Reviewerly/exHarmony huggingface dataset:
Improvement of Energy Consumption in Cloud Computing

For small to large scale cloud computing data centers, the power consumption of physical and virtual machines is a major challenge. Two states of the virtual machine, active state if selected by cloudlet and idle state if not selected. Two types of works are applied which are existing work and proposed work. In existing work non-power aware data center, the power consumption used time-shared data center that discarded cloudlet file size to selected VMs. To performance-enhanced of power consumption in existing work applied power-aware data center in proposed work used the intelligent distribution of cloudlets that according to cloudlet file size organized cloudlet on five range to selected VMs. The result shows in existing work that five VMs selected by five cloudlets, which consumed all power of the host. VM1 difference in existing work to proposed work by selected cloudlet1 with file size 300 to consumed 1000 second in existing work and selected three cloudlets which are cloudlet1 with file size 300, cloudlet2 with file size 400 and cloudlet3 with file size 500 to consumed 2999.99 seconds in proposed work, so there is a positive relationship of VM execution time and number of cloudlets that selected by VM. That positive relationship affected positively the power consumption of VM, but at the same time, the VM2 and VM3 not selected by cloudlet stayed in the idle state near to zero power consumption and zero execution time in proposed work to the reduced power consumption of host from 7.134 KW to 6.562 kW with difference 0.572 kW. VM4 and VM5 were selected by cloudlet4 and cloudlet5 in two work there is no change similar to VM2 and VM3. Performance enhancement of VMs power consumption reduced the cost to increased lifetime and reduced carbon footprints to make environment-friendly.
Adaptive Context-Aware Discriminative Correlation Filters for Robust Visual Object Tracking

In recent years, Discriminative Correlation Filters (DCFs) have gained popularity due to their superior performance in visual object tracking. However, existing DCF trackers usually learn filters using fixed attention mechanisms that focus on the centre of an image and suppresses filter amplitudes in surroundings. In this paper, we propose an Adaptive Context-Aware Discriminative Correlation Filter (ACA-DCF) that is able to improve the existing DCF formulation with complementary attention mechanisms. Our ACA-DCF integrates foreground attention and background attention for complementary context-aware filter learning. More importantly, we ameliorate the design using an adaptive weighting strategy that takes complex appearance variations into account. The experimental results obtained on several well-known benchmarks demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art approaches.
Validation of Synthetic Solar Irradiance Data

Solar irradiance data synthesis aims to generate realistic data for many engineering applications when obtaining real-world data can be costly and often entirely impractical. Once synthetic irradiance data is generated, its validity needs to be established before use. Many validation approaches in the literature examine the statistical equivalence between the generated synthetic data and real ground observation data, but there is no official standard with which to validate. This raises questions about which methods are the most suitable and should be adopted for synthetic solar irradiance methodologies. A critical finding is that a varied approach to validation is necessary, as each intended application of synthetic solar irradiance will prioritize certain aspects over others. In this chapter, a review of the main existing approaches for robust validation of synthetic solar irradiance data is presented, as well as some insights into the metrics.
A New Proposed Method For A Statistical Rules-Based Digital Image Compression

Abstract Image compression depends on data compression of digital images. Its central objective is to decrease the redundancy of the image data for reducing space and the cost of transmitting data in public communication channels. This research suggests a new compression technique based on the statistical rules. The proposed technique is one of the lossy compression techniques is based upon the statistics of the pixel values of the gray-scale image. The quality of these compressed images have been evaluated using some factors like the Image size before/after the compression process, Compression Ratio, (CR), and Peak Signal to Noise Ratio, (PSNR), Mean Square Error (MSE), and Mean Space Saving (MSS). Experimental results have demonstrated that the proposed technique provides sufficient higher compression with minimal to lose data.
Research on image text generation based on word2vec visual vocabulary attention

A method of image text generation based on the combination of word2vec keyword extraction and attention mechanism is proposed. First, the co-occurring words with visual entities in the description set were extracted for each image in the dataset; Then the similarity was calculated for the extracted keywords, the similar words were filtered out to expand the keyword list, and the words in the vocabulary were retained to create new descriptions for the images. Finally, the test set images were combined with attention mechanism to generate description text. The experiments prove that the method proposed in this paper can achieve automatic annotation of images and can effectively solve the attention diffusion problem in the process of image text generation.
Computer literacy as a condition for the formation of future specialists in education

