Reviewerly/exHarmony huggingface dataset:
Treasure Hunt Framework: Distributing Metaheuristics on High Performance Computing Systems

This work proposes a multilevel framework called Treasure Hunt, which is capable of distributing independent search algorithms to a large number of processing nodes. Treasure Hunt ensures quick propagation of information and explores a driving mechanism, obtaining joint convergences between working nodes. Experiments on classic, random and competition benchmarks of various sizes and complexities, using the search algorithms PSO, DE and CCPSO2, show that Treasure Hunt boosts the inherent characteristics of these search algorithms, consistently improving the optimization results, making poor performer algorithms to become comparable to good ones, and good performer algorithms to extend their limits to larger problems. Results suggest that the sampling mechanism provided by Treasure Hunt reduces the need for large population sizes, increasing cooperation between multiple evolving populations, which is specially important in problems with time-consuming fitness functions, due to superior quality of optimization obtained per time frame. Experiments distributing Treasure Hunt instances, in a cooperative network up to 96 processes, show that the robust scaling of the framework reduces the importance of using complex search algorithms on complex problems.
Counter-Misdirection in Behavior-based Multi-robot Teams

When teams of mobile robots are tasked with different goals in a competitive environment, misdirection and counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the number of approaches on counter-misdirection for multi-robot systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by deploying a new type of agent: counter-misdirection agents (CMAs). These agents can detect the misdirection process and “push back” the misdirected agents collaboratively to stop the misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical robotic testbed to study its effectiveness. It shows that this approach can stop the misdirection process effectively with a sufficient number of CMAs. This novel counter-misdirection approach can potentially be applied to different competitive scenarios such as military and sports applications.
Research on Vertical Search Method of Multidimensional Resources in English Discipline Based on Edge Computing

The traditional vertical search method only considers the content of the webpage, and the global master node is not enough, which will lead to premature convergence and fall into the local optimum, resulting in insufficient multi-dimensional search of resources. Therefore, this paper proposes a multidimensional resource vertical edge based on the calculation of English subject search method. This paper analyzes the architecture of search engine firstly and then introduces the multiaccess edge computing architecture. At last, it constructs the vertical search task computing model of multidimensional resources in English discipline. By associating and traversing the attributes of multidimensional resources of English discipline, the vertical search of attribute information is realized offline, and the vertical search method of multidimensional resources of English discipline based on edge calculation is designed. In order to verify the effectiveness of the proposed method, a comparative experiment is designed. Experimental results show that the method can improve the resource search ratio and recall ratio, and it can also effectively improve the search efficiency. For an English subject resource data of 50 MB, the calculation methods of edge multidimensional resource data search recall rate can reach 97% and multidimensional resource data search time consumption is only 39 ms. The experimental results show that the performance of English subject multidimensional resources vertical search is much better.
Activation Functions: Experimentation and Comparison

Activation functions are mathematical functions that are used to activate the neurons of an Artificial Neural Network. Non-linear activation functions mainly help a neural network to converge faster while learning and finding patterns in the complex input data. A neural network learns by updating the weights, which is done using the Back Propagation algorithm, which uses first-order derivatives of the activation functions to calculate the gradient descent. This paper tests various existing and proposed activation functions against Minst and Cifar10 datasets for image classification using a shallow Convolutional Neural Network (CNN) Architecture. Based on the results, some of the proposed activation functions: SMod = x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sub> tanh ( x), the Absolute/Mod Function, a scaled version of Swish, and some other activation functions, are found to be promising. Some of these are then tested against Deeper Neural Networks for various datasets, and it is observed that the average error rate is improved by 2.77. Along with that, suggestions on which activation functions to be used for shallow and deep layers of a Deep Neural Network are provided, resulting in better performance.
Research on Sports Computer Integrated Management System Based on Network Environment under the Background of Smart Sports

With the development of economic and cultural level, computer and network technology have also developed rapidly and become more and more popular. Computer information management technology has also been widely used in education system. For various reasons, physical education management has been in a backward state in using computer and network technology, so it is necessary to develop the physical education management network system. The application of computer integrated management system in college teaching can effectively integrate and distribute resources and give full play to the efficiency, convenience and openness of computer technology. Physical education management network system should have functional modules such as physical education management and sports competition management, and realize effective connection with campus network. In order to make modern computer and network technology better applied to school physical education teaching and management under the background of intelligent sports, this paper discusses the working content and management process of sports computer integrated management system.
Gating Feature Dense Network for Single Anisotropic Mr Image Super-Resolution

