Reviewerly/exHarmony huggingface dataset:
Skeleton-based human action recognition with context awareness

Human action recognition is the process of automatically recognizing human activities in digital video sequences. It is an important research topic in computer vision, particularly in the field of video surveillance. Conventional approaches for skeleton-based action recognition mainly focus on modeling the temporal evolution of the skeleton data, while not taking the contextual information of the scene into consideration. In this paper, we propose a novel human action recognition framework with context awareness: YOLOv5 is used for context recognition first, then the obtained information is integrated with ST-GCN for skeleton-based behavior recognition. Experimental results show the superiority of our algorithm over ST-GCN alone in industrial scenes.
Computational Creativity or Automated Information Production?

Algorithms and automated learning systems have been successfully applied to produce images, pieces of music, or texts that are appealing to humans and that are often compared to artworks. Computational technologies are able to find surprising and original solutions–new patterns that humans cannot anticipate– but does this mean we ascribe to them the kind of creativity that is expressed by human artists? Even though AI can successfully detect humans’ preferences as well as select the objects that satisfy taste, can we ascribe to them the capacity of recognizing the intrinsic value of artworks? To answer these questions, I am first going to explain the kind of creativity that is expressed by contemporary predictive systems, then, in the second part of this paper, I will try to show the difference between the creativity of algorithms and the creativity of artists by expanding on Deleuze’s reflections.
An Approach for Integrating a Prominent Conversational Chatbot with a Contact Center

A conversational chatbot has plenty of advantages and the capability to grow a business. Telecommunication software like contact center has been increasingly collaborating with conversational chatbots. It uses artificial intelligence that helps increase customer satisfaction scores and reduce the customer waiting time and effort to request the service. Depending on the use cases and requirements of a business, a prominent chatbot with a certain set of features has been integrated into a contact center. The integration problem arises when use cases have been changed, and there is a need for a prominent chatbot that satisfies the specific set of requirements. Therefore, this research work proposes a Bot Bridge which integrates a prominent chatbot into a contact center based on the requirement of a business. It can resolve the integration issues of multiple conversational AI chatbots interacting with telecommunication systems.
Bidirectional Machine Translation for Punjabi-English, Punjabi-Hindi, and Hindi-English Language Pairs

Machine translation (MT) aims to remove linguistic barriers and enables communication by allowing languages to be automatically translated. The availability of a substantial parallel corpus determines the quality of translations produced by corpus-based MT systems. This paper aims to develop a corpus-based bidirectional statistical machine translation (SMT) system for Punjabi-English, Punjabi-Hindi, and Hindi-English language pairs. To create a parallel corpus for English, Hindi, and Punjabi, the IIT Bombay Hindi-English parallel corpus is used. This paper discusses preprocessing steps to create the Hindi, Punjabi, and English corpus. This corpus is used to develop MT models. The accuracy of the MT system is carried out using an automated tool: Bilingual Evaluation Understudy (BLEU). The BLEU score claimed is 17.79 and 19.78 for Punjabi to English bidirectional MT system, 33.86 and 34.46.46 for Punjabi to Hindi bidirectional MT system, 23.68 and 23.78 for Hindi to English bidirectional MT system.
Decoherence and the ultraviolet cutoff: non-Markovian dissipative dynamics of a charged particle in a magnetic field

Abstract We derive a non-Markovian master equation for a charged particle in a magnetic field coupled to a bath and study decoherence by analyzing the temporal decay of the off-diagonal elements of the reduced density matrix in the position basis. The coherent oscillations characterized by the cyclotron frequency get suppressed as a result of decoherence due to coupling with the environment. We consider an Ohmic bath with three distinct models for the high-frequency cutoff for the spectral density of the bath and compare the three cases. As expected, the three cutoff models converge in the limit of the uppermost frequency of the bath tending to infinity. We notice a dramatic slowing down of loss of coherence in the low-temperature limit dominated by zero point quantum fluctuations compared to the high-temperature classical limit dominated by thermal fluctuations. We also go beyond the Ohmic model and study super-Ohmic and sub-Ohmic baths with the spectral densities deviating from a linear dependence on the frequency. Our results are testable in a state of the art cold atom laboratory.
ADAM: An Auction-based Datacenter Management in Vehicular Cloud

