Reviewerly/exHarmony huggingface dataset:
Maximizing the influence of bichromatic reverse k nearest neighbors in geo-social networks

Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially relevant to social influencers who are expected to largely promote the POIs online. In other words, the problem aims to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various real-life applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBRkNN with theoretical guarantees is challenging because of the prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and #P-hardness of finding the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BRkNN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.
Evaluation of the Combination of Artificial Intelligence and Radiologist Assessments to Interpret Malignant Architectural Distortion on Mammography

To compare the mammographic malignant architectural distortion (AD) detection performance of radiologists who read mammographic examinations unaided versus those who read these examinations with the support of artificial intelligence (AI) systems.This retrospective case-control study was based on a double-reading of clinical mammograms between January 2011 and December 2016 at a large tertiary academic medical center. The study included 177 malignant and 90 benign architectural distortion (AD) patients. The model was built based on the ResNeXt-50 network. Algorithms used deep learning convolutional neural networks, feature classifiers, image analysis algorithms to depict AD and output a score that translated to malignant. The accuracy for malignant AD detection was evaluated using area under the curve (AUC).The overall AUC was 0.733 (95% CI, 0.673-0.792) for Reader First-1, 0.652 (95% CI, 0.586-0.717) for Reader First-2, and 0.655 (95% CI, 0.590-0.719) for Reader First-3. and the overall AUCs for Reader Second-1, 2, 3 were 0.875 (95% CI, 0.830-0.919), 0.882 (95% CI, 0.839-0.926), 0.884 (95% CI, 0.841-0.927),respectively. The AUCs for all the reader-second radiologists were significantly higher than those for all the reader-first radiologists (Reader First-1 vs. Reader Second-1, P= 0.004). The overall AUC was 0.792 (95% CI, 0.660-0.925) for AI algorithms. The combination assessment of AI algorithms and Reader First-1 achieved an AUC of 0.880 (95% CI, 0.793-0.968), increased than the Reader First-1 alone and AI algorithms alone. AI algorithms alone achieved a specificity of 61.1% and a sensitivity of 80.6%. The specificity for Reader First-1 was 55.5%, and the sensitivity was 86.1%. The results of the combined assessment of AI and Reader First-1 showed a specificity of 72.7% and sensitivity of 91.7%. The performance showed significant improvements compared with AI alone (p<0.001) as well as the reader first-1 alone (p=0.006).While the single AI algorithm did not outperform radiologists, an ensemble of AI algorithms combined with junior radiologist assessments were found to improve the overall accuracy. This study underscores the potential of using machine learning methods to enhance mammography interpretation, especially in remote areas and primary hospitals.
[DC] Exploration of Context and Physiological Cues for Personalized Emotion-Adaptive Virtual Reality

Immersive Virtual Reality (VR) can create compelling context-specific emotional experiences, but very few studies explore the importance of emotion-relevant contextual cues in VR. In this thesis, I investigate how to use combined contextual and physiological cues to improve emotion recognition in VR and enhance shared VR experiences. The main novelty is the creation of the first Personalized Real-time Emotion-Adaptive Context-Aware VR (PerAffectly VR) system which provides significant insight into how to create, measure, and share emotional VR experiences.
Training of artificial neural networks with the multi-population based artifical bee colony algorithm

ABSTRACTNowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers’ acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.KEYWORDS: Artificial neural networksmetaheuristic algorithmsartificial bee colony algorithmmulti-population-based strategy Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, understanding misbehavior on WhatsApp is an important issue. The sending of unwanted junk messages by unknown contacts via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. We address this gap by studying junk messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp groups in India. We characterise both junk content and senders. We find that nearly 1 in 10 messages is unwanted content sent by junk senders, and a number of unique strategies are employed to reflect challenges faced on WhatsApp, e.g., the need to change phone numbers regularly. We finally experiment with on-device classification to automate the detection of junk, whilst respecting end-to-end encryption.
Learning to Augment for Casual User Recommendation

Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large body of content upon each visit, while others are casual users who tend to visit the platform occasionally and consume less each time. As a result, consumption activities from core users often dominate the training data used for learning. As core users can exhibit different activity patterns from casual users, recommender systems trained on historical user activity data usually achieve much worse performance on casual users than core users. To bridge the gap, we propose a model-agnostic framework L2Aug to improve recommendations for casual users through data augmentation, without sacrificing core user experience. L2Aug is powered by a data augmentor that learns to generate augmented interaction sequences, in order to fine-tune and optimize the performance of the recommendation system for casual users. On four real-world public datasets, L2Aug outperforms other treatment methods and achieves the best sequential recommendation performance for both casual and core users. We also test L2Aug in an online simulation environment with real-time feedback to further validate its efficacy, and showcase its flexibility in supporting different augmentation actions.
Image fragile watermarking algorithm based on deneighbourhood mapping

To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, a self-embedding fragile image watermarking algorithm based on deneighbourhood mapping are proposed. First, the image is divided into several 2 × 2 blocks, and authentication watermark and recovery watermark are generated based on the average value of the image blocks. Then, the denighbourhood mapping is implemented as, for each image block, its mapping block is randomly selected outside its neighbourhood. Finally, the authentication watermark and the recovery watermark are embedded into the image block itself and its mapping block. Theoretical analysis indicates that in the case of continuous area tampering, the proposed watermarking algorithm can achieve a better recovery rate than that of the method based on the random mapping. The experimental results verify the rationality and effectiveness of the theoretical analysis. Moreover, compared with the existing embedding algorithms based on random mapping, chaos mapping, and Arnold mapping, in the case of continuous area tampering, the proposed algorithm also achieves a higher average recovery rate.
Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.
Investigating the Tradeoffs of Everyday Text-Entry Collection Methods

Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.
Universal calibration for a ring camera array based on a rotational target

Panoramic dynamic and static measurements of objects in the application of vision measurement are difficult due to the constraints of a camera field of view and multicamera calibration technology. This paper proposes a universal global calibration method for ring multicamera systems based on rotating target and multi-view vision technology. This method uses a rotating target to establish the relationship between ring multi-camera arrays, retrieves the coordinates of the target corners from the fields of view of different cameras, and combines them with the rotation angle to complete the coordinate unification of the system. The unification of coordinates is unaffected by the overlapping fields of view between cameras, and the number of cameras can be configured arbitrarily. The calibration accuracy, validity, and precision of the proposed method are verified through reprojection error, dynamic tensile test, and 3D reconstruction.
How Humans Perceive Human-like Behavior in Video Game Navigation

The goal of this paper is to understand how people assess human-likeness in human- and AI-generated behavior. To this end, we present a qualitative study of hundreds of crowd-sourced assessments of human-likeness of behavior in a 3D video game navigation task. In particular, we focus on an AI agent that has passed a Turing Test, in the sense that human judges were not able to reliably distinguish between videos of a human and AI agent navigating on a quantitative level. Our insights shine a light on the characteristics that people consider as human-like. Understanding these characteristics is a key first step for improving AI agents in the future.
Hierarchical One-Class Model With Subnetwork for Representation Learning and Outlier Detection

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/W1AE/OCC</uri> .
Design and Evaluation of Window Management Operations in AR Headset + Smartphone Interface

Combining the use of an AR headset and a smartphone can provide wider display and precise touch input simultaneously; it can redefine the way we use applications today. Unfortunately, users are deprived of such benefit because of the independence of two devices. There lacks a kind of intuitive and direct interactions across them. In this paper, we conduct a formative study to understand the window management requirements and interaction preferences of using an AR headset and a smartphone simultaneously and report the insights we gained. Also, we introduce an example vocabulary of window management operations in AR headset + smartphone interface. It allows users to manipulate windows in virtual space and shift windows between devices efficiently and seamlessly.
What We Talk About When We Talk About Human-Computer Integration

In 2017, a CHI panel titled “Integration vs Powerful tools” debated whether our relationship with digital technology had begun to shift from interaction to integration. Today, Human-Computer integration developed into an emerging paradigm, rapidly gaining traction and developing a theoretical basis over a number of recent symposiums and publications. However, as more contributions are made to the establishment of Human-Computer Integration, the more its concepts and principles seem to diverge, such that we now find that each theorist talks about something different when they say “integration”. Building on the 2017 panel, in this panel, we ask “What is the essence of Human-Computer Integration? And what are its implications for the future of HCI?” This panel seeks to facilitate discourse between leading thinkers from diverse backgrounds with the audience to come to a shared vision and mutual understanding of human-computer integration.
Verifying Neural Network Controlled Systems Using Neural Networks

