Reviewerly/exHarmony huggingface dataset:
Analyses of Algorithms to Detect DeepFake Videos

&lt;p&gt;Deep learning has impacted various aspects of artificial intelligence ranging from computer vision to reinforcement learning. However, it has also inspired malicious actors to spread misinformation without detection. Fake videos can now be generated with precision high enough to go undetected by traditional video forensics. These videos, popularly referred to as DeepFakes, are being used to spread misinformation. Deep learning based techniques have evolved over the years to detect such DeepFakes. This article analyzes the DeepFake detection algorithms and implements five such algorithms on a standard dataset. The advantages and challenges of the implemented algorithms are explored in detail.&lt;/p&gt;
Scalar trajectories in design: The case of DIY cloth face masks during the COVID-19 pandemic

The article examines an artefact of everyday design – the Do-It-Yourself (DIY) cloth face mask employed against respiratory infections – to interrogate scale and scalar relationships. This lens reveals new perspectives on how practice-based design research can mobilize scale in more nuanced ways. The authors propose that DIY face masks, as artefacts of mundane design engagements both with material (cloth and thread) and with sharing of knowledge (about design, craft and practice), globally and within local networks and communities, direct our attention to scale as a matter of relations, engagements and emergent trajectories. Through empirically led exploration combined with approaching making as sensemaking, the article highlights the multiplicity of design artefacts emerging in DIY mask design spanning several scales and introduces the notion of scalar trajectories across multiple design engagements.
DIP-QGA: a secure and robust watermarking technique based on direct image projection and quantum genetic algorithm

This paper presents three novel methods for hiding and extracting a watermark. The strategy of the first method is to project pixels into a new image. The key is a combination of the rotation and distance related to the viewer. The new pixel positions resulted from the direct image projection (DIP) are then used to hide the secret message. The aim of the second method relies on improving the security of a message against image manipulations (i.e., histogram analysis and RS steganalysis) where a combination between DIP and genetic algorithm (DIP-GA) is done. After that, we highlight the main challenges and issues that occur from the use of GA. Due to that, we propose the main contribution in our paper which is the use of quantum genetic algorithm (QGA) in the DIP method (DIP-QGA). QGA is based on the concepts and principles of quantum computing, such as quantum bits, quantum gates, and superposition of states. We evaluate our proposal's performance on the USC-SIPI dataset As a result, an improvement in PNSR and MSE is obtained (90.15% and 94.34% respectively).
Object-Centric Scene Representations using Active Inference

Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this paper, we propose a novel approach for scene understanding, leveraging a hierarchical object-centric generative model that enables an agent to infer object category and pose in an allocentric reference frame using active inference, a neuro-inspired framework for action and perception. For evaluating the behavior of an active vision agent, we also propose a new benchmark where, given a target viewpoint of a particular object, the agent needs to find the best matching viewpoint given a workspace with randomly positioned objects in 3D. We demonstrate that our active inference agent is able to balance epistemic foraging and goal-driven behavior, and outperforms both supervised and reinforcement learning baselines by a large margin.
High-Capacity Double-Layer Data Hiding Technique Based on 3D Magic Cube

Information technology and the Internet have progressed rapidly in people’s lives, the privacy of information has become an important issue due to the accessibility of data. Therefore, to enhance information security and privacy protection, this paper proposes a new high-capacity method for data hiding. The proposed method is based on 3D magic cube with double layers embedding. In the field of data hiding, the reference matrix-based methods attract many researchers because of its high-capacity. Recently, as the needs of the high payloads of the stego-image, the multi-layers-based technique is applied to provide larger embedding capacity. However, the image after data embedding will cause serious distortions, especially on multi-layers embedding, hence, keeping the image at a good visual quality and achieving high-payloads are the main challenges for data hiding. In this scheme, we take advantage of 3D magic cube and 3D reference space with double layers embedding for our data hiding technique to improve the embedding payloads. The experimental results show that our method not only provides good embedding capacity (4.00 bpp) but also keeps the image at acceptable quality (In terms of PSNR > 30).
Keeping the Noise Down in a Quad Shaker System

Engineers have improved data quality by installing a drive-signal isolator that lowered the noise floor in a shaker system
Central bank digital currency: features and functionality limitations

