Reviewerly/exHarmony huggingface dataset:
Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility

High utility itemset mining is an important model with many real-world applications. But the popular adoption and successful industrial application of this model has been hindered by the following two limitations: (i) computational expensiveness of the model and (ii) infrequent itemsets may be output as high utility itemsets. This paper makes an effort to address these two limitations. A generic high utility-frequent itemset model is introduced to find all itemsets in the data that satisfy user-specified minimum support and minimum utility constraints. Two new pruning measures, named cutoff utility and suffix utility, are introduced to reduce the computational cost of finding the desired itemsets. A single phase fast algorithm, called High Utility Frequent Itemset Miner (HU-FIMi), is introduced to discover the itemsets efficiently. Experimental results demonstrate that the proposed algorithm is efficient.
Potentials of Virtual Reality as an Instrument for Research and Education

Abstract Virtual Reality (VR) is currently on everyone’s lips for various purposes. Although the hype is currently nearing its productive plateau, there are still uncertainties about the utilization and potential of the technology in research and educational contexts. Together with the two following articles on the subject of VR research, this article provides an overview of the inherent potential as well as remaining challenges in the use of VR as an instrument for research and education. These will be clarified and critically examined on the basis of specific academic projects. Based on this, the article concludes with a comprehensive look on the need for further discussion of ethical issues as the key for VR success in research and education.
Diurnal and seasonal variations of the incoming solar radiation flux at a tropical station, Ile-Ife, Nigeria

The diurnal and seasonal variations of the incoming solar radiation have been studied by analysing two years data measured between January, 2016 to December, 2017 at a Tropical station, Ile-Ife (7.53°N; 4.54°E), Nigeria. The maximum incoming solar radiation flux which occurs between 13:00 – 14:00 LT and varies in the course of the year from 639.5 ± 171.6 Wm-2 (with large fluctuations) in the wet months (March – October) to 700.7 ± 105.2 Wm-2 in the dry months (November – February). The large differences in the values, diurnal and seasonal variation of the measured incoming solar radiation between the dry and wet seasons are attributed to the attenuation of the flux by aerosol particles in the dry season and increased cloudiness and humidity in the wet season. The monthly maximum values of 760.3 Wm-2 and 732.8 Wm-2 indicated a double peak from March to May and October to November respectively while a minimum of about 492.7 Wm-2 was recorded from July to August. Similarly, the daytime average had a double peak of 412.5 Wm-2 and 361.3 Wm-2 in March/April/May and October/November respectively, equally a minimum value of about 249.8 Wm-2 was recorded in July/August. The maximum value of the air temperature (which occurs around 15:00 LT) was observed to lag behind the maximum value of the incoming solar radiation (which occurs around 13:00 LT) by 2 hours at the study site. The statistical analysis of the monthly daytime averages of the incoming solar radiation showed that the intensity of the flux received at Ile-Ife (a tropical location) is high (about 67% of the incoming solar radiation are between the interval 325 and 400 Wm-2) throughout the year.
Identification of Phishing Attack in Websites Using Random Forest-SVM Hybrid Model

Phishing attacks have increased in the last few years with the rapid growth of economy and technology. Attackers with less technical knowledge can also perform phishing with sources that are available in public. Financial losses are experienced by businesses and customers thus decreasing confidence in e-commerce. Hence there is a necessity to implement countermeasures to overcome phishing attacks in the website. In this paper, a hybrid model is proposed integrating Random Forest and Support Vector Machine (SVM) techniques. Machine learning models are efficient in prediction and analyze large volumes of data. Experimental results on the phishing datasets how that an accuracy of 94% is obtained by the hybrid model in comparison to the base classifier SVM accuracy of 90% and Random Forest accuracy of 92. 96%. Thus, the model is superior in classifying the phishing attacks.
Blockchain in Distributed CAD Environments

Distributed and collaborative CAD (Computer Aided Design) environments gained wide popularity recently, in engineering fields such as BIM (Building Information Modelling) and GIS (Geographical Information Systems) in Civil Engineering, or PDM/PLM (Product Data Management/Product Lifecycle Management) in Mechanical Engineering. One of key issues in these applications is data integrity and confidence in information stored in information systems. Blockchain technology, initially invented to support the cryptocurrency trusted authority, opened its way to other areas, and it could be the solution for the data integrity issue in distributed CAD environments.
Research on hot news classification algorithm based on deep learning

