Reviewerly/exHarmony huggingface dataset:
Transferring off-road control concepts to watercraft used in flooded areas

Climate researchers predict that heavy rain and flooding are becoming more frequent due to global warming. Flooding alters environments by raising the water level, creating new waterways, destroying buildings and roads. Due to these drastic changes, existing environmental maps become outdated and can only be used to a limited extent as references for planning and navigation tasks in disaster areas. This work proposes deploying semi-autonomous robotic systems to map the environmental conditions below and above water. Rescue forces then use the collected information for operation planning and navigation. The proposed concept is exemplified by a prototypical raft, a commercial water drone, and a crewed pontoon boat. Although the vehicle classes and propulsion systems differ, all three systems use the same control architecture. The architecture is a specialization of the behavior-based REACTiON framework for commercial land vehicles with a focus on off-road scenarios.
The Public Information Disclosure in State Madrasah Aliyah

This study aimed to determine the implementation model of public information disclosure at MAN 2 Pesisir Selatan in West Sumatra province. This study used content analysis research with the data originating from websites, along with the documents and developments downloaded from the websites related to public information disclosure activities at MAN 2 Pesisir Selatan. This study was conducted from November to December 2022. The data validity used source triangulation by validating data in documents on the websites under study in one feature and across features. The data were analyzed using an interactive analysis model. The results show that the public information disclosure implemented in MAN 2 Pesisir Selatan was through providing a place and the main website connected to the PPID website. The PPID website contained complete profiles and types of report information following existing regulations. There were types of information and reports with institutional dimensions, and institutional actors, so good and clean institutions could reflect excellent and clean executors.&#x0D;
Classification of Imbalanced Datasets Using Various Techniques along with Variants of SMOTE Oversampling and ANN

Using Machine Learning and / or Deep Learning for early detection of diseases can help save people’s lives. AI has already been making progress in healthcare as there are newer and improved software to maintain patient records, produce better imaging for error free diagnosis and treatment. One drawback working with real-life datasets is that they are predominantly imbalanced in nature. Most ML and DL algorithms are defined keeping in mind that the dataset is equally distributed. Working on such imbalanced datasets cause the models to end up having high type-1 and type-2 error which is not ideal in the medical field as it can misdiagnose and be fatal. Handling class imbalance thus becomes a necessity lest the ML/DL model fails to learn and starts memorizing the features and noises belonging to the majority class. PIMA Dataset is one such dataset with imbalances in classes as it contains 500 instances of one type and 268 instances of another type. Similarly, the Wisconsin Breast Cancer (Original) Dataset is also a dataset containing imbalanced data related to breast cancer with a total of 699 instances where 458 instances are of one class (Benign tumor images) while 241 instances belong to the other class (Malignant tumor images). Prediction/detection of onset of diabetes or breast cancer with these datasets would be grossly erroneous and hence the need for handling class imbalance increases. We aim at handling the class imbalance problem in this study using various techniques available like weighted class approach, SMOTE (and its variants) with a simple Artificial Neural Network model as the classifier.
Preserving Data Integrity in Legal Documents Using On-Chain NFTs

Data integrity and tamper-proofing are of paramount importance in legal documents. To mitigate these issues of data tampering and data corruption in a centralized system, blockchain technology and Non-fungible tokens can be used. Blockchain is used to establish a trust-less system, eliminating the need for a facilitator or a centralized body to validate the correctness of data. Non Fungible Tokens can be used for their properties of immutability. The limitations of traditional NFTs such as data security and data corruption in a centralized and decentralized storage services are also discussed and a new method for data storage is proposed, i.e. On-Chain NFTs and their possible advantages and disadvantages, and how they provide an additional layer of security, making it more reliable than our current Off-Chain Non Fungible Token standards. Three novel approaches have been proposed, along with their respective pros and cons.
Artificial Intelligence Based Chatbot for Healthcare Applications

During the epidemic, managing the flow of a large number of patients for consultation has been a tough game for hospitals or healthcare workers. It is becoming more difficult to contact a doctor considering the recent situation, especially in rural areas. It's obvious that well-designed and operated chatbots may actually be helpful for patients by advocating precautionary measures and cures, as well as taken to prevent harm inflicted by worry. This paper describes the development of a complicated computer science (AI) chatbot for advising prompt actions when they need to see a doctor. Moreover, offering a virtual assistant may suggest which sort of doctor to consult.
Applying Gamma and Histogram Equalization Algorithms for Improving System-performance of Face Recognition-based CNN

