Reviewerly/exHarmony huggingface dataset:
Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the “Pay-As-PerUse” model. Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud. Cloud computing offers numerous advantages over conventional computing. However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance among available resources. Heterogeneous cloud resources also impact cloud systems overall performance. We proposed an enhanced load balancing algorithm in this research paper for efficient VM allocation in a heterogeneous cloud. Our proposed algorithm allocates independent user tasks or requests to available virtual machines in cloud datacentre efficiently to manage proper load balancing. This algorithm is aimed at minimizing user request response time and the time required for data centre processing. The results obtained showed a significant reduction in user request response time and data centre processing time as compared to “Throttled” and “Round Robin (RR)” algorithms.
Current Trends in E-Learning Development

E-learning, electronic or digital education, is one of the areas in which information and communication technologies, if although not in the most spectacular way, but have certainly had the deepest impact. All that first seemed to be just an extension of distance learning, and then of the so-called computer-assisted education, has now become an independent, ever-expanding and increasingly dynamic industry. E-learning tools are now used at all levels and in all areas of education and training, including non-formal and lifelong extensions. The aim of my study is to present the current trends in this extremely fast-moving and enormously innovative area. When outlining trends, I take into account the opinions and forecasts of leading individuals, companies and organizations in the profession and the industry.
Nominal Unification and Matching of Higher Order Expressions with Recursive Let

A sound and complete algorithm for nominal unification of higher-order expressions with a recursive let is described, and shown to run in nondeterministic polynomial time. We also explore specializations like nominal letrec-matching for expressions, for DAGs, and for garbage-free expressions and determine their complexity. We also provide a nominal unification algorithm for higher-order expressions with recursive let and atom-variables, where we show that it also runs in nondeterministic polynomial time. In addition we prove that there is a guessing strategy for nominal unification with letrec and atom-variable that is a trade-off between exponential growth and non-determinism. Nominal matching with variables representing partial letrec-environments is also shown to be in NP.
Research on Automatic Dance Arrangement Method Based on Augmented Reality Technology

In order to improve the accuracy of augmented reality for automatic choreography, it is necessary to perform automatic choreography and augmented reality processing. This paper proposes an automatic choreography motion feature detection technology based on augmented reality image feature detection, which combines multi-resolution image recognition and detection methods to perform automatic choreography augmented reality computer vision image feature analysis and dance key motion feature point detection processing, and track tracking method based on morphology. According to the detailed feature collection results of dance automatic choreography augmented reality computer vision images, the region segmentation of dance automatic choreography augmented reality is carried out by adopting three-dimensional recombination method of key motion feature points data of computer vision dance, the feature analysis of dance automatic choreography augmented reality is carried out by combining fuzzy image dance key motion feature points detection technology, the fringe pattern analysis of computer vision is carried out by adopting edge contour feature detection method, the error correction and compensation processing of dance motion feature detection are carried out according to light resolution characteristics, and the motion feature detection of dance automatic choreography is realized by combining shape feature distribution. The simulation results show that this method has better resolution of atlas and higher accuracy of parameter estimation of key dance action feature points, which effectively realizes the action correction of automatic dance arrangement and improves the accuracy of automatic dance arrangement.
Bias field correction in 3D-MRIs using convolutional autoencoders.

Bias Field correction is a crucial step in MRI preprocessing. The bias field affects the intensity uniformity in MRI images. This effect is mostly due to the in-homogeneity in the magnetic fields or variation in magnetic susceptibility during acquisition. The presence of bias field affects the tissue classification stage, as most of the common methods assume uniform intensities across same tissue. We present a deep learning approach that uses an autoencoding architecture to predict the bias field. The performance of the method is evaluated based on tissue classification accuracy compared to the ground truth result. The proposed method outperforms a traditional histogram based method and results in a more accurate tissue classification.
Analysis of Power Generation Characteristics of a Photovoltaic System in Korea

