Reviewerly/exHarmony huggingface dataset:
Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis

Medical imaging techniques have been widely used for diagnosis of various diseases. However, the imaging-based diagnosis generally depends on the clinical skill of radiologists. Computer-aided diagnosis (CAD) can help radiologists improve diagnostic accuracy as well as the consistency and reproducibility. Although convolutional neural network (CNN) has shown its feasibility and effectiveness in CAD, it generally suffers from the problem of small sample size when training CAD models. Nowadays, self-supervised learning (SSL) has shown its effectiveness in the field of medical image analysis, especially when there are only limited training samples. However, the backbone of downstream task sometimes cannot be well pre-trained in the conventional SSL framework due to the limitation of the pretext task and fine-tuning mechanism. In this work, an improved SSL framework, named Hybrid-supervised Bidirectional Transfer Networks (HBTN), is proposed to improve the performance of CAD models. Specifically, a novel Gray-Scale Image Mapping (GSIM) task is developed, which still takes the widely used image restoration task in SSL as the pretext task, but further embeds the class label information into it to improve discriminative feature learning of its corresponding network model. The proposed HBTN then integrates two different network architectures, i.e. the image restoration network for the pretext task and the classification network for the downstream task, into a unified hybrid-supervised learning (HSL) framework. It jointly trains both networks and collaboratively transfers the knowledge between each other. Consequently, the performance of downstream network is thus improved. The proposed HBTN is evaluated on two medical image datasets for CAD tasks. The experimental results indicate that HBTN outperforms the conventional SSL algorithms for CAD with limited training samples.
A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this article, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole-slide images of routine hematoxylin and eosin-stained HNSCC sections. We show that our Digital-HPV score generated from hematoxylin and eosin slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated stroma. High levels of B cells and T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in patients with HNSCC.
Multi-stage Proof-of-Works: Properties and vulnerabilities

Since its appearance in 2008, Bitcoin has attracted considerable attention. So far, it has been the most successful cryptocurrency, with the highest market capitalization. Nevertheless, due to the method it uses to append new transactions and blocks to the blockchain, based on a Proof-of-Work, Bitcoin suffers from poor scalability, which strongly limits the number of transactions per second and, hence, its adoption as a global payment layer for everyday uses. In this paper we analyze some recent proposals to address this issue. In particular, we focus our attention on permissionless blockchain protocols, whose distributed consensus algorithm lies on a Proof-of-Work composed of k>1 sequential hash-puzzles, instead of a single one. Such protocols are referred to as multi-stage Proof-of-Works. We consider a simplified scenario, commonly used in the blockchain literature, in which the number of miners, their hashing powers, and the difficulty values of the hash-puzzles are constant over time. Our contribution is threefold. Firstly, we derive a closed-form expression for the mining probability of a miner, that is, the probability that the miner completes the Proof-of-Work of the next block to be added to the blockchain, before any other miner does. Secondly, we show that in multi-stage Proof-of-Works the mining probability might not be strictly related to the miner hashing power. This feature could be exploited by a smart miner, and could open up potential fairness and decentralization issues in mining. Finally, we focus on a more restricted scenario and present two attacks, which can be applied successfully against multi-stage Proof-of-Works: a Selfish Mining attack and a Selfish Stage-Withholding attack. We show that both are effective, and we point out that Selfish Stage-Withholding can be seen as a complementary strategy to Selfish Mining, which in some cases increases the selfish miner profitability in the Selfish Mining attack.
Ensemble Scene Processing is Regulated by Feature Complexity

Past literature has suggested that summary statistics for groups (i.e., ensembles) of faces or objects can be rapidly extracted, with visual ensemble perception typically becoming more efficient as set size increases. Tharmaratnam and colleagues (VSS 2019) recently demonstrated that the average scene content (i.e., perceived naturalness or manufacturedness) and spatial boundary (i.e., perceived openness or closedness) of scene ensembles can be extracted without reliance on visual working memory (VWM) resources. However unlike past literature, the task difficulty of extracting average scene ensemble features increased with increasing set sizes. To investigate this further, in the present study we varied scene ensemble task difficulty by manipulating scene feature complexity. In Experiment 1, participants were asked to report the average orientation of ensembles of rotated scenes (simpler feature). In Experiment 2, participants were asked to report the average sound level of ensembles of scenes that varied in perceived sound quality (i.e., noisy or quiet; complex feature). In both experiments, we varied set size by randomly presenting 1, 2, 4, or 6 scenes to participants on each trial, and additionally measured VWM capacity using a two-alternative forced-choice task. We found that participants were able to accurately extract summary statistics for both ensemble scene features. Importantly, all 6 items were integrated into their percepts without relying on VWM, as less than 1.3 scenes were remembered on average. Interestingly, when set-size increased, task performance did not change when rating average scene orientation, but decreased when rating average sound level. This latter finding is consistent with our previous findings measuring average scene content and spatial boundary. Taken together, these results broaden our understanding of the cognitive mechanisms governing ensemble perception by demonstrating that the number of items and the feature complexity of the incoming sensory information both contribute to the formation of ensemble representations.
Feature selection by ant colony optimization and experimental assessment analysis of PV panel by reflection of mirrors perpendicularly