Nowadays, the Internet development in full swing, has given rise to a variety of network applications, network news as the basic application in the information age, has been more and more customers favor, however, news website to report news type number many, every day the user focus on hot news, so I need to forecast the hot problem in the news, so in view of the study of this problem, this paper studies deep learning application in text categorization, combined with the characteristics of the news text, put forward the double Bi-Gated Recurrent Unit(GRU) + attention deep learning model to predict hotspots, and achieved good results.
Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners’ Rapid Access

MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> has built in five derivative-free optimizers (DFOs), including two direct search algorithms (simplex search, pattern search) and three heuristic algorithms (simulated annealing, particle swarm optimization, and genetic algorithm), plus a few in the official user repository, such as Powell’s conjugate (PC) direct search recommended by MathWorks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> . To help a practicing engineer or scientist to choose a MATLAB DFO most suitable for their application at hand, this paper presents a set of five benchmarking criteria for optimization algorithms and then uses four widely adopted benchmark problems to evaluate the DFOs systematically. Comprehensive tests recommend that the PC be most suitable for a unimodal or relatively simple problem, whilst the genetic algorithm (with elitism in MATLAB, GAe) for a relatively complex, multimodal or unknown problem. This paper also provides an amalgamated scoring system and a decision tree for specific objectives, in addition to recommending the GAe for optimizing structures and categories as well as for offline global search together with PC for local parameter tuning or online adaptation. To verify these recommendations, all the six DFOs are further tested in a case study optimizing a popular nonlinear filter. The results corroborate the benchmarking results. It is expected that the benchmarking system would help select optimizers for practical applications.
A measurement study of YouTube 360° live video streaming

360° live video streaming is becoming increasingly popular. While providing viewers with enriched experience, 360° live video streaming is challenging to achieve since it requires a significantly higher bandwidth and a powerful computation infrastructure. A deeper understanding of this emerging system would benefit both viewers and system designers. Although prior works have extensively studied regular video streaming and 360° video on demand streaming, we for the first time investigate the performance of 360° live video streaming. We conduct a systematic measurement of YouTube's 360° live video streaming using various metrics in multiple practical settings. Our key findings suggest that viewers are advised not to live stream 4K 360° video, even when dynamic adaptive streaming over HTTP (DASH) is enabled. Instead, 1080p 360° live video can be played smoothly. However, the extremely large one-way video delay makes it only feasible for delay-tolerant broadcasting applications rather than real-time interactive applications. More importantly, we have concluded from our results that the primary design weakness of current systems lies in inefficient server processing, non-optimal rate adaptation, and conservative buffer management. Our research insight will help to build a clear understanding of today's 360° live video streaming and lay a foundation for future research on this emerging yet relatively unexplored area.
A Mobile-Based Patient-Centric Passive System for Guiding Patients Through the Hospital Workflow: Design and Development

Background: A hospital is an unfamiliar place to patients because of its style, atmosphere, and procedures. These hospital characteristics cause patients to become confused about responding to protocols, which slows down the procedural flows. Some additional information technology infrastructure facilities and human resources may be needed to solve these problems. However, this solution needs high investment and cannot guarantee an accuracy of information sent to patients. To handle this limitation, EasyHos has been developed to help patients recognize their status (for example, “waiting for an appointment at 11am“) during their stay in a hospital using all existing infrastructure and hospital data and without changing existing hospital's process.
Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics

Language use varies across different demographic factors, such as gender, age, and geographic location. However, most existing document classification methods ignore demographic variability. In this study, we examine empirically how text data can vary across four demographic factors: gender, age, country, and region. We propose a multitask neural model to account for demographic variations via adversarial training. In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.
Experimental measurement of bridge deflection using Digital Image Correlation

Civil engineering bridges usually face a multitude of loads, ranging from strong winds to intense traffic. Thus, it is important to understand their behaviour for adequate maintenance design and prevention of catastrophic failure. The present work reports an image sensing methodology for deflection measurement that was successfully employed on the Entre-Águas bridge in Caniçal, Madeira, Portugal. For the application of two-dimensional Digital Image Correlation, it was necessary to build and deploy a patterned target onto the bridge in the monitored area and a camera setup in stable ground on a plane orthogonal to the target. Two different 2D DIC software packages were used for calculations, one commercial and one self-developed, enabling their comparison and increasing the confidence in the experimental results. The developed system was applied in order to analyze the effects of the passage of 30-ton trucks in the bridge, with promising results.
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

