Reviewerly/exHarmony huggingface dataset:
Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).
Why I No Longer Believe Social Media Is Cool . . .

Web 2.0. seemingly offered empowering opportunities for women globally. While #hashtags gained momentum, liberating women to speak-out against sexual oppression, forging networks for the right to learn to drive, to bridge the gender pay gaps, and to close digital divide, the era of the posthuman, postdigital, and postgender seemed to be just around the corner. A key aspect of this apparent empowerment has been the visual scope of social media that allows women to show the world who they are and how they want to be seen. Teaching in a media and communications program at a university in Dubai, I became interested in the emerging trend of Arab women presenting their lives on social media via image-sharing platforms such as Instagram and Snapchat and assumed that these practices challenged gender inequalities.
A Study on Customers’ Sentiment Analysis Based on Big Data Using Twitter Data

This paper focuses on mining the value of customers emotional behaviors using Twitter data.Using Apache Flume to collect tweets data from Twitter.192,390 tweets were collected.Then the natural language processing (NLP) technology has been used to divide and filter tweets for customers' emotional behaviors analysis.We picked five main hot topics among these tweets.Choose one of the hot topics HUAWEI honors 9 for sentiment analysis (SA).Compared with Native Bayes, Maximum Entropy Classifier.Decision Tree Classifier is the most effective classification method for our data sets.According to our experiment, the result shows that 45% of customers are satisfied with HUAWEI honors 9, but there is still having 36% of customers unsatisfied with it.Specifically, in the field of battery, game and stand-by power consumption, it needs a great of an improvement.
PAtt: Physics-based Attestation of Control Systems

Ensuring the integrity of embedded programmable logic controllers (PLCs) is critical for safe operation of industrial con-trol systems. In particular, a cyber-attack could manipulatecontrol logic running on the PLCs to bring the process ofsafety-critical application into unsafe states. Unfortunately,PLCs are typically not equipped with hardware support thatallows the use of techniques such as remote attestation to ver-ify the integrity of the logic code. In addition, so far remoteattestation is not able to verify the integrity of the physicalprocess controlled by the PLC.In this work, we present PAtt, a system that combines re-mote software attestation with control process validation. PAttleverages operation permutations—subtle changes in the op-eration sequences based on integrity measurements—whichdo not affect the physical process but yield unique traces ofsensor readings during execution. By encoding integrity mea-surements of the PLC’s memory state (software and data) intoits control operation, our system allows to remotely verifythe integrity of the control logic based on the resulting sensortraces. We implement the proposed system on a real PLCcontrolling a robot arm, and demonstrate its feasibility. Ourimplementation enables the detection of attackers that ma-nipulate the PLC logic to change process state and/or reportspoofed sensor readings (with an accuracy of 97% againsttested attacks).
Gaussian Process Regression for Maximum Entropy Distribution

Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical closure settings. Motivated by recent success of Gaussian processes, we investigate the suitability of Gaussian priors to approximate the Lagrange multipliers as a map of a given set of moments. Examining various kernel functions, the hyperparameters are optimized by maximizing the log-likelihood. The performance of the devised data-driven Maximum-Entropy closure is studied for couple of test cases including relaxation of non-equilibrium distributions governed by Bhatnagar-Gross-Krook and Boltzmann kinetic equations.
A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.
Developing cyber peacekeeping: Observation, monitoring and reporting

Cyberphysical societies are becoming reliant upon the cyber domain for everyday life. With cyber warfare increasingly becoming part of future conflicts, new and novel solutions are needed to assist governments in securing their national infrastructure. Cyber peacekeeping is one such solution: an emerging and multi-disciplinary field of research, touching upon technical, political, governmental and societal domains of thought. In this article we build upon previous works by developing the cyber peacekeeping activity of observation, monitoring and reporting. We take a practical approach: describing a scenario in which two cyberphysical societies experience the negative effects of cyber warfare and require cyber expertise to restore services their citizens depend upon. We explore how a cyber peacekeeping operation could start up and discuss the challenges it will face. The article makes a number of proposals, including the use of a virtual collaborative environment to bring multiple benefits. We conclude by summarising our findings, and describing where further work lies.
How Online Learning Evaluation (Kahoot) Affecting Students’ Achievement and Motivation (Case Study on it Students)

It is undeniable that the development of technology in the present impacts on various aspects, especially in the field of learning technology. In learning, lecturers are required to implement innovations in the field of technology. One of the implementations of education technology is the implementation of evaluation by using Kahoot. Kahoot is a game-based learning platform. By implementing this learning evaluation, we expect students' achievement and motivation in learning to improve. The research method of this research is simple qualitative descriptive using a questionnaire that were distributed to the first semester Informatics Engineering students as the object of this research. From the questionnaire the results showed that 82.2% of students stated that Kahoot was able to increase their learning motivation. Other results is the increasing of students’ scores in this evaluations and the obstacle found in implementing this evaluation is the umstable connections or internet networks.
Project Geollery.com: Reconstructing A Live Mirrored World With Geotagged Social Media

