Reviewerly/exHarmony huggingface dataset:
Design of a quantum true random number generator using quantum gates and benchmarking its performance on an IBM quantum-computer

Random numbers are used in various domains, including quantum communication and cryptography applications such as key generation and authentication. Quantum mechanics has the intrinsic ability to generate truly random numbers, making it an ideal alternative for scientific applications that require randomness. Quantum wireless communication is proposed as a new means of communication that is safe, secure, and efficient. The simplicity of our source rotation gate, combined with its independently verifiable solitary unpredictability, is essential to obtain quantum random number generators without spending any money. In this chapter, we explore and study the 24-qubit random number. The worst-case entropy value for such randomly produced integers is 0.999445, with the min-entropy of such numbers being 0.0008. Additionally, steering restart tests were used to validate later verification of true randomness, and the statistical characteristics of the True Random Number Generation (TRNG) were assessed using an autocorrelation study and the statistical test suites.
STUDI KOMPARASI PENGGUNAAN TOOLS CAHAYA OMNI SEBAGAI PENDUKUNG CAHAYA SPOTLIGHT PADA RENDER EXTERIOR DENGAN SOFTWARE RENDERING LUMION 11, ENSCAPE 3.4 DAN TWINMOTION EDU 2022

Light and architecture have a very strong relationship. Light is essential in the creation of attractive and successful buildings, but it may also show flaws. During the day, light glows along the building's façade, giving it a new appearance. From ancient temple and church builders to today's notable architects such as Tadao Ando, Jean Nouvel, and Louis I. Kahn, architects have always used this aspect. Light can also be employed to create an impact on architecture, as shown in the Empire State Building's various lighting usage. Artificial light is particularly significant in architectural displays, and light is very important in the construction of home buildings at night. This study uses qualitative methods with a descriptive-comparative approach to night lighting settings in outdoor spaces, applying Lumion 11, Enscape 3.4, and Twinmotion Edu 2022 rendering software. Models imported from SketchUp's 3D Warehouse are provided spotlight lighting at the same point in each application, as well as Omni lights to support lighting at night. Based on the results of the analysis of the use of Lumion 11, Enscape 3.4, and Twinmotion Edu 2022 rendering software, it is possible to conclude that Lumion 11 and Twinmotion Edu 2022 provide a very photorealistic outdoor space rendering experience at night, with each software platform offering good lighting features.
Federating Learning Attacks: Maximizing Damage while Evading Detection

Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed to protect FL against such attacks, attackers are continuously developing more advanced techniques to bypass these protection mechanisms.In this context, this paper proposes a novel attack mechanism that allows malicious users to optimize their crafted reports, maximizing potential damage while limiting the chances of being detected. Our proposed attack technique is a robust approach designed to bypass existing defense mechanisms in FL. Our contributions are mainly investigating the FL model attack from the attacker’s perspective, proposing a model relaxation approach to optimize a single poisoning ratio variable, and formulating a compromise between the chances of being detected and the amount of damage that the attack could cause. Additionally, we introduce three new attack designs, namely DTA, ATA, and NEA, which maximize the effect of the attack. The proposed Distance Target Attack (DTA) minimizes the distance from the target attack model, while the Accuracy Target Attack (ATA) deteriorates the accuracy of the global model. Furthermore, the Number Estimation Attack (NEA) aims to maximize the expected number of attackers that could bypass the aggregation detection mechanisms.The numerical results based on the KDD dataset confirm the ability of the proposed approach to deteriorate the global model accuracy. The experiments showed that the proposed DTA, ATA, and NEA attacks can significantly reduce the accuracy of the global model. These results demonstrate also the effectiveness and robustness of the proposed attack mechanism in compromising the accuracy and security of FL models.
BC-FL Location-Based Disease Detection in Healthcare IoT

