Reviewerly/exHarmony huggingface dataset:
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices

The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments.
Perspective of Yemeni students on use of online learning

Purpose Online learning has evolved into a necessary means of learning because of its capability to enhance the education quality with minimum resources and infrastructure. Nevertheless, while academics have studied the espousal and use of online learning in various settings, the effect of compatibility and transformational leadership (TL) still remains to be seen, with regards to the Yemeni context. The purpose of this paper is to forward the Delone and Mclean Information System (IS) success Model by integrating compatibility and TL constructs as precursors to user contentment and actual use for estimating performance of students. Design/methodology/approach The questionnaire technique was utilised for gathering primary data from 448 students in nine state-funded Yemeni universities. The six variables in the recommended framework were gauged utilising current scales. Data analysis was done by deploying structural equation modelling through SmartPLS 3.0. Findings The outcomes encompassed three key results: overall quality (data, system and service quality), compatibility and TL have a favourable effect on user satisfaction and actual use; actual use considerably estimates user satisfaction; and user satisfaction and actual use have a favourable effect on performance of students. Research limitations/implications Because the research populace comprised students from nine state-funded universities, it did not include administrative and academic staff. Furthermore, as the study was cross-sectional, it studied the variables at a single point of time. Attaining experience in utilising online learning would transform the convictions of students, and this cannot be traced through a cross-sectional scrutiny. Moreover, the research relies upon self-testified measures for ascertaining the recommended research model. The reason behind this is that obtaining objective information regarding performance was not likely because of the privacy concern. Practical implications Despite the fact that Yemen is a low-income emerging nation with inadequate resources (World Development Indicators, 2017), it can capitalise on online-based learning that provides the advantage of excellent education and that too with limited supplies (Dokhan and Akkoyunlu, 2016; Yang et al. , 2016). Additionally, online learning can enhance administration and communication, empower learning anywhere and anytime, and endorse fairness of education. Originality/value This study supplements the existing studies on information systems by blending overall quality, compatibility, TL, actual use and client satisfaction for estimating the effect of online learning among students from nine state-funded Yemeni universities. Moreover, the recommended model’s predictive prowess has a higher capability to elucidate and estimate the performance effect as against those acquired from few of the previous studies.
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to "concept drift", which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.
Adversarial camera stickers: A physical camera-based attack on deep learning systems

Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on an object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Our demo video can be viewed at: this https URL
Certifying Geometric Robustness of Neural Networks

The use of neural networks in safety-critical computer vision systems calls for their robustness certification against natural geometric transformations (e.g., rotation, scaling). However, current certification methods target mostly norm-based pixel perturbations and cannot certify robustness against geometric transformations. In this work, we propose a new method to compute sound and asymptotically optimal linear relaxations for any composition of transformations. Our method is based on a novel combination of sampling and optimization. We implemented the method in a system called DeepG and demonstrated that it certifies significantly more complex geometric transformations than existing methods on both defended and undefended networks while scaling to large architectures.
Multi-label zero-shot human action recognition via joint latent ranking embedding

Human action recognition is one of the most challenging tasks in computer vision. Most of the existing works in human action recognition are limited to single-label classification. A real-world video stream, however, often contains multiple human actions. Such a video stream is usually annotated collectively with a set of relevant human action labels, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate sufficient video clips regarding all these human actions for training a supervised learning model. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem. To address this problem, a joint latent ranking embedding framework is proposed. Our framework holistically tackles the issue of unknown temporal boundaries between different actions within a video clip for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. Specifically, our framework consists of two component neural networks for visual and semantic embedding respectively. Thus, multi-label zero-shot recognition is done by measuring relatedness scores of concerned action labels to a test video clip in the joint latent visual and semantic embedding spaces. We evaluate our framework in different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning. The experimental results on two weakly annotated multi-label human action datasets (i.e. Breakfast and Charades) demonstrate the effectiveness of our framework.
Phishing Detection Based on Machine Learning and Feature Selection Methods

With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.
AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation

In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based method. Hand joint coordinates are estimated as discrete integration of all pixels in dense representation, guided by adaptive weight maps. This learnable aggregation process introduces both dense and joint supervision that allows end-to-end training and brings adaptability to weight maps, making network more accurate and robust. Comprehensive exploration experiments are conducted to validate the effectiveness and generality of AWR under various experimental settings, especially its usefulness for different types of dense representation and input modality. Our method outperforms other state-of-the-art methods on four publicly available datasets, including NYU, ICVL, MSRA and HANDS 2017 dataset.
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize well to partially occluded objects and to mask attacks. In contrast to DCNNs, compositional models are robust to partial occlusion, however, they are not as discriminative as deep models. In this work, we combine DC-NNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks. Our model is learned in two steps. First, a standard DCNN is trained for image classification. Subsequently, we cluster the DCNN features into dictionaries. We show that the dictionary components resemble object part detectors and learn the spatial distribution of parts for each object class. We propose mixtures of compositional models to account for large changes in the spatial activation patterns (e.g. due to changes in the 3D pose of an object). At runtime, an image is first classified by the DCNN in a feedforward manner. The prediction uncertainty is used to detect partially occluded objects, which in turn are classified by the compositional model. Our experimental results demonstrate that combining compositional models and DCNNs resolves a fundamental problem of current deep learning approaches to computer vision: The combined model recognizes occluded objects, even when it has not been exposed to occluded objects during training, while at the same time maintaining high discriminative performance for non-occluded objects.
Investor attention and bitcoin liquidity: Evidence from bitcoin tweets

This study employs the number of tweets as a proxy for investor attention. We use high-frequency data to investigate tweets’ real-time effects on Bitcoin liquidity. We find that a 1% increase in tweets leads to about 7% of liquidity improvement in the next five to 10 min. In a precise timeline, we show that the positive impact of tweets decays after approximately an hour. We further find that impacts on liquidity are stronger when tweets draw more attention. This study thus suggests that active investor attention can significantly improve Bitcoin liquidity in real time.
Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.
Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the classification of the comparison group images, the AUC (0.913) of the InceptionV3 model was larger than that (0.846) obtained by sonographers, showing a statistically significant difference (P < 0.05). The breast ultrasound computer-aided prediction model based on CNN showed high accuracy in the prediction of breast cancer.
Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review

The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based decision-making have effective applications in big data. Derivations are made based on the use of deep algorithms and multicriteria. Due to its effectiveness and potentiality, it is exploited in several domains such as computer science and information technology, agriculture, and business sector. The aim of the proposed study is to present a systematic literature study in order to show the applications of deep learning algorithms and multicriteria decision approaches for the problems of big data. The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches.
Differentiable quantum architecture search

Abstract Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS. For instance, unitary operations are decomposed into quantum gates, noisy circuits are re-designed to improve accuracy, and circuit layouts for quantum approximation optimization algorithm are automatically discovered and upgraded for combinatorial optimization problems. These results not only manifest the vast potential of DQAS being an essential tool for the NISQ application developments, but also present an interesting research topic from the theoretical perspective as it draws inspirations from the newly emerging interdisciplinary paradigms of differentiable programming, probabilistic programming, and quantum programming.
The power of block-encoded matrix powers: Improved regression techniques via faster Hamiltonian simulation

We apply the framework of block-encodings, introduced by Low and Chuang (under the name standard-form), to the study of quantum machine learning algorithms and derive general results that are applicable to a variety of input models, including sparse matrix oracles and matrices stored in a data structure. We develop several tools within the block-encoding framework, such as singular value estimation of a block-encoded matrix, and quantum linear system solvers using block-encodings. The presented results give new techniques for Hamiltonian simulation of non-sparse matrices, which could be relevant for certain quantum chemistry applications, and which in turn imply an exponential improvement in the dependence on precision in quantum linear systems solvers for non-sparse matrices. In addition, we develop a technique of variable-time amplitude estimation, based on Ambainis' variable-time amplitude amplification technique, which we are also able to apply within the framework. As applications, we design the following algorithms: (1) a quantum algorithm for the quantum weighted least squares problem, exhibiting a 6-th power improvement in the dependence on the condition number and an exponential improvement in the dependence on the precision over the previous best algorithm of Kerenidis and Prakash; (2) the first quantum algorithm for the quantum generalized least squares problem; and (3) quantum algorithms for estimating electrical-network quantities, including effective resistance and dissipated power, improving upon previous work.
Social-Chain

Pervasive Social Networking (PSN) supports online and instant social activities with the support of heterogeneous networks. Since reciprocal activities among both familiar/unfamiliar strangers and acquaintances are quite common in PSN, it is essential to offer trust information to PSN users. Past work normally evaluates trust based on a centralized party, which is not feasible due to the dynamic changes of PSN topology and its specific characteristics. The literature still lacks a decentralized trust evaluation scheme in PSN. In this article, we propose a novel blockchain-based decentralized system for trust evaluation in PSN, called Social-Chain. Considering mobile devices normally lack computing resources to process cryptographic puzzle calculation, we design a lightweight consensus mechanism based on Proof-of-Trust (PoT), which remarkably improves system effectivity compared with other blockchain systems. Serious security analysis and experimental results further illustrate the security and efficiency of Social-Chain for being feasibly applied into PSN.