Reviewerly/exHarmony huggingface dataset:
Extreme multi-label learning : A large scale classification approach in machine learning

In digital world, the amount of data is growing exponentially in day to day life. It is difficult to analyze and extract knowledge from large amount of data with millions of categories in Big Data environment. Therefore, it is challenging problem to develop model that classify large volume of documents available on Internet. However, Multi-Label Classification approach is used to classify data with multiple categories or labels but it is inefficient way to deal with millions of categories. Hence Extreme Multi-Label Classification approach is used to overcome this limitation by selecting subset of labels for the new instance from millions of labels. Recently Extreme Multi-Label Classification has attracted research attention in different application areas like document categorization in Wikipedia, people identification in social networking, gene prediction in bio-informatics etc. Extreme Multi-Label Classification is also opened up new challenge to reformulate existing machine learning problems like ranking, tagging and recommendation. This survey paper focuses on approaches and reviewing current research challenges on eXtreme Multi Label Classification. Also discussed state-of-the-art algorithms to handle eXtreme Multi-Label Classification Problem.
Robust structured-light depth mapping via recursive decomposition of binary codes

Structured-light depth cameras rely on projecting and resolving coded patterns on a three-dimensional scene with high contrast. The front-end optics of such depth cameras impose a fundamental restriction on the depth-sensing range and accuracy: the patterns only remain sharp within the depth of field jointly determined by the camera and projector. We present here a robust method to improve the depth-sensing range and accuracy for a structured-light depth camera without changing the underlying optical design. Moreover, it shows the unique advantage in macrophotography of highly light-scattering objects. We analyze the proposed method theoretically and validate it in experiments.
Analysis of respondents' opinions and attitudes toward the security of payment systems

The number of financial and cyber-attacks is increasing. The latter trend in financial and cyber security incidents is global and, as such it is being monitored globally. Cyber and financial statistical data and the growing number of certified information management systems show the practical importance of data security at the international level. The decisions to solve the data security problems are based on the technical point of view, protection motivation theory, and security standards. By analysing the security of payment system, this article aspires to aid in the development of secure systems. Its aim is to contribute to the knowledge and comprehension of the behaviour of payment systems users with special focus on the aspect of their security. The article analyses the opinions and attitudes of respondents toward the questions dealing with the security of payment systems and their behaviour when using payment cards. The analysis is carried out from the aspect of gender, age and education of respondents by using multidimensional statistical methods, namely factor analysis and analysis of dispersion.
Tight Security Bounds for Generic Stream Cipher Constructions

The design of modern stream ciphers is strongly influenced by the fact that Time-Memory-Data tradeoff (TMD-TO) attacks reduce their effective key length to half of the inner state length. The classical solution is to design the cipher in accordance with the Large-State-Small-Key principle, which implies that the state length is at least twice as large as the session key length. In lightweight cryptography, considering heavily resource-constrained devices, a large amount of inner state cells is a big drawback for these type of constructions. Recent stream cipher proposals like Lizard, Sprout, Plantlet and Fruit employ new techniques to avoid a large inner state size. However, when considering indistinguishability, none of the ciphers mentioned above provide a security above the birthday barrier with regard to the state length. In this paper, we present a formal indistinguishability framework for proving lower bounds on the resistance of generic stream cipher constructions against TMD-TO attacks. In particular, we first present a tight lower bound on constructions underlying the Large-State-Small-Key principle. Further, we show a close to optimal lower bound of stream cipher constructions continuously using the initial value during keystream generation. These constructions would allow to shorten the inner state size significantly and hence the resource requirements of the cipher. We thus believe that Continuous-IV-Use constructions are a hopeful direction of future research.
A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental Analysis

