Reviewerly/exHarmony huggingface dataset:
Sparse Algorithms for Markovian Gaussian Processes

Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian processes combine the use of inducing variables with efficient Kalman filter-like recursions, resulting in algorithms whose computational and memory requirements scale linearly in the number of inducing points, whilst also enabling parallel parameter updates and stochastic optimisation. Under this paradigm, we derive a general site-based approach to approximate inference, whereby we approximate the non-Gaussian likelihood with local Gaussian terms, called sites. Our approach results in a suite of novel sparse extensions to algorithms from both the machine learning and signal processing literature, including variational inference, expectation propagation, and the classical nonlinear Kalman smoothers. The derived methods are suited to large time series, and we also demonstrate their applicability to spatio-temporal data, where the model has separate inducing points in both time and space.
Decoding Supercodes of Gabidulin Codes and Applications to Cryptanalysis

This article discusses the decoding of Gabidulin codes and shows how to extend the usual decoder to any supercode of a Gabidulin code at the cost of a significant decrease of the decoding radius. Using this decoder, we provide polynomial time attacks on the rank metric encryption schemes Ramesses and Liga.
Logistic Q-Learning

We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic Relative Entropy Policy Search (REPS) algorithm of Peters et al. (2010), with the key difference that our method introduces a Q-function that enables efficient exact model-free implementation. The main feature of our algorithm (called QREPS) is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error. We provide a practical saddle-point optimization method for minimizing this loss function and provide an error-propagation analysis that relates the quality of the individual updates to the performance of the output policy. Finally, we demonstrate the effectiveness of our method on a range of benchmark problems.
Blockchain-Based Employment Contract System Architecture Allowing Encrypted Keyword Searches

We introduce a blockchain-based online employment contract system to protect the rights and interests of both employees and employers. In the proposed model, an employer and a worker can interactively create a new electronic online contract, and the mutually signed contract is saved on a contract blockchain so that the contract becomes certifiable but cannot be forged by the contract signers. In particular, the blockchain in our system provides transactional privacy to protect sensitive personal information such as social identifier, contact information, income, and so forth, contained in the contract. Since a remote cloud server must provide not only secure storage, accessibility, and availability of all signed contracts, but also increased security in the server, we propose a new encrypted keyword search mechanism with enhanced search accuracy. Each contract is associated with encrypted keywords generated from the names of contractual parties and must remain confidential and anonymous even to the server. Although, the contracts must always be accessible by the contract signers, only the cloud server should be able to retrieve each user’s contract without decrypting the contract or identifying the contract signer. To meet these requirements, we propose a new encrypted keyword search mechanism based on Gentry’s homomorphic encryption technology; the server can find each user’s contract when two encrypted arbitrary keywords are homomorphic to each other. Since the keywords in the proposed system are based on person names or business names, they are easily predictable, and, thereby, many synonyms for a keyword can exist. Therefore, the proposed encrypted keyword search takes into account not only the keywords but also the ownership of each contract; in this way, the proposed search scheme is secure against a keyword guessing attack and provides strong search accuracy against the keyword synonyms. As a result, users can only access their own contracts, and the cloud server can exactly retrieve the requester’s contracts. Implementations for the proposed system and corresponding analysis on its security and simulated performance are provided.
Underestimation Bias and Underfitting in Machine Learning

Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias.
Blockchain and Machine Learning: Background, Integration Challenges and Application Areas

The blockchain is a new era technology that gives secure mechanical effects on different trades. It thoroughly rotates over the disseminated database. That system assists with disposing of the disappointment at any single point that may influence in sparing time and for the decrease of cost. Till now, a large portion of the work in regards to blockchain is restricted to developing advancements that are identified with the scrambled type of cash. With the progression of different shrewd gadgets and the field of artificial intelligence and machine learning, interdisciplinary cooperation with blockchain technology might be extremely useful for future inquiries about. In this paper, we present a decentralized structure and immense measure of information is to be broken down in the machine learning zone, there are gigantic investigating capacities in the joint effort of the innovation. Right now, parts of the use of the blockchain innovation structure with regards to machine learning are examined.
Chinese Spelling Error Detection Using a Fusion Lattice LSTM

