Reviewerly/exHarmony huggingface dataset:
QuPAA: Exploiting Parallel and Adaptive Architecture to Scale up Quantum Computing

Quantum computing has been gradually developed from a theory to practice in recent years. The desire for more powerful computational capability keeps promoting the scaling up of quantum computers. For quantum machines with large scales, limited coherence time and bloated instruction bandwidth may obstruct the continuous scaling up. In this paper, we propose a new quantum computing architecture named QuPAA, which leverages multiple layers of arbitrary waveform generator (AWG) units to exploit the parallelism among quantum operations. QuPAA provides a series of mechanisms, including parallelism exploitation, microcode streaming, and a quantum flexible instruction set (QuFIS). These mechanisms assist QuPAA to generate instructions adaptively and reduce the requirement for coherence time and instruction bandwidth. The evaluation results show that, compared to the baseline quantum computer with a single AWG unit, QuPAA reduces 25.9% to 97.4% coherence time requirement, and saves 26.9% to 79.1 % instruction bandwidth. The improvement effectively breaks the limitations and provides strong support for the extension of the scale for quantum computers.
Quality Control in Sapphire Growing: From Automated Defect Detection to Big Data Approach

We illustrate how automated scanners visualise internal defects in raw sapphire prior to its processing, and present some defect statistics that Scientific Visual has collected over five years of serving key sapphire suppliers in Europe and Asia. The article illustrates use of defect location and morphology data to reveal trends in sapphire quality, compare production modes, and to find out the optimal parameters for sapphire growth.
Evaluation of immersive virtual reality locomotion mechanisms

Motion sickness is one of the most common issues that affects the user experience in immersive virtual reality environments. In general, a user feels motion sickness because the brain perceives a movement but his/her body is not actually moving. In immersive virtual reality, this is caused because, frequently, the user's field of view is blocked by a head-mounted display and the locomotion in the virtual world is performed by moving analog sticks in a gamepad-like device (this is also known as artificial locomotion). In addition to some general best practices and guidelines to prevent motion sickness and provide immersion, several different locomotion mechanisms have been conceived in the last few years, both in the academy and in the industry. This paper presents the findings from usability tests to compare different locomotion mechanisms. The evaluation took into account not only the level of motion sickness observed in the participants, but also the ease of learn and use, the subjective preferences and the sense of immersion. It is expected that the data from these tests will assist the development of immersive virtual reality environments and future enhancements of locomotion mechanisms for immersive virtual reality.
CAS MAXIMA AS A TOOL FOR FORMING RESEARCH SKILLS IN THE PROCESS OF PRE-SERVICE INFORMATICS TEACHERS TRAINING

The article looks at the means of forming the research skills of students of pedagogical universities. Considerable attention is paid to the formation of students' creative thinking in the process of solving problems with parameters. A large number of applied problems, economic, physical, chemical, biological, technical, medical, etc., involve solving problems with parameters (within the framework of the constructed mathematical model). Tasks with parameters require more general research than ordinary equations, inequalities and their systems. In the article, solving problems with parameters is accompanied by graphical visualization followed by the use of the analytical method. CAS Maxima is used for graphical visualization (animation). The choice of tasks with parameters is due to the fact that mathematical, logical thinking and the ability to analyze, compare, synthesize, develop research skills evolve in the course of their solution. All this should lead to the search and introduction of new forms of pedagogy and technologies in education. The main component of the methodology here is computer-oriented, namely the use of a computer mathematics system. Using systems of computer mathematics, the student can improve the programming technique and ability to focus on the analysis of methods, immerse in the features of such concepts as the conditionality of the problem, the stability of the method, evaluation of the results of calculations. In this paper, we used the CAS Maxima animation tools with the wxMaxima graphical shell to investigate the problems with the parameters. The choice of the Maxima system is due to the following reasons: it is a freely distributed system, distributed under the GNU/GPL license; there are implementations under various operating systems, including Windows, Linux, MacOS; it offers intuitive interface and is easy and reliable in operation. The results of the study show that training of modern specialists, the development of their professional potential can only be effective provided that students are involved in research activities throughout their course of study.
Context-based Model of the Progression of Information Capture in Software Requirements Elicitation

