Reviewerly/exHarmony huggingface dataset:
Automation of Fabric Pattern Construction using Genetic Algorithms

This paper introduces the use of Genetic Algorithms to evolve fabric patterns from randomly generated seeds. The patterns are evolved from random, often dull coloring of the image, to bright multi-color patterns that are aesthetically pleasing in nature. The main problem that this paper intends to solve is to introduce complete automation in the design process of patterns, which have historically been dependent upon human arbitrators to judge the quality of intermediate outputs. In its stead, the proposed algorithm evaluates the quality of the image using inherent latent features present in the image itself. Our algorithm takes into account the distribution of color, global contrast, and the overall dullness score of the image to evaluate the quality of the generated patterns. To create diverse patterns that feel more natural, different approaches are experimented with. These include the use of L-systems and image processing techniques, in a bid to construct a pattern which seems more human-like, rather than just rudimentary digital art.
SemanTV: A Content-Based Video Retrieval Framework

With the increased adaption of CCTV for surveillance, challenges in terms of retrieval have recently gained attention. Most Surveillance Video Systems can only retrieve footage based on its metadata, (date, time, camera location, etc.) which limits the diversity of meaningful footage intended to be retrieved by the user. To solve this, a content-based video retrieval framework was proposed to retrieve relevant videos based on their content and match it to the user's query. This framework composes of two (2) methods: A method for Video Content Extraction that utilizes Google's Video Intelligence API for Optical Character Recognition and Label Detection, and a method for Video Retrieval. Various setups for the Video Retrieval method are explored; this includes the usage of SBERT and Okapi BM25. Each setup was tested against various text queries with equivalent test video results based on the MSVD dataset. To measure each setup's performance in terms of relevance, Recall and Precision at K and Median and Mean Rank were used. It was concluded that the framework composed of the Video Intelligence API along with SBERT alone performed better than the other proposed setup for returning videos relevant to the user's text query more accurately than the other setups of the method.
An infinitary propositional probability logic

Abstract We introduce a logic for a class of probabilistic Kripke structures that we call type structures, as they are inspired by Harsanyi type spaces. The latter structures are used in theoretical economics and game theory. A strong completeness theorem for an associated infinitary propositional logic with probabilistic operators was proved by Meier. By simplifying Meier’s proof, we prove that our logic is strongly complete with respect to the class of type structures. In order to do that, we define a canonical model (in the sense of modal logics), which turns out to be a terminal object in a suitable category. Furthermore, we extend some standard model-theoretic constructions to type structures and we prove analogues of first-order results for those constructions.
A New Discrete Whale Optimization Algorithm with a Spiral 3-opt Local Search for Solving the Traveling Salesperson Problem

The whale optimization algorithm is a metaheuristic inspired by the hunting strategy of humpback whales. This paper proposes a new discrete spiral whale optimization algorithm (DSWOA) to solve the traveling salesperson problem (TSP). Our approach uses sequential consecutive crossover and spiral 3-opt search, a modified version of the popular 3-opt local search. Spiral 3-opt search works like the original 3-opt heuristic but only uses part of the tour to generate 3-opt moves. We show that spiral 3-opt achieves results similar to the original 3-opt technique and significantly reduces runtime. We evaluate DSWOA's performance on 19 TSP instances against six benchmark algorithms. Our results suggest that DSWOA produces TSP solutions that are as good or better than our competitors. For five of the six benchmark algorithms, we demonstrated statistically significant improvements.
“I Use YouTube Now in COVID”: Understanding Technology Adoption of Indigenous Communities during COVID-19 Pandemic in Bangladesh

Indigenous communities in Bangladesh are comparatively disadvantaged and face several barriers regarding rights. Access to technology and ICT can help indigenous communities open new economic, political, and social dimensions. The recent COVID-19 pandemic necessitated technology adoption for routine use, which is equally important for indigenous communities, but their technology adoption scenario remains unexplored in HCI research. Considering the research gap, we interviewed n=36 (Female 26 and Male 10) indigenous people from six different indigenous communities in Chattogarm and Sylhet divisions in Bangladesh. We found that they are strongly connected in communities, have independent technology access, and have no gender differences. They have a strong interest and eagerness to learn available technologies that help them in their professions, enrich their technical skills, communication, social participation, and expand the business. The study also revealed some challenges while using technology, but that did not negatively impact their usage. The study also discussed the community-centric strengths that helped them fight against the COVID-19 crisis and work for their development. This research impacts HCI literature, revealing the technology adoption scenarios of Indigenous communities in Bangladesh.
A study on Accessibility of Google ReCAPTCHA Systems

