Reviewerly/exHarmony huggingface dataset:
Machine Learning on Volatile Instances: Convergence, Runtime, and Cost Tradeoffs

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes. However, running distributed SGD can be prohibitively expensive because it may require specialized computing resources such as GPUs for extended periods of time. We propose cost-effective strategies to exploit volatile cloud instances that are cheaper than standard instances, but may be interrupted by higher priority workloads. To the best of our knowledge, this work is the first to quantify how variations in the number of active worker nodes (as a result of preemption) affect SGD convergence and the time to train the model. By understanding these trade-offs between preemption probability of the instances, accuracy, and training time, we are able to derive practical strategies for configuring distributed SGD jobs on volatile instances such as Amazon EC2 spot instances and other preemptible cloud instances. Experimental results show that our strategies achieve good training performance at substantially lower cost.
Adaptive trainer for preparing students for math exams

Due to the specificity of organizing the learning process in school classes, often students do not have enough time for a practice to solve tasks (like a math or in similar theme) under the supervision of a teacher. In this case, using the services of tutors or selftraining. In recent years, online educational services have become most popular for self-learning. This approach has some limitations, one of which is the inability to individually configure for a specific user. The presented project is aimed at automating the process of e-learning, regarding the acquisition of practical skills in solving mathematical tasks, determining the level of knowledge and reducing the duration of training by reducing the number of tasks depending on the level of training (adaptability). An adaptive testing approach has been implemented to satisfy these requirements. The created Markov models of adaptive testing became the basis for the development of an adaptive trainer for teaching nonformalized skills and abilities necessary for solving mathematical and other problems of sufficiently high complexity that require mastering both the standard technique of constructing reasoning and elements of creative thinking. A web service has been developed to demonstrate how it works. First of all, an adaptive trainer is considered the most effective in cases where it is necessary to order the knowledge and skills, such as to prepare for exams when solving non-formalized tasks (mathematical, technical, algorithmic, etc.). This service is not a substitute for a teacher, but rather complements the existing learning process, expanding the possibilities for self-study.
Fostering Science Teaching and Learning in a Multicultural Environment Through the Culturo-Techno-Contextual Approach

The multicultural science classroom provides new vistas of opportunities for teachers and students to experiment and innovate. This chapter describes the emergence, after about four decades of experimentation in multicultural science classrooms, of the culturo-techno-contextual approach (CTCA) for breaking barriers to meaningful learning of science. CTCA is now being piloted in several African countries. It is a method that is set within the cultural context of the learner and recognizes the increasing use of technology in African science classrooms, a development that will pervade the environment and shape teaching and learning far into the twenty-first century. Beginning with a brief description of CTCA, the chapter provides step-by-step guide to using CTCA. A review of empirical studies which explored the impact of CTCA on students' performance in science is reported, and a special focus was given to the indigenous knowledge and cultural practice elements of CTCA. The chapter also highlights the development, deployment, and guide to the use of the CTCA mobile app which is now available for STEM teachers and students all over the world. The concluding section forecasts the future of CTCA in the desire to improve science teaching and learning especially in multicultural classrooms.
Research on Virtual Exhibition System Platform of Museum Based on VR Technology

With the rapid development of information technology, mobile internet technology and the increasing popularity of intelligent terminals, the digital, networked, virtualized and mobile information age is quietly changing the way people get information, and digital museums have emerged. VR (Virtual Reality) technology is to use computer simulation to generate a three-dimensional virtual world, providing users with the simulation of visual, auditory and tactile senses. The user can observe things in three-dimensional space in time and without restriction. Traditional museum exhibition is facing the impact of new media technology, which can no longer meet the increasing demand of the audience for museums. Based on this, this paper takes VR technology to realize interactive display of virtual museum as the research content, analyzes and discusses the present situation of interactive display of virtual museum, puts forward the problem of lack of interactive display in current virtual museum display, and puts forward the idea of building a museum virtual display system platform based on VR technology.
A segmentation based approach for improved detection of ships from SAR images using deep learning models

