Reviewerly/exHarmony huggingface dataset:
A novel sub-Kmeans based on co-training approach by transforming single-view into multi-view

Semi-supervised learning is dedicated to solving the problem of poor model performance caused by the scarcity of labeled samples. Co-training algorithm, as a representative of semi-supervised learning algorithms, through constructing the diversity of classifiers, is used to solve the shortage of label samples that leads to low classifier accuracy. However, traditional Co-training style algorithms have strong constraints: (1) there are multiple views of data, and (2) the randomness of data views is relatively large, which leads to the low stability of the Co-training style algorithms. To optimize and solve the shortcomings of traditional Co-training style algorithms, a novel Co-training method based on sub-Kmeans named Op-FSCO is proposed. The proposed approach uses an optimal subspace construct method to extend Co-training to single-view data, greatly improving the application field of Co-training style algorithm. The clustering subspace is generated by plotting the probability of the correlation between the feature and the mutual information measure between the feature and the class label. For the resulting cluster subspace, the top two are selected to construct different views. Finally, experiments on a synthetic dataset, UCI dataset, and real-world dataset demonstrate that the proposed approach is more effective than other Co-training style algorithms. On the UCI benchmark dataset, the performance of the Op-FSCO algorithm on 15 UCI datasets is significantly better than the traditional Co-training algorithm. And on the cere bral stroke dataset, our approach is 2%–5% higher than that of traditional Co-training style algorithms.
Blockchain Technology: Introduction, Applications, Challenges

Blockchain—the technology behind the popular cryptocurrency bitcoin is revolutionizing the world and bringing significant changes in our technology and business environment. Blockchain is a tamper-proof, distributed and decentralized peer-to-peer technology that could track and verify digital transactions. It aims to bring in transparency, security and integrity of data. This most trusted, decentralized ledger finds it application in almost all the domains varying from finance, education, energy, supply chain, health care and many more. This chapter elaborates the different components that makes this technology more trustable and reliable. It uncovers the various application areas of blockchain technology. While detailing the space with blockchain it also brings out the shortcomings, difficulties and threats of this this technology. This chapter will act as a guide for people interested to explore blockchain and blockchain based solution for any application.
Calibration Procedure of Camera With Multifocus Zoom Lens for Three-Dimensional Scanning System

With zoom lenses, both the magnification of the camera and the distance between the lens and the object plane can be changed.Changes to zoom lens magnification is accompanied by changes in the depth of field and the distance between the lens and the object plane, complicating the calibration of zoom cameras-specifically, that of each focus and zoom control, for which separate configurations are required.To overcome this problem, a procedure for the calibration of a multifocus zoom lens in a three-dimensional (3D) scanning system was developed in the present study.Specifically, a pragmatic, five-step calibration procedure was proposed.In the first two steps, the characteristics of the zoom lens were obtained.In the third and fourth steps, the calibration was normalized.Finally, the effectiveness of the process was assessed using a laboratory-developed prototype.High reconstruction accuracy was achieved.
Falsification of Hybrid Systems Using Adaptive Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.
Learning Scalable ℓ<sub>∞</sub>-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression

We propose a novel joint lossy image and residual compression framework for learning ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> -constrained near-lossless image compression. Specifically, we obtain a lossy reconstruction of the raw image through lossy image compression and uniformly quantize the corresponding residual to satisfy a given tight ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> error bound. Suppose that the error bound is zero, i.e., lossless image compression, we formulate the joint optimization problem of compressing both the lossy image and the original residual in terms of variational auto-encoders and solve it with end-to-end training. To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks. We further correct the bias of the derived probability model caused by the context mismatch between training and inference. Finally, the quantized residual is encoded according to the bias-corrected probability model and is concatenated with the bitstream of the compressed lossy image. Experimental results demonstrate that our near-lossless codec achieves the state-of-the-art performance for lossless and near-lossless image compression, and achieves competitive PSNR while much smaller ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> error compared with lossy image codecs at high bit rates.
Early Detection of Health Changes in the Elderly Using In-Home Multi-Sensor Data Streams

The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this article, we investigate a methodology for tracking the evolution of the behavior trajectories over long periods (years) using high-dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms: Principal Component Analysis and t-distributed Stochastic Neighbor Embedding. Moreover, our tracking algorithm in the original high-dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over 10 years. We used the TigerPlace electronic health records to understand the residents’ behavior patterns and to evaluate the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the electronic health records, providing strong support for a prospective deployment of the approach.
Creative Media Accessibility: Placing the Focus Back on the Individual

