Reviewerly/exHarmony huggingface dataset:
Autoconfiguration of a Vibration-Based Anomaly Detection System with Sparse a-priori Knowledge Using Autoencoder Networks

This paper presents a method for machine component supervision with little to none prior knowledge of the machine, operating conditions and wear behavior. A hybrid approach based on unsupervised learning methods, consisting of an autoencoder network and clustering, to identify machine states and possible failure preceding anomalies is proposed. In order to cope with information sparsity, the model parameters of the unsupervised methods are derived automatically based on data distribution and a physical motivation. The approach was validated on a dataset of artificially introduced bearing faults. The gained clustering results show a general usability of the approach for condition monitoring with vibration data.
Predicting Stack Overflow Question Tags

This work proposes to predict the tags assigned for the posts on Stack Overflow platform. The raw data was obtained from the stackexchange.com including more than 50K posts and their associated tags given by the users. The posts' questions and titles are pre-processed, and the sentences in the posts are further transformed into features via Latent Dirichlet Allocation. The problem is a multi-class and multi-label classification and hence, we propose 1) one-against-all models for 15 most popularly used tags, and 2) a combined multi-tag classifier for finding the top K tags for a single post. Three algorithms are used to train the one-against-all classifiers to decide to what extent a post belongs to a tag. The probabilities of each post belonging to a tag are then combined to give the results of the multi-tag classifier with the best performing algorithm. The performance is compared with a baseline approach (kNN). Our multi-tag classifier achieves 55% recall and 39% F1-score.
Automatic Labelling of Genre-Specific Collections for Word Sense Disambiguation in Russian

Supervised word sense disambiguation (WSD) models suffer from the knowledge acquisition bottleneck: the semantic annotation of large text collections is very time-consuming and requires much effort from experts. In this article we address the issue of the lack of sense-annotated data for the WSD task in Russian. We present an approach that is able to automatically generate text collections and annotate them with word senses. This method is based on the substitution and exploits monosemous relatives (related unambiguous entries) that can be located at relatively long distances from a target ambiguous word. Moreover, we present a similarity-based ranking procedure that enables to sort and filter monosemous relatives. Our experiments with WSD models, that rely on contextualized embeddings ELMo and BERT, have proven that our method can boost the overall performance. The proposed approach is knowledge-based and relies on the Russian thesaurus RuWordNet.
Understanding Malvestuto’s Normalized Mutual Information

Malvestuto’s version of the normalized mutual information is a well-known information theoretic index for quantifying agreement between two partitions. To further our understanding of what information on agreement between the clusters the index may reflect, we study components of the index that contain information on individual clusters, using mathematical analysis and numerical examples. The indices for individual clusters provide useful information on what is going on with specific clusters.
UAV Altitude Measurement Method Based on Data Fusion and Kalman Filter

Abstract Accurate altitude measurement of UAV is very important for its flight mission. Based on the analysis of the measurement performance of different altitude measurement sensors, this paper designs a multi-altitude measurement sensor fusion filtering structure, and then describes the fusion filtering method. This method mainly provides weighted fusion of data from various altitude measurement sensors to obtain altitude information with higher accuracy than any sensor. Through Kalman filtering of the fusion data, more accurate flight altitude data of UAV can be obtained. The method is validated by a typical example. The results show that the proposed method can solve the problem of precise measurement of altitude data in UAV flight process, and has certain theoretical significance and good application value.
Decentralized Privacy-Preserving Framework for Health Care Record-Keeping Over Hyperledger Fabric

Some of the exciting features of blockchain technology are immutability, distributed ledger technology, peer-to-peer network, and consensus mechanisms. Blockchain applications include IoT, supply chain, finance institutions, and e-governance. Blockchain technology is emerging in the health care applications for maintaining electronic healthcare records to improve the accuracy of diagnosis. Privacy is more important in health care systems, especially for electronic medical records. Here, privacy is defined in two ways, i.e. user privacy and data privacy. For user privacy, the anonymity of the user is very important as it defines that no one can find who the user is and who is the validator. But, user anonymity is not maintained in the existing systems. Hence, the proposed model provides a novel solution for privacy-preserving of electronic medical records. The proposed architecture implements group signatures for providing privacy-preserving to the electronic medical records. Using group signatures also avoids problems like Unforgeability, Anonymity, Traceability, and Unlinkability.
The Combined Method of Automated Knowledge Acquisition from Various Sources: The Features of Development and Experimental Research of the Temporal Version

