Reviewerly/exHarmony huggingface dataset:
A Double Obfuscation Approach for Protecting the Privacy of IoT Location Based Applications

Connected vehicles and smart cars have become highly reliant on location-based services (i.e.LBS) to provide accurate, personalized and intelligent services.However, location-based services have endangered its users to considerable risks concerning the privacy and security of users' personal data.Although existing research provides a myriad of methods to improve and protect user privacy in LBS applications, most of these methods are concerned with handling static queries and non-mobile objects only.Moreover, various issues and challenges still persist with regards to the need to trust third parties, overloading of the user, and low accuracy of the returned results.This paper contributes a Double Obfuscation Approach (referred to as DOA) that applies two phases of obfuscation consecutively whilst integrating two differing privacy protection approaches, namely Obfuscation and Trusted Third Party, and two techniques, namely fog caching technology and mix zone.In essence, the DOA obfuscates and hides the identity and location of its users using the fog nodes, which operate as a trusted third party (TTP), and without the need to reveal the identity of the users or trust the cooperating nodes.Moreover, this paper presents a DOA algorithm that improves the overall user privacy and system performance using the fog nodes, which split the responses of each query into five parts, thus reducing the processing time of the results by the user and enhancing the overall accuracy where the user directly selects the most suitable parts based on his current location.Overall, the hybrid DOA approach empowers the users of connected vehicle applications to protect their privacy through an algorithm that caters for the dynamic nature of user queries and mobility of objects.The results of our comparative simulations against well-known hybrid privacy protection methods demonstrate the superiority of the proposed Double Obfuscation Approach especially with respect to user privacy whilst maintaining a nominal overhead on the user, reduced response time and high accuracy of the obtained results.
An effective recommendation model based on deep representation learning

Recommender system has recently attracted a lot of attention in the information service community. Currently, most recommendation models use deep neural networks to learn user preferences for items and make the final recommendations. However, these current models have not effectively captured the deep semantic features of users and items and have not fully used the auxiliary information of the items. This may result in unsatisfactory recommendations for users’ projects. In order to solve the above problem, in this paper, a novel recommendation model called RM-DRL (Recommendation Model based on Deep Representation Learning) was proposed. It mainly consists of two modules: Information Preprocessing and Feature Representation. The former generates the user’s primitive feature vectors and the items used in the latter. The latter consists of two phases: Representation Learning for Item Features (RL-IF) and Representation Learning for User Features (RL-UF). The RL-IF takes the primitive feature vectors of the item as input and uses a multi-layer Convolutional Neural Network (CNN) to learn to accurately produce the semantic feature vector of the item through multi-task learning. In RL-UF, the user primitive feature vectors and semantic feature vectors of the user preference history, and the positive and negative items were taken as input, and a novel Attention-Integrated Gated Recurrent Unit (AIGRU) neural network was proposed to learn to accurately produce user semantic feature vector. After the Feature Representation module converges, the semantic feature vectors of the users and the items can be used to calculate the users’ preferences on the items via vector dot product. Extensive experiments on five real-world datasets show that RM-DRL remarkably outperforms state-of-the-art baselines in solving the recommendation problem.
JPEG-ACT: Accelerating Deep Learning via Transform-based Lossy Compression

A reduction in the time it takes to train machine learning models can be translated into improvements in accuracy. important factor that increases training time in deep neural networks (DNNs) is the need to store large amounts of temporary data during the back-propagation algorithm. To enable training very large models this temporary data can be offloaded from limited size GPU memory to CPU memory but this data movement incurs large performance overheads. We observe that in one important class of DNNs, convolutional neural networks (CNNs), there is spatial correlation in these temporary values. We propose JPEG for ACTivations (JPEGACT), a lossy activation offload accelerator for training CNNs that works by discarding redundant spatial information. JPEGACT adapts the well-known JPEG algorithm from 2D image compression to activation compression. We show how to optimize the JPEG algorithm so as to ensure convergence and maintain accuracy during training. JPEG-ACT achieves 2.4× higher training performance compared to prior offload accelerators, and 1.6× compared to prior activation compression methods. An efficient hardware implementation allows JPEG-ACT to consume less than 1% of the power and area of a modern GPU.
Alzheimer’s Disease stage identification using deep learning models

The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in order to provide the optimal treatment and the prevention of complications. We employed data from 35 patients with AD collected by smartphones for a week in a daycare center. The data sequences of each patient recorded the accelerometer changes while daily activities were performed and they were labeled with the stage of the disease (early, middle or late). Our methodology processes these time series and uses a Convolutional Neural Network (CNN) model to recognize the patterns that identify each stage. The CNN-based method achieved a 90.91% accuracy and an F1-score of 0.897, greatly improving the results obtained by the traditional feature-based classifiers. In our research, we show that mobility data can be a valuable resource for the treatment of patients with AD as well as to study the progress of the disease. The use of our CNN-based method improves the accuracy of the identification of AD stages in comparison to common supervised learning models.
Imbalanced data learning by minority class augmentation using capsule adversarial networks

