Reviewerly/exHarmony huggingface dataset:
Context Aggregation Network for Semantic Labeling in Aerial Images

Multi-scale object recognition and accurate object localization are two major problems for semantic segmentation in high resolution aerial images. To handle these problems, we design a Context Fuse Module to aggregate multi-scale features and propose an Attention Mix Module to combine different level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. Experiments demonstrate that our network outperforms other state-of-the-art models on the large-scale ISPRS Vaihingen 2D Semantic Labeling Challenge dataset. The model implementation code is made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Refining the Imprecise Meaning of Non-determinism in the Web by Strategic Games

Nowadays interactions with the World Wide Web are ubiquitous. Users interact through a number of steps consisting of site calls and handling results that can be automatized as orchestrations. Orchestration results have an inherent degree of uncertainty due to incomplete Web knowledge and orchestration semantics are characterized in terms of imprecise probabilistic choices. We consider two aspects in this imprecise semantic characterization. First, when local knowledge (even imprecise) of some part of the Web increases, this knowledge goes smoothly through the whole orchestration. We deal formally with this aspect introducing orchestration refinements. Second, we analyze refinement under uncertainty in the case of parallel composition. Uncertain knowledge is modeled by an uncertainty profile. Such profiles allow us to look at the uncertainty through a zero-sum game, called angel/daemon-game, or $$\mathfrak {a}/\mathfrak {d}$$ -game. We propose to use the structure of the Nash equilibria to refine uncertainty. In this case the information improves not through cooperation but through the $$\mathfrak {a}$$ and $$\mathfrak {d}$$ competition.
Delegated Adversarial Training for Unsupervised Domain Adaptation

In this paper, we tackle unsupervised domain adaptation, where a target domain is unlabeled and lies on a considerably different distribution from a source domain. To alleviate such data discrepancies, we coin a novel deep neural network architecture that consists of a classifier and a domain discriminator on top of a shared feature extractor. Toward efficient regularization, we delegate a generation of the adversarial attacks to the domain discriminator. We then leverage the domain adversarial images to let the classification network learn important semantic features across the domains. Specifically, we employ consistency loss function that enables the joint use of clean and adversarial data. We present extensive experimental results on various domain adaptation benchmarks to show the efficacy of the proposed method.
Designing Security System for IoT

In recent era of information technology, Internet of Things is becoming popular rapidly. Billions of objects will be connected to communicate with each other and each of these objects will have computation power. Because of which an integrated system will work for an individual or for a company where these objects will be communicated without any human intervention. Two most important challenges are involved in the whole system, one is powerful network connection system and the second is maintaining security. This paper is focused to provide some security system for the Internet of Things. Security has been proposed in two levels, in the first level, hybrid encryption is proposed for encrypting data when data will be in communication medium and in the second level, machine learning based real time Intrusion Detection System is proposed which will be installed in the device level and in server level. If security is maintained in these two levels, in medium, in device and in server, Internet of Things can work efficiently and effectively.
Misalignment Detection for Web-Scraped Corpora: A Supervised Regression Approach

To build state-of-the-art Neural Machine Translation (NMT) systems, high-quality parallel sentences are needed. Typically, large amounts of data are scraped from multilingual web sites and aligned into datasets for training. Many tools exist for automatic alignment of such datasets. However, the quality of the resulting aligned corpus can be disappointing. In this paper, we present a tool for automatic misalignment detection (MAD). We treated the task of determining whether a pair of aligned sentences constitutes a genuine translation as a supervised regression problem. We trained our algorithm on a manually labeled dataset in the FR–NL language pair. Our algorithm used shallow features and features obtained after an initial translation step. We showed that both the Levenshtein distance between the target and the translated source, as well as the cosine distance between sentence embeddings of the source and the target were the two most important features for the task of misalignment detection. Using gold standards for alignment, we demonstrated that our model can increase the quality of alignments in a corpus substantially, reaching a precision close to 100%. Finally, we used our tool to investigate the effect of misalignments on NMT performance.
A Data-Driven Service Creation Approach for Effectively Capturing Events from Multiple Sensor Streams

