Reviewerly/exHarmony huggingface dataset:
TPTS: Text pre-processing Techniques for Sindhi Language

The Internet is a significant source of textual data, with users generating vast amounts of information through social media and news agencies daily. The extraction of meaningful information from large datasets is a challenging and costly process. Text pre-processing is a crucial initial step in any Natural Language Processing (NLP) task, as it can impact the overall performance of the study. The main objective of text pre-processing is to transform unstructured text into a linguistically meaningful (standard form) format, making extracting information for any text-processing task easier. This paper introduces TPTS, a model for text pre-processing in the Sindhi language. TPTS performs essential NLP tasks such as text tokenization, normalization, stop-word removal, stemming, and POS tagging for the Sindhi language. The Sindhi Text Corpus (STC), consisting of 1.5k Sindhi text documents collected from various online news websites, is used for experimentation. The TF-IDF approach is employed to identify high-frequency stop-words in the Sindhi language. Furthermore, a rule-based system tags words with their part of speech in Sindhi input text. The ROUGE evaluation metric is used to assess the effectiveness of the proposed TPTS technique, achieving 89% accuracy on the STC corpus. The Sindhi language is spoken by over 30 million people globally, and the lack of adequate NLP tools and resources limits the development of technology and natural language applications that can benefit Sindhi speakers. The proposed TPTS model can aid in developing such applications, making it beneficial not only for text pre-processing tasks but also for other Sindhi language text-processing tasks such as text summarization, sentiment analysis, speech-processing applications, text mining, and information retrieval systems.
Development and Implementation of Document Management System for Ilocos Sur Polytechnic State College, Tagudin Campus

With the exponential growth of paper documents daily, organizations face significant challenges in storing, maintaining, and managing these piles of paper data. Even with the increasing trend toward digitalization, many businesses and institutions still rely heavily on paper documents. However, accumulating physical copies can quickly become overwhelming, resulting in limited storage space, difficulty finding specific information, and increased operational costs. As the ISPSC prepares to become a university with additional campuses and a larger population of students and staff, managing paper-based documents remains a challenge for the institution. This study developed and implemented a web-based Document Management System (DMS) at ISPSC, Tagudin Campus, as a tool designed to capture, store, organize, retrieve, and manage documents and files in a centralized digital environment. The study analyzed the respondents' user-satisfaction using a standardized 20- statement of the Website Analysis and Measure Ment Inventory (WAMMI) questionnaire. An online survey was conducted on the respondents (n=40) who had experience in using the DMS. The respondents were generally very satisfied with how the document management system was used to arrange, store, track, and manage paper files inside the campus.
Graph convolutional network with tree-guided anisotropic message passing

Graph Convolutional Networks (GCNs) with naive message passing mechanisms have limited performance due to the isotropic aggregation strategy. To remedy this drawback, some recent works focus on how to design anisotropic aggregation strategies with tricks on feature mapping or structure mining. However, these models still suffer from the low ability of expressiveness and long-range modeling for the needs of high performance in practice. To this end, this paper proposes a tree-guided anisotropic GCN, which applies an anisotropic aggregation strategy with competitive expressiveness and a large receptive field. Specifically, the anisotropic aggregation is decoupled into two stages. The first stage is to establish the path of the message passing on a tree-like hypergraph consisting of substructures. The second one is to aggregate the messages with constrained intensities by employing an effective gating mechanism. In addition, a novel anisotropic readout mechanism is constructed to generate representative and discriminative graph-level features for downstream tasks. Our model outperforms baseline methods and recent works on several synthetic benchmarks and datasets from different real-world tasks. In addition, extensive ablation studies and theoretical analyses indicate the effectiveness of our proposed method.
Design and Implementation of Campus Information Aggregation Platform

With the rapid development of informatization and digitization, the tide of digital services such as digital campus and digital government affairs is rising. However, many scattered, disordered and even redundant data exist on campus due to the non-interoperability of information used by various systems. In view of the current situation analysis, the B/S architecture was adopt in this paper, and the Vue.js, PHP, MySQL, Python and WeChat applet were used to develop a campus information aggregation platform. With the convenience of obtaining information on the Internet, information about life and learning on campus was collected and processed, which was convenient for teachers and students to quickly obtain the desired information.
Quantitative Evaluation System of Indoor Fitness Equipment for the Elderly Based on AHP-TOPSIS