Text Classification is a renowned machine learning approach to simplify the domain-specific investigation. Consequently, it is frequently utilized in the field of sentimental analysis. The demanding business requirements urge to devise new techniques and approaches to improve the performance of sentimental analysis. In this context, ensemble of classifiers is one of the promising approach to improve classification accuracy. However, classifier ensemble is usually done for classification while ignoring the significance of feature selection. In the presence of right feature selection methodology, the classification accuracy can be significantly improved even when the classification is performed through a single classifier. This article presents a novel feature selection ensemble approach for sentimental classification. Firstly, the combination of three well-known features (i.e. lexicon, phrases and unigram) is introduced. Secondly, two level ensemble is proposed for feature selection by exploiting Gini Index (GI), Information Gain (IG), Support Vector Machine (SVM) and Logistic Regression (LR). Subsequently, the classification is performed through SVM classifier. The implementation of proposed approach is carried out in GATE and RapidMiner tools. Furthermore, two benchmark datasets, frequently utilized in the domain of sentimental classification, are used for experimental evaluation. The experimental results prove that our proposed ensemble approach significantly improve the performance of sentimental classification with respect to well-known state-of-the-art approaches. Furthermore, it is also analyzed that the ensemble of classifiers for the improvement of classification accuracy is not necessarily important in the presence of right feature selection methodology.
Personalized Sketch-Based Brushing in Scatterplots

Brushing is at the heart of most modern visual analytics solutions and effective and efficient brushing is crucial for successful interactive data exploration and analysis. As the user plays a central role in brushing, several data-driven brushing tools have been designed that are based on predicting the user's brushing goal. All of these general brushing models learn the users' average brushing preference, which is not optimal for every single user. In this paper, we propose an innovative framework that offers the user opportunities to improve the brushing technique while using it. We realized this framework with a CNN-based brushing technique and the result shows that with additional data from a particular user, the model can be refined (better performance in terms of accuracy), eventually converging to a personalized model based on a moderate amount of retraining.
Design of Reversible Shift Register Using Reduced Number of Logic Gates

Background: Over the last few decades, reversible logic system/circuits have received considerable attention in the diversified fields such as nanotechnology, quantum computing, cryptography, optical computing and low power design of VLSI circuits due to their low power dissipation characteristics. Methods: In this paper, we proposed the design of reversible shift register (SR) i.e. serial-in-serial out (SISO), serial-in-parallel out (SIPO), parallel-in-serial out (PISO) and parallel-in-parallel out (PIPO) SR using a reduced number of reversible logic gates and garbage output. Results: As compared to previously reported results, the improvement in our proposed model of SISO, SIPO, PISO and PIPO was found to be 50 – 66.66 %, 42.85 – 66.66 %, 12.5 – 53.33 % and 50 – 66.66 % respectively, in terms of the number of reversible logic gates.
Tikkoun Sofrim

This paper briefly describes aspects of the Tikkoun Sofrim crowdsourcing webApp. Tikkoun Sofrim is a webApp which allows users to correct automatic transcriptions (AT) done by an AI Neural network engine. We look at the background of the crowdsourcing phenomenon in the use of automatic transcription of digital humanities documents. System structure is briefly described. We then examine personalization and adaption aspects at different stages of the user/application lifecycle Finally, we briefly list future challenges.
Mining Compact Predictive Pattern Sets Using Classification Model

In this paper, we develop a new framework for mining predictive patterns that aims to describe compactly the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important for improving the overall class prediction performance. We test our approach on data derived from MIMIC-III EHR database, focusing on patterns predictive of sepsis. We show that using our classification approach we can achieve a significant reduction in the number of extracted patterns compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model.
Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis

Recent studies have shown that pre-trained contextual word embeddings, which assign the same word different vectors in different contexts, improve performance in many tasks. But while contextual embeddings can also be trained at the character level, the effectiveness of such embeddings has not been studied. We derive character-level contextual embeddings from Flair (Akbik et al., 2018), and apply them to a time normalization task, yielding major performance improvements over the previous state-of-the-art: 51% error reduction in news and 33% in clinical notes. We analyze the sources of these improvements, and find that pre-trained contextual character embeddings are more robust to term variations, infrequent terms, and cross-domain changes. We also quantify the size of context that pre-trained contextual character embeddings take advantage of, and show that such embeddings capture features like part-of-speech and capitalization.
Development of digital image techniques with a low-cost unmanned aerial vehicle to form the three-dimensional mode of dam and affiliated structure