High resolution (HR) magnetic resonance (MR) images are crucial for medical diagnosis. However, in practice, low resolution MR images are often acquired due to hardware limitation. In this work, we propose a gating feature dense network to reconstruct HR MR images from low resolution acquisitions, where we use local residual dense block (LRDB) as the backbone. We propose gating mechanism, which includes absorption gate and release gate, to adaptively introduce the informative features of previous LRDBs to current LRDB to solve the problem of insufficient features sharing. The absorption gate can fuse the output feature of LRDBs with adaptive weights, which allows the model to adaptively learn the effects of different LRDBs for MR image super-resolution (SR). Experimental results show that our proposed method achieves a new state-of-the-art quantitative and visual performance in anisotropic MR image SR.
FastCompletion: A Cascade Network with Multiscale Group-Fused Inputs for Real-Time Depth Completion

Completing sparse data captured with commercial depth sensors is a vital and fundamental procedure for many computer vision applications. For execution in real-world scenarios, a good trade-off between accuracy and speed is increasingly in demand for depth completion methods. Most previous methods achieve satisfactory accuracy on standard benchmarks. However, they extensively rely on heavy models to handle diverse structures and require additional run time on multimodal data. In this paper, we present an efficient method of depth completion. We propose a grouped fusion strategy for efficiently extracting depth and guidance features in parallel and fusing them naturally in the feature spaces to achieve high performance. Instead of a monolithic architecture, we employ cascaded hourglass networks, each of which is specialized for certain structures and has a lightweight architecture. Given the sparsity of the depth maps, we downsample the inputs to multiple scales to further accelerate the computation. Our model runs at over 39 FPS on an embedded GPU with high-resolution inputs. Evaluations on the KITTI benchmark demonstrate that the proposed model is an ideal approach for real-world applications.
A Rasch model analysis on the ability to analyze descriptive statistics: A case Study of FMIPA Universitas Syiah Kuala new students

Abstract Data literacy skills are essential in the current industrial 4.0 era. Data literacy skills include the ability to read and analyze data in a descriptive form. This study aims to measure the ability to analyze descriptive data and analyze the statistical analysis results of responses from new students of the Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala. The test instrument consisted of 29 questions divided into three parts: understanding, application, and analysis. The tool was tested with a time limit of 30 minutes to 436 respondents from 7 study programs at the Faculty of Mathematics and Natural Sciences. The average score obtained by students was 67.66, with a standard deviation of 12.48. The implementation of Rasch model provides a difficulty index ranging from-3.80 to 2.45, with a difficulty average approaching zero and a standard deviation of 1.4. The result concluded that the respondent’s ability to answer the questions is average.
English Teaching Analysis Algorithm Design Based on Bidirectional Association Rules

English teaching evaluation is an effective way to evaluate the teaching quality and promote the professional development of English teachers. However, the traditional evaluation of college English teaching is troubled by the randomness and the fuzziness of evaluation criteria of evaluation process. In this paper, with the comprehensive evaluation by a fuzzy-algorithmic approach, a fuzzy comprehensive evaluation model is built on the basis of statistics to provide a scientific basis for teaching evaluation. First, aiming at the characteristics of English teaching, according to the principle and methods of the fuzzy comprehensive evaluation method, with teaching expert, fellow teachers and students as the main body of evaluation and combined with four comprehensive evaluation indicators, the indicator system and the evaluation grade standard of the English teaching evaluation are established. Then the hierarchy analysis method is used to get the weight of each evaluation indicator, and establish the fuzzy comprehensive evaluation model of English teaching.
Priam's Folly: United States v. Alvarez and the Fake News Trojan Horse