Although the emergence of integrated and collaborative approaches to media accessibility and audiovisual translation, such as accessible filmmaking, often require creative approaches, professionals trained in this area are hard to come by. The aim of this paper is, firstly, to explore why media accessibility does not seem to have pursued this creative route consistently until now. The focus is placed here on the currently prevailing experimental turn, which favours quantitative approaches and has been used to inform current media accessibility guidelines. Secondly, the paper defines creative media accessibility and seeks to identify and analyse the work of an emerging movement of artists, with especial attention to Liza Sylvestre, who uses media accessibility as a source of creativity. Her work constitutes a valuable contribution to research and training in media accessibility, placing the focus back on the individual as a necessary complement to the currently prevailing emphasis on quantitative studies in this field.
Different GARCH model analysis on returns and volatility in Bitcoin

&lt;abstract&gt; &lt;p&gt;The aim of this study was to examine the returns and volatility of Bitcoin. The study uses the daily closing price of Bitcoin from October 1, 2013 to July 31, 2020 as the sample data, which include 2496 observations. About the methodology, the paper describes the utilisation of GARCH models to analyse Bitcoin's returns and volatility. First, the data were tested by using the augmented Dickey-Fuller test to verify the stability and diagram tests sequence. After that, the lag order and determination results of the mean value equation show that the Lag 4 period is the best. Additionally, the paper describes an autocorrelation test of the residual series, which revealed that there is no significant autocorrelation in the residual term for the Bitcoin returns, but that the residual squared has significant autocorrelation. In addition, a linear graph of squared residuals was formulated and the ARCH-LM test was used to find the data that are suitable for modelling with GARCH models since the data have a strong ARCH effect. As result, a GARCH (1, 1) model was used; the findings indicated that the returns and volatility of Bitcoin have clustering characteristics, and that the returns and volatility of Bitcoin constitute a persistent process although the effects gradually reduce over time. Because of the limitations of the GARCH (1, 1) model and researching asymmetry of the returns and volatility of Bitcoin, TARCH and EGARCH models were adopted; the findings indicated that the returns and volatility of Bitcoin are without a "leverage effect". To further explain this special phenomenon, safe-property is quoted in this research. In the end, this paper demonstrates that Bitcoin, as a safe-haven property, can hedge financial risks in times of economic depression. Besides, Bitcoin has a revised asymmetric effect between positive and negative shocks that makes it a viable asset to add to the portfolios of investors.&lt;/p&gt; &lt;/abstract&gt;
Cross-lingual transfer learning for relation extraction using Universal Dependencies

This paper focuses on the task of cross-language relation extraction, which aims to identify the semantic relations holding between entities in the text. The goal of the task is to train classifiers for low-resource languages by means of the annotated data from high-resource languages. Related methods usually employ parallel data or Machine Translator (MT) to project annotated data from a source to a target language. However, the availability and the quality of parallel data and MT are big challenges for low-resource languages. In this paper, a novel transfer learning method is presented for this task. The key idea is to utilize a tree-based representation of data, which is highly informative for classifying semantic relations, and also shared among different languages. All the training and test data are shown using this representation. We propose to use the Universal Dependency (UD) parsing, which is a language-agnostic formalism for representation of syntactic structures. Equipping UD parse trees with multi-lingual word embeddings makes an ideal representation for the cross-language relation extraction task. We propose two deep networks to use this representation. The first one utilizes the Shortest Dependency Path of UD trees, while the second employs the UD-based positional embeddings. Experiments are performed using SemEval 2010-task 8 training data, whereas French and Farsi are the test languages. The results show 63.9% and 56.2% F1 scores, for French and Farsi test data, respectively, which are 14.4% and 17.9% higher than the baseline. This work can be considered a simple yet powerful baseline for further investigation into the cross-language tasks.
On PeerCoin Proof of Stake for Blockchain Consensus

Proof of Stake (PoS) has been talked about extensively as an alternative way of reaching consensus in blockchain systems. However, there are few publications on how PoS can be used to create new blocks in detail. The undisputed lead proponent for PoS is Ethereum. However, virtually all discussions regarding PoS for Ethereum are centered on selecting a block using PoS after one or more candidate blocks have already been somehow created. PeerCoin was the first blockchain system that incorporated PoS in block creation. Unfortunately, there is no known documentation on how PoS works in PeerCoin. In this paper, we fill this gap by presenting a detailed explanation of the PeerCoin PoS algorithm based on PeerCoin source code. We also dispel the misconception that PeerCoin PoS is based on Proof of Work (PoW) and hence would consume a lot of energy just like proof of work (PoW). In fact, it resembles PoW only on surface and differs from PoW substantially in terms of how to meet the difficulty target.
An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy

This paper addresses the notion of semantic similarity between concepts organized according to a taxonomy, based on the well-known information content approach.This approach has been widely experimented in the literature over the years and, in general, outperforms other proposals which do not originate from it.However, it shows some limitations related to the notion of generic sense of a concept.In this paper we illustrate the problem arising by using the traditional approach, and a novel information-theoretic definition of semantic similarity in a taxonomy is proposed which also takes into account the intended sense of a concept in a given context.This proposal has been applied to some among the most representative stateof-the-art similarity measures based on the information content approach, and the experiment shows that it achieves very high correlation values with human judgment.
Handwritten Mathematical Symbols Classification Using WEKA