The spread of infectious diseases in crowded spaces such as shopping malls, markets, and hospitals is a growing concern. In order to mitigate this risk, it is crucial to develop a method that leverages the power of distributed crowd to learn, de- tect, and alert individuals about potential health hazards. Hence, the integration of federated learning (FL), and blockchain (BC) to provide intelligent platforms that facilitate pervasive AI and trust amongst IoT devices and smart phones can play a significant role in achieving this goal. In this study, we propose a new technique named BC-FL Location-Based, which utilizes smart applications installed on IoT devices and smart phones to detect and predict imminent health risks. The technique works by using algorithms such as maximal clique to detect individuals in close proximity and sharing their health data through a blockchain network. A smart contract then triggers a node with sufficient resources to gather users' learning experiences from the blockchain, aggregate it, and run a model to determine if any of the individuals present in the area are infected. To demonstrate the effectiveness of the proposed technique, we conducted simulation experiments using Ethereum-based private blockchain network, where nodes represent individuals in different locations. We used the maximal clique algorithm to simulate the movement of individuals and compared the results of the model run on individual data versus aggregated data. Experiments showed promising results, with accuracy of detection increasing to 99% when using iid data and 90% when using non-iid data.
AES Algorithm Based Data Possession in Cloud Using Blockchain

Remote secure data storage is key importance in cloud computing. So, for checking remote data integrity, an important paradigm called PDP schemes is introduced. There are still PDP schemes which bilinear matching can be used. One huge file can be broken into various blocks. There is much computation cost and communication cost that arises due to inefficient PDP implementation. Anyway, this is not the right solution therefore, to solve this kind of problem, we propose a new one The PDP model: a blockchain-based private PDP. This new one the solution can be leveraged using blockchain which is vital role in cryptocurrency. For this new concept, paper formalizes its system model and security model. And then a specific blockchain-based private PDP scheme is created designed using blockchain and AES. It is very much safer and we also analyze its performance two different parts: theoretical analysis and implementation the prototype was our analyzes show that the proposed PDP the system is safe, efficient and practical.
FanRay: Using Fan Mechanism in Ray-casting to Improve Target Selection in VR

Raycasting is the most commonly used target selection technique in virtual reality. However, the accurate selection of small and remote objects remains challenging. Inspired by the 2D fan cursor method, we present FanRay: a selection technique applying a fan mechanism to ray-casting to select the nearest target within a fan-shaped activation area, which is defined by the movement velocity and orientation of the controller. We first introduce the selection principle and display styles of FanRay. We then design an experiment to identify the appropriate fan angle range and evaluate the performance of FanRay with Raycasting, the most commonly used selection technology. The results show that FanRay performs better than Raycasting, with shorter selection times, smaller movement angles, and a better user experience.
Edge-Aware Spatial Propagation Network for Multi-view Depth Estimation

Deep learning has made great improvements in multi-view stereo. Recent approaches typically adopt raw images as input and estimate depth through deep networks. However, as a primary geometric cue, edge information, which captures the structures of scenes well, is ignored by the existing multi-view stereo networks. To this end, we present an Edge-aware Spatial Propagation Network, named ESPDepth, a novel depth estimation network that utilizes edges to assist in the understanding of scene structures. To be exact, we first generate a coarse initial depth map with a shallow network. Then we design an Edge Information Encoding (EIE) module, to encode edge-aware features from the initial depth. Subsequently, we apply the proposed Edge-Aware spatial Propagation (EAP) module, to guide the iterative propagation on cost volumes. Finally, the edge optimized cost volumes are utilized to obtain the final depth map, serving as a refinement process. By introducing the edge information in the propagation of cost volumes, the proposed method performs well when capturing geometric shapes, thus alleviating the negative effects of the greatly changed depth on edges of real scenes. Experiments on ScanNet and 7-Scenes datasets demonstrate our method produces precise depth estimation, gaining improvements both on global structures and detailed regions.
Political Communication Strategy through Unique Posters: A Critical Discourse Analysis Study

Political communication is an important part of a country. Through this kind of communication, a piece of information will be properly conveyed. Using unique sentences in posters’ demonstrations is a political communication strategy to attract the government’s attention. This study discussed the unique posters used by students when conducting demonstrations as a form of political communication. This study explained the discourse patterns used by students in demonstration posters through critical discourse analysis using Norman Fairclough’s theory. In addition, this study also revealed how the posters’ language was used as a form of social practice in political communication from the public to the government. Based on the analysis that the researcher had done, it can be concluded that the political communication strategy conducted by students through unique posters in demonstrations has three dimensions: text, discourse practice, and sociocultural practice.
Empowering Language Learning: Harnessing Padlet to Assist Non-English Department Students in English Learning