The paper aims to reduce the subjectivity and one-sidedness in the choice of fitness products for the elderly.The method of the Analytic Hierarchy Process (AHP) is applied to establishing the hierarchical structure of evaluation on indoor fitness equipment for the elderly, and then the quantitative index of evaluation on fitness equipment for the elderly is obtained by constructing evaluation matrix analysis, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to calculate the distance of each scheme to the ideal solution so as to obtain the best fitness products for the elderly.The result is that a more scientific evaluation system of indoor fitness products for the elderly is achieved on basis of the model integrating AHP and TOPSIS.The conclusion is that this method reduces the subjectivity of decision-makers and enhances the rationality, which can provide reference ideas for the evaluation and selection of fitness products of the same kind for the elderly.
Acquisition Practice of High Signal-to-Noise Ratio in YTB Block of Sichuan Basin

YTB block in Sichuan basin is a favorable area to exploit oil and gas in shallow tight rock.3D seismic project of this zone has two characteristics.Firstly, it has high requirements for the tolerance rate of the construction process and the acquisition of high signal-to-noise ratio seismic data; Second, there are widely obstacles and noises that lead to difficult acquisition construction organization.In acquisition practice, high signal-to-noise ratio seismic data was obtained by reasonable design of construction scheme, optimization of excitation parameters, improvement of receiving conditions and optimization of obstacle crossing observation system.
ERROR ANALYSIS: INVESTIGATING THE PARAGRAPH WRITING OF ESL MALAYSIAN LEARNERS

The study investigates an impromptu writing task administered to diploma students studying in one of the technical universities on the East Coast of Malaysia. In particular, it identifies their errors in writing a one-paragraph essay, the kind of errors students made in completing the task, and the least errors students made in writing the one-paragraph. The samples were 48 students undergoing Preliminary Semester taking English as one of the subjects in the particular semester. The study found that students made errors to a certain degree in using articles, tenses, and subject-verb-agreement (SVA), to name a few in the current study. More specifically, the highest number of errors students made were in tenses, SVA, and word choices, while the least errors were in possessive and attributive structures, gerunds, and infinitives. The study implied that assigning students impromptu writing contributed to their performance errors. Despite their low vocabulary knowledge, it also made them less expressive when writing in the condition.&#x0D;
An Improved YOLO for Road and Vehicle Target Detection Model

The yolo series is the prevalent algorithm for target identification at now. Nevertheless, due to the high real-time, mixed target parity, and obscured target features of vehicle target recognition, missed detection and incorrect detection are common. It enhances the yolo algorithm in order to enhance the network performance of this method while identifying vehicle targets. To properly portray the improvement impact, the yolov4 method is used as the improvement baseline. First, the structure of the DarkNet backbone network is modified, and a more efficient backbone network, FBR-DarkNet, is presented to enhance the effect of feature extraction. In order to better detect obstructed cars, a thin feature layer for focused detection of tiny objects is added to the Neck module to increase the recognition impact. The attention mechanism module CBAM is included to increase the model’s precision and speed of convergence. The lightweight network replaces the MISH function with the H-SWISH function, and the improved algorithm improves by 4.76 percentage points over the original network on the BDD100K data set, with the mAP metrics improving by 8 points, 8 points, and 7 points, respectively, for the car, truck, and bus categories. Compared to other newer and better algorithms, it nevertheless maintains a pretty decent performance. It satisfies the criteria for real-time detection and significantly improves the detection accuracy.
Cluster analysis of type II Diabetes Mellitus Patients with the Fuzzy C-means method

Cluster analysis has been widely used in the fields of mathematics and health sciences. This study aims to classify distance-based data which are divided into several clusters. Accurate prediction from the outcome or survival rate of diabetic patients can be the key for the stratification of prognosis and therapy. A retrospective study of 447 medical record data of type II diabetes mellitus patients aged 18 years old or above and were hospitalized in the PKU Muhammadiyah Gamping Hospital from 2015-2019. Clustering is using the PCA-Fuzzy C-Means method based on patients’ survival status, demographic characteristics, therapy, and blood glucose (BG) levels. Clustering evaluation by Davies Bouldin Index (DBI). Data analysis is using Jupyter Notebook programme. Cluster formation are first cluster consists of 171 members, second cluster consists of 9 members, third cluster consists of 267 members with DBI 2,2645. 401 patients (89,7%) were recorded as alive and 46 patients (10,3%) were recorded as dead. A total of 447 patients: 54,1% were male; 90,6% were ≥ 45 years old; 66,4% has comorbidities; 51,7% had BG level of more than 200 mg/dl, and 57,7% received combination insulin+oral antidiabetic therapy.
Cyberbully Retarder System using Machine Learning

