Reviewerly/exHarmony huggingface dataset:
Construction of portrait model of core professional researchers in the technical field and analysis of their research topics

In order to support scientific evaluation and decision-making, this paper characterizes and identifies core researchers and their research topics by modeling the portrait of core researchers in technical fields.In this paper, we constructed a portrait model of core researchers, explained the information attributes and their label data in each dimension of the portrait.Selected six identification indicators that can be measured, used the CRITIC method and gray correlation method to determine the weight coefficient and comprehensive score of the index, and designed quantitative identification methods for core researchers using Price's calculation formula and golden section coefficient.Took the field of artificial intelligence as an example for empirical analysis and result verification, selected representative core researchers to track the changes of research topics at the two levels of their teams and individuals, and characterize them based on the portrait model.
Splatting-Based View Synthesis for Self-supervised Monocular Depth Estimation

Self-supervised method has shown great potential in monocular depth estimation, since it does not need expensive ground-truth depth labels but only uses the photometric error of synthesized images as the supervision signal. However, although many methods have been proposed to improve its performance, the occlusion problem has not been clearly handled. This paper introduces a novel view synthesis module to deal with occluded pixels in the process of image reconstruction. Specifically, we use bilinear splatting to forward warp the source image, and average pixels projected to the same location by the predicted depth. In addition, a valid pixel mask is generated with projection to ignore invalid pixels. The proposed approach clearly handles overlapping pixels and invalid areas of the synthesized image, thus improving the performance of self-supervised learning. We conduct various experiments, and the results show that our model can generate clear and complete depth maps and achieves state-of-the-art performance.
A Study of the Security Management System for University Privileged Accounts Linked with Fortification Machines: Practical Research

In practice, the management mode and methods of privileged account in information systems often have significant security risks.This paper studies and elaborates on the security management system of privileged accounts and the technical characteristics and advantages of bastion host.Through the seamless linkage of the two, an efficient, long-term, and secure privileged account security management system is proposed, which can achieve full coverage and lifecycle management of different types of privileged accounts, timely identify account risks and dispose of them, and meet industry regulatory requirements.The system satisfies the requirements of industry regulations and laws.
An Efficient Data Retrieval Method for Blockchain

As blockchain has gained its popularity in different areas, there would be an unimaginable amount of data to be tackled to support its much richer functionalities. As a result, an efficient data retrieval method is of great significance for the development of blockchain. Thus, in this paper, a novel and efficient data retrieval scheme that is compatible with the decentralized nature of blockchain is proposed. Specifically, a new data structure for storing complex data is firstly put forward to reduce the redundancy of data storage on the chain in distributed storage scenario. Then, a counted bloom filter jump table structure that integrating bloom filter and jump table structures is designed to improve tag retrieval efficiency and enrich semantic queries in a low space occupation. Finally, extensive experiments have been conducted to verify the performance of the proposed method in terms of the retrieval efficiency and space ratio.
FLEXIBILITY OF HUMAN RESOURCE MANAGEMENT DURING COVID-19 IN JORDANIAN TOURISM COMPANIES

The purpose of this cross-sectional study is analyzing the impact of human resources management flexibility on employee efficiency and effectiveness during the COVID-19 pandemic.A questionnaire was distributed electronically using a convenient sampling of 261 managers from Jordanian tourism companies.The results showed a statistically significant impact (α ≤ 0.05) of the flexibility of managers, including flexibility of skills, capabilities, and human resource management functions, especially performance evaluation, on the efficiency and effectiveness of employees.Finally, Jordanian tourism companies should involve their employees in many qualification courses to train and develop them to acquire more skills in using IT systems and adopt flexible incentive, promotion, compensation, and reward systems.
FSVM: Federated Support Vector Machines for Smart City

By putting digital technology and vast volume of data together, smart city becomes an emerging city paradigm for intelligent city management and operation. As one of the most popular artificial intelligent algorithms, support vector machines (SVMs) have been widely adopted for classification in various smart city applications. Due to the explosion of data and rigorous privacy requirements, an SVM classifier needs to be trained in a distributed and privacy-preserving manner. To achieve this, a federated SVM (FSVM) scheme is proposed to collaboratively and privately train an SVM classifier by combining the alternating direction method of multipliers (ADMM) with secret sharing. Specifically, the FSVM consists of FSVM-C and FSVM-S to deal with two cases of data partitioning by examples and features, respectively. By implementing the FSVM scheme on the real-word dataset MNIST, the efficiency and effectiveness of both FSVM-S and FSVM-C are verified by comprehensive experimental results.
Study on big data -related position portrait based on recruitment data

