Reviewerly/exHarmony huggingface dataset:
BUAH SAMPELO SEBAGAI IDE PENCIPTAAN KARYA SENI GRAFIS

The creation of this work entitled Fruits Sampleo as an idea for the creation of a work of printmaking art. by creating a visual of sample fruit or papaya using visual elements and visual principles, the artist's interest is in raising the sample fruit with a family background that has problems with poor digestion. for humans, the creation of this work aims to hone technical and material skills in printmaking works. The theory used in this creation uses the theory of the surreal form approach and the use of distortion and disformation transformation along with the use of visual elements and visual principles. The creation method is carried out through the preparation stage, reference drawings, then to the design stage by making alternative sketches and entering the selection of the selected sketches, totaling five sketches. or print reference. The coloring of the work uses black and white, and performs the printing process on paper which has five series of prints. The final stage is entering the stage of presenting the work, with the titles, "Transformation #1", "Transformation #2", "Transformation #3", "Transformation #4", "Transformation #5".
Extension of the generalized Bézier operators by wavelets

Abstract In this paper we introduce a novel extension of generalized Bézier operators by replacing the sample values <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>f</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mfrac> <m:mi>k</m:mi> <m:mi>n</m:mi> </m:mfrac> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:mrow> </m:math> f\left({{k\overn}}\right) with the wavelet expansion of the function f. Using the compactly supported Daubechies wavelets, we construct a wavelet type extension of the generalized Bézier operators defined by Gupta [7]. Moreover, we investigate some properties of these operators in some function spaces.
SEISMIC EVALUATION OF CROSSOVER PIPING ON A SODIUM COOLED FAST REACTOR WITH THREE-DIMENSIONAL ISOLATION SYSTEM

A three-dimensional seismic isolation system is planed for application to the conceptual design of a sodium-cooled fast reactor in Japan. The main steam crossover piping is laid between the nuclear building with the isolation system and the turbine building without the isolation system. A large displacement of the nuclear building with the isolation system is imposed on the crossover piping, which situation is a particular seismic issue because of the isolation system employment. Furthermore, because the SFR operates at elevated temperatures compared with light water reactors, the crossover steam piping design must comply with the design code for elevated temperature components. In this study, seismic evaluation using an example of a crossover piping layout was performed in accordance with the elevated temperature code of Japan Society of Mechanical Engineers. According to the evaluation results and the up to date technologies such as knowledge obtained from existing dynamic failure tests of piping components, an appropriate seismic evaluation method for the crossover piping was studied to realize the sodium-cooled fast reactor with a three-dimensional isolation system.
SSATS—Enhancement of Semantic Similarity of Abstractive Text Summarization Using Transformer

After the development of the internet in such an electronic environment, the volume and necessity for textual data in every sector of work have become vital to keep themselves informed. Delivering only the necessary information with clarity in a complete text is a useful strategy for ensuring that everyone who requires a quick summary understands the entire content well. As a result, a computerized text summary is used to extract only the necessary information. Abstractive summarization is thought to be a challenging task for a computer because it calls for real-world knowledge, semantic class analysis, and an understanding of the document to produce the summary. The produced summary may wind up creating irrelevant information as well, thus enhancing the semantic similarity of the contents becomes a critical concern. In this research the proposed SSATS approach is to boost enhancing the connectivity between the original text and the output text summary, addressing the problem of OOV terminology, and reducing the repetition of phrases. The suggested model compared with the pre-trained transformer implied that the model performed better, and tests using the CNN Daily Mail Dataset have shown that it has better accuracy and an acceptable level of summary.
Comparative Analysis of Balanced Code Smell Detection Using Machine Learning

Code smells can hamper the efficient working of the program the user creates in long run. It is also an important reason for technical debt. To enhance the efficiency of your program it is crucial that code smells are detected and removed regularly. There are various methods that have been proposed for code smell detection majority of which are based on the heuristics approach wherein a metrics is created which is further used to detect smell in the codes, but however there are certain limitations to this approach which we’ll further discuss. We further use SMOTE for data balancing on the dataset we will obtain by use of a code smell detection tool. Machine learning is used in variety of fields these days ranging from medical to food industry and it’s been flourishing and growing at a decent speed since last decade.
An Empirical Study to Investigate Class Imbalance Issue for Improving Security Bug Report Classification Prediction