Researchers have shown that most vehicles spend the majority of their time parked in parking garages, lots, or driveways. During this time, their computing resources are unused and untapped. This has led to substantial interest in Vehicular Cloud, an area of research in which each vehicle acts as a computation node. The main difference between traditional cloud computing and vehicular cloud computing is the availability of nodes. In traditional clouds, nodes are available 24/7, while in vehicular clouds, nodes (vehicles) are only available while parked in parking lots. This creates a dynamic environment as vehicles enter and exit parking garages at random. In this paper, we present a novel framework called ADAM (Auction-based Datacenter Management) for Vehicular Cloud. It uses auction and market design approaches and makes the following contributions: (1) integration of software agents that can search, bid, price, and allocate jobs on behalf of stakeholders, (2) formulation of a truthful auction-based job management system that unifies job allocation, scheduling, and pricing strategies, and (3) simulation studies demonstrating substantial performance benefits. The results of our simulations show that the proposed interactive agents enable efficient processing of large amounts of data, leading to cost savings for stakeholders, reducing the load on conventional clouds, and improving the utility of parked vehicles and parking facilities.
Wildcarded identity-based ring signatures based on linearly homomorphic signatures

In identity-based ring signatures (IBRS) (Zhang and Kim, 2002), a binary string ID∈{0,1}L for L∈N, representing a unique information about a signer (e.g., e-mail address, telephone number), is associated with a secret-key. Its owner can anonymously signs a message under a set of IDs called ring including the signer’s ID. Wildcarded IBRS (WIBRS) (Ishizaka&Kiyomoto, ISC’20) is a generalization of IBRS, in which the ring consists of wildcarded IDs. The signer correctly signs iff her ID matches at least one of the wildcarded IDs. To the best of our knowledge, the only one known WIBRS scheme adaptively secure under standard assumptions is obtained as an instantiation of the attribute-based signatures scheme by Sakai et al. (PKC’16). It has signatures of length O(kL) and secret-keys of (constant) length O(1), where k denotes cardinality of the ring. In this paper, we propose a generic WIBRS construction, built by linearly homomorphic signatures and non-interactive witness-indistinguishable proof. We instantiate it to obtain a concrete WIBRS scheme secure under standard assumptions, whose length of signatures (resp. secret-keys) O(k+L) (resp. O(L)).
Supplemental Material: Cd isotopic constraints on the sources of Zn-Sb deposits: A case study of the Jianzhupo Zn-Sb deposit, Guangxi Province, China

&lt;p&gt;Table S1: EPMA trace-element contents of jamesonite (Jam) and sphalerite (Sp) from the Jianzhupo deposit. Figure S1: General paragenetic sequence of minerals in the Jianzhupo deposit. Figure S2: Backscattered electron images (A to J) and photomicrographs (K to L; under reflected light) of sulphides from the Jianzhupo deposit. Table S2: LA-ICP-MS data (in ppm) obtained for sulfides from the Jianzhupo deposit. Table S3: End-members compositions and mixing modeling on Zn, Cd, and δ114/110Cd values.&lt;/p&gt;
Unmanned aerial vehicle visual scene understanding based on multitask learning network

With the continuous development of intelligent unmanned aerial vehicles reconnaissance, single-object detection or semantic segmentation can no longer meet the diversified requirements, while simple model stacking will cause the model to be too complex, which will seriously affect the real-time running effect. We propose a visual scene understanding algorithm based on a multitask learning network with an encoder–decoder structure. First, the efficient classification network VoVNet is selected as the feature-sharing network to obtain multiscale coding features. Second, based on the one-stage anchor-free object detector, the feature screening supplementary module implements feature interaction with segmented characters to enhance the detection capability of potential targets. Then for semantic segmentation and depth estimation pixel-level classification tasks, a cascaded chained residual pooling module is used as a parameter-sharing decoder, through the parameter-sharing mechanism to reduce the repeated decoding process to ensure the running speed. Finally, to improve the generalization ability of the model, a general-purpose dataset was constructed and network training was carried out based on the idea of knowledge distillation. Experiments on the dataset show that the performance of the multitask learning network can reach the mainstream algorithm level in semantic segmentation and depth estimation branches, whereas the object detection branch is better in multitarget recall rate, and the average time of each subtask is 39 ms, which meets the requirements of real-time performance and proves the effectiveness of the multitask network.
Text Encryption Using Improving Key of Hi Sec Algorithm Using Rubik's Cube

