Reviewerly/exHarmony huggingface dataset:
A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System

Falls in older adults are a major cause of morbidity and mortality and are a key class of preventable injuries. This paper presents a patient-specific (PS) fall prediction and detection prototype system that utilizes a single tri-axial accelerometer attached to the patient's thigh to distinguish between activities of daily living (ADL) and fall events. The proposed system consists of two modes of operation: 1) fast mode for fall predication (FMFP) predicting a fall event (300-700 msec) before occurring and 2) slow mode for fall detection (SMFD) with a 1-sec latency for detecting a fall event. The nonlinear support vector machine classifier (NLSVM)-based FMFP algorithm extracts seven discriminating features for the pre-fall case to identify a fall risk event and alarm the patient. The proposed SMFD algorithm utilizes a Three-cascaded 1-sec sliding frames classification architecture with a linear regression-based offline training to identify a single and optimal threshold for each patient. Fall incidence will trigger an alarming notice to the concern healthcare providers via the Internet. Experiments are performed with 20 different subjects (age above 65 years) and a total number of 100 associated falls and ADL recordings indoors and outdoors. The accuracy of the proposed algorithms is furthermore validated via MobiFall Dataset. FMFP achieves sensitivity and specificity of 97.8% and 99.1%, respectively, while SMFD achieves sensitivity and specificity of 98.6% and 99.3%, respectively, for a total number of 600 measured falls and ADL cases from 77 subjects.
Impact of Mobile Learning on Students’ Achievement Results

Today, mobile learning is a well-established methodology thanks to its countless benefits such as accessing learning content anytime and anywhere, adjusting the content to students’ needs, and timely feedback. The purpose of this pilot study is to illustrate that foreign language learning supported by a personalized smartphone app can be effective in the enhancement of university students’ performance by implementing smartphone app learning in a continuous assessment. The methodology is a case study analysis of students’ needs, with statistical processing of the collected data. The results reveal that foreign language learning, particularly studying and revising English vocabulary and phrases via smartphones is effective in the enhancement of university students’ performance. However, such learning must be designed around students’ needs and continuously facilitated by a teacher. Only then can it contribute to positive learning outcomes. In addition, the results also confirm that mobile learning can serve as an appropriate complementary method to other forms of course delivery. Further research should extend the mobile app to the Apple platform and iOS.
Query-Guided End-To-End Person Search

Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii. the person search should leverage the query image extensively (e.g. emphasizing unique query patterns). However, so far, no prior art realizes this. We introduce a novel query-guided end-to-end person search network (QEEPS) to address both aspects. We leverage a most recent joint detector and re-identification work, OIM [37]. We extend this with i. a query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global context from both the query and gallery images, ii. a query-guided region proposal network (QRPN) to produce query-relevant proposals, and iii. a query-guided similarity subnetwork (QSimNet), to learn a query-guided re-identification score. QEEPS is the first end-to-end query-guided detection and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46] datasets, we outperform the previous state-of-the-art by a large margin.
Topic-Enriched Word Embeddings for Sarcasm Identification

Sarcasm is a type of nonliteral language, where people may express their negative sentiments with the use of words with positive literal meaning, and, conversely, negative meaning words may be utilized to indicate positive sentiment. User-generated text messages on social platforms may contain sarcasm. Sarcastic utterance may change the sentiment orientation of text documents from positive to negative, or vice versa. Hence, the predictive performance of sentiment classification schemes may be degraded if sarcasm cannot be properly handled. In this paper, we present a deep learning based approach to sarcasm identification. In this regard, the predictive performance of topic-enriched word embedding scheme has been compared to conventional word-embedding schemes (such as, word2vec, fastText and GloVe). In addition to word-embedding based feature sets, conventional lexical, pragmatic, implicit incongruity and explicit incongruity based feature sets are considered. In the experimental analysis, six subsets of Twitter messages have been taken into account, ranging from 5000 to 30.000. The experimental analysis indicate that topic-enriched word embedding schemes utilized in conjunction with conventional feature sets can yield promising results for sarcasm identification.
Ensemble Approach of Optimized Artificial Neural Networks for Solar Photovoltaic Power Prediction