Web sites and social media should be developed keeping in mind the widest range of users, regardless of their abilities or disabilities. Instead they often use CAPTCHAs to prevent robotic access to data even if they base their tests on the use of sight, since the ability to see an image or a text and recognize or understand its content is a way to make out a human being from a bot. This study shows that Google reCAPTCHA v2 discriminates against users with visual impairments, while reCAPTCHAv3 doesn’t and, for this reason, it is the best available solution nowadays from an accessibility point of view.
Novel graphical family tree representation to develop an interactive expert system

Abstract Family inheritance distribution is an interesting and challenging problem. When a person dies, all of their wealth is passed on to their heirs. Heirs are family members of the deceased with specific relationships. The distribution process of wealth in Islam has many rules that make it difficult to understand and require an expert to solve. Many interrelated rules make it difficult to teach and learn. Therefore, there have been multiple attempts to develop an expert system to help solve the family inheritance problem. The existing solutions have some limitations in terms of the completeness of rules, and user‐system interactions, which make these systems unreliable. In this study, we developed an expert system to calculate family inheritance. The two main contributions of this work are the development of a complete set of inheritance rules and the design of intelligent user‐system interaction. We innovate the design of the family tree, which plays the role of an interaction layer with the user. The results of our proposed system demonstrate an accuracy of approximately 93%.
A Critical Analysis on the Application of Next-Generation Internet of Things (Iot) for Building Efficient Energy Systems for Improved Temperature Control and Better Energy Consumption

This research article is focused on analyzing the application of next-generation Internet of Things (IoT) in creating energy-efficient systems so as to improvise the temperature and manage the energy consumption effectively. The term IoT is a new concept that has been growing imminently due to growing needs and requirements of government, individuals, corporations, etc. The IoT is mainly stated as a critical system of computing machines that possess better and enhanced connection so as to automate various aspects of the routine needs of the individuals, it also assists in creating better information approach for evaluation and implementation of different approaches for sustainable living. The IoT system connects the different smart devices and senses through cloud-based controllers, the sensors in the building can enable in collecting and transmitting the real-time data on the environment, this information is then used by the controller’s team to offer quick responses on the temperature control ana management. The systems enable in applying predictive and adaptive algorithms which will enable in executing the real-time operational responses, like turning on or switching off the lights based on the signals from in-room occupancy and turning on the air conditioning based on the level of occupancy in the building, this enables in optimizing the heating, ventilation and air-conditioning system so as to enhance better energy consumption. This article presents a critical analysis of the usage of IoT in enhancing the energy-efficient system in the building for improved temperature control and enhanced energy consumption.
mixComp: An R package for estimating complexity of a mixture

allow for modeling heterogeneous data.The number of mixture components may be known in advance, in which case the model parameters can be easily estimated (e.g., their maximum likelihood estimates (MLE) can be computed using the EM (Expectation-Maximization) algorithm (Dempster et al., 1977)).However, in many applications the number of components is unknown and has to be inferred from the data.mixComp provides three categories of methods for estimating the unknown complexity of a (univariate) finite mixture:• methods built upon the determinants of the Hankel matrix of moments of the mixing distribution;
Visual Analysis of Sports Actions Based on Machine Learning and Distributed Expectation Maximization Algorithm

In order to improve the scientificity of sports action analysis, this paper constructs a sports action analysis model based on machine learning based on the greedy algorithm and the bat algorithm. According to the structural characteristics of the model, the structure of the model is reflected in the form of face order, that is, the face neighborhood structure. Moreover, this paper judges the degree of similarity between model faces through the pros and cons of the order and applies it to the structural similarity matrix between models. In addition, this paper establishes corresponding mathematical models for the shape and structure of the model and constructs the shape similarity matrix, the surface neighborhood structure similarity matrix, and the structure similarity matrix between the source model and the target model. Finally, this paper designs and implements CAD model retrieval methods based on greedy algorithm and bat algorithm and designs experiments to compare the performance of the algorithm proposed in this paper with traditional algorithms. The result of the experiment shows that the algorithm proposed in this paper has obvious advantages in sports action analysis compared with the traditional algorithm.
Decomposition Makes Better Rain Removal: An Enhanced Attention-Guided Image De-raining Using Deconvolutions Network

