Reviewerly/exHarmony huggingface dataset:
Interactive fuzzy search algorithm: A new self-adaptive hybrid optimization algorithm

Current investigation deals with presenting a new hybrid metaheuristic optimization technique which is named as Interactive Fuzzy Search Algorithm (IFSA). Proposed method combines the affirmative features of Integrated Particle Swarm Optimizer (iPSO) and Teaching and Learning Based Optimizer (TLBO) techniques with a fuzzy decision mechanism. Proposed IFSA benefiting its fuzzy module provides a self-adaptive synchronization between local and global search strategies during the optimization process. Since in the proposed approach the tunable parameters are automatically assigned, IFSA acts as an ad-hoc free algorithm. To authenticate the validity of the proposed method, its performance is verified over the number of different types of mathematical, mechanical and structural optimization problems. Achieved outcomes indicate that the introduced algorithm has a high competitive potential on solving different class of optimization problems.
Feature Based Approach for Detection of Smishing Messages in the Mobile Environment

Smishing is a security attack that is performed by sending a fake message intending to steal personal credentials of mobile users. Nowadays, smishing attack becomes popular due to the massive growth of mobile users. The smishing message is very harmful since its target to financial benefits. In this article, the authors present a new feature-based approach to detect smishing messages in the mobile environment. This approach offers ten novel features that distinguish the fake messages from the ham messages. In this article, the authors have also identified the nineteen most suspicious keywords, which are used by the attacker to lure victims. This article has implemented these features on benchmarked dataset and applied numerous classification algorithms to judge the performance of the proposed approach. Experimental outcomes indicate that proposed approach can detect smishing messages with the 94.20% true positive rate and 98.74% overall accuracy. Furthermore, the proposed approach is very efficient for the detection of the zero hour attack.
Sentiment classification of twitter data belonging to renewable energy using machine learning

The paper disccuses about the sentimenal analysis process which is a classification problem. To study the emotions of people about alternate energy sources, we carried out sentiment analysis on Twitter data. Sentiment analysis is used to analyze opinions of the user for decision making with the help of natural language processing techniques. In this paper, we carried out sentiment analysis and classification task of tweets belonging to #RenewableEnergy. We applied five different machine learning algorithms for the classification of tweets into three categories. We have carried classification without feature selection technique and with feature selection techniques. We have used CfsSubsetEvaluation and Information Gain feature selection methods to reduce the number of features from the dataset. The result obtained through the techniques followed in this paper, shows that the accuracy of sentiment classification is better with feature selection methods. The best accuracy (92.96%) is achieved with Support Vector Machine(Using PUK Kernel) and CfsSubsetEval feature selection method.
Let Your World Open

Virtual reality (VR) games are currently becoming part of the public-space entertainment (e.g., VR amusement parks). Therefore, VR games should be attractive for players, as well as for bystanders. Current VR systems are still mostly focused on enhancing the experience of the head-mounted display (HMD) users; thus, bystanders without an HMD cannot enjoy the experience together with the HMD users. We propose the "ReverseCAVE": a proof-of-concept prototype for public VR visualization using CAVE-based projection with translucent screens for bystanders toward a shareable VR experience. The screens surround the HMD user and the VR environment is projected onto the screens. This enables the bystanders to see the HMD user and the VR environment simultaneously. We designed and implemented the ReverseCAVE, and evaluated it in terms of the degree of attention, attractiveness, enjoyment, and shareability, assuming that it is used in a public space. Thus, we can make the VR world more accessible and enhance the public VR experience of the bystanders via the ReverseCAVE.
DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery

Links between issue reports and corresponding code commits to fix them can greatly reduce the maintenance costs of a software project. More often than not, however, these links are missing and thus cannot be fully utilized by developers. Current practices in issue-commit link recovery extract text features and code features in terms of textual similarity from issue reports and commit logs to train their models. These approaches are limited since semantic information could be lost. Furthermore, few of them consider the effect of source code files related to a commit on issue-commit link recovery, let alone the semantics of code context. To tackle these problems, we propose to construct code knowledge graph of a code repository and generate embeddings of source code files to capture the semantics of code context. We also use embeddings to capture the semantics of issue- or commit-related text. Then we use these embeddings to calculate semantic similarity and code similarity using a deep learning approach before training a SVM binary classification model with additional features. Evaluations on real-world projects show that our approach DeepLink can outperform the state-of-the-art method.
A novel GA-ANFIS hybrid model for short-term solar PV power forecasting in Indian electricity market

