Reviewerly/exHarmony huggingface dataset:
Detecting shilling attacks in social recommender systems based on time series analysis and trust features

In social recommender systems or trust-based recommender systems, malicious users can bias the recommendations by injecting a large number of fake profiles and by building bogus trust relationships. The existing shilling attack detection methods suffer from low precision when detecting attacks in social recommender systems because they focus mainly on the rating pattern differences between attack profiles and genuine ones and ignore the trust relationships between users. In this paper, we propose an approach for detecting shilling attacks in social recommender systems based on time series analysis and trust features (TSA–TF). Firstly, we construct rating distribution time series for items and propose a dynamic rating distribution prediction model to detect suspicious items by using a single exponential smoothing method. Then, we filter out a part of genuine user profiles by analyzing suspicious items and obtain the set of suspicious user profiles. Secondly, we propose four features by combining rating patterns and trust relationships and train a support vector machine (SVM) classifier to discriminate attack profiles in the set of suspicious user profiles. Experiments on the CiaoDVD dataset and Epinions dataset show that the proposed approach can improve the detection precision while maintaining a high recall.
A fast approach to refraction-aware eye-model fitting and gaze prediction

By temporally integrating information about pupil contours extracted from eye images, model-based methods for glint-free gaze estimation can mitigate pupil detection noise. However, current approaches require time-consuming iterative solving of a nonlinear minimization problem to estimate key parameters, such as eyeball position. Based on the method presented by [Swirski and Dodgson 2013], we propose a novel approach to glint-free 3D eye-model fitting and gaze prediction using a single near-eye camera. By recasting model optimization as a least-squares intersection of lines, we make it amenable to a fast non-iterative solution. We further present a method for estimating deterministic refraction-correction functions from synthetic eye images and validate them on both synthetic and real eye images. We demonstrate the robustness of our method in the presence of pupil detection noise and show the benefit of temporal integration of pupil contour information on eyeball position and gaze estimation accuracy.
TopoX

Traditional graph partitioning methods attempt to both minimize communication cost and guarantee load balancing in computation. However, the skewed degree distribution of natural graphs makes it difficult to simultaneously achieve the two objectives. This paper proposes topology refactorization (TR), a topology-aware method allowing graph-parallel systems to separately handle the two objectives: refactorization is mainly focused on reducing communication cost, and partitioning is mainly targeted for balancing the load. TR transforms a skewed graph into a more communication-efficient topology through fusion and fission , where the fusion operation organizes a set of neighboring low-degree vertices into a super-vertex, and the fission operation splits a high-degree vertex into a set of sibling sub-vertices. Based on TR, we design an efficient graph-parallel system (TopoX) which pipelines refactorization with partitioning to both reduce communication cost and balance computation load. Prototype evaluation shows that TopoX outperforms state-of-the-art PowerLyra by up to 78.5% (from 37.2%) on real-world graphs and is significantly faster than other graph-parallel systems, while only introducing small refactorization overhead and memory consumption.
Quantum Interference between Photons from an Atomic Ensemble and a Remote Atomic Ion

Many remote-entanglement protocols rely on the generation and interference of photons produced by nodes within a quantum network. Quantum networks based on heterogeneous nodes provide a versatile platform by utilizing the complementary strengths of the differing systems. Implementation of such networks is challenging, due to the disparate spectral and temporal characteristics of the photons generated by the different quantum systems. Here, we report on the observation of quantum interference between photons generated from a single ion and an atomic ensemble. The photons are produced on demand by each source located in separate buildings, in a manner suitable for quantum networking. Given these results, we analyze the feasibility of hybrid ion-ensemble remote entanglement generation.
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context

