Reviewerly/exHarmony huggingface dataset:
A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient's critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM's homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.
Identifying microseismic events using a dual-channel CNN with wavelet packets decomposition coefficients

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data.
Technical analysis in cryptocurrency markets: Do transaction costs and bubbles matter?

The study of technical analysis in cryptocurrencies has largely ignored the implications of often high transaction costs and bubble periods on trade rule performance. We study the daily and 1-minute returns of 69 technical trade rules in the form of moving average and breakout strategies, with and without transaction costs, during price bubbles in the 2016–2021 period. For the most profitable trade rules, we find that bubble periods increase the likelihood that Ethereum, Ripple and Litecoin beat buy-and-hold, but not Bitcoin and Bitcoin Cash. Transaction costs decrease this likelihood for Ripple and Litecoin, but increase it for Bitcoin and Ethereum.
MICQ-IPSO: An effective two-stage hybrid feature selection algorithm for high-dimensional data

In machine learning and pattern recognition tasks, classification performance is often degraded due to the existence of irrelevant and redundant features, especially for high-dimensional data. As a data preprocessing tool, feature selection can improve classification performance while reducing the number of features. Focusing on high-dimensional data, we propose a novel two-stage hybrid feature selection method that combines the maximum information coefficient (MIC) based Q-learning algorithm and the improved particle swarm optimization (PSO) based algorithm, named as MICQ-IPSO. In the first stage, we employed an intelligent feature pre-screening operation to get a rough feature subset, which introduced MIC value as the correlation measure and automated the determination of the screening threshold by Q-learning. In the second stage, we applied an improved PSO-based method to get an optimal feature subset. During this stage, a swarm initialization strategy based on MIC correlation was used to narrow the search range and accelerate swarm convergence. To further enhance the exploitability, a deeper local search operation was performed in the search region. Moreover, a particle reset strategy was adopted to help particles jump out of the local optimal solution. Finally, we evaluated our algorithm against several state-of-the-art feature selection approaches on 17 benchmark datasets. The experimental results demonstrate the effectiveness and competitiveness of the proposed algorithm.
Using global information to refine local patterns for texture representation and classification

Local binary pattern (LBP) and its variants have been successfully applied in texture feature extraction. However, it is hard for most LBP-based methods to effectively describe and distinguish the local neighborhoods with similar structures (that is, the calculated feature patterns are identical) but different contrasts or grayscales. To alleviate such problems, we propose a novel global refined local binary pattern (GRLBP) by analyzing the nature of pixel intensity distribution in local neighborhoods. GRLBP consists of two descriptors called magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. And CRLBP_M identifies local neighborhoods with grayscale differences by employing global central grayscale anchors to refine local magnitude patterns. Finally, frequency histograms of MRLBP_S and CRLBP_M from each image are cascaded to generate the GRLBP. Extensive experimental results on seven benchmark texture databases: Outex, CUReT, KTH-TIPS, UMD, UIUC, KTH-T2b, and DTD demonstrate that the proposed GRLBP can represent the detailed information of texture images. Furthermore, compared with state-of-the-art LBP variants, GRLBP has competitive advantages in classification accuracy, feature dimension, and computational complexity, respectively.
Microplastic pollution assessment with digital holography and zero-shot learning

Microplastic (MP) pollution poses severe environmental problems. Developing effective imaging tools for the identification and analysis of MPs is a critical step to curtail their proliferation. Digital holographic imaging can record the morphological and refractive index information of such small plastic fragments, yet due to the heterogeneous sampling environments and variations in the MP shapes, traditional supervised learning methods are of limited use. In this work, we pioneer a zero-shot learning method that combines the holographic images with their semantic attributes to identify the MPs in heterogeneous samples, even if they have not appeared in the training dataset. It makes use of the attention mechanism for image feature extraction and the Kullback–Leibler divergence both to alleviate the domain shift problem and to guide the training of the mapping function. Experimental results demonstrate the effectiveness of our approach and the potential use in a wide variety of environmental pollution assessments.
We need a broader debate on the sustainability of blockchain