Downpour streaks noticeable all around show assorted qualities with various shapes, headings, densities, even the complex covered marvel, causing extraordinary difficulties for the de-pouring assignment. As of late, profound learning-based picture de-pouring techniques have been broadly examined because of their amazing exhibition. In any case, the majority of the current calculations actually have constraints in eliminating precipitation streaks while saving rich textural subtleties under muddled downpour conditions. To this end, we propose to disintegrate downpour streaks into numerous downpour layers and separately gauge every one of them along the organization stages to adapt to the expanding abstracts. To all the more likely portray downpour layers, a further developed non-neighborhood block is intended to abuse the self-similitude of downpour data by learning the all-encompassing spatial element relationships while lessening the estimation intricacy. Also, a blended consideration instrument is applied to direct the combination of downpour layers by zeroing in on the neighborhood and worldwide covers among these downpour layers. Broad tests on both manufactured blustery/downpour cloudiness/raindrop datasets, certifiable examples, the murkiness, and low-light situations show generous enhancements both on quantitative pointers and special visualizations over the present status-of-the-craftsmanship advances.
The Opportunities and Challenges of Implementing Green Internet of Things (IoT) Towards Energy Saving Practices in the Current Environment

The application of internet of things is considered as one of the key cornerstones in the new fifth generation network, it is estimated that more than 40 billion IoT devices will be in place around the work by 2025. The current focus of government and non-profit organisations revolves around implementing energy saving practices, emission of carbon and managing the ecology for sustainable future. The basic objective of Green IoT is to reduce the emission of harmful greenhouse gases and other toxic pollutants, support the government and other stakeholders in conserving the environment and reduce the power consumption. The adoption of the various techniques through Green IoT will lead to creating better and sustainable environment. The Green IoT enables in applying efficient procedures which enables in facilitating the reduction of the harmful substances and to enhance sustainable ecology for the future generation. The smart environment is the one which is focused in leveraging the IoT services, enable in deploying the resources effectively and use the resources so that the energy can be used effectively. This paper attempts to provide the enhanced opportunities and critical challenges in implementing the Green IoT towards energy saving practices. The Green IoT enables in indicating the application of critical practices for green environment and also supports in saving more cost and energy. This enables in offering systematic solution which will create sustainable growth for the community and also enable in addressing the societal challenges effectively.
Efficient Resource Allocation using Federated Learning in Cellular Networks

Federated learning (FL) has been introduced as a promising solution to address data privacy issues. FL is receiving widespread attention in various applications, including wireless communication technology. Using FL performs local model training and uploads model parameters for global synthesis at the server. In wireless communication technology, the most concerning issue is resource allocation, when tens or hundreds of devices are involved in the operation. Therefore, this paper proposes three algorithms, random scheduling (RS), round-robin (RR), and proportional fair (PF), to allocate resources using FL with Poisson Point Process (PPP) distributed in the cellular networks. The efficiency of resource allocation is evaluated using the MNIST Fashion multi-modal dataset and considering each user's (UE) convergence speed and time.
Review Based on Named Entity Recognition for Hindi Language Using Machine Learning Approach

Named Entity Recognition (NER) is a critical job in machine learning that automatically recognizes and explains Named Things in writing, such as an Individual, a Position, or an Association. NER has played a critical role in numerous applications, including information removal and recovery, machine transformation, question answering (Q–A), and writing summarization. While much research has been conducted on NER in Hindi, no instrument with high accuracy has been created yet, as per the Literature Review. Developing a NER system for Hindi is very difficult due to the language's ambiguity, morphological richness, and resource scarcity. We provide a state-of-the-art review of different natural language processing methods (NER) for the primary language of Hindi in this article.
Applying Graph Convolution Networks to Recommender Systems based on graph topology

The recommender systems are widely used in online applications to suggest products to the potential users. The main aim of recommender system is to produce meaningful recommendation to a potential user by monitoring user’s purchasing habits, history, and useful information. Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative filtering. The GCN performs neighborhood aggregation mechanism to extract high level representation for both user and items. In this paper, we propose a recommendation algorithm based on node similarity convolutional matrices with topological property in GCNs where the linkage measure is illustrated as a bipartite graph. The experiments indicate the necessity of capturing user–item graph structure in recommendation. The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches.
The Role of Blockchain Technology on Human Rights Management and Business Ethics—Utopia or Dystopia

