Miracle-dz/UniFAR-FTUs huggingface dataset:
iEnvironment: Perspectives on Metadata-Oriented Testing of Research Software

As a research software platform, iEnvironment has been proposed to support open and big data sharing and reuse for researchers working on surface water issues. Research software refers to software development tools that accelerate discovery and simplify access to digital infrastructures. Although research software platforms are becoming increasingly more innovative and powerful, this increasing complexity hides a greater risk of failure as unplanned and untested program scenarios arise. As systems age and are maintained by different programmers the risk of a change impacting the overall system increases. In contrast, systems that are built with less emphasis on program code and more emphasis on the metadata that describes the application can be more readily changed and maintained by individuals who are less technically skilled but are often more familiar with the application domain. Such systems can also be tested using automatically generated testing regimes.
An Ensemble Approach for Suspicious Traffic Detection from High Recall Network Alerts

Web services from large-scale systems are prevalent all over the world. However, these systems are naturally vulnerable and incline to be intruded by adversaries for illegal benefits. To detect anomalous events, previous works focus on inspecting raw system logs by identifying the outliers in workflows or relying on machine learning methods. Though those works successfully identify the anomalies, their models use large training set and process whole system logs. To reduce the quantity of logs that need to be processed, high recall suspicious network alert systems can be applied to preprocess system logs. Only the logs that trigger alerts are retrieved for further usage. Due to the universally usage of network traffic alerts among Security Operations Center, anomalies detection problems could be transformed to classify truly suspicious network traffic alerts from false alerts.In this work, we propose an ensemble model to distinguish truly suspicious alerts from false alerts. Our model consists of two sub-models with different feature extraction strategies to ensure the diversity and generalization. We use decision tree based boosters and deep neural networks to build ensemble models for classification. Finally, we evaluate our approach on suspicious network alerts dataset provided by 2019 IEEE BigData Cup: Suspicious Network Event Recognition. Under the metric of AUC scores, our model achieves 0.9068 on the whole testing set.
KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
Extracting Grammatical Error Corrections from Wikipedia Revision History

This paper describes the process of extracting and filtering Wikipedia revision history as a resource for grammatical error correction (GEC). Edits in Wikipedia revision history vary widely, including grammatical error corrections, information supplements, format amendments, and even vandalism. To extract only GEC-related revisions, we use an automated error annotation toolkit, ERRANT1, and extend it to process large data in parallel efficiently. With error-type analysis, we can then identify GEC-related edits and omit other unrelated edits (i.e., only the correction parts are reserved). The resulting corpus is — to our knowledge — the largest publicly available corpus of parallel possibly erroneous and correct sentences with error type labels.
ABR-HIC: Attention Based Bidirectional RNN for Hierarchical Industry Classification

Accurate industry classification of national economic activities as an important component in the construction of economic structure and as the basis of the formulation of economic policies and management of national economic activities has been gaining increasing attention. However, owing to the rapid growth in the number of industries, it is become increasingly difficult for tax bureaus to classify the registered taxpayers’ industries. Conventional industrial classification methods only focus on the text features, which can not be analyzed and judged comprehensively according to the registration information, and can only carry on single-label classification since they neglect the primary and secondary relationships between the main and subsidiary industries, which can not meet application requirements. To better address these challenges, this paper proposes a model known as attention based bidirectional RNN for hierarchical industry classification (ABR-HIC), which is the first approach, to the best of our knowledge, to simultaneously address comprehensive registration information utilization and multi-label classification for the main and subsidiary industries. Our architecture establishes a bidirectional RNN using a word-attention mechanism, which is able to capture and fully utilize the text and non-text registration information for feature representation. By separating the taxpayer’s primary and secondary multi-label classification problem corresponding to the main and subsidiary industries, respectively, into two subtasks and through multi-task learning, our model can provide comprehensive primary and secondary multi-industrial labels. Experiments were conducted on real tax data-sets of the Shaanxi Province, China and the results demonstrate the outstanding performance of our architecture in terms of both the classification effect and training time compared with those of state-of-the-art approaches.
A Congestion Diffusion Model with Influence Maximization for Traffic Bottlenecks Identification in Metrocity Scales