The complex interventions among sensor streams bring new challenges for IoT applications to derive meaningful information from large amounts of sensor streams. This paper aims to provide a data-driven service creation method for effectively capturing events based on our previous service abstraction – proactive data service. For improving the effectiveness of proactive data service, we consider the potential correlations among sensor streams besides user's pre-definitions when creating service. Based on the assumption that events frequently co-occurred in history have high probability to co-occur again, we regard frequent event sets as one kind of correlations among sensor streams, and propose an algorithm called FP-MFIM to efficiently find the maximum frequent event sets co-occurred in multiple sensor streams. For providing more effective information, we create PD-services with frequent co-occurred event types besides user-defined event types. This paper reports the tryout use of the method in China power grid for power quality event detection and location. Through a series of experiments based on real sensor data from power grid, we verified the efficiency of FP-MFIM algorithm and the effectiveness of our PD-services in real-world scenario.
Creating 3D Bounding Box Hypotheses From Deep Network Score-Maps

There are two common paradigms for indoor scene understanding, pixel-level labeling and bounding box generation. The two tasks have a complementary nature but are normally achieved separately with different computational flows. We propose a novel method to bridge the two tasks by creating category-specific 3D bounding box hypotheses from score-maps of any deep networks trained on pixel-level semantic labels along with depth data. Those hypotheses can be further used to locate all objects as different non-overlapping bounding boxes by incorporating high-level knowledge, such as common room settings, co-existence or co-exclusiveness etc. We develop an objective function that involves confidence scores and the depth visibility to initialize and optimize multiple hypotheses for each category-specific score map. Experiment results show that our method significantly outperforms direct bounding box generation using pixel-level labeling.
Testing of Statistical Hypotheses about The Distribution Laws of Deviations Probabilities of The F2 Layer Critical Frequency for The Longitudinal Chain of Observation Stations

A comparative analysis of the distribution histograms of deviations of the ionospheric F2 layer critical frequency from the median value at different solar activity, different seasons, different times of day and different latitudinal locations of the stations is carried out. Assumptions are made about the law of the distribution of the probabilities of deviations of the critical frequency of the F2 layer of the ionosphere. The dependence of the shape of the histogram of distribution on the above factors was determined. Examples of distribution histograms and values of their characteristics are given.
Overview of the CLEF 2019 Personalised Information Retrieval Lab (PIR-CLEF 2019)

The Personalised Information Retrieval Lab (PIR-CLEF 2019) lab is an initiative aimed at both providing and critically analysing the evaluation of Personalization in Information Retrieval (PIR) applications. PIR-CLEF 2019 is the second edition of the Lab after the successful Pilot lab organised at CLEF 2017 and the first edition of the Lab at CLEF 2018. PIR-CLEF 2019 provided registered participants with two tracks: the Web Search Task and the Medical Search Task. The Web Search Task continues the activities introduced in the previous editions of the PIR-CLEF Lab, while the Medical Search Track focuses on personalisation within an ad hoc search task introduced in previous editions of the CLEF eHealth Lab.
An Effective Adversarial Training Based Spatial-Temporal Network for Abnormal Behavior Detection

Unsupervised abnormal behavior detection has attracted much attention in recent years. It is a challenging task due to the undefinition and the sparsity of abnormal behaviors, etc. Existing generative model based methods usually perform poorly due to the unknown types of abnormal behaviors and insufficient exploiting of spatial-temporal information. In this paper, we propose a novel adversarial training based spatial-temporal network to tackle these problems. Firstly, we introduce an adversarial training strategy to deal with unknown types of abnormal behaviors. Secondly, to better explore spatial-temporal information, we design an effective two-stream spatial-temporal network, which is identity mapping free and spatial-temporal complementary. Finally, we combine them together to get the final adversarial spatial-temporal network. Our method is evaluated on various challenging public datasets and achieves the state-of-the-art performance.
Analyzing the Effect of Different Partial Overlap Sizes in Perceiving Visual Variables

