Reviewerly/exHarmony huggingface dataset:
Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and Categorical Mixed Data Clustering

Statistical data analysis in machine learning and data mining usually uses the clustering technique. However, data with both attributes or mixed data exists universally in real life. K-prototype is a well-known algorithm for clustering mixed data because of its effectiveness in handling large data. However, practically, k-prototype has two main weaknesses, the use of mode as a cluster center for categorical attributes cannot accurately represent the objects, and the algorithm may stop at the local optimum solution because affected by random initial cluster prototypes. To overcome the first weakness, we can use fuzzy centroid, and for second weakness is to implement the genetic algorithm to search the global optimum solution. Our research combines the genetic algorithm and Fuzzy K-Prototype to accommodate these two weaknesses. We set up two multivariate data with high correlation and low correlation to see the robustness of the proposed algorithm. According to four value indexes of clustering result evaluation, Coefficient Varians Index, Partition Coefficient, Partition Entropy, and Purity, show that our proposed algorithm has a better result than K prototype. Based on the evaluation result, we conclude that our proposed algorithm can solve two weaknesses of the k-prototype algorithm.
Multi-Turn Dialogue Reading Comprehension With Pivot Turns and Knowledge

Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. The major challenges involve noisy history contexts and especial prerequisites of commonsense knowledge that is unseen in the given material. Existing works mainly focus on context and response matching approaches. This work thus makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances and utilizing external knowledge to enhance the representation of context. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension. In detail, our model first picks out the pivot utterances from the conversation history according to the semantic matching with the candidate response or question, if any. Besides, knowledge items related to the dialogue context are extracted from a knowledge graph as external knowledge. Then, the pivot utterances and the external knowledge are combined together with a well-designed mechanism for refining predictions. Experimental results on four dialogue comprehension benchmark tasks show that our proposed model achieves great improvements on baselines. A series of empirical comparisons are conducted to show how our selection strategies and the extra knowledge injection influence the results.
KTBoost: Combined Kernel and Tree Boosting

Abstract We introduce a novel boosting algorithm called ‘KTBoost’ which combines k ernel boosting and t ree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous trees and continuous RKHS regression functions complement each other, and that this combination allows for better learning of functions that have parts with varying degrees of regularity such as discontinuities and smooth parts. We empirically show that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy in a comparison on a wide array of data sets.
MixGAIL: Autonomous Driving Using Demonstrations with Mixed Qualities

In this paper, we consider autonomous driving of a vehicle using imitation learning. Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation learning. This algorithm leverages positive demonstrations to imitate the behavior of an expert. In this paper, we propose a novel method, called mixed generative adversarial imitation learning (MixGAIL), which incorporates both of expert demonstrations and negative demonstrations, such as vehicle collisions. To this end, the proposed method utilizes an occupancy measure and a constraint function. The occupancy measure is used to follow expert demonstrations and provides a positive feedback. On the other hand, the constraint function is used for negative demonstrations to assert a negative feedback. Experimental results show that the proposed algorithm converges faster than the other baseline methods. Also, hardware experiments using a real-world RC car shows an outstanding performance and faster convergence compared with existing methods.
On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