Satellite image processing is one of the rapidly emerging fields in research today. Several objects such as buildings, roads, ships, boats etc. extracted from satellite images can be used further in applications involving tracking and surveillance. In sea and ocean images, ships can be found in different sizes which can complicate the task of finding a pattern or some regularity. It is comparatively easier to identify a ship in a homogenous environment rather than a heterogenous environment that consists of elements such as coasts, harbor, vessel, rocks, islands etc. This paper focuses on ship detection from Synthetic Aperture Radar images using deep learning models like CNN, ResNet, VGG and DenseNet. A segmentation approach that eliminates the noisy regions in the image is proposed in this paper. Performance of the different models with and without segmentation were evaluated based on F2-score. The ResNet model with segmentation has outperformed other models with an F2-score of 0.86.
Magic-hand: Turn a smartwatch into a mouse

We present the design, implementation and evaluation of a smartwatch-based, freehand human–computer interaction system, called Magic-Hand, which consists of two modules: the operation detection module and the motion tracking module. In the operation detection module, five gestures are selected to represent typical operations including right-click, left-click and double-click, and we carefully design the detection algorithm to identify gestures even when the hand is in motion. In the motion tracking module, we map the movement of the hand to the screen cursor movement with the Sigmoid function. We verify the performance of Magic-Hand by comparing it with two existing human–computer interaction techniques: the mouse and the AirMouse. The experimental results show that the gesture recognition in the operation detection module can reach an accuracy of 95%, and the motion tracking is comparable with the benchmarks. Our work facilitates the use of the smartwatch as an effective interaction device in ubiquitous display environments.
A Greedy Control Policy for Latency and Energy Constrained Wireless Federated Learning

For federated learning, each device participates in the model learning in a collaborative training manner. Due to the constraint of delay and energy consumption in actual wireless environment, resource allocation is essential for the convergence speed of federated learning. This paper analyzes the convergence bound of federated learning from a theoretical perspective, based on which, we propose a greedy control policy that combines aggregation frequency control and device scheduling together. The proposed policy minimizes the loss of model training under a given time and energy budget with a greedy strategy which eliminates the device with the worst performance gain in each step. Simulation results show that under different wireless environments, the proposed global control policy achieves higher accuracy than the commonly used federated learning algorithms and has a good robustness to non-i.i.d. data.
Prediction of PV Solar Panel Output Characteristics Using a Multilayer Artificial Neural Network (MLANN)

Abstract In this paper, PV solar collector was tested experimentally from 1st July to 31th August 2018 between 7:00 am and 6:00 pm under the weather conditions of Iraq. The PV output power was calculated by using the measured data of voltage and current obtained from experiments. To predict the PV solar output characteristics, four structures of a multilayer artificial neural network MLANN with Error Back-Propagation EBP were designed in MATLAB software. The MLANN structures have two inputs (temperature and irradiance) and three outputs (voltage, current and power). From experiment tests, a dataset of 434 hourly points was collected to investigate the structures of MLANN model. A 70% of the data used for training stage and 30% was distributed between the testing and validating stages. From test stage, the average of output value was taken for 14 numbers of data to compare with experimental values. The MLANN results show that the structures 2-4-4-1, 2-1-1-1 and 2-5-5-1 were the optimum testing model in the voltage, current and power output respectively with high accuracy and good agreement with experimental results.
Channel Estimation using Conventional Methods and Deep Learning

Despite the evolution of wireless communications, there are some undesirable effects on the signal transmitted through a wireless channel resulting from its physical characteristics. Thus, signals arriving at the receiver are attenuated, distorted, and delayed. Channel estimation is an important part of receivers. A perfect channel estimation compensates for the effects of the channel and restores the original signals. This paper aims at implementing different channel estimation techniques like Least squares estimation, Minimum mean square estimation. Also equalization techniques like Adaptive equalizer - LMS, CMA equalizer and MLSE are analyzed. The implementation is done initially using MATLAB software and then using GNU Radio. Deep learning is also explored for channel estimation in Orthogonal Frequency Division Multiplexing in MATLAB.
A Pilot Study on Extending the SUS Survey: Early Results