Blockchains are a type of technology that uses cryptography's unique capabilities to securely store data. They do not rely on a central source to preserve records, instead of disseminating data over a network, unlike traditional types of record-keeping. Blockchains are the technological underpinnings of digital currencies like Bitcoin. Beyond transactions, blockchains, both private and public, have a variety of present and proposed applications. They can be used to verify people and data, as well as serve as a foundation for file systems and content distribution. The Paper explains why public permissionless blockchains exist in a world apart from the real world. Several architectural elements of such blockchains make it partially law-proof, or, to a degree, a technology that creates a realm in which the law cannot be implemented as it is today. For starters, these blockchains use pseudonymity to protect their users’ privacy. When using blockchain platforms and services, users show their “public key,” which is an encrypted identification, rather than their real-life identity. Second, because of their distributed and decentralized character, these blockchains are immutable, posing a substantial obstacle to enforcement. A promising technology has gotten a lot of attention over the last decade: blockchain. It has a lot of promise, according to predictions and testimony. Despite this proliferation of material, there is still a shown need to comprehend and overcome the technology's limits, as well as inadequate empirical research of its stated values. This Paper particularly examines two contemporary blockchain applications for human rights business practice and argues that established legal and regulatory functions and systems of accountability must not be replaced with technology for blockchain's actual value to be realized. We conducted a qualitative analysis using a literature study and primary research to highlight avenues to examine for future uses of the technology in supply chain initiatives, as well as existing evolutions of blockchain's current implementations.
The Internet of Things and Its Aspect for Digital Marketing During COVID-19 Pandemic

This paper is prepared to identify the influence of the Internet of things and its aspects in digital marketing during the COVID-19 pandemic. The research examines the various aspects of digital marketing before and after the pandemic. The paper also evaluates the tools of IoT for digital marketing. The COVID-19 pandemic has inevitably resulted in digital technologies due to social distancing customs and nationwide lockdowns. It has been found that the COVID-19 outbreak has had an impact on marketing in general and digital marketing in particular. IoT and digital technologies have become essential enablers of connectivity, allowing people to maintain their everyday lives while connecting in digital ways to which they have never been connected before. In this research, the investigator uses secondary data to obtain reliable results. This research methodology employs a descriptive qualitative approach to analyze diverse digital marketing and IoT. In addition, this study proposes a more straightforward method for predicting customer tastes and preferences on digital platforms.
Compact modular multiplier design for strong security capabilities in resource-limited Telehealth IoT devices

Telehealth is an emerging model of delivering quality health to remote communities and stay-at-home users. This is motivated by the rising health care costs and by the benefits of many patients staying at home as opposed to extended hospital stays. Telehealth relies on IoT technology, but IoT devices present the weakest security link to the system. The challenge is to implement strong security capabilities in resource-limited IoT devices. This justifies the use of elliptic curve cryptography (ECC) over the other traditional and resource-consumed approaches such as RSA. Efficient modular multiplication is a basic operation needed for ECC systems. Therefore, the compact and efficient implementation of this operation will significantly affect the performance of ECC in resource-limited applications. This work presents a compact serial-in/serial-out word-based systolic implementation of modular multiplication. The proposed structure is derived using a formal and systematic technique for mapping regular iterative algorithms (RIA) onto processor arrays. The proposed methodology enables control of the processor array workload as well as the workload of each processing element. Controlling the processor word size allows control of system speed, latency, and area. The proposed processor structure saves area and energy consumption by a factor up to 96.3% and 98.5%, respectively.
TELEGRAM APPLICATIONS IN HISTORY LEARNING OF SMA NEGERI 2 KABANJAHE

Learning limitations as a result of the covid-19 pandemic which requires distance learning or online learning (on the network) of history subjects based on the telegram application. This type of qualitative descriptive research aims to find out the events, facts and circumstances that occurred during the research by presenting what happened. This research was conducted to see the process and impact of the use of the telegram application on students' history learning at SMA Negeri 2 Kabanjahe. The data instrument in this study used observations, online interviews and instrument filling in class XI IPS 2 students as samples using google forms, as well as documentation. The method used to analyze the data is a qualitative technique. The result of the research is that by utilizing the telegram application in history learning for class XI students at SMA Negeri 2 Kabanjahe, it runs well with few obstacles on the network in the event of rain or power outages. This can be seen in the smooth learning process and the results of interviews with teachers and students who say there are no obstacles in the use of this telegram application as a medium in the history learning process, other than only network constraints in the event of rain or power outages. It can also be proven in the learning outcomes of students in the cognitive (knowledge) and psychomotor (skills) aspects that are good with the percentage of completeness reaching (64.7%) in the cognitive (knowledge) and (85.2%) psychomotor aspects (Skills).