Reviewerly/exHarmony huggingface dataset:
Hyperscale Hardware Optimized Neural Architecture Search

Recent advances in machine learning have leveraged dramatic increases in computational power, a trend expected to continue in the future. This paper introduces the first Hyperscale Hardware Optimized Neural Architecture Search (H2O-NAS) to automatically design accurate and performant machine learning models tailored to the underlying hardware architecture. H2O-NAS consists of three key components: a new massively parallel “one-shot” search algorithm with intelligent weight sharing, which can scale to search spaces of O(10280) and handle large volumes of production traffic; hardware-optimized search spaces for diverse ML models on heterogeneous hardware; and a novel two-phase hybrid performance model and a multi-objective reward function optimized for large scale deployments.
How we Work, Share, and Re-use at CHIIR

In this paper, we present the results of an initial study of the research, sharing, and re-use practices at the CHIIR conference through a systematic analysis of all CHIIR papers published from 2016 to 2022. We find that CHIIR is a conference predominantly focused on empirical, multi-methods research that over the years has undergone a focusing in terms of the type of research methods that are being used. A modest number of papers re-use existing data and design resources, but infrastructure component re-use is much more rare. Only a fraction of CHIIR papers actually share their own resources, which suggests that there is much to gain in terms of reproducibility of research presented at CHIIR and could potentially be used to support changes in reviewing practices.
Tampering Detection Driving License in RTO Using Blockchain Technology

Manufactures, dealers, buyers, registration authorities, and insurance companies are the entities involved in motor vehicle registration. All entities must work together for the successful registration of the vehicle. However, errors may occur as a result of incorrect or incomplete data entry, as well as other effects like data tampering, could pose a menace to the system. Regional Transport Office is in charge of the vehicle driving license process (RTO). Maintaining automotive procedures in traditional way may obstruct the route to a more dependable, and detection of fraud is a difficult with the existing system. If fraud occurs, it is problematic to track back because many entities such as insurance company, dealer, manufacturer, and RTO function with their norms. SHA256 algorithm is used for hashing the data in blockchain. Hashing scramble, the raw data and hence, it is hard to get back the original data. By incorporating blockchain technology, we are able to obtain a more dependable solution, that is more promising, which aids the creation of a more dependable, secured and efficient system. It is a strategy to solve the problems underlying in the vehicle driving license system and connect them using blockchain technology. Binding them under one gateway will increase the system's reliability because all of them will be on the chain; with blockchain, every transaction record will be available together with the timestamp.
Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data. Material and methods . As part of a cohort study, a retrospective prediction of in-hospital mortality after coronary artery bypass grafting (CABG) was performed in 2182 patients with stable coronary artery disease. Patients were divided into 2 following samples: learning (80%, n=1745) and training (20%, n=437). The initial ratio of surviving (n=2153) and deceased (n=29) patients in the total sample indicated a pronounced class imbalance, and therefore the resampling method was used in the training sample. Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. As a result, five predictive models with the best parameters were obtained. The resulting predictive models were applied to the learning sample, after which their performance was compared in order to determine the most effective model. Results . Predictive models implemented on ensemble classifiers (CatBoost, LightGBM, XGBoost) showed better results compared to models based on logistic regression and random forest. The best quality metrics were obtained for CatBoost and LightGBM based models (Precision — 0,667, Recall — 0,333, F1-score — 0,444, ROC AUC — 0,666 for both models). There were following common high-ranking parameters for deciding on the outcome for both models: creatinine and blood glucose levels, left ventricular ejection fraction, age, critical stenosis (&gt;70%) of carotid arteries and main lower limb arteries. Conclusion. Ensemble machine learning methods demonstrate higher predictive power compared to traditional methods such as logistic regression. The prognostic models obtained in the study for preoperative prediction of in-hospital mortality in patients referred for CABG can serve as a basis for developing systems to support medical decision-making in patients with coronary artery disease.
Influence Mechanism of Students' Learning Enthusiasm Based on Educational Big Data