The security risk of a software product can be decreased by finding security bug reports (SBRs). Although the number of SBRs is frequently restricted, the class imbalance problem still remains for SBR prediction, and this topic hasn’t been well explored in prior works. As the empirical subjects for our study, we selected five datasets of various sizes comprising thousands of bug reports. First, we examine how the class imbalance problem affects SBR prediction and contrast its effects with class-balanced prediction performance. Then, we compare five well-known supervised classification algorithms with class rebalancing techniques to predict SBR. Finally, we summarized our key conclusions based on the outcomes of our empirical investigation.
Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].
Using semantic story maps to describe a territory beyond its map

The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.
When Serverless Computing Meets Different Degrees of Customization for DNN Inference

Serverless computing provides a method to develop application services without the burden of run-time execution environment management overhead. Since the initial offerings of serverless computing using function-as-a-service (FaaS), other variants of execution environments have been proposed, such as a special-purpose FaaS (SPF) for deep neural network (DNN) inference and a serverless container service (SCS) for general web applications. This paper qualitatively summarizes the characteristics of a general-purpose FaaS (GPF), SPF, and SCS from the perspective of customizability when setting up execution environments. To judge whether various serverless computing environments can be feasible solutions for an interactive DNN model inference application, we conduct extensive experiments and conclude that there are rooms for performance improvement serverless DNN inference, and allowing a custom environment setup can make the serverless computing platform for an interactive DNN application.
Enhancing Body Percussion Learning: An ICT Supplement for Home Practice Using Gesture Recognition

The first instrument played by primary school students was their own bodies, utilizing techniques such as hitting, clapping, and rubbing. By employing gesture recognition techniques, body percussion can be controlled by capturing the specific movements required for each percussion exercise. In this paper, an ICT supplement for the study of body percussions at home is proposed. By utilizing an app running on smartphones or tablets, students can observe and listen to exercises provided by their teacher. Furthermore, they can practice and receive feedback on their performance errors through the apps. These functionalities are enabled by the implementation of Artificial Intelligence techniques for gesture recognition. Additionally, this proposal aims to surpass mere execution of body percussion exercises, focusing instead on the promotion of students’ psychomotor skill development.
Proactivity in Conversational Assistants: The mPLiCA Model Based on a Systematic Literature Review

In the last years, we have seen a rise in the use of assistants that are becoming more and more natural in their interactions with people. An emerging characteristic is the proactivity in the assistant interaction. The areas of use for these types of assistants range from health, education, to general assistance for tasks, among others., and the proactivity is usually a means to an end, usually to improve user engagement. Given the growing popularity, we have taken the opportunity in this paper to perform a systematic literature review which focuses on agents with a primary focus on them being proactive. During this, we have observed several interesting patterns, such as the main form of interaction for these agents is through verbal interaction, or the fact that they are usually robots. Many of these papers study user response and feelings to different levels of proactivity, with some defining a time-based proactive response, and other focusing on user involvement when defining proactivity levels. All these findings regarding proactivity make it possible to propose, a model based on the proactivity level and the agent’s ability to learn from each interaction, which is what we are presenting in this paper.
Translation and Validation of the AttrakDiff User Experience Questionnaire to Spanish

The AttrakDiff questionnaire is a widely used instrument for measuring User Experience. However, a Spanish version of the questionnaire has yet to be validated. This represents a significant limitation, given the importance of the Spanish-speaking community. This study aims to translate and validate AttrakDiff to Spanish. Several techniques for translation were used, and the results were joined in a translation proposal. The translated version was evaluated in two scenarios. First, an evaluation with 200 + participants to assess the translation proposal. Second, an evaluation of three systems to perform a factorial analysis and determine the correlations between questions of the same dimension. The results of this study will contribute to the advancement of UX research and practice in the Spanish-speaking context and provide a valuable tool for practitioners and researchers who work with Spanish-speaking users.
Detection of Sets and Repetitions in Strength Exercises Using IMU-Based Wristband Wearables