Covid-19 has switched almost every facet of life to online mode. Therefore, parents are forced to buy gadgets for their children for learning purposes. As a result, cyberbullying has also increased. Nowadays, youngsters get bullied online while using social media and playing online games. Everyday nearly thousands of users deal with bullying related to body shame, facial appearance, behavior, racism, sexual harassment, and other kinds of online bullying. To prevent this harassment, Machine learning algorithms are used to automatically detect the use of abusive words used by the bullies, and the developers will be notified if any type of abusive words are found and the necessary action can be taken. Moreover, a message will be sent if there is any abusive content in the chat. Therefore, the proposed method is efficient in identifying a cyber bullying activity on social media. This system will undoubtedly be useful as many students create social media accounts to keep track of their school life. Now that everything is online, this system proves beneficial in preventing cyberbullying.
The Students’ Perspectives About the Effectiveness Argumentative Speaking Tasks Given on English Class

Argumentative speaking skill is a communication process that aims to convince others to accept our opinions based on facts and strong evidence. The purpose of this study was to find out the students' perspectives about the effectiveness of argumentative speaking tasks given. This study used a qualitative method. The participants in this study were third semester students in English education of Universitas Muhammadiyah Surakarta. There were twenty participants in this study in class G. Data were collected using interviews using notes to record the results of the interviews and documentation in the form of student data instruments and taking pictures during the interviews. The result showed the students perceived that argumentative speaking tasks given was effective for students to form critical thinking and increased their speaking skills.
Comparative Analysis of Human Hand Gesture Recognition in Real-Time Healthcare Applications

There is strong evidence that people will be able to communicate with various sensor-based devices more effortlessly and intuitively thanks to future human–computer interfaces (HCI), creating a more human-like interaction. Human–computer interaction has advanced as a result, and new technologies have been created that allow humans to connect with computers in increasingly intuitive and natural ways. Computer systems that use these technologies exhibit improved efficiency, speed, power, and realism. Real-time gesture interpretation is utilized by a vision-based system to modify items in a medical data visualization environment. This study suggests a technique for recognizing gestures that makes use of a convolutional neural network's sequential model made out of a collection of near-infrared pictures obtained by a Leap Motion sensor. The CNN model is made up of 13 different layers. The study also discusses numerous hand gesture recognition applications in the field of healthcare as well as the methodologies employed by these applications.
Decentralized Expert System for Donation Tracking and Transparency

Charities operate in a challenging financial climate, mostly because they are not very transparent. Every day, it becomes harder to tell if donations were going to the correct places or whether they were going in another direction, like funding shady campaigns or terrorist efforts. Donors thus lose confidence in these groups, which ends communication with philanthropic organizations. Because of this, it is more challenging for organizations to generate money at high expense. Blockchain technology, a decentralized database that offers security, transparency, and cheaper financing costs by removing the involvement of third parties between donors and organizations, was recommended in this study as a potential solution to the described problem. A new track donation model that included a number of additional participants that controlled the contribution process and allayed any scepticism about the charity was offered. Blockchain enables the tracking of all donations, enabling donors to trace the usage of their funds. Smart contracts are used for all donations, enabling donors to know exactly when and how their contributions will be used.
Traceability of Unwitting Disclosure Using Explainable Correlation in Procurement and Supply Chain

Many firms maintain highly complicated and worldwide supply chains in today’s world, which results in tons of transactions. Because of the gap in record maintenance for transactions, there arises the possibility of an anomalous transaction, i.e., any suspicious transaction that does not follow the historical pattern of regular transactions, thus creating a demand for disclosure of procurement and supply chain transactions. In this paper, the authors define the method of segregation of transactions and obtain derived parameters from a study. This also establishes a model-based correlation between the derived parameters and the blockchain. Blockchain plays a critical role in creating immutable records. It will help to reduce human intervention and induced errors, maintain transparency in the transactions, and improve the verifiability and traceability of an anomalous transaction during an audit.
Automated Helpline Service Using a Two-Tier Ensemble Framework

For many years, emergency calls have been handled by humans. Under supervision, a group of people would undergo training for a minimum of three months before they were allowed to become call operators. But this procedure is quite tedious and time-consuming. A person typically calls such an operator to get some kind of immediate assistance when they find themselves in an emergency. However, based on the caller’s emergency information, the operator must decide which department’s services must be sent out. Additionally, all incoming calls are handled in a queue-like manner. Therefore, if the operator spends a significant amount of time talking to a caller, it is possible that the callers in the queue that follow them will receive assistance very late. The aim of this project is to develop an application that could simulate an automated call operator. The front-end interface for distressed users to notify the system of their emergency would be a chatbot. Respective departments are then alerted based on the emergency label it gets classified into, that is carried out using a two-tier Ensemble framework with a final accuracy of 94.7%.
An Efficient Secure Cloud Image Deduplication with Weighted Min-Hash Algorithm

