Reviewerly/exHarmony huggingface dataset:
HRM: An Intelligent Helmet Recognition Model in Complex Scenes

This paper presents an intelligent helmet recognition model in complex scenes based on YOLOv5. Firstly, in construction site projects, consider that the photograph which needs to be identified has numerous problems. For example, helmet’s pixels are too tiny to detect, or a large number of workers makes helmets appear densely. A SE-Net channel attention module is added in different parts of the network layer of the model, so that the improved model can pay more attention to the global variables and increase the detection performance of small target information and dense target information. In addition, this paper constructs a helmet data set based on projects and adds training samples of dense targets and long-range small targets. Finally, the modified mosaic data enhancement reduces the influence of redundant background on the model and improves the recognition accuracy of the tiny target. The experimental results show that in the project, the average accuracy of helmet detection reaches 92.82%. Compared with SSD, YOLOv3, and YOLOv5, the average accuracy of this algorithm is improved by 6.89%, 8.28%, and 2.44% and has strong generalization ability in dense scenes and small target scenes, which meets the accuracy requirements of helmet wearing detection in engineering applications.
Belief merging in absence of reliability information

Abstract Merging beliefs depends on the relative reliability of their sources. When this is information is absent, assuming equal reliability is unwarranted. The solution proposed in this article is that every reliability profile is possible, and only what holds according to all of them is accepted. Alternatively, one source is completely reliable, but which one is not specified. These two cases motivate two existing forms of merging: maxcons-based merging and disjunctive merging.
Big Data in Insurance Innovation

Purpose: To analyse the insurance market breakthroughs through 'Big Data' and the possibility of new techniques of services provided, creating access for information gathering and fraud detection. This can contribute to improved risk management processes and mitigation strategies referred to as 'InsurTech'.Methodology: We catalogue the technique which is especially useful and being evaluated as having the ability to bring innovations to the insurance business. In doing this, we reveal which marketplaces actively participate in start-ups and how insurers engage in them and present them, highlighting the impact of blockchain technology, ride services, robo-advice, and data analysis on the insurance industry.Findings: Findings show that because emerging economies have fewer organisation needs to ensure the distribution model, technology and research may significantly influence such areas. Nonetheless, whether industrialised or emergent, relevant legislative inspections should be carried out to protect subscribers' welfare.Practical implication: Since 'Big Data' impacts insurers' constant monitoring of business risks and corporate governance, an overview of how information is harnessed should be carefully studied. Moreover, it is essential to study the handling of algorithms to guarantee that the expectations are reasonable and that unforeseen effects are avoided to the greatest extent feasible, and regulators have a mechanism for engaging in this review.
Deep Correlated Image Set Compression Based on Distributed Source Coding and Multi-Scale Fusion

In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder. With the reference structure, the side information of each image to be decoded can be generated based on the decoded reference image. And our network learns the correlation between an image and its side information in the training phase. Based on the principle of DSC, the side information can provide additional information such as rich details at decoder side. To make full use of the side information, the initially decoded image and the additional side information are fused at different scales. A decompressed image enhancement network is introduced to reduce the compression artifacts of the decoded images. Extensive experimental results compared with other mainstream methods validate the superior performance of our scheme in both terms of subjective and objective quality.
Design and Characterization of Embroidered Electroacoustic Transducer

This paper is focused on designing and characterization of embroidered electroacoustic transducer placed on the magnet. This type of transducer can be used as a feedback or notification element in an advanced smart textile system. Multiple transducer designs and threads were used in the experiment. Results from measuring of acoustic level have showed, that embroidered electroacoustic transducer can be used as a full-featured speaker for the feedback system. The results have showed the ability to play simple sounds and melodies through this electroacoustic transducer.
Characteristic Analysis Technology of Moving People Based on Image Recognition

