Reviewerly/exHarmony huggingface dataset:
Python

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
The use of INTEGRO GIS in the integrated geophysical study&#x0D; of the deep structure of oil and gas prospective regions (part 1)

Using the materials of integrated geophysical studies of the Yenisei-Khatanga trough as an example, the methodological outline and techniques for constructing deep physical-geological models (PGM) of large regions using the INTEGRO GIS complex are demonstrated. The presented graph is based on spatial analysis of potential field data, as well as analysis of seismic and geoelectric sections along the lines of integrated profiles of CDP-2D and MTS with the involvement of geological data, borehole data and materials on the physical properties of rock complexes. The results of the analysis are then used to construct the PGM according to the data of individual methods. The research focuses on the construction of a volumetric density model of the region that integrates all geological and geophysical information and is intended to serve as the background for the development/correction of structural-tectonic and evolutionary models of oil and gas bearing region for predictive purposes. The actual results of such constructions for the Yenisei-Khatanga trough region and their significance for oil and gas exploration are discussed.
Research on the Construction System of Computer Big Data in the Community Integrated Garbage Classification AI Image Recognition Platform

Based on computer big data combined with AI technology and closed-loop control principle, this paper designs an intelligent system that can help users to classify garbage. Based on the closed-loop control principle, this paper designs and installs the controller equipment, and combines AI technology to detect the position of the stepping motor. Then, based on the Inceptionv3 network feature extraction model and transfer learning, this paper trains and tests on the self-built garbage dataset. The experimental study found that the method can accurately identify the type of garbage, and the average accuracy rate reaches 0.99. The intelligent garbage classification system can improve the efficiency of garbage delivery, greatly reduce the gathering of people near the garbage station, and achieve non-contact garbage disposal. This can effectively avoid the risk of cross-infection caused by contact.
HIENet: A Hardware Impairment Estimation Network for OFDM Systems

In orthogonal frequency division multiplexing (OFDM) systems, carrier frequency offset (CFO) and in- and quadrature-phase (IQ) imbalance are two critical hardware impairments that may lead to severe amplitude and phase mismatches and therefore greatly degrade the demodulation performance. Due to the noise impact and the coupling effects between different hardware impairments, it is considered to be highly challenging to perform joint estimation of CFO and IQ imbalance. In this paper, we propose a novel multi-task learning-based hardware impairment estimation network (HIENet) to simultaneously estimate the CFO and IQ imbalance in OFDM systems. The proposed HIENet can address the noise impact by averaging the task-dependent noise patterns and overcome the coupling effects by extracting correlated features from different estimation tasks. Numerical results show that the proposed HIENet can achieve comparable estimation accuracy to conventional one-shot and iterative methods, while at the same time having the smallest computation time.
An Adversarial Learning-based Tor Malware Traffic Detection Model

Attackers often use Tor to launch cyberattacks and conduct illegal transactions, threatening cyberspace's security and people's daily lives. Existing methods for malware traffic detection on Tor can be classified as rule-based and network-based, both of which apply machine learning extensively. Tor malware traffic detection systems are often deployed in open network environments. Their machine learning systems are the first to be attacked by adversarial samples. To ensure that Tor is not abused, this paper proposes an Adversarial Learning-based Tor Malware Traffic Detection model, AL-TMTD. We generate realistic attack samples that can evade detection and use these samples to produce an augmented training set for producing hardened detectors. In such a way, we obtain a more resilient Tor malware traffic detection model that achieves adversarial robustness. We validate our proposal through an extensive experimental campaign that considers multiple machine learning algorithms and shadow models. We simulate the adversary to construct functionally approximate shadow models through black-box model extraction and generate adversarial samples to validate the adversarial robustness of our proposed AL-TMTD model. Our experimental results demonstrate that the average accuracy of AL-TMTD after the adversarial retraining is as high as 0.995 in detecting adversarial samples, which is 0.314 without the adversarial retraining, a significant improvement.
Comparison of automated segmentation techniques for magnetic resonance images of the prostate

Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs.This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables.The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables.Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice).
Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Abstract Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.
MediBlock: A Pervasive Way to Create Healthcare Value in Secured Manner for Personalized Care

These days, thanks to the fast development of new technologies like the internet of things and wearable gadgets, the healthcare industry is expanding at a breakneck pace. These gadgets saw widespread use as a means of facilitating remote patient monitoring. On the other end blockchain is a highly acclaimed technology that offers a wide variety of interesting characteristics, provides more trust and security. Information pertaining to patients’ health is subject to stringent regulatory and safety regulations. The implementation of a distributed architecture is required in order to successfully overcome these problems and remain in compliance with applicable security standards. Blockchain has attracted a lot of attention as a potentially useful advanced technology because of its decentralized design and the security benefits it promises to provide for Internet of Things-based applications. These considerations served as the impetus for this research, which presents Medi-Block, a protected healthcare system that combines Blockchain technology with the Internet of Things. It is possible to monitor patients remotely using this method. The study proposes an extra security level in a high value creation process of personalized healthcare through remote patient monitoring. This chapter proposes the framework on how patient layer, physician layer and technological layer interacts to provide better value of healthcare with the maximum privacy and security.
Digital Literacy Competence Levels of Indonesian Junior High School Students Based on Activities Through The 5m Approach