Spelling error detection serves as a crucial preprocessing in many natural language processing applications. Unlike English, where every single word is directly typed by keyboard, we have to use an input method to input Chinese characters. The pinyin input method is the most widely used. By intuition, pinyin should be helpful in detecting spelling errors. However, when detect spelling errors, most of the current methods ignore the pinyin information and adopt a pipeline framework that leads to error propagation. In this article, we propose a fusion lattice-LSTM model under the end-to-end framework to integrate character, word, and pinyin features for error detection. Experiments on the SIGHAN Bake-off-2015 dataset show that pinyin is a discriminating feature, and our end-to-end model outperforms the baseline models obviously.
Crawling Wikipedia Pages to Train Word Embeddings Model for Software Engineering Domain

Word embeddings allow words used in similar contexts to have similar meanings. Due to this property word embeddings are widely used as an input feature for classification, clustering and sentiment analysis of natural language (NL) textual data produced during the software development process. Since the accuracy of these machine learning (ML) tasks depends on how well the word embeddings vectors capture the contextual information, it is important to train these models on software engineering (SE) specific text corpus. This paper proposes a pre-trained word embeddings model for SE which captures and reflects the domain-specific meanings of SE related words. We create a domain-specific text corpus by crawling the SE category on Wikipedia and use it for training the word embeddings model. We show that our model can outperform the state-of-the-art word embeddings model trained on Google news in terms of its representational power. More specifically, our model is able to express the SE specific meaning of polysemous words and recognize SE-specific technical words. Additionally, we also show that for almost all the words related to the fundamental SE activities, our model is either comparable or better than the SE-specific model trained over Stack Overflow (SO) posts.
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks

Abstract Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline’s test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%∼2.84%. We open source our implementation at https://github.com/ytchx1999/PyG-OGB-Tricks.
EvoSets: Tracking the Sensitivity of Dimensionality Reduction Results Across Subspaces

Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, which is usually illustrated as a scatterplot for analysts to easily understand and explore. This process involves human prior knowledge and domain-specific requirements. Thus, quantifying and tracking the changes of dimensionality reduction results across subspaces remain challenging. Existing methods can neither quantify the subsets-based changes of dimensionality reduction results when switching subspaces, nor automatically and comprehensively display the overall and subtle differences among dimensionality reduction results. To address this, we developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EvoSets</i> , a novel visual analytics system designed to help users understand how subspaces affect dimensionality reduction results. The effects are quantified based on the distribution of subsets within projections to tracking the sensitivity of dimensionality reduction results across subspaces. In addition, the system supports the exploration of the overall evolution of the dimensionality reduction results for helping users track the convergence and divergence behavior changes of subsets based on an extended <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bubble Sets</i> visualization. Similarities are intuitively illustrated, and dissimilarities are highlighted among the generated dimensionality reduction results across subspaces based on different layout constraints. The usefulness and effectiveness of the system are further evaluated with a user study and two case studies on multi-dimensional datasets.
A Multi-Objective Genetic GAN Oversampling: Application to Intelligent Transport Anomaly Detection\

The Internet of Things (IoT) enables the automation of data collection and processing functions but exposes a huge amount of data to the cyberattacks risk. To tackle this issue, anomaly detection allows to identify data points, events, and/or observations that deviate from a dataset's normal behaviour indicating eventual critical incidents. In this paper, we focus on the imbalance data and the minority classes problem where the number of abnormal samples is much less than normal (secure) samples. In particular, this paper presents a new equilibrium model based on a Genetic Algorithm to improve Generative Adversarial networks (GANs). This model addresses the problem of class imbalance to anomaly detection system performance. The proposed approach use is illustrated by a case study: an intelligent transport system-based scenario.
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks

In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source of cross-lingual transfer can be as straightforward as lexical overlap between languages (e.g., use of the same scripts, shared subwords) that naturally forces text embeddings to occupy a similar representation space. Recently introduced cross-lingual language model (XLM) pretraining brings out neural parameter sharing in Transformer-style networks as the most important factor for the transfer. In this paper, we aim to validate the hypothetically strong cross-lingual transfer properties induced by XLM pretraining. Particularly, we take XLM-RoBERTa (XLM-R) in our experiments that extend semantic textual similarity (STS), SQuAD and KorQuAD for machine reading comprehension, sentiment analysis, and alignment of sentence embeddings under various cross-lingual settings. Our results indicate that the presence of cross-lingual transfer is most pronounced in STS, sentiment analysis the next, and MRC the last. That is, the complexity of a downstream task softens the degree of cross-lingual transfer. All of our results are empirically observed and measured, and we make our code and data publicly available.
A Large-Scale Chinese Long-Text Extractive Summarization Corpus

Recently, large-scale datasets have vastly facilitated the development in nearly domains of Natural Language Processing. However, lacking large scale Chinese corpus is still a critical bottleneck for further research on deep text summarization methods. In this paper, we publish a large-scale Chinese Long-text Extractive Summarization corpus named CLES. The CLES contains about 104K <summary, article> pairs, which is originally collected from Sina Weibo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . To verify the quality of the corpus, we also manually tagged the relevance score of 5,000 <summary, article> pairs. Our benchmark models on the proposed corpus include conventional deep learning based extractive models and several pre-trained Bert-based algorithms. Their performances are reported and briefly analyzed to facilitate further research on the corpus. We will release this corpus for further research <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Industrial Augmented Reality: Connecting Machine-, NC- and Sensor-Data to an AR Maintenance Support System

Access to machine data, e.g. axis positions inside an AR maintenance application can potentially increase the usefulness of AR in maintenance. Technicians walking to the machine control for looking up information would be avoided. However, the machine control interface and data are machine manufacturer depended, making it necessary to customize the interface between the machine control and the AR maintenance application. Here, we present a solution integrating machine control access from three different machines using a middleware box. A qualitative assessment with technicians confirms the usefulness direct machine data access from an AR maintenance application.
Influence of active methodologies: projects and cases in the development of digital competences with mobile devices

Purpose The purpose of this paper is to analyze the influence of two active methodologies on digital competences development using mobile devices. The first methodology is project-oriented learning (POL); the second one is case-based learning (CBL). The analyzed digital competences belong to the communication and collaboration area of framework DIGCOMP. Design/methodology/approach This article shows the results of the quantitative stage with a design pre-experimental pre-test–post-test. A questionnaire was designed and applied to an intentional sample from two different courses. In total, 178 students completed the questionnaire in the pre-test, and after five months, 38 students completed the questionnaire in the post-test. Findings The results show that students to whom POL was applied increase by 7% competence of interaction with mobile technology. The results also show that the students to whom CBL was applied to increase all four competencies (interaction +8%, share +6%, collaboration +5%, netiquette +4%). Research limitations/implications Self-perception for the evaluation of digital competence and the short study time are limitations to generalize the results, so a longitudinal study is necessary and complemented with qualitative analysis, to present a better validation of the contribution of active methodologies to the development of digital competences. Originality/value The rapid advance of technology and the results of various investigations make evident the need of digital competences development. The most common process is digital literacy through techno-functional training. However, these research results confirm that it is possible to promote these digital competences from a practical view and implicitly in active methodologies educational practices.
Self-Augmented Multi-Modal Feature Embedding

Oftentimes, patterns can be represented through different modalities. For example, leaf data can be in the form of images or contours. Handwritten characters can also be either online or offline. To exploit this fact, we propose the use of self-augmentation and combine it with multi-modal feature embedding. In order to take advantage of the complementary information from the different modalities, the self-augmented multi-modal feature embedding employs a shared feature space. Through experimental results on classification with online handwriting and leaf images, we demonstrate that the proposed method can create effective embeddings.
Quantifying the Use of Domain Randomization