In recent years, the visual analysis of human motion has become a frontier direction in the field of computer vision. It recognizes, identifies, and tracks people in image sequences, as well as understands and describes their actions. Using computer vision technology to study the field of image processing and pattern recognition, as well as extracting and effectively identifying human motion features from video images, has become a hot topic of concern with the rapid development and popularization of information technology. This study presents a method for analyzing the characteristics of moving human bodies based on image recognition, introduces the extraction function method, and analyzes the characteristics of the extraction function to improve the ability to analyze the characteristics of moving human bodies. To achieve fine segmentation, the hierarchical clustering algorithm is used to segment the periodic motion in each motion. Different benchmark databases and self-built data were used in the experiments. Experiments show that the algorithm can achieve good classification and recognition results while maintaining low computational complexity and extracting less feature data. It can also organically integrate the static and dynamic features of human walking.
Deepdive: a learning-based approach for virtual camera in immersive contents

A 360° video stream provide users a choice of viewing one's own point of interest inside the immersive contents. Performing head or hand manipulations to view the interesting scene in a 360° video is very tedious and the user may view the interested frame during his head/hand movement or even lose it. While automatically extracting user's point of interest (UPI) in a 360° video is very challenging because of subjectivity and difference of comforts. To handle these challenges and provide user's the best and visually pleasant view, we propose an automatic approach by utilizing two CNN models: object detector and aesthetic score of the scene. The proposed framework is three folded: pre-processing, Deepdive architecture, and view selection pipeline. In first fold, an input 360° video-frame is divided into three sub-frames, each one with 120° view. In second fold, each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score. Finally, decision pipeline selects the sub-frame with salient object based on the detected object and calculated aesthetic score. As compared to other state-of-the-art techniques which are domain specific approaches i.e., support sports 360° video, our system support most of the 360° videos genre. Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360° videos.
PENGARUH PENGGUNAAN METODE SQ3R (SURVEY, QUESTION, READ, RECITED, REVIEW) TERHADAP KEMAMPUAN MEMBACA INTENSIF SISWA KELAS 4 SDN TAMBAKROMO 1 GENENG

The aim of the researchers in this study was to improve students' intensive reading skills at SD Negeri Tambakromo 1 Geneng in Indonesian subjects. This study uses a quantitative method (experimental) with a one group pretest-posttest design. This study uses interviews and tests. The result of the interview is that the students' reading ability is good but there are 8 students who have low reading ability. The method used by the teacher is a lecture in every lesson. The learning outcomes of students who reached the KKM were only 15 students in intensive reading learning. From the research results tcount&gt; t table = 8.419&gt; 1756 SQ3R method has a significant effect on intensive reading ability in 4th grade students of SDN Tambakromo 1. Because if t Count &gt; tTable then it means, H0 is rejected and Ha is accepted. Likewise with the results of the significance of Sig.(2-tailed) is 0.015, the result is less than 0.05 then H0 is rejected and Ha is accepted. SQ3R method has a significant effect on intensive reading ability in grade 4 students at SDN Tambakromo 1.
The GKR Protocol Revisited

The proof-of-work interactive protocol by Shafi Goldwasser, Yael T. Kalai and Guy N. Rothblum (GKR) [STOC 2008, JACM 2015] certifies the execution of an algorithm via the evaluation of a corresponding boolean or arithmetic circuit whose structure is known to the verifier by circuit wiring algorithms that define the uniformity of the circuit. Here we study protocols whose prover time- and space-complexities are within a poly-logarithmic factor of the time- and space-complexity of the algorithm; we call those protocols 'prover-nearly-optimal.' We show that the uniformity assumptions can be relaxed from LOGSPACE to polynomial-time in the bit-lengths of the labels which enumerate the nodes in the circuit. Our protocol applies GKR recursively to the arising sumcheck problems on each level of the circuit whose values are verified, and deploys any of the prover-nearly-optimal versions of GKR on the constructed sorting/prefix circuits with log-depth wiring functions.
An Approach towards Deployable Hybrid Product Recommendation Systems for E-Commerce

