Reviewerly/exHarmony huggingface dataset:
Differential Impact of CD34+ Cell Dose for Different Age Groups in Allogeneic Hematopoietic Cell Transplantation for Acute Leukemia: A Machine Learning-Based Discovery

Allogeneic hematopoietic cell transplantation (alloHCT) is curative for hematologic malignancies; however, it is associated with risks and complications. Identification of risk factors remain the topic of ongoing investigation. Specifically, CD34+ cell dose impact on overall survival (OS) has been investigated in various studies with contradicting conclusions. We utilized machine learning (ML) techniques to predict alloHCT outcomes based on patient and transplant-related characteristics, using single-center data to identify critical and often overlooked variable interactions. Our predictive model improves on the conventional Cox regression analysis with the ability to capture complex high-dimensional relationships. The application of SHAP (SHapley Additive exPlanations), an explainable artificial intelligence (XAI) technique, allowed us to discern new and clinically relevant feature-outcome relationships. The cohort used for developing the machine learning model covers 1153 patients across different diagnoses, transplanted between 2010 to 2019. In particular, we chose XGBoost, a tree-based ML model, that is known for its high performance with tabular data. After further investigation by SHAP, we narrowed the study cohort to 674 acute leukemia patients (AML n=549, ALL n=125) with a median age of 54 (18-76), where we identified clear interaction between CD34+ cell dose of the peripheral blood stem cell (PBSC) grafts and patient age at alloHCT for acute leukemia patients (Figure 1). Three cell dose groups (low, medium and high) were determined using maximally selected rank statistics method, separately for the younger (≤45-years-old) and older (>45-year-old) patient age groups. On univariate analysis for OS, within the younger cohort, the group with a lower CD34+ dose resulted in the highest 2-year OS: low vs. medium vs. high dose, 84.6% vs. 48.2% vs. 59.1% (overall p-value=0.034, Figure 2a). For the older cohort, the group with a low CD34+ dose resulted in the lowest 2-year OS: low vs. medium vs. high dose, 33.3% vs. 48.8% vs. 55.1% (overall p-value=0.054, Figure 2b). Multivariable analysis affirmed the interaction effect: for younger acute leukemia patients, a cell dose no higher than 4.3 × 106 CD34+/kg was associated with improved OS against the medium (hazard ratio [HR] 0.22, p=0.004) and higher (HR 0.25, p=0.009) cell dose groups. Conversely, for older acute leukemia patients, low CD34+ cell dose <3.8 × 106/kg was associated with worse OS against a high dose ³6.1 × 106/kg (HR 1.73, p= 0.009). The high dose group in older patients retained superior OS compared to low and medium dose groups combined (HR 0.73, p=0.024). Our findings suggest that CD34+ cell dose should be tailored by patient age for acute leukemia patients undergoing alloHCT, and XAI showcases excellent proficiency in revealing such interactions.
Crossing the digital frontier: are older adults ready for virtual reality workouts?

In the dynamic web of our digital age, virtual reality (VR) is crystallizing as a promising medium for promoting physical activity in older adults, overcoming age-old barriers. This perspective article explores the intricate synergy between VR and physical activity in older adults and highlights the many challenges of aging within a virtual environment. While VR heralds the potential for multisensory interaction and cognitive enhancement, a thorough assessment of its impact is paramount. The reach of VR goes beyond immediate applications and permeates the emotional and psychological realms of the human journey. Such an exploration requires a comprehensive perspective that goes beyond a purely technical assessment. The aim of this article is not to present clear-cut solutions, but to encourage reflection on the diverse impact of VR on our aging population. We argue for a future in which technology complements, rather than eclipses, the depth of human experience.
Digital practices at school, safety and digital citizenship