Social media in virtual reality is in a high-growth market segment with influential products and services in virtual tourism, remote education, and business meetings. Nevertheless, previous systems have never achieved an online platform which renders a 6DoF mirrored world with geotagged social media in real time. In this paper, we introduce the technical detail behind Geollery.com which reconstructs a mirrored world at two levels of detail. Given a pair of latitude and longitude coordinates, our pipeline streams and caches depth maps, street view panoramas, and building polygons from Google Maps and OpenStreetMap APIs. At a fine level of detail for close-up views, we render textured meshes using adjacent local street views and depth maps. When viewed from afar, we apply projection mappings to 3D geometries extruded from building polygons for a coarse level of detail. In contrast to teleportation, our system allows users to virtually walk through the mirrored world at the street level. Our system integrates geotagged social media from both internal users and external sources such as Twitter, Yelp, and Flicker. We validate our real-time strategies of Geollery.com on various platforms including mobile phones, workstations, and head-mounted displays.
Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding

This article describes Amobee's participation in "HatEval: Multilingual detection of hate speech against immigrants and women in Twitter" (task 5) and "OffensEval: Identifying and Categorizing Offensive Language in Social Media" (task 6).The goal of task 5 was to detect hate speech targeted to women and immigrants.The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target.We present a novel type of convolutional neural network called "Multiple Choice CNN" (MC-CNN) that we used over our newly developed contextual embedding, Rozental et al. ( 2019) 1 .For both tasks we used this architecture and achieved 4th place out of 69 participants with an F 1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B -automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).
Per-instance Differential Privacy

We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold for every instance separately. We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold for every instance separately. When the data is drawn from a distribution, we show that per-instance DP implies generalization. Moreover, we provide explicit calculations of the per-instance DP for the output perturbation on a class of smooth learning problems. The result reveals an interesting and intuitive fact that an individual has stronger privacy if he/she has small ``leverage score'' with respect to the data set and if he/she can be predicted more accurately using the leave-one-out data set. Simulation shows several orders-of-magnitude more favorable privacy and utility trade-off when we consider the privacy of only the users in the data set. In a case study on differentially private linear regression, provide a novel analysis of the One-Posterior-Sample (OPS) estimator and show that when the data set is well-conditioned it provides $(\epsilon,\delta)$-pDP for any target individuals and matches the exact lower bound up to a $1+\tilde{O}(n^{-1}\epsilon^{-2})$ multiplicative factor. We also demonstrate how we can use a ``pDP to DP conversion'' step to design AdaOPS which uses adaptive regularization to achieve the same results with $(\epsilon,\delta)$-DP.
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism

Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources only to the best individual of each niche: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity-preserving mechanism. Using rigorous runtime analysis to explain how and why it is a powerful method, we prove that a mutation-based evolutionary algorithm with a large enough population size, and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that with phenotypic and genotypic distances clearing is able to find both optima for Twomax and several general classes of bimodal functions in polynomial expected time. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and to support the theoretical results.
Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos

While learning based depth estimation from images/videos has achieved substantial progress, there still exist intrinsic limitations. Supervised methods are limited by a small amount of ground truth or labeled data and unsupervised methods for monocular videos are mostly based on the static scene assumption, not performing well on real world scenarios with the presence of dynamic objects. In this paper, we propose a new learning based method consisting of DepthNet, PoseNet and Region Deformer Networks (RDN) to estimate depth from unconstrained monocular videos without ground truth supervision. The core contribution lies in RDN for proper handling of rigid and non-rigid motions of various objects such as rigidly moving cars and deformable humans. In particular, a deformation based motion representation is proposed to model individual object motion on 2D images. This representation enables our method to be applicable to diverse unconstrained monocular videos. Our method can not only achieve the state-of-the-art results on standard benchmarks KITTI and Cityscapes, but also show promising results on a crowded pedestrian tracking dataset, which demonstrates the effectiveness of the deformation based motion representation. Code and trained models are available at https://github.com/haofeixu/rdn4depth.
DCR: A new distributed model for human activity recognition in smart homes

Human Activity Recognition (HAR) is an important research issue for pervasive computing that aims to identify human activities in smart homes. In the literature, most reasoning approaches for HAR are based on centralized approach where a central system is responsible for processing and reasoning about sensor data in order to recognize activities. Since sensor data are distributed, heterogeneous, and dynamic (i.e., whose characteristics are varying over time) in the smart home, reasoning process on these data for HAR needs to be distributed over a group of heterogeneous, autonomous and interacting entities in order to be more efficient. This paper proposes a main contribution, the DCR approach, a fully Distributed Collaborative Reasoning multi-agent approach where agents, with diverse classifiers, observe sensor data, make local predictions, communicate and collaborate to identify current activities. Then, an improved version of the DCR approach is proposed, the DCR-OL approach, a distributed Online Learning approach where learning agents learns from their collaborations to improve their own performance in activity recognition. Finally, we test our approaches by performing an evaluation study on Aruba dataset, that indicates an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the centralized approach and also compared to a distributed approach existing in the literature.
Balanced Coherence Times of Atomic Qubits of Different Species in a Dual <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>3</mml:mn><mml:mo>×</mml:mo><mml:mn>3</mml:mn></mml:math> Magic-Intensity Optical Dipole Trap Array