Several techniques have been developed to generate keys using Rubik's Cube, which has been researched as a potential source of randomness for cryptography. This study suggests an innovative technique for creating a key for the Hi Sec algorithm using a Rubik's Cube image. The process entails creating a binary key for text encryption by leveraging the cube's color information. The Rubik's Cube principle, which includes choosing a subset of cube faces to generate the key, serves as the foundation for the key creation process. Experimental findings indicate that the proposed approach can produce safe keys that are resilient to a variety of assaults. The technique offers a fresh and effective way to generate keys for the Hi Sec algorithm, which may be used in several secure communication systems. The proposed method was programmed in VisualBaic.Net 2012.
A Case Study of Test Automation in Agile Software Development

Agile software development has revolutionized the industry by allowing developers to produce high-quality software in shorter timeframes.However, testing continues to be an important part of software development to ensure that the product fulfills its necessary quality requirements.Manual testing can be time-consuming and error-prone, resulting in software delivery delays.Test automation, on the other hand, may significantly reduce testing time while enhancing the accuracy and consistency of results.The paper examines the relationship between agile software development and testing, with a specific focus on web-based testing.It covers the issues of web testing in an agile environment and examines how test automation can assist to overcome these challenges.Furthermore, it compares manual testing with test automation and analyses the benefits and drawbacks of each approach, provides an overview of popular test automation tools, such as Selenium and JMeter, and explains why they are the best options for automated testing, and the paper highlights the benefits of web testing, including as increased software quality, reduced costs, and improved user satisfaction.
Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers.However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems.In this paper, we first construct a largescale multi-objective cloud task problem considering the time and cost functions.Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem.This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems.This reduces the computational burden of the algorithm.Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer.Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform.Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.
Effectiveness of Digital-Based COVID-19 Prevention Supplement Books to Empower High School Students' Scientific Literacy and Critical Thinking Skills

The development of teaching materials such as digital-based supplementary books really needs to be done considering that COVID-19 has not yet been integrated with biology subjects. Students need to dig up information about COVID-19 through scientific literacy and solve problems through critical thinking. This study aims to determine the effectiveness of digital-based supplement books through quasi-experiments by looking at the comparison of pre-test and post-test results. The data were analyzed quantitatively to determine the category of effectiveness and deepened by a qualitative analysis of the influencing factors. Sig. The value of (2-tailed) Paired T-test is 0.000. which shows that there is a difference in the average learning outcomes before and after using the supplement book. This means that the COVID-19 prevention supplement books greatly affect students' scientific literacy skills and critical thinking skills. Scientific literacy is enhanced because the content in the book explores COVID-19 information and is integrated into the material. Meanwhile, critical thinking is empowered with direction analysis and decision making in books. The book developed is predicted to be very useful and supportive in learning biology, especially relevant material such as material about viruses, the respiratory system, and the immune system
Integration of methods and means of distance learning of mathematics during the process of training future teachers of mathematics

The article aims to study the peculiarities of applying different distance education methods and tools in training future teachers of mathematics. The task of the article can be conditionally divided into two stages: differentiation of means and methods of distance education in the conditions of higher education institutions; searching for a model (combination of social, pedagogical, and other elements) of forming professional competence through a combination of distance education tools and methods. The methodological basis of the study was scientific and pedagogical methods. The results of the study highlight the elements of distance education, capable in their totality to provide full and high-quality training of future teachers of mathematics in the digital environment of universities.
Improved Harris Hawks Optimization Algorithm Based Data Placement Strategy for Integrated Cloud and Edge Computing

Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows. The individual tasks of a scientific workflow necessitate a diversified number of large states that are spatially located in different datacenters, thereby resulting in huge delays during data transmission. Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets. Therefore, this fixed storage strategy creates huge amount of bottleneck in its storage capacity. At this juncture, integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge. In this paper, Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm (ACF-DFS-HHOA) is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow. This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows. The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points. The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution.
Analisis Sentimen Pengunjung Pulau Komodo dan Pulau Rinca di Website Tripadvisor Berbasis CRISP-DM