AbstractAbstractSolar photovoltaic (PV) power is a most important green energy source of electricity. Accurate solar PV power forecasting is a vital requirement for integration of PV power with electricity grid, stability of grid and its management. This paper proposed, a novel hybrid model, combining genetic algorithm (GA) and adaptive network based fuzzy inference system (ANFIS) for short term solar PV power forecasting in Indian electricity market. The developed hybrid model is applied for study of PV power forecasting in Indian electricity market. Performance of developed model is compared with four different models and proposed model has given more accurate forecasting.Keywords: Restructured marketGenetic algorithmFuzzy logicNetwork based fuzzy inference systemANFIS and PV power forecasting
STELLAR: A Generic EM Side-Channel Attack Protection through Ground-Up Root-cause Analysis

The threat of side-channels is becoming increasingly prominent for resource-constrained internet-connected devices. While numerous power side-channel countermeasures have been proposed, a promising approach to protect the non-invasive electromagnetic side-channel attacks has been relatively scarce. Today's availability of high-resolution electromagnetic (EM) probes mandates the need for a low-overhead solution to protect EM side-channel analysis (SCA) attacks. This work, for the first time, performs a white-box analysis to root-cause the origin of the EM leakage from an integrated circuit. System-level EM simulations with Intel 32 nm CMOS technology interconnect stack, as an example, reveals that the EM leakage from metals above layer 8 can be detected by an external non-invasive attacker with the commercially available state-of-the-art EM probes. Equipped with this `white-box' understanding, this work proposes STELLAR: Signature aTtenuation Embedded CRYPTO with Low-Level metAl Routing, which is a two-stage solution to eliminate the critical signal radiation from the higher-level metal layers. Firstly, we propose routing the entire cryptographic core within the local lower-level metal layers, whose leakage cannot be picked up by an external attacker. Then, the entire crypto IP is embedded within a Signature Attenuation Hardware (SAH) which in turn suppresses the critical encryption signature before it routes the current signature to the highly radiating top-level metal layers. System-level implementation of the STELLAR hardware with local lower-level metal routing in TSMC 65 nm CMOS technology, with an AES-128 encryption engine (as an example cryptographic block) operating at 40 MHz, shows that the system remains secure against EM SCA attack even after 1M encryptions, with 67% energy efficiency and 1.23× area overhead compared to the unprotected AES.
Learning Bayesian network parameters via minimax algorithm

Parameter learning is an important aspect of learning in Bayesian networks. Although the maximum likelihood algorithm is often effective, it suffers from overfitting when there is insufficient data. To address this, prior distributions of model parameters are often imposed. When training a Bayesian network, the parameters of the network are optimized to fit the data. However, imposing prior distributions can reduce the fitness between parameters and data. Therefore, a trade-off is needed between fitting and overfitting. In this study, a new algorithm, named MiniMax Fitness (MMF) is developed to address this problem. The method includes three main steps. First, the maximum a posterior estimation that combines data and prior distribution is derived. Then, the hyper-parameters of the prior distribution are optimized to minimize the fitness between posterior estimation and data. Finally, the order of posterior estimation is checked and adjusted to match the order of the statistical counts from the data. In addition, we introduce an improved constrained maximum entropy method, named Prior Free Constrained Maximum Entropy (PF-CME), to facilitate parameter learning when domain knowledge is provided. Experiments show that the proposed methods outperforms most of existing parameter learning methods.
Multi-view face hallucination using SVD and a mapping model

Multi-view face hallucination (MFH) presents a challenge issue in face recognition domain. In this paper, an efficient method based on singular value decomposition (SVD) and a mapping model is proposed for multi-view face hallucination. Based on an approximately same linear mapping relationship across different views, two corresponding matrices obtained from the SVD of the low resolution (LR) image for the high-resolution (HR) multi-view face images can be constructed via the mapping model using global reconstruction. Experiments show that our proposed multi-view face-hallucination scheme is effective and produces promising super-resolved results.
Comparing ANN, SVM, and HMM based Machine Learning Methods for American Sign Language Recognition using Wearable Motion Sensors