The System Usability Scale (SUS) is a popular method to measure the subjective usability of a system, due largely to the simplicity and rapidity of both collecting and analyzing data. A drawback is that the SUS generates a single unidimensional usability score from 0-100. Several researchers have amassed larger datasets across multiple projects to allow for analysis on additional methods to glean insights from the SUS survey. Along these lines, we investigate the practical value of extending the SUS survey with additional items such as open text responses, and test underlying assumptions of how SUS results are interpreted. We found that while a lower SUS score does generally correlate to a stronger desire to modify the system, people generally want to make modifications to a system regardless of its usability. Further, we found that the amount of user feedback related to modifications to a system provided predicted subjective usability ratings.
When Worlds Collide: AI-Created, Human-Mediated Video Description Services and the User Experience

This paper reports on a user-experience study undertaken as part of the H2020 project MeMAD (‘Methods for Managing Audiovisual Data: Combining Automatic Efficiency with Human Accuracy’), in which multimedia content describers from the television and archive industries tested Flow, an online platform, designed to assist the post-editing of automatically generated data, in order to enhance the production of archival descriptions of film content. Our study captured the participant experience using screen recordings, the User Experience Questionnaire (UEQ), a benchmarked interactive media questionnaire and focus group discussions, reporting a broadly positive post-editing environment. Users designated the platform’s role in the collation of machine-generated content descriptions, transcripts, named-entities (location, persons, organisations) and translated text as helpful and likely to enhance creative outputs in the longer term. Suggestions for improving the platform included the addition of specialist vocabulary functionality, shot-type detection, film-topic labelling, and automatic music recognition. The limitations of the study are, most notably, the current level of accuracy achieved in computer vision outputs (i.e. automated video descriptions of film material) which has been hindered by the lack of reliable and accurate training data, and the need for a more narratively oriented interface which allows describers to develop their storytelling techniques and build descriptions which fit within a platform-hosted storyboarding functionality. While this work has value in its own right, it can also be regarded as paving the way for the future (semi)automation of audio descriptions to assist audiences experiencing sight impairment, cognitive accessibility difficulties or for whom ‘visionless’ multimedia consumption is their preferred option.
Data Augmentation in Training Neural-Network Language Model for Ontology Population

This paper is a continuation of the research focused on solving the problem of ontology population using training on an automatically generated training set and the subsequent use of a neural-network language model for analyzing texts in order to discover new concepts to add to the ontology. The article is devoted to the text data augmentation - increasing the size of the training set by modification of its samples. Along with this, a solution to the problem of clarifying concepts (i.e. adjusting their boundaries in sentences), which were found during the automatic formation of the training set, is considered. A brief overview of existing approaches to text data augmentation, as well as approaches to extracting so-called nested named entities (nested NER), is presented. A procedure is proposed for clarifying the boundaries of the discovered concepts of the training set and its augmentation for subsequent training a neural-network language model in order to identify new concepts of ontology in the domain texts. The results of the experimental evaluation of the trained model and the main directions of further research are considered.
Analysis of Counterfeits Using Color Models for the Presentation of Painting in Virtual Reality

The black market for counterfeit works of art is the third most lucrative in the world. The digitalization of art and introduction into the virtual environment allows for a high rate of these counterfeit works. The issue of verifying original works in the online environment currently brings new challenges in the field of forensic science, in particular fields of color vision and working with light, colors, and color models, thus opening new approaches of art verification and presentation. This article deals with image processing and digitalization in color fields and their definition in color spaces. In order to determine a color model of a digitally processed image and for a specific color used, authentic artwork is analyzed by graphic software. This text aims to find and define the same color tone with the actual color tone of the original artwork in the digital environment and color spaces. The definition of color can be an attribute for verifying the originality of a work of art in a digital environment. At present, a precise definition of the color of a painting can help to achieve more accurate reproduction of artwork in a virtual reality environment and its visual perception by the user.
Embodying the Number of an Entity’s Relations for Knowledge Representation Learning

Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information — the number of an entity’s relations which means the number of an entity’s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity’s relations between entities into the entities’ embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity’s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.
The Influence of Team Workload Demands During a Cyber Defense Exercise on Team Performance