The experience in the development and evolutionary development of technology of knowledge acquisition from various sources on the basis of the original combined method of knowledge acquisition, which is an important part of the problem-oriented methodology for building integrated expert systems for static and dynamic problem domains, are analyzed. Particular emphasis is placed on experimental software modeling of the processes of temporal knowledge acquiring from experts, NL texts and temporal databases and analysis of the results (using the example of medical diagnostics).
CNN-Based Model for Chinese Information Processing and Its Application in Large-Scale Book Purchasing

The demand for library books has been growing year by year, and manual methods for purchasing books have been unable to keep with these ever-increasing Chinese book demands. As deep learning techniques have developed, they have achieved remarkable results in various fields. In this study, we applied a convolution neural network (CNN)-based model to the book purchasing task. We also built a Chinese book dataset based on historical data from the Wuhan University Library. Experimental results on this dataset show that our CNN-BOOK model can reach an accuracy of 83% on the book purchasing task. This method greatly improves the efficiency of book purchases and can provide new ideas for how library purchasing is conducted in the future.
$$\alpha $$-UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation

UNet++, an encoder-decoder architecture constructed based on the famous UNet, has achieved state-of-the-art results on many medical image segmentation tasks. Despite improved performance, UNet++ introduces densely connected decoding blocks, some of which, however, are redundant for a specific task. In this paper, we propose \(\alpha \)-UNet++ that allows us to automatically identify and discard redundant decoding blocks without the loss of precision. To this end, we design an auxiliary indicator function layer to compress the network architecture via removing a decoding block, in which all individual responses are less than a given threshold \(\alpha \). We evaluated the segmentation architecture obtained respectively for liver segmentation and nuclei segmentation, denoted by UNet++\(^C\), against UNet and UNet++. Comparing to UNet++, our UNet++\(^C\) reduces the parameters by 18.89% in liver segmentation and 34.17% in nuclei segmentation, yielding an average improvement of IoU by 0.27% and 0.11% on two tasks. Our results suggest that the UNet++\(^C\) produced by the proposed \(\alpha \)-UNet++ not only improves the segmentation accuracy slightly but also reduces the model complexity considerably.
Two-Dimensional Semi-Supervised Feature Selection

Data with a few labels are common in reality and corresponding semi-supervised learning methods have good application percepts such as image process and computer vision. However, most of conventional semi-supervised feature selection methods, they first convert the image data into corresponding vectors and then utilize the vectors for feature selection. However, these vector-based methods fail to consider the intrinsic location information of the matrix data resulting in degrading performance. To overcome these problems, in this paper, we propose a two-dimensional semi-supervised feature selection model, which directly selects the features from the matrix data by using both labeled and unlabeled data. Specifically, our proposed method unifies the sparse matrix regression and label prediction procedures to conduct feature selection. To solve our model, an alternating optimization algorithm is also proposed. Furthermore, we provide analysis about our proposed model including convergence analysis and computational complexity. Extensive experimental results confirm the effectiveness of the proposed model.
SentiMem: Attentive Memory Networks for Sentiment Classification in User Review

Sentiment analysis for textual contents has attracted lots of attentions. However, most existing models only utilize the target text to mine the deep relations from text representation features to sentiment values, ignoring users' historicalally published texts, which also contain much valuable information. Correspondingly, in this paper we propose SentiMem, a new sentiment analysis framework that incorporates user's historical texts to improve the accuracy of sentiment classification.
An Analysis of Scientific Production in Big Data Knowledge Domain on Google Books, YouTube and IEEE Explore® Digital Library

The paper aims to reveal the current state of book, video and article production in Big Data Knowledge Domain on particular platforms by examining the capabilities of Application Programming Interface (API) technology in conducting scientific data-driven research. Queries append public records from Google Books, YouTube and IEEE Explore® Digital Library to two research paradigms (sets of data sets): Big Data (incl. Analysis, Engineering, Architecture, Governance, Management, Frameworks) and Big Data interdisciplinary fields (Data Science, Data Mining, Deep Learning, Machine Learning, Artificial Intelligence). Metadata from more than 25 000 conference papers, 2000 books over the past 50 years, and 4 000 videos for the last 12 years, matching the searching criteria, has been stored and analyzed. The outputs are summarized in statistics, forecasting, rating key findings by various attributes: title, author, publisher, research field, category, subject, publication year, description, view count, and a combination of mentioned metadata in cross-tables. Nearly a half of billion video views; a half of million article reference count; a twofold increase in the number of papers in Machine learning over past three years compared to the total number in the same field for entire 1988-2016 period; 1:2:12 overall books-to-videos-to conference papers ratio; 61.3% of last year's video production just in a month (Jan-2020); the earliest found usage of "Artificial Intelligence" expression in a printed law document dated 1848 are few curious examples of analysis findings. The paper presents non-commercial research and retrieved data is collected entirely from public records.
Hierarchical Cluster and Multidimensional Scaling Analysis of Video Websites Based on URL Co-occurrence