The emergence of cryptocurrencies has motivated the central banks to issue a centralised and trusted digital currency instead of fiat currency named central bank digital currency (CBDC). CBDC is a substitute for fiat currency to meet some wants instead of cryptocurrency. However, it is still in the testing stage and has some challenges that should be addressed and focused on. The study of different research papers and published articles show three significant issues and many minor issues that reflect CBDC's challenges, including its functional limitations due to its used technology features, its effects on monetary policy, and its effects on commercial banks. The primary purpose of this study is to concisely present the previous work on the above three mentioned issues.
Classification of Breast Tumor from Histopathological Images with Transfer Learning

Breast cancer is one of the most common cancers occurring in women and is the main cause of mortality among women aged 20 to 59. Detection of breast cancer in the early stage increases survival rate to 80%, and the rate decreases to around 40% if diagnosed in its later stage. There exists significant need to diagnose breast cancer in its early stage itself among women suspected with the disease. The hematoxylin and eosin (H&E)–stained histopathology tissue samples taken by pathologists from suspected breast areas are viewed with whole slide imaging (WSI) and can be stored as digital histopathology images. Manual analysis of histopathology images is time-consuming and leads to inter- and intraobserver variability. Hence, automated classification methods are in research focus. Breast tumors are classified as benign and malignant tumors, which are non-cancerous and cancerous tumors, respectively. Currently, deep learning through convolutional neural networks is an emerging technology and has proven to be successful for image segmentation and classification applications. These deep CNNs require large labeled datasets, which are lacking in the field of medicine, especially for breast cancer images. Also, high computational power, time, and memory are required if CNN is to be trained from scratch. To overcome this, transfer learning from natural images like ImageNet, comprising of 1.2 million natural images belonging to 1,000 classes, is used. Pretrained models like VGG16, ResNet50, Inception, AlexNet, and DenseNet are employed for transfer learning, and fine-tuning is to be performed for refinement. In the proposed method, four different network architectures, namely, AlexNet, VGG16, Inception v3, and DenseNet121, are used for classification of histopathological images using transfer learning. The top dense layers of these architectures are removed, and custom dense layers are added for classification. Also, the weights of the last two layers are fine-tuned with the histopathology input images dataset. The publicly available breast cancer hisopathology images dataset BreakHis is used for validation of the proposed method. The experimental results prove that transfer learning–based CNN gives better classification accuracy compared to networks learned from scratch. Also, it is inferred from the experimental results that the pretrained model of DenseNet121 gives the highest classification accuracy of 0.9520 compared to the other models and state-of-the-art methods.
The Effects of Nearpod-Incorporated English Classes on Korean University Students’ Speaking Performance and A ffective A spects

Nearpod, an online teaching cloud-based platform, offers great potential to generate pedagogical tasks and yield better results in teaching communication skills. This study thus examined the effects of Nearpod-based English-speaking classes. To this end, one-on-one speaking tests were conducted twice in a semester, and a questionnaire was administered to assess university students’ perceptions of the usefulness of Nearpod in offline classroom. The participants comprised 39 university students who took a mandatory English class titled Communication in English. The paired samples t-test results indicated that the students’ overall speaking performance has improved over time. Specifically, compared to the high-level students, the low-level students showed a significant increase in speaking performance. The questionnaire analysis demonstrated that most students were satisfied with Nearpod-based English speaking classes because of enhanced interest, active participation in class, and increased interaction with class content and peers. The pedagogical implications are suggested and the limitations of the study are further discussed.
Classifying Composition of Software Development Team Using Machine Learning Techniques

Software development projects still reportedly have high failure rates. The ineffective composition of the software team has been recognized as the main aspect of the failure of the software project. In this study, a classification model of the composition of an effective software development team was developed. The model developed consists of three predictor variables: personality, role, and gender. Outcome variables to determine team effectiveness are seen in the quality of the team. To measure the quality of the team, two metrics were used: team development level assessment and team dysfunction assessment. The techniques used for classification are logistic regression and decision trees. The experimental results show that the best method is produced by a decision tree with the highest accuracy value of 70%. Therefore, the results conclude that the use of the decision tree method can determine an effective team as software development team.
map-A*: A Goal Oriented Optimum Path Finding Algorithm for Differential Drive Movement