At present information technologies are rapidly developing all over the world, so a man of today should be engaged in self-education and self-improvement. Due to the rapid society informatization and learning conditions during a pandemic, there is an increasing need to improve the computer literacy of current and future teachers, as a computer is the main device in the work of a teacher and in teaching a student in the modern world. In this regard, the formation of computer literacy among students of pedagogical universities and teachers in the current learning environment is an urgent problem in modern society. In accordance with the problem, the main goal of the article is to analyze the works of both domestic and foreign scientists, devoted to the problems of training future teachers in the context of society informatization and the pandemic. For a more detailed analysis and study of the problem of computer literacy formation, the article uses a theoretical research method, which is implemented in considering the fundamental definitions of computer literacy, in highlighting the main levels of computer literacy. The result of the analysis is a listing and description of the basic skills that a pedagogical university student and an employee in the field of education must possess to work with computer information, as well as the use of computer programs in the professional activity of a teacher in the pandemic. Thus, increasing computer literacy is an integral process in the information society, especially during the period of active use of distance learning technologies.
An FPGA Accelerator for Bayesian Network Structure Learning with Iterative Use of Processing Elements

A Bayesian network is one of the graphical models that represent the causality or correlation relationship among multiple observed phenomena. The structure learning of this network is generally NP difficult, and the computational time to obtain an approximate solution becomes huge. This paper proposes an FPGA accelerator for structure learning of Bayesian networks. The proposed method employs a dataflow type architecture and executes processes without dependency in dynamic programming in parallel. By iteratively using processing elements at each processing stage, we can efficiently use limited resources while taking advantage of the parallel performance of FPGAs. We implemented the proposed method for Xilinx Alveo U200 using high-level synthesis. Evaluation results showed that we achieved up to 12.6 times faster than single-core execution of software and up to 2.98 times faster than on multi-core execution. We further applied the proposed method to the Local-to-Global algorithm and achieves 8.6 times faster than the software execution in the structure learning of a practical network with 37 nodes.
Kernearl-Based Lifelong Policy Gradient Reinforcement Learning

Policy gradient methods have been widely used in reinforcement learning (RL), especially thanks to their facility to handle continuous state spaces, strong convergence guarantees, and low-complexity updates.Training of the methods for individual tasks, however, can still be taxing in terms of the learning speed and the sample trajectory collection.Lifelong learning aims to exploit the intrinsic structure shared among a suite of RL tasks, akin to multitask learning, but in an efficient online fashion.In this work, we propose a lifelong RL algorithm based on the kernel method to leverage nonlinear features of the data based on a popular union-of-subspace model.Experimental results on a set of simple related tasks verify the advantage of the proposed strategy, compared to the single-task and the parametric counterparts.
On Followers Search

Although followership has been widely studied in sociology and management, the problem of finding followers has not drawn attention in the field of artificial intelligence. We refer to the problem of finding followers of a given object as followers search. In sociology, followers are close to their leaders, and leaders are superior to their followers. In this article, aimed at finding followers of a given object, we formulate followers on the basis of both superiority and closeness. The former means that the given object should be superior to followers, and the latter means that followers should be close to the given object. We present a followers search algorithm to find the followers of the given object. Furthermore, we apply the ideas of followers to the market basket and recommender system datasets. The experimental results demonstrate the rationality of the discovered followers on the market basket dataset and the improved performance of the TrustPMF algorithm by adopting followership on recommender system datasets, which indicate a promising future for followers search.
Self-Play or Group Practice: Learning to Play Alternating Markov Game in Multi-Agent System

The research in reinforcement learning has achieved great success in strategic game playing. These successes are thanks to the incorporation of deep reinforcement learning (DRL) and Monte Carlo Tree Search (MCTS) to the agent trained under the self-play (SP) environment. By self-play, agents are provided with an incrementally more difficult curriculum which in turn facilitates learning. However, recent research suggests that agents trained via self-play may easily lead to getting stuck in local equilibria. In this paper, we consider a population of agents each independently learns to play an alternating Markov game (AMG). We propose a new training framework-group practice- for a population of decentralized RL agents. By group practice (GP), agents are assigned into multiple learning groups during training, for every episode of games, an agent is randomly paired up and practices with another agent in the learning group. The convergence result to the optimal value function and the Nash equilibrium are proved under the GP framework. Experimental study is conducted by applying GP to Q-learning algorithm and the deep Q-learning with Monte-Carlo tree search on the game of Connect Four and the game of Hex. We verify that GP is the more efficient training scheme than SP given the same amount of training. We also show that the learning effectiveness can even be improved when applying local grouping to agents.
Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

