Reviewerly/exHarmony huggingface dataset:
Probe misalignment calibration in fiber point-diffraction interferometer

The measuring probe integrated with multiple fiber point-diffraction sources can be applied to measure both the three-dimensional coordinates and highly accurate point-diffraction wavefront. The probe determines the achievable measurement accuracy of fiber point-diffraction interferometer (PDI), in which the fiber exit end plane is required to be parallel with the detector plane. The probe misalignment due to fabrication error could introduce significant measurement error. A high-precision method is proposed to calibrate the probe misalignment in fiber PDI, including the central positioning based on phase difference and tilt adjustment based on Zernike polynomials fitting. Both numerical simulation and experiments have been carried out to demonstrate the feasibility and accuracy of the proposed probe misalignment calibration method. The proposed method provides a feasible way to address the processing uncertainty on measuring probe in fiber PDI, and enables high-precision geometry alignment and misalignment calibration in the interferometric testing systems with case of no imaging lens.
One-Class Active Learning for Outlier Detection with Multiple Subspaces

Active learning for outlier detection involves users in the process, by asking them for annotations of observations, in the form of class labels. The usual assumption is that users can provide such feedback, regardless of the nature and the presentation of the results. This is a simplification, which may not hold in practice. To overcome it, we propose SubSVDD, a semi-supervised classifier, that learns decision boundaries in low-dimensional projections of the data. SubSVDD de-constructs the outlier classification so that users can comprehend and interpret results more easily. For active learning, SubSVDD features a new update mechanism that adjusts decision boundaries based on user feedback. In particular, it considers that outliers may only occur in some of the low-dimensional projections. We conduct systematic experiments to show the effectiveness of our approach. In a comprehensive benchmark, SubSVDD outperforms alternative approaches on several data sets.
Remote Sensing Image Retrieval Based on Semi-Supervised Deep Hashing Learning

As an useful solution of the approximate nearest neighbor (ANN) search, hashing attracts growing attention in the topic of large-scale image retrieval. In this paper, we propose a semi-supervised deep hashing method based on the adversarial autoencoder (AAE) network for remote sensing image retrieval (RSIR), and we name it SSHAAE. Here, we assume the RS images have been represented by the visual features, and the target of our SSHAAE is mapping those features into the binary codes. First, a hashing layer is adopted to replace the part of original latent layer in AAE. In addition, the classical reconstruction loss function is selected to generate the hash code. Second, two discriminators are added simultaneously to make sure the hash code is bit balanced and the generated label variable is one-hot. Third, we design the hash loss function to guarantee the obtained hash code is discriminative, similarity persevering, and low quantization error. The presented SSHAAE model can be trained by the minimax optimization. The encouraging experimental results counted on a high-resolution RS image archive demonstrate our SSHAAE model is effective to RSIR.
Design and build of a scale model unmanned inland cargo vessel: actuation and control architecture

Abstract This study describes the actuation design and construction of an unmanned scale model inland vessel of type CEMT-I. This proposed design could help to increase the competitiveness of smaller inland vessels which are slowly diminishing. Moreover, this idea aligns with the ambition of the European Commission to increase the cargo flow over waterborne transport. Therefore, this study scaled down a recently designed barge of the European project Watertruck+. These barges have a 360-degrees-steerable steering grid in the bow together with a 360-degrees-steerable four-channel thruster at the stern. This configuration unlocks new and more advanced motion control possibilities compared to conventional actuation systems. The performance of this actuation design, at different propeller speeds and angles, was experimentally identified for the scale model. Furthermore, the implemented back-seat driver control paradigm is discussed at its two levels of implementation. Firstly, the lowest level control, to reach certain desired sytem states, is shown. Secondly, the higher level control, the autonomy system provided by the MOOS-IvP software, is discussed and its interaction with the low level control is demonstrated. The authors believe that the combination of this actuation and control design can unlock new cargo transport opportunities for the European inland waterways.
Word Embeddings in Low Resource Gujarati Language