Cognitive intelligent path searching procedures are evolving as significant perspective in both research and application level of WRN (Wheeled Robot Navigation). The superimposition of Machine Vision and Augmented Reality have provided clarity, precision and optimality to PTP (Point To Point) robot locomotion strategies. Performance of various existing graph theoretic as well as bio inspired path planning techniques have been computationally evaluated and improvised in certain research works. This concerned work primarily focuses on achievement of optimum Goal-oriented robot navigation with proposed map-A * algorithm. This structured algorithm is inspired from the characteristic features of RGB-D SLAM (Red, Green Blue Depth Simultaneous Localization And Mapping) for real-time trajectory scan of the explored route by the considered differential drive design. It has been followed by a simultaneous intelligent Grid-based path finding mechanism for desired goal completion. The interposition of path mapping has made the mobile agent comparative easier and quicker to get a snap of already traversed non-optimum route and decide its further movement. Input 3D data format has been captured by RGB-D sensor for its superiority in obstacle detection using its colour gradient feature. The algorithmic construction of the proposed procedure has been included with experimental analysis. Performance of the algorithm is bench-marked with conventionally used technique which cites its efficiency and reliability in the concerned working domain.
Implementation of MTCNN Facial Feature Extraction on Sleepiness Scale Classification Using CNN

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a ‘csv’ file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%.
APLIKASI SISTEM PAKAR DIAGNOSA PENYAKIT GASTROENTERITIS BERBASIS ANDROID DENGAN METODE CLASSIFICATION AND REGRESSION TREE

Gastroenteritis is a disease that attacks the body's digestive system, especially in thesmall intestine and stomach through viruses and bacteria. In addition to its transmission causedvirus and bacteria, it can also caused by unhygienic food and drink. However, people generallyequate gastroenteritis with diarrheal disease, causing medical mismanagement that can befatal. Therefore, this research trying to build an Android based application that can help peoplein diagnose gastroenteritis. So in this research, the application was build using ClassificationAnd Regression Tree using an Android platform and flutter extension. This research usinghistorical data of 780 patients with 2 classes of disease, namely gastroenteritis and gastritis.The testing results is done to determine the peformance of application such as accuracy,precision , and recall of the application in diagnosing disease. According to confusion matrixtesting results, the application that was build able to diagnose gastroenteritis and gastritisdisease by obtaining an Accuracy of 85.1 %, Precision of 84 %, and Recall (applicationsensitivity in diagnosing) of 98 %.
Dubai Smart City as a Knowledge Based Economy

Innovation, emerging technologies and digitalization have significant impacts on personal, social, environmental, and business aspects. This study explores the initiatives taken by the United Arab Emirates (UAE) to accelerate its knowledge-based economy to ensure the best living experience for its people. Knowledge is the key factor in growth and labor market expansion where skills, knowledge, and experience possessed by individuals, are the driver of creativity and innovation, with dependence on information and communication technology (ICT) as an enabler. The UAE is moving towards a knowledge-based economy in order to maintain a diversified economy in line with "The Abu Dhabi Economic Vision 2030" which was launched in 2009. Through a systematic literature review, researchers have developed a perspective on transformation of economy and society through innovation, use of Information and Communication Technology (ICT), as well as government performance management frameworks. Theory of sense data, nomothetic explanations, predictive and prescriptive analytics, along with Smart Dubai as a case study, are used to assess the achievements, emerging trends and expectations of knowledge-based economy in the UAE. Smart Dubai is a unique example of a high-level quality system that strives towards making Dubai the happiest city on earth. Smart Dubai has been adopting many projects that enhance the life of people and support the economy of the UAE. This study highlights the challenges related to the implementation of the Smart Dubai project. The study also provides a clear view of smart cities transformation into a knowledge-based economy and utilizes predictive and prescriptive analytics tools that can assist other researches in the future to improve and develop research regarding smart cities and gain a better understanding. Ten recommendations are made in this study, for the Smart Dubai to consider, in order to maintain high-quality performance and accelerate the transformation process of Knowledge-based economy.
COVID-19 Vaccination Data Visualization: Issues and Challenges

