Reviewerly/exHarmony huggingface dataset:
Generalised fuzzy cognitive maps: Considering the time dynamics between a cause and an effect

Fuzzy Cognitive Maps (FCMs) have been used to quantitatively model the dynamics of complex systems and predict their behaviours. However, they are usually unable to address the issues arising from time lags between causes and effects. Accordingly, Generalised Fuzzy Cognitive Maps (GFCMs) have been introduced to overcome this problem. This article deals with a breed of GFCMs that addresses time lags between cause(s) and effect(s), demonstrated by a case-study that deals with the social, economic and technological consequences of heavy rainfall in Kampala, Uganda. The results show that the inclusion of time lags alters both, the final steady-state values of the social, economic and technological consequences of heavy rainfall and the time taken to stabilise. Thus, the inclusion of time lags increases the reliability of GFCMs as a means to quantitatively model the dynamics of complex systems.
Learning residual refinement network with semantic context representation for real-time saliency object detection

Salient object detection (SOD) aims to precisely segment out the most attractive areas in a single image. With the rapid development of deep learning, much effort has been paid to learn an effective representation for SOD from bottom-up or top-down pathways. However, they fail to precisely separate out the whole salient object with fine boundaries due to the repeated subsampling operations such as pooling and striding leading to the loss of fine structures and spatial details. To address these issues, in this paper, we propose a residual refinement network with semantic context features for SOD. First, we design an encoder-decoder structure with side-connections to capture the sharper object boundaries, which can not only gradually recover the spatial details in each feature map from top to down, but also enhance the features at all scales with high-level semantic context information. The semantic context enhanced features are further strengthen by using a set of atrous convolutional filters with multiple atrous rates to encode multi-scale context information. Finally, using the side-output features as input, we develop a recurrent residual module to gradually learn to recover the missing boundary details in the previous coarsely predicted saliency map in a coarse-to-fine manner. Extensive evaluations on six popular SOD benchmark datasets demonstrate leading performance of the proposed approach compared with state-of-the-art methods. Especially, our approach runs in real-time at a speed of 29 fps.
Sentiment Enhanced Multi-Modal Hashtag Recommendation for Micro-Videos

Recommending hashtags for micro-videos is a challenging task due to the following two reasons: 1) micro-video is a unity of multi-modalities, including the visual, acoustic, and textual modalities. Therefore, how to effectively extract features from multi-modalities and utilize them to express the micro-video is of great significance; 2) micro-videos usually include moods and feelings, which may provide crucial cues for recommending proper hashtags. However, most of the existing works have not considered the sentiment of media data for hashtag recommendation. In this paper, the senTiment enhanced multi-mOdal Attentive haShtag recommendaTion (TOAST) model is proposed for micro-video hashtag recommendation. Different from previous hashtag recommendation models, which merely consider content features, sentiment features of modalities are further incorporated in TOAST to improve the recommendation performance of the sentiment hashtags (e.g., #funny, #sad). Specifically, the multi-modal content features and the multi-modal sentiment features are modeled by a content common space learning branch based on self-attention and a sentiment common space learning branch, respectively. Furthermore, the varying importance of the multi-modal sentiment and content features are dynamically captured via an attention neural network according to their consistency with the hashtag semantic embedding by an attention neural network. Extensive experiments on a real-world dataset have demonstrated the effectiveness of the proposed method compared with the baseline methods. Meanwhile, the findings from the experiments may provide new insight for future developments of micro-video hashtag recommendation.
Website Fingerprinting Through the Cache Occupancy Channel and its Real World Practicality

Website fingerprinting attacks use statistical analysis on network traffic to compromise user privacy. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who observes traffic traveling between the user's computer and the network. In this article we investigate a different attack model, in which the adversary sends JavaScript code to the target user's computer. This code mounts a cache side-channel attack to identify other websites being browsed. Using machine learning techniques to classify traces of cache activity, we achieve high classification accuracy in both the open-world and the closed-world models. Our attack is more resistant than network-based fingerprinting to the effects of response caching, and resilient both to network-based defenses and to side-channel countermeasures. We carry out a real-world evaluation of several aspects of our attack, exploring the impact of the changes in websites and browsers over time, as well as of the attacker's ability to guess the software and hardware configuration of the target user's computer. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that it reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser.
Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

