Reviewerly/exHarmony huggingface dataset:
Learning the representation of raw acoustic emission signals by direct generative modelling and its use in chronology-based clusters identification

Acoustic emission (AE) is a passive monitoring technique used for learning about the behaviour of an engineered system. The streaming obtained by continuously recording AE transient signals is treated by a four steps procedure: 1) The detection of salient AE signals by distinguishing noise against non-noise signals using wavelet denoising, 2) the statistical representation of randomly selected AE signals using Autoregressive Weakly Hidden Markov Models, 3) an inference phase by applying those models to unknown AE signals and generating a set of novelty scores reflecting differences between signals, 4) the clustering of novelty scores using constraint-based consensus clustering. Compared to the standard way relying on the transformation of all AE signals by manual feature engineering (MFE) before clustering, the main breaktrough proposed in this paper holds in the use of the raw AE signals, with different lengths and various scales, to build high level information and organise the low level streaming data. Validated first on simulated data, we show the potential of this methodology for interpreting acoustic emission streaming originating from composite materials.
An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system.In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively.A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes.This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system.ELM performs efficient class prediction of the query image based on the pre-trained data.Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM.The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system.To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated.Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets.The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.
Deep imitator: Handwriting calligraphy imitation via deep attention networks

Calligraphy imitation (CI) from a handful of target handwriting samples is such a challenging task that most of the existing writing style analysis or handwriting generation methods do not exhibit satisfactory performance. In this paper, we propose a novel multi-module framework to address the problem of CI. Firstly, we utilized a deep convolution neural network (CNN) to extract personalized calligraphical features. Then we built a calligraphy-clustering attention module and a mata-style matrix (msM) to compute an embedding of calligraphy. The structure of conditional gated recurrent unit (cGRU) is then improved to predict the probabilistic density of pen tip movement displacement by dual condition inputs. Finally, we generated personalized handwriting stroke sequences through iterative sampling with Gaussian mixture model (GMM). Experiments on public online handwriting databases verify that the proposed method could achieve satisfactory performance; the generated samples achieved high similarities with original handwriting examples.
Mobile Technologies and Knowledge Management in Higher Education Institutions: Students’ and Educators’ Perspectives

Mobile devices are increasingly included for knowledge management (KM) in academic contexts. The purpose of this study was to examine how the integration of mobile technologies affects KM among students and educators in higher education settings in Saudi Arabia. Interviews with educators and students at two universities explored the factors determining the use of mobile technologies for learning. Content analysis of the participants’ responses found that the students and educators perceived four key factors determining mobile technology use: the capacity of mobile technologies to enhance learning processes, teaching practices, and student-student and student-educator communicative interactions, and hardware and infrastructure components. The main conclusion was that Saudi universities must utilise mobile technologies to identify, encapsulate, transform, and disseminate usable knowledge effectively.
The application of Deep Learning in Persian Documents Sentiment Analysis

Nowadays the amount of textual information on the web is grown rapidly. The huge textual data needs more accurate classification algorithms. Sentiment analysis is a branch of text classification that is used to classify user opinions in case of market decisions, product evaluations or measuring consumer confidence. With the rise of the production rate of Persian text data in a commercial area, improvement of the efficiency of algorithms in Persian is a must. The structure of the Persian language such as word and sentence structures poses some challenges in this area. Deep learning algorithms are recently used in NLP and especially sentiment text classification for many dominant languages like Persian. The goal is to improve the performance of classification using deep learning issues. In this work, the authors proposed a hybrid method by a combination of structural correspondence learning (SCL) and convolutional neural network (CNN). The SCL method selects the most effective pivot features so the adaptation from one domain to similar ones cannot drop the efficiency drastically. The results showed that the proposed hybrid method that is learned from one domain can act efficiently in a similar domain. The result showed that applying a combination of SCL+CNN can improve the result of sentiment classification for two domains more than 10 percent.
EQSA: Earthquake Situational Analytics from Social Media