Co-link analysis is an important method in link analysis. This paper attempts to research the categories of video websites with an alternative method of co-link analysis, called URL co-occurrence analysis. In the research, hierarchical cluster analysis and multidimensional scaling analysis are used to study the competition situation. Results show that the video websites in China can be divided into 4 - layer gradient websites. Overall, some comprehensive web sites hold the most share of the market and characteristic websites highlights the market and small websites coexist at the same time.
CIC-PIM: Trading spare computing power for memory space in graph processing

Shared-memory graph processing is usually more efficient than in a cluster in terms of cost effectiveness, ease of programming and runtime. However, the limited memory capacity of a single machine and the huge sizes of graphs restrains its applicability. Hence, it is imperative to reduce memory footprint. We observe that index compression holds promise and propose CIC-PIM, a lightweight encoding with chunked index compression, to reduce the memory footprint and the runtime of graph algorithms. CIC-PIM aims for significant space saving, real random-access support and high cache efficiency by exploiting the ubiquitous power-law and sparseness features of large scale graphs. The basic idea is to divide index structures into chunks of appropriate size and compress the chunks with our lightweight fixed-length byte-aligned encoding. After CIC-PIM compression, two-fold larger graphs are processed with all data fit in memory, resulting in speedups or fast in-memory processing unattainable previously.
Innovative Methods of Mastering Economic Knowledge by Learning a Foreign Language

The authors analyze innovative methods that can help students of cooperative universities to master economic knowledge through a foreign language. The types of professional activities of specialists in the cooperative sphere, where knowledge of a foreign language is necessary, are identified. The difficulties and their causes that students may encounter when mastering a foreign language in order to use it in their future professional activities are identified. The authors consider the importance of improving professional and communicative competence for students of cooperative universities, which is an integral component of professional growth and improvement, as it serves as an indicator of the manifestation of many professional qualities in the field of the cooperative sector of the economy. The following research methods were used: the analysis of scientific literature on the issues of the pedagogical experiment, pedagogical observation; questioning; student testing; the method of expert assessment; mathematical data processing. The authors concluded that such innovative methods as the portfolio method, case studies, and presentations not only arouse the interest of students and contribute to the development of economic knowledge, but also provide an opportunity to create a motivational environment, a creative learning atmosphere, allowing teachers to solve pedagogical, educational, developmental tasks.
iGYM: An Inclusive Augmented Reality Exergame for People of All Abilities

iGYM is an inclusive augmented reality (AR) exergame that allows people of all abilities to play the emulated hockey game through floor display projection that interacts with the body motion. iGYM creates a peripersonal circle around each player whose radius is modulated by either player motion or pressing a wireless button attached to the player's body. Expanding peripersonal circle impacts a virtual puck projected on the floor to manipulate its velocity. In this paper, we provide a technical description of iGYM system design with emphasis on player detection and tracking. We evaluate two adaptive filtering-based tracking methods; Kalman filter and particle filter to quantify the performance of each scheme for the player tracking. iGYM with Kalman filter based real-time player tracking was successfully demonstrated to public for inclusive play among children and parents with and without disabilities.
Application of Block Chain in EHR’s System for Maintaining the Privacy of Patients Record

Electronic health records (EHR’s) system provides improved care of patients and allows easy access to information about their data about health. According to Health Insurance Portability and Accountability Act (HIPPA), security rules are accountable for maintaining security and privacy of patients. There are three main issues that we face with EHR’s, i.e., legislative gaps, lack of trust and lack of patient control. To address these issues, we collaborate EHR’s with blockchain to provide a platform where we have increased transparency, tracking information, ledger (hard to tamper), cost reduction, immutability and unforgeability. It allows patients to have complete access to their medical data which instead were controlled by hospitals only. Attribute-based signature allows us to sign a message without displaying any information; it only displays an evidence that he has signed it. By using DES and RSA, we provide unforgeability and privacy of attribute signer. SHA-2 as a hashing technique ensures security of stored data.
Control of a Twin Rotor MIMO System using a TS Fuzzy TITO PD Controller