This study presents a model for classifying four strength exercises (chest, back, biceps, and triceps). The system is built on the data collected from an IMU sensor embedded in a wearable wristband device. We gathered data from multiple sessions involving three users while they performed these exercises on fitness machines in real-world scenarios. A Deep Learning model incorporating CNN and LSTM networks was developed to classify these exercises and estimate repetition progressions accurately. The performance results are highly promising, suggesting the feasibility of integrating such exercise recognition capabilities into both mobile and wearable devices.
New Trends in Machine Learning Techniques for Human Activity Recognition Using Multimodal Sensors

The ageing of today’s society, according to demographic and epidemiological data, presents a significant increase in the elderly population. Following the experienced pandemic, research in telemedicine to improve the lives of elderly people through a comprehensive program developed by multidisciplinary teams has become a top priority. This enables the provision of remote healthcare services, facilitating access to specialists, disease monitoring, medication management, and health indicator tracking to address the medical, social, and emotional needs of elderly individuals. This study proposes a sensor-based approach to identify activity patterns without prior labels. The system architecture responsible for collecting data from the monitored user in the assisted living facility consists of a beacon, multiple anchors, and various sensors for motion, opening and closing, temperature, and humidity. The experimentation was carried out with distinct activities such as sleeping, eating, taking medication, walking, showering, and brushing teeth, inferred from the identified patterns. This approach offers an automatic and objective way to understand the routines and behaviours of older individuals, thereby improving their care and attention through personalized interventions tailored to their individual needs. Furthermore, it lays the groundwork for future research on the detection and monitoring of changes in activities over time, identifying possible signs of impairment or changes in the health of elderly people.
Development of floating point operating devices

The paper shows a well-known approach to the construction of cores in multi-core microprocessors, which is based on the application of a data flow graph-driven calculation model. The architecture of such kernels is based on the application of the reduced instruction set level data flow model proposed by Yale Patt. The object of research is a model of calculations based on data flow management in a multi-core microprocessor. The results of the floating-point multiplier development that can be dynamically reconfigured to handle five different formats of floating-point operands and an approach to the construction of an operating device for addition-subtraction of a sequence of floating-point numbers are presented, for which the law of associativity is fulfilled without additional programming complications. On the basis of the developed circuit of the floating-point multiplier, it is possible to implement various variants of the high-speed multiplier with both fixed and floating points, which may find commercial application. By adding memory elements to each of the multiplier segments, it is possible to get options for building very fast pipeline multipliers. The multiplier scheme has a limitation: the exponent is not evaluated for denormalized operands, but the standard for floating-point arithmetic does not require that denormalized operands be handled. In such cases, the multiplier packs infinity as the result. The implementation of an inter-core operating device of a floating-point adder-subtractor can be considered as a new approach to the practical solution of dynamic planning tasks when performing addition-subtraction operations within the framework of a multi-core microprocessor. The limitations of its implementation are related to the large amount of hardware costs required for implementation. To assess this complexity, an assessment of the value of the bits of its main blocks for various formats of representing floating-point numbers, in accordance with the floating-point standard, was carried out.
Schizophrenia and the Role of Artificial Intelligence in Detecting and Treating it: Cognitive Frontiers

A persistent mental illness that changes thoughts, feelings, behaviour, and communication with people is schizophrenia. Delusions, hallucinations, distorted thinking, lack of motivation, and fear of society are often its hallmarks. Although there is currently no cure for schizophrenia, patients can greatly benefit from early diagnosis and treatment. The field of artificial intelligence (AI) is very promising for the diagnosis and treatment of schizophrenia. Artificial intelligence systems can scan and analyse vast amounts of data, including genetic and hereditary data, brain scans, and medical records, and predict when a person might develop schizophrenia if the factor is hereditary. This can help medical providers act quickly to start treating patients before their symptoms worsen. AI can also help patients with schizophrenia manage and regulate their medications. Artificial intelligence systems can analyse patient data to determine the best medications and dosage amounts while also highlighting any potential side effects of treatment. This can help medical providers customize strategies structure treatment for each patient and enhance treatment outcomes. Considering all the above-mentioned matters, the use of artificial intelligence in diagnosing, analysing, and treating schizophrenia promises to improve patient outcomes and treatment, deepen our knowledge of this complex disease, early detection of the disease, and find appropriate treatment.
Monocrystalline Photovoltaic Panel Response Pattern to Relative Humidity and Temperature under Distinct Wavelengths in Mangrove Swamp Environment in Calabar, Cross River States, Nigeria

