Reviewerly/exHarmony huggingface dataset:
Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informative sentences from biomedical articles is always challenging.This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information.The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model.The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them.The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences.The quality of the framework is assessed via different parameters like information retention, coverage, readability, cohesion, and ROUGE scores in clustering and non-clustering modes.The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption.The configurable settings of combined parameters reduce execution time, enhance memory utilization, and extract relevant information outperforming other biomedical baseline models.An improvement of 17% is achieved when the proposed model is checked against similar biomedical text summarizers.
Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

This article focuses on developing an expert system applicable to the area of neurocognitive rehabilitation. The benefit of this interdisciplinary research is to propose an expert system that has been adapted based on real patients’ results from the Addenbrooke’s cognitive examination (ACE-R). One of this research’s main results is a unique proposal to transfer the ACE-R result to the CHC (Cattell–Horn–Carroll) intelligence model. This unique approach enables transforming the CHC model domains according to the modified ACE-R factor analysis, which has never been used before. The expert system inference results allow the automated optimized design of a neurorehabilitation plan to train patients’ cognitive functions according to the CHC model. A set of tasks in 6 difficulty levels (Level 1–Level 6) was proposed for each of the nine CHC model domains. For each patient, the ACE-R results helped determine specific CHC domains to be rehabilitated as well as the starting game level for the rehabilitation within each domain. The proposed expert system has been verified on real data of 705 patients and achieved an average error of 5.94% for all CHC model domains. The proposed system is to be included in the outcomes of the research project of the Technology Agency of the Czech Republic as a verified procedure for healthcare providers.
Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users' daily lives.However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services.Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users' daily lives.The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards.In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system.Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly.As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling).When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall.Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values.The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.
Defining Cyber Risk Scenarios to Evaluate IoT Systems

The growth of the Internet of Things (IoT) has accelerated digital transformation processes in organizations and cities. However, it has also opened new security challenges due to the complexity and dynamism of these systems. The application of security risk analysis methodologies used to evaluate information technology (IT) systems have their limitations to qualitatively assess the security risks in IoT systems, due to the lack of historical data and the dynamic behavior of the solutions based on the IoT. The objective of this study is to propose a methodology for developing a security risk analysis using scenarios based on the risk factors of IoT devices. In order to manage the uncertainty due to the dynamics of IoT behaviors, we propose the use of Bayesian networks in conjunction with the Best Worst Method (BWM) for multi-criteria decision-making to obtain a quantitative security risk value.
ML-Based Traffic Classification in an SDN-Enabled Cloud Environment

Traffic classification plays an essential role in network security and management; therefore, studying traffic in emerging technologies can be useful in many ways. It can lead to troubleshooting problems, prioritizing specific traffic to provide better performance, detecting anomalies at an early stage, etc. In this work, we aim to propose an efficient machine learning method for traffic classification in an SDN/cloud platform. Traffic classification in SDN allows the management of flows by taking the application’s requirements into consideration, which leads to improved QoS. After our tests were implemented in a cloud/SDN environment, the method that we proposed showed that the supervised algorithms used (Naive Bayes, SVM (SMO), Random Forest, C4.5 (J48)) gave promising results of up to 97% when using the studied features and over 95% when using the generated features.
Digital skills in the workplace - from the employees' and supervisors' perspective

For a long time digital skills, due to their relational and relative nature, have been a challenge for researchers trying to create a framework catalogue of these competences. As technology develops and the working environment changes, this task is becoming increasingly difficult and the gradation of digital skills is getting less and less clear. Employers and managers have similar doubts: What does it mean that an employee has digital skills? How can you assess their level? How can you check whether an employee's digital skills will help them fulfil their tasks? Since the pandemic, emphasis has been put on the importance of the digital competences of employees, with many companies adapting the way they operate to the new conditions (remote work, hybrid work). Despite many existing compilations it is difficult to find a unified method of assessing digital skills in modern workplaces. In this article the author presents the results of research on digital skills of employees of a financial company. The DigComp model was applied as the technical basis of the research, which allowed for a diagnosis of digital skills in specific areas (qualitative research). The other part of the research compares employees' claims with their supervisor's description of their digital skills (qualitative research) on the operational level - in a professional environment. The research showed discrepancies in the perception of employees' digital skills related to cybersecurity, the use of digital tools for streamlining work processes, as well as keeping a work-life balance.
Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging

Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification.In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials (gauze, instruments), collected from HS images of 100 patients during surgeries.The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%.In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images.
Taking play and tinkering seriously in AI education: cases from Drag vs AI teen workshops