Word embeddings/vectors are becoming an extremely important component of natural language processing tasks. Word2vec and fastText are few of the most common word embedding techniques. While large amount of work has been done to obtain embeddings in resource rich languages like English, work still remains to be done for low resource languages. Our focus has been to develop word vectors for one such low resource language, Gujarati which is spoken in western part of India. We also developed analogy test data set to evaluate the accuracy of the embeddings obtained. We also compared the performance of the models with the pre trained Gujarati models already available.
Design and Implementation of Storage System for Real-time Blockchain Network Monitoring System

Recently, due to the popularity of Bitcoin, interest in the blockchain which is the core technology of Bitcoin, has also increased. Blockchain is a distributed ledger technology that stores transaction information that occurs in P2P(Peer-to-Peer) networks on the ledger of all nodes in a different way than centralized method and verifies whether they are stored correctly. As a result of blockchain technology, not only Bitcoin but also various cryptocurrencies such as Ethereum, Litecoin, Ripple and Bitcoin Cash are being developed. Blockchain is used in various fields because of its features such as integrity and anonymity, but it is also used for illegal transactions and drug transactions. To solve these problems, monitoring system is needed to collect data from blockchain networks and detect abuse and illegal transactions. In this paper, we propose monitoring system for detecting and tracking illegal transactions by collecting and analyzing information in a blockchain network. In addition, we introduce an efficient storage system implemented by Apache Kafka and Apache Storm among the Blockchain monitoring systems.
BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature

The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years.In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies.Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes.For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents.We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature.More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% Fscore.In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
Semi-Supervised Deep Time-Delay Embedded Clustering for Stress Speech Analysis

Real stressed speech is affected by various aspects (individual characteristics and environment) so that the stress patterns are diverse and different on each individual. To this end, in our previous work, we performed an unsupervised clustering method that able to self-learning manner by mapping the feature representations of the stress speech and clustering tasks simultaneously, called deep time-delay embedded clustering (DTEC). However, DTEC has not confirmed yet the compatibility between the output class and informational classes. Therefore, we proposed semi-supervised time-delay embedded clustering (SDTEC) as a new framework of semi-supervised in DTEC. SDTEC incorporates the prior information of pairwise constraints in the embedding layer and simultaneously learns the feature representation and the clustering assignments. The prior information was used to guide the clustering procedure so that the points that belong to the incorrect cluster can be corrected. The effectiveness of the proposed SDTEC was evaluated by comparing it with some baseline methods in terms of the clustering error rate (CER). Moreover, to demonstrate SDTEC’s capabilities, we conducted a comprehensive ablation study. Based on experiment results, SDTEC outperformed the baseline methods and achieves state-of-the-art results in semi-supervised clustering.
Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions

The heteroscedastic nonlinear regression model (HNLM) is an important tool in data modeling. In this paper we propose a HNLM considering skew scale mixtures of normal (SSMN) distributions, which allows fitting asymmetric and heavy-tailed data simultaneously. Maximum likelihood (ML) estimation is performed via the expectation-maximization (EM) algorithm. The observed information matrix is derived analytically to account for standard errors. In addition, diagnostic analysis is developed using case-deletion measures and the local influence approach. A simulation study is developed to verify the empirical distribution of the likelihood ratio statistic, the power of the homogeneity of variances test and a study for misspecification of the structure function. The method proposed is also illustrated by analyzing a real dataset.
Robust optical flow estimation involving exponential fractional-order derivatives

This work presents a fractional-order method for optical flow estimation. The proposed method is based on the Classic+NL algorithm and the Caputo-Fabrizio fractional-order derivative. Therefore, the proposed operator can be seen as a generalization of this integer-order model. The performance of the algorithm is analyzed by using the Average End Point Error (EPE) and the Average Angular Error (AAE) considering the MPI Sintel and Middlebury Datasets. Experimental results on these dataset show that the proposed algorithm performs better than other existing techniques through the analysis of the two error metrics mentioned above. The firefly optimization algorithm was applied to obtain the fractional-order, which minimizes the EPE, for each of images sequence used.
Martyrs for Free Speech? Disentangling the Effects of Legal Prosecution of Anti-immigration Politicians on their Electoral Support