Data Visualization plays an important role for patterns and trends analysis in trillion of data rows Big Data analysis, where the data can be represented in some graphical forms. Hence, the data could be more comprehensible in its visual summary in dashboards and storyboards. This study aims to discuss some issues and challenges in visualizing COVID-19 vaccination datasets. There are some possible issues in data visualization, as it is not easy and may be challenging to produce a good dashboard that are interesting and easy for viewers to understand. Therefore, this study focuses on some issues that may arise during performing a data visualization on the COVID-19 dataset. In this study, there are three dashboards have been studied, which are the COVID-19 tracker, its effectiveness, and its acceptance. The first two dataset are derived from Ministry of Health Malaysia bank data, whereas the third dataset is from a survey to support this analysis. The selected attributes are states, the number of people who have received the vaccine as adults, children, and teenagers, and the number of people who already received boosters, and reasons to not get a booster. The visualization issues found within the dashboard are mis-choice of colors, mis-choice of visual object type, lack of interactivity, and plotting too much data. As a result, this proposed alternative solutions for those issues such as color deliberately, pick a suitable visual object, create an interactive dashboard, and reduce the information overload in visualizing the data.
AUGMENTED REALITY HYDRANT PEMADAM KEBAKARAN BERBASIS ANDROID

Augmented Reality (AR) is a technology used to display a 3-dimensional animatedobject with marker as a medium for displaying 3-dimensional animation in real time. AugmentedReality Hydrant for Fire Extinguishers Based on Android is an application used for learningmedia in the introduction of fire extinguishers with the aim of making it easier for the public orfirefighters to find out their form and function through this application. The Fire ExtinguisherHydrant Augmented Reality application is made in several stages, namely identifying problems,determining goals and objectives, analyzing needs, designing systems, designing designs,making systems and testing systems. This Android-based Augmented Reality Hydrant candisplay several Hydrant objects or Fire Extinguishers and display information about theseobjects.
Final frontiers: Science fiction and techno-science in Non-Aligned India

"Final frontiers: Science fiction and techno-science in Non-Aligned India." Journal of Postcolonial Writing, ahead-of-print(ahead-of-print), pp. 1–2
Regular Patterns of Quadratic Residues

For an odd prime p we determine four kinds of patterns concerning the distribution of quadratic residues depending on three parameters, according to the class of p modulo 8.
Blockchain Adoption and Optimal Reinsurance Design

We study blockchain adoption in insurance-reinsurance markets. Unlike standard operational costs related to claim processing that scale linearly with the volume of claims, blockchain operating costs per firm are independent of claim volume and decrease with the adoption rate since verification and storage costs are distributed among the adopters. In a consortium of insurance firms, we quantify how the equilibrium adoption decisions depend on the reinsurance contract characteristics, the risk aversions of insurance companies, the distributions of their potential losses and the blockchain cost structure. Under the optimal contract, the reinsurance firm internalizes the benefits of adoption on other insurance firms, thereby acting as a central planner. We then characterize the adoption gap between decentralized (Nash) and centralized blockchain consortium.
Security Frameworks as a Foil for Larger Management Issues

Information technology (IT) in general has had a disconnect from the rest of the organization. Information management techniques and processes began evolving well before the development of digital computers and associated technology management disciplines. What makes IT especially challenging in healthcare organizations is that there are, in fact, numerous hegemonic professional subcultures under one roof attempting to operate as one organization. When it comes to information risk management, there are frameworks already in place for quality that have also been used for Information Security. These include Just Culture, Failure Mode and Effects Analysis, Comprehensive Unit-based Safety Program, and numerous quality improvement frameworks such as the National Database of Nursing Quality Indicators. Healthcare isn't retail or supply chain, where solutions can be duplicated and scaled much more easily. Healthcare is based on centuries of methods and traditions that are a modern analogue of the medieval guild system.
Emerging Technology and Interactive Feedback

Innovation and advancement in technologies continue to rise on an industrial scale, with an incredible influx of new, fast and efficient systems able to seemingly streamline our everyday lives. If athletes are to make improvements, then they require feedback as an essential tool in their armoury. As time and technology grew on, the specificity and power of analysis tools has grown immeasurably. Now, there are a seemingly bewildering number of new and emerging technologies available to analysts, in a growing market with innovation at an all-time high. This chapter seeks to provide an overview of some of those technologies, unearthing the significant capabilities and possibilities that they bring, helping to align together and bring understanding of what might be suitable for a particular given environment. Computer hardware was invented to ‘compute’ and solve problems, whilst also storing and retrieving data alongside the management of communications and the manipulation of multimedia.