Alexander Rieger is a postdoctoral researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. His research interests include innovative digital technologies such as blockchain, decentralized digital identities, and artificial intelligence. Alex leads the scientific advisory team for the FLORA blockchain project of Germany's Federal Office for Migration and Refugees and acts as advisor to the European Blockchain Partnership and various public and private sector partners in Germany and Luxembourg. Tamara Roth is a postdoctoral researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. Her research interests include the interplay of innovative technologies such as blockchain and decentralized digital identities with sociocultural and socioethical constructs in public administration and healthcare. Moreover, she investigates technology adoption and implications of technologies for sustainable development from a psychological and neurobiological perspective. Johannes Sedlmeir is a researcher on information systems at the FIM Research Center, University of Bayreuth. In his research, he focuses on the energy consumption and performance benchmarking of different blockchains, the application of cryptographic methods such as zero-knowledge proofs for scalable and privacy-oriented blockchain solutions in different sectors, and decentralized digital identities. He received his M.Sc. in theoretical and mathematical physics. Gilbert Fridgen is professor and PayPal-FNR PEARL chair in digital financial services at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. In his research, he analyzes the transformative effects of digital technologies on individual organizations and on the relationship between organizations. He addresses potentially disruptive technologies like blockchain, decentralized digital identities, artificial intelligence, or the Internet of Things. His research involves information systems engineering and IT strategy and (risk) management, as well as regulatory compliance. In his projects and partnerships, he collaborates with partners in financial services, energy, mobility, manufacturing, and consulting, as well as with public bodies and governments.
Pseudo-labeling generative adversarial networks for medical image classification

Semi-supervised learning has become a popular technology in recent years. In this paper, we propose a novel semi-supervised medical image classification algorithm, called Pseudo-Labeling Generative Adversarial Networks (PLGAN), which only uses a small number of real images with few labels to generate fake images or mask images to enlarge the sample size of the labeled training set. First, we combine MixMatch to generate pseudo labels for the fake and unlabeled images to do the classification. Second, contrastive learning and self-attention mechanisms are introduced into PLGAN to exclude the influence of unimportant details. Third, the problem of mode collapse in contrastive learning is well addressed by cyclic consistency loss. Finally, we design global and local classifiers to complement each other with the key information needed for classification. The experimental results on four medical image datasets show that PLGAN can obtain relatively high learning performance by using few labeled and unlabeled data. For example, the classification accuracy of PLGAN is 11% higher than that of MixMatch with 100 labeled images and 1000 unlabeled images on the OCT dataset. In addition, we also conduct other experiments to verify the effectiveness of our algorithm.
A software-based cost estimation technique in scrum using a developer's expertise

Background: Scrum methodology is the most useful and adopted part of the agile methodology.Many organizations are adopting Scrum for the last decade to meet their software requirements.It is facilitating the software industries to develop software applications according to the user's requirements.Objective: Scrum helps to fulfill the customer's requirements by interacting with the client and developer.Instead of this, scrum still has some challenges that influence the cost of a scrum.These challenging factors include coordination, team size, change request, complexity, and issues in daily meeting sessions.Due to these factors mostly the cost and time exceed scrum-based projects.To overcome these issues from the accurate and precise estimation in the scrum project we have developed a web-based software system to estimate the effort of a user story as well as the budget.Methodology: There are some sophisticated cost estimation techniques for scrum-based projects.But all estimation techniques have some limitations that make them less useful for estimation and overcoming the effects of these factors in the scrum.We have surveyed the multiple software industries to get the static findings of the survey.We have designed the 30 hypotheses that main aim to reveal how much the proposed estimation technique is valuable for the accuracy of estimation.For the inclusion of participants, we have set the age criteria to get the opinions of the most experienced programmers.Results: The main aim of this paper is to provide knowledge about key challenging factors in Scrum that cause exceed in cost and time, and also briefly explain why existing scrum cost estimation techniques are not pertinent for projects.In the last, we propose a framework as a solution to manage these factors and control cost and time from exceeding.We also adopted this estimation technique in different case studies in software industries for findings and results.The technique calculates the efforts autonomously.We design a software-based estimation technique that collects inputs from team/s and estimates the cost and time of a project. Conclusion:In outcomes, we found that our cost estimation approach reduced the issues from scrum software project development and make it more proficient for estimation of projects.Our proposed estimation technique introduced the new concept of estimation that facilitate the client, software industry, and developers as well to meet the requirements of the client, fulfill the changes during the sprints/s development and deliver the project within the time and cost.
Video summarization with a convolutional attentive adversarial network

