Reviewerly/exHarmony huggingface dataset:
Impact of digital services of hybrid cloud-based learning environment on efficiency of education

This paper aims to analyse the impact of the hybrid cloud-based learning environment providing resources and services to implement interdisciplinary projects in the course of studies on the efficiency of education. A combination of modern methods of pedagogical studies was applied to address the set problems. The authors used theoretical methods, such as analysis, generalisation, comparison, synthesis, and systematisation of research literature and teaching aids providing visibility in terms of the current outlook of the analysed problem and developing and introducing a hybrid cloud-based learning environment to support interdisciplinary projects. The authors also used empirical methods, such as pedagogical experiments, to determine the impact of digital services in the hybrid cloud-based learning environment on students' performance in each discipline of interdisciplinary projects and their profiles in the course of the project. For numeric processing of research findings and statistical tests for objectivity, the authors used methods of mathematical statistics. The case is made for using cloud computing in the education of future professionals. Experimental analysis is conducted to determine the impact of the hybrid cloud-based learning environment providing resources and services to implement interdisciplinary projects in the course of studies on the efficiency of education. The positive impact is shown for the hybrid cloud-based learning in terms of students' progress. The results can make the basis for developing and introducing a hybrid cloud-based learning environment to support interdisciplinary projects. The scientific novelty of this study is underscored by the analysis of the impact of digital services of the hybrid cloud-based learning environment on the efficiency of educating future professionals in the course of an interdisciplinary project.
Driver Alert System Using Deep Learning and Machine Learning

The rising number of road injuries is one of the most pressing challenges facing the world today.One of the main causes of traffic accidents were unsafe and inattentive driving.Drowsiness or a loss of focus on the part of the driver is believed to be a significant factor in such incidents.Driver drowsiness tracking research can assist in the reduction of road accidents.This journal presents a good approach for applying a driver's sleepiness alarm system that uses Machine Learning and Deep Learning techniques to identify and track the driver's yawning and sleepiness.For face detection and recognition, the device utilises the Histogram Centred Gradient (HOG) function descriptor, which is widely used in image processing.The SVM is then used to determine if the image being identified is a face or not.It also checks the driver's Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) up to a certain countable frames to see whether he or she is sleepy or yawning.Since the driver's drowsiness or fatigue is proportional to the number of hours spent behind the wheel, an extra element for changing theface and mouth reference frames have been added.This increases the sensitivity to detect drowsiness.This also necessitates the introduction of face recognition so that each driver can be tracked individually.This Project aims to provide a Driver Alert System consisting of three sections Face Recognition to unlock vehicle, traffic light detection and Drowsiness alert system.
Content Characteristics of Knowledge Integration in the eHealth Field: An Analysis Based on Citation Contexts

Abstract Purpose This study attempts to disclose the characteristics of knowledge integration in an interdisciplinary field by looking into the content aspect of knowledge. Design/methodology/approach The eHealth field was chosen in the case study. Associated knowledge phrases (AKPs) that are shared between citing papers and their references were extracted from the citation contexts of the eHealth papers by applying a stem-matching method. A classification schema that considers the functions of knowledge in the domain was proposed to categorize the identified AKPs. The source disciplines of each knowledge type were analyzed. Quantitative indicators and a co-occurrence analysis were applied to disclose the integration patterns of different knowledge types. Findings The annotated AKPs evidence the major disciplines supplying each type of knowledge. Different knowledge types have remarkably different integration patterns in terms of knowledge amount, the breadth of source disciplines, and the integration time lag. We also find several frequent co-occurrence patterns of different knowledge types. Research limitations The collected articles of the field are limited to the two leading open access journals. The stem-matching method to extract AKPs could not identify those phrases with the same meaning but expressed in words with different stems. The type of Research Subject dominates the recognized AKPs, which calls on an improvement of the classification schema for better knowledge integration analysis on knowledge units. Practical implications The methodology proposed in this paper sheds new light on knowledge integration characteristics of an interdisciplinary field from the content perspective. The findings have practical implications on the future development of research strategies in eHealth and the policies about interdisciplinary research. Originality/value This study proposed a new methodology to explore the content characteristics of knowledge integration in an interdisciplinary field.
Image Classification for Binary Classes Using Deep Convolutional Neural Network: An Experimental Study