The potential growth in data mining has an important aspect on security due to the consideration of the data as an asset.The provisioning of protection in a public infrastructure fails to ensure privacy disclosure of an individual's information.Differential Privacy (DP) is a promising solution for assuring privacy protection by injecting noise using the Laplace mechanism or Exponential mechanism.The access of data by analysts is performed via edge devices.A common problem identified from previous research work is the leakage of privacy at the edge layer and data accessed by unauthorized people.To address the problem, this paper proposes DP-FCNN, that implements Differential Privacy using a Fuzzy Convolution Neural Network (FCNN) with Laplace Mechanism for injecting noise.The processes handled here are data processing and query processing.The dataset is uploaded by the data owner to the data provider, who is responsible for injecting noise and then encrypting with Piccolo encryption before uploading it into the cloud.Based on the uploaded dataset, the data owner constructs a hash index from the extracted key attributes by using the BLAKE2s algorithm for performing hashing.The hash index is fed into the edge server to form a Merkle hash tree due to the data leakage at the edge is eliminated.On the other hand, requests/queries by the data analyst are authenticated by the data provider.The hash tree in the edge server then searches for the corresponding data, extracting it from the cloud and delivers it to the data analyst in an encrypted format.Every authenticated data analyst is provided with a decryption key for retrieving the query result.This is implemented using Java and the results show better efficiency in terms of scalability, processing time and accuracy.
Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention.This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the complex data.However, two limitations may still deteriorate their performances: (1) the local invariance of the input data is insufficiently explored, that is, the intrinsic geometrical structures of the original data in the data and feature spaces are not considered simultaneously; (2) the sparseness that can greatly improve the ability of learning parts is also ignored.In this paper, we propose a novel approach to address the above two problems, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), which can learn sparse and informative deep features while sufficiently exploring the local invariance of the data to discover valuable information underlying the input data.Specifically, SDG Deep NMF learns the informative deep features by performing the dual graph regularization in the deep NMF framework, which can respect the intrinsic geometrical structures of the input data in the data and feature spaces while mining the data information in hidden layers.Meanwhile, SDG Deep NMF also imposes sparse constraints on the basis matrix during the feature learning to improve the part-based learning capabilities.Moreover, we construct the objective function of SDG Deep NMF in the form of the Euclidean distance for convenience, the iterative updating scheme is chosen to optimize it.Comprehensive experiments on four benchmark datasets can demonstrate the effectiveness of the proposed approach in image clustering.
A Personalized Movie Recommendation System based on LSTM-CNN

In the context of such an era where nearly everything is based on big data, personalized recommendation systems are becoming increasingly valuable for research. Deep learning has attained great achievements in numerous fields by virtue of its powerful computing power and extraordinary nonlinear transformation capabilities. Applying deep learning to a recommendation system that needs to mine and extract features from massive amounts of data will not only help the development of recommendation algorithms, but also improve the algorithm performance and thus improve the user experience. This project introduces a recommendation algorithm based on LSTM-CNN and applies it to the recommendation of movies by mining user behavior data and recommending movies with higher ratings to them. This article uses the data provided by the movie website MovieLens. It is testing set and training set that the data is divided into, and Top-N recommendation list is produced for the training set, while the algorithm is evaluated on the testing set. It is the features of the data that LSTM-CNN can effectively extract and complete the recommendation from the results.
The Use of Blockchain to Support Distributed AI Implementation in IoT Systems

This article presents a distributed and decentralized architecture for the implementation of distributed artificial intelligence (DAI) using hardware platforms provided by the Internet of Things (IoT). A trained DAI system has been implemented over IoT, where each IoT device acts as one or more of the neurons within the DAI layers. This is accomplished by the utilization of decentralized, self-managed blockchain technologies that allow trusted interactions and information to be exchanged between distributed neurons. The platform was built and customized to be used within the IoT system, and it is capable of handling DAI-related tasks. A new consensus mechanism based on Proof of Authority (PoA) and Proof of Work (PoW) has been designed and implemented, along with bespoke block and transaction formats. The proposed architecture was analyzed, implemented, and tested using a dedicated testbed with low-cost IoT devices. A quantitative measurement and performance evaluation of the system based on a real-world IoT application was conducted. The implemented DAI is found to have an accuracy of 92%–98%, with an energy cost of 0.12 joules (J) when utilizing a Raspberry Pi to run one neuron. The measured hash per joule (h/J) when using a Raspberry Pi for mining is 13.8 Kh/J compared to 54 Kh/J using an ESP32. The results showed that it is feasible to implement a DAI system utilizing the IoT hardware platform while maintaining the system’s accuracy. The integration of the blockchain has added an element of security and trust to the data and the interaction between system components.
Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset

Recent research has questioned the fairness of face-based recognition and attribute classification methods (such as gender and race) for dark-skinned people and women. Ocular biometrics in the visible spectrum is an alternate solution over face biometrics, thanks to its accuracy, security, robustness against facial expression, and ease of use in mobile devices. With the recent COVID-19 crisis, ocular biometrics has a further advantage over face biometrics in the presence of a mask. However, fairness of ocular biometrics has not been studied till now. This first study aims to explore the fairness of ocular-based authentication and gender classification methods across males and females. To this aim, VISOB 2.0 dataset, along with its gender annotations, is used for the fairness analysis of ocular biometrics methods based on ResNet-50, MobileNet-V2 and lightCNN-29 models. Experimental results suggest the equivalent performance of males and females for ocular-based mobile user-authentication in terms of genuine match rate (GMR) at lower false match rates (FMRs) and an overall Area Under Curve (AUC). For instance, an AUC of 0.96 for females and 0.95 for males was obtained for lightCNN-29 on an average. However, males significantly outperformed females in deep learning based gender classification models based on ocular-region.
Outcomes prediction in longitudinal data: Study designs evaluation, use case in ICU acquired sepsis