While collaborative filtering (CF) is the most popular approach for recommendation systems, it only makes use of the ratings given to items by users and neglects side information about user attributes or item features.In this work, a natural language processing (NLP) technique is applied to generate a more consistent version of Tag Genome, a side information which is associated with each movie in the MovieLens 20M dataset.Subsequently, we propose a 3-layer autoencoder to create a more compact representation of these tags which improves the performance of the system both in accuracy and in computational complexity.Finally, the proposed representation and the well-known matrix factorization techniques are combined into a unified framework that outperforms the state-of-the-art models by at least 2.87% and 3.36% in terms of RMSE and MAE, respectively.
Information flow between bitcoin and other financial assets

Since bitcoin launched in 2009, its popularity has soared, with global trading volume reaching 203.1 billion dollars as of August 2019. Given the trend that bitcoin is increasingly recognized as an investment asset, we analyzed its relationship with other financial assets. We found significant flows of information between bitcoin and exchange rates in most countries. Further, we observed a directional pattern in information flow according to the economic conditions in each country (i.e., developed and emerging markets). The results showed that bitcoin responded more strongly to the exchange rates and stocks in developed markets than in emerging markets.
Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution

The large spread of Distributed Energy Resources (DERs) and the related cyber-security issues introduce the need for monitoring. The proposed work focuses on an anomaly detection strategy based on the physical behavior of the industrial process. The algorithm extracts some measures of the physical parameters of the system and processes them with a neural network architecture called autoencoder in order to build a classifier making decisions about the behavior of the system and detecting possible cyber-attacks or faults. The results are quite promising for a practical application in real systems.
Human-to-AI Coach: Improving Human Inputs to AI Systems

Humans increasingly interact with Artificial intelligence (AI) systems. AI systems are optimized for objectives such as minimum computation or minimum error rate in recognizing and interpreting inputs from humans. In contrast, inputs created by humans are often treated as a given. We investigate how inputs of humans can be altered to reduce misinterpretation by the AI system and to improve efficiency of input generation for the human while altered inputs should remain as similar as possible to the original inputs. These objectives result in trade-offs that are analyzed for a deep learning system classifying handwritten digits. To create examples that serve as demonstrations for humans to improve, we develop a model based on a conditional convolutional autoencoder (CCAE). Our quantitative and qualitative evaluation shows that in many occasions the generated proposals lead to lower error rates, require less effort to create and differ only modestly from the original samples.
Optimizing Mass-Scale Multi-Screen Video Delivery

We discuss the design of a mass-scale streaming video delivery system, targeting devices with different decoding, playback, and network capabilities. This system incorporates several advanced tools: dynamic packaging, dynamic devices detection, dynamic manifest generation, rules engine, analytics engine, and context-aware encoding (CAE). We explain how this system is implemented using the current cloud computing and content delivery network platforms and how it is tuned to achieve optimal end-to-end performance, delivering the best possible user experience with minimized transcoding, bandwidth, and storage costs.
3D augmentation of the surgical video stream: Toward a modular approach

Background and Objective. We present an original approach to the development of augmented reality (AR) real-time solutions for robotic surgery navigation. The surgeon operating the robotic system through a console and a visor experiences reduced awareness of the operatory scene. In order to improve the surgeon's spatial perception during robot-assisted minimally invasive procedures, we provide him/her with a solid automatic software system to position, rotate and scale in real-time the 3D virtual model of a patient's organ aligned over its image captured by the endoscope. Methods. We observed that the surgeon may benefit differently from the 3D augmentation during each stage of the surgical procedure; moreover, each stage may present different visual elements that provide specific challenges and opportunities to exploit for organ detection strategies implementation. Hence we integrate different solutions, each dedicated to a specific stage of the surgical procedure, into a single software system. Results. We present a formal model that generalizes our approach, describing a system composed of integrated solutions for AR in robot-assisted surgery. Following the proposed framework, and application has been developed which is currently used during in vivo surgery, for extensive testing, by the Urology unity of the San Luigi Hospital, in Orbassano (To), Italy. Conclusions. The main contribution of this paper is in presenting a modular approach to the tracking problem during in-vivo robotic surgery, whose efficacy from a medical point of view has been assessed in cited works. The segmentation of the whole procedure in a set of stages allows associating the best tracking strategy to each of them, as well as to re-utilize implemented software mechanisms in stages with similar features.
Object Detection using Ryze Tello Drone with Help of Mask-RCNN

The following topics are dealt with: learning (artificial intelligence); Internet of Things; cloud computing; neural nets; diseases; support vector machines; feature extraction; medical image processing; pattern classification; Internet.
Saving social media data: Understanding data management practices among social media researchers and their implications for archives

