Reviewerly/exHarmony huggingface dataset:
HOW QUALITY ASSURANCE DRIVES CREATIVITY AND INNOVATION IN JORDAN'S PRIVATE UNIVERSITIES?

This study sought to assess the effects of quality assurance on innovation and creativity at Jordanian private universities across three of its dimensions: input quality, operations quality, and quality audit. Four private universities in Jordan completely invented the study's sample. The deans and heads of the academic departments were also included in the study sample, making a total of 140 participants. A questionnaire was created as a tool for data collecting as part of the study's descriptive analytic technique. The distribution of the questionnaire was conducted through a thorough surveying procedure. Depending on the results of the statistical analysis, there is a high level of application of quality assurance standards by academic departments’ deans and heads, through supporting quality assurance programs in Jordanian private. In addition, the study found that there is a statistically significant impact of quality assurance on creativity and innovation in Jordanian private universities.
Matter and Information

It is obvious that living bodies are made of substances. But this is only one aspect of their existence. It has now been established that information plays an equally important role in their lives.
Information Constancy

Time is one of the basic characteristics of the material world, which determines the dynamism, direction, and interconnection of processes in space. This is a form of being of matter, which characterizes the duration of existence, and the sequence and direction of changes in the states of developing material systems. This is the vector of motion of matter, the duration of motion of matter, and a measure of changes in the motion of matter. In fact, it is not even a physical, but rather a philosophical category, introduced by humans for the convenience of orientating ourselves in space. It is a measure of changes, directed exclusively from the past into the future.
Application of ISM in Evaluating Inter-relationships Among Software Vulnerabilities

Complicated digital communication networks generate faults in development, implementation and governance, which are the main cause of software vulnerabilities. The growth of these vulnerabilities urges the software development and application security teams to rely on vulnerability detection tools throughout the development phase of a software. These developers are often overwhelmed with a steady stream of security alerts that must be addressed without slowing down the pace of software development. Vulnerability prioritization often helps the security team to remove the highly severe vulnerabilities first, but to check if the vulnerability is being linked to another vulnerability needs to be taken into the consideration. The purpose of this study is to establish a quantitative evaluation framework to discover and analyze the link between vulnerability categories, leading to an improved elimination of vulnerabilities using the Interpretive Structural Modeling (ISM) technique. The model is validated for an Indian software development.
Secure Shift-Invariant ED Mask-Based Encrypted Medical Image Watermarking

Security is a major concern for huge medical imaging data captured on regular basis by medical authorities. In this regard, many watermark algorithms have been proposed in past using edge detection, but improving robustness is an open challenge. Thus, in this paper, a novel two-pass image watermarking algorithm is designed. The proposed algorithm takes the advantage of shift invariance (SI) mask for generating asymmetric edge detection (ED) in wavelet transform domain. Then, in order to enhance the security and robustness, the simple and fast random key-based encryption is combined to securely store the watermarked images in cipher form. The image is first decrypted using the private key, and then, watermark reconstruction is performed. The performance is evaluated under various noisy attacks for qualitative evaluation. The parameter quantitative evaluation is presented in terms of MSE and SNR.
KONFLIK SOSIAL DALAM NOVEL DOKTER YANG DIRINDUKAN KARYA ASMA NADIA DAN RANCANGAN PEMBELAJARAN SASTRA DI SMA

The problem in this study is the social conflict in the novel the doctor missed by Asma Nadia and the design of learning literature in high school. The purpose of this study is to describe the social conflict in the novel the doctor missed by Asma Nadia and the design of learning literature in high school. The method used in this study is a qualitative descriptive method. The data source in this study is the novel Doctor Yang Dirindukan by Asma Nadia. The data in this study are quotes that contain social conflicts in the novel Doctor Yang Dirindukan by Asma Nadia. The results of this study indicate that social conflicts that often appear in the novel Dokter Yang Dirindukan by Asma Nadia are social conflicts between individuals with a total of 26 conflicts. As for social conflicts that do not often arise, namely social conflicts, conflicts of interest, as many as 17 conflicts. For social conflicts, cultural differences only appear 12 times in the novel, while very few social conflicts appear, namely social changes that are too fast in society as many as 11 conflicts.
Research on a Guide Dog Robot for Expressing Visual Environment by Voice

This paper proposed a guide dog robot that can express visual environment by voice. The guide dog robot uses NIC, a combination of CNN and LSTM methods, to recognize the visual scene and generate captions. The generated sentences are then converted to speech using speech synthesis to guide the visually impaired. The guide dog robot can not only guide the visually impaired person safely to their destination, but also providing explanation services of street scenes, enabling the visually impaired person to enjoy visual scenes. The system is consisted of visual scene recognition, caption, and speech synthesis. The effectiveness of the system is confirmed through experiments using the proposed guide dog robot.
Deterministic approach to classify the power efficient S-Boxusing machine learning