Outcomes’ prediction in Electronic Health Records (EHR) and specifically in Critical Care is increasingly attracting more exploration and research. In this study, we used clinical data from the Intensive Care Unit (ICU), focusing on ICU acquired sepsis. Looking at the current literature, several evaluation approaches are reported, inspired by epidemiological designs, in which some do not always reflect real-life application’s conditions. This problem seems relevant generally to outcomes’ prediction in longitudinal EHR data, or generally longitudinal data, while in this study we focused on ICU data. Unlike in most previous studies that investigated all sepsis admissions, we focused specifically on ICU-Acquired Sepsis. Due to the sparse nature of the longitudinal data, we employed the use of Temporal Abstraction and Time Interval-Related Patterns discovery, which are further used as classification features. Two experiments were designed using three different outcomes prediction study designs from the literature, implementing various levels of real-life conditions to evaluate the prediction models. The first experiment focused on predicting whether a patient would suffer from ICU-acquired sepsis and when during her admission, given a sliding observation time window, and the comparison of the three study designs behavior. The second experiment focused only on predicting whether the patient will suffer from ICU-acquired sepsis, based on data taken relatively to his admission start time. Our results show that using Temporal Discretization for Classification (TD4C) led to better performance than using the Equal-Width Discretization, Knowledge-Based, or SAX. Also, using two states abstraction was better than three or four. Using the default Binary TIRP representation method performed better than Mean Duration, Horizontal Support, and horizontally normalized horizontal support. Using XGBoost as a classifier performed better than Logistic Regression, Neural Net, or Random Forest. Additionally, it is demonstrated why the use of case-crossover-control is most appropriate for real life application conditions evaluation, unlike other incomplete designs that may even result in “better performance”.
Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements

The subpixel displacement estimation is an important step to calculation of the displacement between two digital images in optics and image processing. Digital image correlation (DIC) is an effective method for measuring displacement due to its high accuracy. Various DIC algorithms to compare images and to obtain displacement have been implemented. However, there are some drawbacks to DIC. It can be computationally expensive when processing a sequence of continuously deformed images. To simplify the subpixel displacement estimation and to explore a different measurement scheme, a convolutional neural network with a transfer learning based subpixel displacement measurement method (CNN-SDM) is proposed in this paper. The basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by CNN, and thereby to achieve a coarse-to-fine subpixel displacement estimation. The proposed CNN is a classification model consisting of two convolutional neural networks in series. The results of simulated and real experiments are shown that the proposed CNN-SDM method is feasibly effective for subpixel displacement measurement due its high efficiency, robustness, simple structure and few parameters.
Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization

In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage.In this paper, an optimized deep learning method is presented for multiple stomach disease classi cation.The proposed method work in few important steps-preprocessing using the fusion of ltering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and nally fusion of optimal vectors and classi cation using Multi-Layered Perceptron Neural Network (MLNN).In the feature extraction step, pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step.Later on, the activation function is applied to Global Average Pool (GAP) for feature extraction.However, the extracted features are optimized through two different nature-inspired algorithms-Particle Swarm Optimization (PSO) with dynamic tness function and Crow Search Algorithm (CSA).Hence, both methods' output is fused by a maximal value approach and classi ed the fused feature vector by MLNN.Two datasets are used to evaluate the proposed method-CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques, it is shown that the proposed method shows signi cant performance.
Opinion Mining based Fake Product review Monitoring and Removal System