Software requirements elicitation is a process affected by several contextual factors, like the problem domain and the nature of the stakeholders and requirements engineers, among others. Various approaches have been proposed to model this process, but few take into account how the information capture progress in elicitation sessions. This paper proposes a Model of the Progression of Information Capture that seeks to represent the advance over time of the requirements process by considering the information that is gathered during the elicitation sessions in order to become requirements. For this, an original technique, with graphical notation and procedure to modelling, is created. The proposal is validated through a case study where its practical application is illustrated. However, although it appears to be a useful and novel tool for requirements engineers, the model requires further validation through more empirical studies.
Pedagogical conditions for the development of the competence of the teacher's self-improvement

Based on the analysis of current scientific literature on the problems of self-improvement of the teacher, we formulated the definition of the concept "competence of self-improvement of the teacher". We considered this competence of the teacher as an important component of his vocational and pedagogical competence; we specified understanding of essence and structure of self-improvement as activity, process, result of personal growth and development of teacher acting as its competent subject, initiator. We disclosed an understanding of the essence of pedagogical conditions as an essential component of the pedagogical process, integrating into itself a certain set of measures aimed at achieving the desired goal; defined and formulated a set of external and internal pedagogical conditions, having a certain potential for influencing the development of the competence of self-improvement of the teacher in the process of intra-school advanced training. In detail, we considered the internal pedagogical conditions, the creation of which ensured the development of the competence of the teacher's self-improvement: increasing the motivation for professional and personal growth; development of the self-worth of education (self-development); strengthening the subject position. We noted important aspects of increasing motivation in the form of a productive form of self-development of a teacher; we disclosed developing possibilities of self-worth of education for formation of competence of self-improvement of teacher; we indicated the relevance of strengthening the subject position of the teacher in conditions of purposeful advanced training.
Storage efficient algorithm for Hermite Normal Form using LLL

Computing HNF has a long history, but designing a storage efficient algorithm is a challenging issue for matrices of large sizes. One of the main challenges in the design of storage efficient HNF algorithm is to control the rank and the size of the intermediate input. In our proposed algorithm, we use a multiple extended gcd algorithm and show that the rank of the intermediate input matrix decreases as the number of iteration increases. The determinant of the intermediate input matrix of our algorithm is a factor of the determinant d of the input matrix and thus size reduction modulo d can be done in the computations of our algorithm. By using a lattice reduction algorithm and a proven quality of LLL reduced basis, we prove that the storage of the intermediate input matrix Bk of our algorithm is less than (n−k+1)((n−k+1)(n−k)4+log2⁡d) in bits. Therefore, it is expected that a smaller storage for kth iteration is required as k closes to n. We compare the intermediate computations of our algorithm to MW-type algorithm which has an optimal asymptotic space requirement. Our experimental example and results on intermediate size suggest that the intermediate storage of our HNF algorithm is comparable to MW-type algorithm and well controlled by the intermediate input size.
Data Analyzing via Probabilistic Modeling

Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.
Discovery of Topic Derivative Relationship in Social Networks

Detecting social topics and discovering emergencies are necessary for the detection and control of public opinion. One social topic may derive one and more new topics as information spreads in social networks. This paper proposes the concept of derivative topics to describe the trend of topic change in the process of information dissemination, which benefits to discover public opinion and its evolutionary direction. We aggregate the posts into pseudo-documents and construct subgraphs of pseudo-documents with words as nodes. By extracting the topic words to determine whether there is a derivative relationship between documents, and form a visual derivative relationship graph. First, we group the original dataset into time slices and use paragraph2Vec to train each Microblog post as paragraph vectors. Second, we calculate the similarity between the posts in the same group through their paragraph vectors. The posts with high similarity are aggregated into a pseudo-document. Finally, we extract topic words in each pseudo-document and describe the derivation relationship between the topics by constructing the derivative relationship graph. The experimental results show that the concept of derivative topics we proposed has validity. The structure of the graph shows the derivative relationship between derivative topics and makes the derivative relationship visualization.
Recognition Of Handwritten Word Images

Handwritten Character Recognition (HCR) is one of the fields in which a lot of research work is going on. In recent years it has taken quite a significant place in the research area. The objective of HCR technology is to transcript the handwritten scripts to digital form which is much more advantageous in storing than manual scripts. This paper presents a method for hand written word image recognition, which can be later extended for entire document. The proposed system employs Histogram of Oriented Gradients (HOG) features of the character images to train the classifier. The trained classifier is then used to recognize the segmented characters of word image to form a computer readable word. This work also compares the results using K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers. The SVM classifier is found to provide better word recognition rate of 75%.
Temporal Action Proposal Generation via Multi-Task Feature Learning