The substitution box (s-box) serves as one of the key elements of cryptography responsible for uncertainty and nonlinear properties. These boxes must have a variety of security qualities in order to protect a cipher against various attacks, including side-channel attacks. Designing a 16*16 s-box that is both cryptographically secure and energy efficient is a difficult task. Thus, in order to automate the process of determination and verification of dynamic power efficiency of s-boxes, a method of supervised machine learning approach has been used here. Additionally, utilizing the outcomes of supervised learning, in order to construct s-boxes that are both cryptographically secure and low-power, we propose a deterministic model that may be used in an optimization strategy to estimate the dynamic strength of an s-box. A machine learning- approach has been integrated to automate and categorize the power efficiency. It is evident that there is a 4% increase in the accuracy of power prediction of s-box.
Structural flood control model based on Eco-hydraulic in small watersheds (Case study of the Akelaka-Halmahera river)

Various factors, such as the physical condition of the area, nature, and humans, influence the phenomenon of flooding. Thus, the flood control application model will differ in each region. The application of flood control structurally lowers the flood water level significantly, but it changes the ecosystem around the river. The eco-hydraulic concept is a new paradigm in watershed management to reduce the impact of floods while still paying attention to river ecosystems. This study aims to integrate structural methods with eco-hydraulic concepts in flood control in small-scale watersheds. The locus is the Akelaka watershed on Halmahera Island. Watershed morphometric analysis using the ArcGIS application, hydrological analysis for five and 25-year return floods, and hydraulics testing using the HecRAS application are used. The application of this concept uses vegetation on river slopes after normalization. The results showed that flood control which combined normalization, eco-hydraulic and structural ideas in Akelaka village, showed excellent results. The potential for inundation is reduced by up to 93% for a five-year return period flood discharge and 89% for a 25-year return period flood discharge.
Studi Efektivitas Koagulan Kitosan-Kapur Dalam Menurunkan COD, MBAS dan Fosfat pada Limbah Laundry

Abstract. Environmental pollution that is often encountered in daily life that comes from laundry waste. Laundry waste includes pollutants or substances that pollute the environment because in it there is a substance called linear alkylbenzene sulphonate (LAS). LAS is a detergent that is classified as hard to brake down by microorganisms (non-biodegradable) so that it can cause environmental pollution. One method that is often used in laundry wastewater treatment is coagulation using chitosan and lime as a coagulant. The purpose of this study was to analyze the efficiency and effectiveness in reducing pollutant levels in laundry waste using chitosan-lime coagulant. This study used a completely randomized design with 200 mg/L chitosan and 0.1-0.5 g lime. The test parameters used were COD, MBAS, and phosphate. Data were analyzed using calculation of efficiency and effectiveness of reduction, linear regression, and one-way ANOVA test. The results showed that under the best conditions, chitosan 200 mg/L and lime as much as 3.5 g resulted in a reduction efficiency of 68.52%, 9.15%, and 92.44%. Chitosan-lime is effective in reducing MBAS and phosphate levels to quality standard, but chitosan-lime coagulant is less effective in reducing COD levels because it still exceeds the the established quality standards
Design &amp; Development of Hybrid Electric Fish-Harris Hawks Optimization-Based Privacy Preservation of Data in Supply Chain Network with Block Chain Technology

Due to the expansion of privacy protection, and data sharing gets complicated and is known to be a hot issue in the research areas. Many business scenarios in the supply chain network have been highly important in research areas. Especially, blockchain technology ensures the essential solution in the supply chain network for securing information sharing. Yet, it is difficult to maintain security at every blockchain level, whereas it is suggested to use “public–private key cryptography.” The fundamental goal of this research work is to present new privacy preservation of data using hybrid meta-heuristic algorithms with blockchain technology. The secured supply chain network is built with blockchain technology. The privacy preservation of data sharing in blockchain technology is handled by the electric fish-Harris hawks optimization (EF-HHO). This new algorithm is utilized for the key generation phase of two processes like “data sanitization and restoration.” The optimal key generation follows a multi-objective problem is solved by the variables like “Hiding Failure (HF) rate, information preservation (IP) rate, and false rule (FR) generation, and degree of modification (DM)”. The result analysis shows the offered model provides effective performance when compared with existing conventional techniques.
Experimental Realization of Quantum Cryptography by Arduino