Cyber defense is dependent on individual functioning as well as teamwork. Research has identified teamwork factors, such as communication and coordination, that increase performance, while more supporting behaviours, i.e. emotional and team support, have shown to inhibit team performance but currently the understanding of impact workloads have on performance is still limited. This study investigated the role of team workload demands on team performance.Methods: Data was collected during the Norwegian Defense Cyber Academy’s annual Cyber Defense Exercise. We investigated how team workload demands influenced performance outcomes (Team Effectiveness and Team Dissatisfaction).Results: Teamwork demands, i.e. communication, coordination, and team performance monitoring could predict Team Effectiveness, while Task-team workload demands, i.e. team support, team emotion, and time-share demands could predict Team Dissatisfaction.Discussion: Results support the hypotheses that teamwork demands could predict team effectiveness while task-team demands could predict more negative aspects of team performance and the results are in line with previous research.Future research in cyber security operations should incorporate team workload demands to assess performance.
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 9 June 2020Accepted: 06 July 2021Published online: 08 November 2021Keywordsgenerative modeling, manifold, GANs, scientific ML, unsupervisedAMS Subject Headings68T99, 90C47, 91A40Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
A Critical Review on Service Oriented Architecture and its Maintainability

Over the last decade, migration methodologies from Legacy to SOA design have been broadly explored. Language migration, database migration, and platform migration were the primary issues with legacy systems. Legacy systems are a time-consuming and costly process for maintaining application and programming source code. It is difficult to change a single module of the software system without affecting other modules. SOA, on the other hand, enables organizations to manage business processes, business rules, enterprise content management, and management security. In this study A survey is carried out on the SOA maintainability challenges namely Analyzability, Changeability, Stability and Testability.
Beyond neighbourhood-preserving transformations for quantization-based unsupervised hashing

An effective unsupervised hashing algorithm leads to compact binary codes preserving the neighborhood structure of data as much as possible. One of the most established schemes for unsupervised hashing is to reduce the dimensionality of data and then find a rigid (neighborhood-preserving) transformation that reduces the quantization error. Although employing rigid transformations is effective, we may not reduce quantization loss to the ultimate limits. As well, reducing dimensionality and quantization loss in two separate steps seems to be sub-optimal. Motivated by these shortcomings, we propose to employ both rigid and non-rigid transformations to reduce quantization error and dimensionality simultaneously. We relax the orthogonality constraint on the projection in a PCA-formulation and regularize this by a quantization term. We show that both the non-rigid projection matrix and rotation matrix contribute towards minimizing quantization loss but in different ways. A scalable nested coordinate descent approach is proposed to optimize this mixed-integer optimization problem. We evaluate the proposed method on five public benchmark datasets providing almost half a million images. Comparative results indicate that the proposed method mostly outperforms state-of-art linear methods and competes with end-to-end deep solutions.
MPC-Friendly Commitments for Publicly Verifiable Covert Security

We address the problem of efficiently verifying a commitment in a two-party computation. This addresses the scenario where a party P1 commits to a value x to be used in a subsequent secure computation with another party P2 that wants to receive assurance that P1 did not cheat, i.e. that x was indeed the value inputted into the secure computation. Our constructions operate in the publicly verifiable covert (PVC) security model, which is a relaxation of the malicious model of MPC, appropriate in settings where P1 faces a reputational harm if caught cheating.
<scp>Real‐time</scp> traffic anomaly detection based on Gaussian mixture model and hidden Markov model

Abstract With the rapid development and wide application of visual sensor technology and image processing technology, the traffic anomaly events detection methods of intelligent transportation system (ITS) are being constantly updated. Traditional machine learning methods do not require high computing power but lack high accuracy, while deep learning methods provide high accuracy but demand of high computing power for edge computing hardware. To improve accuracy and real‐time performance of detecting abnormal traffic events in edge computing hardware, a novel traffic anomaly detection model was developed in the following steps. In the first moving object tracking stage, a hybrid model was designed by combining Gaussian mixture model (GMM) with hidden Markov model, and optimized the tracking accuracy of target trajectories in multiframe images from traffic surveillance video. In the second abnormal events classification stage, principal component analysis was applied to reduce the dimension of the trajectories features, and then the abnormal traffic behaviors were classified by K‐Nearest Neighbor. Experiments demonstrate that the model can achieve higher detection accuracy than GMM method as well as a faster detection speed than You only look once deep learning method in moving object tracking stage.