&lt;p&gt;Students’ competence to utilize digital technology to access information as learning sources has become a necessity as experienced during the covid-19 pandemic. Students should have the digital literacy competence to support the students’ learning processes. This research was conducted to reveal the digital competence levels covering internet searching, hypertextual navigation, content evaluation, and knowledge assembly based on activities made by the students of Junior High Schools in Singkawang. The samples were selected using a proportional random sampling technique and 75 respondents were selected representing the students of Junior High Schools in Singkawang. The collected data were then processed using a descriptive statistical analysis with a quantitative approach. The statistical test was conducted using mini-step Program with Rasch modeling to measure the students’ digital literacy competence levels at Junior High Schools in Singkawang. The research results showed that the digital literacy competence level test measuring instrument was considered valid and reliable. The digital literacy competence level with high logit value of +3.78 logit also showed high problem-solving competence level. The students’ digital literacy competence level based on activities was classified into high category. Meanwhile, the activities made by the students included 5M: searching and selecting information (M1), managing information (M2), analyzing information (M3), utilizing information (M4), and sharing information (M5).&lt;/p&gt;
An Ontology for Context-aware Middleware for Dependable Medical Systems

In healthcare systems, there is an ecosystem of heterogeneous biosensors. A middleware is required for transmitting and establishing dependable communication with multiple integrations and information exchange through messages. However, in environments in which a distributed and dynamic network exists, a purely traditional middleware would not minimize the effect failures at data transmission and network congestion can cause. To preserve data integrity and provide relevant services as the environment changes, the use of context-aware middleware is recommended. This article describes an ontology for context-aware middleware to handle challenges faced by medical system networks and environment changes.
The global digital divide: Younger and older teachers at odds in this COVID19-induced remote teaching experiment.

Abstract COVID-19 was anticipated to target aging population and surely it did in world of health, but did collateral damage in education. Lack of preparedness and digital divide became more obvious among old-aged teachers as compared to younger faculty. The later found themselves more equipped with skill sets and more motivated to embrace the realities of the virtual world. We studied a cohort of teachers across the spectrum of age or years of service compared to younger or newly hired faculty. We solicited teachers in both cohorts from Asia, Europe and Americas and surveyed their responses to a given set of questions. We also recruited a group of students to independently and anonymously verify if they noticed any major effect in their learning process. Our global data showed a clear digital divide among the aged and younger faculty. Both groups found themselves unprepared and lacking necessary skill set to deal with this unprecedented, sudden and mandatory online education being forced upon them. However, younger faculty reported to have adapted to the onslaught of technology in an abrupt and timely manner as compared to their senior colleagues. Mostly, both group of teachers considered the learning experience at best as “on job training module”. High anxiety levels with fear of lack of performance, proper delivery methods also played a role in this panic-stricken educational world. Both group of teachers also reported lack of historical perspective and pilot studies for the use of technological gadgets as one of the artifacts in this experiment. The students being at the recipient end also reported in their surveys that the haste and rush of delivery and assessment methods negatively influenced the teaching methods and reciprocally their learning process.
Improve polyp semi-supervised segmentation with prioritizing the reliability of unlabeled images

We propose a simple strategy when training the semi-supervised segmentation model. Traditional semi-supervised segmentation methods use all unlabeled data without any priority. However, it is not reasonable to consider all pseudo labels have the same confidence. Our idea is to divide pseudo labels into two sets, high stable and low stable, to alleviate the negative impact of the wrongly pseudo labeled images. We propose a simple algorithm to calculate the reliability scores of pseudo labels and retraining strategies. The experiments on popular colonoscopy polyp datasets show that our method yields satisfactory results and achieves a Dice Coefficient higher than the result of the traditional semi-supervised method using all unlabeled data about 2.3%. Code is available at https://github.com/sun-asterisk-research/prioritizing_stable_images_ssl
Computer-Aided Assessment System for Calisthenics Exercises

Amidst the global pandemic, Physical Education classes had to resort to online submission of exercise performance videos, which is a more tedious and time-consuming task for PE instructors to individually view and assess. As a possible tool to assist with this problem, this paper presents a computer-aided assessment system that outputs a summarized assessment for three calisthenics exercises, namely the push-up, the squat, and the sit-up. The system uses OpenCV as a platform for computer vision and utilizes Mediapipe's pose detection library to detect the pose of the target within a video. By calculating the angle between the landmark body parts associated with the execution of a specific exercise, the system can count the reps as well as provide insight on the angle depth of the exercise execution and its range of motion. It outputs this data as an Excel spreadsheet for convenience since the system is intended to assist Physical Education instructors with checking the exercise videos of their students. The system was validated by comparing its summary output with that of a human PE professor and statistically treated using SPSS. The results show that it provides a similar rating to that of the human in terms of counting total number of reps and correct reps for push-ups and squats but had some issues with assessing range of motion for sit-ups.
Can cryptocurrency solve the problem of financial constraint in corporates? A literature review and theoretical perspective