Safety verification is an essential requirement of neural network controlled systems when they are adopted in safety-critical fields. This paper proposes a novel approach to synthesizing neural networks as barrier certificates, which can provide safety guarantees for neural network controlled systems. We first propose the construction conditions of neural network barrier certificates, followed by an iterative framework to synthesize them. Each iteration trains a neural network as the candidate barrier certificate using the training datasets sampled from the neural network controlled system. After training, identifying whether the candidate barrier certificate is a real one for the neural network controlled system is transformed into a group of mixed-integer programming problems, which the numerical optimization solver solves with guaranteed results. We implement the tool NetBC and evaluate its performance over 6 practical benchmark examples. The experimental results show that NetBC is more effective and scalable than the existing polynomial barrier certificate-based method.
Multi-task manifold learning for partial label learning

In partial label learning (PLL), each instance is associated with a candidate label set, and only one label is ground-truth. PLL aims to identify the ground-truth label out of these candidate labels. Most of the existing PLL approaches focus on single-task PLL, and ignore the auxiliary information of the related tasks. This paper puts forward a novel multi-task manifold learning method for partial label learning (MT-PLL), which learns multiple PLL tasks jointly, and incorporates the auxiliary information of the related tasks to improve the performance of PLL classifiers. MT-PLL assumes that the graph manifold structure guides the generation of labeling confidence for instances in each task. In addition, the information of related tasks can be used to boost the performance of the overall classification model. Then, a heuristic framework is used to optimize the objective function. Numerical experiments have demonstrated that MT-PLL can deliver better performance than state-of-the-art single-task PLL methods.
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs

Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a powerful formalism capable of describing a wide range of models.Recent research found it beneficial to use large state spaces for HMMs and PCFGs.However, inference with large state spaces is computationally demanding, especially for PCFGs.To tackle this challenge, we leverage tensor rank decomposition (aka.CPD) to decrease inference computational complexities for a subset of FGGs subsuming HMMs and PCFGs.We apply CPD on the factors of an FGG and then construct a new FGG defined in the rank space.Inference with the new FGG produces the same result but has a lower time complexity when the rank size is smaller than the state size.We conduct experiments on HMM language modeling and unsupervised PCFG parsing, showing better performance than previous work.Our code is publicly available at https://github.com/VPeterV/RankSpace-Models.
Weapon Detection Using ML for PPA

Security is always a top issue in every domain. Computer vision is used extensively in abnormal detection and monitoring to solve a variety of problems in physical protection applications (PPA). Old technology like pixel-based video analytics has a loophole in identifying the object, and the object identified with old technologies is less accurate. Weapon identification using cutting-edge technology like machine learning (ML), artificial intelligence, and deep learning can be used to identify weapons in the security area, as the world is constantly threatened by conflict or terror attacks. Using a convolution neural network (CNN), this paper shows an implementation of an automatic gun (or) weapon detection using (CNN). It focuses on detecting, classifying, and locating guns with pinpoint accuracy. This research proposes a groundbreaking real-time automatic weapon identification system for both surveillance purposes. Weapon detection in images/videos is accomplished by determining whether the image/video contains any weapons, as well as image localization.
Reservoir-based convolution

Reservoir computing (RC) has attracted attention and has been used in many applications because of its low training cost. Multiple studies using RC for image recognition have been proposed, and some have achieved accuracy rates of greater than 99% on the MNIST dataset. For the Fashion-MNIST and CIFAR-10 datasets, however, they have not yet achieved high accuracy. This study proposes a novel convolutional neural network based on RC that can be optimized by ridge regression rather than back-propagation. The reservoir-based network has multiple reservoirs with various leak rates to extract features with various spatial frequencies from the inputs. The experimental results show that the performance of the proposed model achieves higher accuracy rates in the mentioned datasets compared with those of other reservoir-based image recognition approaches.
Automated identification of borrowings in multilingual wordlists

Although lexical borrowing is an important aspect of language evolution, there have been few attempts to automate the identification of borrowings in lexical datasets. Moreover, none of the solutions which have been proposed so far identify borrowings across multiple languages. This study proposes a new method for the task and tests it on a newly compiled large comparative dataset of 48 South-East Asian languages from Southern China. The method yields very promising results, while it is conceptually straightforward and easy to apply. This makes the approach a perfect candidate for computer-assisted exploratory studies on lexical borrowing in contact areas.