Reviewerly/exHarmony huggingface dataset:
Deep Real-time Hand Detectoin Using CFPN on Embedded Systems

Real-time HI (Human Interface) systems need accurate and efficient hand detection models to meet the limited resources in budget, dimension, memory, computing, and electric power. In recent years, object detection became a less challenging task with the latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide the desired efficiency and accuracy for HI systems on embedded devices due to their complex time-consuming architecture. In addition, the detection of small hands ( pixels) is still a challenging task for all the above existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) to provide above mentioned performance for small hand detection. The superiority of CFPN is confirmed on a HandFlow dataset with mAP:0.5 of 95.6 and FPS of 33 on Nvidia TX2. The COCO dataset is also used to compare with other state-of-the-art method and shows the highest efficiency and accuracy with the proposed CFPN model. Thus we conclude that the proposed model is useful for real-life small hand detection on embedded devices.
Extracurricular Activities as a Condition for the Intellectual Development of Young Teenagers

The paper introduces the organizational and methodological aspects of extracurricular activities as a necessary condition for the intellectual development of young teenagers, who usually demonstrate a striking imbalance in mathematical, non-science, and technical abilities, as well as a partial decrease in non-verbal abilities. The article describes various psychological and pedagogical approaches to the issue of intellectual abilities. The research objective was to study the forms and methods of organizing extracurricular activities that ensure the intellectual and personality development in 11–13-year-olds. A set of controlled experiments proved their efficiency and demonstrated some intellectual improvement in the test subjects. For instance, the research resulted in an obvious increase in the number of test subjects with a medium level of logical thinking, mathematical skills, speech development, and the ability to compare concepts.
Analyzing Motivation in Personal Software Process Education Course with a Qualitative Approach

Kyushu Institute of Technology offers a class of the PSP (Personal Software Process) training course. One of the problems of the class is low completion rate and professors have been trying to resolve the situation. One of the trials is formalizing the motivation process of the PSP course trainees by using state transition modelling with the state, values of the factors regarding the trainee's motivation and a set of stimuli from the course instructors and environment. Conceptually, instructors can decide effective scenarios for the trainees and develop effective learning environment with the assumption on the state and corresponding state transition function of the trainees. However, it is difficult to develop and analyze such scenarios and environment based on the actual motivation states inside trainees. We use a qualitative approach, GTA with systems engineering modeling in analyzing causal conditions, intervening conditions, action strategies for success scenarios and environment.
The Impulse Outlier Suppression Techniques Using ROAD and VMF for Color Portraits

Due to the immense necessity of admirable electronic paintings, which must be restored from an impulsive outlier, during the last two decades, there are immense researches and developments on outlier restoration algorithms. As a results, this paper proposes the alternative impulsive noise restoration algorithm based on ROAD (Rank-Ordered Absolute Differences) and VMF (Vector Median Filter) for color electronic paintings under an impulsive noise. From theoretical point of view, ROAD is one of the superior outlier allocating and VMF is one of the superior outlier restoration for color electronic painting. By exhaustive experiment, the proposed outlier restoration algorithm is implemented on many standard electronic paintings, which are undermined by an impulsive outlier with many distribution quantities. Moreover, the state-of-art outlier restoration algorithms, for instant SMF (Standard Median Filter) and VMF (Vector Median Filter), are comparatively experimented for exposing the efficiency of the proposed algorithm.
A Probe into the National Customs Clearance Integration Mechanism under "The Belt and Road Initiative"

research-article A Probe into the National Customs Clearance Integration Mechanism under "The Belt and Road Initiative" Share on Authors: Wanpu Wang Shanghai Business School, China Shanghai Business School, ChinaView Profile , Lujin Huang East China University, China East China University, ChinaView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 205Pages 1–8https://doi.org/10.1145/3448734.3448735Published:28 January 2021 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
MFST: Multi-Features Siamese Tracker

Siamese trackers have recently achieved interesting results due to their balance between accuracy and speed. This success is mainly due to the fact that deep similarity networks were specifically designed to address the image similarity problem. Therefore, they are inherently more appropriate than classical CNNs for the tracking task. However, Siamese trackers rely on the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not the optimal choice within the deep similarity framework, as multiple convolutional layers provide several abstraction levels in characterizing an object. Starting from this motivation, we present the Multi-Features Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust deep similarity tracking. MFST proceeds by fusing hierarchical features to ensure a richer and more efficient representation. Moreover, we handle appearance variation by calibrating deep features extracted from two different CNN models. Based on this advanced feature representation, our algorithm achieves high tracking accuracy, while outperforming several state-of-the-art trackers, including standard Siamese trackers.
Five Approaches to Insuring Cyber Risks

