Reviewerly/exHarmony huggingface dataset:
Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model

Modern healthcare practice, especially in intensive care units, produces a vast amount of multivariate time series of health-related data, e.g., multi-lead electrocardiogram (ECG), pulse waveform, blood pressure waveform and so on. As a result, timely and accurate prediction of medical intervention (e.g., intravenous injection) becomes possible, by exploring such semantic-rich time series. Existing works mainly focused on onset prediction at the granularity of hours that was not suitable for medication intervention in emergency medicine. This research proposes a Multi-Variable Hybrid Attentive Model (MVHA) to predict the impending need of medical intervention, by jointly mining multiple time series. Specifically, a two-level attention mechanism is designed to capture the pattern of fluctuations and trends of different time series. This work applied MVHA to the prediction of the impending intravenous injection need of critical patients at the intensive care units. Experiments on the MIMIC Waveform Database demonstrated that the proposed model achieves a prediction accuracy of 0.8475 and an ROC-AUC of 0.8318, which significantly outperforms baseline models.
PILOT

During the development phase, software programmers usually introduce code that contains issues intentionally left for additional treatment. To allow for future fixing, they mark such code using textual comments, resulting in Self-Admitted Technical Debt (SATD). Detecting SATD contained in source code has become crucial in the development cycle since it helps programmers locate issues that need to be solved, thus improving code quality. We introduce PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques. First, the semantic among SATD comments is captured using feature extraction steps. Then, neural network algorithms are applied to classify comments, represented as vectors. We built a PILOT prototype with a feed-forward neural network and evaluated it using real-world datasets as proof of concept. The empirical evaluation shows that PILOT obtains an encouraging performance and outperforms a well-established baseline. We anticipate that our tool will come in handy, as once being embedded in the IDE, it can help developers recognize SATD manifested in their code, allowing them to conveniently identify and fix issues.
High speed ultraviolet digital image correlation (UV-DIC) for dynamic strains at extreme temperatures

Digital Image Correlation (DIC) is a camera-based method of measuring full-field displacements and strains from the surface of a deforming object. It can be applied at any length scale (determined by the lenses) and any time scale (determined by the camera), and because it is non-contacting, it can also be used at temperatures much higher than can be withstood by bonded strain gauges. At extreme temperatures, materials emit light in the form of blackbody radiation, which can saturate the camera sensor. It has previously been shown that the emitted light can be effectively screened by using ultraviolet (UV) cameras, lenses, and filters; however, commercially available UV cameras are relatively slow, which limits the speed of combined UV-DIC measurements. In this study, a UV intensifier was paired with a high-speed camera, and its ability to perform UV-DIC at high temperature and high speed was investigated. The system was compared over three different experiments: (A) a quasi-static thermal expansion test at high temperature, (B) a vibration test at room temperature, and (C) the same vibration test repeated at high temperature. The system successfully performed DIC up to at least 1600 °C at frame rates of 5000 fps, which is more than 100 times faster than other examples of UV-DIC in the literature. In all cases, measurements made using the UV intensifier were much noisier than those made without the intensifier, but the intensifier enabled measurements at temperatures well above those which an unfiltered high-speed camera otherwise saturates.
Co-Communication Graph Convolutional Network for Multi-View Crowd Counting

We study and address the multi-view crowd counting (MVCC) problem which poses more realistic challenges than single-view crowd counting for better facilitating crowd management/public safety systems. Its major challenge lies in how to fully distill and aggregate useful, complementary information among multiple camera views to create powerful ground-plane representations for wide-area crowd analysis. In this paper, we present a graph-based, multi-view learning model called Co-Communication Graph Convolutional Network (CoCo-GCN) to jointly investigate intra-view contextual dependencies and inter-view complementary relations. More specifically, CoCo-GCN builds a view-agnostic graph interaction space for each camera view to conduct efficient contextual reasoning, and extends the intra-view reasoning by using a novel Graph Communication Layer (GCL) to also take between-graph (cross-view), complementary information into account. Moreover, CoCo-GCN uses a new Co-Memory Layer (CoML) to jointly coarsen the graphs and close the ‘representational gap’ among them for further exploiting the compositional nature of graphs and learning more consistent representations. Finally, these jointly learned features of multiple views can be easily fused to create ground-plane representations for wide-area crowd counting. Experiments show that the proposed CoCo-GCN achieves state-of-the-art results on three MVCC datasets, i.e., PETS2009, DukeMTMC, and City Street, significantly improving the scene-level accuracy over previous models.
Extortion of a Staking Pool in a Proof-of-Stake Consensus Mechanism