Cloud storage services have emerged as a competitive alternative to other methods of data storage, but its operators must balance privacy with optimization. Clients would want to utilize end-to-end data encryption to protect data security; however, effective encryption nullifies the value of all established storage-optimization methods like data deduplication. To save cost and storage space, image deduplication in cloud computing is proposed. To protect the confidentiality of the image, the notion of a hybrid secure approved deduplication algorithm with a weighted min-hash pre-processing method is presented in the paper. The image will be encrypted or decrypted in the deduplication system using a weighed min-hash encryption key that is produced by computing the hash value of the image content. The ciphertext used to verify the duplicate image copy will be produced by identical image copies in the same way. This system has undergone security analysis to ensure its safety.
Additive Congruential Kupyna Koorde Cryptographic Hashfor Secured Data Storage in Cloud

Cloud computing is the process of storing data without direct active management by the user. Cloud computing security is an essential concern to protect the cloud environment, data, information, and applications against unauthorized access. Storage security is concerned with securing data storage systems. Many works have been developed for protecting data communication. However, the data integrity rate and data confidentiality rate were not improved. In order to address these problems, Additive Congruential Ephemeral Cryptographic Kupyna Koorde Hash Storage (ACECKKHS) Method is introduced for efficient secured data storage using better data confidentiality as well as integrity rate. ACECKKHS method comprises key generation, encryption, as well as decryption to enhance privacy performance in a cloud environment. From ACECKKHS Method, the user registers his/her information with CS to achieve protected data storage. After registration, CS provides an ephemeral public key as well as an ephemeral private key to each registered user using Ephemeral Castagnos–Laguillaumie Cryptography by means of an additive congruential generator. After key generation, the cloud user encrypts the information by ephemeral public key as well as transmits with the server to secure data storage. Davies-Meyer Kupyna Koorde distributed hash is employed by CS for storing the encrypted cloud user data with the help of their hash value to minimize the storage complexity. After that, the stored information gets retrieved by the cloud user for performing the decryption with the sender’s private key. In this way, the data gets stored in a secure manner by using ACECKKHS Method. Experimental evaluation is performed with different parameters namely confidentiality rate, data integrity rate, computational cost, as well as storage cost using a number of cloud users’ data. ACECKKHS increases data confidentiality and integrity, and minimizes the computational cost as well as storage cost than the conventional approaches.
Blockchain Technology and Its Application in 3D Parts Procurement: A Case Study

In this paper, we present a review of Blockchain based technologies and their application in supply chain procurement. The blockchain has two main characteristics that may be leveraged for adoption in supply chain networks and logistics. First, the exchange of information in blockchain is real time, secure, verifiable, trustable and these are accessible to all members of the network. 3D Printing is an excellent application for Blockchain based procurement. In Blockchain based 3D printing, the aim is to secure contracts from suppliers that may be used in assemblies or for spare parts. An OEM can issue a call for proposals in real time from 3D printing suppliers. The bids can be received securely using the blockchain with partial/complete order quantities. In this paper, we show how the blockchain can be used to integrate the four stages of 3D printing procurement: Scan and Design, Build and Monitor, Test and Validate, and Deliver and Manage. Finally, we present a case study for scheduling 3D printers in manufacturing network. We examine a 3D printer scheduling problem by considering multiple printing alternatives in which various types of products can be printed in different forms using Mixed-Integer linear programming (MILP) under scenario-based experiments.
A Survey Paper: On Path Planning Strategies Based on Classical and Heuristic Methods

Path Planning algorithm is widely used in unmanned vehicles and autonomous robots. The applications of these robots and vehicles are huge. Though the basic requirement of these algorithms is to find the path from source to goal, the implementation process is different for different algorithms. Based on their characteristics, these algorithms can be differentiated into two types. One is the classical method, and another is the heuristic method. In this paper, some popular classical methods like Sub-Goal Method, Potential Field, Dijkstra’s Algorithm, Rapidly-explore Random Tree Cell, and Probabilistic Road Map, and a few heuristic methods like Ant Colony Optimization, Genetic Algorithm, and Bug Algorithm are briefly described.