In this study, reflecting long-term climate characteristics, we analyzed electricity generation and generation characteristics of 3kWp PV system, which was semi-integrated with air duct behind. Using PVsyst as a simulation analysis tool, we inputted "National reference standard weather data" of 16 regions as a typical climatic data. The result is summarized as follows: First, the national average annual electricity generation was 1,312 kWh/kWp (StDev, σ = 71). It was most abundant in Mokpo with 1,434 kWh/kWp, which was average 21% greater than the lowest with 1,165 kWh/kWp in Seoul and 1,197 kWh/kWp in Jeju. National average daily generating time based on STC was 3.6 hours (σ=0.43), and that of Mokpo and Seoul was 3.9 and 3.2 hours respectively. Second, Jeju showed the great difference of annual monthly generation by month (annual average = 99.7 kWh/kWp, σ = 25.5), while Jinju showed the smallest difference (annual average = 115.5 kWh/kWp, σ = 10.6). Generation in Jeju was at the largest in April with 132.2 kWh/kWp, which was 2.3 times greater than the lowest 55.2 kWh/kWp in January. However, generation in Jinju was at the largest in March with 129.3 kWh/kWp, which was only 1.3 times greater than the lowest 101.1 kWh/kWp in June. Third, the annual average PR was the highest in Incheon with 85.8% and the lowest in Jeju with 83.2%. PR of Mokpo was 84.3%, which was lower than that of national average.
Machine Reading Comprehension

Reading comprehension is one of the fundamental skills for humans, which one must learnsystematically since elementary school. Considering we have a question in mind and hope the internet cananswer us. As a start, we formulate the question with some keywords and type them into the search box. Thesearch engine returns a set of matched documents, that is, the search engine only helps you filter out irrelevantdocuments, leaving the hard tasks such as reading, comprehending and summarizing to oneself. In other words,the mighty internet doesn‟t actually answer your question, we answer it by ourselves. Having a search systemwith the reading comprehension ability can make the user experience smoother and more efficient, as all timeconsumingtasks such as retrieving, inferring and summarizing are left to the computers. For impatient userswho don‟t have time to read the text carefully, such a system can be very useful. In this paper, we propose anextraction framework to extract the answers from the passage given as input. Specifically, the answer extractionmodel is employed to extract the most important sub-spans from the passage which are used along with thequestion to further elaborate the final answers
A Study of the Influence of Data Complexity and Similarity on Soft Biometrics Classification Performance in a Transfer Learning Scenario

Transfer learning is a paradigm that consists in training and testing classifiers with datasets drawn from distinct distributions. This technique allows to solve a particular problem using a model that was trained for another purpose. In the recent years, this practice has become very popular due to the increase of public available pre-trained models that can be fine-tuned to be applied in different scenarios. However, the relationship between the datasets used for training the model and the test data is usually not addressed, specially where the fine-tuning process is done only for the fully connected layers of a Convolutional Neural Network with pre-trained weights. This work presents a study regarding the relationship between the datasets used in a transfer learning process in terms of the performance achieved by models complexities and similarities. For this purpose, we fine-tune the final layer of Convolutional Neural Networks with pre-trained weights using diverse soft biometrics datasets. An evaluation of the performances of the models, when tested with datasets that are different from the one used for training the model, is presented. Complexity and similarity metrics are also used to perform the evaluation.
Quantum supremacy in end-to-end intelligent IT. PT. III. Quantum software engineering – quantum approximate optimization algorithm on small quantum processors

Principles and methodologies of quantum algorithmic gate-based design on small quantum computer described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. A new approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. SW &amp; HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation on small quantum programmable computer algorithm gate (that can program in SW to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates) described
A novel and fast blurred image matching method

In this paper, a novel image matching method is proposed in order to improve the performance of image registration, especially for blur images.Firstly, A set of Scale Invariant Feature Transform (SIFT) points are extracted.Secondly, in order to further improve the distinctiveness of the SIFT descriptors, three scale invariant concentric circular regions are applied to produce descriptors.Thirdly, for the purpose of decreasing the high dimensional and complexity of SIFT descriptors, The Local Preserving Projection (LPP) technic is applied to reduce the dimensions of the descriptors.Lastly, the Euclidean distance similarity measurements are used to obtain the results of matching feature points.The experimental results show that the novel image matching method can not only reduce the data amounts, but also improve the matching speed and the matching precision.
Mass Media Information Validation Models for Decision Making

