Reviewerly/exHarmony huggingface dataset:
Quantum-enhanced data classification with a variational entangled sensor network

We report the experimental demonstration of supervised learning assisted by an entangled sensor network (SLAEN). We show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals.
Sequence-Level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models

Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR) systems lack universal interpretation and vary with updates to the underlying confidence or acoustic models (AMs). In this work, we build interpretable confidence scores with an objective to closely align with ASR accuracy. We propose a new sequence-level CC with a richer context providing CC scores highly correlated with ASR accuracy and scores stable across CC updates. Hence, expanding CC applications. Recently, AM customization has gained traction with the widespread use of unified models. Conventional adaptation strategies that customize AM expect well-matched data for the target domain with gold-standard transcriptions. We propose a cost-effective method of using CC scores to select an optimal adaptation data set, where we maximize ASR gains from minimal data. We study data in various confidence ranges and optimally choose data for AM adaptation with KL-Divergence regularization. On the Microsoft voice search task, data selection for supervised adaptation using the sequence-level confidence scores achieves word error rate reduction (WERR) of 8.5% for row-convolution LSTM (RC-LSTM) and 5.2% for latency-controlled bidirectional LSTM (LC-BLSTM). In the semi-supervised case, with ASR hypotheses as labels, our method provides WERR of 5.9% and 2.8% for RC-LSTM and LC-BLSTM, respectively.
Cloud Computing Improves Accounting Computerization Information System

At present, facing the need of the value mining of accounting data, the disadvantages of the existing accounting information system begin to highlight. Due to the high cost, low efficiency, long construction cycle and other factors, the existing accounting information system is difficult to obtain massive accounting data, mining knowledge, to provide timely scientific basis for enterprise managers. Using the front-end information technology -- cloud computing, this paper expounds the overall goal and process decomposition of computerized accounting information system based on cloud computing technology. The establishment of an efficient accounting big data analysis platform based on the internal relations of resources, events and participants not only provides a theoretical basis for improving the accounting informatization theoretical system, but also provides practical significance for enhancing the core competitiveness of enterprises.
Skeleton action recognition using Two-Stream Adaptive Graph Convolutional Networks

Using skeleton data to classify human behavior has been received a lot of attention and various technologies have been developed because it can be utilized in various domains. Among them, Graph Convolutional Networks (GCN) shows high accuracy in action recognition through the effective extraction of features from graph data. GCN has been further developed and two stream adaptive graph convolution networks (2s-AGCN) are made. This model utilizes bone data as well as spatial and temporal information of joints to compensate for the problems of conventional GCN limited to local region. Although 2s-AGCN show the highest accuracy among existing technologies, new approaches using CNN, LSTM and auto encoder continue to emerge and present several methods. In this paper, we verify if 2s-AGCN show higher accuracy than recent CNN-LSTM networks and compare structures and features of their models.
Practical Spatial Hash Map Updates

Spatial hashing is a very efficient approach for storing sparse spatial data in a way that easily allows merging information among areas with similar properties. Though having a very simple core, this technique requires careful implementation to achieve high efficiency. In this chapter we first consider some key principles and implementation aspects. The original spatial hashing is initially limited to storing simple data types, which can be updated using atomic calls. The second part of this chapter adds support for arbitrary data storage in the hash map, while still guaranteeing the atomicity of the updates. We achieve this using on-the-fly generation of change lists for each modified hash map entry.
COVID-19 Tweet Analysis using Hybrid Keyword Extraction Approach

Aim of this paper is to identify the key issues discussed among the people on Twitter using machine learning and NLP techniques regarding COVID-19. One of the most important way to produce these insights is Automatic Keyword Extraction. It is a method to obtain the most important words from the text by summarizing thus providing an insight into the whole context. Text Summarization is the process to condense the content without the loss of significant data. This paper applies a hybrid model of graph-based and topic modeling approaches to extract keywords from a large dataset of approximately 1 million tweets.
A deterministic single ion fountain

Abstract We present an alternative approach for interconnecting trapped ion processor nodes by deterministic single ion transfer. In our experiments, we demonstrate the single ion extraction out of a linear Paul trap, into a free space trajectory, followed by recapture in the trapping potential. We recapture in the same trap, coined the ion fountain operation after a free-space travel of distance 110 mm and a time of flight of 7 μ s. Our experimental realization yields a success probability of 95.1%, namely 715 out of 752 extracted ions are retrapped, cooled and observed. Based on such high success rate, we discuss the future perspective for an application towards scalable ion trap quantum computing and advanced quantum sensing.
Research University: Principles, Environment, Innovations, Traditions. Historical Aspect

This paper studies the basic principles of the Russian research university, its scientific, educational, social and cultural environment with the aim to study the transformation of the classical Humboldt’s model of university. The author emphasizes the role of the university environment as a key factor of shaping the personality of a research university graduate. There is discussed the importance of scientific and pedagogical schools and their leaders in the university’s development and functioning. The innovation activity is defined, and it is proposed to consider the innovative university system as a new essence of its structure. The turn of the XX century is characterized by the significant role of university traditions and of the Ministers of Education and Science in keeping and developing Russian higher education. The article is addressed to the heads of higher education institutions, to the experts in the sphere of university management, as well as to the researchers in the sphere of education and science.
Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment

Heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. Recently, number of multi-objective based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these model induce significant overhead when demand and number of processing core increases. For addressing research problem this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Thus, this paper present Energy Aware Resource Utilization (EARU) model by minimizing energy dissipation and utilizing cache resource more efficiently. The EARU model achieves much lesser execution time, energy consumption, and power consumption when compared with existing multi-objective based and DVFS-based workload scheduling algorithm.
Rendering Scenes for Simulating Adverse Weather Conditions

Most of the object detection schemes do not perform well when the input image is captured in adverse weather. Reason being that the available datasets for training/testing of those schemes didn’t have many images in such weather conditions. Thus in this work, a novel approach to render foggy and rainy datasets is proposed. The rain is generated via estimation of the area of the scene image and then computing streak volume and finally overlapping the streaks with the scene image. As visibility reduces with depth due to fog, rendering of fog must take depth-map into consideration. In the proposed scheme, the depth map is generated from a single image. Then, the fog coefficient is generated by modifying the 3D Perlin noise with respect to the depth map. Further, blending the corresponding density of the fog with the scene image at a particular region based on precomputed intensities at that region. Demo dataset is available in this https://github.com/senprithwish1994/DatasetAdverse .
Multi-Scale Segmentation For Detecting Mass In Mammograms Using Deep Learning Techniques

This paper tends to the issue of fragmenting an image into the segment. Proposed system characterizes a predicate for estimating the proof for a limit between two districts utilizing a diagram-based portrayal of the picture. Proposed system at that point build up an efficient division calculation dependent on this predicate and demonstrate that although this calculation settles on ravenous choices it produces divisions that fulfill worldwide properties. Proposed system applies the calculation to picture division utilizing two different sorts of nearby neighbourhoods in building the chart and show the outcomes with both genuine and engineered pictures. The calculation keeps running in time about straight in the number of chart edges and is additionally quick by and by. A significant normal for the strategy is its capacity to safeguard detail in low-changeability picture districts while overlooking points of interest in high-fluctuation locales. Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from initial stages respectively. Our purpose is to develop a mammography-based DL breast cancer risk model that is more accurate than established clinical breast cancer risk models
The use of corpora and other electronic tools in historical research on translation

Translation history and historiographical approaches to translation have traditionally relied on the knowledge provided by the historical context and both contextual and paratextual features of the translated texts together with their reception. Nonetheless, only by correlating historiographical insights with empirical evidence obtained from the translated texts will it be possible to produce a coherent and sound translation history. In this line of work, technology and digital humanities offer tools to the translation historian which that can complement non-computational methods and more traditional approaches to the sources and which that can be very beneficial if implemented correctly. This chapter advocates the use of tools such as corpora derived from linguistics to complement the research carried out from a historiographical point of view, while also indicating some of their possible drawbacks or limitations. In this increasingly technological world, the translation history researcher should be aware of both the opportunities and challenges provided by these tools and embrace their use with the aim of facilitating interdisciplinary avenues and progress in the field.
Learning Incorrect Verdict Patterns of the Established Face Recognizing CNN Models Using Meta-Learning Supervisor ANN

We explore the performance of the established state-of-the-art Convolutional Neural Network (CNN) models on the novel face recognition data set with artistic facial makeup and occlusions. The strength and weaknesses of different CNN architectures are probed on particular types of makeup and occlusions of the benchmark data. Apart from the practical value of the knowing effectiveness of the face camouflaging techniques, such a data set magnifies the reliability and robustness problem of the established CNN models in real-life settings. A flexible and lightweight approach of isolating uncertainty of the CNN models verdicts’ trustworthiness is investigated, aiming to increase the trusted recognition accuracy. A separate supervising Artificial Neural Network (ANN) is attached to the established CNNs and is trained to learn patterns of the erroneous classifications of the underlying CNN models.
Human Detection and Segmentation via Multi-view Consensus

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion. For scenes with dynamic activities and camera motion, we propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training via coarse 3D localization in a voxel grid and fine-grained offset regression. In this manner, we learn a joint distribution of proposals over multiple views. At inference time, our method operates on single RGB images. We outperform state-of-the-art techniques both on images that visually depart from those of standard benchmarks and on those of the classical Human3.6M dataset.
Scientific and methodological support practical training future teachers in the process of production practices