Cyber risks are some of the most dangerous risks of the twenty-first century. Many types of businesses, including retail stores, healthcare entities, and financial institutions, as well as government entities, are the targets of cyber attacks. The simple reality is that no computer security system is completely safe. They all can be breached if the hackers are skilled enough and determined. Consequently, the worldwide damages caused by cyber attacks are predicted to reach $10.5 trillion by 2025. Insuring such risks is a monumental task.The cyber insurance market currently is fragmented with hundreds of insurers selling their own cyber risk insurance policies that cover different types of cyber risks. This means the purchasers of cyber insurance must be experts in both insurance and cyber security in order to make a knowledgeable purchase. And, even knowledgeable purchasers of cyber insurance can only obtain limited coverage for cyber risks. This is because the insurance is sold on a named peril, as opposed to all-risk, basis and the policies contain numerous exclusions. Cyber policies also have relatively low policy limits in comparison to other lines of insurance and the enormity of the risks presented. This Article explores ways the cyber insurance market could be improved. In doing so, it analyzes the current cyber insurance market, including the history of cyber insurance and the challenges that insuring cyber risks present. The Article then offers five different approaches to insuring cyber risks moving forward that address many of the problems with the current cyber insurance market. Ultimately, the Article concludes the fifth approach, the novel “All-Risk Private-Public” approach, would be the best one.
Evaluating the Robustness of Question-Answering Models to Paraphrased Questions

Understanding questions expressed in natural language is a fundamental challenge studied under different applications such as question answering (QA). We explore whether recent state-of-the-art models are capable of recognizing two paraphrased questions using unsupervised learning. Firstly, we test QA models’ performance on an existing paraphrased dataset (Dev-Para). Secondly, we create a new paraphrased evaluation set (Para-SQuAD) containing multiple paraphrased question pairs from the SQuAD dataset. We describe qualitative investigations on these models and how they present paraphrased questions in continuous space. The results demonstrate that the paraphrased dataset confuses the QA models and decreases their performance. Visualizing the sentence embeddings of Para-SQuAD by the QA models suggests that all models, except BERT, struggle to recognize paraphrased questions effectively.
WEBSITE AS A MEANS OF ADVERTISING CITIES OF THE WORLD: STRUCTURAL-SEMANTIC AND PRAGMATIC ASPECTS

The article actualizes the study of websites as a means to promoting cities around the world and highlights the main structural and semantic characteristics of advertising sites hosted on Internet platforms. The pragmatic role of a lexical unit of advertising texts is investigated. The concepts of website and advertising text are defined, the main ways of information transfer through advertising text were described. Definitions of the concepts such as advertising, advertising text, strategy, website, and related concepts are provided. After researching the selected advertising texts, the structural and semantic characteristics of advertising on websites, and the main ways of transmitting information, such as verb sentences, simple nominative sentences, and elliptical sentences are analyzed. The pragmatic means of advertising campaigns on websites includes constructions, such as Top + Numeral, Must + V., Let + Inf, and so on. Examples of pragmatic websites are also considered. Samples and purposes of using this approach are given. There are various strategies for advertising and promoting cities and countries around the world on Internet platforms. The most popular and effective are the use of different language techniques (motivational, interrogative, negative sentences) and the strategy of the association. The advertising text is noted to embody a communicative and pragmatic guideline. In the advertising message, there is a presentation of the goods to the target audience. Thus, the main load is the language code, which is aimed at a special category of people. A small number of works on this topic indicate the need for a more detailed study of the issue.
Answer Ranking with Weighted Scores in Indonesian Hybrid Restricted Domain Question Answering System

The increased availability of information on the WWW has made it more challenging to select and filter information that we require. As a result, there is a greater demand for question-answering systems. Question Answering System responds to natural language-based questions from the user. However, traditionally, ranking the answers is based on the availability of the user's query terms in the candidate documents, which results in lower performance. The goal of this paper is to address the problem of ranking answer candidates for factoid-based questions. We improved the Answer Extraction by adding weight and several features and tokens of questions to determine the candidate's answer. We also enhanced the Question Analysis phase by joining phrase queries and boolean combination queries. Based on the results of our experiments, our proposed framework had the best performance of 54%.
Decentralized Domain Authentication - Exploratory Literature Survey