Machine learning tools have been extensively used for the prediction and classification of mathematical symbols, formulas, and expressions. Although the recognition and classification in handwritten text and scripts have reached a point of commensurate maturity, yet the recognition work related to mathematical symbols and expressions has remained a stimulating and challenging task throughout. So, in this work, we have used Weka, a machine learning tool, for the classification of handwritten mathematical symbols. The current literature witnesses a limited amount of research works for classification for handwritten mathematical text using this tool. We have endeavored to explore the potential classification rate of handwritten symbols while analyzing the performance by comparing the results obtained by several clustering, classification, regression, and other machine learning algorithms. The comparative analysis of 15 such algorithms has been performed, and the dataset used for the experiment incorporates selective handwritten math symbols. The experimental results output accuracy of 72.9215% using the Decision Table algorithm.
Movie Recommendation System to Solve Data Sparsity Using Collaborative Filtering Approach

With the increase in numbers of multimedia technologies around us, movies and videos on social media and OTT platforms are growing, making it confusing for users to decide which one to watch for. For this, movie recommendation systems are widely used. It has been observed that two-thirds of the films watched on Netflix are the recommended ones to its users. The target of this work is to use implicit feedback given by other users to recommend movies, i.e., ratings given by them. Implicit feedback will help to enhance Data Sparsity as for a replacement logged-in user, the system won't have details of their past liked movies. So, matching the similarity with other users is often a plus point to recommend movies that they would like. The anticipated result will depend upon the positive attitude; i.e., if the predicted rating is high, then it'll be recommended; otherwise it'll not be recommended. The performance of the methodology is measured with accuracy and precision values for different strategies. It gives the best accuracy and highest precision values using Logistic Regression (LR) and lowest recall value as compared to other algorithms. This technique gives an accuracy, precision, and recall value of 81.9%, 69.82%, and 32.5%, respectively, using LR.
A Tutorial on Singular Value Decomposition with Applications on Image Compression and Dimensionality Reduction

This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as principal component analysis (PCA), eigen decomposition, matrix decomposition, Cholesky decomposition and others. SVD is utilized in many applications such as data analysis and dimensionality reduction, image compression, Google's PageRank algorithm, Netflix's recommender system and many more. This paper overviews the mathematics behind SVD in a simple way. It also applies SVD technique in image compression and in dimensionality reduction as the underlining technique of the PCA and data analysis.
Cross-domain Recommendation with Bridge-Item Embeddings

Web systems that provide the same functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the cross-domain recommendation task. Despite many research efforts on this task, the main drawback is that they largely assume the data of different systems can be fully shared . Such an assumption is unrealistic different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive. In this work, we consider a more practical scenario to perform cross-domain recommendation. To avoid the leak of user privacy during the data sharing process, we consider sharing only the information of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus (e.g., legal policy) on data sharing since the data to be shared is user-irrelevant and has no explicit semantics. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR (short for N eural A ttentive T ransfer R ecommendation ). We perform extensive experiments on two real-world datasets, demonstrating that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the efficacy of NATR in using the transferred item embeddings to alleviate the data sparsity issue.
Using transformers to improve answer retrieval for legal questions

Transformer architectures such as BERT, XLNet, and others are frequently used in the field of natural language processing. Transformers have achieved state-of-the-art performance in tasks such as text classification, passage summarization, machine translation, and question answering. Efficient hosting of transformer models, however, is a difficult task because of their large size and high latency. In this work, we describe how we deploy a RoBERTa Base question answer classification model in a production environment. We also compare the answer retrieval performance of a RoBERTa Base classifier against a traditional machine learning model in the legal domain by measuring the performance difference between a trained linear SVM on the publicly available PRIVACYQA dataset. We show that RoBERTa achieves a 31% improvement in F1-score and a 41% improvement in Mean Reciprocal Rank over the traditional SVM.
DAIR: A Query-Efficient Decision-based Attack on Image Retrieval Systems

There is an increasing interest in studying adversarial attacks on image retrieval systems. However, most of the existing attack methods are based on the white-box setting, where the attackers have access to all the model and database details, which is a strong assumption for practical attacks. The generic transfer-based attack also requires substantial resources yet the effect was shown to be unreliable. In this paper, we make the first attempt in proposing a query-efficient decision-based attack framework for the image retrieval (DAIR) to completely subvert the top-K retrieval results with human imperceptible perturbations. We propose an optimization-based method with a smoothed utility function to overcome the challenging discrete nature of the problem. To further improve the query efficiency, we propose a novel sampling method that can achieve the transferability between the surrogate and the target model efficiently. Our comprehensive experimental evaluation on the benchmark datasets shows that our DAIR method outperforms significantly the state-of-the-art decision-based methods. We also demonstrate that real image retrieval engines (Bing Visual Search and Face++ engines) can be attacked successfully with only several hundreds of queries.