This paper mainly studies big data-related position portrait construction based on recruitment data crawled from the 51job website.We first use text mining technique to classify job information and accurately obtain the texts of two important aspects: work content and job requirements.We then apply the information extraction technique to extract labels describing different aspects of positions from structured and unstructured text data, and also adopt the Kano model to obtain more labels.Finally, we construct a multi-aspect and multi-dimensional position portrait through the sunburst chart.The position portrait constructed in this paper provides multi-dimensional analysis of the requirements of big data-related jobs and can help job seekers, enterprises, universities, and even third-party training institutions know the demand for talents and quickly determine the pertinence of a candidate's resume.
Algorithms mention in full-text content of article from NLP domain: A comparative analysis between English and Chinese

Algorithms play an increasingly important role in scientific work, especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language, which is hard to comprehensively investigate the use of algorithms.For example, in papers of Chinese conference, is the mention of algorithms consistent with it in English conference papers?In order to answer this question, this paper takes NLP as an example, and compares the mention frequency, mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference, Annual Meeting of the Association for Computational Linguistics (ACL), and the Chinese conference, China National Conference on Computational Linguistics (CCL).The results show that compared with ACL, the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed, while the distribution of mention location is similar.This study can provide a reference for the research related to the mention, citation and evaluation of knowledge entities.
AI based Approach for Solar PV Power Prediction with Varying Dust Accumulation Levels

Energy demand and concerns over greenhouse gases have made the integration of solar PV into the grid imperative. Solar power forecasting models must have a high prediction accuracy to address the intermittent nature of solar irradiation. Solar PV power is significantly affected by the dust deposited on the PV panel surface. The amount of dust deposited on PV panel as one of the input parameters to predict solar PV power and solar irradiation is performed in this paper. Multivariate analysis of three deep learning techniques that is LSTM (Long short-term memory), 1D CNN (Convolution Neural Network) and BilSTM (Bidirectional Long short-term memory) to predict the solar PV power and solar irradiation with varying dust accumulated levels for the 335 watt PV module installed on the rooftop of the lab at Delhi Technological University is presented. An artificial dust scenario is created by continually incrementing the dust level by 1.258 mg/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Fusion Object Detection and Action Recognition to Predict Violent Action

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.
Pengaruh Model Pembelajaran Problem Based Learning terhadap Hasil Belajar IPS Siswa Kelas IV SD Negeri 097325 Bandar Siantar

This study aims to determine whether the Problem Based Learning (PBL) Learning Model Affects the Learning Outcomes of Class IV Students in Subtheme 3 Learning "Gratitude for Diversity" at SD Negeri 097325 Bandar Siantar exists or not. There are 2 hypotheses in the study, namely, (1) there is an effect of applying the Problem Based Learning (PBL) model on the learning outcomes of Grade IV students in subtheme 3 learning "Gratitude for Diversity" at SD Negeri 097325 Bandar Siantar (H1) and there is no effect of applying the model Problem Based Learning (PBL) on Class IV Student Learning Outcomes in Subtheme 3 Learning "Gratitude for Diversity" at SD Negeri 097325 Bandar Siantar (Ho).This research method is an experimental method with the type of pre-experimental design that uses the One Group Pretest-Posttest Design. The population of this study were all fourth grade students at SD Negeri 097325 Bandar Siantar, totaling 30 students. Because the total population was less than 50, the population was used as the sample in this study. Data collection was carried out by giving tests and observation techniques. The research data was analyzed using descriptive analysis and inferential analysis. Based on the results of data analysis it is known that the results of tcount are 11.27 with a significant level of 0.05. This means that there is an influence of the Problem Based Learning (PBL) Learning Model on Class Student Learning Outcomes IV In Learning Subtheme 3 "Grateful for Diversity" at SD Negeri 097325 Bandar Siantar. This means H1 is accepted and HO is rejected.
Towards Practical Binary Code Similarity Detection: Vulnerability Verification via Patch Semantic Analysis