In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%. Index Terms— Viola-Jones, HE, AHE, Gamma, CNN.
Holographic Digital Image Watermarking Based on Chaos Techniques

Digitally, a large number of information was generated, stored and exchanged. This growth leads to problems that needed to solve. Digital watermarking has been one of the key terms to secure and authenticate the owner's information. The watermarking image technology is a procedure for embedding secret data into an original image. In this paper, encrypted holographic watermark image was proposed by using chaotic technique, which utilizing three distinct chaos maps: logistic, Arnold and Baker; to ensure the security to the system. Performance evaluation of embedding and decrypted Watermark image measured by Peak to signal ratio (PSNR), Structural Similarity (SSIM), Mean square error (MSE), Root mean square (RMSE), and Normalize Root mean square (NRMSE). Results and outcomes of measurements confirmed the robustness of the chaotic technique and also the Histogram showed the good distribution of the encrypted holographic image pixels and observed that the encrypted holographic image in bit are significantly uniform and different from that of the Watermark image that mean the encryption image in bit interleaver is change the level values and position of pixel and also good similarity SSIM about 0.9 for both test 1 and 2. Index Terms— Arnold cat map, Baker map, Digital watermark image, Holographic image, Logistic map.
A classification method for breast images based on an improved VGG16 network model

&lt;abstract&gt; &lt;p&gt;Breast cancer is the cancer with the highest incidence in women worldwide, and seriously threatens the lives and health of women. Mammography, which is commonly used for screening, is considered to be the most effective means of diagnosing breast cancer. Currently, computer-assisted breast mass systems based on mammography can help doctors improve film reading efficiency, but improving the accuracy of assisted diagnostic systems and reducing the false positive rate are still challenging tasks. In the image classification field, convolutional neural networks have obvious advantages over other classification algorithms. Aiming at the very small percentage of breast lesion area in breast X-ray images, in this paper, the classical VGG16 network model is improved by simplifying the network structure, optimizing the convolution form and introducing an attention mechanism. The improved model achieves 99.8 and 98.05% accuracy on the Mammographic Image Analysis Society (MIAS) and The Digital Database for Screening Mammography (DDSM), respectively, which is obviously superior to some methods of recent studies.&lt;/p&gt; &lt;/abstract&gt;
A unified calibration method of 3D laser profile measurement with different of laser-line lengths

Purpose Existing calibration methods mainly focus on the camera laser-plane calibration of a single laser-line length, which is not convenient and cannot guarantee the consistency of the results when several three-dimensional (3D) scanners are involved. Thus, this study aims to provide a unified step for different laser-line length calibration requirements for laser profile measurement (LPM) systems. Design/methodology/approach 3D LPM is the process of converting physical objects into 3D digital models, wherein camera laser-plane calibration is critical for ensuring system precision. However, conventional calibration methods for 3D LPM typically use a calibration target to calibrate the system for a single laser-line length, which needs multiple calibration patterns and makes the procedure complicated. In this paper, a unified calibration method was proposed to automatically calibrate the camera laser-plane parameters for the LPM systems with different laser-line lengths. The authors designed an elaborate planar calibration target with different-sized rings that mounted on a motorized linear platform to calculate the laser-plane parameters of the LPM systems. Then, the camera coordinates of the control points are obtained using the intersection line between the laser line and the planar target. With a new proposed error correction model, the errors caused by hardware assembly can be corrected. To validate the proposed method, three LPM devices with different laser-line lengths are used to verify the proposed system. Experimental results show that the proposed method can calibrate the LPM systems with different laser-line lengths conveniently with standard steps. Findings The repeatability and accuracy of the proposed calibration prototypes were evaluated with high-precision workpieces. The experiments have shown that the proposed method is highly adaptive and can automatically calibrate the LPM system with different laser-line lengths with high accuracy. Research limitations/implications In the repeatability experiments, there were errors in the measured heights of the test workpieces, and this is because the laser emitter had the best working distance and laser-line length. Practical implications By using this proposed method and device, the calibration of the 3D scanning laser device can be done in an automatic way. Social implications The calibration efficiency of a laser camera device is increased. Originality/value The authors proposed a unified calibration method for LPM systems with different laser-line lengths that consist of a motorized linear joint and a calibration target with elaborately designed ring patterns; the authors realized the automatic parameter calibration.
Nilai Keislaman dalam Cerita Wayang Kulit dengan Lakon Babad Alas Wanamarta di Kabupaten Karanganyar