This paper introduces EQSA, an interactive exploratory tool for earthquake situational analytics using social media. EQSA is designed to support users to characterize the condition across the area around the earthquake zone, regarding related events, resources to be allocated, and responses from the community. On the general level, changes in the volume of messages from chosen categories are presented, assisting users in conveying a general idea of the condition. More in-depth analysis is provided with topic evolution, community visualization, and location representation. EQSA is developed with intuitive, interactive features and multiple linked views, visualizing social media data, and supporting users to gain a comprehensive insight into the situation. In this paper, we present the application of EQSA with the VAST Challenge 2019: Mini-Challenge 3 (MC3) dataset.
Seismic Exploration Wireless Sensor System Based on Wi-Fi and LTE

In present seismic exploration wireless sensor systems with large acquisition channels, it is difficult to achieve a high data rate, high reliability and long distance in wireless data transmission simultaneously. In this paper, a wireless seismic exploration system using a dual-layer network is proposed. The dual-layer network is designed based on Wi-Fi and LTE, so that long-distance high-rate seismic data transmission with a high reliability can be achieved. In the proposed system, the sensor array is composed of two kinds of nodes, the gateway node and the collecting node. Based on the proposed nodes, collecting node positioning, seismic data acquisition, seismic local data storage and quasi real-time remote seismic data transmission can be realized. Reliability mechanisms have been put forward to deal with the exceptions. An experiment was carried out to test the data transmission efficiency of the proposed system. The results show that the seismic exploration wireless sensor system with a dual-layer network structure can achieve quasi real-time remote seismic data transmission with no packet loss.
Collecting and provenance: a multidisciplinary approach

In the past three decades, the practice of provenance research has developed into a dedicated field, primarily driven by the renewed public awareness of Nazi art looting. Provenance research is not...
Quantification of Water Sources in a Coastal Gold Mine through an End-Member Mixing Analysis Combining Multivariate Statistical Methods

Mixing calculations have been widely applied to identify sources of groundwater recharge, but these calculations have assumed that the concentrations of end-members are well known. However, the end-members of water remain unclear and are not easily available in practical applications. To better determine end-members and mixing ratios, an end-member mixing analysis combining multivariate statistical methods was used on a large, complex water chemistry dataset collected from the Shashandao gold mine in China. Multivariate statistical methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were applied to determine the specific end-members (these two methods verified each other). On the basis of the identified end-members, a maximum likelihood method was then used to estimate the mixing ratios of the water sources. The combined method proposed in this study can help to identify more accurate end-members and deal with uncertainty in end-member concentrations, and it can also adjust the concentrations until the optimal mixing ratios for the calculation are obtained. This method can be a powerful tool for groundwater management and in predicting water inrush in mining operations.
QoS-Constrained Service Selection for Networked Microservices

In the microservice system, microservices are generally deployed as many microservice instances connected by a network system, and there are several composite services (CSs) in the system, each of which consists of several tasks that need to be executed on instances and has some Quality of Service (QoS) constraints to represent the user requirement for QoS.At runtime, it is necessary to select the microservice instance for each task under constraints and form an execution path of the CS.However, traditional service selection methods focus on the QoS optimization of a single CS, while ignoring sharing and competition of instances.To address this problem, this paper presents a Microservice Service Selection algorithm (MSS) based on list scheduling.Firstly, a workflow model is employed to describe the CS and to analyze the processing speed of instances, the transferring speed of the network and the degree of task concurrency, to calculate the sub-deadline of each task.Then, according to the sub-deadline and other information, the scheduling urgency of each task is calculated and updated in real-time.Finally, two service selection strategies based on the sub-deadline and urgency are proposed to complete the microservice instances selection and constitute the service path.Experiments have been carried out using the standard workflow examples of real-world applications.The results show that the proposed selection strategies can effectively improve the performance of service selection in network systems.
Browser-Based Deep Behavioral Detection of Web Cryptomining with CoinSpy