The relevance of the study is due to the processes of reforming the structure and content of higher education in the world, the transformation of the domestic system of professional training, focused on achieving high indicators in the professional career of university graduates. The article presents the experience of using electronic scientific and methodological support for the practical training of students at the university. The need for scientific and methodological development of issues of electronic support for the practical experience of students, the study of forms, content, mechanisms for the implementation of digital and electronic learning technologies, as well as assessing the quality of electronic channels for obtaining information, actualizes the problem of this study. The purpose of the article is the theoretical substantiation and development of the content, forms and methods of scientific and methodological electronic support for the training of a future teacher in terms of industrial practice. The scientific novelty of the research lies in the development of the content and mechanisms for the implementation of scientific and methodological electronic support for the practical training of future teachers on the example of organizing practical training for bachelors of psychological and pedagogical education. The theoretical significance of the study lies in the fact that its results contribute to the expansion of scientific ideas about the process of practical training of a future teacher, taking into account the informatization of modern education. The study of the difficulties encountered by students in the course of distance online learning reveals contradictions of both objective and subjective nature. The resolution of contradictions is possible when organizing independent work of students in practice by means of electronic support for the experience of professional activity, aimed at enriching the spectrum of obtaining a posteriori knowledge. The article offers options for the use of distance technologies in a university, stages, forms and methods of interaction between participants in the educational process. The result of the research and the subject of discussion is the substantiation of the ways to implement the a posteriori component of the industrial practice program as a form of blended learning, which provides a seamless combination of the possibilities of traditional work and electronic support of students' practical activities. The reference point for an independent search for answers when performing practical tasks, their reflective analysis and theoretical interpretation in the course of solving pedagogical problems is provided by means of an electronic educational environment and a study developed by the authors scientific and methodological support for the practical training of future teachers. The article is addressed to teachers, researchers, heads of educational organizations interested in improving teacher education.
How We Explore, Interpret, and Solve Complex Problems: A Cross-National Study of Problem-Solving Processes

Complex problem-solving (CPS) is considered an important educational outcome in the 21st century. Despite its importance, we have no knowledge of its measurability, development, and comparability in Arab countries, where there is a short history of computer-based assessment. The results of the current study provide important insights into the international validity of CPS measurements and shed light on the different hidden behavioral patterns and test-taking behavior of Jordanian (N=431) and Hungarian (N=1844) students as they solve complex problems. CPS proved to be measurement-invariant in Jordan and Hungary among university students. Analyzing log data, we have identified large differences in students’ test-taking behavior in terms of the effectiveness of their exploration strategy, time-on-task, and number of trials at an international level. Low-achieving students typically engaged in less interaction with the problem than high achievers, however the differences identified become smaller compared to the increasing complexity of the tasks. Hungarian students became increasingly aware of their effective exploration behavior and required ever fewer trial-and-error moves and ever fewer trials despite the increasing complexity of the tasks in comparison to the Jordanian students, who spent almost the same time and used almost the same number of trials during the whole test, which explain the different research results for time-on-task and high CPS achievement. This study contributes to an understanding of how students from different educational contexts behave while solving complex problems.
Key Analysis of Research Progress on Lower Limb Exoskeleton Robots

The lower limb exoskeleton robot is an intelligent device that is worn on the human body and performs auxiliary exercises. This device can help patients and the elderly complete rehabilitation actions such as walking and standing. This article analyzes the research progress of lower limb exoskeleton robots. The author has studied the key points of lower limb exoskeleton robot driving platform design and control system design, through research and combing the human gait cycle, trajectory algorithm analysis and other key points of gait trajectory planning. The purpose of this article is to optimize the lower extremity exoskeleton robot system and promote the research and development of this content.
Independent Sets in Semi-random Hypergraphs

A set of vertices in a hypergraph is called an independent set if no hyperedge is completely contained inside the set. Given a hypergraph, computing its largest size independent set is an NP-hard problem. In this work, we study the independent set problem on hypergraphs in a natural semi-random family of instances. Our semi-random model is inspired by the Feige-Kilian model [9]. This popular model has also been studied in the works of [9, 30, 35] etc. McKenzie, Mehta, and Trevisan [30] gave algorithms for computing independent sets in such a semi-random family of graphs. The algorithms by McKenzie et al. [30] are based on rounding a “crude-SDP”. We generalize their results and techniques to hypergraphs for an analogous family of hypergraph instances. Our algorithms are based on rounding the “crude-SDP” of McKenzie et al. [30], augmented with “Lasserre/SoS like” hierarchy of constraints. Analogous to the results of McKenzie et al. [30], we study the ranges of input parameters where we can recover the planted independent set or a large independent set.
Affective classification model based on emotional user experience and visual markers in YouTube video

On the other hand, video classification has been viewed as a critical means of ensuring the efficient retrieval of videos [6][7][8][9].Previous research has indicated that affective computing-based video retrieval is the future direction and presents eminently compelling research issues [7,10,11].Additionally, due to the computer revolution, emerging issues such as an extremist group infiltrating social media to spread propaganda and execute various illicit activities have been widespread more than they were.Extremist groups are becoming tech-savvy and have their own media outlets to achieve their goals [12,13].They want to provoke the interest of potential new members, supporters and sympathizers, particularly young adults [14][15][16][17].One way is through video Re search
A Darwinian Differential Evolution Algorithm for Multilevel Image Thresholding

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.