With the explosive growth of video data, video summarization, which attempts to seek the minimum subset of frames while still conveying the main story, has become one of the hottest topics. Nowadays, substantial achievements have been made by supervised learning techniques, especially after the emergence of deep learning. However, it is extremely expensive and difficult to construct a large-scale video summarization dataset through human annotation. To address this problem, we propose a convolutional attentive adversarial network (CAAN), whose key idea is to build a deep summarizer in an unsupervised way. Upon the generative adversarial network, our overall framework consists of a generator and a discriminator. The former predicts importance scores for all the frames of a video while the latter tries to distinguish the score-weighted frame features from original frame features. To capture the global and local temporal relationship of video frames, the generator employs a fully convolutional sequence network to build global representation of a video, and an attention-based network to predict normalized importance scores. To optimize the parameters, our objective function is composed of three loss functions, which can guide the frame-level importance score prediction collaboratively. To validate this proposed method, we have conducted extensive experiments on two public benchmarks SumMe and TVSum. The results show the superiority of our proposed method against other state-of-the-art unsupervised approaches. Our method even outperforms some published supervised approaches.
Flight Anomaly Detection via a Deep Hybrid Model

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.
Improving unsupervised stain-to-stain translation using self-supervision and meta-learning

In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in this field by addressing variabilities without the manual overhead. Here, we tackle the variation of different histological stains by unsupervised stain-to-stain translation to enable a stain-independent applicability of a deep learning segmentation model.We use CycleGANs for stain-to-stain translation in kidney histopathology, and propose two novel approaches to improve translational effectivity. First, we integrate a prior segmentation network into the CycleGAN for a self-supervised, application-oriented optimization of translation through semantic guidance, and second, we incorporate extra channels to the translation output to implicitly separate artificial meta-information otherwise encoded for tackling underdetermined reconstructions.The latter showed partially superior performances to the unmodified CycleGAN, but the former performed best in all stains providing instance-level Dice scores ranging between 78% and 92% for most kidney structures, such as glomeruli, tubules, and veins. However, CycleGANs showed only limited performance in the translation of other structures, e.g. arteries. Our study also found somewhat lower performance for all structures in all stains when compared to segmentation in the original stain.Our study suggests that with current unsupervised technologies, it seems unlikely to produce "generally" applicable simulated stains.
Excavator joint node-based pose estimation using lightweight fully convolutional network

Current deep-learning-based excavator pose estimation methods usually face problems such as high memory consumption and low operation speed owing to large parameter redundancy. This paper presents a joint node-based excavator pose estimation approach using a lightweight fully convolutional network (FCN) that achieves higher accuracy with lower computation and storage requirements. The method directly encodes excavator joint nodes into multilevel features, and employs a deconvolution head to decode them into heat maps to provide joint node coordinates. The lightweight design is made at two levels: block level (employing depth-wise separable convolution instead of conventional convolution for efficiency) and layer level (employing the slimming technique to optimize layer channels for redundant depth removal). Using images collected from real construction sites, the superiority of the method was validated by comparing it with other state-of-the-art algorithms using various hardware platforms. The results indicate the high potential of excavator pose estimation for edge device deployment.
Transformation of University Models: Analysis of the Development Strategies of Universities in the World

The paper presents an analysis of the key characteristics of the typological models of modern universities and outlines the directions of their transformation. The concept of University 3.0 was used as a research methodology in view of the existing approaches to the classification of university models. The research method was the qualitative analysis of the strategic development documents of the various universities of the world posted on their websites. The selection of the universities for analysis was carried out on the basis of the results of the QS WUR 2022 ranking. The sample was made up of 30 universities from North America, Europe, Asia, and Russia, occupying various ranking positions. As a result, there were revealed the features of the strategic priorities for the development of Russian and foreign universities, as well as universities aimed at global and regional influence. A set of basic characteristics of typological models of universities 1.0-4.0 has been developed. It has been established that the transformation of these models takes place in the direction of increasing the scope of their mission, expanding the areas of activity, using new digital technologies, developing integration potential and strengthening the impact on society. The defining parameters for the formation of the model of the university of the future are focus on innovation and advanced continuous education, managed broad integration, diversification of income sources, social involvement and contribution to sustainable development. The presented complex of the main characteristics of typological models of universities can serve as a tool for assessing the compliance of a particular university with a particular model, as well as contribute to goal-setting in the development of the national higher education system and particular universities.