Reviewerly/exHarmony huggingface dataset:
Anomaly detection in multidimensional time series—a graph-based approach

Abstract As the digital transformation is taking place, more and more data is being generated and collected. To generate meaningful information and knowledge researchers use various data mining techniques. In addition to classification, clustering, and forecasting, outlier or anomaly detection is one of the most important research areas in time series analysis. In this paper we present a method for detecting anomalies in multidimensional time series using a graph-based algorithm. We transform time series data to graphs prior to calculating the outlier since it offers a wide range of graph-based methods for anomaly detection. Furthermore the dynamic of the data is taken into consideration by implementing a window of a certain size that leads to multiple graphs in different time frames. We use feature extraction and aggregation to finally compare distance measures of two time-dependent graphs. The effectiveness of our algorithm is demonstrated on the numenta anomaly benchmark with various anomaly types as well as the KPI-anomaly-detection data set of 2018 AIOps competition.
Blockchain Based Authentication for Identity Management

The website currently has no separate provisions to distinguish between an organization or an individual. In other words, cooperatives are expected to be able to collect and store their client data. Such a rule has a high cost for specialist cooperatives, as most of the data is copied to multiple servers, difficult to prove by a large amount of user information around the world, and various certification arrangements for various administrations. The aim of this research is that individual data can be collected safely and reliably for information mining, profiling, and data storage. The ideal order would act naturally with a sovereign personality, another type of character that each client has and controls. This setup will incorporate compressed people's way of life like cryptographically checked characteristic settings backed by trusted backers. Individuals provide evidence of personality and participation by providing a workable part of their character to organizational specialists. Consent to access may also be revoked as a result of giving the individual full control over his or her information. Using a systematic literature review method, this research fundamentally examines the various capabilities, boards, and structures of Blockchain technology. Synopsis of the latest Blockchain-based personalities, executives, and lever arrangements from 2017 to 2021. The expected result of this research is that Blockchain technology can create trust and security in cooperative settings.
Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development

Bayesian optimization (BO) is a well-developed machine learning (ML) field for black-box function optimization. In BO, a surrogate predictive model, here a Gaussian process, is used to approximate the black-box function. The estimated mean and uncertainty of the surrogate model are paired with an acquisition function to decide where to sample next. In this study, we applied this technique to known ferromagnetic thin-film materials such as ferromagnetic (Fe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100−<i>y</i></sub> Ga <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>y</i></b> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1−<i>x</i></sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> = 0−21 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> = 9−17) to demonstrate optimization of structure–property relationships, specifically the dopant concentration or stoichiometry effect on magnetostriction and ferromagnetic resonance linewidth. Our results demonstrated that BO can be deployed to optimize structure–property relationships in FeGaB and FeGaC thin films. We have shown through simulation that using BO methods to guide experiments reduced the number of samples required to statistically determine the maximum or minimum by 50% compared to traditional methods. Our results suggest that BO can be used to save time and resources to optimize ferromagnetic films. This method is transferrable to other ferromagnetic material structure–property relationships, providing an accessible implementation of ML to magnetic materials development.
Significance and Limitations of Deep Neural Networks for Image Classification and Object Detection

Nowadays, Deep Neural Networks are very popular for solving computer vision problems. The motivation of this study is to explore the different deep learning-based architectures used for image classification and object detection. There are various Convolutional Neural Networks(CNNs) based models that perform well for image classification where images are very similar to the dataset. But these models have certain Limitations if tilted or rotated images are processed for classification. Moreover, CNNs are unable to identify the location of different objects in an image. Therefore, to identify the location and recognize the object in the image along with bounding box information, region proposal based object detection algorithms (i.e., R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN) come into the picture. In these object detection algorithms, a bounding box is drawn around the object of interest to locate it within the image. This research article has explored these region-based models along with their pros and cons. Moreover, the performance and purpose of utilization for these object detection-based deep neural network has also been analyzed. Eventually, a summary is provided about these deep neural network-based architectures, where the suitability of a model(s) to solve a particular problem has been discussed.
Microwave Quantum Radar using a Josephson Traveling Wave Parametric Amplifier

Detection of low-reflectivity objects can be improved by the so-called Quantum Illumination (QI) procedure. However, quantum detection error probability exponentially decays with the source bandwidth. The Josephson Parametric Amplifiers (JPAs) technology utilized as a source, generating pairs of entangled signals called two-mode squeezed vacuum states, shows a very narrow bandwidth limiting the operation of the Microwave Quantum Illumination (MQI) systems. In this paper, for the first time, a microwave quantum radar setup based on quantum illumination protocol and using a Josephson Traveling Wave Parametric Amplifier (JTWPA) is proposed. We experimentally demonstrate the generation and control of three-wave mixing modes produced by a JTWPA, key enabler radiation source for MQI schemes. Measurement results of the developed JTWPA, pumped at 12 GHz, show the capability to generate entangled modes in the X-band, making our MQI system a promising candidate for the detection of stealth objects.
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships

