Reviewerly/exHarmony huggingface dataset:
Computational efficient deep learning-based super resolution approach

Deep learning-based Single Image Super-Resolution (SISR) has recently provided great performances when compared with state-of-the-art approaches. However, these performances are usually at the cost of high computational complexity and memory management, even at the inference stage. In this paper, we aim to reduce the structural complexity of a state-of-the-art Deep Neural Networks (DNN)1 approach to propose a cost-effective solution to the problem of SISR. We have investigated how the different components of the model (baseline) may affect the overall complexity while minimizing the negative effect on its quality performances. This has provided a solution, which yields quality performances comparable to the baseline model, while approximately reducing more than one order of magnitude the number of parameters, the spatial complexity (GPU memory) up to 1/6 and inference time by 1/2.
Vehicle Detection, Classification and Counting

According to logic, turnpike chiefs are becoming increasingly dependent on the differentiating evidence and counting of watchful automobiles. However, due to the wide range of vehicle sizes, discovery is still difficult, which affects the accuracy of the vehicle count. The review includes a further excellent thruway vehicle dataset with around 6000 clarified events in 12659 images. In contrast to existing publicly available datasets, the suggested dataset retains clarified tiny picture-related components, providing comprehensive information for deep learning-based vehicle detection. The proposed vehicle location and counting framework eliminates the parkway street surface from the image and divides it into a remote and proximal zone using a previously proposed division strategy; the method is essential for advancing vehicle discovery. Finally, video handling is used to obtain driving directions. A few parkway observation recordings from various scenes are used to validate the suggested procedures. Results from tests show how applying the suggested division technique might improve identification accuracy, especially for small vehicle items. Additionally, the original methodology described in this paper does a commendable job of determining driving route and counting automobiles. This paper has broad implications for the board and control of the Parkway Scene.
Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Background Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health. Objective This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team. Methods This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants’ experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot? Results A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%). Conclusions This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.
A formalization of multi-agent planning with explicit agent representation

We present a formalization of multi-agent planning problems in which agents are explicitly represented. In contrast with previous formalisations, we discuss the advantages of representing agents explicitly and show the implementation in the Unified Planning formalism and some practical examples. The proposed formalism is equivalent to other models, in particular to Multi Agent Planning Domain Definition Language (MA-PDDL), and can thus be compiled into it and solved by existing multi-agent planning solvers. Moreover, we present a further extension to define more complex multi-agent problems with explicit models of other agents.
LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli

Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP (Local Binary Patterns) feature extraction and statistical pooling is proposed to classify spam or raw (non-spam) images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM (Support Vector Machine) algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.
Stochastic Gradient Descent on Modern Hardware for Business Environment

Cost is the primary justification for outsourcing an organization’s IT hardware acquisition. By avoiding the need to hire more staff members and reducing the number of hours your present workers are required to work, outsourcing the purchase of hardware may help the business save money. Additionally, you won’t need to continually purchase brand-new IT hardware or software. Additionally, outsourcing can boost production and enable you to provide your consumers with a better and more effective good or service. An iterative technique for maximizing an objective function with sufficient smoothness qualities is stochastic gradient descent or SGD for short. Given that it substitutes an estimate for the real gradient, it may be thought of as a stochastic approximation of gradient descent optimization. This lightens the computational load, especially in high-dimensional optimization problems, leading to quicker iterations at the cost of a reduced convergence rate. We suggest a Stochastic Gradient Descent model for use in a commercial setting where large corporations must purchase the necessary hardware with their software. Our approach assists in determining the necessary hardware requirements based on the demands of the customers.
Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study

In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted.We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351.Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods.Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.
NILAI SUSILA PADA TOKOH MAGI DIELA DALAM NOVEL PEREMPUAN YANG MENANGIS KEPADA BULAN HITAM KARYA DIAN PURNOMO

This study aims to describe the moral value of conveying the life phenomenon of the character Magi Diela in the novel is something amazing to guide the reader's life values. Therefore, the author is interested in analyzing the novel Perempuan yang Menangis Kepada Bulan Hitam Karya Dian Purnomo. Moral values come from conscience, even though it is not written, this norm exists in the human heart, this value distinguishes between right and wrong, guiding daily life according to human nature. The type of research used in this study is descriptive research. The approach used is qualitative. Qualitative is a research approach that the author will use in processing data. Based on the research that has been conducted on the Novel Perempuan yang Menangis Kepada Bulan Hitam Karya Dian Purnomo about the form of moral value of Magi Diela's character, the research results can be obtained as follows: (1). Fairness is obtained as many as 11 quotes, (2) Honesty is obtained as many as 10 quotes, (3). Help each other is obtained as many as 13 quotations, then the total number of quotations obtained is 34 quotations. So it can be concluded that the character of Magi Diela is a figure who can be emulated and inspired to fight for women's right
The effect of government roles, family education, and community attitudes on juvenile delinquency in Gorontalo City, Indonesia