Although the cryptocurrency hype over the past year may be seen by some as a benign social fad, to the Web community it is the center point for a series of ethically dubious ransomware attacks.Browser based cryptomining, or cryptojacking has gained widespread attention.Cryptojacking consists of Web servers delivering cryptocurrency mining scripts to clients, and using the client resources to play part in a distributed coin mining scheme.Although Web server operators defend the ethics of their involvement by quoting mining as a substitute for advertisement revenue, these scripts can hog massive amounts of client-side resources and can be delivered without client consent, presenting a high potential for abuse.Regardless of how ethical these campaigns are, what remains constant is the need for their detection.While there has been an array of work in defending against such cryptojacking campaigns, these defenses remain quite preliminary.We present CoinSpy, an entirely in-browser tool built using deep learning techniques for the detection of cryptomining activity within Web pages.A key challenge is that there is limited visibility into the client resource usage from within the browser sandbox.CoinSpy extracts several signals from information available from the browser and combines them using deep learning to build a powerful cryptojacking classifier.We argue why CoinSpy is the most robust defense against current and future cryptojacking attacks as compared to recent work, and show that it can detect various cryptojacking campaigns with 97% accuracy.
Design, development and validation of a system for automatic help to medical text understanding

The paper presents a web-based application, SIMPLE, that facilitates medical text comprehension by identifying the health-related terms of a medical text and providing the corresponding consumer terms and explanations.The comprehension of a medical text is often a difficult task for laypeople because it requires semantic abilities that can differ from a person to another, depending on his/her health-literacy level. Some systems have been developed for facilitating the comprehension of medical texts through text simplification, either syntactical or lexical. The ones dealing with lexical simplification usually replace the original text and do not provide additional information. We have developed a system that provides the consumer terms alongside the original medical terms and also adds consumer explanations. Moreover, differently from other solutions, our system works with multiple languages.We have developed the SIMPLE application that is able to automatically: 1) identify medical terms in a medical text by using medical vocabularies; 2) translate the medical terms into consumer terms through medical-consumer thesauri; 3) provide term explanations by using health-consumer dictionaries. SIMPLE can be used as a standalone web application or can it be embedded into common health platforms for real time identification and explanation of medical terms. At present, it works with English and Italian texts but it can be easily extended to other languages. We have run subjective tests with both medical experts and non-experts as well as objective tests to verify the effectiveness of SIMPLE and its simplicity of use.Non-experts found SIMPLE easy to use and responsive. The big majority of respondents confirmed they were helped by SIMPLE in understanding medical texts and declared their willingness to continue using SIMPLE and to recommend it to other people. The subjective tests, conducted with medical experts on a set of Italian radiology reports, showed an agreement between SIMPLE and the experts, on the highlighted medical terms, that ranges between 74.05 % and 81.16 % as well as an agreement of around 60 % on the consumer term translation. The objective tests showed that the consumer terms, provided by SIMPLE, are, on average, eighteen times more familiar than the relative medical terms so proving, once more, the effectiveness of SIMPLE in simplifying the medical terms.The performed tests demonstrate the effectiveness of SIMPLE, its simplicity of use and the willingness of people in continuing with its use. SIMPLE provides, with a good agreement level, the same information that medical experts would provide. Finally, the consumer terms are 'objectively' more familiar than the related technical terms and as a consequence, much easier to understand.
2T3C HCA, a new hollow cylinder device using Digital Image Correlation to measure properties of interfaces between asphalt layers