The purpose of this article is to show that wayang kulit is a vehicle for the spread of Islam in the land of Java and to show the Islamic values ​​contained in the story of wayang kulit with the Lakon Babat Alas Wanamarta. The researcher has two problem formulations, why wayang kulit is used as a vehicle for the spread of Islam in Java and what Islamic values ​​are contained in the wayang kulit story with the Lakon Babat Alas Wanamarta. This study used a descriptive qualitative method with data collection techniques through documentation and interviews. Documentation is done by viewing video recordings of wayang performances. Interviews were conducted with the puppeteers who often perform wayang performances. The result of this study is that wayang kulit became a medium for spreading Islam by Sunan Kalijaga. Sunan Kalijaga spread Islam through a cultural approach. Wayang kulit was chosen because at that time wayang kulit was something that was popular. So that the spread of Islam through wayang kulit is easily accepted by the community by being associated with the play in each wayang story. In this Lakon, one of them is the Lakon Babat Alas Wanamarta which contains Islamic values, such as ridho, istiqomah, and tawakal.
The transformative AI initiative: from process digitization to AI strategic digitalization

Purpose Strategically focused AI transformation will almost certainly be more profound than the era of AI process optimization that has preceded it because it potentially changes the way the organizations operate and compete in the marketplace. Design/methodology/approach Potentially transformative strategic digitalization, although built on the AI technology to create process efficiencies that preceded it, will need leaders to adopt a new decision mindset first, and therefore is currently an aspirational initiative in progress. Findings Strategic digitalization shifts the focus of attention from the technical features of process digitization to the transformation of the context and of the organization itself, triggered or enabled by the advent of new technological possibilities. Practical implications Strategic digitalization has the potential to automate the decision-making in middle-management functions in order to greatly potentiate the value of their work. Contrary to the conventional organizational pyramid, strategic digitalization will likely be most effective in an hourglass form of organization, one that replaces many middle management tasks through AI and related technologies in order to create higher-value jobs for managers. Originality/value Guidelines derived from effective initiatives offer a roadmap to the ongoing experimental approach to applying AI to both organization and strategy, a transformation that will be crucial to the future success of organizations.
A Study on the Perception of SNS Users on Artificial Intelligence Design Using Text Mining

Current artificial intelligence technology enables image recognition, voice recognition, unmanned driving, and automatic translation through deep learning. Artificial intelligence technology is developing also in the field of design beyond human limitations. However, negative perceptions such as ethical distrust and anxiety over mass layoffs are also emerging, which can delay the spread of artificial intelligence technology. Therefore, the theme of the study is “a study on the perception of SNS users on artificial intelligence design using text mining
Temperature and Low-Stakes Cognitive Performance

This paper offers one of the first evidence in a developing country context that transitory exposure to high temperatures may disrupt low-stakes cognitive activities across a range of age cohorts. By matching eight years of repeated cognitive tests among all the participants in a nationally representative longitudinal survey in China with weather data according to the exact time and geographic location of their assessment, we show that exposure to a temperature above 32 °C on the test date, relative to a moderate day within 2224 °C, leads to a sizable decline in their math scores by 0.066 standard deviations (equivalent to 0.23 years of education). Also, the effect on the math test scores becomes more pronounced as people age, especially for males and the less educated. However, the test takers living in hotter regions or those with air conditioning installed on site are less vulnerable to extreme high temperatures, indicating the role of adaptation.
A Framework for Auxiliary Diagnosis of Pet Disease using Deep Learning

To date, the number of people who have companion animals has gradually increased and the need for advancement in veterinary care and pet health care has been increased. Deep learning models are taking their places in healthcare and can be used for detecting diseases. We aimed to build and validate a framework for auxiliary diagnosis of pet diseases in everyday life before hospital visits. Our framework utilizes disease image classification and natural language models with Swin-Transformer and Bidirectional Encoder Representations from Transformers as the backbone, respectively, and both presented the accuracy of 84.5% and 84%, respectively. This proposed framework can be useful in understanding animals’ symptoms for pet owners as well as assisting a veterinarian for diagnosis.
Dissoanalytic Psychohistory: Dissoanalysis of the Traumatic History of Humanity and the Construction of a New Societal Reality