This research is aimed at analyzing (1) the effect of government roles on community attitudes in Gorontalo City, (2) the effect of family education on community attitudes in Gorontalo City, (3) the effect of community attitudes on juvenile delinquency in Gorontalo City, (4) the effect of government roles on juvenile delinquency in Gorontalo City, (5) the effect of family education on juvenile delinquency in Gorontalo City, and (6) the simultaneous effect of government roles, family education, and community attitudes on juvenile delinquency. The method was a survey using a quantitative approach with path analysis. The population covered all adolescents in Gorontalo City, 15,342 in number. 99 adolescents acted as samples. Data were collected through questionnaire distribution. The conclusions were: (1) Government roles had a direct, positive, and significant effect of 62% on community attitudes. That was, the higher the government roles, the higher the community attitudes, (2) Family education had a direct, positive, and significant effect of 71.8% on community attitudes. That was, the higher the family education, the higher the community attitudes, (3) Community attitudes had a direct, positive, and significant effect of 65.3% on juvenile delinquency. That was, the higher the community attitudes, the lower the juvenile delinquency, (4) Government roles had a direct, positive, and significant effect of 43.2% on juvenile delinquency. That was, the higher the government roles, the lower the juvenile delinquency, (5) Family education had a direct, positive, and significant effect of 73% on juvenile delinquency. That was, the higher the family education, the lower the juvenile delinquency, and (6) Government roles, family education, and community attitudes had a simultaneous, direct, positive, and significant effect of 76.6% on juvenile delinquency. That was, the higher the government roles, the lower the juvenile delinquency. Accordingly, higher government roles, family education, and family attitudes would simultaneously bring about lower juvenile delinquency. In other words, higher government roles, family education, and community attitudes would simultaneously bring about lower juvenile delinquency.
Hadamard Layer to Improve Semantic Segmentation in Medical Images

Interest in medical image segmentation has grown in the last decade due to advances in machine learning. A recent breakthrough, the Hadamard Layer, is a new simple, computationally efficient way to improve results in semantic segmentation tasks. This layer is a free parameter that requires training. Therefore it does not increase the number of model parameters, and the extra computational cost is marginal. Experimental results show that the Hadamard layer substantially improves the performance of the investigated models in two different organ datasets, liver, and lung. The specific loss function allows a stable and fast training convergence.
Action Recognition Based on Multidimensional Palm Action Features

In the present study, a small sample palm motion detection method based on two hand motion features was established to overcome the problem of low accuracy of hand motion recognition. Firstly, the contour of hand motion was accurately extracted by the self-designed two-segment skin color extraction algorithm. A 5-layer convolutional neural network was then used to identify hand movements with high initial contour differentiation. Subsequently, for movements of which there are difficulties in directly distinguishing contours, skeleton data of the hand were extracted through OpenPose, and feature parameters were constructed to distinguish three hand movements with a high degree of contour confusion, namely, holding, opening and merging of five fingers. Finally, a simultaneous analysis algorithm was established to accurately identify the 10 kinds of hand movements. The overall recognition accuracy of the 10 kinds of hand movements reached over 96%, and the average recognition accuracy reached 98.4%.
White Blood Cells Recognition and Classification using Convolutional Neural Network

Effectively recognizing and counting each type of blood cell contributes significantly to the diagnosis and effective management of patients with blood diseases. The majority of hematology laboratories in West Africa do not have equipment dedicated to the automatic classification of blood cells. The integration of AI in Hematology helps to reduce time taken by specialists for the classification of blood cells, to make a rapid diagnosis and to reduce the mortality rate. Following PRISMA model guidelines, this article reviews the main techniques of recognition and classification of blood cells to identify potential approaches that can assist doctors in diagnosis and rapid decision-making. Based on patient dataset from the CNHU-HKM, Benin reference hospital located at Cotonou, the performance of our automatic cell recognition model reached 99% in training, 88% and 90% in validation respectively for images 32*32 and 16*16.
Multi-Criteria Decision Making Using the WASPAS Method in Webcam Selection Decision Support Systems