This study aims to address the massification of the internet in today's world, how much people depend on digital technologies today for almost everything in their lives, as well as the digital practices that exist today in schools, as well as security issues. online or digital and digital citizenship. Which, by the way, are topics little discussed in schools or with timid reflections still in the school environment. Because it is a bibliographical research, important contributions from authors who dominate on such subjects and address digital security issues as a whole and that the school universe is still unaware of many issues were described. Research by Cristina Paludo Santos was also described in her article Education, Digital Practices and New Network Risks regarding a project with high school students where the fragility of knowledge regarding digital security was detected, where part of it is shared in this bibliographical research. In view of the above, it is concluded in this work that digital practices exist in schools, however, the risks also and about digital security is a topic that is still little known by the school community and society in general. The risks are inevitable, but it is possible that society can be better informed about them.
Pointer Meter Reading Recognition by Joint Detection and Segmentation

To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.
An Overview of Deepfake Methods in Medical Image Processing for Health Care Applications

AI has become a part and parcel of the medical industry. Especially machine learning-based deep learning models with their ability to handle large amount of data continuously made significant contributions to the healthcare industry. One of the most promising technological developments in the field of deep learning is “deepfake”. Deepfake is a method of synthesizing fake images, audio, or video through deep learning models. Deepfake medical image processing is used in different applications like medical image synthesizing, modality transfer, data augmentation, dataset expansion, resolution enhancement, denoising, and reconstruction of medical images. Therefore, its wide range of applications provided a new realm of opportunity for the medical professional for quick, easy, and accurate diagnosis of dis-ease. This study provides an overview of deepfake implementation in the field of medical image processing. This paper is focused on providing clarity for the research questions (RQs), What are deepfake? What are the most common methods to generate a medical deepfake? What are the major applications of deepfake in the medical field? What is the need for deepfake detection? These research questions may help the researchers and academicians to understand the domain specified.
E-Comic Based on SETS: A Digital Learning Media to Improve Student’s Character and Critical Thinking Skills

This research aims to test the feasibility and effectiveness of electronic science comic learning media based on character education and SETS in improving students' character and critical thinking skills. This research is development research oriented towards testing product effectiveness. The data collection methods employed in this study included observation, tests, and questionnaires. The research findings indicate that, 1) the feasibility of SETS-based e-comics is measured through expert assessments of media and materials with the criteria of a very viable product; 2) The average gain test results show that SETS-based science electronic comic media is effective in improving students' character moderately by 5,04 and students' critical thinking abilities with a score of 4,03 are included in the medium category. This can be interpreted as saying that the use of SETS-based science e-comics is able to improve student behavior and influence students to be more enthusiastic and broad-minded to actively ask scientific questions.
Anti-Eavesdropping by Exploiting the Space–Time Coupling in UANs

Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is.
A Novel Default Prediction Model Based on DNN and LightGBM

Given the credit risk losses brought by Loan default to commercial banks, this paper, based on the America Express data set of the Kaggle platform, builds a hybrid model to predict customer default, to reduce credit risk. We first conduct data cleaning and feature engineering processing, dividing the data into category data and continuous data, and calculating the correlation between different features. We build DNN and lightGBM for credit default prediction. From the comprehensive results, the prediction effect of the mixed model is significantly better than that of the single prediction model. Among these models, our hybrid approach achieves the leading metric of 0.798. This metric is higher than XGBoost, LightGBM, and DNN by 0.013, 0.002, and 0.008 respectively.
Understanding and treating body image disturbances in eating disorders through body illusion interventions: a scoping review protocol

