Reviewerly/exHarmony huggingface dataset:
Semantic Data Management for a Virtual Factory Collaborative Environment

Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.
Sequential Monte Carlo with transformations

Abstract This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives.
Characterization of Illite Clays associated with the Sinongduo low sulfidation epithermal deposit, Central Tibet using field SWIR spectrometry

Field measurements using shortwave infrared (SWIR) reflectance from drill hole samples provided an effective method to map variations in illite crystallinity and the illite assemblage minerals associated with hydrothermal alteration zonation of the Sinongduo low sulfidation epithermal Ag-Pb-Zn deposit, Tibetan Plateau, China. The SWIR mineral spectral information was combined with electron probe micro-analysis (EPMA) measurements, and microscope observations to provide geological insights into: (a) the likely metallogenic fluid nature of the mixture of magmatic fluids and a cooler water source; (b) the illite physico-chemical properties of the associated hydrothermal fluids; (c) the illite, montmorillonite and sericite evolutionary interactions among these fluids, country rocks, and alteration minerals; and (d) the alteration minerals relationships and illite crystallinity to the economic mineralization. The results suggest that illite characterizations can be used as an indicator for epithermal deposit prospecting.
Speech to text conversion and summarization for effective understanding and documentation

&lt;p class="western" style="margin-top: 0.21cm; margin-bottom: 0cm;" align="justify"&gt;&lt;span&gt;Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on. Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed method&lt;/span&gt;&lt;/p&gt;
An Editorial Network for Enhanced Document Summarization

We suggest a new idea of Editorial Networka mixed extractive-abstractive summarization approach, which is applied as a postprocessing step over a given sequence of extracted sentences.We further suggest an effective way for training the "editor" based on a novel soft-labeling approach.Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractiveonly baselines.
The Ability to Think Creatively on SSCS Using Schoology Apps, How is the Student’s Language Metacognitive Awareness?

This paper aims to evaluate the students' ability to solve syntactic problems through creative thinking, when they are engaged in Search, Solve, Create and Share (SSCS) learning, using schoology apps.For this purpose, several fourth-semester students were selected as subjects of an essay test, a structured interview and an observation.The test reveals their metacognitive awareness in solving syntax problems, the interview reflects their errors and causes of such errors, and the observation shows their conditions in SSCS learning using schoology apps.The data validity was checked through triangulation of data sources.The results show that the students, who are good at solving syntax problems in schoology apps through creative thinking, have high level of metacognitive awareness, and are able to make reflective use and strategic use of metacognitive awareness.
Open Practices and Resources for Collaborative Digital Pathology

In this paper, we describe open practices and open resources in the field of digital pathology with a specific focus on approaches that ease collaboration in research and education settings. Our Mini-Review includes open access journals and open peer review, open-source software (libraries, desktop tools, and web applications), and open access collections. We illustrate applications and discuss current limitations and perspectives.
Embedding Projection for Targeted Cross-lingual Sentiment: Model Comparisons and a Real-World Study

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.
A Novel Color-Texture Descriptor Based on Local Histograms for Image Segmentation

In this paper, we propose a novel color-texture image segmentation method based on local histograms.Starting with clustering-based color quantization, we extract a sufficient number of representative colors.For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained.After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation.Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors.Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries.Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.
Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation

In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from the projected intensity by using a fully convolutional network (FCN). The color image should be generated from a very sparse projected intensity image. For this reason, the FCN is designed to have an asymmetric network structure, i.e., the layer depth of the decoder in the FCN is deeper than that of the encoder. The well-known KITTI dataset for various scenarios is used for the proposed FCN training and performance evaluation. Performance of the asymmetric network structures are empirically analyzed for various depth combinations for the encoder and decoder. Through simulations, it is shown that the proposed method generates fairly good visual quality of images while maintaining almost the same color as the ground truth image. Moreover, the proposed FCN has much higher performance than conventional interpolation methods and generative adversarial network based Pix2Pix. One interesting result is that the proposed FCN produces shadow-free and daylight color images. This result is caused by the fact that the LiDAR sensor data is produced by the light reflection and is, therefore, not affected by sunlight and shadow.
Cloud-based people counter

Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.
Detecting Toxicity in News Articles: Application to Bulgarian

Online media aim for reaching ever bigger audience and for attracting ever longer attention span.This competition creates an environment that rewards sensational, fake, and toxic news.To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content.While previous research primarily focused on English, here we target Bulgarian.We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups.Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic.We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features.Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier.The evaluation results show an accuracy of 59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over the majority-class baseline (Acc=30.3%,macro-F1=5.2%).While most previous research has focused almost exclusively on English, here we target Bulgarian.In particular, we built a dataset for our experiments based on the knowledge base of Media Scan, which has catalogued and characterized many of the Bulgarian online media in the past five years.If a medium published a toxic news, this was recorded and the article, as well as the medium, got labelled accordingly.The analyzed media vary from digital newspapers, to media groups and blogs.For some articles there is detailed explanation with examples about why they were labelled like that.In some cases, the Media Scan website describes attempts to contact the authors of an article asking for clarification about some questionable facts that are being reported.Here we use this information by performing multi-class classification over the toxicity labels: fake news, sensations, hate speech, conspiracies, anti-democratic, pro-authoritarian, defamation, delusion.Note that we allow multiple of these labels simultaneously.We further add a non-toxic label for articles that represent good news.
A Robust Active Safety Enhancement Strategy With Learning Mechanism in Vehicular Networks