This study ranks the effective factors in the output power of the low concentrator photovoltaic system (LCPVS) with 4 mirrors using Feature Selection (FS) approach through Ant Colony Optimization (ACO) method based on the analysis of the system improvements in summer and winter. The aim was to improve the intensity of solar irradiation on the same surface area of photovoltaic (PV) panel using mirrors, which led to an increase in power output. The proposed system comprised 4 mirrors that reflected solar radiation to PV panel surface, that was cooled by water spraying, in the most perpendicular position possible. Having done with the related experiments, the results were compared with reference panel. It was demonstrated that the average DC power output of the LCPVS with 4 mirrors was roughly the same, and it was 2.8 times more than that of the reference panel in summer and winter. Moreover, the average DC power output improvement in PV panel was 184.01% in summer and 179.42% in winter. The parameters influencing the output power of PV panel were ranked accordingly by FS approach through ACO. The developed system has the potential to be useful throughout the winter and can also be set up in the majority of regions across the globe.
Geochemical studies and resource potential of magnetite in Taiwanese beach sands and related source rocks: Implications for geochemical fingerprinting of ore minerals

This study investigated the compatible and discriminant elements of magnetite in rocks and beach sands on Taiwan’s western and eastern shores. Scanning electron microscopy, X-ray diffraction, and optical microscopy were used to evaluate the purity of the studied magnetite grains, while laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-Ms) was used to determine the chemical composition. Multivariate diagrams and principal component analysis (PCA) were also used to better determine Taiwan’s magnetite resource and mining potential. Two types of magnetite were identified in this study: primary magmatic and hydrothermal. Three steps were followed to determine the compatible and discriminant variables. In the first step, a multivariate diagram was used to reduce the number of variables. The results revealed that Zn, P, Ca, Cr, Cu, Na, and K, as well as Fe, Mg, Al, Ti, V, Mn, Co, Sc, Ni, Ga, Zr, Nb, and Sn, were compatible variables in magmatic and hydrothermal magnetite, respectively. In the second step, box-and-whisker plots, hierarchical clustering, and variable importance in projection (VIP) plots were used to identify discriminant variables based on the aforementioned compatible elements. The results indicated that Fe, Al, Ti, Mg, Mn, V, and Zn were highly discriminant variables. In contrast, Zr, Sn, Sc, and Nb were low discriminant variables in hydrothermal and magmatic magnetite. Other elements, such as Co, Cr, Cu, Ca, Ga, Ni, K, and Na, exhibited varying discriminant values. The discriminant variables obtained from the second step were arranged using the PCA ranking model for each study sample in the third step. In summary, multivariate diagrams, box-and-whisker plots, VIP plots, and PCA were useful tools for determining magnetite-compatible and discriminant elements. Regarding its resource potential, Taiwanese magnetite was found to be of high grade (>65%), suitable for manufacturing pig, cast, wrought, and steel iron based on its Mn content. Finally, this study highlighted the magnetite resource potential of andesite, migmatite, and serpentinite units exposed at the surface. Exploration and evaluation of these units are strongly recommended.
LWED: Lightweight white-box encryption communication system for drones over CARX algorithm