The fact that image datasets are often imbalanced poses an intense challenge for deep learning techniques. In this paper, we propose a method to restore the balance in imbalanced images, by coalescing two concurrent methods, generative adversarial networks (GANs) and capsule network. In our model, generative and discriminative networks play a novel competitive game, in which the generator generates samples towards specific classes from multivariate probabilities distribution. The discriminator of our model is designed in a way that while recognizing the real and fake samples, it is also requires to assign classes to the inputs. Since GAN approaches require fully observed data during training, when the training samples are imbalanced, the approaches might generate similar samples which leading to data overfitting. This problem is addressed by providing all the available information from both the class components jointly in the adversarial training. It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples. Furthermore, the generator is trained with feature matching loss function to improve the training convergence. In addition, prevents generation of outliers and does not affect majority class space. The evaluations show the effectiveness of our proposed methodology; in particular, the coalescing of capsule-GAN is effective at recognizing highly overlapping classes with much fewer parameters compared with the convolutional-GAN.
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Background Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. Objective This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. Methods We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. Results The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. Conclusions The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.
A Comparison of Deep Learning Methods for ICD Coding of Clinical Records

In this survey, we discuss the task of automatically classifying medical documents into the taxonomy of the International Classification of Diseases (ICD), by the use of deep neural networks. The literature in this domain covers different techniques. We will assess and compare the performance of those techniques in various settings and investigate which combination leverages the best results. Furthermore, we introduce an hierarchical component that exploits the knowledge of the ICD taxonomy. All methods and their combinations are evaluated on two publicly available datasets that represent ICD-9 and ICD-10 coding, respectively. The evaluation leads to a discussion of the advantages and disadvantages of the models.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology

Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This paper explores 1)how graph signal processing (GSP) can be used to extend CNN components to graphs in order to improve model performance; and 2)how to design the graph CNN architecture based on the topology or structure of the data graph.
Interacting with Three-Dimensional Molecular Structures Using an Augmented Reality Mobile App

Visualization of three-dimensional (3D) elements has always played a huge role in chemistry education. At the same time, it is a challenge to teach with most representations being shown in two-dimensional (2D) media. With the recent rise of extended reality (XR) that includes virtual and augmented reality (VR/AR) technology in higher education, attempts have been made at presenting 2D representations to students in a manner that is easier to understand. However, the effectiveness of said attempts has limitations. Our AR project has developed a free-to-use mobile application “Nucleophile’s Point of View” (NuPOV) that aims to address these limitations. By allowing users to not only view chemistry concepts in an AR setting but also interact with them by hand, they are able to learn and understand at a deeper level through an individualized and self-directed learning experience. Our study has shown that such an approach proved to be relatively well-received by students.
On Scalability of Association-rule-based Recommendation

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.
Multicopy provable data possession scheme supporting data dynamics for cloud-based Electronic Medical Record system

In the era of big data, there are several insuperable research challenges in establishing Electronic Medical Record (EMR) for updating massive data with traditional methods. It is an attractive option to create the cloud-based EMR system, since cloud provides elastic and affordable data storage and management services. However, once the medical records are uploaded into the cloud, the owner will lose the control over the data, and sensitive contents might be accessed or modified by unauthorized entities. To address this issue, we propose a multicopy provable data possession for cloud-based EMR systems, which ensures the integrity and privacy of EMR data. In particular, to achieve data updates, we design a novel dynamic structure that improves the Merkle Hash Tree for multicopy storage, which achieves full dynamics efficiently and safely. Moreover, a random masking technique is employed in our proposal to generate distinguishable replica blocks of one block. Our construction prevents a verifier from obtaining medical records from challenge responses, but also eliminates exposing the content to unauthorized entities. Our security analysis shows that our scheme is provably secure. Evaluation experiments demonstrate that the proposal has lower communication and computation costs in comparison with the existing schemes.
Applications of Machine Learning Techniques to Predict Diagnostic Breast Cancer

This article compares six machine learning (ML) algorithms: Classification and Regression Tree (CART), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Linear Regression (LR) and Multilayer Perceptron (MLP) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by estimating their classification test accuracy, standardized data accuracy and runtime analysis. The main objective of this study is to improve the accuracy of prediction using a new statistical method of feature selection. The data set has 32 features, which are reduced using statistical techniques (mode), and the same measurements as above are applied for comparative studies. In the reduced attribute data subset (12 features), we applied 6 integrated models AdaBoost (AB), Gradient Boosting Classifier (GBC), Random Forest (RF), Extra Tree (ET) Bagging and Extra Gradient Boost (XGB), to minimize the probability of misclassification based on any single induced model. We also apply the stacking classifier (Voting Classifier) ​​to basic learners: Logistic Regression (LR), Decision Tree (DT), Support-vector clustering (SVC), K-Nearest Neighbors (KNN), Random Forest (RF) and Naïve Bays (NB) to find out the accuracy obtained by voting classifier (Meta level). To implement the ML algorithm, the data set is divided in the following manner: 80% is used in the training phase and 20% is used in the test phase. To adjust the classifier, manually assigned hyper-parameters are used. At different stages of classification, all ML algorithms perform best, with test accuracy exceeding 90% especially when it is applied to a data subset.
Blockchain-Based Multi-Party Authorization for Accessing IPFS Encrypted Data

