Reviewerly/exHarmony huggingface dataset:
Visualizing Access Logs of a Scientific Digital Library Effectively as Multiple Time Series Using Modified Horizon Graphs

Libraries usually subscribe to digital data sources. Their members (students, teachers, researchers, ...) gain access to resources stored in these external services via proxy servers managed by the local libraries. Library managers have the need to know the usage of subscribed services by members so that they can make decisions, for example, whether to renew a subscription or not. Proxy servers access logs store valuable information for them, however, it's difficult to get the answers directly from log files because the raw data is unstructured and massive. In this paper, we describe a two-step technique to support library managers in viewing and understanding usage information more effectively using the data provided by proxy servers' log files. The first step consists of preprocessing access logs from proxy servers and constructing multiple time series from the data. A modified version of horizon graphs was developed in the second step to visualize many time series on one screen. The screen space is divided unequally to different time series based on their relative importance with each other. In addition, an interaction method and a dynamic legend are also introduced to give a better understanding of the graphic representation. The proposed method is implemented as an application running in a web browser. Experiment results with real world data gathered in one year show that the proposed technique can improve users' performance on data exploration and analysis tasks notably.
Detection of Harassment on Twitter with Deep Learning Techniques

Online harassment is a common issue since the beginning of social networks and it’s still present nowadays, causing serious consequences to victims because their gender, race, sexuality, among others. We have seen efforts to fight these behaviors creating automated systems to detect and report this kind of bad conduct. However, these solutions tend to perform well only on a specific type of data without generalizing well. In this paper, we present a new dataset of harassment detection on Twitter with four classes, presented for the SIMAH competition. Then we apply three different deep learning architectures (CNN, LSTM, and BiGRU) to classify these tweets showing that it is a hard problem to solve especially because of the lack of annotated data within some classes. The results only on the test set reach 46% in f1-score and using all data to train gives 55% using the same metric.
Security strategy for vulnerabilities prevention in the development of web applications

Abstract In recent years, Higher Education Institutions through their Systems departments have strengthened security for the development of applications on web environment, because of their vulnerability to possible computer attacks. This research proposes a security strategy to reduce the risk presented by the web applications developed in the systems department of the Simón Bolívar University, in San José de Cúcuta, Colombia, based on a diagnosis of the current state of its security policy compared to other institutions of the department of Norte de Santander, the analysis of current regulations and the state of the art of security in web applications, as an object of study. This strategy of safe web software development arises in order to establish the security parameters that should be applied by the web software developers of the Institution, shielding the developed applications and thus guaranteeing the integrity of the information that is manipulated through them. The strategy was validated through expert judgment in the field of web application development, emphasizing the importance of applying it to prevent vulnerabilities in institutional web software and thus provide greater reliability in the management of information.
Geological and geochemical constraints on the genesis of the Bengge gold deposit in the Yidun Terrane, SE Tibet

The Bengge gold deposit is one of a small number of deposits in the Zhongdian-Muli region, which is part of the southern segment of the Yidun Terrane in the eastern Tibetan Plateau. The deposit is hosted in a Late Triassic alkaline igneous complex comprising mainly biotite–pyroxene syenite and quartz syenite, emplaced into Triassic slate. These syenites show phyllic alteration with mineral association of quartz–sericite–pyrite–carbonate. The main gold mineralization occurs in fault zones as gold–sulfide–quartz veins, in altered syenite as telluride in veinlets, and as fissure-filling gold in disseminated pyrite. Four stages of veins are recognized: sheeted K-feldspar–quartz–magnetite veins (dikes) (stage I), quartz–carbonate–K-feldspar veins with sericite–quartz–pyrite alteration haloes (stage II), polymetallic sulfide–telluride–sericite–quartz veins with pervasive phyllic alteration (stage III), and quartz–carbonate–stibnite veins (stage Ⅳ). The stage II and III veins are closely related to gold mineralization. 40Ar/39Ar dating of hydrothermal sericites from the phyllic alteration zone, which are intergrown with gold-bearing pyrite and telluride, yielded an age of ~126 Ma, indicating the mineralization event occurred in the Cretaceous extensional setting. Raman spectroscopy and microthermometry study for fluid inclusions in quartz from stage II revealed heterogeneous trapping of low-salinity (0.14–6.63 wt% NaCl equivalent) and mesothermal (270–300 °C) CO2–H2O–NaCl ore-forming hydrothermal fluids. The calculated fluid-trapping pressures range mainly from 750 to 850 bar. δ18O values of quartz from the gold mineralization stage (stage II) vary between 15.21‰ and 19.35‰. The δ18O values for waters in equilibrium with quartz vary from 6.96‰ to 10.73‰, which are consistent with values for magmatic waters. We propose that the auriferous fluids were derived from an underlying intrusion during the Cretaceous. The geological characteristics, low salinity, and mesothermal CO2–H2O–NaCl fluids, and the magmatic source of the ore-forming fluids, all support the hypothesis that the Bengge deposit is an intrusion-related gold deposit.
FQL: An Extensible Feature Query Language and Toolkit on Searching Software Characteristics for HPC Applications

