Reviewerly/exHarmony huggingface dataset:
Air Big Data Outlier Detection Based on Infinite Gauss Bayesian and CNN

Air quality has always been a hot issue of concern to the people, the environmental protection department and the government. Among the massive air quality data, abnormal data can interfere with subsequent experiments and analysis. Therefore, it is necessary to detect abnormal data to improve the accuracy of the data. However, traditional air outlier detection methods require at least one year's data to make inferences about air quality. This paper firstly analyzes the characteristics of air quality big data, and then proposes a framework based on Bayesian non-parametric clustering, namely Dirichlet Process (DP) clustering framework, to realize the outlier detection of air quality. The framework optimizes Gaussian mixture model into infinite Gaussian mixture model according to the results of data analysis, and uses neural network to cluster the data processed by infinite Gaussian mixture model, which effectively improves the clustering accuracy and avoids the need of collecting a large number of training data.
A GIS Approach of Potential Photovoltaic Power Sites in Niamey, Niger

Nowadays, the daily conversations at summits and conferences are mainly about energy, climate change, and environmental issues. Fossil fuels are the main causes of global warming due to the release of greenhouse gases (GHG) into the atmosphere, leading to a change in the climate. Niger, as an emerging nation, has significant energy potential that is weakly exploited due to socioeconomic factors. Multi-criteria analysis and satellite imagery were utilized to generate the potential photovoltaic (PV) energy maps. Three sites were selected as the most suitable places: 1) Site A with 22.5% or 12.154km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ; Site B with 53.33% or 28.82 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ; and Site C with 10.23% or 5.53 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The average annual energy potential of the selected sites was found to be 45.13 GWh. This energy potential was based on the: 1) calculated average annual solar radiation per unit surface area; 2) total suitable areas; and 3) efficiency of the PV panels.
A gradient aggregate asymptotical smoothing algorithm for training max–min fuzzy neural networks

A gradient aggregate asymptotical smoothing algorithm is proposed for training fuzzy neural networks (FNNs), which develops the smoothing algorithm (SA) described in Li et al. (2017). By introducing the framework of asymptotical approximation to the algorithm, any degree of approximation for the error function of max–min FNNs can be obtained by an aggregate smoothing function with a variable precision parameter. The algorithm minimizes a sequence of asymptotically approximate functions using the steepest descent algorithm for solving a nondifferentiable max–min optimization problem of max–min FNNs. The proposed update rule on the precision parameter reconciles the conflict between the high-accuracy approximation and the numerical ill-conditioning. The algorithm is globally convergent under Armijo line search. As shown in the simulation results for three artificial examples and a real-world problem in fault diagnosis, compared with SA, the proposed algorithm can efficiently deal with the numerical oscillations and has better performance.
Digital Signage Personalization Through Analysis of the Visual Information About Viewers

Digital signage has been gaining more and more popularity. However, personalization of digital signage is still challenging and it is getting even more difficult with the appearance of low regulating the ways of personal data storage and processing. Most advertisement frameworks can detect general information about viewers such as age and gender that is not enough to recognize his/her real interests. There are efforts aimed at detection of emotions and prediction interests based on the mood as well as usage of modern 3D cameras for collecting distance to the viewers and their heights. Also, the topic of detection logotypes in images is getting more popular. In this paper, we are researching possibilities of integrating possible information collection techniques for better prediction of viewers' interests. These are integrated into the concept of the system that contains human detection, analytics, advertisement, and statistic modules.
In-process Memory Isolation Using Hardware Watchpoint

Memory disclosure vulnerabilities have been exploited in the leaking of application secret data such as crypto keys (e.g., the Heartbleed Bug). To ameliorate this problem, we propose an in-process memory isolation mechanism by leveraging a common hardwarefeature, namely, hardware debugging. Specifically, we utilize a watchpoint to monitor a particular memory region containing secret data. We implemented the PoC of our approach based on the 64-bit ARM architecture, including the kernel patches and user APIs that help developers benefit from isolated memory use. We applied the approach to open-source applications such as OpenSSL and AESCrypt. The results of a performance evaluation show that our approach incurs a small amount of overhead.
FAST: Fast and Accurate Synoptic Texts

