Reviewerly/exHarmony huggingface dataset:
An Investigation of 2D Keypoints Detection on Challenging Scenarios Using Depthwise Separable Convolutions: A Hand Pose Estimation Case Study

2D keypoints detection is a computer vision task applicable to several fields such as hand, face, and body tracking, which provides useful information for spatial analytics, gestural interactions, and augmented reality applications. This work investigates the usage of depthwise separable convolutions (an optimized convolution operation) to speed up the inference time on a largely used architecture for 2D keypoints estimation. We evaluate the impacts on the precision and performance of such optimization on a hand pose estimation task. We also extend the evaluation towards simulated challenging scenarios of defocused lens, motion blur, occlusions, and noisy images to understand how these stress situations affect both the original and the optimized architectures. We show that the execution time can be improved on average by 12.8% with an accuracy compromise of less than 1 pixel (mean EPE). The experiments on challenging scenarios revealed that the model powered by depthwise separable convolutions is most fit for the occlusion cases and noisy environments while suffering more on the motion blur simulated scenarios.
Influence of enablers on processes and benefits of knowledge management in closed environment: a structural equation modelling

Knowledge management (KM) itself can be divided into two essential components, the process (KMP) and the benefits (KMB). This has led to the analysis of certain enablers which influence the processes of knowledge management and also identifies the benefits. The purpose of this study is to analyse issues which affect the success of knowledge management and the relationship between enablers; knowledge management process and the benefits of knowledge management using structural equation modelling (SEM). Visualising their complex relationship in a closed environment like the army, it was imperative that these facets be analysed and tested using structural equation modelling. Partial least square structural equation modelling (PLS-SEM) has been adopted for this study. This paper studied the shortlisted enablers and analysed its relationship with knowledge management. To elaborate: "how they influenced the process and ensure the true benefits of knowledge management implementation." The study aligns the reason for using PLS-SEM with the software SMARTPLS. It brings out the statistical relevance and the degree of impact of each enabler on knowledge management process (KMP) and knowledge management benefits (KMB).
SFE2D: A Hybrid Tool for Spatial and Spectral Feature Extraction

A crucial task for integrated geoscientific image (geo-image) interpretation is the relevant geological representation of multiple geo-images, which demands high-dimensional techniques for extracting latent geological features from high-dimensional geo-images. A standalone mathematical tool called SFE2D (spatiospectral feature extraction in two-dimension) is developed based on independent component analysis (ICA), continuous wavelet transform (CWT), k-means clustering segmentation, and RGB color processing that iteratively separates, extracts, clusters, and visualizes the highly correlated and overlapped geological features from multiple sources of geo-images. The SFE2D offers spatial feature extraction and wavelet-based spectral feature extraction for further extraction of frequency-dependent features. We show that the SFE2D is a robust tool for automated pattern recognition, fast pseudo-geological mapping, and detection of regions of interest with a wide range of applications in different scales, from regional geophysical surveys to the interpretation of microscopic images.
Self-Aware Contextual Behavior Analysis for Service Quality Assurance over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.
PROBLEM-BASED LEARNING IS A MEANS OF ORGANIZING STUDENTS ' ACTIVE ACTIVITIES IN PRIMARY SCHOOLS

The article considers the development of active activity of students in primary school through problem-based learning. The psychological and pedagogical features of problem-based learning are described. Based on the research, special tasks of problem-based learning are taken as a basis, i.e. (the use of logical methods or individual methods of creative activity); education of skills for the creative application of the acquired knowledge (in new situations) and skills for solving educational problems. The skills expected from problem-based learning are concretized by the desire and ability to learn, preparation for self-education, speech and logical thinking. The article also analyzes that the essence of a problem lesson can be covered in one sentence, for example, "creative assimilation of knowledge". The phrase "creative mastery of knowledge" means that in the lesson the student goes through all the links of scientific creativity: setting a problem and finding a solution - at the stage of knowledge introduction; expression of the solution and productive implementation – reproduction (utterance) of knowledge, etc. In problem-based learning, the difference between the dialogue leader and the motivator is analyzed.
Unsupervised Machine Learning: Clustering