The amount of large-scale scientific computing software is dramatically increasing. In this work, we designed a new query language, named Feature Query Language (FQL), to collect and extract HPC-related software features or metadata from a quick static code analysis. We also designed and implemented an FQL-based toolkit to automatically detect and present software features using an extensible query repository. A number of large-scale, high performance computing (HPC) scientific applications have been studied in the paper with the FQL toolkit to demonstrate the HPC-related feature extraction and information/metadata collection. Different from the existing static software analysis and refactoring tools which focus on software debug, development and code transformation, the FQL toolkit is simpler, significantly lightweight and strives to collect various and diverse software metadata with ease and rapidly.
Exploring Work Environment Usage Behaviour through Context-Aware Mobile Methods

This paper presents our findings on knowledge work environment usage behaviour through a combined automated mobile indoor positioning system and self-reports collected from the environment's inhabitants.Contemporary work environments are increasingly flexible multi-occupant environments as opposed to cellular offices.Understanding persons' task-related and situation-related environmental needs is critical to improve the design of future knowledge work environments.This study is conducted in a team office environment prior to and following an intervention in which the office layout was re-organized.The combined methodological approach described in this paper provides a new tool for architecture researchers aiming to understand the use of workspaces.Importantly, combining self-reports with context-aware location data collection provides researchers an efficient in situ tool to access participants experiences and decision-making process in choosing their workstation or workspace.
Formal Modeling and Verification of Scalable Process-Aware Distributed IoT Applications

IoT applications are distributed over multiple IoT computing and communicating resources across the network including but not limited to small devices, machines, and public clouds. These IoT applications are composed of clearly separated and communicating fragments that can be modeled as large scale business processes. Process fragments can be unique, deployed in one location such as the cloud. They can also be replicated over multiple locations, such as different rooms in a smart building. Formal modeling and verification of such business processes composed of replicated fragments is a challenging task. In this paper we present a formal approach of modeling and verification of IoT applications that takes into account the scalability and repeatability properties of processaware IoT applications. We propose a concise way of modeling and a general framework for the verification that simplifies the burden of IoT application modeling and deployment.
Anti-forensics of Image Sharpening Using Generative Adversarial Network

Image sharpening is an image enhancement method which has been widely used to improve the quality of images. Therefore, in image forensics, it is required to be identified as all possible manipulations applied in images need to be detected. In recent years, sharpening detection get evolved with new detectors proposed every year to gradually boost the detection performance. This situation continues for several years till the introduction of convolutional neural networks (CNNs). With the assistance of CNNs, the detection of sharpening seems to be completely solved that the detection performance for sharpening achieves perfect, even when the images are weakly sharpened. Is it true that we should no longer pay attention to sharpening forensics any more? To answer this question, in this paper, an anti-forensics method based on generative adversarial network (GAN) is proposed to investigate the philosophy. The images generated via our method possess the feature of sharpening, however, they cannot be simply considered as sharpened images because no traditional sharpening manipulation is applied during the procedure. Observed from the experimental results, even the state-of-art sharpening detector based on CNN can be deceived with the GAN generated images.
A classification method based on encoder‐decoder structure with paper content