Learning around artificial intelligence (AI)-powered technologies that attends to power is an urgent and widely felt priority among the learning sciences and CS ed broadly. Popular approaches to AI education focus on technical skills, with far less theoretical and practical work around critical and justice-centered AI learning. Adding to this literature, we discuss tool design and observed interactions in Drag vs AI workshops, where participants use hands-on makeup art as a medium for fooling, subverting, and refusing facial recognition. Our broader analysis asks how participants make sense of the technical and political aspects of AI, as they interact with AI through the Drag vs AI workshops’ modes of aesthetic transformation, tinkering, and resistance. In this paper, we focus on participants’ embodied algorithmic tinkering with AI and affordances for justice-centered computing education. Our analysis highlights how tinkering and play modes of interaction with AI materials can promote critical and agentive learning.
VERD: Emergence of Product-Based Video E-Commerce Retrieval Dataset from User’s Perspective

Customer demands for product search are growing as a result of the recent growth of the e-commerce market. According to this trend, studies on object-centric retrieval using product images have emerged, but it is difficult to respond to complex user-environment scenarios and a search requires a vast amount of data. In this paper, we propose the Video E-commerce Retrieval Dataset (VERD), which utilizes user-perspective videos. In addition, a benchmark and additional experiments are presented to demonstrate the need for independent research on product-centered video-based retrieval. VERD is publicly accessible for academic research and can be downloaded by contacting the author by email.
Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced data in poverty classification

Poverty data in official statistics data is important for development planning. The lower percentage of the poor recorded yearly indicates good development of a country. Moreover, there is always a problem when performing an inferential and classification analysis because of the imbalanced data, thereby leading to biases in the estimation results and prediction errors in the classification. One of the solutions to this problem is using Synthetic Minority Over-sampling Technique (SMOTE). Therefore, this study aims to evaluate the inference and classification quality using the binary logistic regression model without and with SMOTE. The data utilized was the poverty status of households in the rural and urban areas in East Java, Indonesia as contained in the 2019 National Socio-Economic Survey. Furthermore, the variables used are poverty status of the household, the age of the household head (HH), the ratio of household members who are employed, gender of the HH, number of household members, education level of HH, and occupation of the HH. It was concluded that the model with SMOTE approach was better at inference and classifying the results.
Spotting Acronyms and Initialisms with the Help of Informatics

Abstract The growing popularity of streaming services has led to innumerable audiovisual material available for the audience. As movies, documentaries, or TV shows are part of the entertainment industry, they aim at reaching viewers worldwide with the help of dubbed and subtitled versions. Our aim is to collect the acronyms used in the transcripts/subtitles of several American political TV shows (24, Designated Survivor, House of Cards, and The West Wing) and analyse their translated versions into Hungarian. However, the strenuous activity of opening each subtitle file one by one and browsing through them to spot and collect the acronyms and initialisms would result in countless mouse clicks. Hence, a specific software (SRT Manager) was designed to speed up the process. As the majority of definitions regarding acronyms and initialisms focus on the fact that they result from the combination of at least two capital letters, once the software gets the input (multiple subtitle files of entire seasons), it provides all the consecutive two- or more capital letter instances (with or without periods) found in the raw data, such as AA or A.A. Further statistical data (the source file of each instance, counting all unique values and numbering occurrences, and adding sample lines from the subtitle) also saves a lot of time and energy, as it can easily be exported to spreadsheet programs for further data analysis.
Network Pathway Extraction Focusing on Object Level

In this paper, I propose an efficient method of identifying important neurons that are related to an object’s concepts by mainly considering the relationship between these neurons and their object concept or class. I first quantify the activation values among neurons, based on which histograms of each neuron are generated. Then, the obtained histograms are clustered to identify the neurons’ importance. A network-wide holistic approach is also introduced to efficiently identify important neurons and their influential connections to reveal the pathway of a given class. The influential connections as well as their important neurons are carefully evaluated to reveal the sub-network of each object’s concepts. The experimental results on the MNIST and Fashion MNIST datasets show the effectiveness of the proposed method.
A Comparative Analysis of Augmented Reality Frameworks Aimed at Diverse Computing Applications

Immersive systems such as Augmented Reality (AR) and Virtual Reality (VR) have proven useful in diverse computing domains. However, there is little effort on accuracy measurements within AR applications, which could greatly impact outcomes and decisions in certain domains, such as crime scene investigations, among others. This paper aims to analyze and evaluate two existing prominent AR frameworks, ARCore and ARKit, which support the development of diverse mobile computing applications for immersive systems. This research developed prototype applications and conducted comparison tests of measurement accuracy within the applications. The accuracy was tested using four distance criteria across six different devices, spanning ARCore and ARKit frameworks. A control experiment was used to benchmark the measurement accuracy. Relatively, an instance of the experiment presented ARCore as reliable. Overall, ARKit proved to be more accurate between the two frameworks, with an average accuracy of 99.36% as opposed to 89.42% scored by ARCore. The obtained results can give insight on the choice of framework to consider during AR application development for a specific domain, hence boosting quality of experience.
Identity-Based Unidirectional Collusion-Resistant Proxy Re-Encryption from U-LWE