Cryptocurrencies to date, most notably Bitcoin, have primarily relied on a proof-of-work system to validate and process transactions on the blockchain. Proof-of-work systems have, however, several limitations, such as enormous energy demands, and so are likely to be replaced by proof-of-stake systems. These systems use a mechanism that does not rely on mining power but the amount of stake owned by a node, allowing randomly selected validators to create blocks and verify blocks created by other validators. Proof-of-stake systems naturally result in staking pools where in a third party organisation operates validators on behalf of investors who have staked in the currency. Given they have oversight for a large amount of staked currency, staking pools are a prime target for malicious actors. In this paper we explore the economic implications of an attack on a staking pool. We pay particular attention to how the staking pool and clients could resolve an extortion attack by a malicious actor who has accessed relevant signing keys.
Home care system for the elderly and pathological conditions

Rapid population growth and the increase in older people in need of care produce significant changes in healthcare and quality of life in many nations. Home care, compared to hospitalization, can be a valid and less expensive alternative. To help people with disabilities, we need to identify daily physical activities without supporting others. There are several automatic techniques for recognizing human activities. Nowadays, development in I CT and Artificial Intelligence enable intelligent systems to monitor the conditions and activities of elderly people. Moreover, nanotechnology such as MEMS offers advantages of small size, low power consumption, and analyzing human motion. These technologies can recognize when it is necessary to act in dangerous situations. The proposed solution in this manuscript represents an integrated prototype that provides an efficient technological tool to caregivers serving promptly and assuring efficient performance throughout the entire health care system process. In the present work, we use an IoT platform in which, through inertial sensors, we collect kinematic data transferred to a convolutional neural network to classify human activities. For accurate identification of the localization of the subjects, we integrated the system with an ultrasonic network. Then to verify the correct inter-pretation, we used Virtual Reality.
Classifier Adaptation Based on Modified Label Propagation for Unsupervised Domain Adaptation

Unsupervised domain adaptation endeavors to learn a desirable classifier for a target domain by transferring knowledge learned from a related (source) domain. Existing approaches focus on deriving domain-invariant feature representations by aligning the domain distributions. However, those works often require an extra classifier. In contrast, this paper proposes a classifier adaptation method based on modified label propagation (CAMLP) for unsupervised domain adaptation. Inspired by pseudolabeling, CAMLP proposes a simple but effective measurement for relationships among cross-domain samples. Thus, samples from distinct domains are constructed in a same graph. The true labels can then propagate from the source domain to the target one along the graph. We also propose a consistency-aware pseudolabel annotation to alleviate the problem of negative transfer caused by unreliable pseudo labels. Extensive experiments on several benchmark datasets confirm that the proposed method performs favorably against the state-of-the-art approaches.
Mahler/Zeta Correspondence

The Mahler measure was introduced by Mahler in the study of number theory. It is known that the Mahler measure appears in different areas of mathematics and physics. On the other hand, we have been investigated a new class of zeta functions for various kinds of walks including quantum walks by a series of our previous work on “Zeta Correspondence”. The quantum walk is a quantum counterpart of the random walk. In this paper, we present a new relation between the Mahler measure and our zeta function for quantum walks. Firstly we consider this relation in the case of one-dimensional quantum walks. Afterwards we deal with higher-dimensional quantum walks. For comparison with the case of the quantum walk, we also treat the case of higher-dimensional random walks. Our results bridge between the Mahler measure and the zeta function via quantum walks for the first time.
Financial crisis early warning system of Bio-Pharma Listed Enterprises Based on particle swarm optimization algorithm