Abstract Several anti-immigration politicians in Europe have been prosecuted for hate speech; some of these trials were highly mediatized. To what extent, and how, does hate speech prosecution of anti-immigration politicians affect voting for their party? We address this question by an experiment (N = 372) using manipulated versions of a television news story about a politician of the Dutch Party Forum for Democracy (FvD). We go beyond prior studies by disentangling the mechanisms driving the electoral ramifications of hate speech prosecution, assessing the moderating role of multiculturalist attitudes separately and in combination with six mediators (anti-establishment attitudes, issue salience immigration, perceived party’s effectiveness and legitimacy, support for free speech, and perceived party visibility). Among voters who are positive toward multiculturalism, exposure to a news story about prosecution boosts support for free speech and perceived visibility and support for the FvD. Both aspects are positively related to voting for FvD. This improves our understanding of the mechanisms of hate speech prosecution, informing public debates of how to react to controversial speech by politicians.
High‐performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks

Abstract This research paper shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high‐level features from convolutional Neural Network (CNN) system. By recognize inherent deep feature representations in image patches for morphological changes in medicinal imaging information discovery. Various performance measurements, High Frequency Error Norm (HFEN), Mean Squared Error MSE, peak Signal‐to‐noise‐ratio (PSNR), Structural Similarity Index (SSI), are utilized to inspect different dataset. As presented in the paper outcomes, the proposed method can accomplish predominant performance compared with other cutting‐edge techniques with either low or high‐level features in terms of the synthesis and reconstruction rate. The proposed deep learning algorithm has a training time of 5 seconds with 0.97 SSI and 0.15 HFEN. In all investigations, the outcome shows that the proposed image synthesis and reconstruction framework reliably exhibited progressively precise outcomes when contrasted with best in class.
FPGA Based Real-Time Multi-Face Detection System With Convolution Neural Network

The AdaBoost-based real-time face detections have been widely used in current video surveillance. However, the AdaBoost-based face detection has poor performances in detecting multi-face with different scales, multiple poses, and occlusion in complex lighting environment. Recent research shows that the convolutional neural network (CNN) can improve its accuracy. In this work, a FPGA based real-time multi-face detection system for crowded area surveillance application using CNN is presented. A hardware friendly fully quantization strategy is proposed and the result is tested on WIDER FACE dataset. With acceptable loss of accuracy, the FPGA based system can achieve a frame rate of 37 FPS at $512 \times 288$ resolution with only 65 ms processing delay.
An overview of Affective Models and ICT in Education

Emotion, human intelligence and learning have inextricable connections. Making sure learners' emotions are positive during the learning procedure can increase and optimize the learning outcome. However, until recently, cognition and emotion were viewed as two separate notions. Learning materials and pedagogical strategies focusing more on how to increase and sustain the volume of knowledge, rather than how to actively engage the learner, through positive and enjoyable learning experiences, were in the focus of attention. However, in the last years, the advent of a wide variety of learning (digital) resources, such as serious games, robots, mobile devices, virtual and augmented reality, has provided the means to involve the learner in more immersive and active contexts, that place engagement and human emotions in the centre of the interaction. Moreover, the advances in artificial intelligence are now allowing for a wide availability of instruments that allow for estimating emotions based on a plethora of means, such as facial expressions, heart rate measurements, digital log files, personality analysis. The above are leading to personalized learning that tailors the learning procedure to the (emotional and cognitive) needs of the individual learner. This paper is presenting an introduction to the role of emotion in educational settings and describes influential and promising emotional models. A brief overview of ways to infer emotions follows, while examples of works intended to make use of measured emotion in learning conditions is presented at the end of this work.
System analysis of the business process of attracting, selecting and involving into the teamwork of young specialists in the space industry