Multi-party authorization (MPA) typically involves multiple parties to control and grant access to shared data. MPA is used to solve the insider’s attack problem by ensuring that a single authority or party is not acting alone. Currently, almost all existing implementations of MPA are centralized and fall short in providing logs and events related to provenance of granting permissions in a trusted, secure, immutable, auditable, and decentralized manner. Moreover, for sharing data, proxy re-encryption algorithms are often used to give secure access to encrypted shared data. These schemes and algorithms are also centralized and cannot be trusted. In this paper, we propose a fully decentralized blockchain-based solution in which MPA is implemented using Ethereum smart contracts, and proxy re-encryption algorithms (which are computationally expensive) are implemented using multiple oracles to give access to encrypted shared data stored on a public and decentralized storage platform, such as the Interplanetary File Systems (IPFS). The smart contracts help to validate results based on the majority of encrypted results determined by the oracles. For this, we incorporate reputation mechanisms in the proposed smart contracts to rate the oracles based on their malicious and non-malicious behaviors. We present algorithms along with their full implementation, testing, and validation details. We evaluate the proposed system in terms of security, cost, and generalization to show its reliability and practicality. We make the smart contract source code publicly available on Github.
Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model

Breast Cancer is a highly aggressive type of cancer generally formed in the cells of the breast. Despite significant advances in the treatment of primary breast cancer in the last decade, there is a dire need to attempt of an accurate predictive model for breast cancer prognosis prediction. Researchers from various disciplines are working together to develop methods to save people from this fatal disease. A good predictive model can help in correct prognosis prediction of breast cancer. This accurate prediction can have several benefits like detection of cancer in the early stage, spare patients from getting unnecessary treatment and medical expenses related to it. Previous works rely mostly on uni-modal data (selected gene expression)for predictive model design. In recent years, however, multi-modal cancer data sets have become available (gene expression, copy number alteration and clinical). Motivated by the enhancement of deep-learning based models, in the current study, we propose to use some deep-learning based predictive models in a stacked ensemble framework to improve the prognosis prediction of breast cancer from available multi-modal data sets. One of the unique advantages of the proposed approach lies in the architecture of the model. It is a two-stage model. Stage one uses a convolutional neural network for feature extraction, while stage two uses the extracted features as input to the stack-based ensemble model. The predictive performance evaluated using different performance measures shows that this model produces better result than already existing approaches. This model results in AUC value of 0.93 and accuracy of 90.2 percent at medium stringency level (Specificity = 95 percent and threshold = 0.45). Keras 2.2.1, along with Tensorflow 1.12, is used for implementing the source code of the model. The source code can be downloaded from Github: https://github.com/nikhilaryan92/BreastCancer.
Network embedding: Taxonomies, frameworks and applications

Networks are a general language for describing complex systems of interacting entities. In the real world, a network always contains massive nodes, edges and additional complex information which leads to high complexity in computing and analyzing tasks. Network embedding aims at transforming one network into a low dimensional vector space which benefits the downstream network analysis tasks. In this survey, we provide a systematic overview of network embedding techniques in addressing challenges appearing in networks. We first introduce concepts and challenges in network embedding. Afterwards, we categorize network embedding methods using three categories, including static homogeneous network embedding methods, static heterogeneous network embedding methods and dynamic network embedding methods. Next, we summarize the datasets and evaluation tasks commonly used in network embedding. Finally, we discuss several future directions in this field.
A unified framework of deep networks for genre classification using movie trailer

Affective video content analysis has emerged as one of the most challenging and essential research tasks as it aims to analyze the emotions elicited by videos automatically. However, little progress has been achieved in this field due to the enigmatic nature of emotions. This widens the gap between the human affective state and the structure of the video. In this paper, we propose a novel deep affect-based movie trailer classification framework. We also develop an EmoGDB dataset, which contains 100 Bollywood movie trailers annotated with popular movie genres: Action, Comedy, Drama, Horror, Romance, Thriller, and six different types of induced emotions: Anger, Fear, Happy, Neutral, Sad, Surprise. The affect-based features are learned via ILDNet architecture trained on the EmoGDB dataset. Our work aims to analyze the relationship between the emotions elicited by the movie trailers and how they contribute in solving the multi-label genre classification problem. The proposed novel framework is validated by performing cross-dataset testing on three large scale datasets, namely LMTD-9, MMTF-14K, and ML-25M datasets. Extensive experiments show that the proposed algorithm outperforms all the state-of-the-art methods significantly, as reported by the precision, recall, F1 score, precision–recall curves (PRC), and area under the PRC evaluation metrics.