Word embeddings are a key component of high-performing natural language processing (NLP) systems, but it remains a challenge to learn good representations for novel words on the fly, i.e., for words that did not occur in the training data. The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space. Currently, two approaches for learning embeddings of novel words exist: (i) learning an embedding from the novel word’s surface-form (e.g., subword n-grams) and (ii) learning an embedding from the context in which it occurs. In this paper, we propose an architecture that leverages both sources of information – surface-form and context – and show that it results in large increases in embedding quality. Our architecture obtains state-of-the-art results on the Definitional Nonce and Contextual Rare Words datasets. As input, we only require an embedding set and an unlabeled corpus for training our architecture to produce embeddings appropriate for the induced embedding space. Thus, our model can easily be integrated into any existing NLP system and enhance its capability to handle novel words.
Google AutoML: Cloud Vision

Google Cloud AutoML Vision facilitates the creation of custom vision models for image recognition use cases. This managed service works with the concepts of transfer learning and neural architecture search under the hood to find the best network architecture and the optimal hyper-parameter configuration of that architecture that minimizes the loss function of the model. This chapter will go through a sample project of building a custom image recognition model using Google Cloud AutoML Vision. In this chapter, we will build an image model to recognize select cereal boxes.
Redirected Smooth Mappings for Multiuser Real Walking in Virtual Reality

We propose a novel technique to provide multiuser real walking experiences with physical interactions in virtual reality (VR) applications. In our system, multiple users walk freely while navigating a large virtual environment within a smaller physical workspace. These users can interact with other real users or physical props in the same physical locations. The key of our method is a redirected smooth mapping that incorporates the redirected walking technique to warp the input virtual scene with small bends and low distance distortion. Users possess a wide field of view to explore the mapped virtual environment while being redirected in the real workspace. To keep multiple users away from the overlaps of the mapped virtual scenes, we present an automatic collision avoidance technique based on dynamic virtual avatars. These avatars naturally appear, move, and disappear, producing as little influence as possible on users’ walking experiences. We evaluate our multiuser real walking system through formative user studies, and demonstrate the capability and practicability of our technique in two multiuser applications.
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation

Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entanglement of the latent space.We impose an explicit constraint on the Kullback-Leibler (KL) divergence term inside the VAE objective function.While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel.Within this framework, we explore different properties of the estimated posterior distribution, and highlight the trade-off between the amount of information encoded in a latent code during training, and the generative capacity of the model. 1
Neural aesthetic image reviewer

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high‐ or low‐aesthetic score should be assigned to an image. From such a point of view, the authors propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, they propose three models based on shared aesthetically semantic layers and task‐specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, they collect the AVA‐Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi‐task learning, the proposed models can rate aesthetic images as well as produce comments in an end‐to‐end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA‐Reviews dataset. Moreover, they demonstrate experimentally that the authors’ model can generate textual reviews related to aesthetics, which are consistent with human perception.
Inverse Attention Guided Deep Crowd Counting Network

In this paper, we address the challenging problem of crowd counting in congested scenes. Specifically, we present Inverse Attention Guided Deep Crowd Counting Network (IA-DCCN) that efficiently infuses segmentation information through an inverse attention mechanism into the counting network, resulting in significant improvements. The proposed method, which is based on VGG-16, is a single-step training framework and is simple to implement. The use of segmentation information does not require additional annotation efforts. We demonstrate the significance of segmentation guided inverse attention through a detailed analysis and ablation study. Furthermore, the proposed method is evaluated on three challenging crowd counting datasets and is shown to achieve significant improvements over several recent methods.
A Novel LSTM-GAN Algorithm for Time Series Anomaly Detection

Time series anomaly detection is an important part of Prognostic and Health Management (PHM), and has been widely studied and followed with interest. The data with time series features often has non-stationary properties, and its fluctuation amplitude changes with time. Traditional anomaly detection algorithms can achieve the detection of shallow level anomalies when facing such data, however they fail to detect outliers on deep features of time series data. The gate structure of the long short-term memory network (LSTM) shows obvious advantages in processing time series data, while the confrontation training of generative adversarial network (GAN) performs well in detecting and acquiring deep features of data. Therefore, this paper focuses on the anomaly detection of time series data with the fusion model of LSTM and GANs, which is named the LSTM-GAN, and the performance of the algorithm is verified from two sets of time series data. The experimental results demonstrated that the proposed algorithm achieved superior performance in processing time series data compared to conventional algorithms. The research content of this article has profound guiding significance for time series anomaly detection.
Enhanced Potential Field-Based Collision Avoidance for Unmanned Aerial Vehicles in a Dynamic Environment