A sentiment analysis is needed to identify tourist preferences for products and services in a tourist destination. Therefore, this study uses the Cross-Industry Standard Procedure for Data Mining (CRISP-DM) method to classify visitor review data for Komodo Island and Rinca Island obtained from the Tripadvisor website based on negative and positive sentiments, then recommends the development of an information system that can optimize the products and services of tourist destinations on Komodo Island and Rinca Island. Meanwhile, the algorithms used are k-Nearest Neighbor (k-NN), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and Decision Tree (DT). Based on the results of data classification using the word weighting method or Term Frequency Inverse Document Frequency (TF-IDF) through the sentiment extract operator, it can be known that the five words that most often appear in tourist reviews of Komodo Island are as follows: Komodo dragons (1894), dragons (1596), island (1492), tour (840), boat (774). Meanwhile, the five words that most often appear in tourist reviews of Rinca Island are as follows: komodo dragon (1042), dragons (962), island (882), rinca (606), and boat (372). In addition, the results of the classification of 564 Komodo Island tourist review data and 364 Rinca Island tourist review data using CRISP-DM-based k-NN, NBC, SVM, and DT algorithms, show that the Support Vector Machine (SVM) is the best-performing algorithm where the accuracy value is 99.69%, precision 100%, recall 99.39%, f-measure 99.69%, Area Under Curve (AUC) 100% and t-Test 0.958. Meanwhile, the results of processing tourist review data on Rinca Island products and services show that the Support Vector Machine (SVM) algorithm has the best performance with 100% accuracy, 100% precision, 100% recall, 100% f-measure, 100% Area Under Curve (AUC) and 0.964 t-Test. After comparing the performance of SVM before and after using the Synthetic Minority Oversampling Technique (SMOTE), it can be seen that the implementation of the algorithm becomes more optimal when using the SMOTE operator. Thus, SVM is a relevant algorithm used as a model for analyzing tourist sentiment on Komodo Island and Rinca Island based on CRISP-DM.
Pengembangan E-Learning Management System Untuk Tingkat Sekolah Dasar Berbasis Website dengan Metode Agile

Information Technology has played an important role as a medium of information and communication in the field of education, one of which is in the Academic Management of Schools. In the world of educational technology it is not only related to the internet but can also be seen from the system or how to teach in schools. At present, the learning process using E-learning can be interactive so that students are able to interact with technology as a learning medium. Web-based learning media (e-learning) is a web that can help educators in the rapidly developing era of information and communication technology development. In this research, the problem that arises in the school environment is that there is no special place for students and teachers to be organized in obtaining learning materials and collecting assignments, documentation of knowledge of learning materials that are not stored properly, so it takes a long time to find the required knowledge back. Therefore used data collection methods, namely the method of observation, interview methods (interviews), and literature study. The Agile method is the most appropriate method used to design this system with the aim that the author can help ease the task of students and teachers in carrying out their duties. The results achieved in making the e-learning application at SDN Tengah 01 KramatJati are that students can understand material without a time limit, consult, take quizzes, upload assignments and can immediately see their learning results in person, while teachers can upload material, create questions and give them directly live.
Pengimplementasian Fitur Data Profiling Pada Aplikasi Data Governance Berbasis Open Source Tools dengan Metode Itterative/Incremental

Data is one element that is considered important in a business. If the processing is carried out correctly and as needed, the data has high quality. High data quality can have a positive impact, that is, it can create information and knowledge to support decision-making. In fact, many organizations accumulate data without a good data storage or management system and cause the data to become large so that it is difficult to understand and produce information in it that cannot be conveyed properly which makes a decision not have a maximum positive impact. Data Quality Management (DQM) is considered to be able to assist in maintaining quality in the data and ensure that no data is lost or damaged. Quality data is obtained by one of the stages in DQM, namely data profiling, which performs the process of checking the available and stored data to determine a content structure and data quality. The research focuses on implementing the DQM module data profiling feature on the Data Governance website application. This research produces data profiling features that can help people in data processing where the modules produced are value distribution, data completeness, clustering, value similarity and data deduplication.