This chapter introduces the readers to clustering algorithms as a part of unsupervised machine learning algorithms. This chapter describes the state-of-theart clustering algorithms. This chapter gives an elaborative definition of k-mean clustering, hierarchical clustering, and self-organizing map. It also defines the algorithmic framework of each algorithm with hands-on examples with detailed Python codes and outputs.
Phone-Free Texting: HamMessenger Makes it Easy to SMS over VHF

My first exposure to radio communication happened when I was around 5 or 6 years old. My dad was working as an airport electrician. He would bring walkie-talkies home, and my brothers and I would play with them around the yard. That's as far as my radio experience went, until a friend and I decided to get our amateur radio licenses together. This was only months before the COVID-19 lockdown, so it turned out to be the perfect time to learn to communicate using amateur radio!
Analysis of needs and availability of information on depression based on the ZHIHU Q&amp;A community

This study analyzes users' needs based on questions and the adequacy and quality of content in responses from participants in the ZHIHU Q&A online community (the largest Q&A platform in China). It aims to provide a basis for improving the management of online Q&A systems and enhancement of depression literacy for platform users. Python was used to crawl the question and response records found on depression in the ZHIHU, and the hot question records were classified by k-means clustering. The top 100 hot question records were then selected, and this research manually marked the answers with more than 40 likes for each question based on five dimensions of objectivity, integrity, professionalism, persuasiveness and practicality. The query content contained 685 hot questions. The results of cluster analysis showed that the questions about depression could be divided into four categories: basic knowledge, social life, self-management/prevention and education. In the annotated answers, the frequency of responses of higher quality of objectivity, persuasiveness and practicality was 89.81%, 65.64% and 73.24%, respectively, while the proportion of those regarding higher quality of professionalism and integrity was 33.21% and 16.79%. Users were found to have a high rate of demand for depression health information, but most of the responses on depression in the ZHIHU community were low-level and inadequate. Professionals and qualified volunteers should be encouraged to provide standardized answers to popular questions and contribute to a high-quality Q&A system. More rigorous management of the ZHIHU community should be designed to provide a high quality educational resource for platform users.
Min-Punishment Sweep Coverage based Path Plan Algorithm for USV

Scanning coverage problem of unmanned surface vehicles (USVs) is hot problem. Considering the cost of USVs, this paper proposes the minimum penalty scanning coverage problem and assigns a time penalty factor to each target point under limited number of sensors. The paper gives the resolution based on equal allocation strategy, branch-and-bound strategy and greedy strategy, respectively. As the branch-and-bound strategy has high time complexity and is not applicable to the case of large-scale target points, experiments of different scales are set up in this paper to compare the branch-and-bound algorithm, the greedy algorithm and the basic algorithm – equal allocation strategy, respectively. In the experiments, the penalty value of the greedy algorithm is only 3.26%, which is better than that of the branch-and-bound algorithm. The proposed method outperforms the equal allocation strategy on both small and large scale data.
Ancient ceramic restoration platform based on virtual reality technology

It is proposed to adopt a research method combining the successful cases of modern virtual reality technology in the restoring of ancient ceramics and the comprehensive analysis of the literature. This article first puts forward the irritating gas generated in the offline restoration of ancient ceramics and the irreversibility of the ceramics restoration process. Secondly, the virtual reality technology itself has the characteristics of immersion, interactivity, and intuitiveness. Through the combination of virtual reality technology and ancient ceramic restoration, we can build an ancient ceramic restoration platform to solve the actual problems in ancient ceramic restoration.
DISRUPTIVE TECHNOLOGIES FOR ACCOUNTING OF THE FUTURE

Disruptive technologies in accounting represent a new evolutionary phase of accounting impacted by emerging technologies that are part of industrial revolution 4.0. The relevance of emerging technologies, their potential and the opportunities they offer for the accounting profession attract both academia and professionals with accelerated research efforts. Academia and scientific researchers must research and provide an appropriate theoretical basis to help practitioners better adapt and increase their awareness and trust in technology. This paper provides early quantitative research data on publication trends related to most disruptive technologies in accounting such as big data, data analytics, cloud, artificial intelligence and blockchain. We identified these five emerging technologies through literature review and elaborated in detail how they can change and advance the accounting profession. The research was conducted using bibliometric analysis to examine the level of coverage of each of the technologies in the period from 2016 to 2020 by analyzing the published articles by the Big Four accounting firms, professional accounting associations and institutions and high-ranking academic journals. The purpose of the research was to identify a potential gap in research preferences related to selected technologies between academia and development professionals and experts in the field. The findings highlight that there are no significant discrepancies or different views of academia and practitioners. It is a positive result indicating that academia and scientific researchers exploit in the same direction as practitioners, thus providing support for adaptation and alignment to technology trends.
THE ALGORITHM OF BLOCK ENCRYPTION "AL03" AND THE RESULTS OF ITS ANALYSIS