Collision avoidance in aerial environments using the conventional artificial potential field (APF) often faces local minima problems and the results prevent unmanned aerial vehicles (UAVs) from performing their missions. In addition, an UAV’s paths planned based on the conventional method are safe trajectories only in a certain static environment. To generate optimal and collision-free paths in a dynamic environment, the authors propose a novel APF approach, called “enhanced curl-free vector field”. For the repulsive potential field of the approach proposed, one computes each angle between the velocity vectors of UAVs and the relative position vectors of moving obstacles to the UAVs. The comparisons of the computed angles and the velocity of UAVs determine the direction of the curl-free vector field. Results from two case simulations, static obstacles with local minima and dynamic obstacles, show that our approach solves the local minima problems of path planning and generates more efficient paths for avoiding potential collisions caused by dynamic obstacles compared to the existing APF methods.
On the security of privacy‐preserving authentication scheme with full aggregation in vehicular ad hoc network

Abstract Certificateless aggregate signature (CLAS) scheme is a very important cryptographic technique used in many internet of things (IoT) applications like healthcare wireless sensor networks, industrial IoT, smart agriculture, and smart transportation to achieve privacy and integrity of transmitted information, and improved efficiency. Recently, a privacy‐preserving authentication scheme based on CLAS scheme for secure communication in vehicular ad hoc network (VANET) which can achieve complete aggregation was proposed. The authors demonstrated that their scheme is semantically secure in the random oracle model based on the intractability of the computational Diffie‐Hellman (CDH) problem under the consideration of type I and II attacks. However, by giving two concrete attacks, we show that the scheme is insecure in the standard security model. Consequently, we propose a fix by modifying the sign, verify, and aggregate‐verify algorithms of the scheme. Afterwards, we demonstrate that with this modification, the improved scheme is semantically secure against forgery attacks in the random oracle model under the intractability of the CDH problem. An analysis of the performance of the proposed scheme and the related schemes shows the former is much more efficient and suitable for practical application.
Aspect-Based Sentiment Classification Using Interactive Gated Convolutional Network

Aspect-based sentiment classification aims to detect the sentiment polarity of a target in a given context.Most previous approaches use long short-term memory (LSTM) and attention mechanisms to predict the sentiment polarity of targets, which are usually complex and need more training time.Some previous approaches are based on convolutional neural networks (CNN) and gating mechanisms, which are much simpler, efficient and takes lesser convergence time than LSTM due to parallelized computations during training.However, such CNN-based networks ignore the separate modeling of targets via context-specific representations.In this paper, we propose a novel interactive gated convolutional network (IGCN) that uses a bidirectional gating mechanism to learn mutual relation between the target and corresponding review context.IGCN also uses positional information of context words with respect to the given target, POS tags, and domain-specific word embeddings for predicting the sentiment of a target.The experimental results on SemEval 2014 datasets show the effectiveness of our proposed IGCN model.
Distributed Bayesian optimization of deep reinforcement learning algorithms

Significant strides have been made in supervised learning settings thanks to the successful application of deep learning. Now, recent work has brought the techniques of deep learning to bear on sequential decision processes in the area of deep reinforcement learning (DRL). Currently, little is known regarding hyperparameter optimization for DRL algorithms. Given that DRL algorithms are computationally intensive to train, and are known to be sample inefficient, optimizing model hyperparameters for DRL presents significant challenges to established techniques. We provide an open source, distributed Bayesian model-based optimization algorithm, HyperSpace, and show that it consistently outperforms standard hyperparameter optimization techniques across three DRL algorithms.
Software development with feature toggles: practices used by practitioners