Traffic bottlenecks identification plays an important role in traffic planning and provides decision-making for prevention of traffic congestion. Although traffic bottlenecks widely exist, they are difficult to predict because of the changing traffic condition and traffic demand. In this paper, we introduce a traffic congestion diffusion (TCD) model with traffic flow influence (TFI) to capture the traffic dynamics and give a panoramic view for the city by cross domain data fusion. We proposed novel definition of bottleneck from the perspective of influence spread under TCD. The bottlenecks identification problem is modeled as an influence maximization problem, i.e., selecting the top K influential nodes in road networks under certain traffic conditions. We establish the submodularity of influence spread and solve the NP-hard optimal seed selection problem by using an efficient heuristic algorithm (TCD-IM) with provable near-optimal performance guarantees. To the best of our knowledge, this should be the first model for a metro-city scale from the influence perspective. The TCD-IM model is able to identify the dynamic traffic bottlenecks.
The OTree: Multidimensional Indexing with efficient data Sampling for HPC

Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.
DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation

One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations.
Optimal Design and Redundancy Resolution of a Novel Robotic Two-Fingered Exoskeleton

This article deals with the optimal design and redundancy resolution of a seven DOF robotic two-fingered hand exoskeleton intended for rehabilitation purposes. The exoskeleton is designed to track the human digit motion accurately. As the human digit joint cannot be modeled by single revolute joint due to instantaneously varying center of rotation, a 4-bar mechanism is employed to model each phalanx of a finger. Optimal 4-bar linkages are designed by minimizing the error between the Cartesian trajectories of the human phalanx and those of the coupler points in respective 4-bar mechanisms. It is demonstrated that the designed 4-bar based exoskeleton can track the human digit motion accurately. Performance analysis of the developed device has been carried out by resolving its kinematic redundancy during a fine object translation motion by instantaneously optimizing manipulability measure.
Cold High-Frequency Plasma Versus Afterglow Plasma in the Preservation of Mobile Cultural Heritage on Paper Substrate

This article presents a comparison between the results obtained at the application of a conservation treatment in cold high-frequency (HF) and afterglow plasma on historical papers dating in the 19th century. This article is focused on plasma biocide effects and on the impact of active species from the two types of plasma on the surface, highlighted by mechanical and physical–chemical analyses. The working gas was nitrogen and the treatment time was varied from 5 to 10 and 15 min. The result of physical–chemical analyses as SEM, pH determination, color, and tensile strength analyses, as well as microbiological investigations, converges to the conclusion that a treatment of 10 min achieves a complete decontamination, and the impact of active species is less intense in the afterglow plasma as compared to the direct discharge in cold HF plasma.
In Silico Investigation of the CACNA1C N2091S Mutation in Timothy Syndrome

Experimental studies demonstrated that CACNA1C-N2091S led to a gain-of-function in the L-type calcium current (I<inf>CaL</inf>) linked to heritable Timothy Syndrome, but mechanisms by which the N2091S mutation promotes and perpetuates ventricular fibrillation remain unclear. This study sought to investigate the proarrhythmic effects of N2091S-induced I<inf>CaL</inf>. Using a dynamic ventricular myocyte model, we simulated I<inf>CaL</inf>, APs, Ca<sup>2+</sup> transients ([Ca<sup>2+</sup>]<inf>i</inf>) and sarcoplasmic reticulum (SR) calcium profiles ([Ca<sup>2+</sup>]<inf>SR</inf>) in three cell types. Effects of the N2091S mutation on cell electrophysiology were quantified by changes in I<inf>CaL</inf> density, [Ca<sup>2+</sup>]<inf>i</inf> amplitude ([Ca<sup>2+</sup>]i(max)), SR calcium content ([Ca<sup>2+</sup>]SR(max)), action potential duration (APD) and AP shape. It was shown that the N2091S mutation increased ICaL(max), [Ca<sup>2+</sup>]i(max), [Ca<sup>2+</sup>]SR(max) and APD in three cell types. Compared with ENDO and EPI cells, MCELL cells with excessive prolongation of APD due to the N2091S mutation facilitated inducibility of early afterdepolarization (EAD)-mediated triggered activity. And the different EAD inducibility among the three cell types can amplify the electrical difference and thereby dispersion of repolarization, increasing susceptibility to ventricular arrhythmias. Thus, the N2091S mutation confers not only a trigger, but also a substrate for lethal ventricular arrhythmias.
VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have inherent limitations to query video streams due to their unstructured data model and lack of expressive query language. In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns. In this context, we propose- i) VidCEP, an in-memory, on the fly, near real-time complex event matching framework for video streams. The system uses a graph-based event representation for video streams which enables the detection of high-level semantic concepts from video using cascades of Deep Neural Network models, ii) a Video Event Query language (VEQL) to express high-level user queries for video streams in CEP, iii) a complex event matcher to detect spatiotemporal video event patterns by matching expressive user queries over video data. The proposed approach detects spatiotemporal video event patterns with an F-score ranging from 0.66 to 0. S9. VidCEP maintains near real-time performance with an average throughput of 70 frames per second for 5 parallel videos with sub-second matching latency.
Named Entity Extraction for Knowledge Graphs: A Literature Overview