From a spherical image, a gaze point, instead of gaze vectors, can be estimated directly because a remote spherical camera can observe a user's face and a gaze target simultaneously. This paper investigates the problem of estimating a gaze point in a spherical image from facial landmarks. In contrast with the existing methods which usually assume gaze points move on a narrow plane, the proposed method can cope with the situation where gaze points vary in depth for a relatively wide field of view. As shown in the results of comparative experiments, we find the orthogonal coordinates of facial landmarks on a unit sphere is a reasonable representation in comparison with spherical polar coordinates; the cues of head pose is helpful to improve the accuracy of gaze points. Consequently, the proposed method achieves a performance on the accuracy of gaze points estimation which is comparable to the state of the art methods.
Fast Local Representation Learning with Adaptive Anchor Graph

Dimension reduction is an effective technology to embed data with high dimension to lower dimension space, where Linear Discriminant Analysis (LDA), one of representative methods, only works with Gaussian distribution data. However, in order to solve non-Gaussian issue that only one cluster cannot well fit the distribution of same class, many graph-based discriminant analysis methods are proposed which capture local structure through measuring each pairwise distance. This is expense of time complexity because of the full-connections. In order to solve this issue, we propose a fast local representation learning with adaptive anchor graph to learn local structure information through similarity matrix in anchor-based graph. Notably, anchor points and similarity matrix are updated in subspace which is more precisely to capture local discriminant information. Experimental results on several synthetic and well-known datasets demonstrate the advantages of our method over the state-of-the-art methods.
Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis

Generating videos with content and motion variations is a challenging task in computer vision. While the recent development of GAN allows video generation from latent representations, it is not easy to produce videos with particular content of motion patterns of interest. In this paper, we propose Dual Motion Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while learning disentangled content and motion representations. Our Dual-MTGAN is able to perform deterministic motion transfer and stochastic motion generation. Based on a given image, the former preserves the input content and transfers motion patterns observed from another video sequence, and the latter directly produces videos with plausible yet diverse motion patterns based on the input image. The proposed model is trained in an end-to-end manner, without the need to utilize pre-defined motion features like pose or facial landmarks. Our quantitative and qualitative results would confirm the effectiveness and robustness of our model in addressing such conditioned image-to-video tasks.
Stable Hand Pose Estimation under Tremor via Graph Neural Network

Hand pose estimation, which predicts the spatial location of hand joints, is a fundamental task in VR/AR applications. Although existing methods can recover hand pose competently, the tremor issue occurring in hand motion has not been completely solved. Tremor is an involuntary motion accompanied by a desired gesture or hand motion, leading to hand pose that deviates from user's intentions. Considering the characteristic of tremor motion, we present a novel Graph Neural Network for stable 3D hand pose estimation. The input is depth images. The constraint adjacency matrix is devised in Graph Neural Network for dynamically adjusting the topology of a hand graph during message passing and aggregation. Firstly, since there are rich potential constraints among hand joints, we utilize the constraint adjacency matrix to mine the suitable topology, modeling spatial-temporal constraints of joints and outputting the precise tremor hand pose as the pre-estimation result. Then, for obtaining a stable hand pose, we provide a tremor compensation module based on the constraint adjacency matrix, which exploits the constraint between control points and tremor hand pose. Concretely, the control points represented the voluntary motion are employed as constraints to edit the tremor hand pose. Our extensive quantitative and qualitative experiments show that the proposed method has achieved decent performance for 3D tremor hand pose estimation.
Robust Blind Watermarking against Geometrical Attacks with Medical Images for EMR/EHR

In this paper, an efficient solution for robust watermarking against geometrical attacks with medical images is proposed. In the embedding process, at first, we select the robust non-overlapping Scale-Invariant Feature Transform regions. Then, we embed the same information into these regions by using the even-odd quantization technique. In the extraction process, blind recovery of information is performed by exploiting the correlation among all detection regions. Both fan-shaped and half-ring-shaped regions are suggested for multi-bit watermarking. The embedded image quality is analyzed and compared to other image watermarking approaches based on Spread Spectrum or Least Significant Bit techniques. The reliability and the robustness are investigated with various geometrical attacks such as rotation, scaling and cropping. Moreover, our solution is also better than existing methods in terms of usefulness and security for electronic medical/health record applications.
An Asynchronous WFST-Based Decoder for Automatic Speech Recognition