Convolutional neural networks (CNNs) have proved itself a well-built model for image recognition in these modern computing days. Inclined by CNN's successes, we present an elaborative experimental assessment of CNN on image classification using a newly fabricated dataset of high-resolution images belonging to two different classes. The dataset partitioned into two distinct categories of high-resolution images of cats and dogs. This chapter presents an extensive experimental study of training size on training and validation accuracy and loss. We designed a fine-tuned predictive two-class image classification model for a large training size, which achieved a training accuracy of 100%, with validation accuracy close to 99.13%.
Intelligent Agent-based Technique For Virtual Machine Resource Allocation For Energy-Efficient Cloud Data Centres

In recent years, cloud computing technology has emerged as a promising solution to many medium and small scale business enterprise. It leverages cost, encourages collaboration, and it is so flexible to use. Despite its attractive features, it still has some challenging issues such as power consumption rate. Although significant efforts have been devoted to optimising power usage through scheduling and virtualisation, it though remains a challenging problem. This paper introduces a new model on how to reduce the power consumption during live virtualisation and schedule based on intelligent mobile agent technology. The simulation results show that the proposed algorithm minimises power consumption used in the data centre at each stage by more than 30% compared to the current state-of-the-art techniques.
Moodle e-course management system as a tool for holding distance Olympiads at university

The article reveals the functionality of the e-courses management system Moodle as a tool for holding Olympiads in a distance format. The technology of development of tasks for the Olympiad is described, taking into account their implementation in Moodle, and two ways of presenting tasks are shown: 1) as a single test with a given number of questions and with different forms of test questions; 2) in the form of several separate tests, distributed over course modules. It is substantiated that the choice of the way of presenting tasks depends on the specifics of the Olympiad and on the presence/absence of the need to divide tasks into thematic modules. A description of the types of test questions that can be used for the tasks of the Olympiad is given: open form, closed form, multiple choice, essays, etc. As an example, the stages of developing an Olympiad in information security for students of pedagogical university are described. The system of tasks of the Olympiad is given and it is shown how to present these tasks in Moodle for automatic and manual checking. The technology described in the article allows the development of distance Olympiads for both students and schoolchildren. Students of pedagogical university were selected as the target audience. The procedure for evaluating the Olympiad tasks completed by students in manual and automated form is demonstrated with specific examples.
Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition

Matching infrared (IR) facial probes against a gallery of visible light faces remains a challenge, especially when combined with cross-distance due to deteriorated quality of the IR data. In this paper, we study the scenario where visible light faces are acquired at a short standoff, while IR faces are long-range data. To address the issue of quality imbalance between the heterogeneous imagery, we propose to compensate it by upgrading the lower-quality IR faces. Specifically, this is realized through cascaded face enhancement that combines an existing denoising algorithm (BM3D) with a new deep-learning-based deblurring model we propose (named SVDFace). Different IR bands, short-wave infrared (SWIR) and near-infrared (NIR), as well as different standoffs, are involved in the experiments. Results show that, in all cases, our proposed approach for quality balancing yields improved recognition performance, which is especially effective when involving SWIR images at a longer standoff. Our approach outperforms another easy and straightforward downgrading approach. The cascaded face enhancement structure is also shown to be beneficial and necessary. Finally, inspired by the singular value decomposition (SVD) theory, the proposed deblurring model of SVDFace is succinct, efficient and interpretable in structure. It is proven to be advantageous over traditional deblurring algorithms as well as state-of-the-art deep-learning-based deblurring algorithms.
PornNet: A Unified Deep Architecture for Pornographic Video Recognition

In the era of big data, massive harmful multimedia resources publicly available on the Internet greatly threaten children and adolescents. In particular, recognizing pornographic videos is of great importance for protecting the mental and physical health of the underage. In contrast to the conventional methods which are only built on image classifier without considering audio clues in the video, we propose a unified deep architecture termed PornNet integrating dual sub-networks for pornographic video recognition. More specifically, with image frames and audio clues extracted from the pornographic videos from scratch, they are respectively delivered to two deep networks for pattern discrimination. For discriminating pornographic frames, we propose a local-context aware network that takes into account the image context in capturing the key contents, whilst leveraging an attention network which can capture temporal information for recognizing pornographic audios. Thus, we incorporate the recognition scores generated from the two sub-networks into a unified deep architecture, while making use of a pre-defined aggregation function to produce the whole video recognition result. The experiments on our newly-collected large dataset demonstrate that our proposed method exhibits a promising performance, achieving an accuracy at 93.4% on the dataset including 1 k pornographic samples along with 1 k normal videos and 1 k sexy videos.
Persona2vec: a flexible multi-role representations learning framework for graphs

