Reviewerly/exHarmony huggingface dataset:
PUMAD: PU Metric learning for anomaly detection

Anomaly detection task, which identifies abnormal patterns in data, has been widely applied to various domains. Most recent work on anomaly detection have focused on an accurate modeling of the normal data based on unsupervised methods. To get a satisfactory anomaly detection accuracy, they need pure normal data without abnormal data. This scenario requires many labels to get pure normal data. In many real-world scenarios, there exist abundant unlabeled data and a limited number of partially labeled anomalies. This paper proposes a novel anomaly detection method, PUMAD, which uses a Positive and Unlabeled (PU) learning approach to learn from abundant unlabeled data and a small number of partially labeled anomalies (i.e., positives). PUMAD successfully works on the anomaly detection scenario by exploiting deep metric learning with a hashing-based filtering method. Extensive experimental results on real-world benchmark datasets demonstrate that our approach based on PU learning is effective to detect anomalies. PUMAD achieves a much higher accuracy of up to 24% than state-of-the-art competitors.
Health–Related ICT Solutions of Smart Environments for Elderly–Systematic Review

By improving the quality of life and extending the length of life, Western society is becoming an increasingly ageing population with a higher proportion of seniors.From another point of view, there is a critical shortage of care staff, both in hospitals and for in-home care.Thanks to new technology trends such as Smart Homes and Smart Furniture, there is an opportunity for increased support for seniors by utilizing new technologies.This paper presents the current trends and possibilities in applying smart information and communications technology (ICT) solutions for in-home care concerning diseases in old age.The paper consists of a systematic review according to the PRISMA methodology of the available literature in Web of Science, IEEE Xplore, PubMed, Springer, and the Espacenet patent database.Publications report the usage of some types of artificial intelligence and their implementation and non-intrusive sensing technologies.The patents review identified solutions with a focus on monitoring the state of older adults and mobility improvement.Existing ICT smart solutions must address the following issues: (1) ease-of-use; (2) invisibility and disuse that isolate older adults; (3) privacy and security; (4) affordability of technology in terms of cost; and (5) supporting elderly individuals to stay in their homes or move in different environments independently.There is a significant gap between a large number of scientific publications and commercial solutions.The existing products reflect the specifics of the diseases in a rather wider context instead of the fulfilment of exact needs.It is often stated that such devices can be used across diseases, but the direct connection and benefits for the disease is still rather weak.The challenge remains to tap the existing potential of a large number of innovative ideas on the market and improve the quality of life.
Hand Gesture Recognition in Complex Background Based on Convolutional Pose Machine and Fuzzy Gaussian Mixture Models

Abstract Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when confronted with complex background, illumination variation and occlusion in real-world scenarios. In this paper, a two-stage hand gesture recognition method is proposed to tackle these problems. At the first stage, hand pose estimation is developed to locate the hand keypoints using the convolutional pose machine, which can effectively localize hand keypoints even in a complex background. At the second stage, the Fuzzy Gaussian mixture models (FGMMs) are tailored to reject the nongesture patterns and classify the gestures based on the estimated hand keypoints. Extensive experiments are conducted to evaluate the performance of the proposed method, and the result demonstrates that the proposed algorithm is effective, robust, and satisfactory in real-time scenarios.
DNet: A lightweight and efficient model for aspect based sentiment analysis

Aspect based sentiment analysis (ABSA) is the task of identifying fine-grained opinion polarity towards a specific target in a sentence, which is empowering experts and intelligent systems with enriched interaction capabilities. Most of approaches to date usually capture semantic relations between target and context words based on RNNs (Recurrent Neural Networks) or pre-trained models (e.g. BERT). However, due to computational complexity and size constraints, these models are often hosted in the cloud. Enabling ABSA models to run on resource-constrained end-devices with quick response time is still challenging and not yet well studied. This paper presents distillation network (DNet), a lightweight and efficient sentiment analysis model based on gated convolutional neural networks for on-device inference. Through combining stacked gated convolution with attention mechanism, DNet can distill aspect-aware context information from unstructured text progressively, achieving high performance with less inference latency and reduced model size. Experiments on SemEval 2014 Task 4 and ACL14 Twitter datasets demonstrate that our approach achieves the state-of-the-art performance. Furthermore, compared with the BERT-based model, DNet reduces the model size by more than 50 times and improves the responsiveness by 24 times.
LSTM Based Phishing Detection for Big Email Data

In recent years, cyber criminals have successfully invaded many important information systems by using phishing mail, causing huge losses. The detection of phishing mail from big email data has been paid public attention. However, the camouflage technology of phishing mail is becoming more and more complex, and the existing detection methods are unable to confront with the increasingly complex deception methods and the growing number of emails. In this article, we proposed an LSTM based phishing detection method for big email data. The new method includes two important stages, sample expansion stage and testing stage under sufficient samples. In the sample expansion stage, we combined KNN with K-Means to expand the training data set, so that the size of training samples can meet the needs of in-depth learning. In the testing stage, we first preprocess these samples, including generalization, word segmentation and word vector generation. Then, the preprocessed data is used to train a LSTM model. Finally, on the basis of the trained model, we classify the phishing emails. By experiment, we evaluate the performance of the proposed method, and experimental results show that the accuracy of our phishing detection method can reach 95 percent.
Multi-label optimal margin distribution machine