An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other’s context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.
Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices

Today’s low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves – a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine’s improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.
Manipulation of Multiple Fano Resonances Based on a Novel Chip-Scale MDM Structure

A novel chip-scale metal-dielectric-metal (MDM) refractive-index sensor is proposed and investigated in this paper by using finite-difference time-domain (FDTD) method and multimode interference coupled-mode theory (MICMT), respectively. A slot cavity with an embedded tooth-shape cavity and a side-coupled semi-ring cavity are inserted between the input and output MDM waveguides. According to the simulation results, dual ultra-sharp and asymmetrical Fano peaks emerge in transmission spectrum with high performances. Besides, the transmission responses for the primary parameters of this structure are also investigated. To focus on developing integrated photonic devices, the original structure is successfully expanded by two additional semi-ring cavities through an innovative coupling approach, generating up to eight Fano peaks with outstanding characteristics. It is believed that this novel MDM structure will be a guideline for designing the chip-scale plasmonic devices.
Using Margin-based Region of Interest Technique with Multi-Task Convolutional Neural Network and Template Matching for Robust Face Detection and Tracking System

Real-time face detection and tracking systems suffer from low accuracy and slow processing speed that lead to poor robustness. This problem is vital in real-time setups including human robot interactions (HRI) and video analysis systems. This paper presents margin-based region of interest (MROI) approach to speed up the processing time. Further a hybrid approach is also presented that combines Multi-task Convolutional Neural Networks (MTCNN) with template matching to improve face detection accuracy. The MROI approach which is responsible to speed up the processing time is presented in two variants with fixed and dynamic margin concepts. Dataset used in this work comprises of twenty RGB video files. Each video file is fifteen seconds long and been created from real-life videos containing a person in lecture delivering environment. Each video file contains a person in which the person moves, turns head and the camera also moves. The highest face detection and tracking accuracy achieved in this paper is 99.31% with a processing time of 14.93 milliseconds per frame. The proposed hybrid algorithm significantly improves the ability to detect and track faces in sideway orientation apart from frontal face. The proposed algorithm has the ability to process above 65 frames per second (FPS). The system presented has increased FPS processing ability by more than 400% as well as given boost to the accuracy if compared to the conventional MTCNN full frame scanning techniques.
Modeling human attention by learning from large amount of emotional images

Recent resurgence of neural networks in computer vision have resulted in tremendous improvements in saliency prediction, eventually, saturating some saliency metrics. This leads researchers to devise higher-level concepts in images in order to match the key image regions attended to by human observers. In this paper, we propose a saliency model which utilizes the top-down attention mechanism through the involvement of emotion-inducing region information in the predictor’s feature space. The proposed framework is inspired by psychological and neurological studies that emotion attracts attention. Using three publicly available datasets with emotion-rich images, we were able to show that awareness of the emotion-inducing region improves saliency prediction of images. Saliency metrics for probabilistic models, particularly information gain and KL-divergence, have improved with respect to the same architecture without emotion information. Statistical tests show that emotional regions generally have higher improvement than neutral regions corroborating psychological studies that emotion attracts attention.
How Does Fake News Spread: Raising Awareness & Educating the Public with a Simulation Tool

In this study, we analyze the phenomenon of fake news’ spreading in the Internet. In recent years, the number of fake news and misinformation spreading cases increased. We analyze vaccine-related fake news spreading in Twitter and discover techniques used by the participants of fake news spreading. We present a simulation game designed to teach the public about the techniques behind fake news spreading.
Performance of Lacunarity Features for Classifying Thyroid Nodule using Thyroid Ultrasound Images

Diagnosis of thyroid cancer can be analysis using nodule characteristic. The diagnosis analysis of thyroid nodule is depended on how long the radiologist gets their experience. To reduce radiologist dependency, a computerized system is necessary to build. This study focuses on classifying the thyroid nodule by using texture features into three classes. This research uses 97 thyroid ultrasound images. The first step of proposed method is pre-processing used to enhance the detection capability. After that, morphological operation and active contour are applied to find the correct nodules. The segmented area is extracted using histogram, GLCM, GLRLM, and lacunarity. The extracted value is used to classify the data using Multilayer Perceptron (MLP). The result shows the highest performance is felt in MLP method using lacunarty features. It is about 98.97% accuracy, 98.92% sensitivity, 99.47% specificity, 99.05% PPV, and 99.50% NPV. It means that lacunarity feature has good performance to classify three classes.