Interactions between entities in knowledge graph (KG) provide rich knowledge for language representation learning.However, existing knowledge-enhanced pretrained language models (PLMs) only focus on entity information and ignore the fine-grained relationships between entities.In this work, we propose to incorporate KG (including both entities and relations) into the language learning process to obtain KG-enhanced pretrained Language Model, namely KLMo.Specifically, a novel knowledge aggregator is designed to explicitly model the interaction between entity spans in text and all entities and relations in a contextual KG.An relation prediction objective is utilized to incorporate relation information by distant supervision.An entity linking objective is further utilized to link entity spans in text to entities in KG.In this way, the structured knowledge can be effectively integrated into language representations.Experimental results demonstrate that KLMo achieves great improvements on several knowledge-driven tasks, such as entity typing and relation classification, comparing with the state-of-the-art knowledge-enhanced PLMs.
Digital Competences of Students of Library Studies: Comparison of Research Results for 2018–2020

This study focuses on the analysis of changes in the digital competence profile of students of Information and Library Studies at Masaryk University in Czechia. As a research tool, we used the DigComp self-assessment questionnaire that students were asked to fill in after completing the course. Our research shows that students are insufficiently prepared for work as highly qualified information specialists. At the same time, we found that their competence profile remained very stable between 2018 and 2020. This finding indicates that students do not readily respond to new societal changes at the level of individual competences. The research results are based on data collected from 152 students during three runs of a compulsory course at the university. Information Science and Library Science students have long perceived their competences to be strongest in the domains of information and data literacy and communication and collaboration. Programming is the weakest competency among the competences, followed by solving technical problems and engaging in active citizenship through digital technologies. These findings can be used to innovate the curriculum to meet the demands of digitally competent information workers.
Secure Access Control for DAG-Based Distributed Ledgers

Access control is a fundamental component of the design of distributed ledgers, influencing many aspects of their functionality, such as fairness, efficiency, traditional notions of network security, and adversarial attacks such as Denial-of-Service (DoS) attacks. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> In this work, we consider the security of a recently proposed access control protocol for directed acyclic graph-based distributed ledgers. We present a number of attack scenarios and potential vulnerabilities of the protocol and introduce a number of additional features which enhance its resilience. Specifically, a blacklisting algorithm, which is based on a reputation-weighted threshold, is introduced to handle both spamming and multirate malicious attackers. A solidification request component is also introduced to ensure the fairness and consistency of the network in the presence of attacks. Finally, a timestamp component is also introduced to maintain the consistency of the network in the presence of multirate attackers. Simulations to illustrate the efficacy and robustness of the revised protocol are also presented.
Security and Privacy Mechanisms for the New Generation Healthcare Applications Using Blockchain Technology

Recently evolving blockchain technology for eHealth will promote a network of record management that is safe, transparent and patient-driven. This emerging technology, however, RPM (Remote patient management) settings can not support the storing of data produced from smart Internet of Things equipment. since This framework requires a system of rapid consensus, key control and better privacy policies. In this chapter, We propose a distributed e - healthcare system consisting of three layers collateralized by Blockchain.: 1) Sensing network-Body Region Sensor System-works include intelligent clinical sensors usually transmitting data to a mobile on or inside a patient body. 2) The NEAR processing layer-Edge Networks consists of single-hop equipment's built by using IoT devices for data sensing. 3) Cloud or other large computing services compose the Fast processing layer-Core networks). A replicated Patient Agent processes medical data To ensure constant, safe and private connectivity through the three layers. The Patient Agent runs a lightweight Blockchain consensus system which uses a Blockchain-leveraged job-offloading mechanisms to maintain patient safety when outsourcing assignments. Performance review of the decentralized e - healthcare framework was performed to prove the feasibility of the scheme in the collection and storing of Remote Patient Management information.
Proof‐of‐concept of an information visualization classification approach based on their fine‐grained features

Abstract The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine‐grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri‐variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.
On the fly image denoising using patch ordering

We introduce an image denoising algorithm which utilizes a novel online dictionary learning procedure together with patch ordering. The developed algorithm employs both the non-local image processing power of patch ordering and the sequential patch-based update of online dictionary learning. The patch ordering process exploits the similarities between patches of a given image which are extracted from different locations. Joint processing of the ordered set of image patches facilitates the non-local image processing ability of the algorithm. The algorithm starts with the extraction of a maximally overlapped set of patches from the given noisy image. Then, the extracted patches are reordered by using a distance measure, and the 3D ordered patch cube is formed. The ordered patch cube is used sequentially to update an overcomplete dictionary. In each iteration, firstly the present patch is denoised using sparse coding over the current overcomplete dictionary. Secondly, the overcomplete dictionary is updated using the current image patch, and the dictionary is passed to the next iteration. We call this process as “on the fly denoising”, because each patch is individually denoised using an instantaneously updated overcomplete dictionary. Patch ordering together with online dictionary learning ensures that the dictionary is adapted to different neighborhoods of patches in the patch cube. This adaptation of the dictionary to specialized local patch structures in the patch cube promises improved denoising performance when compared to dictionary learning algorithms devoid of such adaptation. Simulation results indicate that the introduced online method presents improved denoising performance in comparison to both online and batch dictionary learning algorithms from the literature while maintaining similar computational complexity.
Blockchain for Supply Chain Traceability: Case Examples for Luxury Goods