This paper provides a brief description of the developed block cipher algorithm "AL03" and the results of checking the avalanche effect. This algorithm has the structure of a substitution-permutation network. The check consisted of two stages. At the first stage, the avalanche effect was tested separately for each transformation used in the algorithm. At the second stage, each round of encryption was analyzed. To characterize the degree of the avalanche effect in a transformation, the avalanche parameter is determined and used - the numerical value of the deviation of the probability of changing a bit in the output sequence when a bit in the input sequence changes from the required probability value equal to 0.5. The article presents the results after the 1st, 2nd, 3rd, and 24th rounds in the form of a table. Based on the round results obtained, comparative tests were carried out, as a result of which a positive conclusion was given on further research of this encryption algorithm.
Learning rules for automatic identification of implicit aspects in Portuguese

This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.
Modeling sand and gravel deposits and aggregate resource potential

This chapter examines aspects surrounding one popular approach to modeling aggregate potential. Models have been extensively used in quantitatively assessing grades and tonnages of metal deposits and have recently been adapted to sand and gravel deposits. The chapter shows that aggregate potential can be qualitatively estimated through the application and interpretation of pre-existing geological information, and then quantified through the use of deposit models. As a ‘first approximation’ the methodology provides a guideline for land-use planners, producers and developers to manage aggregate resources. Mineral deposit models essentially consist of a cumulative distribution of data for a given deposit parameter where the values of certain percentiles such as the 10th, 50th and 90th, are given. The chapter presents the geological settings of sand and gravel deposits on Vancouver Island, British Columbia by identifying the geomorphic landform associated with each pit.
Blockchain oriented hyperledger based performance driven framework for mass e-voting

Voting is a process for making collective decisions or to express a mass opinion on list of options available. It is a most commonly used instrument to elect a political representative. It is apparent that the methodology currently followed for voting in India can be improved at many levels to make it more robust and efficient. Currently, the voting methodology followed in Indian political elections has two major issues, one is high cost per voter, and low voter turnout. There are many attempts by other democratic setups to tackle this problem by offering online method of voting, but the most trustworthy and promising solution is considered to be voting platform/infrastructure backed by Blockchain. Most of the currently existing Blockchain based voting solutions are computationally expensive, doesn’t provide a verifiable secret ballot, slow, and Byzantine Fault Tolerant Proof of Work algorithms run on a public Blockchain network. The work presented in this paper aims at addressing these issues by proposing Blockchain based framework that leverages Hyperledger Fabric for Scalable Voting System. The proposed method uses Aadhar number for authentication of voters. The proposed method can efficiently cater the secure, trustworthy, and promising to Indian scale. The proposed method offers a various solution, offline and online voting with features such as cost-effective deployments, instantaneous vote counting, Cast as Intended Verifiability, and an observable and auditable architecture. The proposed method has been tested on real time setup and the experimental results are promising.
Interpretation of clearness day-ahead forecast errors using novel cloud classification