The application field of drones is being broadened with the development of communication and computer technology, but the communication link and data security of drones are facing great threats due to the limited hardware and software resource environment. Attackers can access not only the intermediate data of drone nodes through various means, but even tamper or manipulate their communication environment, when the drone and the control terminal are not trusted and can be regarded as a white-box attack environment. To solve the security problem in drone communication, a lightweight block cipher CARX for drones is proposed and white-boxed for its implementation to cope with the increasingly severe white-box attacks. And over this cryptographic algorithm, a simulated lightweight white-box encryption communication system for drones (LWED) is designed, which can complete the encryption and decryption operations and secure transmission of data and images, etc. The average encryption rate of the 64-bit version of the algorithm is 7.29×10-2Mbps, and the average encryption rate of white-box CARX (WCARX) is 1.18×10-2Mbps, and the average decryption time of the flight status parameters data of the encrypted communication control system is 0.14s. Compared with similar algorithms, the CARX algorithm can guarantee the encryption and decryption efficiency while providing high security and resistance to a variety of attacks, and can fully secure the data during the communication process after being built into the security system of drone.
IRLS: An Improved Reinforcement Learning Scheduler for High Performance Computing Systems

Exploiting current High Performance Computing (HPC) systems is a critical task for resolving urgent worldwide problems. However, existing scheduling heuristics such as First Come First Served (FCFS) have limitations in dealing with the increasing complexity of computing systems and the dynamic nature of application workloads. Reinforcement learning (RL) has emerged as a promising approach to designing HPC schedulers that can learn to adapt to dynamic system configurations and workload conditions. However, existing RL-based schedulers often lack the ability to incorporate important identity features of jobs and do not consider user behavior.To address these limitations, we propose an improvement to the latest Deep Reinforcement Learning Agent for Scheduling (DRAS) model, called Improved Reinforcement Learning Scheduler (IRLS). The IRLS model incorporates additional identity features in the state definition to recognize similarities between tasks from the same source and utilizes an empirical approach to perform job runtime prediction. Our experiments demonstrate that by using the IRLS model, we can significantly improve the performance of real-life HPC workloads, with improvements of up to 15.4% compared to the original DRAS model and 35.7% compared to FCFS.
Vision-based Fall Detection System: Novel Methodology and Comprehensive Experiments

Falls are a significant health concern for older adults and people with disabilities. It can result in serious injuries such as hip fractures, head trauma, and even death. Therefore, a fall detection system is essential for preventing and mitigating the abovementioned negative consequences. In this study, we propose a fall detection system with a low-cost camera that can run in real-time and obtain persuading accuracy. Concretely, our proposed system first detects and tracks entities using the Yolo-V4-tiny model and SORT algorithm, respectively. Subsequently, the MediaPipe framework is utilized to extract the skeletons of each entity for physical characteristics calculations. Last but not least, the provided skeletons per frame with their corresponding physical characteristics serve as inputs of the Transformer to release detection results. The experimental result with reliable accuracy suggests that our study has the potential for practical applications. The demo video can be found here https://www.youtube.com/watch?v=dyFUT_SBrfU.
Approach to Research Feature Interactions

Feature crossing (or interaction) is one of the essential data analysis techniques provided to show insights into data by building interactions between features. It consists in creating combinations of original features from a dataset via different methods. Often, such combinations created with a strong domain knowledge contain important information for solving a problem. This process is quite creative and intuitive because number of all possible combinations can be huge and it can be impossible for a human to check all of them. This is highly dependent on the data used in the problem and domain, since even traits that are important statistically can be meaningless in the domain. In this work, we present an approach to the convenient exploration of feature crossings in data, their evaluation, ranking, and visualization. This approach implemented can be useful tool for data analysts. We present an example of the approach with a prototype implemented on Python 3 and Tableau Public on a real-world dataset.
Theoretical Review of Digital Tools Aimed to Development Soft Skills during a Learning Process

The article is devoted to a theoretical review of soft skills development tools in the digital environment in a learning process at the higher education. A generalized definition of soft skills is presented as over-professional skills of communication, thinking, and managing emotions that are formed during a personal life. Authors list typical characteristics of soft skills, such as universality, dynamism, anticipatory nature. The article includes a brief description of such distance learning technologies aimed at development of soft skills as web quests, online challenges, online marathons, mobile applications, infographics, artificial neural network. Also authors describe the technology of use of the artificial neural network in a context of project design for development of soft skills. Image adjustment and demonstration of it during the project presentation allows students to gain a deeper understanding of the field of copyright, including by example, as well as develop such soft skills as flexibility of thinking, persuasion, and presentation skills.
Artificial Intelligence Techniques for Quality Assessments of Immersive Multimedia