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec , a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.
Construction of Guideline-Based Decision Tree for Medication Recommendation

Clinical decision support system (CDSS) plays an essential role nowadays and CDSS for treatment provides clinicians with the clinical evidence of candidate prescriptions to assist them in making patient-specific decisions. Therefore, it is essential to find a partition of patients such that patients with similar clinical conditions are grouped together and the preferred prescriptions for different groups are diverged. A comprehensive clinical guideline often provides information of patient partition. However, for most diseases, the guideline is not so detailed that only limited circumstances are covered. This makes it challenging to group patients properly. Here we proposed an approach that combines clinical guidelines with medical data to construct a nested decision tree for patient partitioning and treatment recommendation. Compared with pure data-driven decision tree, the recommendations generated by our model have better guideline adherence and interpretability. The approach was successfully applied in a real-world case study of patients with hyperthyroidism.
Strongly regular graphs from reducible cyclic codes

Let p be a prime number. Reducible cyclic codes of rank 2 over $$\mathbb {Z}_{p^m}$$ are shown to have exactly two Hamming weights in some cases. Their weight distribution is computed explicitly. When these codes are projective, the coset graphs of their dual codes are strongly regular. The spectra of these graphs are determined.
PMS-Sorting: A New Sorting Algorithm Based on Similarity

Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm, it is mainly suitable for the high duplication of search results, for the independent search results, the effect is not ... | Find, read and cite all the research you need on Tech Science Press
Data-centric disambiguation for data transformation with programming-by-example

Programming-by-example (PBE), can be a powerful tool to reduce manual work in repetitive data transformation tasks. However, few examples often leave ambiguity and may cause undesirable data transformation by the system. This ambiguity can be resolved by allowing the user to directly edit the synthesized programs; however, this is difficult for non-programmers. Here, we present a novel approach: data-centric disambiguation for data transformation, where users resolve the ambiguity in data transformation by examining and modifying the output rather than the program. The key idea is to focus on the given set of data the user wants to transform instead of pursuing the synthesized program's generality or completeness. Our system provides visualization and interaction methods that allow users to efficiently examine and fix the transformed outputs, which is much simpler than understanding and modifying the program itself. The user study suggests that our system can successfully help non-programmers to more easily and efficiently process data.
A Novel Design Approach Exploiting Data Parallelism in Serverless Infrastructure

Serverless computing has emerged as a new application design and execution model. The serverless application is decomposed into granular logical functional units that run on small, low cost, and short-lived compute containers. These containers are dynamically managed by FaaS service providers. Users are charged only for the compute and storage resources needed for the execution of their piece of code. Cloud functions have restrictions on memory usage and execution time-out as imposed by their service providers. Due to this limitation, compute intensive tasks time-out before their completion and hence unable to harness the power of serverless computing. In this paper, we propose a design approach for serverless applications. It exploits data parallelism in embarrassingly parallel computations. Using our approach, compute bound tasks that are implemented in conventional design and fail in serverless environment can get executed successfully without worrying about the limitations imposed by serverless platforms. For this, several extensive experimentations using Amazon’s AWS Lambda service have been performed. Further, a serverless application designed using our approach exploits the auto-scalability feature of serverless computing to achieve faster execution benefit.
Pseudoinverse graph convolutional networks

Abstract Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised classification on graph-based datasets. We propose a new GCN variant whose three-part filter space is targeted at dense graphs. Our examples include graphs generated from 3D point clouds with an increased focus on non-local information, as well as hypergraphs based on categorical data of real-world problems. These graphs differ from the common sparse benchmark graphs in terms of the spectral properties of their graph Laplacian. Most notably we observe large eigengaps, which are unfavorable for popular existing GCN architectures. Our method overcomes these issues by utilizing the pseudoinverse of the Laplacian. Another key ingredient is a low-rank approximation of the convolutional matrix, ensuring computational efficiency and increasing accuracy at the same time. We outline how the necessary eigeninformation can be computed efficiently in each applications and discuss the appropriate choice of the only metaparameter, the approximation rank. We finally showcase our method’s performance regarding runtime and accuracy in various experiments with real-world datasets.
Revisiting the Tag Relevance Prediction Problem