In legal scholarship over the past few years, fake news has been criticized and pondered repeatedly. In many ways, 2020 was a year of reckoning which brought to the fore the myriad problems posed by fake news. This Article uses the context of 2020 to critique the Supreme Court decision in United States v. Alvarez, the latest Supreme Court ruling on the issue of whether fake news is protected by the First Amendment. Alvarez was decided in 2012, before the true dangers of fake news during the Internet Age were made fully apparent to the public. While Alvarez upheld the noble idea of truth ultimately triumphing in the marketplace of ideas, in reality, Alvarez opened the gates to the pernicious dangers posed by fake news.
Application of Case-study for solving problems of measuring the level of phenol pollution of the Orel River in the training of future ecologists

Abstract In the modern world, rapid changes are taking place in many areas of human activity. The processes of globalization and the acceleration of scientific and technological progress require the search and introduction of new educational teaching technologies in the educational process. The requirements for teaching methods and their quality are steadily increasing. The methods of professional training used in the 20th century cannot provide the necessary level of quality in the new conditions, this creates the need for a constant search for even more effective approaches to the professional training of future specialists, including environmentalists. Motivating students to actively participate in the educational process, increasing their creative activity is important for them to gain new knowledge and skills. The most important condition for the formation of motivation is a close correlation between the educational process and the professional activities of future specialists. Case-study is, in our opinion, one of the most effective tools for improving the quality of training of modern specialists. Its functional capabilities largely meet the requirements that society imposes on the modern education system.
Learning Pose-Adaptive Lip Sync with Cascaded Temporal Convolutional Network

Speech-driven lip sync has become a promising technique for generating and editing talking-head videos. These studies mainly use 3D morphable models or 2D facial landmarks as the intermediate face representations. However, 2D-based methods have been stagnant recently due to their inability to handle out-of-plane rotations, even though the 2D landmarks have the advantage of fast and accurate extraction. In this paper, we design a cascaded temporal convolutional network to successively generate mouth shapes and corresponding jawlines based on audio signals and template headposes. Instead of explicitly calibrating the rotation between the predicted mouth and the template face, we employ neural networks to learn the pose-adaptive mapping implicitly. We also propose an image-to-image translation-based neural rendering method for producing high-resolution and photo-realistic videos. Experiments show our solution improves both the mapping accuracy and visual performance than baselines. This work could benefit many real-world applications like virtual anchors, telepresence, and conversational agents.
Hybrid Algorithm on Semantic Web Crawler for Search Engine to Improve Memory Space and Time

World Wide Web is a colossal reservoir of hyperlink documents, these hyperlinks documents lay the foundation for communication on this omnipresent computing world. An acute need has arisen to develop and modify or design search algorithms that helps in efficiently and competently searching the specific required data from the huge repository available. A variety of search engines employ diverse web crawlers for obtaining search results efficiently. A variety of search engines use diverse web crawlers for obtaining search results proficiently. Some search engines use focused web crawler that collects different web pages that usually satisfy some specific property, by effectively prioritizing the crawler frontier and managing the exploration process for hyperlink. In this paper the main objective of this research is identifying the bottlenecks in the conventional framework. It conceives the experiment architecture which will showcase an improved technique to speed up the crawling process. This will lay the foundation for a future generation of current web. It will expand the horizon of crawling to diversify its application to the industry specific crawling mechanism. Shannon gain algorithm is used to determine the Threshold value of dynamic dataset. Experiments have been conducted on Apriori, Eclat, Declat Algorithms, Proposed hybrid algorithm. The comparative assessment of memory usage reflects the minimal consumption by the hybrid architecture. One of the main features of our proposed Method is its ability to tunnel through pages with a low score.
Graph Learning Under Spectral Sparsity Constraints

Graph inference plays an essential role in machine learning, pattern recognition, and classification. Signal processing based approaches in literature generally assume some variational property of the observed data on the graph. We make a case for inferring graphs on which the observed data has high variation. We propose a new signal processing based inference model and a new learning criterion that allow for wideband frequency variation in the data and derive an algorithm for graph inference. The proposed inference algorithm consists of two steps: 1) learning orthogonal eigenvectors of a graph from the data; 2) recovering the adjacency matrix of the graph topology from the given graph eigenvectors. The first step is solved by an iterative algorithm with a closed-form solution. In the second step, the adjacency matrix is inferred from the eigenvectors by solving a convex optimization problem. Numerical results on synthetic data show the proposed inference algorithm can effectively capture the meaningful graph topology from observed data under the wideband assumption.
Sustaining a Networked Community Resource: Findings from a Longitudinal Situated Display Deployment