Taiwan is located in earthquake zone, bears hundreds of felt earthquakes each year. Otherwise, steep mountains of Taiwan, lack of capacity to accumulate rain, need to rely on reservoirs to storage rainwater for supplying people’s livelihood and industrial water. Global warming is caused by abnormal weather, subjoined with poor geological conditions and over developed of mountainous areas. Therefore, earthquakes, torrential rains and typhoons bring about the collapse of the slopes around the reservoir to cause landslide. Resulting in decreased capacity of reservoir water collection, affected the safety of reservoirs. Poor construction, material fatigue, deterioration and other factors lead to cracks of concrete structure of dams. These cracks in the tip had the phenomenon of stress concentration and rapid changes in stress. It caused reservoirs gradually destroyed, and even effected the overall safety of the reservoirs and their service life. This study is aimed at the safety assessment technique as development of three-dimensional, 3D digital imaging technology for monitoring of dams and its affiliated structures. The study achievements are: 1. Application of new type Open Source Pixhwak 2 flight controller; 2. Image processing conversion and analysis technology in dynamic flight; 3. 3D surround shot technique and establishment of the numerical criterion of 3D modelling; 4. Forming 3D contour model of the whole reservoir
Monitoring of drinking-water quality by means of a multi-objective ensemble learning approach

This paper proposes the use of multi-objective ensemble learning to monitor drinking-water quality. Such problem consists of a data set with an extreme imbalance ratio where the events, the minority class, must be correctly detected given a time series denoting water quality and operative data on a minutely basis. First, the given data set is preprocessed for imputing missing data, adjusting concept drift and adding new statistical features, such as moving average, moving standard deviation, moving maximum and moving minimum. Next, two ensemble learning techniques are used, namely SMOTEBoost and RUSBoost. Such techniques have been developed specifically for dealing with imbalanced data, where the base learners are trained by adjusting the ratio between the classes. The first algorithm focuses on oversampling the minority class, while the second focuses on under-sampling the majority class. Finally, multi-objective optimisation is used for pruning the base models of such ensembles in order to maximise the prediction score without reducing generalisation performance. In the training phase, the model is trained, optimised and evaluated using hold-out validation on a given training data set. At the end, the trained model is inserted into a framework, which is used for online event detection and assessing the model's performance.
Towards Empathetic Planning and Plan Recognition

Every compassionate and functioning society requires its members to have a capacity to adopt others' perspectives. As Artificial Intelligence (AI) systems are given increasingly sensitive and impactful roles in society, it is important to enable AI to wield empathy as a tool to benefit those it interacts with. In this paper, we work towards this goal by bringing together a number of important concepts: empathy, AI planning, and plan recognition (i.e., the problem of inferring an actor's plan and goal given observations about its behavior). We formalize the notions of Empathetic Planning and Empathetic Plan Recognition which are informed by the beliefs and affective state of the actor, and propose AI planning-based computational approaches. We illustrate the benefits of our approach by conducting a study with human participants.
Comparative Study for Multiple Coordinated Views Across Immersive and Non-immersive Visualization Systems

Our objective is to qualitatively assess how users interact with and explore heterogeneous views of data in novel hybrid 2D and 3D visual analytic applications, and assess attitudinal responses on the usefulness of such applications. The application used for the study visualizes simulated network communication data of multiple assets over time, and shows topological, geospatial, and temporal aspects of the data in multiple views. We first perform a usability test, where the participants complete a couple of exploratory tasks: one, identifying corresponding assets in a visualization, and two, identifying patterns/relationships between particular assets. Participants perform the same tasks using several different system configurations: using only 2D visualizations, using 2D and 3D visualizations together but as separate applications, and using the 2D and 3D visualizations together with multiple coordinated views across the two systems. Afterwards, participants complete a user survey of questions that probe at user preferences and opinions about the relative effectiveness of each system towards accomplishing the given tasks. We discuss how the results of the study confirm current system design decisions, and also evaluate additional user-centric characteristics that must be considered to inform future design decisions. These results will inform hypotheses and guidelines for a future behavioral, quantitative study.
Proposal of Usability Metrics to Evaluate E-commerce Websites

Usability form as an important aspect nowadays for software products, even more, when we talk about transactional websites. However, in the present, there are not enough metrics to help us measuring usability level from an E-commerce portal, for this reason, we present a new proposal of metrics of usability. While working on the proposal, we did a research to identify 39 metrics and 10 existing aspects, then we created usability metrics and finally we validated the proposal with five specialists, based on interviews and questionnaires.
Design of Virtual Reality for Humanoid Robots with Inspiration from Video Games