Millions of people with speech and hearing impairments, worldwide, communicate through sign languages every day. In the same way that voice recognition provides a simple communication platform for most users, gesture recognition is a natural means of correspondence for the hearing-impaired. In this paper, we explore the problem of translating/converting sign language to speech, and propose an improved solution using different machine learning techniques. We seek to build a system that can be employed in the daily lives of people with hearing impairments, in order to enhance communication and collaboration between the hearing-impaired community and those untrained in American Sign Language (ASL). The system architecture is based on using wearable motion sensors and machine learning techniques. In this study, we propose a solution using Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and compare their accuracy with the Hidden Markov Model (HMM) results from our previous work to recognize ASL words. Experimental results show that using ANN gives an overall higher accuracy in recognizing ASL words, compared to other machine learning techniques.
Semantic textual similarity between sentences using bilingual word semantics

Semantic textual similarity between sentences is indispensable for many information retrieval tasks. Traditional lexical similarity measures cannot compute the similarity beyond a trivial level. Moreover, they only can capture the textual similarity, but not semantic. In this paper, we propose a method for semantic textual similarity that leverages bilingual word-level semantics to compute the semantic similarity between sentences. To capture word-level semantics, we employ distribute representation of words in two different languages. The similarity function based on the concept-to-concept relationship corresponding to the words is also utilized for the same purpose. Multiple new semantic similarity measures are introduced based on word-embedding models trained on two different corpora in two different languages. Apart from these, another new semantic similarity measure is also introduced using the word sense comparison. The similarity score between the sentences is then computed by applying a linear ranking approach to all proposed measures with their importance score estimated employing a supervised feature selection technique. We conducted experiments on the SemEval Semantic Textual Similarity (STS-2017) test collections. The experimental results demonstrated that our method is effective for measuring semantic textual similarity and outperforms some known related methods.
AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks

We propose a collection of three shift-based primitives for building efficient compact CNN-based networks. These three primitives (channel shift, address shift, shortcut shift) can reduce the inference time on GPU while maintains the prediction accuracy. These shift-based primitives only moves the pointer but avoids memory copy, thus very fast. For example, the channel shift operation is 12.7× faster compared to channel shuffle in ShuffleNet but achieves the same accuracy. The address shift and channel shift can be merged into the point-wise group convolution and invokes only a single kernel call, taking little time to perform spatial convolution and channel shift. Shortcut shift requires no time to realize residual connection through allocating space in advance. We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet. Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy.
Forensic Analysis of Tor Browser: A Case Study for Privacy and Anonymity on the Web

Web browsers are among the most commonly used applications to access the web from any platform nowadays. With recent digital incidents involving breach of data, users are becoming more cognizant of the threat posed by malicious actors having access to personal data as well as vulnerable applications which may compromise their data. For this very reason, users are being offered privacy preserving solutions for trust maturity. The onion router (Tor) browser is one such application which not only ensures the privacy preservation goals but also provides promising anonymity. Due to this feature, majority of the users use Tor browser for normal use as well as malign activities. In order to validate the claims of Tor browser and help digital forensic investigators and researchers, we created different scenarios to forensically analyze the Tor browser privacy and anonymity. As a result of the findings, it can be concluded that the Tor browser leaves plethora of sensitive digital artifacts on host machine, which can be further used to compromise user data.
Safe Route Determination for First Responders in the Presence of Moving Obstacles

Determining safe and fast routes for first responders is an important issue in a disaster response. Especially when different types of disasters (e.g., toxic plumes, fires, and floods) occur and affect transportation networks simultaneously, special routing strategies (e.g., detour) would be needed to ensure the safety for responders. On the other hand, after disasters happen, a quick response time is required, and the responders should move as fast as possible and even go through certain obstacles to reach the disaster sites to deliver emergency services. In this paper, we study path planning through moving obstacles, taking into account the influence of obstacles on the status of road networks and the speed of rescue vehicles. A set of algorithms is proposed to deal with not only geometries but also the properties of moving obstacles to support route generation. Based on the Dijkstra algorithm, a new routing algorithm is designed and developed, which aims at minimizing the risk while constraining the travel time of routes. We validate our approach with a set of experiments on some navigation cases. The experimental results show the promise of the algorithm in the generation of feasible and safe routes for first responders to pass through moving obstacles.
Detection of Gold Nanoparticles in Hydrothermal Fluids