Abstract Social media data (SMD) offer researchers new opportunities to leverage those data for their work in broad areas such as public opinion, digital culture, labor trends, and public health. The success of efforts to save SMD for reuse by researchers will depend on aligning data management and archiving practices with evolving norms around the capture, use, sharing, and security of datasets. This paper presents an initial foray into understanding how established practices for managing and preserving data should adapt to demands from researchers who use and reuse SMD, and from people who are subjects in SMD. We examine the data management practices of researchers who use SMD through a survey, and we analyze published articles that used data from Twitter. We discuss how researchers describe their data management practices and how these practices may differ from the management of conventional data types. We explore conceptual, technical, and ethical challenges for data archives based on the similarities and differences between SMD and other types of research data, focusing on the social sciences. Finally, we suggest areas where archives may need to revise policies, practices, and services in order to create secure, persistent, and usable collections of SMD.
A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics.
On inexact solution of auxiliary problems in tensor methods for convex optimization

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Application of Blockchain and IoT towards Pharmaceutical Industry

Blockchain and IoT technology has been gaining importance globally in different domain at future perspective. It has gained enormous attention with a great interest in a surfeit of various applications varying from financial services, security analysis, food, agriculture to health industry. Among the fast growing sectors, pharmaceutical industry acclaims to be the major forefront of the healthcare delivery in a largest aspect. There embarks the need of utilizing applications of IoT and Blockchain for safe delivery of healthcare management. The pharmaceutical sector helps in delivery of potential drugs at the outset of growth of medical science into the market. The need of safety and proper validation of the products of medical drugs sold to the consumers had to be taken care of much evaluation. Blockchain and IoT use cases provides the need of security of drugs and also delivery at the right time without the false interaction. In this review paper complete analysis of development in the healthcare associated with the pharmaceutical industry is done by the approach of implementation of Blockchain and IoT. This paper projects the applications of the domain with the future analysis and also the challenges in demand.
Space-Reclaiming Icicle Plots

This paper describes the space-reclaiming icicle plots, hierarchy visualizations based on the visual metaphor of icicles. As a novelty, our approach tries to reclaim empty space in all hierarchy levels. This reclaiming results in an improved visibility of the hierarchy elements especially those in deeper levels. We implemented an algorithm that is capable of producing more space-reclaiming icicle plot variants. Several visual parameters can be tweaked to change the visual appearance and readability of the plots: among others, a space-reclaiming parameter, an empty space shrinking parameter, and a gap size. To illustrate the usefulness of the novel visualization technique we applied it, among others, to an NCBI taxonomy dataset consisting of more than 300,000 elements and with maximum depth 42. Moreover, we explore the parameter and design space by applying several values for the visual parameters. We also conducted a controlled user study with 17 participants and received qualitative feedback from 112 students from a visualization course.
Interactive visual labelling versus active learning: an experimental comparison

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts, and is often a tedious and time-consuming process. Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. Interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations, similarity map, scatterplot matrix (SPLOM), and parallel coordinates, with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy, and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling. Users also employ different labelling strategies depending on the visualisation used.
Analysis of High Frequency Data of a Machine Tool via Edge Computing

New technological capabilities of digitalization are enablers of processing a broad range of machine data. While so-called Low-Frequency Data (LFD) is captured at a sampling rate of several hundred milliseconds, High-Frequency Data (HFD) is based on a sampling rate in the single-digit millisecond range. In this paper, HFD is used to implement an edge-based analytics application for prediction purposes in a machine tool. This edge application leverages Siemens SINUMERIK Edge to capture HFD from a machine tool to recognize anomalies of any kind. The edge application is implemented as a show case in the Learning Factory of Graz University of Technology, the [email protected]
Applications of Virtual Data in Subsea Inspections

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented demonstrating how the inspection process can be enhanced through the use of virtual data. The first case study looks at how immersive virtual underwater scenes can be created to help divers and inspectors plan and implement real-world inspections. The second case study shows an example where deep learning-based computer vision methods are trained on datasets comprised of instances of virtual damage, specifically instances of barnacle fouling on the surface of a ship hull. The trained deep models are then applied to detect real-world instances of biofouling with promising results. The final case study shows how image-based damage detection methods can be calibrated using virtual images of damage captured under various simulated levels of underwater visibility. The work emphasizes the value of virtual data in creating a more efficient, safe and informed underwater inspection campaign for a wide range of built infrastructure, potentially leading to better monitoring, inspection and lifetime performance of such underwater structures.
Temporal association rule mining: An overview considering the time variable as an integral or implied component

Abstract Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies &gt; Association Rules Algorithmic Development &gt; Association Rules