Enterprise financial crisis early warning system can help Bio-Pharma enterprises find and avoid potential financial risks in time, which is widely used in Bio-Pharma enterprise financial management, and the actual use efficiency directly affects the development of Bio-Pharma enterprises. This paper analyzes the function of the Bio-Pharma enterprise financial crisis early warning system and the existing problems of the current Bio-Pharma enterprise financial crisis early warning system and puts forward the countermeasures to improve the enterprise financial crisis early warning system.
Blockchain Integrated with Principal Component Analysis: A Solution to Smart Security against Cyber-Attacks

Digitalization of financial institutes, industries, and organizations to provide fast online services needs a large number of Internet connections and different types of networks. The presence of a large number of digital networks makes operational communication and data transfer vulnerable to different cyber-attacks. So, to secure the system, there must be a mechanism present to detect malicious activities and give an alarm to the operator/HMI. A numerous intrusion detection system is present in this era but still, the intruders get access to the network and create an economical loss. Blockchain is a new path to gain the trust in the cyber-security field. Researchers have been working on the term “blockchain technology” for the past few years. In a growing digital environment, blockchain is a novel technology that provides various advantages. This paper identifies and examines the possible way to enhance the blockchain capability by integrating with principal component analysis (PCA). The bad data are removed from the real-time data set and then pass through the blockchain mechanism to identify the threats. The results of the investigation and analysis demonstrate that, when compared to the traditional approach, the PCA integration with the blockchain method proposed in this paper may reduce detection time and boost detection rate.
Semi-supervised Context Discovery for Peer-Based Anomaly Detection in Multi-layer Networks

User-related cyber security attacks could cause tremendous losses to any organization. Detecting such threat can be formulated as anomaly detection problem in multilayer networks where each layer of the multilayer networks contain different contextual information regarding the users. While there have been many works proposed for peer-based anomaly detection, there has been little endeavor in discover the appropriate context (peers) for anomaly detection in multilayer networks. In this paper, we propose a context discovery method, which integrates the relations provided by each individual network layer and detects the anomalous nodes in networks based on the optimized peers of nodes with (or without) limited feedback from cybersecurity experts. The proposed system addresses the frequently encountered challenges when conducting anomaly detection, i.e., feedback sparsity, and the newly emerged challenge associated with multilayer networks, i.e., finding peers of each node based on conflicting information provided by individual layers. The proposed system is capable of capturing the anomalies in multilayer networks and outperforms the widely used peer-based anomaly detection algorithms on both synthetic and real-world sensor network and cybersecurity datasets.
OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers’ tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.
ReaderBench: Multilevel analysis of Russian text characteristics

This paper introduces an adaptation of the open source ReaderBench framework that now supports Russian multilevel analyses of text characteristics, while integrating both textual complexity indices and state-of-the-art language models, namely Bidirectional Encoder Representations from Transformers (BERT). The evaluation of the proposed processing pipeline was conducted on a dataset containing Russian texts from two language levels for foreign learners (A - Basic user and B - Independent user). Our experiments showed that the ReaderBench complexity indices are statistically significant in differentiating between the two classes of language level, both from: a) a statistical perspective, where a Kruskal-Wallis analysis was performed and features such as the “nmod” dependency tag or the number of nouns at the sentence level proved the be the most predictive; and b) a neural network perspective, where our model combining textual complexity indices and contextualized embeddings obtained an accuracy of 92.36% in a leave one text out cross-validation, outperforming the BERT baseline. ReaderBench can be employed by designers and developers of educational materials to evaluate and rank materials based on their difficulty, as well as by a larger audience for assessing text complexity in different domains, including law, science, or politics.
Human Action Recognition Based on Improved FCN Framework

Human motion recognition is a highly active area of research. In this paper, we propose a Spatial Transformer Fully Convolutional Network (STFCN) for human action recognition, which leverages the advantages of both full convolution network (FCN) and spatial transformation network (STN). Firstly, in the stage of video image feature extraction, the proposed method integrates the STN network into the Convolutional Neural Networks (CNN) and the obtained feature maps are then passed through the FCN network. The upsampled operation of the FCN restores the feature maps to the size of the original input image. Finally, a softmax classifier is used to classify the human action at the pixel level. Extensive experiments are conducted on the standard human action datasets, i.e., HMDB51 and UCF101. The experimental results show that the STFCN achieves better performance than other compared methods.
A New Critical Risk on the Block: Cyber Risks as an Example of Technical Knowledge Risks in Organizations