Artificial Intelligence techniques are being applied in the quality assessment of immersive multimedia content, such as virtual and augmented reality scenarios. The immersive nature of these applications poses a unique challenge to traditional quality assessment methods. In fact, estimating user acceptance of immersive technologies is complex due to multiple aspects, such as usability, enjoyment, and cyber sickness. Artificial Intelligence-based approaches offer a promising solution to this problem, enabling objective evaluations of immersive multimedia such as spatial audios, point clouds, and light field images. This work presents an overview of different artificial intelligence techniques that have been used for quality assessments of immersive multimedia content, including machine learning algorithms, deep learning, and computer vision. The advantages of these techniques and some examples of practical application are provided. Future works are presented, underlining the possible outcomes of a Ph.D. study in this field.
Digital Teaching Reform and Practice of Supply Chain Management Course

Digital technology is widely and deeply applied in the field of education and teaching. It has had a revolutionary impact on higher education and effectively promote the development of digital education. Supply chain management course as a comprehensive core course of logistics management and engineering majors, the traditional education model has problems such as emphasizing theory over practice, and teaching methods that are too single. This paper explores in depth the content of digital teaching reform of supply chain management course from several aspects such as digital teaching resources construction, digital teaching mode innovation and digital teaching evaluation. Finally, we analyze the effectiveness of the digital teaching reform of the curriculum. It aims to provide certain reference value for the digital transformation of education and teaching.
Mobile Learning and the Future of Work: Shaping the Skills and Competencies Required for the Digital Economy Workforce.

The digital economy is transforming the nature of work, and mobile learning is becoming increasingly vital in shaping the skills and competencies needed for the future workforce. This paper provides an overview of mobile learning and its role in developing the skills required for the digital economy workforce. We examine the potential for mobile learning to provide flexible and accessible education, especially for learners who face challenges in accessing traditional brick and mortar learning environments. We present a comprehensive review of the literature that highlights the challenges and opportunities of mobile learning, including issues of the digital divide, equity, and access on a global scale. Our analysis reveals that mobile learning has the potential of enhancing key digital literacy, entrepreneurship, and soft and hard skills competencies as identified by industry stakeholders. Besides, we present case studies that demonstrate how mobile learning programs can be implemented successfully to develop the competencies required for the digital economy workforce. These case studies showcase best practices and initiatives from a diverse range of contexts, including developed and developing countries. In conclusion, we provide insights into the significant implications of mobile learning for the future workforce of the digital economy and the challenges that must be addressed to ensure that mobile learning is accessible and equitable to all learners. Our paper highlights the importance of continuous research and collaboration to harness the potential of mobile learning in shaping the future of work. The findings of this paper have significant implications for policymakers, educational leaders, teachers, and learners who are concerned with preparing students for the digital economy workforce.
Homomorphic Filtering Adversarial Defense for Automatic Modulation Classification

Deep neural networks provide an intelligent means for automatic modulation classification (AMC) in the communication field. However, due to their interpretability flaws, neural networks are vulnerable to adversarial examples that lead to decision anomalies. In this paper, we propose a homomorphic filtering adversarial defense (HFAD) algorithm for filtering in the signal frequency domain to defend against adversarial examples and promote the safe and reliable application of AMC models. The algorithm attenuates the low frequency components of the signal by performing homomorphic filtering on it, effectively alleviates the error induction of the model output by the adversarial perturbation. Our experimental results demonstrate that the defense algorithm we propose can not only ensure a high recognition accuracy for the original signal, but also effectively resist a variety of white-box adversarial attacks and improve the robustness of the AMC model against adversarial examples.
Hyperledger based Verifiable and Secure Cloud Data Deletion

In today's era of diversified computational advancements, cloud computing and its application have become an inevitable part of the industry and academia. Chunks of data are stored and removed everyday from the cloud instances. Any remaining trace of this deleted data may lead to illicit data leakage. Cloud Service Consumers (CSC)/users mostly depend on third-party auditors (TPA)/verification systems for guaranteeing that their data has been completely deleted from the cloud premises once their mutual service tenure ends. In this paper, blockchain technology is used to provide an authentication and verification platform for executing a secured and complete removal of user data from Cloud Service Provider (CSP) premises and providing a proof for the same. The Elliptic Curve Digital Signature Algorithm (ECDSA) signature scheme has been integrated into the existing hyperledger fabric framework to achieve the required results. A performance analysis based on hyperledger caliper is presented. It manifests a negligible performance overhead of 13% due to the additional ECDSA layer. Additionally, the comparative study shows the benefits of TPA independence, deletion request authentication and user identity management which makes the proposed scheme, a more secure and trustworthy datacenter design option as compared to contemporary solutions.