Homomorphic Encryption (HE) is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). Several Leveled-HE (LHE)-enabled Convolutional Neural Networks (LHECNNs) are proposed to implement MLaaS to avoid the large bootstrapping overhead. However, prior LHECNNs have to pay significant computational overhead but achieve only low inference accuracy, due to their polynomial approximation activations and poolings. Stacking many polynomial approximation activation layers in a network greatly reduces the inference accuracy, since the polynomial approximation activation errors lead to a low distortion of the output distribution of the next batch normalization layer. So the polynomial approximation activations and poolings have become the obstacle to a fast and accurate LHECNN model. In this paper, we propose a Shift-accumulation-based LHE-enabled deep neural network (SHE) for fast and accurate inferences on encrypted data. We use the binary-operation-friendly leveled-TFHE (LTFHE) encryption scheme to implement ReLU activations and max poolings. We also adopt the logarithmic quantization to accelerate inferences by replacing expensive LTFHE multiplications with cheap LTFHE shifts. We propose a mixed bitwidth accumulator to expedite accumulations. Since the LTFHE ReLU activations, max poolings, shifts and accumulations have small multiplicative depth, SHE can implement much deeper network architectures with more convolutional and activation layers. Our experimental results show SHE achieves the state-of-the-art inference accuracy and reduces the inference latency by 76.21% ~ 94.23% over prior LHECNNs on MNIST and CIFAR-10.
Split or Merge: Which is Better for Unsupervised RST Parsing?

Rhetorical Structure Theory (RST) parsing is crucial for many downstream NLP tasks that require a discourse structure for a text.Most of the previous RST parsers have been based on supervised learning approaches.That is, they require an annotated corpus of sufficient size and quality, and heavily rely on the language and domain dependent corpus.In this paper, we present two language-independent unsupervised RST parsing methods based on dynamic programming.The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones.The second builds the optimal tree in terms of a similarity score function that is defined for merging two adjacent spans into a large one.Experimental results on English and German RST treebanks showed that our parser based on span merging achieved the best score, around 0.8 F 1 score, which is close to the scores of the previous supervised parsers.
Perception of Looming Motion in Virtual Reality Egocentric Interception Tasks

Motion in depth is commonly misperceived in Virtual Reality (VR), making it difficult to intercept moving objects, for example, in games. We investigate whether motion cues could be modified to improve these interactions in VR. We developed a time-to-contact estimation task, in which observers (n = 18) had to indicate by button press when a looming virtual object would collide with their head. We show that users consistently underestimate speed. We construct a user-specific model of motion-in-depth perception, and use this model to propose a novel method to modify monocular depth cues tailored to the specific user, correcting individual response errors in speed estimation. A user study was conducted in a simulated baseball environment and observers were asked to hit a looming baseball back in the direction of the pitcher. The study was conducted with and without intervention and demonstrates the effectiveness of the method in reducing interception errors following cue modifications. The intervention was particularly effective at fast ball speeds where performance is most limited by the user's sensorimotor constraints. The proposed approach is easy to implement and could improve the user experience of interacting with dynamic virtual environments.
Optimal Dubins Paths to Intercept a Moving Target on a Circle

We present a path planning problem for a pursuer to intercept a cooperating target traveling on a circle. The pursuer considered here has limited yaw rate, and therefore its path should satisfy the kinematic constraints. We assume that the distance between initial position of the pursuer and any point on the target circle is greater than four times the minimum turn radius of the pursuer. We prove the continuity of the length of the Dubins paths of type Circle-Straight line-Circle with respect to the position on the target circle. This is used to prove that the optimal interception path is a Dubins path, and we present an iterative algorithm to find the optimal interception point on the target circle.
Distributed Contribution-Based Quantum-Behaved Particle Swarm Optimization With Controlled Diversity for Large-Scale Global Optimization Problems

Many cooperative coevolution optimization algorithms have been proposed recently for solving large-scale global optimization problems. These algorithms first decompose a large-scale global optimization problem into several subproblems, each with a specific number of decision variables, and then optimize the subproblems separately. However, if computing resources are not reasonably allocated to subproblems, computational resources may be wasted. In this paper, we propose a distributed contribution-based quantum-behaved particle swarm optimization with controlled diversity (DC-QPSO) for large-scale global optimization problems. According to the level of optimized contribution of each subproblem, the computing resources are reallocated automatically in each stage, guaranteeing that subgroups with more contribution get more computational resources. Moreover, a parallel diversity control strategy is proposed to enhance the capability of finding better solutions to problems. CEC’2010 and CEC’2013 benchmark function suits are selected to test the performance of the proposed algorithms. The experimental results demonstrate the better performance of the proposed DC-QPSO on the benchmark functions, compared to other state-of-the-art large-scale global optimization algorithms.
Bounding Sequential Estimation Errors Due to Gauss-Markov Noise with Uncertain Parameters