Twin Rotor MIMO System (TRMS) is a highly complex nonlinear two-input two-output (TITO) system. Its quite challenging to control the pitch and yaw motion of TRMS simultaneously due to coupling. Therefore, this paper aims at 2-degree of freedom (DoF) control of a laboratory based TRMS. We deploy the simplest TS fuzzy TITO proportional derivative (PD) controller to control the pitch and yaw motions simultaneously. A modified form of rule base is considered to reduce the computational complexity. Further minimum number of fuzzy sets is employed on each input. Simulation studies are conducted on TRMS with TS fuzzy TITO PD controller and linear PD controller to compare the performances.
Mispronunciation Detection and Diagnosis Through a Chatbot

The interaction between humans and machines has evolved; thus, the idea of being able to communicate with computers as we usually do with other people is becoming increasingly closer to coming true. Nowadays, it is common to come across intelligent systems named chatbots, which allow people to communicate by using natural language to hold conversations related to a specific domain. Chatbots have gained popularity in different kinds of sectors, such as customer service, marketing, sales, e-commerce, e-learning, travel, and even in education itself. This chapter aims to present a chatbot-based approach to learning English as a second language by using computer-assisted language learning systems.
Investigating robustness of adversarial camouflage (AC) for naval vessels

The use of camouflage is widespread in the biological domain, and has also been used extensively by armed forces around the world in order to make visual detection and classification of objects of military interest more difficult. The recent advent of ever more autonomous military agents raises the questions of whether camouflage can have a similar effect on autonomous agents as it has on human agents, and if so, what kind of camouflage will be effective against such adversaries. In previous works, we have shown that image classifiers based on deep neural networks can be confused by patterns generated by generative adversarial networks (GANs). Specifically, we trained a classifier to distinguish between two ship types, military and civilian. We then used a GAN to generate patterns that, when overlaid on parts of military vessels (frigates), made the classifier confuse the modified frigates with civilian vessels. We termed such patterns "adversarial camouflage" (AC) since these patterns effectively camouflage the frigates with respect to the classifier. The type of adversarial attack described in our previous work is a so-called white box attack. This term describes adversarial attacks that are devised given full knowledge of the classifier under attack. This is as opposed to black box attacks, which describe attacks on unknown classifiers. In our context, the ultimate goal is to design a GAN that is capable of black box attacks, in other words: a GAN that will generate AC that has effect across a wide range of neural network classifiers. In the current work, we study techniques to improve the robustness of our GAN-based approach by investigating whether a GAN can be trained to fool a selection of several neural network-based classifiers, or reduce the confidence of the classifications to a degree which makes them unreliable. Our results indicate that it is indeed possible to weaken a wider range of neural network classifiers by training the generator on several classifiers.
A Dual-Layer Architecture for the Protection of Medical Devices from Anomalous Instructions

Complex medical devices are controlled by instructions sent from a host PC. Anomalous instructions introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical components (e.g., manipulation of device motors) devices, or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human errors (e.g., a technician's configuration mistake), or host PC software bugs. To protect medical devices, we propose to analyze the instructions sent from the host PC to the physical components using a new architecture for the detection of anomalous instructions. Our architecture includes two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instructions' content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies with respect to the classifier's output, relative to the clinical objectives.
Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games

Game balancing is one of the most challenging features to be implemented in a typical game design process. Approaches for evaluating and achieving game balancing include extensive playtesting - which typically requires several iterations of games with subtle adjustments in the components and adopted strategies that resemble brute force - and algorithmic solutions that use qualitative and measurable design goals when developing game components. The literature contains examples of methods that employ artificial intelligence to generate maps in computer games that offer balanced and fairness of starting conditions for the players. The use of such methods for tabletop games, however, has been scarce in the academic literature, for the best of the authors' knowledge. This paper investigates the application of the ant colony optimisation metaheuristic to generate content and improving game balance for two well-known tabletop games, namely, Terra Mystica and Settlers of Catan. The resultant configurations satisfy complex game-dependent requirements while optimising a model for game balancing. Moreover, the results showed to be promising when compared with existing game maps and setup.
Universities, Jisc and the journey to open

Open access, research and scholarship have increasingly become embedded in the research environment, culture and activity of UK universities. The chapter considers the key role that Jisc has played in the integration of ‘open’ into the academies and the changes in culture, policies and practice, through the development of its programmes, tools and services and its close working relationships with academic libraries.