Abstract Background We plan a scoping review aimed to synthesize what is known about the use of sensory-driven body illusion (BI) interventions for understanding and treating body image disturbance (BID) in people diagnosed with clinical eating disorders (EDs) and people with subclinical ED symptomatology. Our study will provide an outline of the current literature, identify gaps within the literature, and suggest novel directions for future research. Methods/design The scoping review process will be guided by the methodological framework of Arksey and O’Malley, subsequent recommendations by Levac et al., and the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols Extension for Scoping Reviews guidelines. The following electronic databases will be systematically searched: MEDLINE (via PubMed), Web of Science, PsycINFO, and Scopus. Furthermore, to identify additional studies, we will use a search engine such as Google Scholar, and for grey literature, we will include Proquest for Dissertations and Theses. A search strategy has been identified and agreed upon by the research team in conjunction with a research librarian. Two researchers will screen the titles and abstracts independently and then assess the full text of the selected citations for the inclusion criteria. A third reviewer will be involved in cases of disagreement. Data will be extracted, collated, and charted to summarize all the relevant methods, outcomes, and key findings in the articles. Discussion A better understanding of this topic will aid in the development and refinement of current treatments aimed at treating BID in people with EDs. Implications and recommendations for research, policy, and practice in the context of the ED community will be discussed. Systematic review registration https://osf.io/3bcm6/?view_only=83b2e8a2445d4266909992e3dfb51929
Adaptive identification of supply chain disruptions through reinforcement learning

Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain’s aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim.
On the use of smart contracts to cope with Arrow’s Information Paradox on trade secrets exchange: method and prototype

The state of the art contains systems for the transfer of intangible assets protected by intellectual property rights, consolidated by legal monopolies, be them attributive systems (such as patents and industrial designs) or reliant on automatic protection (such as copyright and neighboring rights). Some of these systems, or their respective methods, are described in the scientific literature. Of this set, some methods employ blockchain technology and a smaller subset also employs smart contracts technology, despite both technologies being mutually independent. Although trade secrets also constitute monopolies, computerized solutions or cryptographic environments used for other types of goods protected by other forms of intellectual property cannot be transferred to these assets, due to their sui generis nature, lacking of monopoly regardless of its non-disclosure. The need for maintenance of secrecy is due to Arrow Information Paradox, the phenomenon by which the disclosure of the secret constitutes by itself its transference. This phenomenon impairs the potential for transference of industrial secrets and prevents the adoption of the existing systems applicable to other types of intangible assets. Through this work a method for transferring industrial secrets is presented, using smart contracts embedded on a blockchain, for solving two of the three component elements of the Paradox, namely capability and reliability, and presupposes the third, relevance. The method is also validated through the development of a prototype, based on Ethereum blockchain.
Construction and Improvement of a Vocational Education and Teaching System Oriented to “Internet+”

Given the current rapid development of informatization, people will increasingly use online teaching methods to learn for their quality improvement. Starting from the fundamental theories, such as the concept, classification, basic process, task, and method of data mining under the background of Internet Plus, this article analyzes the problems that must be considered in data mining application. Secondly, the Apriori algorithm is studied, the FP-Tree flow chart is established, and the Internet Plus vocational education teaching system model is constructed using multiple databases as data sources. Finally, the results of the teaching system are analyzed and verified, with the model analysis showing that the minimum confidence boost of the system is 0.65, and the minimum support reaches 0.03. The maintainability of the database based on the association rules Apriori algorithm is good; the data entry can be completed smoothly, and the update, deletion, and modification can also be completed smoothly.
A Comprehensive and Privacy-Aware Approach for Remote Qualified Electronic Signatures

The current shift towards digital transactions emphasizes the need for robust Qualified Electronic Signature (QES) frameworks that safeguard integrity and privacy. Having the potential to become the leading type of adopted QES, the main challenge that Remote QESs present to end users is choosing between transmitting the entire document or only its digest to the Trust Service Provider (TSP). The first option compromises the document’s confidentiality, while the second one requires the development of signature applications compliant with advanced signature formats, a task that often needs additional time and resources. In this paper, we introduce a comprehensive strategy for remote QESs, designed for seamless integration with current client applications, while simultaneously maintaining user privacy. The main topics approached in this paper are the following: a comprehensive architecture for privacy-aware remote QES systems, relevant standards and legislation, integration scenarios for clients, and remote QES standard protocols to assure communication between client and TSP environments. Furthermore, we also explore the integration of our proposed solution with an enhanced long-term preservation service that uses Ethereum smart contracts and methodologies to implement signature applications with advanced electronic signatures via open-source libraries while ensuring document privacy. The main result of this work is a flexible on-premise module that provides the ability to sign, validate, and preserve documents, with a minimal integration effort.
Detection of arabic sign language by machine learning techniques with PCA and LDA