Configuring community technology to ensure its sustainability has proved challenging. We present a 3-year longitudinal study and evaluation of two independent situated community display networks in rural contexts. We describe how the design of the display systems evolved to reflect the needs and desires of the community. We report on the way stakeholders' perceptions of the displays changed over time, and examine the community dynamics involved in the administration, maintenance and moderation of the systems. Drawing from our findings, we further explore the role of the community champion and their impact on sustainability and scalability. We provide recommendations for the design of community network display technology that supports democratic inter-community politics and governance, and is sensitive to the hidden emotional labor and social resources that are required from communities to fully adopt and sustain display technology.
Mobile Augmented Reality: Fast, Precise, and Smooth Planar Object Tracking

We propose an innovative method for combining sparse optical flow tracking and descriptor matching algorithms. The proposed approach solves the following problems that are inherent to keypoint-based and optical flow based tracking algorithms: spatial jitter, extreme scale transformation, extreme perspective transformation, degradation in the number of tracking points, and drifting of tracking points. Our algorithm provides smooth object-position tracking under six degrees of freedom transformations with a small computational cost for providing a high-quality real-time AR experience on mobile platforms. We experimentally demonstrate that our approach outperforms the state-of-the-art tracking algorithms while offering faster computational time. A mobile augmented reality (AR) application, which is developed using our approach, delivers planar object tracking with 30 FPS on modern mobile phones for a camera resolution of 1280×720. Finally, we compare the performance of our AR application with that of the Vuforia-based AR application on the same planar objects database. The test results show that our AR application delivers better AR experience than Vuforia in terms of smooth transition of object-pose between video frames.
Computer Security Based Question Answering System with IR and Google BERT

Machine learning perception and question answering is a fundamental errand in natural language processing. As of late, pre-prepared contextual embeddings (PCE) model, bidirectional encoder representations from transformers (BERT) has pulled in loads of consideration because of its incredible execution in a wide scope of natural language processing (NLP) undertakings. In this venture, the BERT model is fine-tuned with extra undertaking question–answer specific layers to improve its exhibition on Stanford Question Answering Dataset (SQuAD 2.0). A closed domain question answering system is developed, which is ‘computer security’ domain-specific for the use of interactive learning which makes learning very exciting. The system has also been extended by adding document retrieval and cache for faster access.
Exploring User Experience of Automatic Documentation Tools

Currently, researches about automatic documentation tools seldom pay attention to user experience. In attempt to explore how existing works consider user experience of automatic documentation tools and user experience challenges automatic documentation tools are faced with, we qualitatively analyze evaluation sections in 21 papers. We find (1) User experience of automatic documentation tools is usually considered as the supplementary for document quality. (2) Automatic documentation tools are faced with three user experience challenges. Developers do not trust the generated documents. High expectations towards automatic documentation tools also hurt user experience. How developers work and acquire information presents another challenge to the design of automatic documentation tools. Apart from identifying three user experience related challenges, this paper also benefits none SE professionals who are interested in automatic documentation tools by selecting representative papers and summarizing how evaluations are conducted.
Training and inference for integer-based semantic segmentation network

Semantic segmentation has been a major topic in research and industry in recent years. However, due to the computation complexity of pixel-wise prediction and backpropagation algorithm, semantic segmentation has been demanding in computation resources, resulting in slow training and inference speed and large storage space to store models. Existing schemes that speed up segmentation network change the network structure and come with noticeable accuracy degradation. However, neural network quantization can be used to reduce computation load while maintaining comparable accuracy and original network structure. Semantic segmentation networks are different from traditional deep convolutional neural networks (DCNNs) in many ways, and this topic has not been thoroughly explored in existing works. In this paper, we propose a new quantization framework for training and inference of segmentation networks, where parameters and operations are constrained to 8-bit integer-based values for the first time. Full quantization of the data flow and the removal of square and root operations in batch normalization give our framework the ability to perform inference on fixed-point devices. Our proposed framework is evaluated on mainstream semantic segmentation networks like FCN-VGG16 and DeepLabv3-ResNet50, achieving comparable accuracy against floating-point framework on ADE20K dataset and PASCAL VOC 2012 dataset.