Abstract Many classical texts are available in multiple versions that almost always differ from each other due to transcription error and editorial discretion. One of the central challenges in the study of such texts is the preparation of a ‘synoptic’ text: an aligned presentation of the various versions in which corresponding words or phrases, even if not identical, are mapped to each other. Multiple text alignment of this sort must take into account orthographic and conceptual relationships between words. In this article, we define this text alignment problem as an optimization problem by providing a formal measure of alignment quality. Unlike previous measures, our measure uses word embeddings to take into account conceptual similarity between aligned words. We propose an efficient and scalable alignment method in accordance with the proposed criteria. This method splits the texts to be aligned into smaller subtexts, thus improving both efficiency and accuracy. Empirical comparisons on sample data indicate our method is significantly faster than existing methods, often rendering intractable problems tractable, and that the alignment obtained by our method is considerably better than that obtained by other methods.
Air pollution distribution in Telkom University: spatial interpolation map

Currently, air quality information in a region becomes an important thing to know. Some efforts have been conducted to show air quality in certain region. One of the efforts that has been done is information regarding air quality in several big cities in Indonesia which can be seen in the official website of the Ministry of Environment and Forestry that have several weaknesses. One of the problems to be overcome in this research is visualization of air quality data that is monitored at one point only in which that point is the placement location of air quality monitoring station. Because of that, we need an application that can display a map of air pollution distribution using the spatial interpolation method. The solution offered is the depiction of air quality by using heatmap on the map. The method used to produce heatmap with smooth result is natural cubic spline interpolation method. The production of heatmap uses API which is provided by Google Maps. The final result obtained is the map view with the coloration in the form of color gradation in accordance with the air quality value that is obtained.
Thermal Imagery Noise Influence on Face Recognition Performance

The utilization of a thermal band imaging sensor with aim to overcome the disadvantages of visible light face recognition systems, such as variation in lighting conditions, has become more popular in wide range of security and safety applications. Thermal imaging sensor enhances reliability of face recognition systems under different lighting conditions, including complete darkness. However, thermal imagery also possesses several disadvantages such as usual presence of noise, originating both from sensor and environment. This paper examines the influence of thermal imagery noise on face recognition performance in thermal-to-thermal face recognition applications.
Temporal Sentiment Tracking and Analysis on Large-scale Social Events

Online Social Networks (OSNs) are emergent resources for largescale multi-purpose data analytics. Sentiment analysis (SA) is a trending research area on OSNs. SA approaches for studying and analyzing events are still missing several shortcomings. Unlike other approaches that analyzed micro-scaled events such as "marriage", "graduation", we analyzed the sentiment of large-scale social events such as "festivals". In this paper, we address the problem of finding the sentiment of large-scale social events and introduce a novel method for this goal. To address this problem, we utilize a lexical approach. The features used in our method are universal and composed of auxiliary and essential features from OSNs. Auxiliary features are non-textual features used to emphasize the sentiment polarity. Moreover, we track the temporal interchanges of audience sentiment on OSNs.We finally empirically validate that our method can outperform with high precision and recall values.
Hinted Star Coordinates for Mixed Data

Abstract Mixed data sets containing numerical and categorical attributes are nowadays ubiquitous. Converting them to one attribute type may lead to a loss of information. We present an approach for handling numerical and categorical attributes in a holistic view. For data sets with many attributes, dimensionality reduction (DR) methods can help to generate visual representations involving all attributes. While automatic DR for mixed data sets is possible using weighted combinations, the impact of each attribute on the resulting projection is difficult to measure. Interactive support allows the user to understand the impact of data dimensions in the formation of patterns. Star Coordinates is a well‐known interactive linear DR technique for multi‐dimensional numerical data sets. We propose to extend Star Coordinates and its initial configuration schemes to mixed data sets. In conjunction with analysing numerical attributes, our extension allows for exploring the impact of categorical dimensions and individual categories on the structure of the entire data set. The main challenge when interacting with Star Coordinates is typically to find a good configuration of the attribute axes. We propose a guided mixed data analysis based on maximizing projection quality measures by the use of recommended transformations, named hints, in order to find a proper configuration of the attribute axes.
Sizing of a Car Parking Photovoltaic System: An Economic Analysis

The world dependency on fossil fuels to meet growing demand for energy is a significant contributor to global warming. Additionally, with the depletion of such energy sources, it has brought to attention the importance of research, development, and utilization of other low to non-carbon emitting sources to meet the rising energy demand and reduce environmental impact. Solar energy is available in abundance and the decreasing cost of photovoltaic (PV) panels is promoting their use. A grid-tied power generation scheme, using PV panels deployed on unshaded car parking areas with the proper balance of system, were investigated. Solar shading of car parking lots is an efficient use of space and can be installed practically anywhere. The merit of this technique is twofold: 1) generate free electricity and 2) provide shade simultaneously. This study initiates a case study of solar shading on a campus, located in Ras Al Khaimah, UAE. The study focuses on five car parking lots situated on the South side of the engineering building. Cost analysis assessment revealed that: 1) the initial cost of the whole system is $25,000 USD and 2) the payback period is thirteen (13) years. The methodology developed can be globally implemented to cut-down greenhouse gas emissions.
Predictive multi‐view content buffering applied to interactive streaming system