Synthetic image generation provides the ability to efficiently produce large quantities of labeled data, which addresses both the data volume requirements of state-of-the-art vision systems and the expense of manually labeling data. However, systems trained on synthetic data typically under-perform systems trained on realistic data due to mismatch between the synthetic and realistic data distributions. Domain Randomization (DR) is a method of broadening a synthetic data distribution to encompass a realistic data distribution and provides better performance when the exact characteristics of the realistic data distribution are not known or cannot be simulated. However, there is no consensus in the literature on the best method of performing DR. We propose a novel method of ranking DR methods by directly measuring the difference between realistic and DR data distributions. This avoids the need to measure task-specific performance and the associated expense of training and evaluation. We compare different methods for measuring distribution differences, including the Wasserstein and Fréchet Inception distances. We also examine the effect of performing this evaluation directly on images and features generated by an image classification backbone. Finally, we show that the ranking generated by our method is reflected in actual task performance.
Decentralisation in the blockchain space

The rapidly evolving blockchain technology space has put decentralisation back into the focus of the design of techno-social systems, and the role of decentralised technological infrastructures in achieving particular social, economic, or political goals.In this entry we address how blockchains and distributed ledgers think about decentralisation.
Low-Power Reconfigurable Architecture of Elliptic Curve Cryptography for IoT

Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially in the Internet of Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which is capable of resisting simple power analysis attacks (SPA) and can be configured to support all of point operations and modular operations on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) is the most complex and time-consuming operation of ECC, while modular multiplication (MM) and modular division (MD) have high computational complexity among modular operations. For decreasing power dissipation and increasing reconfigurable capability, a Reconfigurable Modular Multiplication Algorithm and Reconfigurable Modular Division Algorithm are proposed, and MM and MD are implemented by two adder units. Combining with the optimization of operation scheduling of PM, on 55 nm CMOS ASIC platform, the proposed architecture takes 0.96, 1.37, 1.87, 2.44 ms and consumes 8.29, 11.86, 16.20, 21.13 uJ to perform one PM on 160-bit, 192-bit, 224-bit, 256-bit field orders. It occupies 56.03 k gate area and has a power of 8.66 mW. The implementation results demonstrate that the proposed architecture outperforms the other contemporary designs reported in the literature in terms of area and configurability.
An ant colony algorithm for drone path planning

The UAV path planning described in this paper is to use the ant colony algorithm to plan the path of the UAV in two-dimensional space. Because of these shortcomings of the traditional ant colony algorithm, this paper first explains what the traditional ant colony algorithm is, as well as some defects of the traditional ant colony algorithm. Then an improved method is used to solve the ant colony algorithm, the basic central idea is to add a new set of ants to the original ant colony algorithm, so that the path algorithm can be calculated from both ends. When two sets of ants meet, you can add up the two sets of paths, and the optimal path is obtained. At the same time, the starting point can be set differently on the algorithm, the setting of obstacles can also be changed, the final path is compared to see what is different between the two algorithms, whether the resulting path changes. Keywords-component; Drones; Ant colony algorithm; Improve the algorithm.
Digital Forensic Investigation Process in Railway Environment

The digitalization process did not circumvent either railway domain. With new technology and new functionality, such as digital interlocking system, automated train operation, object recognition, GPS positioning, traditional railway domain got a vulnerability that can be exploited. Another issue is usage of CotS (Commercial-of-the-Shelf) hardware and software and openness of traditionally closed system. Most of published similar paper are focused on cyber security and security & safety model for securing of assessment in this kind of domain, but this paper will deal with this upcoming railway technology and digital investigation process in such kind of environment. Digital investigation process will be presented, but not only in ICS and SCADA system, but also in specific, railway environment. Framework for investigation process and for maintaining chain of custody in railway domain will be proposed.