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method.
Verifiable Attribute-Based Keyword Search Over Encrypted Cloud Data Supporting Data Deduplication

Searchable encryption is a promising technique that allows cloud service provider return the interested files to the user, even if the shared files are encrypted. However, once encrypted data is uploaded to the cloud, the data owner loses the physical control of the data; the cloud service provider may face the problem of duplicate data occupying the storage space; how can the data user obtain the legal access rights? Therefore, data integrity detection, data deduplication and reasonable access authorization become the focus of attention. To address these problems, we propose a novel verifiable attribute-based keyword search over encrypted data supporting data deduplication. In the scheme, attribute-based encryption is introduced to achieve effective access authorization based on its one-to-many advantage, as well data confidentiality. A data label of each shared file is generated to finish data deduplication, while the third-party auditor and hash function are adopted to ensure data integrity, including ciphertext, plaintext and symmetric key. Moreover, in order to solve the problem of high computational load caused by attribute-based encryption, outsourcing decryption is used to optimize the scheme. The formal security analysis shows that our scheme can implement fine-grained authorization to search and realize indistinguishable keywords, unforgeability of signatures and confidentiality of ciphertexts. Meanwhile, the results of the experiment reveal that our scheme has a low band-width communication, storage and computability.
A Two-stage Chinese text summarization algorithm using keyword information and adversarial learning

At present, most Chinese text summarization algorithms use the sequence-to-sequence model, but this model is prone to the problems of unknown words and incomplete content generation. To address these problems, we propose a new two-stage automatic text summarization method using keyword information and adversarial learning in this paper. On the one hand, the proposed method integrates the keyword information into the sequence-to-sequence model. The main information and keywords of the article are considered simultaneously through the attention mechanism to improve the information of summary generation. On the other hand, adversarial learning is introduced into the proposed model to avoid the problem that the semantic vector after passing through the encoder cannot save the context information better. Experiments are carried out on the Chinese dataset LCSTS, and the comparison results show that the proposed method has advantages in abstractive summarization.
Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

Breast cancer is the major cause of death among women. The best and most efficient approach for controlling cancer progression is early detection and diagnosis. As opposed to biopsy, mammography helps in early detection of cancer and hence saves lives. Mass classification in mammograms remains a major challenge and plays a vital role in helping radiologists in accurate diagnosis. In this work, we propose a MobileNet based architecture for early breast cancer detection and further classify mass into malignant and benign. It requires less memory space and provides faster computations with 86.8% and 74.5% accurate results for DDSM and CBIS-DDSM, respectively. We have achieved better results than other deep CNN models such as AlexNet, VGG16, GoogleNet, and ResNet.
Towards automatic cross-language classification of cognitive presence in online discussions

This paper presents a study that examined automated cross-language classification of online discussion messages for the levels of cognitive presence, a key construct from the widely used Community of Inquiry (CoI) model of online learning. Specifically, we examined the classification of 1,500 Portuguese language discussion messages using a classifier trained on a corpus of the 1,747 English language discussion messages. In the study, a random forest classifier was developed using a small set of 108 validated indicators of psychological processes, linguistic coherence, and online discussion structure. The classifier obtained 67% accuracy and Cohen's κ of 0.32, showing a moderate level of inter-rater agreement above chance and the general viability of the proposed approach. Most importantly, the findings suggest that certain aspects of cognitive presence construct are highly generalizable and transfer across different languages. Finally, the paper also presents a novel method for addressing class imbalance problem using a generic algorithm heuristic technique, which provided substantial improvements over the use of imbalanced dataset. Results and practical implications are further discussed.
Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity

Learning sentence similarity is a fundamental research topic and has been explored using various deep learning methods recently. In this paper, we further propose an enhanced recurrent convolutional neural network (Enhanced-RCNN) model for learning sentence similarity. Compared to the state-of-the-art BERT model, the architecture of our proposed model is far less complex. Experimental results show that our similarity learning method outperforms the baselines and achieves the competitive performance on two real-world paraphrase identification datasets.
JSCleaner: De-Cluttering Mobile Webpages Through JavaScript Cleanup

A significant fraction of the World Wide Web suffers from the excessive usage of JavaScript (JS). Based on an analysis of popular webpages, we observed that a considerable number of JS elements utilized by these pages are not essential for their visual and functional features. In this paper, we propose JSCleaner, a JavaScript de-cluttering engine that aims at simplifying webpages without compromising their content or functionality. JSCleaner relies on a rule-based classification algorithm that classifies JS into three main categories: non-critical, replaceable, and critical. JSCleaner removes non-critical JS from a webpage, translates replaceable JS elements with their HTML outcomes, and preserves critical JS. Our quantitative evaluation of 500 popular webpages shows that JSCleaner achieves around 30% reduction in page load times coupled with a 50% reduction in the number of requests and the page size. In addition, our qualitative user study of 103 evaluators shows that JSCleaner preserves 95% of the page content similarity, while maintaining nearly 88% of the page functionality (the remaining 12% did not have a major impact on the user browsing experience).
ROSE: Role-based Signed Network Embedding