Blockchain presents multiple use cases for supply chain management. As supply chains are increasingly complex and global inter-organizational networks, trust and transparency are key success factors. Blockchain provides a promising solution to address supply chain transparency by enabling product tracing along the supply chain, potentially ensuring product authenticity and provenance as well as ethical production. This chapter focuses on supply chain traceability for luxury goods, where counterfeits present a special challenge. Three case examples in luxury goods illustrate the potential benefits of blockchain.
Mixup Augmentation for Kidney and Kidney Tumor Segmentation

Abdominal computed tomography is frequently used to non-invasively map local conditions and to detect any benign or malign masses. However, ill-defined borders of malign objects, fuzzy texture, and time pressure in fact, make accurate segmentation in clinical settings a challenging task. In this paper, we propose a two-stage deep learning architecture for kidney and kidney masses segmentation, denoted as convolutional computer tomography network (CCTNet). The first stage locates volume bounding box containing both kidneys. The second stage performs the segmentation of kidney, kidney tumors and cysts. In the first stage, we use a pre-trained 3D low resolution nnU-Net. In the second stage, we employ a mixup augmentation to improve segmentation performance of the second 3D full resolution nnU-Net. The obtained results indicate that CCTNet can provide improved segmentation of kidney, kidney tumor and cyst.
A Three-Year Study on Peer Evaluation in a Software Engineering Project Course

Background: Peer evaluation in software engineering (SE) project courses enhances the learning experience of students.It also helps instructors monitor and assess both teams and individual students.Peer evaluations might influence the way individual students and teams work; therefore, the quality of the peer evaluations should be tracked through the project course.Contribution: In this article, we analyzed the quality and scoring behavior of students in peer evaluation in an undergraduate SE project course over three years.Research Questions: RQ1: What is the quality of peer evaluation of undergraduate students in a SE project course?RQ2: How do undergraduate students in an SE project course score each other?Methodology: The quality of peer evaluation (length, level of detail, etc.) and scoring of peers based on various aspects of peer evaluations of third-year students in a year-long SE project course were studied.Taking into account the grade students received at the end of the course (A, B, C, and F-calibers), peer evaluations were categorized, analyzed over time, and compared between students calibers.Findings: After analyzing 6854 peer evaluations from 193 students, it was found that the quality of peer evaluations across students was mostly consistent throughout the course.Also, it was observed that quantitative aspects of the peer evaluation were scored similarly across student calibers.However, the qualitative aspects of the peer evaluation were impacted by the caliber of students.These findings suggest that weaker students (i.e., C-caliber students) generally receive better quality peer evaluations than stronger students (i.e., A-caliber students).Finally, a preliminary analysis showed a positive connotation of emotions and sentiments found in the textual feedback delivered by students.
Adaptive Path-Following Control for Autonomous Semi-Trailer Docking

Maneuvering a truck-trailer system while docking is extremely challenging. This article aims to alleviate this problem by presenting an enhanced path-following control framework for autonomous semi-trailer docking. In the proposed system, adaptive controllers that utilize gain scheduling are introduced for forward and reverse path-following tasks in docking maneuvers to increase the robustness and path-following performance. The system includes an improved pure pursuit controller with adaptive look-ahead distance for forward path following; a cascade controller of reverse pure pursuit and a gain-scheduled LQ control for reverse path-following. In the evaluation of the path-following performance of forward and reverse controllers, the closed-loop system of path-following controllers with the truck-trailer kinematic model is simulated in MATLAB/Simulink for various test cases, and the results are compared with those of other studies. Furthermore, different docking scenarios are generated via the cascade path planning algorithm for autonomous semitrailer docking. These are tested with a high degree semi-trailer model within the IPG TruckMaker simulation environment, and with a full truck-trailer vehicle in the test field. The results of both simulations and physical testing clearly demonstrate improvements in terms of the control problem formulation, i.e., the stabilized path-following is obtained with acceptable path-following errors.
EnhanceFace: Adaptive Weighted SoftMax Loss for Deep Face Recognition

Loss function is an important topic in the field of face recognition, while the margin-based loss function is one of the most useful methods to enhance discriminability. Recently, the method of dividing the samples into easy and hard ones effectively optimizes the margin-based loss function by emphasizing these two parts at different training stages. However, the hard samples contain not only intra-personal variations that can be easily misclassified but also wrong-labeled samples. To capture and distinguish their difference, an EnhanceFace method is proposed with a new adaptive weighted loss function in this letter to improve the accuracy of face recognition. Firstly, this method partitions the hard samples into semi-hard and harder samples. Then, a new loss function is developed to adaptively adjust corresponding weights for both semi-hard samples and harder samples during the training process. With this new loss function, their effects can be well balanced according to their importance. The EnhanceFace method is tested on different popular benchmarks. Finally, better performance is shown by the experimental results comparing with other state-of-the-art methods.
HARA: A Hierarchical Approach for Robust Rotation Averaging

We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.
The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.