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems, which uses customer’s data like interests, purchase history, ratings to suggest new products, which they may like. Recom-mendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the cus-tomer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online plat-forms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering, Popularity-based and Content-Based Filtering.
Choice-supportive bias affects video viewing experience: Subjective experiment and evaluation

In recent years, with the spread of video streaming services and remote web conferencing systems, the increase in the number of end hosts connected to the Internet, the improvement of end host performance, and the diversification and sophistication of applications, the demand for communication quality of service (QoS) from users and service providers has become even higher. However, the amount of traffic flowing through the network is increasing year by year, and it has become difficult to operate a system that guarantees a certain level of QoS for users. In other words, best-effort type network systems have become the mainstream. Not only QoS, but also the user's own quality of experience (QoE) has become important, and application level control to improve user QoE within limited communication resources has become very important for both users and service providers. Since QoE is a subjective measure of a user's perception of a service, it is considered to be affected by cognitive biases that are observed in human subjective decision-making. In this paper, we conduct an experiment focusing on the choice-supportive bias, which is one of the cognitive biases, and clarify the effect of this cognitive bias on users during video viewing.
Titaniferous-Vanadiferous, Magnetite-Ilmenite Mineralization in a Mafic Suite within the Chhotanagpur Gneissic Complex, Bihar, India

Titanium or vanadium metals or their alloys are important industrial metals/alloys. Because these resources are in short supply, the investigation of potential titaniferous-vanadiferous deposits needs special attention to bridge the supply-demand gap. The study integrates geological, geochemical, remote sensing, and geophysical data for assessing the potentiality of titaniferous-vanadiferous, magnetite-ilmenite mineralization in and around the Sudamakund and Paharpur areas, Gaya and Jehanabad districts, Bihar, India, and delineation of specific targets for detailed exploration. Field visits for large scale mapping on (1:12,500 scale) were used to conduct a reconnaissance survey for magnetite-ilmenite mineralization in parts of toposheet number 72G/04 in the Gaya and Jehanabad districts of Bihar, as well as the collection of bedrock samples (BRS), pit/trench samples (PTS), petrographic samples (PS), and petrochemical samples (PCS), followed by petrographic and ore microscopic study, and interpretation of chemical results. Signatures of oxidized iron-bearing sulphides (iron-oxides ratio) and other ferrous-iron-bearing minerals surrounded by altered rocks (clay bearing minerals) are visible in remote sensing images. The geological work was followed by ground geophysical gravity and magnetic surveys in selected blocks by the Geophysics Division, eastern region (ER) on a 1:12,500 scale. The magnetite ore is hard, compact, crystalline, and at some places, granular in nature. The analytical value of these magnetite ore bodies indicates average Fe content at 49.53% (range 25.85–60.78%), with a considerable amount of TiO2 (average 15.85%, range 1.47–26.77%), and V (average 144.79 ppm, range 30.00–256.00 ppm, from PTS). The trends of these magnetite ore deposits correspond to the major lineaments (NE-SW and NW-SE). The superimposition of gravity and magnetic contour maps with the geological map (1:12,500 scale) helps explain the observed geophysical anomalies, and the possible subsurface (horizontal and vertical) expansion of magnetite ore deposits in alluvium cover regions warrants further investigation.
Model of Educational Ecosystem of Professional Training of Social Sphere Specialists

The implementation of the proposed requirements is of particular importance for the professional development of social work specialists, since the problems of social work practice are primarily the problems of people who need help to overcome them, they should be the focus of the entire educational program for training social workers at the institute. Knowledge of the laws of social design, skills and proficiency in tools for developing and evaluating the effectiveness of social projects, understanding their importance for solving urgent social problems form the necessary competencies of professional social workers. The chapter of the monograph presents a model of the educational ecosystem of professional training of social work specialists.
Cloud Computing - An insight to latest trends and Developments

