quantum information needs to be protected by quantum error-correcting codes due to imperfect physical devices and operations. One would like to have an efficient and high-performance decoding procedure for the class of quantum stabilizer codes. A potential candidate is Gallager’s sum-product algorithm, also known as Pearl’s belief propagation (BP), but its performance suffers from the many short cycles inherent in a quantum stabilizer code, especially highly-degenerate codes. A general impression exists that BP is not effective for topological codes. In this paper, we propose a decoding algorithm for quantum codes based on quaternary BP with additional memory effects (called MBP). This MBP is like a recursive neural network with inhibitions between neurons (edges with negative weights), which enhance the perception capability of a network. Moreover, MBP exploits the degeneracy of a quantum code so that the most probable error or its degenerate errors can be found with high probability. The decoding performance is significantly improved over the conventional BP for various quantum codes, including quantum bicycle, hypergraph-product, surface and toric codes. For MBP on the surface and toric codes over depolarizing errors, we observe error thresholds of 16% and 17.5%, respectively.
Challenges of Virtual Education during the COVID-19 Pandemic: Experiences of Mexican University Professors and Students

Students’ and university professors’ challenges due to the COVID-19 worldwide pandemic have been enormous. Without prior notice, they had to switch from traditional education to virtual teaching. This study’s objective was to identify the perception of students and professors of the Bachelor in the English Language from the University of Chihuahua, Mexico, during the transition to virtual classes due to the pandemic. Researchers applied a survey of 28 items with a Likert scale to 152 students during the spring semester 2020 and the second instrument of 51 items to 26 professors. The students reported having complications in connectivity (21%) and difficulties interacting with professors and classmates (34%). Many students reported experiencing anxiety (69.1%) and feeling isolated (62.5%). Most agree that they learn better in face-to-face classes (85%). In turn, the professors agree that the students had a lower performance than in a face-to-face semester (69.3%). Despite this, the professors affirm that they followed the program to the letter (92.3%) and completed the programmed contents (84.6%). Besides, 92.3% of the academics are confident to face a new semester in virtual modality, if necessary. Although we can say that the transition from face-to-face to virtual classes in the institution was successful, students and professors encountered difficulties and disadvantages in the use of platforms for online classes. The benefit of this research´s findings is understanding the University must have all the necessary elements in place for online teaching. It must establish a formal regulation of virtual programs that includes professor´s and student´s profile, as well as the strategies to be followed. The students’ and professors’ perceptions highlight the crucial elements that professors need to consider to teach virtual classes and take advantage of these lessons to face the future better.
Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)

COVID-19 is a pandemic that is troubling many people. This has led to a lot of public comments on Twitter social media. The comments are used for sentiment analysis so that we know the polarity of the sentiment that appears, whether it is positive, negative, or neutral. The problem when using twitter data is that the tweet data still contains many non-standard words such as abbreviated writing due to the maximum limitation of characters that can be used in one tweet. Preprocessing is the most important initial stage in sentiment analysis when using Twitter data, because it affects the classification performance results. This study specifically discusses the preproceesing technique by performing several test scenarios for the combination of preprocessing techniques to determine which preprocessing technique produces the most optimal accuracy and its effect on sentiment analysis. Feature extraction using N-Gram and word weighting using TF-IDF. Mutual Information as a feature selection method. The classification method used is SVM because it is able to classify high-dimensional data according to the data used in this study, namely text data. The results of this study indicate that the best performance is obtained by using a combination of cleaning and stemming; and normalization of words, cleaning, and stemming with the same accuracy of 77.77%. the use of unigram results in higher accuracy compared to bigram. Mutual Information is able to reduce overfitting problems by reducing irrelevant features so that train and test accuracy is quite stable
An Effective Deep Embedding Learning Method Based on Dense-Residual Networks for Speaker Verification

In this paper, we present an effective end-to-end deep embedding learning method based on Dense-Residual networks, which combine the advantages of a densely connected convolutional network (DenseNet) and a residual network (ResNet), for speaker verification (SV). Unlike a model ensemble strategy which merges the results of multiple systems, the proposed Dense-Residual networks perform feature fusion on every basic DenseR building block. Specifically, two types of DenseR blocks are designed. A sequential-DenseR block is constructed by densely connecting stacked basic units in a residual block of ResNet. A parallel-DenseR comprises split and concatenation operations on residual and dense components via corresponding skip connections. These building blocks are stacked into deep networks to exploit the complementary information with different receptive field sizes and growth rates. Extensive experiments have been conducted on the VoxCeleb1 dataset to evaluate the proposed methods. The SV performance achieved by the proposed Dense-Residual networks is shown to outperform corresponding ResNet, DenseNet or fusions of them, with similar model complexity, by a significant margin.
Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan

Abstract In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training data set for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning data set for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region.
Parents’ Perception on Printed Modular Distance Learning in Canarem Elementary School: Basis for Proposed Action Plan