A meticulous experimental investigation was conducted to study the response pattern of monocrystalline photovoltaic technology to relative humidity and temperature under distinct wavelengths under the mangrove swamp atmosphere in Calabar, Cross River State, Nigeria, by in-situ measurement approach using a precision digital hygrometer, precision digital infrared gun thermometer and an intelligent photovoltaic panel maximum power point tracker (MPPT) to track and determine the maximum power, voltage and current produced by the photovoltaic (PV) technology at a particular panel temperature and humidity level. The result reveals that 100% efficiency from the PV technology may not be possible with the various wavelengths aside from the natural spectrum because the humidity level will have to drop below 10% for 100% efficiency to be achieved. However, under the natural spectrum the results revealed that the PV technology could attain 100% efficiency at a humidity level of 35%. The results also reveal that an increase in efficiency as temperature rises is not indefinite as there is a threshold temperature above which the efficiency will begin to drop for all wavelengths. Results of the study reveal that under the natural and red wavelengths, the maximum efficiency that could be attained is 62% and 41% respectively at a threshold of 440C. With the orange wavelength, 46% efficiency is possible at a threshold of 44.70C. With the lemon, green and violet wavelengths, the maximum efficiency that could be attained is 50%, 40% and 44% respectively at a threshold of approximately 430C. While 43% and 48% efficiency is possible with the blue and peach filter at a threshold of 40.50C and 42.30C respectively.
Evaluation and Mapping of Evapotranspiration in Forest-Savanna Transition Zone of Ogun State, South-Western Nigeria

Evapotranspiration's impact on crop production, determined by water consumption in plants, varies across locations due to surface and climate differences. Traditional ground-based methods for measurement fall short in capturing these variations. In order to address this, the study evaluated and mapped the evapotranspiration in the forest-savanna transition zone of Ogun State, South-western Nigeria using a geo-informatics approach. Over six years, 12 Landsat images were collected, representing dry and wet seasons. These images were used to estimate the Normalized Difference Vegetative Index (NDVI), indicating vegetation density, and compute evapotranspiration values across the area. During the dry season, NDVI ranged from -0.326 to 0.376, and during the wet season, it ranged from -0.435 to 0.780, showing higher vegetation cover in the wet season. Evapotranspiration values varied across different regions. In Abeokuta South, Abeokuta North, and Odeda Local Government Areas, values ranged from 2.83 to 6.37 mm/day, 0.12 to 2.64 mm/day, and 3.12 to 5.44 mm/day, respectively, influenced by varying vegetation characteristics. The geo-informatics approach offered a realistic representation and spatial understanding of evapotranspiration, proving cost-effective and accessible. In conclusion, the study recommends the geo-informatics approach for evapotranspiration measurement due to its ability to consider spatial characteristics. This understanding is essential for effective water resource management and crop planning in the Forest-Savanna transition zone of Nigeria.
Demystifying Metaverse Applications for Intelligent Healthcare

The metaverse is a virtual, interlinked digital world where users can connect, socialise, and participate in numerous activities using virtual, augmented, and other innovative technologies. It is a phrase borrowed from science fiction recently gaining popularity in the information and technology sector. It's a sizable, shared, 3D virtual environment that illustrates a collective, shared online space that stretches beyond traditional internet experiences. The chapter explores and investigates how immersive technologies, such as virtual reality, augmented reality, and others, might improve several facets of healthcare and make it more intelligent, effective, and patient-centred.