To carry out all virtual or online activities, you need hardware that can support it, one of which is a webcam. Many webcam products issued by various electronics companies are compatible for laptops and computers. However, to make a webcam selection the user must know one by one the specifications of each webcam. This of course takes a long time to determine the right webcam. This study aims to implement the Multi-Criteria Decision Making (MCDM) approach with Aggregated Sum Product Assessment (WASPAS) on a webcam selection decision support system, in order to get the best, right and fast alternative. The WASPAS method can determine the best alternative through prioritization that is relevant to the weighting used. Based on the case studies conducted, the WASPAS method was able to determine the best webcam with the best alternative results, namely NYK Nemesis A96 with a value of 0.7053, followed by Aukey PC-LM7 with a value of 0.6826, JETE W2 with a value of 0.6799, Logitech C922 with a value of 0.6499 and Ausdom AF660 with a value 0.6271. Because the findings are identical to manual calculations, the created system generates legitimate WASPAS method calculations. Based on the tests carried out using the black-box testing approach, it shows that all the functions in the system can run as they should
Blockchain Based Medical Records System and QR Based Medicine Validation

Blockchain is an emerging technology used to develop creative solutions in many industries, including healthcare. Health records must be kept secure and private for several reasons. Blockchain helps to prevent specific dangers and provides decentralized data protection for the healthcare industry. Using blockchain networks in the healthcare system improves the efficiency, security and transparency of sharing patient medical data between hospitals and doctors. Medical institutions can acquire insight and enhance the analysis of patient information with the use of this technology. It can provide a distinctive data storage pattern at the highest level of security and lessen the concern about data tampering in healthcare. This paper aims to bring a more advanced medical records system in which the health records of a patient who needs medical attention are kept electronically in a secure and ethical manner. This is accomplished by employing the Ethereum blockchain. Based on recent studies in India, nearly 20 percent of the medicines sold are counterfeit, due to which many people suffer from side effects. The primary advantage of the system is that the patient can verify the validity of the given medicines by checking the manufacturer and fair price by QR validation. This system can be further upscaled and implemented into a physical hospital where patients and doctors can benefit from the system and hence can be confident about the security of their information stored at the hospital with additional features. Blockchain technology solves most of the security and integrity-related problems in the medical field, as everything is transacted and recorded in chains.
Dragons and data

Abstract How do you convince the maths-anxious that statistics can be fun? Sarah R. Christofides shows how she uses simulated data to fire up her introductory statistics classes
A Tag-aware Recommendation Algorithm Based on Deep Learning and Multi-objective Optimization

Social tagging information to describe characteristics. Recent systems introduce tagging user preferences and item work shows that the recommendation accuracy can be remarkably promoted when tag information is handled properly. However, other performance indicators of recommendations, such as diversity and novelty, are also of great importance in practice. Thus, we propose a two-stage tag-aware multi-objective framework for providing accurate and diversity recommendations. Specifically, we formulate a tag-based recommendation algorithm via deep learning to generate accurate items and abstract effective tag-based potential features for users and items. According to these features, two conflicting objectives are designed to estimate the recommendation accuracy and diversity, respectively. By optimizing these two objectives simultaneously, the designed multi-objective recommendation model can pro-vide a set of recommendation lists for each user. Comparative experiments verify that the proposed model is promising to generate improved recommendations in terms of accuracy and diversity.
Zero-shot Scene Graph Generation via Triplet Calibration and Reduction

Scene Graph Generation (SGG) plays a pivotal role in downstream vision-language tasks. Existing SGG methods typically suffer from poor compositional generalizations on unseen triplets. They are generally trained on incompletely annotated scene graphs that contain dominant triplets and tend to bias toward these seen triplets during inference. To address this issue, we propose a Triplet Calibration and Reduction (T-CAR) framework in this article. In our framework, a triplet calibration loss is first presented to regularize the representations of diverse triplets and to simultaneously excavate the unseen triplets in incompletely annotated training scene graphs. Moreover, the unseen space of scene graphs is usually several times larger than the seen space, since it contains a huge number of unrealistic compositions. Thus, we propose an unseen space reduction loss to shift the attention of excavation to reasonable unseen compositions to facilitate the model training. Finally, we propose a contextual encoder to improve the compositional generalizations of unseen triplets by explicitly modeling the relative spatial relations between subjects and objects. Extensive experiments show that our approach achieves consistent improvements for zero-shot SGG over state-of-the-art methods. The code is available at https://github.com/jkli1998/T-CAR .