Background: Using feature toggles is a technique that allows developers to either turn a feature on or off with a variable in a conditional statement. Feature toggles are increasingly used by software companies to facilitate continuous integration and continuous delivery. However, using feature toggles inappropriately may cause problems which can have a severe impact, such as code complexity, dead code, and system failure. For example, the erroneous repurposing of an old feature toggle caused Knight Capital Group, an American global financial services firm, to go bankrupt due to the implications of the resultant incorrect system behavior. Aim: The goal of this research project is to aid software practitioners in the use of practices to support software development with feature toggles through an empirical study of feature toggle practice usage by practitioners. Method: We conducted a qualitative analysis of 99 artifacts from the grey literature and 10 peer-reviewed papers about feature toggles. We conducted a survey of practitioners from 38 companies. Results: We identified 17 practices in 4 categories: Management practices, Initialization practices, Implementation practices, and Clean-up practices. We observed that all of the survey respondents use a dedicated tool to create and manage feature toggles in their code. Documenting feature toggle's metadata, setting up the default value for feature toggles, and logging the changes made on feature toggles are also frequently-observed practices. Conclusions: The feature toggle development practices discovered and enumerated in this work can help practitioners more effectively use feature toggles. This work can enable future mining of code repositories to automatically identify feature toggle practices.
The Effectiveness of a 3D-Virtual Reality Learning Environment (3D-VRLE) on the Omani Eighth Grade Students’ Achievement and Motivation towards Physics Learning

--This study aimed to examine the effectiveness of a 3D-Virtual Reality Learning Environment the Omani 8th-grade students’ achievement and motivation towards physics learning. To achieve the study objectives, data were collected using two instruments: an achievement test and a motivation questionnaire. The sample consisted of sixty-five female students. They were divided into two groups, (32) students in the experimental group and (33) students in the control group. The findings of this research study indicated a greater improvement in students’ achievement in physics after using the 3D-VRLE compared to their counterparts in the traditional learning environment. Furthermore, the results showed that the 3D-VRLE has a positive effect on students’ motivation towards physics learning. In the light of these findings, the study recommended the importance of using the 3D-VRLE in the Ministry of Education schools. Research areas for future investigation in the field of the 3D-VRLEs were also suggested.
θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%).
Open Intent Extraction from Natural Language Interactions

Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance, grouping user utterances into a single intent type from a set of categories known beforehand. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task under an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply a multi-head self-attention mechanism to effectively learn dependencies between distant words. We further use adversarial training to improve performance and robustly adapt our model across varying domains. Finally, we curate and plan to release an open intent annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that our approach outperforms state-of-the-art baselines by 5-15% F1 score points. We also demonstrate the efficacy of OPINE in recognizing multiple, diverse domain intents with limited (can also be zero) training examples per unique domain.
Deepfakes: temporal sequential analysis to detect face-swapped video clips using convolutional long short-term memory

Deepfake (a bag of “deep learning” and “fake”) is a technique for human image synthesis based on artificial intelligence, i.e., to superimpose the existing (source) images or videos onto destination images or videos using neural networks (NNs). Deepfake enthusiasts have been using NNs to produce convincing face swaps. Deepfakes are a type of video or image forgery developed to spread misinformation, invade privacy, and mask the truth using advanced technologies such as trained algorithms, deep learning applications, and artificial intelligence. They have become a nuisance to social media users by publishing fake videos created by fusing a celebrity’s face over an explicit video. The impact of deepfakes is alarming, with politicians, senior corporate officers, and world leaders being targeted by nefarious actors. An approach to detect deepfake videos of politicians using temporal sequential frames is proposed. The proposed approach uses the forged video to extract the frames at the first level followed by a deep depth-based convolutional long short-term memory model to identify the fake frames at the second level. Also the proposed model is evaluated on our newly collected ground truth dataset of forged videos using source and destination video frames of famous politicians. Experimental results demonstrate the effectiveness of our method.