Vulnerability is a major threat to software security. It has been proven that binary code similarity detection approaches are efficient to search for recurring vulnerabilities introduced by code sharing in binary software. However, these approaches suffer from high false-positive rates (FPRs) since they usually take the patched functions as vulnerable, and they usually do not work well when binaries are compiled with different compilation settings. To this end, we propose an approach, named Robin , to confirm recurring vulnerabilities by filtering out patched functions. Robin is powered by a lightweight symbolic execution to solve the set of function inputs that can lead to the vulnerability-related code. It then executes the target functions with the same inputs to capture the vulnerable or patched behaviors for patched function filtration. Experimental results show that Robin achieves high accuracy for patch detection across different compilers and compiler optimization levels respectively on 287 real-world vulnerabilities of 10 different software. Based on accurate patch detection, Robin significantly reduces the false-positive rate of state-of-the-art vulnerability detection tools (by 94.3% on average), making them more practical. Robin additionally detects 12 new potentially vulnerable functions.
Meta-CRS: A Dynamic Meta-Learning Approach for Effective Conversational Recommender System

Conversational recommender system (CRS) enhances the recommender system by acquiring the latest user preference through dialogues, where an agent needs to decide “whether to ask or recommend”, “which attributes to ask”, and “which items to recommend” in each round. To explore these questions, reinforcement learning is adopted in most CRS frameworks. However, existing studies somewhat ignore to consider the connection between the previous rounds and the current round of the conversation, which might lead to the lack of prior knowledge and inaccurate decisions. In this view, we propose to facilitate the connections between different rounds of conversations in a dialogue session through deep transformer-based multi-channel meta-reinforcement learning, so that the CRS agent can decide each action/decision based on previous states, actions, and their rewards. Besides, to better utilize a user’s historical preferences, we propose a more dynamic and personalized graph structure to support the conversation module and the recommendation module. Experiment results on five real-world datasets and an online evaluation with real users in an industrial environment validate the improvement of our method over the state-of-the-art approaches and the effectiveness of our designs.
Multilabel sentiment analysis for classification of the spread of COVID-19 in Indonesia using machine learning

This study aims to use datasets on Twitter to find out public opinion on the spread of coronavirus in Indonesia by conducting sentiment analysis. The resulting sentiment analysis will benefit the community by helping the Indonesian government take various strategic measures to prevent and counter the spread of the COVID-19. This research was conducted through the data collection stage, namely crawling data tweet words in Bahasa Indonesia containing the meaning of the spread of COVID-19, the next stage of the process of creating labels manually. Next, the pre-process stage by removing the character, symbols and special features from Twitter. The last stage, classification using learning machine with 3(three) methods namely K-nearest neighbor (K-NN), Naïve Bayes and decision tree. The study analyzed sentiment of 1,119 valid Tweets data and found that K-NN algorithm had the highest accuracy value compared to Naïve Bayes and decision tree algorithms, which was 95.10%. However, the Twitter data analyzed obtained 78.19% of Tweets that fall into the negative category and only 13.85% of public opinion that is positive. This indicates that most of the Tweets of Indonesians in twitter do not mean the spread of COVID-19 disease somewhere.
Novel extraction and tracking method used in multiplelevel for computer vision

Various scientific applications, including cosmological simulation, fluid simulation, and molecular dynamics, depend heavily on the analysis of particle data. Although there are techniques for feature extraction and tracking regarding volumetric data, it is more difficult to do such tasks for particle data due to the lack of explicit connectivity information. Even though one could transform the particle data to volume beforehand, doing so runs the risk of incurring error and growing the data size. In order to facilitate feature extraction and tracking for scientific particle data, we adopt a deep learning (DL) method in this research. In order to capture the relation between physical features and spatial locations in a neighborhood, we use a DL model that generates latent vectors. Through clustering the latent vectors, characteristics could be retrieved from the vectors. The Cam-shift tracking algorithm, which just needs inference of the latent vector for chosen regions of interest, is implemented in the feature space to accomplish quick feature tracking. With the use of two datasets, we test our approach and contrast it with other approaches already in use.
Privacy aware-based federated learning framework for data sharing protection of internet of things devices

&lt;p align="justify"&gt;Federated learning (FL) has emerged as one of the most effective solutions to deal with the rapid utilization of internet of things (IoT) in big data markets. Through FL, local data at each IoT device can be trained locally without sharing the local data to the cloud server. However, this conventional FL may still suffer from privacy leakage when the local data are trained, and the trained model is shared to the cloud server to update the global prediction model. This paper proposes a FL framework with privacy awareness to protect data including the trained model for IoT devices. First, a data/model encryption method using fully homomorphic encryption is introduced, aiming at protecting the data/model privacy. Then, the FL framework for the IoT with the encryption method leveraging logistic regression approach is discussed. Experimental results using random datasets show that the proposed framework can obtain higher global model accuracy (up to 4.84%) and lower global model loss (up to 66.4%) compared with other baseline methods.&lt;/p&gt;
Automated Attendance Marker Using Facial Recognition