We construct a polarization-mediated magic-intensity (MI) optical dipole trap (ODT) array, in which the detrimental effects of light shifts on the mixed-species qubits are efficiently mitigated so that the coherence times of the mixed-species qubits are both substantially enhanced and balanced for the first time. This mixed-species magic trapping technique relies on the tunability of the coefficient of the third-order cross term and ground state hyperpolarizability, which are inherently dependent on the degree of circular polarization of the trapping laser. Experimentally, polarization of the ODT array for $^{85}\mathrm{Rb}$ qubits is finely adjusted to a definite value so that its working magnetic field required for magic trapping amounts to the one required for magically trapping $^{87}\mathrm{Rb}$ qubits in another ODT array with fully circular polarization. Ultimately, in such a polarization-mediated MI-ODT array, the coherence times of $^{87}\mathrm{Rb}$ and $^{85}\mathrm{Rb}$ qubits are, respectively, enhanced up to $891\ifmmode\pm\else\textpm\fi{}47\text{ }\text{ }\mathrm{ms}$ and $943\ifmmode\pm\else\textpm\fi{}35\text{ }\text{ }\mathrm{ms}$. Moreover, we reveal that the noise of the elliptic polarization causes dephasing effect on the $^{85}\mathrm{Rb}$ qubits but it could be efficiently mitigated by choosing the dynamical range of active polarization device. We also show that light shifts seen by qubits in an elliptically polarized MI-ODT can be more efficiently compensated due to the decrease in the ground state hyperpolarizability. It is anticipated that the novel mixed-species MI-ODT array is a versatile platform for building scalable quantum computers with neutral atoms.
Metadata-driven Task Relation Discovery for Multi-task Learning

Task Relation Discovery (TRD), i.e., reveal the relation of tasks, has notable value: it is the key concept underlying Multi-task Learning (MTL) and provides a principled way for identifying redundancies across tasks. However, task relation is usually specifically determined by data scientist resulting in the additional human effort for TRD, while transfer based on brute-force methods or mere training samples may cause negative effects which degrade the learning performance. To avoid negative transfer in an automatic manner, our idea is to leverage commonly available context attributes in nowadays systems, i.e., the metadata. In this paper, we, for the first time, introduce metadata into TRD for MTL and propose a novel Metadata Clustering method, which jointly uses historical samples and additional metadata to automatically exploit the true relatedness. It also avoids the negative transfer by identifying reusable samples between related tasks. Experimental results on five real-world datasets demonstrate that the proposed method is effective for MTL with TRD, and particularly useful in complicated systems with diverse metadata but insufficient data samples. In general, this study helps in automatic relation discovery among partially related tasks and sheds new light on the development of TRD in MTL through the use of metadata as apriori information.
Learning Generative Adversarial Networks from Multiple Data Sources

Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.
Exploring weight symmetry in deep neural networks

We propose to impose symmetry in neural network parameters to improve parameter usage and make use of dedicated convolution and matrix multiplication routines. Due to the significant reduction in the number of parameters as a result of the symmetry constraints, one would expect a dramatic drop in accuracy. Surprisingly, we show that this is not the case, and, depending on network size, symmetry can have little or no negative effect on network accuracy, especially in deep overparameterized networks. We propose several ways to impose local symmetry in recurrent and convolutional neural networks and show that our symmetry parameterizations satisfy universal approximation property for single hidden layer networks. We extensively evaluate these parameterizations on CIFAR, ImageNet and language modeling datasets, showing significant benefits from the use of symmetry. For instance, our ResNet-101 with channel-wise symmetry has almost 25% fewer parameters and only 0.2% accuracy loss on ImageNet. Code for our experiments is available at https://github.com/hushell/deep-symmetry.
Exponential Taylor Series Method to eliminate the gamma distortion in phase shifting profilometry

Phase shifting profilometry (PSP) is widely applied in three-dimensional (3D) shape measurement because of its high precision and simple hardware configuration. However, the gamma effect of commercial digital projector usually causes nonlinear periodical phase error. In this paper, we propose an approach named as Exponential Taylor Series Method (ETSM) to eliminate the gamma distortion. The ETSM sets the input fringe pattern intensity as an exponential Taylor series function. Along with the decoding algorithm proposed, the nonlinear phase error caused by the gamma effect can be eliminated theoretically. Simulations and experiments are conducted to verify the ETSM's performances by comparing with the conventional method of PSP and another method based on exponent. The results show that the ETSM greatly reduces the phase error caused by gamma distortion without any additional work or pre-process. The ETSM also performs better in resisting the noise due to its quasi-sinusoidal intensity distribution of the captured fringe patterns.