People need a communication channel and a way to communicate with a person or group. Deaf people communicate with others through sign language. The remarkable and rapid development in image and video recognition systems has made researchers use this development to solve many problems, including sign language for the deaf, and reduce their suffering in communicating with ordinary people. This work aims to use and apply machine learning technology to build an automatic recognition system for Arabic Sign Language (ArSL). In this work, images of ArSL characters were recognized using four classification techniques (Naïve Bayes (NB), Decision Trees (DTs), and Adaptive Boosting), and K-Nearest Neighbor (KNN)) with the Python library and using two feature extraction algorithms (PCA & LDA). Data pre-processing steps, including grayscale conversion, Gaussian blur, histogram equalization, and resizing, are applied to enhance the data's suitability for training and testing. The work was tested with five experiments chosen with multiple ratios for training and test data. The first training is 90%, the second training is 80% of the data, the third is 75%, the fourth is 70%, and the last is 60%. The work also played a good role in interpreting ArSL, and the accuracy of the work considers the KNN algorithm more accurate in prediction.
The Role of Libraries in Creating a Safer Cyberspace: Awareness of Estonian Library Employees in Information Security

The purpose of the study was to find out the knowledge, attitude and behavior of library employees related to information security in seven areas of the information security policy involving employees: password management, use of email, use of the Internet, use of social media, use of mobile devices, handling of information and reporting of cyber incidents. The theoretical part provides an overview of the importance of the topic in the context of libraries, the reasons why information security and cyber security must be at the same high level in libraries as, for example, in banks or healthcare institutions – we can only imagine what would happen if the digitized cultural memory of Estonia disappears or user data is leaked. The HAIS-Q (Human Aspects of Information Security Questionnaire) developed by Parsons et al. was used to collect data in order to assess information security awareness (ISA) within the organization. The method is based on three factors developed by the same authors: the Knowledge-Attitude-Behavior (KAB) model, which determines the security awareness of employees. The target group of the online questionnaire was Estonian librarians. A total of 388 librarians from public, research, school and specialist libraries responded to the survey. The mean and standard deviation of each focus area, descriptive statistics, Cronbach’s coefficient and Pearson correlation analysis were used to achieve the research objective. The results revealed that the chosen methodology is well suited for assessing the cyber literacy of not only library staff, but also the staff of all memory institutions (archives, museums) and identifying training needs.
Unveiling the correlates of university students attitude towards online education

The education landscape in India is undergoing massive transformation due to rapid advances in Information and Communication Technologies (ICTs) and its increasing integration in education sector in India. It has transformed the way education services are planned, structured and delivered to the masses. The Corona pandemic has, per force, accelerated the use of ICTs in education sector and brought to center stage the concepts such as online education or e-learning. The present study was carried out to find out the attitude of Undergraduate students towards online education and the factors affecting it at a premier Agriculture University in North India. Using exploratory research design, the study sample comprised of 185 respondents selected using stratified random sampling method, and the data was collected using a pre-tested questionnaire which was e-mailed to the selected respondents. The study findings revealed that a majority of the respondents displayed a ‘neutral to positive’ attitude towards online education. Further, age, academic performance, family education status, Annual family income, Family size, Technology readiness, and Application skills were found to be the major factors that affected attitude towards online education. The value of Coefficient of determination, i.e. R2 (0.5729) reiterated that all the independent variables contributed 57.29% variation in the dependent variable. Hence, concerted efforts have to be made by the policy makers, administrators, and academicians concerned to make the necessary improvements in planning, implementing and strengthening online education strategies.