Element overlap in visualization techniques is a known problem, and high amounts of data and lack of available visual space potentialize this issue. Many studies have applied techniques to reduce occlusion levels in data visualizations, such as random jitter, element transparency, layout rearrangement, and focus+context techniques. However, few studies focus on the presence of occlusion, which is a relevant topic for visualizations where some degree of overlap is inevitable or purposefully explored. This paper takes a step in this direction, and presents a comparative study of visual variables, measuring their robustness to overlap and number of unique values. The study used a grid layout to display visual variables (hue, saturation, shape, text, orientation, and texture), and varied percentage of occlusion (0%, 50%, 60% and 70%) and number of unique values (3, 4 and 5) to measure the effect they cause on the speed and accuracy to locate the visual variables. Hence, 48 volunteers performed locate tasks on a tool that automatically generate a grid of visual variables and collect their answers. The results revealed that hue and shape were robust to high occlusion levels and a high number of unique values. Text and texture had medium loss of performance, while saturation and orientation were the most negatively affected.
Performance Analysis for Statistical Testing of Random and Pseudorandom Generators by Entropy Statistics

The topical problem of performance analysis for statistical testing of "pure randomness" for random and pseudorandom cryptographic generators is considered. Statistical tests based on statistical estimators of Rényi and Tsallis entropy functionals for the observed output sequences with some fixed asymptotic significance level are used. To evaluate asymptotic values of power for these tests we consider two useful in practice families of "pure randomness" alternatives. The first one describes the mathematical model of a pseudo-random sequence generator that is close in its probabilistic properties to the "pure random" sequence. The second one is the Markovian alternative. For the alternative hypotheses, asymptotic probability distributions of entropy estimators are also found. Estimates of the power of the developed tests are calculated.
How Lexical Gold Standards Have Effects on the Usefulness of Text Analysis Tools for Digital Scholarship

This paper describes how the current lexical similarity and analogy gold standards are built to conform to certain ideas about what the models they are designed to evaluate are used for. Topical relevance has always been the most important target notion for information access tools and related language technology technologies, and while this has proven a useful starting point for much of what information technology is used for, it does not always align well with other uses to which technologies are being put, most notably use cases from digital scholarship in the humanities or social sciences. This paper argues for more systematic formulation of requirements from the digital humanities and social sciences and more explicit description of the assumptions underlying model design.
An Automated Structural Approach to Support Theatrical Performances by Introducing Gesture Recognition to a Cuing System

In this article, we explain how to process and interpret stage directions in order to support actors’ performances. To this end, we specify the function of coverbal gesture and movement as an additional statement by an actor. We introduce a classification of communicative gestures by individuals and groups based on a description with typical body triangles and their sensor-detectable vertices moving in time. We claim that with the help of a speech and gesture recognition system, the occurrence of semantic isotopies to characterize a scene with speech and accompanying metaphoric gestures described in the stage directions can be detected and encoded in a feature vector. As a proof of concept, we highlight the ritual theater play Orphée d’Afrique, which was written as a second part of a homonymous chant novel and presents an implementation of a gesture- and voice-detection cuing system.
Applying Supervised Machine Learning to Predict Virtual Machine Runtime for a Non-hyperscale Cloud Provider

Cloud computing offers an online, on-demand and pay-as-you-go access to computing resources. The cloud enables users to adjust their consumption to their needs. Users deploy their application code, libraries and operating systems on the provider's hardware. The resources can be allocated under the form of virtual machines (VMs). Predicting the runtime of VMs can be useful to optimize the resource allocation. We propose a formulation of this objective as a multi-class classification problem by using as much features as available when launching a VM. Experimentation carried out on real traces from the public cloud provider Outscale show that the inclusion of features extracted from tags, which are freely-typed pieces of text used to describe VMs for human operators, improve the model performance.
Small sample-based disease diagnosis model acquisition in medical human-centered computing

With the development of wireless communications and networks, HCC (human-centred computing) has attracted considerable attention in recent years throughout the medical field. HCC can provide an effective integration of various medical auxiliary diagnosis models using machine learning algorithms. In medical HCC, deep learning has demonstrated its powerful ability in the field of computer vision. However, image processing based on deep learning usually requires a large amount of labeled data, which requires significant resources since it needs to be completed by doctors, and it is difficult to collect a large amount of data for some rare diseases. Therefore, how to use the deep learning method to obtain an effective auxiliary diagnosis model based on a small sample or zero sample data set has become an important issue in the study of medical auxiliary diagnosis. We proposes an auxiliary diagnosis model acquisition method based on a variational auto-encoder and zero sample augmentation technology, and the incremental update training program based on wireless communications and networks is designed to obtain the auxiliary diagnosis model to solve the difficulty of collecting a large amount of valid data. The experimental results show that the model obtained by the above method based on a small sample or zero sample data set can effectively diagnose the types of skin diseases, which helps doctors make better judgments.
IoT Services Orchestration and Choreography in the Healthcare Domain