This paper describes the derivation and implementation of a new method to overbound Kalman filter (KF) based estimate error distributions in the presence of time-correlated measurement and process noise.The method is specific to problems where each input noise component is first-order Gauss-Markov with a distinct variance 2 ∈ [ min 2 , max 2 ] and time constant ∈ [ min , max ].The bounds on 2 and are known.Reference [1] derives an overbound for the continuous-time KF, and we extend the result to the more common case of sampled-data systems with discrete-time measurements.We prove that the KF covariance matrix overbounds the estimate error distribution when Gauss-Markov processes are defined using a time constant max and a process noise variance inflated by ( max min ⁄ ).We also show that the overbound is tightest by initializing the variance of the Gauss-Markov process with 0. The new method is evaluated using covariance analysis for an example application in advanced receiver autonomous integrity monitoring (ARAIM) [2].
CART- A Statistical Model for Predicting QoE using Machine Learning in Smartphones

Abstract The proliferation of fast, cheap, and ubiquitous network access, in particular on mobile devices, has significantly changed the way user’s access online benefits on an everyday premise. Alongside the expanded accessibility of a dependably on Internet, new advancements, for example, LTE have empowered the utilization of a wide range of administrations, with stringent necessities regarding system execution and limit requests. We consider different QoE appraisal models in view of administered machine learning strategies, which are proficient to foresee the QoE experienced by the end client of well-known cell phone applications (e.g., YouTube and Face book), utilizing as info the inactive in-gadget estimations. In this extraordinary issue, we investigate the connection between the Quality of Service (QoS) that administrators screen and oversee and the Quality of Experience (QoE) that the clients really get. Utilizing a rich QoE dataset got from field trials in operational cell systems, we benchmark the execution of various machine learning based indicators, and build a choice tree based model which is able to foresee the per-client general understanding and administration worthiness with a win rate of 91% and 98% separately. To the best of our knowledge, this is the initial proposal using end-user, in-device passive measurements and machine learning models to predict the QoE of smartphone users in operational cellular networks.
Spatial Resolution Matching of Microwave Radiometer Data with Convolutional Neural Network

Passive multi-frequency microwave remote sensing is often plagued with the problems of low- and non-uniform spatial resolution. In order to adaptively enhance and match the spatial resolution, an accommodative spatial resolution matching (ASRM) framework, composed of the flexible degradation model, the deep residual convolutional neural network (CNN), and the adaptive feature modification (AdaFM) layers, is proposed in this paper. More specifically, a flexible degradation model, based on the imaging process of the microwave radiometer, is firstly proposed to generate suitable datasets for various levels of matching tasks. Secondly, a deep residual CNN is introduced to jointly learn the complicated degradation factors of the data, so that the resolution can be matched up to fixed levels with state of the art quality. Finally, the AdaFM layers are added to the network in order to handle arbitrary and continuous resolution matching problems between a start and an end level. Both the simulated and the microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite have been used to demonstrate the validity and the effectiveness of the method.
Yodel

Yodel is the first system for voice calls that hides metadata (e.g., who is communicating with whom) from a powerful adversary that controls the network and compromises servers. Voice calls require sub-second message latency, but low latency has been difficult to achieve in prior work where processing each message requires an expensive public key operation at each hop in the network. Yodel avoids this expense with the idea of self-healing circuits, reusable paths through a mix network that use only fast symmetric cryptography. Once created, these circuits are resilient to passive and active attacks from global adversaries. Creating and connecting to these circuits without leaking metadata is another challenge that Yodel addresses with the idea of guarded circuit exchange, where each user creates a backup circuit in case an attacker tampers with their traffic. We evaluate Yodel across the internet and it achieves acceptable voice quality with 990 ms of latency for 5 million simulated users.
India stack---digital infrastructure as public good

research-article Share on India stack---digital infrastructure as public good Authors: Vivek Raghavan UIDAI, Bangalore, India UIDAI, Bangalore, IndiaView Profile , Sanjay Jain IIMA, Bangalore, India IIMA, Bangalore, IndiaView Profile , Pramod Varma UIDAI, Bangalore, India UIDAI, Bangalore, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 11November 2019 pp 76–81https://doi.org/10.1145/3355625Published:24 October 2019Publication History 5citation7,256DownloadsMetricsTotal Citations5Total Downloads7,256Last 12 Months712Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access