he authors study the mathematical models that formalize philosophical notions “information field” and “information” with mathematical means including Gauss’s theorem. They propose a radial hierarchy of information validation that reflects the taxonomic structure of factors affecting it. Besides, the authors work out a cognitive model that contributes to making scientifically based decisions concerning validity of information in the mass media. The model is tested with the help of simplicial analysis which enables to see unevident links between the factors influencing the formation of the structure of the cognitive model. Calculation procedures helped to correct the analyst’s speculations concerning the links between the factors in the cognitive model, applying the notion “coupling network”, which implies a connection between two separate simplexes through a network of intermediary simplexes. The results of the study may add to executive decision-making when designing and implementing programs of ideological purpose.
RETRACTED: Exploration of Teachers’ Effective Teaching Behavior in Mathematics Classroom Based on Big Data Analysis

Abstract With the development of education reform, the quality of classroom teaching has been paid more and more attention. Mathematics teaching is an important part of classroom education, and the quality of students’ learning has a direct impact on their future development. Therefore, only by continuously improving classroom teaching efficiency can the burden of students be truly reduced. Combining reality, using computer big data to analyze the characteristics, problems and improvement strategies of classroom teaching behavior.
RETRACTED: Research on the Promotion Path of Teachers’ Scientific Research and Innovation Ability based on Big Data Analysis of “Double High Program” Construction

Abstract Under the national vocational education reform, HVC (hereinafter referred to as HVC) begin to implement the “double high program”, which is an important measure to promote the rapid development of vocational education. Among them, the improvement of teachers’ scientific research and innovation ability is an important part of the “double high program”, which is a solid foundation for the quality of personnel training. The 21st century is the era of big data, which requires HVC to improve teachers’ scientific research and innovation ability based on big data analysis. At present, there are many problems in HVC, which need us to improve teachers’ comprehensive ability. Firstly, this paper analyzes the basic principle of big data mining technology. Then, it puts forward the factors that restrict the development of teachers’ scientific research and innovation ability. Finally, this paper puts forward some suggestions, which can better build a higher vocational college research and innovation ability promotion system.
Speaker Verification System Based on Deformable CNN and Time-Frequency Attention

Speaker verification (SV), especially short utter-ances SV, needs to be robust under complex noisy and far-field conditions. Majority of recent works apply attention mechanism on aggregation of frame-level speaker embeddings which are extracted by deep neural network. In this paper, a novel speaker verification system based on the deformable convolution module and the time-frequency attention module has been proposed. In the deformable convolution module, the convolutional sampling locations are adaptively adjusted by additional offsets which are learnt from the spectrogram. Meanwhile, in order to extract the more effective speaker discrimination information for short utterances, the time-frequency attention module is used to help the system focus on the important regions of the short utterances along the time and the frequency domain. Experiments on the HI-MIA database show that the proposed modules can improve the equal error rate (EER) of speaker verification system by relatively 24% compared to the baseline model, at a result of 8.51%.
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond

This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works that use explicit, symbolic models which can be computationally expensive and limited in generalization, we propose a simple and effective alternative by revisiting modulated convolutions that fuse the query and the image locally. Following the design of residual bottleneck, we call our method MoVie, short for Modulated conVolutional bottlenecks. Notably, MoVie reasons implicitly and holistically and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong performance for counting: 1) advancing the state-of-the-art on counting-specific VQA tasks while being more efficient; 2) outperforming prior-art on difficult benchmarks like COCO for common object counting; 3) helped us secure the first place of 2020 VQA challenge when integrated as a module for ‘number’ related questions in generic VQA models. Finally, we show evidence that modulated convolutions such as MoVie can serve as a general mechanism for reasoning tasks beyond counting.
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning With Rewards

A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. In this class of problems, we show a simple example in which the desired optimal policy cannot be induced by any weighted linear combination of rewards. Hence, there exist constrained reinforcement learning problems for which neither regularized nor classical primal-dual methods yield optimal policies. This work addresses this shortcoming by augmenting the state with Lagrange multipliers and reinterpreting primal-dual methods as the portion of the dynamics that drives the multipliers evolution. This approach provides a systematic state augmentation procedure that is guaranteed to solve reinforcement learning problems with constraints. Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.
Multi-Camera People Detection and Tracking