In this digital era, the emergence of technology for language instruction is unquestionable. Technology helps students learn a target language more quickly, easily, and happily than traditional methods. The non-English department requires extra inspiration, motivation, and a unique approach. As an ICT platform, Padlet is regarded as a creative medium for English learning. This study provides light on how the padlet application can be used to assist and motivate non-English Department students to learn English. This study utilized a quantitative methodology with a sample of 38 people. These participants were all first-year non-English Department Mandailing Natal, STAIN Mandailing Natal Sumatera Utara. The PIAUD (early childhood Islamic education) department had 19 participants, while the Perbankan Syariah (Islamic banking) department had 19 students. For data collection, the researcher utilized a questionnaire. The questionnaires collect baseline information regarding the use of padlet as an innovative medium for English learning. This study found that the majority of students (80%) believe padlet to be a suitable and dynamic medium for learning English, and students are motivated to study English.&#x0D; Keywords: Non-English department, learning English, padlet.
Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Abstract The use of neural networks in a range of academic and scientific pursuits has introduced a great interest in modeling human behavior and activity patterns to recognize particular events. Various methods have so far been proposed for building expert vision systems to understand the scene and draw true semantic inferences from the observed dynamics. However, classifying abnormal or unusual activities in real-time video sequences is still challenging, as the details in video sequences have a time continuity constraint. A cost-effective approach is still demanding and so this work presents an advanced three-dimensional convolutional network (A3DConvNet) for detecting abnormal behavior of persons by analyzing their actions. The network proposed is 15 layers deep that uses 18 convolutional operations to effectively analyze the video contents and produces spatiotemporal features. The integrated dense layer uses these features for the efficient learning process and the softmax layer is used as the output layer for labeling the sequences. Additionally, we have created a dataset that carries video clips to represent abnormal behaviors of humans in megastores/shops, which is a consequent contribution of this paper. The dataset includes five complicated activities in the shops/megastores: normal, shoplifting, drinking, eating, and damaging. By analyzing human actions, the proposed algorithm produces an alert if anything like abnormalities is found. The extensive experiments performed on the synthesized dataset demonstrate the effectiveness of our method, with achieved accuracy of up to 90.90%.
Factors Contributing to Students' Willingness to Communicate during English Day Program

A lot of habituations are needed to get students accustomed to communicating using a foreign language. One of the habits that schools can hold is through outside classroom program, such as English Day program by SMK Putra Indonesia Malang, held weekly to create an English environment for students to develop their speaking skills. However, in practice, students’ willingness to communicate (WTC) was considered low based on the initial observations conducted by one of the researchers. The present study aimed to investigate the factors contributing to WTC during the English Day program. Using a case study through interview and observations, the participants were eight tenth grade students of Industrial Chemical and Industrial Pharmacy classes represented by high and low achievers. The results showed that there were five factors influence their WTC during the English Day Program, including their surroundings, being afraid of committing grammatical mistakes, lack of vocabulary knowledge, lack of pronunciation mastery, and shyness. Thus, English teachers are recommended to encourage students to speak more through fun learning activities in the classroom. Moreover, further researchers are suggested to conduct research on a wider sample to know the main factor contributing to low WTC during the English Day program.
Image forgery detection using Convolutional Neural Networks

Digital forensics vital aspect of picture identity theft has drawn a lot of notice recently. In order to establish the primitive character of images, earlier studies looked at residual pattern noise, wavelet-transformed data and facts, image pixel resolution histograms, and additional characteristics of images. In an attempt to attain high-level picture illustration with the advancement of neural network-based innovations, convolutional neural networks have recently been utilized for recognizing image counterfeiting. This model suggests constructing a convolutional neural network with a structure that is distinct from previous studies in which we attempt to interpret the features derived from each layer of convolution to recognize a variety of picture manipulation using automated feature recognition. Three convolutional layers, one fully interconnected layer, and a SoftMax classifier constitute the suggested system. Our study utilizes our own data collection as the training data, which includes genuine pictures, spliced images, and further enhanced replicates with retouched and re-compressed images. Experimental findings make it abundantly obvious that the proposed network is optimal and versatile.
Gastric Carinoma Detection using Hybrid Model based TL

-Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research. However, due to the amplification of noise during the upsampling steps, state-ofthe-art methods often fail at reconstructing high-resolution images from noisy versions of their low-resolution counterparts. However, this is especially important for images from unknown cameras with unseen types of image degradation. In this work, we propose to jointly perform denoising and super-resolution.To this end, we investigate two architectural designs: "in-network" combines both tasks at feature level, while "pre-network" first performs denoising and then super-resolution. Our experiments show that both variants have specific advantages: The in-network design obtains the strongest results when the type of image corruption is aligned in the training and testing dataset, for any choice of denoiser. The prenetwork design exhibits superior performance on unseen types of image corruption, which is a pathological failure case of existing super- resolution models. We hope that these findings help to enable super-resolution also in less constrained scenarios where source camera or imaging conditions are not well controlled. Key Words: Super-resolution, Denoising, Deep learning, Image enhancement
EMPLOYING SOCIAL MEDIA BASED WILLINGNESS TO COMMUNICATE (WTC) MODULE IN ENGLISH SPEAKING SKILL INSTRUCTION

Having a sense of willingness to communicate (WTC) in the 2nd language (L2) is one of the important factors in improving ESL students’ speaking skills. WTC model is used as an indicator for successful English learning outcome. This study looks at the impact of the employment of WTC on ESL students’ speaking skill by integrating the employment of social media applications in a digital English-Speaking classroom setting. This study employs multi-methods approach using purposive sampling technique involving questionnaire responses of 421 students to identify successful WTC strategies to be integrated in the module; a pre- and post-test to look at impact of the module and focus group interview of 17 pre-university students to investigate their opinion on the module. The findings of the study revealed that students’ WTC were very much influenced by the use of social media applications in a digital classroom. The interview data also showed that students were more engaged and had higher WTC when social media applications were used. These findings are seen as valuable input especially to English language instructors in integrating social media applications in their teaching and learning in order to engage and increase their WTC in English in the classroom.
Assessment of the effectiveness of broadcast media campaign messages on men’s knowledge of prostate cancer in Etsako West Local Government Area

This study seeks to assess the effectiveness of broadcast media campaign messages on men’s knowledge of prostate cancer. A survey method was used to collect data from 600 respondents from Etsako West Local Government Area of Edo State. The survey instrument was designed to measure respondents’ knowledge of prostate cancer, the extent to which they were exposed to broadcast media campaign messages about the disease, and their perception of the effectiveness of the messages. Descriptive statistics and chi-square tests were used to analyze the data. The results revealed that the broadcast media campaign messages were moderately effective in increasing respondents’ knowledge about prostate cancer. The results also showed that the broadcast media messages had a positive effect on respondents’ perception of the effectiveness of the messages. Findings shows that those familiar with the campaigns in the media have also admitted that they know very little about prostate cancer before the campaigns began. Most men still reject the idea that the disease has a genetic component, although some of them are just learning about genetic testing now. The minority of men who believe that heredity contributes to prostate cancer risk have consented to DNA analysis. Also, the attitudes of underestimating and overestimating risks of prostate cancer are influenced by high or low amounts of information collected on the subject. The study recommends that; the men in the Etsako West Local Government Area need to have their preconceived notions about prostate cancer dispelled through well-planned mass media campaigns, and that since there is no guarantee of a cure for cancer, despite the availability of medical procedures and therapies, prostate cancer prevention should be stressed in subsequent campaigns giving particular attention to the causes of prostate cancer and how it can be prevented and treated.
STUDENT COMMUNITY LEARNING PLATFORM

Abstract- The main aim of this survey paper is to study and figure out the various methods and functionalities to implement an online platform where students of a particular college can share their projects and research done on any particular technology over a secure network which is locally hosted and is accessible to students and staff of a particular institution. The motto behind the project is to help build an interactive student/staff community where each user is able to showcase their talents and knowledge gained over an online platform. The content that can be uploaded ranges from any personal project, project ideas, blogs on new technology learnt or any research done on any topics. This paper covers the various Filtering algorithms &amp; Server hosting. Towards the end of the paper, the future scopes are also discussed. Keywords- Content Filtering, MySQL,PHP, Machine Learning, Student Community, Learning Platform
Knowledge Sharing Behaviour to Accelerate Green Practices Adoption for Construction Industry

Green practices in the construction industry are coined based on the concept of protecting the environment and providing a more environmentally friendly solution for the construction activities. Various green practices had been introduced namely in the use of green materials, green management and also technology. However, after decades of efforts, the industry is still daunted by the issues such as lack of awareness and knowledge that hinder the full adoption of green practices in the industry. Thus, this paper aims to explore the knowledge sharing behaviour among construction stakeholders to accelerate green practices adoption in the construction industry. There are four knowledge sharing behaviours identified from this paper that possibly can influence the green practices acceptance and adoption in the industry. This paper is based on conceptual findings conducted through literature review.