This paper is concerned with the experimental realization of an Arduino-based quantum cryptography system, in which a setup of five laser diodes is constructed in the transmitter that randomly emits photons to encode information consisting of a sequence of "0" and "1" bits. The receiver contains five photoresistor sensors that detect the presence or absence of light as well as determine the intensity of the light. Laser light is used to securely transmit data, such as text or numbers, in this technology.
Performance Aware Egress Path Discovery for Content Provider with SRv6 Egress Peer Engineering

QoS of applications is essential for content providers, and it is required to improve the end-to-end communication quality from a content provider to users. Generally, a content provider's data center network is connected to multiple ASes and has multiple egress paths to reach the content user's network. However, on the Internet, the communication quality of network paths outside of the provider's administrative domain is a black box, so multiple egress paths cannot be quantitatively compared. In addition, it is impossible to determine a unique egress path within a network domain because the parameters that affect the QoS of the content are different for each network. We propose a “Performance Aware Egress Path Discovery” method to improve QoS for content providers. The proposed method uses two techniques: Egress Peer Engineering with Segment Routing over IPv6 and Passive End-to-End Measurement. The method is superior in that it allows various metrics depending on the type of content and can be used for measurements without affecting existing systems. To evaluate our method, we deployed the Performance Aware Egress Path Discovery System in an existing content provider network and conducted experiments to provide production services. Our findings from the experiment show that, in this network, 15.9% of users can expect a 30Mbps throughput improvement, and 13.7% of users can expect a 10ms RTT improvement.
Development of Bilingual Chatbot for University Related FAQs Using Natural Language Processing and Deep Learning

Ever since the rise of the Covid-19 pandemic many organizations have shifted to the new online norm of digitalization. In the list of educational intuitions, the Universities are now offering online programmes to students. The admission process can be frustrating and time-consuming for both the students and the university. Students will have several queries and issues that need to be answered on the spot and clarified before the application process begins. However, chatbots are widely being used everywhere from websites to personal devices. Everyone wants flexibility and ease of access to information faster and in an accurate form, this research paper explores the fundamentals of conversational chatbots in the University system driven by system-chatterbots using neural networks. Focusing on developing a bilingual chatbot that uses Fijian (iTaukei - (Vosa Vakaviti)) and English language through the use of NLP and deep learning algorithms to respond to student FAQs efficiently using intents, a bag of words, and tokenization. This bilingual chatbot can be deployed by any University to provide 24/7 student support to strengthen student communication and ease the flow of information dissemination to its prospective and existing students with prompt responses.
Digital Wisdom Development and Self-reflection of Teacher Candidates

Being digitally wise allows today's students not only to strengthen their natural abilities through existing technologies, but also to respond appropriately to learning networks that are increasingly complex. As a result, it is crucial that teachers and educators continually update and expand their professional knowledge base and improve their practices in order to meet the learning needs of their increasingly diverse students. The research has been conducted during the academic year 2021–2022 with the participation of 1173 teacher trainees enrolled in the Primary Education Degree Program at the University of Palermo. The trainees carried out their activities through digital tools. Brown and Green's Wisdom Development Scale (2006) tool was used to measure eight factors of wisdom development - across 79 seven- point Likert-type scale items: self-knowledge, emotional management. Altruism, judgement, inspirational engagement, life knowledge, life skills and willingness to learn. In addition, some items were selected from the SELFIE for Teachers Tool to help student teachers to review and get feedback on how they are currently using digital tools and technologies for their future profession.
Application of Internet of Things and Virtual Reality Technology in the Intelligent Reconstruction of Old Residential Quarters

In order to speed up the reconstruction of old communities in our country and promote the transformation into a modern smart city. Taking Shenyang as a reference, analyzing the reconstruction situation of old communities, summarizing a series of problems that existed in the initial project design, and having a deeply research on the intelligent reconstruction of the old community in Shenyang. Under the influence of the development trend of artificial intelligence, the feasible method of using Virtual Reality Technology and Internet of Things to assist the initial design is proposed. Can be used as a reference for old communities’ reconstruction in Shenyang, and further providing a new idea for the construction of a modern smart city in the whole country.
FCM-RGM: Segmentation of Nuclei via Exact Contour Enhancement in Pap Smear Images