In the face of all the limitations and criticisms regarding the recognition of consciousness as a psychosocial "singularity experience", the construction of "Dissoanalysis Theory: Psychocommunal Therapy" centered on "multiple consciousness system", "multiple reality perception" and "multiple memory phenomenon", that is, a new societal reality, has become an indispensability. Based on trauma and dissociation studies, i.e. modern psychotraumatology paradigms and modalities, the "Dissoanalysis Theory", structured by Ozturk with the aim of creating a functional society consisting of empathetic, prudent, just, peaceful, and capable individuals, made it possible for the emergence of "dissoanalytic psychohistory" from a psychosocial perspective. Dissoanalytic psychohistory is intervention therapy for psychosocial crises! “Dissoanalytic psychohistory” structured by Ozturk within the framework of the dissoanalytic school is defined as a branch of science that does researches on childhood traumas, psychosocial perceptions of childhood, chronic oppressions, child-rearing styles, dysfunctional families, dysfunctional generations, intergenerational transmission of trauma, intergenerational transfer of psychopathology, psychocommunal dissociation, dominant leaders and mass violence and develops strategies focused on especially preventing childhood traumas and wars. Dissoanalytic psychohistory, emphasizing that the neutralization of dysfunctional generations and the future can be achieved by the administration of "psychocommunal therapy" of all nations in the world, is an original psychology theory that consists of a holistic synthesis of modern psychotraumatology and psychohistory paradigms with the study of intergenerational transmission of trauma and intergenerational transfer of psychopathology. Dissoanalytic psychohistory, which is the dissoanalysis of the traumatic history of humanity and the construction of a new societal reality, both analyzes and integrates the dissociative components of societies with absolute reality. Dissoanalytic psychohistory creates development-oriented shifts, “dissociative revolutions”, “integrative psychosocial movements” and “mass predictions” and even functional and healthy new society profiles by raising awareness of masses of human about their actions.
A Machine Learning Approach for Predicting Execution Statistics of Spark Application

Apache Spark is one of the most popular, widely used and open-source distributed processing framework that can process huge site datasets in time efficient manner due to its in-memory computational capabilities. However, there are several factors that can affect the performance of an application which include the nature and size of the input dataset, computational capability of the system and nature and design of the algorithm. Hence, there are different parameters that are required to correctly predict the execution statistics of a Spark application which include execution time of jobs, stages and tasks, memory requirement and usage at the execution level and I/O cost in the form of read/ write shuffling of data. To address these challenges, a simulation and machine learning based prediction model is presented in this paper that takes only a few initial samples of execution statistics and predicts the performance and execution statistics of the Spark application with high accuracy. The proposed model is evaluated on the Wordcount application and Spark standalone mode and accuracy metrics show that the proposed model achieves high accuracy in predicting execution statistics.
Research on Quantum Computing Standard System Architecture and Roadmap

Abstract Quantum computing is an important branch of quantum information technology. Quantum computing is far more powerful than traditional computing in solving some problems, and it has great potential for commercial and military applications. Firstly, this paper introduces the status quo of quantum computing research and the status of domestic and foreign standards, and then discusses the demands of quantum computing standards, and expounds on the necessity of a quantum computing standard system. Then give the quantum computing architecture standard system diagram. Finally, there is a roadmap for Quantum computing standardization is given, including the short, medium and long term. The architecture and the roadmap will be helpful to guide the standardization work as well as the application development.
EverTrack: A System to Track History of Used Cars Using Blockchain

Because there are so many different parties involved in the automobile trade, it is one among the major sectors that suffers from fraudulent activities and lack of integrity. So, a proper outcome to track and log data of these vehicles are necessary to satisfy a high level of trust, integrity, and traceability. A systematic approach to change the way specific industries operate by eliminating middle layers between the seller and buyer. In this paper, the system that we introduce which we have named it as EverTrack will make it easier for auto owners, repair shops, and insurance-based companies for registering and adding new information regarding their vehicles, as well as include an anti-theft system. EverTrack will address the difficulties of centralised used automobile history reporting systems, including authority and repair agencies, as well as central storage databases.