Mining a large number of students' behavior data can analyze the differences of students' behavior characteristics. Mining based on students' behavior data can explore the influence mechanism of students' learning enthusiasm, and can provide reference opinions and suggestions for improving students' positive emotions in learning and helping students with poor grades to achieve academic success. The data collected and conclusions obtained by the existing research are not accurate and reliable, and other personality characteristics of students are not fully considered. Therefore, this article studies the influence mechanism of students' learning enthusiasm based on educational big data. It studies the performance of students with good learning enthusiasm in social relations, and mines students’ social intercourse at school based on association rules. It analyzes the regularity of students' behavior and their diligence, and realizes the characterization of students' sustainability and enthusiasm in learning. Combining the differences of behavior characteristics between students with and without learning enthusiasm, this article constructs a learning enthusiasm recognition model and compares it by classification. Experimental results verify the effectiveness of the model.
SDPN: A Slight Dual-Path Network With Local-Global Attention Guided for Medical Image Segmentation

Accurate identification of lesions is a key step in surgical planning. However, this task mainly exists two challenges: 1) Due to the complex anatomical shapes of different lesions, most segmentation methods only achieve outstanding performance for a specific structure, rather than other lesions with location differences. 2) The huge number of parameters limits existing transformer-based segmentation models. To overcome these problems, we propose a novel slight dual-path network (SDPN) to segment variable location lesions or organs with significant differences accurately. First, we design a dual-path module to integrate local with global features without obvious memory consumption. Second, a novel Multi-spectrum attention module is proposed to pay further attention to detailed information, which can automatically adapt to the variable segmentation target. Then, the compression module based on tensor ring decomposition is designed to compress convolutional and transformer structures. In the experiment, four datasets, including three benchmark datasets and a clinical dataset, are used to evaluate SDPN. Results of the experiments show that SDPN performs better than other start-of-the-art methods for brain tumor, liver tumor, endometrial tumor and cardiac segmentation. To ensure the generalizability, we train the network on Kvasir-SEG and test on CVC-ClinicDB which collected from a different institution. The quantitative analysis shows that the clinical evaluation results are consistent with the experts. Therefore, this model may be a potential candidate for the segmentation of lesions and organs segmentation with variable locations in clinical applications.
Crowd counting using deep learning based head detection

Scale invariance and high miss detection rates for small objects are some of the challenging issues for object detection and often lead to inaccurate results. This research aims to provide an accurate detection model for crowd counting by focusing on human head detection from natural scenes acquired from publicly available datasets of Casablanca, Hollywood-Heads and Scut-head. In this study, we tuned a yolov5, a deep convolutional neural network (CNN) based object detection architecture, and then evaluated the model using mean average precision (mAP) score, precision, and recall. The transfer learning approach is used for fine-tuning the architecture. Training on one dataset and testing the model on another leads to inaccurate results due to different types of heads in different datasets. Another main contribution of our research is combining the three datasets into a single dataset, including every kind of head that is medium, large and small. From the experimental results, it can be seen that this yolov5 architecture showed significant improvements in small head detections in crowded scenes as compared to the other baseline approaches, such as the Faster R-CNN and VGG-16-based SSD MultiBox Detector.
Classifying Ethnicity of the Pedestrian Using Skin Colour Palette

Nowadays, with the emergence of big data, there is an increasing desire to analyze and understand them. In this area, the focus is on the challenge of identifying pedestrian attributes from CCTV images, especially in terms of ethnicity. Due to a lack of necessary characteristics, ethnicity classification is nigh impossible in this instance. Facial landmarks are a requirement for the existing approaches. Therefore, it is suggested to use the individual’s skin tones as features instead. Segmenting the skin area of each unique face adds multiple dominant colours to the colour palette, which are later employed as characteristics during classification. The P-DESTRE dataset, which provides pedestrian dataset and their properties, including their ethnicities, is used to demonstrate the viability of the suggested method. The accuracy percentage for distinguishing between Caucasian and Indian pedestrians using the P-DESTRE dataset and skin colour palette is 98%. The outcome demonstrates that ethnicity classification is possible when utilizing a colour palette as a feature. On this premise, it is still possible to identify a pedestrian’s ethnicity from CCTV footage even without the use of face landmarks.
Training Feedforward Neural Networks Using Arithmetic Optimization Algorithm for Medical Classification