We introduce asynchronous dynamic decoder, which adopts an efficient A* algorithm to incorporate big language models in the one-pass decoding for large vocabulary continuous speech recognition. Unlike standard one-pass decoding with on-the-fly composition decoder which might induce a significant computation overhead, the asynchronous dynamic decoder has a novel design where it has two fronts, with one performing "exploration" and the other "backfill". The computation of the two fronts alternates in the decoding process, resulting in more effective pruning than the standard one-pass decoding with an on-the-fly composition decoder. Experiments show that the proposed decoder works notably faster than the standard one-pass decoding with on-the-fly composition decoder, while the acceleration will be more obvious with the increment of data complexity.
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video

The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve the extremely uneven human supervision resource and digital cameras. Besides, the number of anomaly types that real-time machine monitoring could recognize has not met the need. This paper presents a fast and robust methodology for real-time anomaly detection under different scenarios. We created the Video-Energy-Vector(VEV) to significantly reduce the dimension of feature maps while maintaining the spatial-temporal information. We applied the proposed method on different computer vision features to evaluate the effectiveness of common features to different types of abnormal events based on SVM. Also, we adopted the voting model among different features, which significantly increased the performance. Further More, the small video size to be trained guaranteed the real-time efficiency. The result of the modified UCF-Crime Dataset has proved that our approach has achieved robust results and had the generalization ability on new anomaly types.
Blockchain-Based Digital Record-Keeping in Land Administration System

Land Administration System (LAS) is a salient infrastructure for any developed or developing country. The LAS is implemented both in digital form (traditional database system) and the manual approaches (paper-based documentations). Both of these systems provide monopoly control to an authority which may increase corrupt behavior in land administration. Again, though the Blockchain technology was originally introduced for keeping a financial ledger; the utilization of this technology can be extended to implement any decentralized computing systems including the digital record keeping and management system. Recent studies found that uses of Blockchain technology in land administration remain new and relatively untested. Therefore, in this work, a blockchain-based framework is proposed to develop a land administration system. The proposed framework was simulated in Ethereum blockchain platform; and showed that the framework contributed to develop a secure, reliable, scalable, effective, and efficient land administration system.
Summarization of Twitter Events with Deep Neural Network Pre-trained Models

Due to the proliferation of online social media services such as Twitter, there is an upsurge in the volume of user-generated textual content. Such voluminous content is difficult to be consumed by users. Therefore, the development of technological solutions to automatically summarise the voluminous texts are essential. The work presented in this paper reports on the development of automatically generating abstractive summaries from a collection of texts from Twitter. Our proposed approach is a two-stage framework which includes: 1) Event detection by clustering and 2) Summarization of the events. We first generated a contextualized vector representation of the tweets and then applied different clustering techniques on the vectors. We evaluated the generated clusters, and based on the evaluation; we chose the best one found suitable for the summarization task. For the summarization task, we used the pre-trained models of two recently developed state-of-the-art deep neural network architectures and evaluated them on the event clusters. Standard measures of ROUGE scores have been used for evaluating the summaries. We obtained best ROUGE-1 score of 46%, ROUGE-2 score of 30%, ROUGE-L score of 41% and ROUGE-SU score of 23% from our experiments.
Meta-Cognition-Based Simple And Effective Approach To Object Detection

Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds. However, frequently, there is a trade-off between speed and accuracy of such models, which encumbers their use in practical applications such as autonomous navigation. In this paper, we explore a meta-cognitive learning strategy for object detection to improve generalization ability while at the same time maintaining detection speed. The meta-cognitive method selectively samples the object instances in the training dataset to reduce overfitting. We use YOLO v3 Tiny as a base model for the work and evaluate the performance using the MS COCO dataset. The experimental results indicate an improvement in absolute precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference time.
Forensic Inspections in the Time of Covid-19

The global pandemic of COVID-19 has impacted the entire world in ways that were unimaginable months ago. It has changed how professionals work and interact with one another. To ensure the safety of experts and attorneys, also forensic inspection procedures must evolve, while still providing a reliable foundation for an expert testimony, relevant to the task at hand. Traditional field inspections require attendees to be physically present and generally in close proximity. Inspecting artifacts may also require that individual components be serially examined by multiple experts, which increases the risk of transmitting the disease. The authors propose a new approach to forensic inspections aimed at protecting consultants from the biological risk due to COVID-19. This approach takes into account the anticontagion safety protocols established in affected countries around the world.