In the Internet of Things (IoT) ecosystem, multiple smart devices communicate among themselves and with people. Similarly, they are primarily characterized by remarkable detection and processing capabilities. On the other hand, a services composition (SC) task involves performing the orchestration or choreography of services. Services orchestration is a centralized process for organizing interactions among the services of an activity or business process; however, orchestrators involved in a same services orchestration task rarely know each other. The choreography of services is collaborative, allowing each involved party to describe their participation in the interaction. The choreography follows the sequences of the messages between the multiple parties and typically obtains the exchanges in the public messages that occur between the services, instead of a specific business process that a single party executes. SC is frequently studied in the context of Web services (WS), where a series of standards have been developed and used in real-world implementations to support SC. Unfortunately, these standards are inadequate in the IoT paradigm, since IoT devices are based on data/events and the resources are restricted. Healthcare is an important application area of the IoT, since is reducing costs and improves to improve service quality. On the other hand, in the healthcare supply chain, the flow of diverse services that allow the integration and coordination of processes to satisfy the patient’s requirements is required. This book chapter proposes the design of language for IoT services composition, which considers the services orchestration and choreography to define business processes in a supply chain in the healthcare domain, while simultaneously discussing important literature on SC, business process, IoT services orchestration, IoT services choreography and IoT services coordination. Additionally, we propose the design of our language for IoT services composition, presenting the expressivity of language, the services orchestrating process characteristics and the services choreography process characteristics. In addition, as a proof-of-concept, we present a study case of the IoT services orchestration and choreography in the healthcare domain, in a timely procurement process of services for overweight or obese patients. Finally, the modeling of the study case and the mapping of the labels of the services’ orchestration and choreography process for the language for IoT services composition are presented.
Detection of Fuzzy Duplicate Texts in News Feeds

The paper is devoted to the problem of fuzzy duplicate texts detection in news feeds. The signature methods of detecting fuzzy duplicate news are considered. Signatures describe the content of a news as one or a group of numbers. It is proposed to use Description words big signature. This signature consist of set of flags for the presence of description words and vector of names. This vector include names of objects, countries, names of politicians. It allow setting the exact direction of the news on this or that event. In paper the results of testing the signature methods are given. Proposed signature showed good results both in recall and in precision of duplicate news detection.
Automatic Generation of Statutory Warnings Using Deep Learning

The media conveyed may contain questionable substance, for example, a smoking scene, and related substance, for example, a heavy drinker scene, and so on. These days the movie producers furthermore, editors need to physically check the whole video feed to recognize the frightful substance. After scanning all the scenes, they put enough time and exertion into putting statutory alerts and signs in video substance. Statutory admonitions are shown in public interest declaring that the motion picture/TV indicates does not encourage smoking, drinking liquor and so on. In this paper, we propose a programmed method of statutory admonitions utilizing profound learning framework dependent on one of the profound learning strategy, a Darknet-53 based Convolutional Neural Network which is called YOLOv3. Utilizing this, it is possible to computerize this procedure of including statutory alerts in movies or TV arrangement or videos. For example, if there is a smoking scene in a film, at that point the undertaking will identify it and will demonstrate a related statutory cautioning as a subtitle.
Storage Fundamentals and Cloud Storage

Data are one of the major and core assets of every organization. Obviously there is a need for the data of any organization to be stored. There are different types of storage devices catering for different characteristics such as capacity, scalability, reliability, performance, and cost. Navigating and selecting a suitable storage device for a given purpose requires an understanding of the different storage devices available. The objective of this chapter is to describe the various storage devices, how they have progressed from being conventional storage devices to cloud storage devices, and how data and the need for digitization have evolved.