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety.
Ship Instance Segmentation from Remote Sensing Images Using Sequence Local Context Module

The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between objects of the same class. The SLC module applies a sequence of dilation convolution blocks to progressively learn multi-scale context information in the mask branch. Besides, we try to add SLC module to different locations in our framework and experiment with the effect of different parameter settings. Comparative experiments are conducted on remote sensing images acquired by QuickBird with a resolution of 0.5m - 1m and the results show that the proposed method achieves state-of-the-art performance.
After you, please: browser extensions order attacks and countermeasures

Abstract Browser extensions are small applications executed in the browser context that provide additional capabilities and enrich the user experience while surfing the web. The acceptance of extensions in current browsers is unquestionable. For instance, Chrome’s official extension repository has more than 63,000 extensions, with some of them having more than 10M users. When installed, extensions are pushed into an internal queue within the browser. The order in which each extension executes depends on a number of factors, including their relative installation times. In this paper, we demonstrate how this order can be exploited by an unprivileged malicious extension (i.e., one with no more permissions than those already assigned when accessing web content) to get access to any private information that other extensions have previously introduced. We propose a solution that does not require modifying the core browser engine, since it is implemented as another browser extension. We prove that our approach effectively protects the user against usual attackers (i.e., any other installed extension) as well as against strong attackers having access to the effects of all installed extensions (i.e., knowing who did what). We also prove soundness and robustness of our approach under reasonable assumptions.
Natural Human–Robot Interface Using Adaptive Tracking System with the Unscented Kalman Filter

Traditional human-robot interfaces usually have limitations in accuracy and/or operational space. This article proposes a natural human-robot interface using an adaptive tracking method, which can effectively expand the operational space while ensuring high accuracy. The natural interface allows the robot to directly reproduce the user's hand movement, making the interaction more intuitive and natural. The leap motion is fixed on the Cartesian platform to capture the movement of the user's hand. Because the Cartesian platform follows the hand and keeps the hand in the center of the detection area, the measurement accuracy is improved and the measurement space can be extended. During the process of acquiring gesture data, the measurement errors were found to increase over time because of the inherent noise of the sensor. To deal with this problem, the unscented Kalman filter is applied to estimate the position of the hand. Moreover, an adaptive velocity control method is proposed to improve the operation accuracy and reduce the task execution time with the consideration of users' habits and easiness of usage. The effectiveness of this interface is verified by a series of experiments, and the results show that the proposed interface can be used by nonprofessional users for object operation tasks and can provide users with superior interactive experiences.
TemporalNode2vec: Temporal Node Embedding in Temporal Networks

The goal of graph embedding is to learn a representation of graphs vertices in a latent low-dimensional space in order to encode the structural information that lies in graphs. While real-world networks evolve over time, the majority of research focuses on static networks, ignoring local and global evolution patterns. A simplistic approach consists of learning nodes embeddings independently for each time step. This can cause unstable and inefficient representations over time. We present a novel dynamic graph embedding approach that learns continuous time-aware node representations. Overall, we demonstrate that our method improves node classification tasks comparing to previous static and dynamic approaches as it achieves up to 14% gain regarding to the F1 score metric. We also prove that our model is more data-efficient than several baseline methods, as it affords to achieve good performances with a limited number of vertex representation features.
Combining SMT and NMT Back-Translated Data for Efficient NMT

Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.Consequently, techniques for augmenting the training set have become popular recently.One of these methods is back-translation (Sennrich et al., 2016a), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model.Generally, NMT models are used for backtranslation.In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches.In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both.The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches.
Optimizing Generalized Rate Metrics with Three Players

We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize a non-decomposable performance metric such as the F-measure or G-mean, and constrained training problems where the classifier needs to satisfy non-linear rate constraints such as predictive parity fairness, distribution divergences or churn ratios. We extend previous two-player game approaches for constrained optimization to an approach with three players to decouple the classifier rates from the non-linear objective, and seek to find an equilibrium of the game. Our approach generalizes many existing algorithms, and makes possible new algorithms with more flexibility and tighter handling of non-linear rate constraints. We provide convergence guarantees for convex functions of rates, and show how our methodology can be extended to handle sums-of-ratios of rates. Experiments on different fairness tasks confirm the efficacy of our approach.
Turkish Tweet Classification with Transformer Encoder

Short-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space.In this study, we aim to analyze and classify Turkish tweets based on their topics.Social media jargon and the agglutinative structure of the Turkish language makes this classification task even harder.As far as we know, this is the first study that uses a Transformer Encoder for short text classification in Turkish.The model is trained in a weakly supervised manner, where the training data set has been labeled automatically.Our results on the test set, which has been manually labeled, show that performing morphological analysis improves the classification performance of the traditional machine learning algorithms Random Forest, Naive Bayes, and Support Vector Machines.Still, the proposed approach achieves an F-score of 89.3% outperforming those algorithms by at least 5 points.