Security has been one of the major issues prevalent in the Internet. To ensure secure, encrypted connections over the Internet, Transport Layer Security (TLS) protocols were established. TLS relies on validating SSL certificates containing public keys of domains and are provided by Certificate Authorities(CAs). However, mis-issued certificates have been used to attack users. To overcome this, Certificate Transparency (CT) was introduced. The centralised nature of CT had several issues, and to overcome them, a decentralised system, IKP was proposed. Although CT and IKP have mitigated certificate authentication issues, they fail to consider domain misbehaviour in using these certificates.
Preliminary Application Analysis of Mixed Teaching Mode in Colleges and Universities Based on MOOC Platform

With the rapid development of information network technology, Internet technology has been widely used in teaching practice. Among them, MOOC is an open online teaching course for the general public. This new online teaching model not only breaks through the limitations of time and space, crosses the boundaries of disciplines, and meets the diverse learning needs of different students, but also changes the shortcomings of the traditional teaching model. Based on the related concepts of MOOC teaching, this article will construct a hybrid teaching mode that combines with classroom teaching. The author of this issue proposes a hybrid teaching implementation process suitable for college teaching. The author hopes that this model will be helpful for cultivating students' independent learning ability and cooperative innovation ability.
Introduction to Second Quantization

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Chaotic Henry Gas Solubility Optimization Algorithm

Meta-heuristic methods have been successfully applied in many real-world optimization problems. Recently, Henry gas solubility optimization algorithm has been introduced which is based on Henry’s law. To improve its solution precision, a new chaotic Henry gas solubility optimization method has been presented in this paper. The proposed variant is validated on 47 benchmark problems of distinct modalities, i.e., unimodal, multi-modal, and fixed-dimension multi-modal, using mean fitness value and standard deviation. The experimental outcomes show the superiority of the proposed method over the existing ones.
Responsiveness Evaluation of Pointing Operations on Active Learning Environment

We focus on facilitator's behavior in group learning and define "Responsiveness" in the pointing environment of active learning. We experimented for evaluating the responsiveness of each pointing devices to record the behavior during pointing in active learning. In the behavior recording experiment, we evaluate the responsiveness of three types of pointing devices. Average response time by Remote Touch Pointing was 2.19 seconds lowest as a result of the experiment. From the results of analysis response time, Remote Touch Pointing is considered to be suitable for pointing operation in the active learning environment.
Enhancing sparse representation of color images by cross channel transformation

Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. The sparsity is firstly considered with respect to the most significant coefficients in the wavelet decomposition of the color image. The discrete cosine transform is singled out as an effective 3 point transformation for this purpose. The enhanced feature is further exploited by approximating the transformed arrays using an effective greedy strategy with a separable highly redundant dictionary. The relevance of the achieved sparsity is illustrated by a simple encoding procedure. On typical test images the compression at high quality recovery is shown to significantly improve upon JPEG and WebP formats.
Jamming Strategy Generation for Hidden Communication Modes Via Graph Convolution Networks

Optimal jamming has important applications in both military and civil communications. There have been a brunch of works investigating the optimal jamming signal design when the signal modes of the opponent are known. In this work, we focus on the less studied hidden mode jamming problem. That is, the jammer has partially recorded the signal modes of the opponent, but there are some hidden modes not revealed to the jammer as of the appearance of these modes. As such, when the hidden modes appear, the jammer has to quickly adapt its jamming strategy to achieve effective jamming. However, it is challenging to do so due to incomplete knowledge of the intrinsic relation between the known and the hidden modes. In this work, a learning-based approach is proposed to attack this problem. Specifically, we custom-devise a jamming network (J-Net) to automatically learn the intrinsic relation among different modes and transfer the jamming strategy from the known modes to the hidden ones. Experimental results demonstrate that the J-Net attains much better jamming effect than pulsed Gaussian jamming and random jamming, and is comparable to the reinforcement learning-based approach, which assumes all the (known and hidden) modes available at the jammer.
Phone Distribution Estimation for Low Resource Languages