The use of data-driven ensemble approaches for the prediction of the solar Photovoltaic (PV) power production is promising due to their capability of handling the intermittent nature of the solar energy source. In this work, a comprehensive ensemble approach composed by optimized and diversified Artificial Neural Networks (ANNs) is proposed for improving the 24h-ahead solar PV power production predictions. The ANNs are optimized in terms of number of hidden neurons and diversified in terms of the diverse training datasets used to build the ANNs, by resorting to trial-and-error procedure and BAGGING techniques, respectively. In addition, the Bootstrap technique is embedded to the ensemble for quantifying the sources of uncertainty that affect the ensemble models' predictions in the form of Prediction Intervals (PIs). The effectiveness of the proposed ensemble approach is demonstrated by a real case study regarding a grid-connected solar PV system (231 kWac capacity) installed on the rooftop of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. The results show that the proposed approach outperforms three benchmark models, including smart persistence model and single optimized ANN model currently adopted by the PV system's owner for the prediction task, with a performance gain reaches up to 11%, 12%, and 9%, for RMSE, MAE, and WMAE standard performance metrics, respectively. Simultaneously, the proposed approach has shown superior in quantifying the uncertainty affecting the power predictions, by establishing slightly wider PIs that achieve the highest confidence level reaches up to 84% for a predefined confidence level of 80% compared to three other approaches of literature. These enhancements would, indeed, allow balancing power supplies and demands across centralized grid networks through economic dispatch decisions between the energy sources that contribute to the energy mix.
Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services

Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and computation requirements exceed the capability of medical institutions. With privacy concerns as well as intellectual property protection issues, the valuable diagnosis classifier and the sensitive user data should be protected against the cloud server. In this paper, we identify a work-flow for cloud-assisted online diagnosis services. We propose an efficient and secure decision tree classification scheme in the proposed work-flow. Specifically, the medical institution transforms a locally pre-trained decision tree classifier to a decision table, and later uses searchable symmetric encryption to encrypt the decision table. Then, the encrypted table is outsourced to the cloud server, and a user can submit encrypted physiological features to the cloud server and obtain an encrypted diagnosis prediction back. We provide formal security proofs to demonstrate that our scheme protects the confidentiality of the decision tree classifier and the user's data. The performance analysis shows that our scheme achieves faster-than-linear classification speed. Experimental evaluations show that our scheme requires several micro-seconds to process a diagnosis request in the tested datasets.
Blockchain and Building Information Modeling (BIM): Review and Applications in Post-Disaster Recovery

Blockchain Technology (BCT) is a growing digital technology that in recent years has gained widespread traction in various industries in the public and private sectors. BCT is a decentralized ledger that records every transaction made in the network, known as a ‘block’, the body of which is comprised of encrypted data of the entire transaction history. BCT was introduced as the working mechanism that forms the operational basis of Bitcoin, the first digital cryptocurrency to gain mainstream appeal. The introduction of decentralized data exchange technology in any industry would require strengthened security, enforce accountability, and could potentially accelerate a shift in workflow dynamics from current centralized architectures to a decentralized, cooperative chain of command and affect a cultural and societal change by encouraging trust and transparency. BCT aims at creating a system that would offer a robust self-regulating, self-monitoring, and cyber-resilient data transaction operation, assuring the facilitation and protection of a truly efficient data exchange system. In the state of Florida, climate change and unpredicted weather disasters have put pressure on state and local decision-makers to adapt quick and efficient post-disaster recovery systems. Part of the recovery efforts is the reconstruction of buildings and infrastructure. The introduction of new technologies in the Architecture, Engineering, and Construction (AEC) industry can contribute to addressing recovery and rebuilding after the event of a natural disaster. With parallel technological advancement in geospatial data and Geographic Information System (GIS), as well as worsening climatic conditions, concerns can be suitably addressed by employing an integrated system of both Building Information Modeling (BIM) and BCT. While several potential applications of BIM must provide solutions to disaster-related issues, few have seen practical applications in recent years that indicate the potential benefits of such implementations. The feasibility of BIM-based applications still rests on the reliability of connectivity and cyber-security, indicating a strong use case for using BCT in conjunction with BIM for post-disaster recovery. This research depicts a survey of BCT and its applications in the Architecture, Engineering, and Construction (AEC) industries and examines the potential incorporation within the BIM process to address post-disaster rebuilding problems. Moreover, the study investigates the potential application of BCT in improving the framework for automating the building permitting process using Smart Contract (SC) technologies and Hyperledger Fabric (HLF), as well as discussing future research areas. The study proposes a new conceptualized framework resulting from the integration of BCT and BIM processes to improve the efficiency of building permit processes in post-disaster events.
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs

Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera, too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd. Therefore, to solve the wide-area counting task requires multiple cameras with overlapping fields-of-view. In this paper, we propose a deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene-level density map on the ground-plane of the 3D world. We consider 3 versions of the fusion framework: the late fusion model fuses camera-view density map; the naive early fusion model fuses camera-view feature maps; and the multi-view multi-scale early fusion model favors that features aligned to the same ground-plane point have consistent scales. We test our 3 fusion models on 3 multi-view counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view counting dataset containing a crowded street intersection. Our methods achieve state-of-the-art results compared to other multi-view counting baselines.
Enhancing Medical Smartphone Networks via Blockchain-Based Trust Management Against Insider Attacks

Internet of Things (IoT) has gradually become one of the most important platforms across different disciplines, by enabling dedicated physical objects to communicate with other Internet-enabled things.With this trend, more devices in medical environments are capable of connecting with each other, named Internet of Medical Things (IoMT).It aims for improving efficiency and reducing communication delay, e.g., monitoring the status of patients and notifying abnormal events.However, due to the distributed nature, insider attacks are still one of the major threats to such IoT environment.How to improve the trust management in IoMT remains a challenge.Motivated by the popularity of blockchain technology, in this paper, our general goal is to investigate the performance of blockchain-based trust management.In particular, we focus on a particular type of IoMT, named medical smartphone networks (MSNs), because of the wide adoption of smartphones in the medical domain.Then, we apply blockchains for enhancing the effectiveness of Bayesian inferencebased trust management to detect malicious nodes in MSNs.In the evaluation, we explore the performance of our approach in two different healthcare environments, and experimental results demonstrate that blockchain technology can help improve the detection efficiency of detecting malicious nodes with reasonable workload.
Local Gradients Smoothing: Defense Against Localized Adversarial Attacks

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently introduced localized attacks, Localized and Visible Adversarial Noise (LaVAN) and Adversarial patch, pose a new challenge to deep learning security by adding adversarial noise only within a specific region without affecting the salient objects in an image. Driven by the observation that such attacks introduce concentrated high-frequency changes at a particular image location, we have developed an effective method to estimate noise location in gradient domain and transform those high activation regions caused by adversarial noise in image domain while having minimal effect on the salient object that is important for correct classification. Our proposed Local Gradients Smoothing (LGS) scheme achieves this by regularizing gradients in the estimated noisy region before feeding the image to DNN for inference. We have shown the effectiveness of our method in comparison to other defense methods including Digital Watermarking, JPEG compression, Total Variance Minimization (TVM) and Feature squeezing on ImageNet dataset. In addition, we systematically study the robustness of the proposed defense mechanism against Back Pass Differentiable Approximation (BPDA), a state of the art attack recently developed to break defenses that transform an input sample to minimize the adversarial effect. Compared to other defense mechanisms, LGS is by far the most resistant to BPDA in localized adversarial attack setting.
A Food Recommender System Considering Nutritional Information and User Preferences

The World Health Organization identifies the overall increasing of noncommunicable diseases as a major issue, such as premature heart diseases, diabetes, and cancer. Unhealthy diets have been identified as the important causing factor of such diseases. In this context, personalized nutrition emerges as a new research field for providing tailored food intake advices to individuals according to their physical, physiological data, and further personal information. Specifically, in the last few years, several types of research have proposed computational models for personalized food recommendation using nutritional knowledge and user data. This paper presents a general framework for daily meal plan recommendations, incorporating as main feature the simultaneous management of nutritional-aware and preference-aware information, in contrast to the previous works which lack this global viewpoint. The proposal incorporates a pre-filtering stage that uses AHPSort as multi-criteria decision analysis tool for filtering out foods which are not appropriate to the current user characteristics. Furthermore, it incorporates an optimization-based stage for generating a daily meal plan whose goal is the recommendation of food highly preferred by the user, not consumed recently, and satisfying his/her daily nutritional requirements. A case study is developed for testing the performance of the recommender system.
Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms

Reconnaissance mission has a wide application in both civil and military fields, which provides intelligence and basis for the following decision-making to accomplish certain goals. Due to numerous advantages of UAV swarms such as strong flexibility, high efficiency, and low cost, conducting reconnaissance missions by UAV swarms has become a trend of future. However, the path planning problem of UAV swarms is a key challenge in the aspect of model construction, algorithm, selection and high computational complexity, especially when the mission is complicated. In this paper, various distributed particle swarm optimization (DPSO)-based path planning algorithms are proposed for UAV swarms conducting a reconnaissance mission, in which targets are gathered in the form of clusters and different tactic needs are taken into consideration. Three algorithms named the maximum density convergence DPSO algorithm (MDC-DPSO), the fast cross-over DPSO algorithm (FCO-DPSO), and the accurate coverage exploration DPSO algorithm (ACE-DPSO) are proposed correspond to the needs of fast convergence, random cross-over, and accurate search, respectively. Different fitness functions and search strategies are specifically designed considering the mobility and communication constraints of the UAV swarms. Besides, the jump-out mechanism and revisit mechanism are designed to save invalid search efforts and avoid falling into local optimum. The simulation results demonstrate that the proposed algorithms are effective in generating paths for UAV swarms conducting a reconnaissance mission, which can be easily applied to large scale swarms.
RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

Detecting a splicing forgery image and then locating the forgery regions is a challenging task. Some traditional feature extraction methods and convolutional neural network (CNN)-based detection methods have been proposed to finish this task by exploring the differences of image attributes between the un-tampered and tampered regions in a image. However, the performance of the existing detection methods is unsatisfactory. In this paper, we propose a ringed residual U-Net (RRU-Net) for image splicing forgery detection. The proposed RRU-Net is an end-to-end image essence attribute segmentation network, which is independent of human visual system, it can accomplish the forgery detection without any preprocessing and post-processing. The core idea of the proposed RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN. The residual propagation recalls the input feature information to solve the gradient degradation problem in the deeper network; the residual feedback consolidates the input feature information to make the differences of image attributes between the un-tampered and tampered regions be more obvious. Experimental results show that the proposed detection method can achieve a promising result compared with the state-of-the-art splicing forgery detection methods.
Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor

In the field of Artificial Intelligence Machine learning provides the automatic systems which learn and improve itself from experience without being explicitly programmed. In this research work a movie recommender system is built using the K-Means Clustering and K-Nearest Neighbor algorithms. The movielens dataset is taken from kaggle. The system is implemented in python programming language. The proposed work deals with the introduction of various concepts related to machine learning and recommendation system. In this work, various tools and techniques have been used to build recommender systems. Various algorithms such as K-Means Clustering, KNN, Collaborative Filtering, Content-Based Filtering have been described in detail. Further, after studying different types of machine learning algorithms, there is a clear picture of where to apply which algorithm in different areas of industries such as recommender systems, e-commerce, etc. Then there is an illustration of how implementations and working of the proposed system are used for the implementation of the movie recommender system. Various building blocks of the proposed system such as Architecture, Process Flow, Pseudo Code, Implementation and Working of the System is described in detail. Finally, in this work for different cluster values, different values of Root Mean Squared Error are obtained. In this proposed work as the no of clusters decreases, the value of RMSE also decreases. The best value of RMSE obtained is 1.081648. The results given by the proposed system are better than the existing technique on the basis of RMSE value.
Experimental error mitigation via symmetry verification in a variational quantum eigensolver

Variational quantum eigensolvers offer a small-scale testbed to demonstrate the performance of error mitigation techniques with low experimental overhead. We present successful error mitigation by applying the recently proposed symmetry verification technique to the experimental estimation of the ground-state energy and ground state of the hydrogen molecule. A finely adjustable exchange interaction between two qubits in a circuit QED processor efficiently prepares variational ansatz states in the single-excitation subspace respecting the parity symmetry of the qubit-mapped Hamiltonian. Symmetry verification improves the energy and state estimates by mitigating the effects of qubit relaxation and residual qubit excitation, which violate the symmetry. A full-density-matrix simulation matching the experiment dissects the contribution of these mechanisms from other calibrated error sources. Enforcing positivity of the measured density matrix via scalable convex optimization correlates the energy and state estimate improvements when using symmetry verification, with interesting implications for determining system properties beyond the ground-state energy.