Feedfoward neural network (FNN) is popular machine learning technique widely implemented for image classification, data clustering, object recognition, etc. due to its outstanding capability in processing data. Backpropagation is commonly employed as a conventional method to adjust the weights and biases of FNNs. As a gradient-based algorithm, backpropagation tends to have slow convergence rate and highly dependent on the initial solution generated. Arithmetic optimization algorithm (AOA) emerges as a promising metaheuristic search algorithm (MSA) to replace conventional method in training FNNs due to its outstanding global search ability. In this paper, AOA is designed to optimize the weights, biases and selection of activation functions of FNN for image classification. Medical datasets are extracted from UCI Machine Learning Repository to assess the capability of AOA in training FNN. Comparative studies report that the promising performance AOA in training FNN for medical classification.
Learning strategies in the virtuality forced by the pandemic: contributions to the design of learning contexts in the post-pandemic stage

The purpose of the article is to describe and analyze the learning strategies used by Economics students with different academic performance in the period of remote teaching by COVID-19. Although the strategies that students use to learn were previously addressed by educational research, the study provides new evidence by placing the analysis in the unusual contexts defined by the pandemic. A non-experimental, descriptive research was conducted, using a self-report questionnaire answered by 94 students. The results reveal that resource regulation strategies were affected by the context of the health emergency. The evidence obtained is an important input for the design of learning contexts that favor the use of strategies and academic performance. According to the reported findings, hybrid proposals, which allow taking advantage of the benefits of virtuality, would be appropriate to favor students' strategic learning.
Conversational Context-sensitive Ad Generation with a Few Core-Queries

When people are talking together in front of digital signage, advertisements that are aware of the context of the dialogue will work the most effectively. However, it has been challenging for computer systems to retrieve the appropriate advertisement from among the many options presented in large databases. Our proposed system, the Conversational Context-sensitive Advertisement generator (CoCoA), is the first attempt to apply masked word prediction to web information retrieval that takes into account the dialogue context. The novelty of CoCoA is that advertisers simply need to prepare a few abstract phrases, called Core-Queries, and then CoCoA automatically generates a context-sensitive expression as a complete search query by utilizing a masked word prediction technique that adds a word related to the dialogue context to one of the prepared Core-Queries. This automatic generation frees the advertisers from having to come up with context-sensitive phrases to attract users’ attention. Another unique point is that the modified Core-Query offers users speaking in front of the CoCoA system a list of context-sensitive advertisements. CoCoA was evaluated by crowd workers regarding the context-sensitivity of the generated search queries against the dialogue text of multiple domains prepared in advance. The results indicated that CoCoA could present more contextual and practical advertisements than other web-retrieval systems. Moreover, CoCoA acquired a higher evaluation in a particular conversation that included many travel topics to which the Core-Queries were designated, implying that it succeeded in adapting the Core-Queries for the specific ongoing context better than the compared method without any effort on the part of the advertisers. In addition, case studies with users and advertisers revealed that the context-sensitive advertisements generated by CoCoA also had an effect on the content of the ongoing dialogue. Specifically, since pairs unfamiliar with each other more frequently referred to the advertisement CoCoA displayed, the advertisements had an effect on the topics about which the pairs spoke. Moreover, participants of an advertiser role recognized that some of the search queries generated by CoCoA fit the context of a conversation and that CoCoA improved the effect of the advertisement. In particular, they learned how to design of designing a good Core-Query at ease by observing the users’ response to the advertisements retrieved with the generated search queries.
CBSOACH: design of an efficient consortium blockchain-based selective ownership and access control model with vulnerability resistance using hybrid decision engine