Identity-based proxy re-encryption (IB-PRE) converts the ciphertext encrypted under the delegator’s identity to the one encrypted under the delegatee’s identity through a semitrusted proxy without leaking delegator’s private key and the underlying plaintext. At present, the security of most IB-PRE schemes relies on the hardness of the discrete logarithm solution or large integer decomposition and cannot resist attacks of the quantum algorithms. The majority of the IB-PRE schemes over lattice are secure only in the random oracle model. Aiming at such problems, the paper constructs a secure IB-PRE scheme over lattice in the standard model. In the scheme, the underlying encryption scheme proposed by Gentry et al. in EUROCRYPT 2010 is adopted to reduce the storage space of ciphertext. The proposed scheme is unidirectional collusion-resistant multihop and anonymous, and it is semantically secure against selective identity and chosen plaintext attack based on Decisional Learning With Errors with uniformly distributed errors (D-U-LWE) hard problem in the standard model.
Real-Time 3D Reconstruction of Large-Scale Scenes with LOD Representation

Real-time 3D reconstruction of static scenes can be achieved based on the RGB-D image sequence fusion. It is a popular practice to divide the space into uniform voxels and use a truncated signed distance function to represent surface information. In order to represent a scene of large scale, the voxel hash algorithm which stores voxels compressively can be used, but most of the conventional methods do not consider the complexity and roughness of the object surface in the scene, so the scene is represented with a uniform resolution. It somewhat limits the range of scene representation and the speed of real-time reconstruction. In this paper, a large-scale scene reconstruction algorithm based on voxel hashing storage with LOD representation is proposed. The main contributions include the following two aspects: (1) By preprocessing the depth image with smooth filtering, which ensures the accuracy of the data, it can effectively reduce the distortion caused by the sensor itself and violent motion and provide better support for the stages of voxel hashing, model rendering, and frame-to-model camera position tracking. (2) The 3D reconstruction with LOD representation is realized. We take the view distance and the roughness of the model surface as criteria to control the adaptive division and representation of spatial voxel blocks. Finally, we carried out qualitative and quantitative evaluations of the algorithm, and confirmed that the algorithm can achieve real-time reconstruction with different levels of detail in the commercial graphics processing hardware environment, and achieve a good fusion effect in large-scale scenes.
Diagnosing Clinical Diseases using an Edge-Enabled Deep Learning Technology

Along with the development of high-speed communication networks, edge-enabled mobile devices have opened new possibilities for diagnosing health conditions or developing suitable treatment plans. While the latest deep learning technology has deployed to restructure and translate complex medical applications, the costly training operation using large-scale neural networks with tremendous amount of data remain the major challenge. In this work, we take advantages of reservoir computing to develop a reliable and low-cost medical diagnostic system for edge-enabled devices. Specifically, an echo state network (ESN) was trained to discover non-obvious correlation and likelihood from biomedical data with respect to various patients. Through the determination of cardiovascular and coronavirus diseases, numerical evaluations demonstrated advantage of ESN against the state-of-the-art. At particularly no computation overhead, ESN precisely described the prediction tasks of health conditions, offering improvements of up to 1000x in sample reduction, 175x in training speedup, and 15 percentage points in prediction accuracy.
QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation

Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend, and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the backpropagated gradients, and training a filter state against an ideal target state.
Mask R-CNN with Multi-Backbones - A Comparative Analysis

Instance segmentation is a computer vision task for detecting and localizing an object in an image. It involves identifying, segmenting, and categorizing each object in an image. The idea behind instance segmentation is to categorize objects that belong to the same class into different instances. With instance segmentation, one can locate the bounding boxes of each instance as well as the object segmentation maps for each instance, allowing one to determine the number of instances in the image. Mask Region-based convolutional neural network is the cutting-edge method for instance segmentation. The backbone architecture of the mask R-CNN consists of a feature pyramid network, a region proposal network, and a region of interest alignment network. In this paper, three CNN models such as ResNet101, ResNet50, and MobileNetV1 are used as backbone network structures to compare the mask R-CNN architecture. The performance of these models is compared with the Penn- Fudan dataset using performance metrics such as mean Average Precision, mean Average Recall, and F1-score. The ResNet-101 output achieves a mean Average Precision of 98.3% over the other two models. The performance scores and losses of the three models were also discussed. Experimental studies show that ResNet-101 outperforms the other two models.