Abstract The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder‐decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder‐decoder structure is formed with labels and words. This improves the original paper classification method based on encoder‐decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder‐decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results.
Predicting System for the Behavior of Consumer Buying Personal Car Decision by Using SMO

Current consumer behavior traders are used to create decision-making tools for entrepreneurs to produce products or services that meet consumer needs. For this reason, therefore focusing on data analysis using SMO techniques for predict system for the behavior of consumer buying personal car decision based on a total of 1,110 data obtained, with a total of 6 relevant car trading information features, consisting of income customers, type of car, down payment/ cash booking, decision results requires that the answer in the forecast is divided into two classes, namely the class of buying cars and not buying cars When dividing the data into 50% training set and using 50% test set, 555 training set will be used and 555 test sets can be used to enter the learning and testing process. By random 50/50 in the selection of algorithms to be tested and the accuracy rate is 95.13%.
Novel Application of an RGB-D Camera for Face-Direction Measurements and Object Detection: Towards Understanding Museum Visitors' Experiences

Gaze measurement techniques can be used to understand the experiences of visitors at museums. In previous research, researchers used a wearable sensor such as an eye tracker to estimate what a visitor was looking at. Although it is possible to measure this experimentally by using a wearable sensor, it is difficult to measure the experience of all museum visitors with such an approach. Therefore, in this study, we propose a method for measuring visitors' experiences with non-wearable type sensors. In this method, we measure the face direction, which is said to be related to the gaze direction of the visitors, by using non-wearable Kinect sensors. We also have to detect objects because we need information about what people are looking at to measure the visitors' experiences. In this paper, we describe the method to estimate the gaze target of the visitor, which is referred to as the visitor's experience, based on the face-direction measurements and object detection data collected simultaneously, and an experiment is conducted to evaluate its effectiveness.
Smartness and Autonomy for Shipboard Power Systems Reconfiguration

Smart Ships represent the next generation of ships that use ICT to connect all the devices on board for integrated monitoring and safety management. In such a context, the Shipboard Power System (SPS) is critical to the survival and safety of the ship because many accidents are due to electrical failures. The SPS reconfiguration consists of a variation of the electrical topology to supply energy to critical services successfully. The proposed reconfiguration procedure uses an autonomous and mission-oriented approach, and it employs a generic-purpose self-adaptive Fault Management System. It delivers a set of possible runtime solutions that properly consider the current ship mission and operating scenario while dealing with multiple failures. Solutions, achieving a partial reconfiguration of the system, are considered when a full recovery strategy is not available according to the current ship conditions.
Research on Multi-Mode Interactive Virtual Reality Scene Enhancement Based on Artificial Intelligence

Aiming at the problem of high distortion of traditional virtual reality scene enhancement methods, a multi-mode interactive vr scene enhancement method based on artificial intelligence is studied. The convolution neural network in artificial intelligence technology is used to extract the cloud of virtual field points and reconstruct the cloud of virtual field points. The coordinate matching relationship between the virtual scene built by computer and the real world was determined, and the virtual scene was enhanced in detail by Gouraud shading algorithm, texture mapping and other processing methods. Combined with multi-mode interactive equipment, the processed virtual scene is projected into the real space to complete the design of multi-mode interactive virtual reality scene enhancement method based on artificial intelligence. Through the comparison experiment with the traditional scene enhancement method, it is verified that the designed virtual reality scene enhancement method based on artificial intelligence can reduce the distortion degree, improve the user satisfaction and have a broader application prospect.
Graphic Representation of Component Algorithms in Multimedia Devices

On the basis of theoretical generalization and work in the field of algorithmization of typical design problems in automated systems, the algorithms for the layout of elements in multimedia designs are analyzed. One of the first problems solved at the design stage of multimedia structures is the task of converting functional circuits into electrical ones. Due to the large variety of elements in multimedia devices, it is necessary to determine the optimal set of these elements for each particular class of schemes, namely minimizing the number of types of elements of the set in the designed device. The layout task was analyzed taking into account two types of algorithms-sequential and iterative.
Application Scheme of Artificial Neural Network in Archives Intelligent Management