Cloud deployments are prone to vulnerabilities and attacks, mitigated via security patches. However, these patches increase the computational complexity of the deployments, thus reducing their quality-of-service performance. To overcome this limitation and maintain high-security levels, this text proposes the design of the CBSOACH model, which is a novel consortium blockchain-based selective ownership and access control model with vulnerability resistance using a hybrid decision engine. The model introduces header-level pattern analysis, which processes all incoming traffic using a light-weight rule-based method. Header-level pattern analysis is backed by a consortium blockchain model that allows for efficient ownership control with minimum overheads. Due to a combination of header-level pattern analysis with consortium blockchain, the model can maintain traceability, trustability, immutability and distributed computing capabilities. The model can reduce attack probability while maintaining lower delay and high-efficiency ownership transfer. This increases the scalability and usability of the model for large-scale deployments.
A novel security analysis for a new NTRU variant with additional private key

This paper proposes a new variant of NTRU with a slightly different critical formulation. The significance of this new variant is that it requires an additional private key to provide a tighter scheme. Because of these changes, modified key generation, encryption and decryption algorithms have been developed accordingly. The new variant is analyzed and tested against several well-known attacks, namely the alternate private key attack, brute force attack, meet-in-the-middle attack, multiple transmission attacks and lattice attack. Security properties related to these attacks have been established and explored to ensure the new variant is secure against the said attacks. Several examples are provided to illustrate the ideas.
Correspondence on “An International Study Evaluating Elemental Analysis”

This Correspondence provides a brief commentary on a recent ACS Central Science article that evaluated the performance of different laboratories in elemental analysis and suggests that a broader conclusion should be drawn instead, recognizing the benefits of metrology and the international quality infrastructure.
Distance learning as an integrative response to contemporary challenges

The COVID-19 pandemic has raised the issue of using distance learning as a separate independent model of organizing the educational process. The purpose of the article is to analyze distance learning as a form of organizing the educational process, which is dynamically developing in response to global challenges, from the point of view of an integral approach. Accordingly, the object of the study is modern forms of distance learning as a way of organizing the educational process. To achieve this goal, an analytical research approach was used that allows correlating and identifying effective solutions taking into account the unique features of the educational system: content analysis (32 items of scientific literature were analyzed), prognostic, systemic, structural methods, as well as methods of specification and abstraction. The empirical data for the study were obtained by using a survey method, which involved 100 higher education students. Its main goal was to determine the readiness of students to perceive distance learning not only as a response to the challenges of quarantine restrictions but also as a separate form of educational activity. The results showed that despite the efforts of teachers and the use of modern digital technologies, students had to study a lot of new educational materials on their own. The transition to distance learning has led to an increase in the importance of homework and independent work compared to traditional forms of educational activities. The majority of respondents noted that despite the increase in the share of online tests and other control activities, the average academic performance has not decreased. It was found that the number of students who improved their performance did not differ from those who used traditional forms of education. As a result, many higher education students expressed the opinion that studying remotely was generally easy, despite all the obstacles. Such a positive assessment allows us to interpret distance learning as a promising and independent vector for the development of future education. The conclusions emphasize that a condition for the further development of distance education is the development of digital technologies that form the basis of the technical implementation of distance learning. We believe that distance learning should be perceived as a separate branch of the educational system. The practical significance of the study lies in proving the functioning of distance education as an original form of organization of the educational process.
Compensating for Sensing Failures via Delegation in Human–AI Hybrid Systems

Given the increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g., perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human-AI teaming case where a managing agent is tasked with identifying when to perform a delegated assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent's failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, these sensing deficiencies. These contexts provide cases where the manager must learn to identify agents with capabilities that are suitable for decision-making. As such, we demonstrate how a reinforcement learning manager can correct the context-delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation.
Immersive Learning: Characteristics of Development of VR Education Technology and the Practice

In recent years, the application of virtual reality (VR) technology has been witnessing explosive growth.In the field of education, VR provides the learning situations of "immersion", "interaction" and "imagination" for modern education.Its application in education brings a series of actions that optimize conventional teaching concept and methodology, as well as a new perspective for future cultivation of teachers' ability.Researchers of educational technology need to rationally understand the potentiality of the application of VR in education, carry out in-depth analysis of the current application of VR in education, and specifically reflect on the existing challenges from the perspective of education.This article gives an overview of the development of VR educational technology, and summarizes the characteristics and practices of this technology.