Pedestrian detection and tracking are essential problems in the field of computer vision, having a wide range of applications in surveillance, security, autonomous driving and robotics areas. Altho ...
RETRACTED: Research on the Construction of Young Teachers in Colleges and Universities of Traditional Chinese Medicine Based on Computer Multimedia System

Abstract In the process of training young teachers, we should combine the cultivation of innovative talents with the cultivation of young teachers, and explore the innovative training mode of graduate students while cultivating the innovative spirit, innovative thinking and innovative ability of young teachers. Through the use of modern computer multimedia technology to train excellent young teachers, so that they play a huge role in the College of traditional Chinese medicine. Modern multimedia technology can be used in class, teaching content and the use of various aspects of teaching, and so on. At the same time, the electronic teaching plan provided for teachers also enriches the class content to a certain extent [1] . His paper focuses on how to use multimedia system correctly to complete teaching in young teachers of TCM colleges and universities.
Anatomy recognition in CT images of head and neck region via precision atlases

Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in the segmentation procedure reduce its practicality in clinical application. In this work, we start from a viewpoint that different parts of the target object can be recognized by different atlases and propose a precision atlas selection strategy. By comparing regional similarity between target image and atlases, precision atlases are ranked and selected by the frequency of regional best match, which have no need to be globally similar to the target subject at either image-level or object-level, largely increasing the implicit patterns contained in the atlas set. In the proposed anatomy recognition method, atlas building is first achieved by all-to-template registration, where the minimum spanning tree (MST) strategy is used to select a registration template from a subset of radiologically near-normal images. Then, a two-stage recognition process is conducted: in rough recognition, sub-image level similarity is calculated between the test image and each image of the whole atlas set, and only the atlas with the highest similarity contributes to the recognition map regionally; in refined recognition, the atlases with the highest frequencies of best match are selected as the precision atlases and are utilized to further increase the accuracy of boundary matching. The proposed method is demonstrated on 298 computed tomography (CT) images and 9 organs in the Head & Neck (H&N) body region. Experimental results illustrate that our method is effective for organs with different segmentation challenge and samples with different image quality, where remarkable improvement in boundary interpretation is made by refined recognition and most objects achieve a localization error within 2 voxels.
Working together: Using student-driven UX projects to improve library websites

Web design standards have evolved considerably since the introduction of smartphones and tablets, but many academic library websites have fallen behind, perhaps due to a lack of internal user experience (UX) research and design practices. This article presents a discussion of various UX research and design methods undertaken by the Pratt Institute Libraries via collaboration with courses in Pratt’s School of Information. Projects discussed include web analytics, user testing, online surveys, personas definition, card sorting, and prototyping. The insights gained from this study may be of assistance to other academic libraries planning to redesign and improve their online experience.
Analysis of key points of road and bridge foundation construction technology

Abstract In the process of road and bridge construction, basic construction technology is most the important. Only when the basic construction technology is improved can the overall quality of the road and bridge be improved. Therefore, the relevant construction units must strengthen the research of basic construction technology and master the technical points to accelerate road and bridge construction. In view of this, this paper mainly analyzes the key points of road and bridge foundation construction technology.
Intelligent Piracy Site Detection Technique with High Accuracy

Recently, with the diversification of media services and the development of smart devices, users have more opportunities to use digital content, such as movies, dramas, and music; consequently, the size of the copyright market expands simultaneously.However, there are piracy sites that generate revenue by illegal use of copyrighted works.This has led to losses for copyright holders, and the scale of copyrighted works infringed due to the ever-increasing number of piracy sites has increased.To prevent this, government agencies respond to copyright infringement by monitoring piracy sites using online monitoring and countermeasure strategies for infringement.However, the detection and blocking process consumes a significant amount of time when compared to the rate of generating new piracy sites.Hence, online monitoring is less effective.Additionally, given that piracy sites are sophisticated and refined in the same way as legitimate sites, it is necessary to accurately distinguish and block a site that is involved in copyright infringement.Therefore, in this study, we analyze features of piracy sites and based on this analysis, we propose an intelligent detection technique for piracy sites that automatically classifies and detects whether a site is involved in infringement.