Abstract: Attendance management is a crucial task in various domains, such as educational institutions, workplaces, and events, to accurately record and monitor the presence of individuals. Traditional attendance marking methods involving manual processes are time-consuming, prone to errors, and lack efficiency. This abstract proposes a novel solution, the "Attendance Marker Using Facial Recognition," which leverages the advancements in facial recognition technology to automate attendance tracking The attendance marker system offers numerous advantages over traditional methods. Firstly, it eliminates the need for manual data entry, reducing human error and increasing efficiency. It also ensures reliable identification, as facial features are difficult to forge or replicate. Moreover, the system can handle a large number of attendees simultaneously, making it suitable for events with high footfall
A comparative study of various optimization techniques for cloud brokerage systems

The term "cloud computing" refers to a concept of resource sharing that enables ubiquitous, convenient, and on-demand network access to a shared pool of configurable computer resources that are provided by commercial providers in accordance with certain service level agreements.The use of cloud computing is significant for a number of reasons, including data analysis and storage.A cloud broker acts as a go-between for their customers and the various service providers.Requests can be made by the client to the internet broker.The cloud broker is responsible for matching the client's request with the various offerings that are made available by the service's provider.The problem of cloud brokerage can be formulated as a multi-objective optimization challenge, with the following three goals in mind: decreasing the amount of time it takes to respond to requests from customers; limiting the amount of energy that is consumed; and maximizing the amount of money that is made by the cloud broker.To overcome this problem using various optimization techniques can be compared.The performance of the cloud brokerage system is compared with that the multi-objective particle swarms optimization, genetic algorithm, and random search algorithm and ant colony optimization.
The Effectiveness of Different Cloud Computing Models on Organizational Performance: An Analytical Study

This analytical study investigates how different cloud computing architectures affect how well businesses perform.Cloud computing enables rapid access to a shared pool of computing resources, enabling efficient provisioning and distribution.The impact of several cloud deployment strategies, such as public, private, hybrid, and community clouds, on scalability and application/data migration is examined in the study.To comprehend the connection between cloud computing utilization, IT capabilities, and business value, the Resource-Based View theory is also used.The report emphasizes cloud computing's financial benefits, technological developments, and performance assessment prospects.Additionally, issues including information asymmetry, lack of differentiation, vendor lock-in, and security issues are addressed.Consideration is given to the application of cloud computing and its potential benefits in India, particularly for small and medium-sized organizations.
Breaking Modulus of the form \(N = p^rq^s\) with Improved Polynomial Attacks

Let \(N=p^r q^s\) be prime power moduli where \(p\) and \(q\) are unbalance prime numbers for \(2 \leq s&lt;r\). If \(q&lt;p&lt;\lambda q\) and \(q^s&lt;p^r&lt;\lambda q^s\), and\[\phi(N) \approx \lambda^{\frac{r-s}{2 r}}\left(N^{\frac{r+s}{2 r}+N^{\frac{r+s-2}{2 r}}}\right)-N^{\frac{r+s-1}{2 r}}\left(\lambda^{\frac{r-s+1}{2 r}}+\lambda^{\frac{r-s-1}{2 r}}\right)\]then\[x&lt;\sqrt{\frac{\lambda^{\frac{r-s}{2 r}}\left(N^{\frac{r+s}{2 r}}+N^{\frac{r+s-2}{2 r}}\right)-N^{\frac{r+s-1}{2 r}}\left(\lambda^{\frac{r-s+1}{2 r}}+\lambda^{\frac{r-s-1}{2 r}}\right)}{2 N^{\frac{1+2 \alpha r}{2 r}}}}\] which leads to the factorization of the moduli \(N=p^r q^s\) in polynomial time. The second assaults on s multi prime power moduli are described \(N_i=p^r_i q^s_i\) for \(i=1,2,..., \omega.\) We use lattice basis reduction techniques to obtain the parameters (x; yi) or (y; xi) after transforming the system of equations into a simultaneous Diophantine approximation problem, and it resulted in simultaneous factorization of s moduli \(N_i\) in polynomial time.