&amp;lt;p&amp;gt;&amp;amp;#160; &amp;amp;#160; &amp;amp;#160; &amp;amp;#160; The formation and dissipation of clouds are one of the longest studied and yet least understood phenomenon in nature. This is crucial in atmospheric and climate science as clouds have a significant impact on radiative forcing. In numerical weather prediction, solar radiation forecasts have lower skill than other parameters as temperature forecasts despite recent progresses. This study aims at better understanding cloud situations over Europe and how solar radiation forecast errors are related to these situations. Therefore, an enhanced cloud class algorithm based on unsupervised Deep Learning and hierarchical clustering is introduced. By using the MODIS optical cloud thickness product, the algorithm is able to classify 14 different daily cloud situations which are applied on defined tile regions (approximately 70,000 km&amp;amp;#178;) of Europe. These different classes differ in both optical cloud phase and the overall structure of the cloud shape. The usefulness of the cloud classes is illustrated by showing regional differences of cloud type frequencies over the last 20 years. To better understand solar radiation forecast errors, the cloud classes are assigned to ECMWF IFS clearness day-ahead forecast errors. We show that high-water content and mixed-cloud phase situations lead to highest absolute forecast errors for single sites. Summed up over an area, we observe an accumulation of forecast errors for mixed-cloud phase situations whereas for other cloud situations forecast errors are more likely to cancel each other out (e.g. broken high-water content clouds). This study is useful for researchers and practitioners to better understand situations of high solar radiation errors by using the developed cloud product.&amp;lt;/p&amp;gt;
Towards an Integrated Model of Electronic Word of Mouth Communication

The term electronic word of mouth has witnessed a constant evolution due to the technological advancements and increased internet mediated consumer conversations.The topic has become a subject of interest for both business professionals and academic scholars with its growing importance in business research.Past studies mostly discussed the dynamic nature of eWOM under the strong influence of emerging concepts and technological innovations.However, very few research studies have viewed its extensive evolution in the context of a basic model of communication.In this research paper, a theoretical review was conducted to systematically organize the literature findings to develop an eWOM communication model.From participants' classification and motivation to generate eWOM to its influence on receivers, the model elaborates all the basic elements of communication process which also include content type and transmitting platform.This paper significantly contributes to elaborate the basic eWOM communication process by the extensive analyzation of the existing body of knowledge which will help in building a strong foundation of the topic for future studies.
Steganography on Digital Color Image Using Modulo Function and Pseudo-Random Number Generator

In this era, many people exchange data digitally either through the internet or other communication channels. However, data sent digitally can be seen by unauthorized people. Therefore, data security is essential. One of the data security techniques is steganography. This study employs steganography based on Pixel Value Modification (PVM) method using the modulo function. This technique will be implemented to insert a text message in digital images in RGB color space. Besides, we use the Pseudo-Random Number Generator with a secret key to enhance the inserted messages’ security. To measure the performance of the proposed method, testing is carried out by comparing the stego image to cover image based on three criteria, namely imperceptibility, fidelity, and recovery. PVM method using modulo function successfully hides text that the length is less than 200 characters into 255×255-pixel color images. Imperceptibility testing is done by distributing a questionnaire to six people randomly. The results showed that all respondents answered that each test image's stego image and cover image had no difference. At the same time, the results of the fidelity test show that the MSE value is close to zero, and the PSNR value is above 40 dB. Furthermore, recovery testing will check whether the extracted message is the same as the secret message inserted in the cover image. The results showed that all messages inserted were extracted correctly for recovery criteria if given the correct secret key.
Warping-based spectral translation network for unsupervised cross-spectral stereo matching

Recently, a pair of RGB and near-infrared (NIR) cameras is applied to stereo vision systems for all-day vision applications. The images captured by the RGB-NIR stereo vision system have spectral ranges that differ significantly. Hence, the NIR image displays richer image information during nighttime. By contrast, during daytime, the RGB image generally provides abundant information. Therefore, these images can complement each other’s disadvantages in all-day environments. However, from the perspective of image matching, it is difficult to search for correspondences between two images because of their different spectral ranges. Although various methods for translating RGB images into NIR images have been proposed to solve this problem, hight-quality conversion results have not been achieved. Incomplete conversion results cause the inaccurate estimation of disparity during stereo matching. Therefore, we propose a warping-based spectral translation network (WASTNet) to enhance the training performance of a disparity estimation network by improving the performance of image translation. The WASTNet involves the following three steps: (1) initial disparity estimation for warping an NIR image to the viewpoint of RGB image; (2) post-processing for enhancing the initial disparity map; (3) image warping and spectral translation. Furthermore, we propose a confidence-based smoothness term that is applied to a loss function to mitigate disparity errors in the disparity map. The WASTNet and the proposed smoothness term are used concurrently with a conventional spectral translation network, which improves the performance of an unsupervised stereo matching network.