To improve pavement resistance, asphalt concrete layers are bonded together with a tack coat. A new device has been developed to describe the mechanical behavior of interfaces between asphalt concrete layers. The 2T3C Hollow Cylinder Apparatus (2T3C HCA) can apply torsion and tension-compression to hollow cylinder samples composed of two asphalt concrete layers with a tack coat in between. Normal and shear stresses can be applied independently with any type of loading (monotonic or cyclic). In addition to global measurements, a local optical method, based on Digital image correlation (DIC), is used to observe the deformation of the interface. The first interface characterization results are presented in this paper.
Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Cancer is one of the leading causes of death in the world.It is the main reason why research in this field becomes challenging.Not only for the pathologist but also from the view of a computer scientist.Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection.The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image.The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data.This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch.We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images.The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images.The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking.For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c.According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations.The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.
Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions

This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research. The purpose of the study was to identify the habits and current experiences of Spanish researchers in the health sciences in relation to the management and sharing of raw research data. Method: An electronic questionnaire composed of 40 questions divided into three blocks was designed. The three Section s contained questions on the following aspects: (A) personal information; (B) creation and reuse of data; and (C) preservation of data. The questionnaire was sent by email to a list of universities in Spain to be distributed among their researchers and professors. A total of 1063 researchers completed the questionnaire. More than half of the respondents (54.9%) lacked a data management plan; nearly a quarter had storage systems for the research group; 81.5% used personal computers to store data; “Contact with colleagues” was the most frequent means used to locate and access other researchers’ data; and nearly 60% of researchers stated their data were available to the research group and collaborating colleagues. The main fears about sharing were legal questions (47.9%), misuse or interpretation of data (42.7%), and loss of authorship (28.7%). The results allow us to understand the state of data sharing among Spanish researchers and can serve as a basis to identify the needs of researchers to share data, optimize existing infrastructure, and promote data sharing among those who do not practice it yet.
Meta-Learning for Few-Shot NMT Adaptation

We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adaptable to many target do- mains with the minimal amount of in-domain data. We frame the adaptation of NMT systems as a meta-learning problem, where we learn to adapt to new unseen domains based on simulated offline meta-training domain adaptation tasks. We evaluate the proposed meta-learning strategy on ten domains with general large scale NMT systems. We show that META-MT significantly outperforms classical domain adaptation when very few in- domain examples are available. Our experiments shows that META-MT can outperform classical fine-tuning by up to 2.5 BLEU points after seeing only 4, 000 translated words (300 parallel sentences).
Discriminant and Sparsity Based Least Squares Regression with l<sub>1</sub> Regularization for Feature Representation

Least squares regression (LSR) has two main issues that greatly limits the improvement of performance: 1) The target matrix is too rigid leading to a large regression error; 2) the underlying geometric structure of the training data is often ignored to learn a more discriminative projection matrix. To solve these dilemmas, this paper presents a discriminant and sparsity based least squares regression with l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization (DS_LSR). In DS_LSR, the sparse coefficient matrix of the training data with l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization is jointly learned with the projection matrix to make the projection matrix discriminative. In addition, an orthogonal relaxed term is introduced to hold the structure of regression targets while relaxing the rigid label matrix. Extensive experimental results demonstrate the effectiveness of the proposed method in classification accuracy.
Urdu handwritten text recognition: a survey

Work on the problem of handwritten text recognition in Urdu script has been an active research area. A significant progress is made in this interesting and challenging field in the last few years. In this study, the authors presented a comprehensive survey for a number of offline and online handwritten text recognition systems for Urdu script written in Nastaliq font style from 2004 to 2019. Following features make their contribution worthwhile and unique among the reviews of a similar kind: (i) their review classifies the existing studies based on types of recognition systems used for Urdu handwritten text, (ii) it covers a very different outlook of the recognition process of the Urdu handwritten text at different granularity levels (e.g. character, word, ligature, or sentence level), (iii) this review article also presents each of surveyed articles in following dimensions: the task performed, its granularity level, dataset used, results obtained, and future dimensions, and (iv) lastly it gives the summary of the surveyed articles according to the granularity levels, publishing years, related tasks or subtasks, and types of classifiers used. In the end, major challenges and tasks related to Urdu handwritten text recognition approaches are also discussed in detail.