In real-world networks, nodes might have more than one type of relationship. Signed networks are an important class of such networks consisting of two types of relations: positive and negative. Recently, embedding signed networks has attracted increasing attention and is more challenging than classic networks since nodes are connected by paths with multi-types of links. Existing works capture the complex relationships by relying on social theories. However, this approach has major drawbacks, including the incompleteness/inaccurateness of such theories. Thus, we propose network transformation based embedding to address these shortcomings. The core idea is that rather than directly finding the similarities of two nodes from the complex paths connecting them, we can obtain their similarities through simple paths connecting their different roles. We employ this idea to build our proposed embedding technique that can be described in three steps: (1) the input directed signed network is transformed into an unsigned bipartite network with each node mapped to a set of nodes we denote as role-nodes. Each role-node captures a certain role that a node in the original network plays; (2) the network of role-nodes is embedded; and (3) the original network is encoded by aggregating the embedding vectors of role-nodes. Our experiments show the novel proposed technique substantially outperforms existing models.
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning

The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task. Our work is inspired by the observation that many KG entities correspond to online items in application systems. However, the two kinds of data sources have very different intrinsic characteristics, and it is likely to hurt the original performance using simple fusion strategy.
A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory

Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models, increasing in this way the efficiency in terms of computational cost. Three public databases (SHOAIB, UCI, WISDM) are used in the evaluation process. The results have shown that HAR-SR can classify activities with 93% accuracy when using a leave-one-subject-out cross-validation procedure (LOSO).
Deconstructing Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a thriving unsupervised machine learning technique that has led to significant advances in various fields such as computer vision, natural language processing, among others. However, GANs are known to be difficult to train and usually suffer from mode collapse and the discriminator winning problem. To interpret the empirical observations of GANs and design better ones, we deconstruct the study of GANs into three components and make the following contributions. Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be connected to the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs, to provably recover meaningful low-dimensional generator approximations when the real distribution is high-dimensional and corrupted by outliers. Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance, to design GANs to meet the population requirement using only finite samples. We implement our principle in three cases to achieve polynomial and sometimes near-optimal sample complexities: (1) learning an arbitrary generator under an arbitrary pseudonorm; (2) learning a Gaussian location family under total variation distance, where we utilize our principle to provide a new proof for the near-optimality of the Tukey median viewed as GANs; (3) learning a low-dimensional Gaussian approximation of a high-dimensional arbitrary distribution under Wasserstein distance. We demonstrate a fundamental trade-off in the approximation error and statistical error in GANs, and demonstrate how to apply our principle in practice with only empirical samples to predict how many samples would be sufficient for GANs in order not to suffer from the discriminator winning problem. Optimization: we demonstrate alternating gradient descent is provably not locally asymptotically stable in optimizing the GAN formulation of PCA. We found that the minimax duality gap being non-zero might be one of the causes, and propose a new GAN architecture whose duality gap is zero, where the value of the game is equal to the previous minimax value (not the maximin value). We prove the new GAN architecture is globally asymptotically stable in solving PCA under alternating gradient descent.
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.
A Low-Cost Smart Glove System for Real-Time Fitness Coaching

Strength training is becoming increasingly popular among all age groups, as it helps the participants increase muscle strength, improve body flexibility, reduce health risks, and reshape physical forms. However, strength training imposes strict regulations on gestures and requires professional instruction in real time for the sake of body-building efficiency and safety. For this purpose, in this article, we propose a novel low-cost system named iCoach, to provide real-time monitoring and coaching service for strength training participants. Specifically, we design and implement a smart fitness glove, which can be seamlessly equipped with a pervasive inertial unit. With this customized but low-cost device, we can recognize various training programs, detect nonstandard behaviors while exercising, and assess exercising qualities of a user. Our primary experimental results show that iCoach can recognize 15 sets of training programs, detect three common nonstandard behaviors, and assess the quality of training with high accuracy and reliability.
Embedded UAV Real-Time Visual Object Detection and Tracking

The use of camera-equipped Unmanned Aerial Vehicles (UAVs, or “drones”) for a wide range of aerial video capturing applications, including media production, surveillance, search and rescue operations, etc., has exploded in recent years. Technological progress has led to commercially available UAVs with a degree of cognitive autonomy and perceptual capabilities, such as automated, on-line detection and tracking of target objects upon the captured footage. However, the limited computational hardware, the possibly high camera-to-target distance and the fact that both the UAV/camera and the target(s) are moving, makes it challenging to achieve both high accuracy and stable real-time performance. In this paper, the current state-of-the-art on real-time object detection/tracking is overviewed. Additionally, a relevant, modular implementation suitable for on-drone execution (running on top of the popular Robot Operating System) is presented and empirically evaluated on a number of relevant datasets. The results indicate that a sophisticated, neural network-based detection and tracking system can be deployed at real-time even on embedded devices.