For the problems of the current digital archives massive data extraction, such as slow extraction speed and poor quality of data extraction, this paper constructs the archives management model based on BP neural network. Using the value domain characteristics of range type massive data attributes, the scheme divides the distribution samples of digital book and archive data into several sub regions to realize data classification. By constructing neuron model, the error term of output is determined, the weight of each layer of BP neural network is adjusted, and the digital book and archives based on BP neural network is established according to the data output of hidden layer and output layer. The rapid extraction model of quantitative data can realize the rapid extraction of massive data of digital books and archives. The experimental results show that the data extraction speed of the proposed method is faster and the efficiency of data extraction is higher.
The use of Ontologies for Enterprise Modeling and Evaluation

The enterprise is a complex entity that demonstrates the need for a model that is descriptive, clear, coherent and consistent to manage this complexity. The use of ontologies for enterprise modeling enables the company's organization and operation to be analyzed, understood and mastered. Hence, it becomes commonly used in this field.In the present work, we are interested in the application of ontologies in enterprise modeling in general, and their use for enterprise evaluation, especially, in performance measurement systems. This paper presents a theoretical bases in the field of Enterprise Ontology, as well as the contribution of their use for performance measurement.
Small Deep Learning Models for Hand Gesture Recognition

Robust hand gesture recognition is a critical ingredient of many human-computer interaction applications. Recently, Deep Neural Networks (DNNs) models have been widely applied to solve different computer vision problems. However, few researchers have applied DNNs to recognize static hand gestures. In this paper, an approach is proposed that leverages the recent progress on Convolutional Neural Networks (CNNs) and uses deep-learning-based strategy to recognize 24 hand gestures from the American Sign Language (ASL). This approach is built on recently released small CNNs architectures to leverage their high accuracy rate, maintaining small model size and the feasibility to deploy on resource constrained-devices. Here, the architectures of SqueezeNet and MobileNets for both RGB and depth images are utilized, and a late-fusion strategy to concatenate features from both RGB and depth modalities is performed. Also, an effective encoding of depth images as rendered RGB images is proposed to handle depth data with CNNs, and thus improve recognition performance. Experiments are executed on a challenging publicly available American Sign Language - Finger Spelling (ASL-FS) dataset acquired by an RGB-D camera to evaluate the proposed architecture. Results show that the proposed approach achieves higher average accuracy, outperforming all previous methods, and has great feasibility to apply the models on resource-constrained devices and embedded visual applications.
Fourier Based Pre-Processing For Seeing Through Water

Consider a scene submerged underneath a fluctuating water surface. Images of such a scene, when acquired from a camera in the air, exhibit significant spatial distortions. In this paper, we present a novel, computationally efficient pre-processing algorithm to correct a significant amount (≈ 50%) of apparent distortion present in video sequences of such a scene. We demonstrate that when the partially restored video output from this stage is given as input to other methods, it significantly improves their performance. This algorithm involves (i) tracking a small number N of salient feature points across the T frames to yield point-trajectories {q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">=Δ</sup> {(x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it</sub> , y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it</sub> )} <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> } <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> , and (ii) using the point-trajectories to infer the deformations at other non-tracked points in every frame. A Fourier decomposition of the N trajectories, followed by a novel Fourier phase-interpolation step, is used to infer deformations at all other points. Our method exploits the inherent spatio-temporal characteristics of the fluctuating water surface to correct non-rigid deformations to a very large extent. The source code, datasets and supplemental material can be accessed at [1], [2].
Context-Aware Trust Estimation for Realtime Crowdsensing Services in Vehicular Edge Networks

This work proposes a context-aware trust estimation scheme that can allow roadside units in a vehicular edge network to provide real-time crowdsensing services in a reliable manner by selectively using information from trustworthy sources. Our proposed scheme is novel in that its trust estimation does not require any prior knowledge towards vehicles on roads but quickly obtains an accurate trust value of each vehicle. To that end, we particularly leverage the concept of I-sharing which removes a cold-start problem during the system bootstrapping period. Based on an extensive simulation study, we prove that the proposed scheme outperforms its competitive counterpart and baseline models in terms of trust bias and malicious vehicle detection accuracy.