Research Article| March 01, 2019 Detection of Gold Nanoparticles in Hydrothermal Fluids Mark Hannington; Mark Hannington 1GEOMAR, Helmholtz Centre for Ocean Research Kiel,Wischhofstrasse 1-3, 24148 Kiel, Germany2Earth and Environmental Sciences, University of Ottawa, 25 Templeton, Ottawa K1N 6N5, Canada †Corresponding author: e-mail, Mark.Hannington@uottawa.ca Search for other works by this author on: GSW Google Scholar Dieter Garbe-Schönberg Dieter Garbe-Schönberg 3Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10, 24118 Kiel, Germany Search for other works by this author on: GSW Google Scholar Economic Geology (2019) 114 (2): 397–400. https://doi.org/10.5382/econgeo.2019.4636 Article history accepted: 10 Jan 2019 first online: 27 Mar 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Mark Hannington, Dieter Garbe-Schönberg; Detection of Gold Nanoparticles in Hydrothermal Fluids. Economic Geology 2019;; 114 (2): 397–400. doi: https://doi.org/10.5382/econgeo.2019.4636 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEconomic Geology Search Advanced Search Abstract The transport and deposition of gold from colloidal suspensions in hydrothermal fluids has been a persistent theme in ore deposits research. Studies of active geothermal systems show that a complete model of gold transport must include both dissolved and particulate forms. However, samples of the hydrothermal fluids are commonly spiked with aqua regia after collection in order to put any solids back into solution, thus preventing a quantitative assessment of the particle load. Although attempts have been made to filter the solids, gold nanoparticles (Au NPs) will mostly pass the 0.2-μm filters that are in common use, and a simple technique for analyzing suspended particles in the liquids has been lacking. In this study, we demonstrate how time-resolved acquisition of mass 197 in a conventional inductively coupled plasma-mass spectrometer (ICP-MS) can be used to detect and measure Au NPs in the filtered liquids, with an example of well-characterized fluids from the Reykjanes geothermal field on Iceland. The technique allows for precise monitoring of the solution as it is introduced into the plasma with the capability of identifying individual particles carried in suspension. Results show that Au particles passing the 0.2-μm filters are abundant in the studied samples, and measurements of the individual particles can be used to determine their size. The experiment highlights the potential of emerging ICP-MS techniques, including very fast data acquisition and multielement analysis of single particles in time-of-flight mode, for characterization of NPs in hydrothermal fluids. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
A deep one-shot network for query-based logo retrieval

Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for practical deployment since it is practically impossible to have such annotated data for new unseen logo. In this work, we develop an easy-to-implement query-based logo detection and localization system by employing a one-shot learning technique. Given an image of a query logo, our model searches for it within a given target image and predicts the possible location of the logo by estimating a binary segmentation mask. The proposed model consists of a conditional branch and a segmentation branch. The former gives a conditional latent representation of the given query logo which is combined with feature maps of the segmentation branch at multiple scales in order to find the matching position of the query logo in a target image, should it be present. Feature matching between the latent query representation and multi-scale feature maps of segmentation branch using simple concatenation operation followed by 1x1 convolution layer makes our model scale-invariant. Despite its simplicity, our query-based logo retrieval framework achieved superior performance in FlickrLogos-32 and TopLogos-10 dataset over different existing baselines.
UAV’s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra

Image segmentation is widely used in the field of agriculture to improve the yields and protecting them from pests, herbs, shrubs, and weeds. Precision agriculture is also contributing to the inter and intra crop monitoring. Recently, unmanned aerial vehicles are used widely for acquiring images. In this paper, we purpose Clifford geometric algebra to enhance the segmented images acquired from the UAVs of different agricultural fields. The Clifford geometric algebra is also sometimes used as a collective term for the diverse range of mathematical fields, both classical and modern algebraic mathematics. Previous image segmentation approaches depend upon the intensity of red, green, and blue colors; but the complete perspective could not be obtained from these approaches. Geometric algebra overcomes this limitation and leads to a genuine color space image processing. It is mainly used in the processing of medical images. Subalgebra of the Clifford algebra is Quaternions. We have used this approach in agricultural images. The image segmentation of foreground and background is enhanced using Clifford geometric algebra; hence, the results obtained are fine-tuned segmented images. The anticipated result of our research would have a positive impact on the amelioration of the condition of the farmers and their livelihood.