Computing technology is rapidly improving and becoming more advanced over time. In order to stay up with the rapid transformation, industry executives pay close attention to the disruptive technologies positioned to deliver value in the cloud. In today's hyperconnected world, staying on top of trends is essential for survival and staying ahead of competitors. Because Cloud Computing has evolved into a large-scale computing system with seamless access to virtually unlimited resources, and various people from various sectors are using Cloud Computing for various reasons, it is now necessary to discuss technology trends and developments in general for Cloud Computing.This paper focuses on the basics of the aspects of understanding of Cloud Computing, as well as the present status, and trends of Cloud Computing. The study also explores the evolving cloud innovation of emerging paradigms likeBlockchain, IoT, AI/AR, Edge, and Green cloud computing, containers, etc. in near future cloud computing systems.
On decompositions of binary recurrent polynomials

Abstract Polynomial decomposition expresses a polynomial f as the functional composition $$f=g\circ h$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>=</mml:mo> <mml:mi>g</mml:mi> <mml:mo>∘</mml:mo> <mml:mi>h</mml:mi> </mml:mrow> </mml:math> of lower degree polynomials g and h , and has various applications. In this paper, we will show that for a minimal, non-degenerate, simple, binary, linearly recurrent sequence $$(G_n(x))_{n=0}^\infty $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> <mml:mi>∞</mml:mi> </mml:msubsup> </mml:math> of complex polynomials whose coefficients in the Binet form are constants, if $$G_n(x)=g(h(x))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mi>g</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>h</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , then apart from some exceptional situations that have to be taken into account, the degree of g is bounded by a constant independent of n . We will build on a general but conditional result of this type that already exists in the literature. We will then present one Diophantine application of the main result.
Accelerating Blockchain-enabled Distributed Machine Learning by Proof of Useful Work

In Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Decentralized machine learning (DML) with blockchain is a potential solution. However, blockchain with proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving real-world problems. We propose a novel uPoW method that exploits PoW mining to accelerate DML through a task scheduling framework for multi-access edge computing (MEC) systems. To provide a good quality-of-service for the system, we minimize the latency by solving a multi-way number partitioning problem in the extended form. A novel uPoW-based mechanism is proposed to schedule DML tasks among MEC servers effectively. Simulation results show that our proposed blockchain strategies accelerate DML significantly compared with benchmarks.
Fast Left Kan Extensions Using the Chase

We show how computation of left Kan extensions can be reduced to computation of free models of cartesian (finite-limit) theories. We discuss how the standard and parallel chase compute weakly free models of regular theories and free models of cartesian theories and compare the concept of “free model” with a similar concept from database theory known as “universal model”. We prove that, as algorithms for computing finite-free models of cartesian theories, the standard and parallel chase are complete under fairness assumptions. Finally, we describe an optimized implementation of the parallel chase specialized to left Kan extensions that achieves an order of magnitude improvement in our performance benchmarks compared to the next fastest left Kan extension algorithm we are aware of.
Korean vocabulary education using Chinese characters adjacently in reading text

In this study, Chinese learners proposed Writing with Chinese characters adjacently as a way to increase the efficiency of learning Korean vocabulary and reading comprehension. To prove this justification, 30 Chinese international students currently studying at Korean universities were randomly selected and surveyed. In addition, the effectiveness of Writing with Chinese characters adjacently on reading education and vocabulary acquisition was examined by writing Chinese characters together in the "Chinese character vocabulary" melted in the learning textbook.&#x0D; Accordingly, based on their background knowledge of Chinese characters, Chinese learners concluded that they not only easily understand the meaning of the text but also are effective in acquiring vocabulary. It should also not be overlooked that this can be an important motivation for learners in Chinese character cultures to learn Korean rather than learners in other cultures.&#x0D; Based on the conclusions drawn here, it is possible to find new teachinglearning methods that can efficiently acquire Korean vocabulary and improve reading skills. In order for this Chinese character method to establish itself as a Korean language teaching method for Korean learners in the Chinese character culture, it must be actively applied to various textbooks such as Korean reading textbooks, Korean vocabulary learning textbooks, and major textbooks. And it is necessary to be sure that Writing with Chinese characters adjacently can be the best means of helping understand Korean language.