Progress in the space industry depends on many factors. The most important is the staffing. At present, three objectives related to personnel are relevant for Russian companies in the space industry: 1) staff quality improvement, 2) openness to innovation, 3) modern technologies (agile, additive technologies and other). The article provides a systematic analysis of the process of attracting, selecting, and involving in the teamwork of young specialists in Russian companies of the space industry. As an ideal model we took a process when the best students of the best universities who got the best theoretical knowledge get a job in their degree field and work among people with practical experience corresponding to the modern world level. And so harmoniously exchanging knowledge, they create more sophisticated samples of space technology, and management creates comfortable working conditions and staff development. It is also important to have a leader who opens up new perspectives that are understood, supported and implemented by the whole team, as a team of like-minded people. As a result of this study, we showed a possibility to organize the process, to attract, select and anchor young specialists in a space industry company, as a business process. Teamwork based on the methods proposed in the article plays an important role in the organization of this business process (Scrum, eduScrum, 4-D systems). Although the study is based on Russian experience, the objectives being solved seem relevant for many companies in the space industry.
Neural Gender Prediction in Microblogging with Emotion-aware User Representation

Demographics of social media users such as gender are very important for personalized online services. However, the gender information of many users is usually not available. Luckily, the messages posted by social media users can provide rich clues for inferring their genders, since male and female users usually have differences in their message content. In addition, users with different genders often have different patterns in expressing emotions. In this paper, we propose a neural approach for gender prediction in social media based on both content and emotion of messages posted by users. The core of our approach is an emotion-aware hierarchical user representation model. Our model first learns message representations from words using message encoder and then learns user representations from messages using user encoder with hierarchical attention networks selecting important words and messages to learn informative user representations. In addition, we propose two methods to incorporate emotion information in messages into user representation learning. The first one is to incorporate emotion-aware message representations generated by a pre-trained emotion classifier into message representations. The second one is to train emotion-aware message encoders via jointly training our model with an auxiliary emotion classification task. Extensive experiments on two real-world datasets validate the effectiveness of our approach.
Estimating Ocean Subsurface Salinity from Remote Sensing Data by Machine Learning

Accurate estimation of ocean's interior salinity information based on surface remote sensing data is quite significant for understanding complex dynamic processes in the ocean. This study adopts two kinds of ensemble learning algorithm, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) to estimate the subsurface salinity anomaly (SSA) in the upper 2000 m of the global ocean from multisource satellite-based sea surface parameters. The model performance is measured by R-square (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and normalized root-mean-square error (NRMSE). The results indicate the RF and GBDT models are both well suitable for retrieving SSA in the global ocean's interior and RF model outperforms GBDT model; the models accuracy generally decreased with the depth below 500 m. This study is helpful in understanding subsurface and deeper ocean environment response to recent global warming.
Extractive Text Summarization Techniques of News Articles: Issues, Challenges and Approaches

With the burst of information on the Web, there is a need to compress without eliminating facts and index for further reference highlighting the need for automatic text summarization techniques. With the increasing social and occupational requisites, humans need to process large amounts of information to obtain key points in a short period of time. The paper reviews various web extraction technologies available to obtain real-time data necessary for new like articles. The paper goes on to describe various automatic text summarization techniques and their inherent mechanisms in pruning text logically. In further evaluation, summarizers' performances are tested against manually generated summaries using ROGUE parameters. The web extractors are analyzed based on subjective time and space efficiency. The basic understanding of web extraction and summary generation is noted, the requirements are outlined and the proposed parameters are presented as a conclusion of the review presented.
Cryptographic Reverse Firewalls for Identity-Based Encryption

The Snowden revelations show that powerful attackers can compromise user's machines to steal users' private information. At the same time, many of the encryption schemes that are proven to be secure in Random Oracle Model (ROM) may present undetectable vulnerabilities when implemented, and these vulnerabilities may reveal a users' secrets, e.g., the machine hides some backdoors without the user's awareness, and an attacker can steal the user's private information through these backdoors. Recently, Mironov and Stephens-Davidowitz proposed cryptographic reverse firewall (CRF) to solve this problem. However, there is no CRF for identity-based encryption (IBE) has been proposed. In this paper, we propose two CRF protocols for IBE. One is a one-round encryption protocol with CRF used on the receiver, and the other is a two-round encryption protocol with CRFs deployed on both sender and receiver. We prove that these two protocols can resist the exfiltration of secret information and one is only secure against a chosen plaintext attack (CPA), the other is semantically secure against an adaptive chosen ciphertext attack (IND-ID-CCA). Moreover, we use JPBC to implement our protocols. The experimental results indicate that our protocols have some advantages in communication cost. Under certain computation cost conditions, our protocols are efficient and practical.