This was a descriptive study which determined and analyzed the perceptions of parents on the printed modular distance learning in an elementary school. There were 50 parent respondents selected via convenience sampling. Parents’ perceptions were along the manner of distributing modules, retrieval of modules, time allotment for learning activities, the learning activities in the module, assessment and observance of safety and health protocols in the distribution and retrieval of modules. Questionnaires were distributed to the parents through the Purok Leaders who were responsible in the distribution and retrieval of the learning modules. Weighted mean was used to analyze the responses of the parents in the questionnaires. Finding revealed that parents mostly agreed to the strategies in the distributing modules, retrieval of modules, time allotment for learning activities, the learning activities in the module, assessment and highly agreed to the observance of safety and health protocols in the distribution and retrieval of modules. However, parents claimed that time allotment in the completion of learning activities was insufficient since the activities were so many. In addition, some parents claimed that they could not understand some topics in the module so they could not help their children in answering the learning activities. The research then recommendedfor a review of the learning activities and conduct seminar for parents to guide them in assisting their children during their “classes “at home. An action plan was developed to improve the implementation of the modular learning in the new normal.
Autonomous Motivation and Information Security Policy Compliance

Many existing studies focus on the effect of external influence mechanisms (e.g., deterrence) impacting information security policy compliance (ISPC). This study explores the formation of ISPC from an autonomous motivation perspective, based on social exchange theory and self-determination theory. Data were gathered by conducting a survey of 261 employees, with hierarchical regression analysis being used to test our hypotheses.The results indicated the following: First, job satisfaction and personal responsibility positively impact ISPC. Second, job satisfaction perceived by employees is positively linked to personal responsibility, where deterrence severity has a negative moderating effect on this relationship. Finally, personal responsibility mediates the relationship between job satisfaction and ISPC. This study suggests that organizational support should focus on promoting perceived self-determination of employees, and that deterrence should be maintained at a moderate level to adapt to the organization's security strategy and information security environment.
A low-cost, high-performance video-based binocular eye tracker for psychophysical research

&#x0D; &#x0D; &#x0D; &#x0D; We describe a high-performance, pupil-based binocular eye tracker that approaches the performance of a well-established commercial system, but at a fraction of the cost. The eye tracker is built from standard hardware components, and its software (written in Visual C++) can be easily implemented. Because of its fast and simple linear calibration scheme, the eye tracker performs best in the central 10 degrees of the visual field. The eye tracker possesses a number of useful features: (1) automated calibration simultaneously in both eyes while subjects fixate four fixation points sequentially on a computer screen, (2) automated real-time continuous analysis of measurement noise, (3) automated blink detection, (4) and real-time analysis of pupil centration artifacts. This last feature is critical because it is known that pupil diameter changes can be erroneously registered by pupil-based trackers as a change in eye position. We evaluated the performance of our system against that of a well-established commercial system using simultaneous measurements in 10 participants. We propose our low-cost eye tracker as a promising resource for studies of binocular eye movements.&#x0D; &#x0D; &#x0D; &#x0D;
Vector Map Encryption Algorithm Based on Double Random Position Permutation Strategy

Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. An attacker can easily obtain the key values by analyzing the relationship between the cipher vector map and the plain vector map, which will lead to the ineffectiveness of the scrambling operation. To solve the problem, a vector map encryption algorithm based on a double random position permutation strategy is proposed in this paper. First, the secret key sequence is generated using a four-dimensional quadratic autonomous hyperchaotic system. Then, all coordinates of the vector map are encrypted using the strategy of double random position permutation. Lastly, the encrypted coordinates are reorganized according to the vector map structure to obtain the cipher map. Experimental results show that: (1) one-to-one mapping between the plain vector map and cipher vector map is prevented from happening; (2) scrambling encryption between different map objects is achieved; (3) hackers cannot obtain the permutation key value by analyzing the pairs of the plain map and cipher map.
Towards Robust Learning with Different Label Noise Distributions

Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important step towards preventing performance degradations in Convolutional Neural Networks. Discarding noisy labels avoids a harmful memorization, while the associated image content can still be exploited in a semi-supervised learning (SSL) setup. Clean samples are usually identified using the small loss trick, i.e. they exhibit a low loss. However, we show that different noise distributions make the application of this trick less straightforward and propose to continuously relabel all images to reveal a discriminative loss against multiple distributions. SSL is then applied twice, once to improve the clean-noisy detection and again for training the final model. We design an experimental setup based on ImageNet32/64 for better understanding the consequences of representation learning with differing label noise distributions and find that non-uniform out-of-distribution noise better resembles real-world noise and that in most cases intermediate features are not affected by label noise corruption. Experiments in CIFAR-10/100, ImageNet32/64 and WebVision (real-world noise) demonstrate that the proposed label noise Distribution Robust Pseudo-Labeling (DRPL) approach gives substantial improvements over recent state-of-the-art. Code is available at https://git.io/JJ0PV.
Secured Data Transmission with Integrated Fault Reduction Scheduling in Cloud Computing

Cloud computing offers end users a scalable and cost-effective way to access multi-platform data. While the Cloud Storage features endorse it, resource loss is also likely. A fault-tolerant mechanism is therefore required to achieve uninterrupted cloud service performances. The two widely used defect-tolerant mechanisms are task relocation and replication. But the replication approach leads to enormous overhead storage and computing as the number of tasks gradually increases. When a large number of defects occur, it creates more overhead storage and time complexity depending on task criticalities. An Integrated Fault Reduction Scheduling (IFRS) cloud computing model is used to resolve these problems. The probability of failure of a VM is calculated by finding the previous failures and active executions in this model. Then a fault-related adaptive recovery timer is retained, modified depending on the fault type. Experimental findings showed that IFRS reached 67% lower storage costs and 24% less response time when comparing with the current technique for sensitive tasks.
Digital Watermarking using Grasshopper Optimization Algorithm

Abstract The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Grasshopper Optimization Algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method outperforms the other methods and can be effectively used for practical digital watermarking.