Phones are critical components in various computational linguistic fields, for example, phone distributions could be helpful in speech recognition and speech synthesis. Traditional approaches to estimate phone distributions typically involve G2P systems which are either manually designed by linguists or trained on large datasets. These prohibitive requirements make research on low resource languages extremely challenging. In this work, we propose a novel approach to estimate phone distributions by only requiring raw audio datasets: We first estimate the phone ranks by combining language-independent recognition results and Learning to Rank results. Next, we approximate the distribution with Expectation-Maximization by fitting Yule distribution. The results on 7 languages show the joint-model has better performance in both ranking estimation and distribution estimation tasks.
Procedure2Command: an AI-based Nuclear Power Plant Control Command Code Generation Prototype System

Nuclear power plant control systems are complex and require extremely high safety. Safety operation procedure flowcharts drawn by experts with many pages are often used as the guidebooks to help human operators avoid improper operations. To help operators better use these flowcharts and reduce their mental burden, here we propose a control command code generation prototype system, which involves artificial intelligence technologies, especially natural language processing, to automatically translate the nuclear power plant safety operation procedure flowcharts into executable codes. The proposed system includes three components: a flowchart decomposition tool, a text-code translation dataset, and a deep-learning based code generation model. Experimental results show that our proposed method can generate command codes with comparable accuracy as manual translation in a much higher inference speed.
Practical Idiomatic Considerations for Checkable Meta-logic in Experimental Functional Programming

Implementing a complex concept as an executable model in a strongly typed, purely functional language hits a sweet spot between mere simulation and formal specification. For research and education it is often desirable to enrich the algorithmic code with meta-logical annotations, variously embodied as assertions, theorems or test cases. Checking frameworks use the inherent logical power of the functional paradigm to approximate theorem proving by heuristic testing. Here we propose several novel idioms to enhance the practical expressivity of checking, namely meta-language marking, nominal axiomatics, and constructive existentials. All of these are formulated in literate Haskell’98 with some common language extensions. Their use and impact are illustrated by application to a realistic modeling problem.
A Triplet Appearance Parsing Network for Person Re-Identification

As one of the specific vision tasks, person re-identification has become a prevalent research topic in the field of multimedia and computer vision. However, existing feature extraction methods, originating from the quality of the bounding boxes which could cause the inhomogeneity and incoherence of person representation for cluttered backgrounds, are difficult to adapt the challenges of the harsh real-world scenarios. This study develops a Triplet person Appearances Parsing Framework (TAPF) which eliminates the surrounding interference factors of bounding boxes for person re-identification. The framework consists of a triplet person parsing network and an integration mechanism for person local and global appearance information. Concretely, the triplet parsing network includes a channel parsing module, a position parsing module and a color parsing module, which are used to extract the person channel parsing descriptor, regional descriptor and color perception descriptor, respectively. Then, a local and global flatten gaussian operations are performed to integrate the person appearance parsing descriptors to obtain more discriminative features for the person representation. The experimental results have been conducted to validate our proposed algorithm can achieve a better performance for person re-identification on several public datasets, i.e., VIPeR and Market-1501, respectively.
Published or Perished: Harnessing Consolidated Procurement of National Reference Books

The procurement of goods and services is generally carried out in isolation without coordination and integration in the process. Consequently, a number of issues may occur, such as the high number of procurement packages, high administrative cost, and high purchasing cost. Strategies are needed to address those issues, and one potential strategy is consolidated procurement of goods/services. Consolidated procurement has successfully been carried out by the Government of Indonesia through the National Public Procurement Agency (LKPP) for the procurement of national textbooks for the primary, junior high, and senior high schools. This paper reports on the implementation, challenges, and benefits at every stage of the consolidated procurement of the national textbooks in Indonesia.This study employed a qualitative approach and method by interviewing informants selected through purposive sampling. The informants in this research came from the Ministry of Education and Culture, the National Public Procurement Agency (LKPP), and book publishers as procurement vendors. Data analysis was conducted through open, selective, and axial coding whereby data from different informants were triangulated. The findings suggested that the consolidated procurement of the national textbooks, which was conducted in three phases, namely planning, tendering, and contract delivery, involved several strategies and faced a number of challenges. Studies on and practice of consolidated procurement of goods/services in Indonesia remain limited. Therefore, this article is expected to serve as a reference for future studies and for practitioners/government who wish to conduct consolidated procurement. Keywords —  Consolidated procurement, Challange of consolidated procurement, Advantage of consolidated procurement, national textbooks, procurement of goods/services in Indonesia,