This paper dealt with segmentation on Pap smear pictures for cervical cancer categorization. A cervical cancer screening may offer information about cervical cancer Pap smear by detecting the cell shape structure on the Pap smear picture. The manual screening method for categorizing cells is a complicated and error-prone task. Several research has been conducted on categorizing cervical cell pictures that are not always segmented initially. Segmentation is critical for obtaining the characteristics included in the picture of cervical cells, such as the cell nucleus and cytoplasm. Despite years of study, the difficulty of cell pictures, such as low contrast, cell imperfection, and overlapping, remains a difficulty. Nucleus and cytoplasm segmentation, the foundation of cervical cancer screening, is a fundamental precondition in automated Pap smear analysis. This work articulates a powerful method for segmenting cervical cells into the nucleus and cytoplasm. Picture enhancement was performed prior to segmentation to increase image quality utilizing the median filter and contrast enhancement approach. In this research, we suggested a novel Fuzzy c-means Region expanding approach of nucleus segmentation (FCM-RGM). Finally, the segmentation contour picture is cropped using the cell size information. This Fuzzy c-means clustering approach also enhances nucleus identification ability significantly. The Herlev public datasets are used to assess the approach. In terms of nucleus segmentation accuracy, experiments show that the suggested strategy dominates state-of-the-art cervical nucleus segmentation techniques.
Application of Artificial Intelligence Technology in Big Data Nining

Data mining is the process of expressing knowledge through data collection and processing. This is an important technique for efficiently extracting batch data. Artificial intelligence (AI) is a simulation technology of the human brain. It makes full use of computer technology and other subject technologies for intelligent learning and machine control. Both are advanced data management technologies, and both have broad application prospects. This article mainly introduces the clustering algorithm and TextRank algorithm. This paper uses AI technology to conduct research and application in big data mining (BDM), and establishes a mathematical model of the underlying TextRank algorithm. The model is solved by the TextRank algorithm, and the status of research and application of AI technology in BDM is assessed,. Experimental results show that the algorithm improves the research and application of embedded big data intelligent technology by 55% and reduces false positive rate and false positive percentage. Finally, by comparing the research and application analysis of artificial intelligence technology in large data storage.
Double Attention Mechanism Text Detection and Recognition Based on Neural Network Algorithm

How to effectively identify these signals and data has become an urgent topic. The neural network model is a stochastic system composed of nonlinear neurons. Therefore, it has strong self adaptability and controllability. This paper proposes a method based on training samples. It classifies the original continuous text through the artificial neural network algorithm. This paper mainly uses experimental method and comparative method to analyze the accuracy, precision, recall rate, F value and its trend in training and the results under different models. The experimental results show that good results have been achieved on the IMDB comment dataset, and the accuracy rate is close to 89.4%.
Water Conservancy Project Construction Supervision Quality Control Information Management System

The quality of water conservancy project is related to the investment benefit, social benefit, environmental benefit and the safety of people's life and property. It directly affects the development of national economy and social stability. It is the key to the success or failure of project construction. In the construction supervision of water conservancy project, the project quality control is the core content of its work. It is not only the most important basis and standard to reflect the quality of supervision service, but also the basic requirement of the owner for supervision. Aiming at the construction of local water conservancy projects, this paper puts forward the necessity of developing the quality control information management system software of water conservancy project construction supervision. In the management of engineering construction quality evaluation, the fuzzy decision-making technology is applied to the comprehensive evaluation of engineering quality. Combined with the supervision practice, the evaluation grade is analyzed, and the four-level evaluation standard is established. Starting from the quality evaluation of the bottom unit project, the comprehensive evaluation of the engineering project is completed layer by layer through subdivisional works, divisional works and unit works.
Home Automation System for People with Limited Upper Limb Capabilities Using Artificial Intelligence

People with physical disabilities (PD) have problems carrying out daily activities, affecting their independence. In this context, a person with PD can communicate through hand gestures or facial gestures, among others. However, selecting the features and patterns that separate one gesture from another is not a trivial problem. In this sense, we propose a real-time domotic system (DS) that works with three subsystems. The first subsystem recognizes hand gestures using a machine learning model and infrared information. The machine learning model consists of pre-processing, feature extraction, classification and post-processing modules. The second subsystem relays the message between the subsystems. Finally, the third subsystem activates the operation of the actuators by gestures. The Hand Gesture Recognition (HGR) model was trained using 6720 observations and tested offline with 1680 observations, giving an accuracy rate of 92.759%. Additionally, the DS was tested online with 1500 observations from 10 users whose persons were not part of the input dataset or model testing. Online testing gives an accuracy rate of 84.07%. Once the hand gesture is recognized, it is sent wireless to an Esp8266 board through the MQTT protocol. The Esp8266 board activates the operation of several actuators. The mosquitto broker embedded in a Raspberry Pi manages the sending and receiving of messages between the computer where the gesture is recognized and the Esp8266 board. The theoretical response time of the DS is 118.87 ms.