Advances in robotics have led to breakthroughs in several areas, including the development of humanoid robots. There are now several different models of humanoid robots available, but operating them remains a difficult challenge. Current operator control interfaces for humanoid robots often require very experienced operators and significant amounts of time for planning. A large amount of the planning and cognitive load is attributable to the operator attempting to gain adequate three-dimensional (3D) situation awareness and task awareness while viewing an interface on a flat, two-dimensional (2D) screen. Virtual reality (VR) has enormous potential to provide benefits to allow the operator to quickly and accurately understand the state of a robot in a scanned 3D environment and to issue accurate commands with less cognitive load. In the gaming sphere, VR headsets remain a new and promising interface for playing video games. In some cases, existing video games are being ported over to VR and, in others, brand new games are being designed with VR in mind. Control schemes and best practices for VR are emerging within the video game industry. This paper aims to leverage their lessons learned and to apply them to the teleoperation of humanoid robots.
Selecting a text similarity measure for a content-based recommender system

Purpose The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance. Design/methodology/approach Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain. Findings The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25. Originality/value This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.
Smart Learning Environments: A Blend of ICT Achievements and Smart Pedagogy for the World Sustainable Development

In the context of a sustainable development the mitigation of the energy consumption could be achieved by the incorporation of Information and Communication Technology (ICT) as well as the exploitation of smart computing in every day sectors, formulating a smart society constituted by energy conscious citizens. According to this assumption a smart society is necessary to be built on foundations of a smart education which aims to orchestrate best practices and methods based on smart pedagogies, in order to accomplish optimized learning through the development of Smart Learning Environments (SLEs). This paper presents the emergency of developing SLEs as a robust solution for the humans’ well-being. It classifies the fundamental features of SLEs highlighted by the international literature the past decade, into affordances arising from services that come from both accelerating technological achievements and pedagogical issues regarding energy. It also provides conclusions that foster research to this direction.
Concrete Efficiency Improvements for Multiparty Garbling with an Honest Majority

Secure multiparty computation is becoming a necessary component in many real-world systems. The efficiency of secure two-party protocols has improved tremendously in the last decade, making such protocols efficient enough for many real-world applications. Recently, much attention is being diverted to making secure multiparty computation (for more than two parties) truly practical as well. In particular, the last couple of years saw a resurgence of interest in constant round secure protocols, based on the multiparty garbling paradigm of Beaver et al. (STOC 1990). Such protocols generally offer improved performance in high latency networks, such as the internet. In this paper we consider the case where a majority of the parties are honest, and construct highly efficient constant round protocols for both the semi-honest setting and the malicious setting. Our protocols in the semi-honest setting significantly improve over the recent multiparty garbling protocols for honest majority of Ben Efraim et al. (ACM CCS 2016), both in asymptotic complexity and in concrete running time. In the malicious setting, we consider security with abort when assuming more than 2/3 of the parties are honest. We show that by assuming the existence of simple preprocessing primitives, which do not require knowledge of the computed function, we get malicious security at almost the same cost as semi-honest security. I.e., the function dependent preprocessing and the online phase are almost identical to the semi-honest setting. We ran experiments to measure the effect of our optimizations and to show that our protocols compete with the state-of-the-art constant round protocols.
Multi-Level Image Segmentation of Color Images Using Opposition Based Improved Firefly Algorithm

Background: Image segmentation is the fundamental step in image processing. Numerous image segmentation algorithms have been proposed already for grey scale images and they are very complex and time-consuming. Since most of the algorithms suffer from over and under segmentation problem, multi-level image thresholding is very effective method against this problem. Nature inspired meta-heuristics algorithms such as firefly are fast and can enhance the performance of the method. Objective: This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function. Methods: Numerous benchmark images were tested for evaluating the performance of the proposed method. Results: The experimental results validate the performance of Opposition Based Improved Firefly Algorithm (OBIFA) for multi-level image segmentation using Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Metric (SSIM) parameter. Conclusion: The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.