The breakout of the COVID-19 pandemic has intensified the appearance of many additional technical knowledge risks in organizations. Cyber risks in particular are becoming a great challenge for organizations. At the same time, academic research on cyber risks, their costs, consequences and ability of overcoming them is still scarce and fragmented. In order to fill this gap, the paper is aimed to identify different types of cyber risks that organizations face and to examine the organizations’ ability to handle those risks. The paper presents research results from a sample of 60 organizations, addressing questions such as: (i) What are the costs of cyber risks the organization has faced? (ii) What is the company ability to address these risks?, and (iii) What is the organization doing to minimize the impact of such risks? Data was collected by means of a questionnaire. This research study has allowed to identify the state of the art concerning cyber risks, which can bear severe consequences for organizations. The findings clearly show that not all organizations suffer from the same level of cyber risks but it is much related to their field of operations. Consequently, also the ability to manage these cyber risks is quite diversified among the examined companies. Research results are limited to a sample of 60 organizations and thus the findings should be taken with caution. The study provides useful insights for managers and owners of organizations in need of dealing with the cyber threats/attacks and other technical knowledge risks threatening their organizations. The paper is enriched with a number of sample solutions that they may apply to mitigate those risks.The paper lays the ground for a better understanding of technical knowledge risks, primarily cyber risks, to which organizations are increasingly exposed today. As such, the paper offers food for thought for researchers dealing with the topic of technical knowledge risks and organizational risk management in general.
A robust statistic‐aided edge detector for SAR images based on RUSTICO

Speckle noise exists inherently due to the special imaging mechanism of synthetic aperture radar (SAR), which is a challenge for edge extraction. The traditional methods failed to make full use of statistical characteristics of SAR images to detect the edges, which results in the loss of weak edges. To address this issue, combined with the robust inhibition-augmented curvilinear operator, this letter proposes a novel edge detector based on the ratio of local statistics (ROLSS) for SAR images. In this letter, the proposed detector achieves the balance between edge resolution and speckle suppression through the proposed intensity calculation method. By taking advantage of image statistic characteristics, the proposed ROLSS edge detector can draw complete continuous edges of many shapes areas and textures without any post-processing, such as edge thinning, extension, and so on. Experiment results show that the proposed detector yields more accurate edge maps with fewer false and missed pixels, and it holds robustness both in the speckle-polluted simulated image and the real X-band airborne SAR image.
Perbandingan Efektivitas Sistem Pembelajaran Luring dan Daring Menggunakan Metode Use case dan Sequence Diagram

Ma’soem University is a formal educational institution that has implemented an offline and online learning system. This study aims to assess the effectiveness of the two learning systems globally and determine which learning system is more suitable to be applied to students and propose improvements to the learning system that is considered less effective. The research method used is a survey in which respondents are asked eight questions related to both learning systems, the technique of analysis and data processing is done by using descriptive statistics, meanwhile the proposed system improvement is visualized using use case diagrams and sequence diagrams. Based on the research results, the respondents stated that the offline learning system was considered more effective with a percentage of 64,99%, compared to online learning system with a percentage of 31,52%, so it is proposed to improve the online learning system by building an e-learning system.
A Microservices Architecture based on a Deep-learning Approach for an Innovative Fruition of Art and Cultural Heritage

Technological innovations have resulted in a digital transformation in a variety of fields, including culture and tourism. We propose an innovative and personalized solution to benefit art and cultural heritage in indoor and outdoor environments by combining Internet of Things-enabled technologies and deep learning-based approaches. A recent Convolutional Neural Network (CNN) architecture to jointly perform local feature detection and description has been adapted and exploited for the first time for image matching in the cultural heritage application context. The performance validation of the proposed system shows that the proposed modular architecture ensures a very low error rate and excellent response time up to 2000 user visits in 700 seconds. The validation of the computer vision module shows as the proposed CNN based feature extraction approach improves image matching performance, especially in poorly textured object areas reaching a F1-Score of 0.9907 (against the 0.9679 obtained by traditional gradient based approaches) on the challenging dataset of images taken from 4 different historical sites and a F1-Score of 0.9807 (against the 0.9798 obtained by traditional approaches) on a public benchmark dataset of artworks.