Temporal action proposal generation is an active topic in computer vision and image/video processing communities, which aims to predict a set of temporal proposals to discover all action instances with high recall and intersection over union (IoU) in real-world untrimmed videos. Many previous approaches rely on the two-stream network which intends to simultaneously extract spatial (appearance) and temporal features for representing each video. However, few work considers to capture the correlations between the two kinds of features, leaving a large room for improving the model. In this paper, we present a new method for generating temporal action proposals based on multi-task feature learning. Specifically, we aim to learn shared representation between the spatial and temporal features in a multi-task learning framework, so as to acquire a compact and precise feature representation. Moreover, we devise a correlation loss to address the `weak-correlation' problem with high IoUs but low confidences cores. Finally, we take an ensemble learning strategy in order to inherit the advantages of existing works. Extensive experimental results on the ActivityNet-1.3 challenge dataset show that the proposed method achieves the best performance, compared with the state-of-the-arts reported in the literature and the official leaderboard. Our code will be released soon.
An Improved Method of Object Detection Based on Chip

In spite of methods for object detection based on convolutional neural networks, there's a problem that the information of objects missing in the convolutional progress with an immeasurable proportion. The reason is that while the network downsample in order to further obtain the abstract features, a certain pixel point in the feature map corresponding to more original image area, so there're less content that can be referred to. To handle this problem, an improved object detection method based on YOLOv3 is demonstrated. Our approach is composed of three steps, initial detector, adaptive chip generator, secondary detector. Firstly, figuring out which chips are worth detecting in the image. Secondly, screening the best associations for reduce the number of duplicate detections from these chips. Finally, detection progress will run on each chip and summarize the output. Benefit from it, this method achieves a significant performance especially in medium and large size objects.
An Adaptive Aggregation Method Based on Movie Genre for Group Recommendation

Group Recommendation means recommending satisfying activities to a group of users in social networks. Most existing aggregation methods mainly focus on aggregating the user's rating for the item according to the rating feature. They ignore the rationality and interpretability of user weights in aggregate function. Moreover, the user weights determined by these methods cannot be adaptively changed with different groups and items. In this paper, we propose an adaptive (AG) aggregation method based on item genre. The AG method does two things: first, uses item genre information to extract a reasonable user weight; second, performs adaptive weight modeling. In addition, we consider that there will be interactions among members in the group decision-making process, so the weights of members may be adjusted appropriately. In order to consider this reasonable factor in our recommendation system, we improved the AG method from the perspective of group member weight deviation. Extensive experimental results on MovieLens dataset clearly demonstrate the effectiveness of our proposed AG.
The System of Computer-Aided Design of Organizational Documentation in The Context of Digitalization of the University

The present can be safely called the era of the revolutionary transformation of the educational system. This is very difficult process. It is accompanied by the creation of a huge number of interrelated documents. Each document has a large number of repetitions of the same information. The result is a structured document, formed taking into account all standards. And its design takes the minimum time from a teacher. Since the organization of this process can be organized as follows. The teacher fills in the input forms and submits them to the member of the project team. He does all the routine work. Thus, an automated system for creating a work program will free the teacher from routine. He will be able to devote more time to improving his skills and creative work with students.
Sequential Anomaly Detection Using Feedback and Prioritized Experience Replay

Time series anomaly detection is essential because it helps in identifying faulty sensors and malicious behaviour in real-time. Most of the research work on anomaly detection revolves around density-based unsupervised learning techniques for batch data and forecasting (threshold-based) techniques for streaming data. Typically in streaming data, we continuously encounter concept drifts due to which the forecasting approaches’ threshold becomes insignificant with time. Also, forecasting techniques cannot identify sequential anomalies, as they try to forecast as per the ingested data. The reason behind less implementations using supervised learning for anomaly detection is because of the class imbalance problem in the dataset and unavailability of the labels. Most of the anomaly datasets contain 5% outliers due to which any learning model will overfit on normal data class and will not be able to learn about the anomalous class. In this work, we address these issues using Prioritized Experience Replay and introduce a novel state function which incorporates feedback to identify sequential anomalies. We evaluate our model on the Yahoo benchmark dataset, which contains 367 time-series datasets (each testing different aspects of anomaly detection), four smart home energy datasets and Numenta Anomaly benchmark datasets consisting of 58 time series data. The paper exhibits better performance of the proposed approach over the baseline approaches across different anomaly datasets.
Singular Value Decomposition and detection of color histogram of an image by using wavelet based symmetry reflection