This Letter discusses the benefits of introducing Machine Learning techniques in multi-view streaming applications. Widespread use of machine learning techniques has contributed to significant gains in numerous scientific and industry fields. Nonetheless, these have not yet been specifically applied to adaptive interactive multimedia streaming systems where, typically, the encoding bit rate is adapted based on resources availability, targeting the efficient use of network resources whilst offering the best possible user quality of experience (QoE). Intrinsic user data could be coupled with such existing quality adaptation mechanisms to derive better results, driven also by the preferences of the user. Head-tracking data, captured from camera feeds available at the user side, is an example of such data to which Recurrent Attention Models could be applied to accurately predict the focus of attention of users within videos frames. Information obtained from such models could be used to assist a preemptive buffering approach of specific viewing angles, contributing to the joint goal of maximising QoE. Based on these assumptions, a research line is presented, focusing on obtaining better QoE in an already existing multi-view streaming system
Demo

This abstract presents Verisig, a scalable tool for verifying safety properties of closed-loop systems with neural network (NN) controllers. Verisig transforms the NN into an equivalent hybrid system and composes this hybrid system with the plant's. This abstract describes this transformation and outlines the tool's building blocks.
Join Algorithms under Apache Spark

Currently, we are dealing with large scale applications, which in turn generate massive amount of data and information. Large amount of data often requires processing algorithms using massive parallelism, where the main performance metrics is the communication cost. Apache Spark is highly scalable, fault-tolerance, and can be used across many computers. So join algorithm is one of the most widely used algorithms in database systems, but it is also a heavily time consuming operation. In this work, we will survey and criticize several implementations of Spark join algorithms and discuss their strengths and weaknesses, present a detailed comparison of these algorithms, and introduce optimization approaches to enhance and tune the performance of join algorithms.
Feature Fusion Attention Visual Question Answering

Visual Question Answering (VQA) is the multitask research field of computer vision and natural language processing and is one of the most intelligent applications among machine learning applications at present. It firstly analyzes and copes with the problem sentences to extract the core key words as well as then seeking out the answers from the figure. In our research, it extracts characteristic values from problem sentences and images by adopting the BI-LSTM and VGG_19 algorithms. Then, after integrating the values into new feature vectors, the paper correlates them into the attention through the attention mechanism and finally predicts the answers finally. Also, the VQA1.0 data set is adopted to train the model. After conducting the training, the accuracy of the test by using the test set reached up to 54.8%.
Spatiotemporal interest point detector exploiting appearance and motion-variation information

As a local invariant feature of videos, the spatiotemporal interest point (STIP) has been widely used in computer vision and pattern recognition. However, existing STIP detectors are generally extended from detection algorithms constructed for local invariant features of two-dimensional images, which does not explicitly exploit the motion information inherent in the temporal domain of videos, thus weakening the performance of existing STIP detectors in a video context. To remedy this, we aim to develop an STIP detector that uniformly captures appearance and motion information for video, thus yielding substantial performance improvement. Specifically, under the framework of geometric algebra, we first develop a spatiotemporal unified model of appearance and motion-variation information (UMAMV), and then a UMAMV-based scale space of the spatiotemporal domain is proposed to synthetically analyze appearance information and motion information in a video. Based on this model, we propose an STIP feature of UMAMV-SIFT that embraces both appearance and motion variation information of the videos. Three datasets with different sizes are utilized to evaluate the proposed model and the STIP detector. We present experimental results to show that the UMAMV-SIFT achieves state-of-the-art performance and is particularly effective when dataset is small.
An Empirical Study of Malicious Threads in Security Forums