Fake review detection and its elimination from the given dataset using different Natural Language Processing (NLP) techniques is important in several aspects. In this article, the fake review dataset is trained by applying two different Machine Learning (ML) models to predict the accuracy of how genuine are the reviews in a given dataset. The rate of fake reviews in E-commerce industry and even other platforms is increasing when depend on product reviews for the item found online on different websites and applications. The products of the company were trusted before making a purchase. So this fake review problem must be addressed so that these large E-commerce industries such as Flipkart, Amazon, etc. can rectify this issue so that the fake reviewers and spammers are eliminated to prevent users from losing trust on online shopping platforms. This model can be used by websites and applications with few thousands of users where it can predict the authenticity of the review based on which the website owners can take necessary action towards them. This model is developed using Naïve Bayes and random forest methods. By applying these models one can know the number of spam reviews on a website or application instantly. To counter such spammers, a sophisticated model is required in which a need to be trained on millions of reviews. In this work “amazon Yelp dataset” is used to train the models and its very small dataset is used for training on a very small scale and can be scaled to get high accuracy and flexibility.
Context-driven detection of distracted driving using images from in-car cameras

Distracted driving on roads has been a perennial problem. There is increasing consensus now among various stakeholders that this practice will not be eliminated with mere warnings from subject matter experts. Instead, these warnings must also be augmented with appropriate technological assistance to facilitate safer driving. There has been an urgent interest in the community to devise newer technologies for detecting when drivers are distracted on roads. In this paper, we design a computer vision techniques that process image data recorded from inside of cars to automatically infer when subjects are driving distracted. However, our innovation lies in adding context to the predictions. We do so, by first detecting and localizing a number of objects in cars that contribute to distracted driving (e.g., hands, smartphones, radio etc.) from images. Then, we process the relative locations of these objects using machine learning algorithms within an image to make predictions on distracted driving. We believe that our proposed context-driven approach is unique. We expect it to better facilitate correction of distracted driving, when real-time feedback to subjects come with appropriate contextual interpretation of the specific aspects that contributed to distracted driving. Performance evaluations of our techniques reveal a) mAP score of 63.90 for an IoU of 0.5 in object localization; and b) an overall accuracy of 94% in detecting instances of distracted driving based on object localization. Processing time incurred by our technique is around 200ms only. As such, we believe that our system is accurate, fast, practical and context-aware also.
Sequential Message Characterization for Early Classification of Encrypted Internet Traffic

Classifying Internet traffic is critical to many network management tasks, including malicious attack detection, usage monitoring, load balancing, etc. As current traffic packets are often transmitted with encryption, at randomized port numbers, and under highly dynamic network conditions, traditional approaches such as port mapping, deep packet inspection, and statistical analysis are no longer effective. In this paper, we first collect extensive traffic flows at the exit router of a university and label them into various source applications. After extracting the message (consisting of multiple consecutive TCP packets) sequence for all collected traffic flows, we find that each application type has distinct sequential message features. By leveraging the message sequential feature, we develop a system, named SMC (Sequential Message Characterization), which can perform online traffic classification with the sequential size information of a few message segments. In SMC, after confirming the long-term dependency among message segments, we create a Long Short-Term Memory (LSTM) neural network to conduct deep learning on message size sequence, and then build a multi-classifier to classify traffic types based on the probability profiles output by deep LSTM models. Extensive experiments are conducted and results demonstrate that the proposed SMC can achieve 97% of classification accuracy on average. Meanwhile, with as few as 6 pieces of message size information as input, SMC enables early online traffic classification especially for heavy-traffic flows with over 35 message segments in median.
A Speech Command Control-Based Recognition System for Dysarthric Patients Based on Deep Learning Technology

Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help dysarthric patients control mobile devices. However, the large computation power requirement for the ASR system increases implementation costs. To alleviate this problem, this study proposed a convolution neural network (CNN) with a phonetic posteriorgram (PPG) speech feature system to recognize speech commands, called CNN–PPG; meanwhile, the CNN model with Mel-frequency cepstral coefficient (CNN–MFCC model) and ASR-based systems were used for comparison. The experiment results show that the CNN–PPG system provided 93.49% accuracy, better than the CNN–MFCC (65.67%) and ASR-based systems (89.59%). Additionally, the CNN–PPG used a smaller model size comprising only 54% parameter numbers compared with the ASR-based system; hence, the proposed system could reduce implementation costs for users. These findings suggest that the CNN–PPG system could augment a communication device to help dysarthric patients control the mobile device via speech commands in the future.