Traditionally, recommender systems provide a list of suggestions to a user based on past interactions with items of this user. These recommendations are usually based on user preferences for items and generated with a delay. Critiquing recommender systems allow users to provide immediate feedback to recommendations with tags and receive a new set of recommendations in response. However, these systems often require rich item descriptions that contain relevance scores indicating the strength, with which a tag applies to an item. For example, this relevance score could indicate how violent the movie "The Godfather" is on a scale from 0 to 1. Retrieving these data is a very demanding process, as it requires users to explicitly indicate the degree to which a tag applies to an item. This process can be improved with machine learning methods that predict tag relevance. In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs. In particular, we define the tag relevance prediction problem, explore the inconsistency of relevance scores provided by users as a challenge of this problem and present a method, which outperforms the state-of-the-art method for predicting tag relevance. We found a moderate inconsistency of user relevance scores. We also found that users tend to disagree more on subjective tags, such as "good acting", "bad plot" or "quotable" than on objective tags, such as "animation", "cars" or "wedding", but the disagreement of users regarding objective tags is also moderate.
Conversational Agent for Daily Living Assessment Coaching Demo

Conversational Agent for Daily Living Assessment Coaching (CADLAC) is a multi-modal conversational agent system designed to impersonate "individuals" with various levels of ability in activities of daily living (ADLs: e.g., dressing, bathing, mobility, etc.) for use in training professional assessors how to conduct interviews to determine one's level of functioning.The system is implemented on the Mind-Meld platform for conversational AI and features a Bidirectional Long Short-Term Memory topic tracker that allows the agent to navigate conversations spanning 18 different ADL domains, a dialogue manager that interfaces with a database of over 10,000 historical ADL assessments, a rule-based Natural Language Generation (NLG) module, and a pre-trained open-domain conversational sub-agent (based on GPT-2) for handling conversation turns outside of the 18 ADL domains.CADLAC is delivered via state-of-the-art web frameworks to handle multiple conversations and users simultaneously and is enabled with voice interface.The paper includes a description of the system design and evaluation of individual components followed by a brief discussion of current limitations and next steps.
META-HEURISTICS TECHNIQUES IN CLOUD COMPUTING: APPLICATIONS AND CHALLENGES

Cloud computing has emerged as highly demanding technology in recent years.The transformation of data and services towards cloud has reduced the increased expenditure on hardware and software in the market.The integration of cloud with many technologies like mobile, Internet of Things, etc. has brought new challenges in cloud computing.Researchers have applied various swarm intelligence, nature inspired, and hybrid algorithms to find a pare-to optimal solution for them.State of art optimization algorithms that are applied in solving these problems is presented in this paper.The applications of these algorithms in load balancing, scheduling, resource allocation, virtual machine allocation, and placement have been discussed and analyzed in cloud computing.The impact of these algorithms on quality of service is also analyzed to present some valuable suggestion.
COVID-19 and the National Library of South Africa: Adapting to the new normal

The National Library of South Africa (NLSA) like many libraries and institutions has been impacted by the COVID-19 pandemic. However, its approach has been that of deciding to embrace the change, adapting to an approach of working with stakeholders and focusing on how the future can best be forecasted so that the work done now can have a greater impact. The NLSA shares its experiences, learnings and approaches for this new normal and hopes this sheds light on how national, and all, libraries can adopt a culture of continuous growing and adapting whether in a pandemic or not.
Research on Path Planning Algorithm Based on Dimensionality Reduction Method and Improved RRT

Autonomous underwater vehicle (AUV) has important significance for understanding, researching and developing the ocean. The research of path planning plays an important role in the safety of AUV and the improvement of work efficiency. Aiming at the complex underwater environment, irregular obstacles and planning difficulties, this paper proposes a dimension reduction path planning method suitable for three-dimensional space path planning. The space is expressed by the combination of horizontal stratification and longitudinal cutting, which simplifies the environmental model and improves the dimensionality disaster of path planning and long planning time. Furthermore, the inverse pruning and node reconstruction methods are used to further optimize the shortening path, which improves the practical value of the path. And the algorithm is fast, which reduces the energy consumption during the AUV travel. The simulation results show that the design path planning method is simple and effective, and it is suitable for complex underwater environment.