How useful is the information that a security analyst can extract from a security forum? We focus on threads of interest, which we define as: (i) alerts of worrisome events, such as attacks, (ii) offering of malicious services and products, (iii) hacking information to perform malicious acts, and (iv) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Here, we leverage our earlier work in thread analysis, and ask the question: what kind of information do these malicious threads provide. Specifically, we analyze threads in three dimensions: (a) temporal characteristics, (b) user-centric characteristics (c) content-centric properties. We study threads pulled from three security forums spanning the period 2012-2016. First, we show that 53% of the users asking/selling malicious Services on average has 3 posts and initiate 1 thread and 1 day lifetime. Second, we argue that careful analysis can help to identify emerging threats reported in security forums through Services and Alerts threads and potentially help security analysts prevent attacks. We see this study as a first attempt to argue for the wealth and type of information that can be extracted from security forums.
Tutorial Gap Identification Towards Student Modeling Using Fuzzy Logic

Tutorial requirements of student modeling are traditionally identified by human tutors through crisp mental calculations, and the accuracy of this identification enhances with the experience of the tutor. These identifications become challenging when the mode of delivery is through e-learning. Automatic identification of tutorial requirements must be included in e-learning through ITS where some intelligent computational paradigms can help in automatic identification of the said requirements. This article proposes some fuzzy logic-based techniques toward the said purpose of automatic tutorial gap identification where analysis of the learner's responses to purposefully designed modular MCQ tests and verbal explanations are taken in to consideration as inputs for the purpose. Variation of the fuzzy logics was taken in to account to justify the usefulness of methods under consideration and to provide a realistic and adaptable approach. The experimental results show that the proposed methods are successful and efficient towards the said purpose.
Parametric Facial Animation for Affective Interaction Workflow for Avatar Retargeting

Virtual avatars/characters are essential in the interaction with virtual environments and 3D video games. While these avatars can be controlled by a bot or a human, they need a suitable model that allows emotion representation regardless of what or who is controlling them, since it increases the interaction and immersion. Provide facial expressions to any avatar require an animation process to emulate a particular gesture or emotion before to include it in a virtual environment. This process is specific to every single facial expression required in the avatar. This paper presents an initial approximation to contribute to the solve this problem with a workflow to apply a well-known parameterized face model called Candide. The proposal aims to adapt Candide into any avatar previously modeled through automatic morphological adjustment and texture mapping. This work includes the application of this workflow into six characters licensed under the Creative Commons copyright. We also present examples of facial emotion animation, as well as subjective assessment by five experts in audiovisual and animation production.
How New is the (RDF) News?

Linked Open Data and the RDF format have become the premier method of publishing structured data representing entities and facts. Specifically, media organizations, such as the New York Times and the BBC, have embraced Linked Open Data as a way of providing structured access to traditional media content, including articles, images, and video. To ground RDF entities and predicates in existing Linked Open Data sources, dataset curators provide links for some entities to existing general purpose repositories, such as YAGO and DBpedia, using entity extraction and linking tools. However, these state-of-the-art tools rely on the entities to exist in the knowledge base. How much of the information is actually new and thus unable to be grounded is unclear. In this work, we empirically investigate the prevalence of new entities in news feeds with respect to both public and commercial knowledge graphs.
Comprehensive Analysis of the Uses of GPU and CUDA in Soft-Computing Techniques

Now a day it is quite often seen that Soft-computing approaches are the cast off frequently in Computer and Information Technology. Fuzzy logic, Genetic Algorithms, and Artificial Neural Network are the fundamentals of Soft computing techniques. Several soft computing approaches such as deep neural network require a huge amount of computational power of the CPU to execute a problem and consume lots of time to execute. GPU (Graphics Processing Unit) provides an efficient way to use efficiently all the computational resources in the form of streaming processors. Further, CUDA (Computed Unified Device Architecture) provides a parallel framework specifically designed for GPU. CUDA and GPU are frequently used to implement different soft computing techniques. This article provides a brief introduction of GPU and CUDA and different soft computing techniques. Further, analytical review of the implementation of different soft computing techniques using GPU and CUDA are given.
Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation

In recent years, considerable efforts based on convolutional neural networks have been devoted to age estimation from face images. Among them, classification-based approaches have shown promising results, but there has been little investigation of age differences and ordinal age information. In this paper, we propose a ranking objective with two novel schemes jointly performed with an age classification objective to take ordinal age labels into account. We first introduce relative triplet sampling in which a set of triplets is constructed considering the relative differences in ages. This also addresses the problem of having limited triplet candidates, that occurs in conventional triplet sampling. We then propose the scale-varying ranking constraint, which decides the importance of a relative triplet and adjusts a scale of gradients accordingly. Our adaptive ranking loss with relative sampling not only lowers the generalization error but ultimately has a meaningful performance improvement over the state-of-the-art methods on two well-known benchmarks.