The SVD method is one of the most important features of image decomposition. The proposed method is based on two-part: one is for decomposing an input image and another is detecting the color histogram of that input image based on symmetry reflection. The second part of this work is performed by using the Log-Gabor filter. SVD decomposition is an effective way of decomposing an input image correctly. Many image decomposition techniques have been performed based on the SVD method. In this paper, we have proposed a combined way to detect the color histogram and decomposition result of an input image. This proposed method is very efficient for detecting the decompose output of the input image. MATLAB tool is effective to implement the proposed method. The proposed system helps to decompose any input image and also detect its color histogram.
Research on Detecting Algorithm for Data Inconsistency in Remote Measurement System

Combining the characteristics of the networked network test system and the strong database distribution, the problems caused by data redundancy and physical distribution in the database-the inconsistency of the data in the database are proposed [1]. The data inconsistency detection algorithm is studied, the commonly used checkpoint algorithm is analyzed, and the checkpoint algorithm is improved. The test site actively sends test information to reduce the system communication overhead, and implemented in the remote test database system. Finally, this improved algorithm is summarized.
Trace Elements Concentration in Domestic Groundwater Wells in Northern Parts of Kelantan, Malaysia

Abstract Groundwater is one of the major alternatives of freshwater supply in rural as well as in urban areas in Kelantan. The need for groundwater has been rising day by day for irrigation purposes. The degree of trace elements pollution and the suitability of groundwater for drinking purpose were assessed through analysis of 32 groundwater samples of shallow aquifer. A total of 15 trace elements investigated includes As, Pb, Sr, Ba, Mn, Co, Ni, Cu, Cd, Fe, Zn, Cr, B, Se, and Al. The study reveals that most of the samples analysed contain marginally low concentrations of trace elements. The majority of toxic elements (As and Pb) are found to be in minute quantities and thus assumed to be inattentive in the wells. The concentration of all the elements except Fe and Mn were found far well within the permissible limits of WHO, thus pointing to the unpolluted source of water supply in the area. The concentration of Fe is higher in eight samples whereas Mn is on the higher side in twelve samples during post-season. Overall, all the elements show higher concentrations during post season owing to the less intensity of rainfall. Overall, the water in the area is safe for various purposes, but the source and contamination levels of Fe and Mn needs to be evaluated in detail. Moreover, appropriate use of law and regulations and effective water management is required amid rising population to preserve the quality of this resource for future generations.
Popular Imperceptibility Measures in Visual Adversarial Attacks are Far from Human Perception

Adversarial attacks on image classification aim to make visually imperceptible changes to induce misclassification. Popular computational definitions of imperceptibility are largely based on mathematical convenience such as pixel p-norms. We perform a behavioral study that allows us to quantitatively demonstrate the mismatch between human perception and popular imperceptibility measures such as pixel p-norms, earth mover’s distance, structural similarity index, and deep net embedding. Our results call for a reassessment of current adversarial attack formulation.
Concept Drift and Avoiding its Negative Effects in Predictive Modeling of Failures of Electricity Production Units in Power Plants

Ensuring the required accuracy of predictive models operating on time series is very important for industrial diagnostics systems. It is especially visible if there are a lot of models covering hundreds of devices and thousands of measurements operating under varying conditions in changing environments. In this work, we analyze the concept drift phenomenon in the context of actual measurements and predictions of the diagnostic system of boiler feed pump working in coal-fired power plants. In the practical part, we adapt algorithms and techniques operating on time series to obtain better results and reduce the negative effects of the concept drift. The results of our experiments show that the application of drift handling methods brings improvement in the effectiveness of the fault prediction process.
The application of fractal theory to diagnostics of composite materials by acoustic methods

The article provides a method for the analysis of diagnostic acoustic signals when defects are detected in composite materials. In this case, methods of the theory of fractals are used. Acoustic signals are analyzed in terms of the characteristics of multifractal spectra. The defective state of the sample shows the monofractal characteristic of the multifractal spectrum.