Purpose This paper aims to attempt to examine some of the unique features of cryptocurrency and the reasons for its growing market acceptability. Given the expanding size of cryptocurrency markets, the present study strives to identify whether it can be used as an alternative financial asset in place of traditional financial assets to meet firms' financial constraints. It also provides issues for future research in the area of cryptocurrency markets. Design/methodology/approach This paper analysed 94 research papers from databases such as ScienceDirect, Proquest, EBSCO, Emerald Insight and Web of Science. Articles connected to cryptocurrency, financial assets and corporate financial constraints research were explored. VOSviewer software has been used to visualise the specified body of literature and identify eight clusters in previous literature using keyword and abstract analysis. Findings Studies reveal that cryptocurrency markets are independent of traditional financial markets and cryptocurrency returns have less correlation with traditional financial asset classes. This can be an advantage to firms, especially during times of crisis when traditional financial assets are impacted by significantly lower returns, while cryptocurrencies can serve as an alternative. Realtime data reveals that during the pandemic, cryptocurrencies had the maximum growth in returns which also happened to be a time when firms faced severe cash constraints. While accepting cryptocurrency as a means of exchange is still under review by regulatory authorities, it can be considered an alternative asset for investment purposes. Firms can take advantage of it to overcome financial constraints and thus reap the gains from holding crypto assets for precautionary reasons. Originality/value The present study investigates using cryptocurrency as an alternative financial asset to solve the financial constraint problem in corporates. The issues regarding volatility, cyber securities, gold returns, long-term and short-term returns have been some of the most prominent studies in the area of cryptocurrency. The present study uses eight theme-based clusters to identify the role of cryptocurrency as an alternative investment class and examines evidence-based research regarding the financial returns from holding cryptocurrency over certain traditional asset classes such as gold, currency or stocks. In recent years, it has been found that investors' growing interest in holding cryptocurrency as part of their financial portfolio has led to the substantial appreciation of cryptocurrency prices. To the best of the authors’ knowledge, the study will be a novel attempt to identify the role of cryptocurrency as an antidote to the companies’ financial constraints and liquidity issues.
TOP-Key Influential Nodes for Opinion Leaders Identification in Travel Recommender Systems

Travel recommender systems, also called (TRS) have recently gained significant attention in the research and industrial communities. These systems aim at identifying the travellers preferences and providing adequate suggestions to them whenever and wherever they want. Thus, TRS are very helpful for travelers, particularly, when they visit a place they have never been to before. Opinion Leaders based-technique attempts to identify the set of most important delegates who can represent as many TRS users as possible to alleviate the cold start user problem when a new user is registered to the system and has no ratings yet and the cold start item problem when a new item is added to the system and has no interactions yet. In this paper, we propose two graph based approaches for Opinion Leaders Detection in Travel Recommender Systems. The first is based on the Minimum Cover Vertex identification, while the second uses the Fragmentation method to detect the set of most influential nodes in the recommender system graph. The obtained experimental results confirm the effectiveness of our proposal.
Application of machine learning in banking and finance: a bibliometric analysis

Machine learning (ML) is a type of artificial intelligence (AI) that empowers a framework to gain information instead of through express programming. The application of ML techniques is not limited to one sector, and it has been used in healthcare, fraud detection, automation etc. The bibliometric analytical approach has not yet been applied to the construct 'application of ML in banking and finance'. The study attempts to connect ML into banking and finance which is intrinsically a multidisciplinary study. The study identifies and put across a comprehensive analysis of the publications made in this construct so as to make significant contributions on the existing body of knowledge in this field with facts that are statistically backed using bibliometric analysis. This study will provide insights to faculty, researchers and industry experts about the research trends and the possible avenues for future research in this construct.
One-Time Training That Transfers to Arbitrary Highly Faulty Optical Neural Networks

We present a one-time error-aware software training technique that outputs matrices that produce near-ideal performance on any arbitrary highly faulty photonic neural network with hardware phase errors up to 5x larger than present-day fabrication tolerances.
Motivating the Interest of Children with Disabilities to Study Mathematics and Computer Science on the Example of the Olympiad at the Faculty of Information Technology

&lt;p&gt;The article describes one of the options for the support and development of gifted children with disabilities - the olympiad in mathematics and computer science for students with disabilities and disabilities, held at the Faculty of IT MSUPE. The goals set by the faculty during the olympiad are described and examples of tasks are given.&lt;/p&gt;