Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual Interest and Trust Driving Collective Intelligence Awareness for Social Recommendation

Social recommendation incorporates the social information of the user, such as friend relationship and trust relationship into the traditional recommendation system. From this point of view, social recommendation expands the function of the traditional recommendation to some extent. However, the existing social recommendation methods mostly focus on the general social relation between users and neglects the refinement of group information based on individual interest and trust. To this end, this paper proposes a novel social recommendation model based on the collective intelligence awareness driven by individual interest and trust. Experiments on two real-world datasets demonstrate that the proposed social recommendation method based on group information and individual feature outperforms the three baseline methods on the two evaluation metrics MAE and RMSE.
SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition

This study focuses on improving the performance of human activity recognition when a small number of sensor data are available under some special practical scenarios and resource-limited environments, such as some high-risk projects, anomaly monitoring and actual tactical scenarios. The Human Activity Recognition (HAR) based on wearable sensors is an attractive research topic in machine learning and ubiquitous computing over the last few decades, and has extremely practicality in health surveillance, medical assistance, personalized services, etc. However, with the limitation of sensor sampling rate, sustainability, deployment, and other restricted conditions, it is difficult to collect enough and resultful sensor data anywhere, anytime. Therefore, the HAR based on wearable sensors always faces the challenges of the low-data regime under some practical scenarios, which leads to a low accuracy of activity recognition and needs to be solved urgently. Currently, the Generative Adversarial Networks (GANs) provide a powerful method for training resultful generative models that could generate very convincing verisimilar images. The framework of GANs and its variants shed many lights on improving the performance of HAR. In this paper, we propose a new generative adversarial networks framework called SensoryGANs that can effectively generate available sensor data used for HAR. To the best of our knowledge, SensoryGANs is the first unbroken generative adversarial networks applied in generating sensor data in the HAR research field. Firstly, we tried exploring and devising three activity-special GANs models for three human daily activities. Secondly, these specific models are trained with the guidance of unbroken vanilla GANs. Thirdly, the trained generators from adversarial optimization process are used to generate synthetic sensor data. Finally, the synthetic sensor data from SensoryGANs are used to enrich the original authentic sensor datasets, which can improve the performance of target activity recognition model. Meanwhile, we propose three visual evaluation methods for assessing synthetic sensor data produced by the trained generators in SensoryGANs models. Experimental results show that SensoryGANs models have the capability of capturing the implicit distribution of real sensor data of human activity, and then the synthetic sensor data generated by SensoryGANs models Have a potential for improving human activity recognition.
A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification

The Fuzzy k Nearest Neighbor (Fuzzy kNN) classifier is well known for its effectiveness in supervised learning problems. kNN classifies by comparing new incoming examples with a similarity function using the samples of the training set. The fuzzy version of the kNN accounts for the underlying uncertainty in the class labels, and it is composed of two different stages. The first one is responsible for calculating the fuzzy membership degree for each sample of the problem in order to obtain smoother boundaries between classes. The second stage classifies similarly to the standard kNN algorithm but uses the previously calculated class membership degree. To deal with very large datasets, distributed versions of the Fuzzy kNN algorithm have been proposed. However, existing approaches remain not fully scalable as they aim to replicate the exact behavior of the Fuzzy kNN. In this work, we present an approximate and distributed Fuzzy kNN approach based on Hybrid Spill-Tree implemented under Apache Spark. The aim of this model is to alleviate the scalability problems and to deal with big datasets maintaining high accuracy. In our experiments, we compare in precision and runtime with the Fuzzy kNN for big data problems existing in the literature, running with datasets of up to 11 million instances. The results show an improvement in the runtime and accuracy with respect to the previous exact model.
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images

Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous questions and problems awaiting answers and solutions, respectively. In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network. Three common image classification methods, including support vector machines, decision trees, and artificial neural networks are used to classify feature vectors obtained by different feature extractors. We use KIMIA Path960, a publicly available dataset of 960 histopathology images extracted from 20 different tissue scans to test the accuracy of classification and feature extractions models used in the study, specifically for the histopathology images. SVM achieves the highest accuracy of 90.52% using local binary patterns as features which surpasses the accuracy obtained by deep features, namely 81.14%.
A Local and Global Discriminative Framework and Optimization for Balanced Clustering

For many specific applications in data mining and machine learning, we face explicit or latent size constraint for each cluster that leads to the “balanced clustering” problem. Many existing clustering algorithms perform well in partitioning but fail in producing balanced clusters and preserving the naturally balanced structure of some data. In this paper, we propose a novel balanced clustering framework that flexibly utilizes local and global information of data. First, we propose the global balanced clustering (GBC), in which a global discriminative partitioning model is combined with the minimization of the distribution entropy of data. Then, we show that the proposed GBC can be further used to globally regularize some widely used local clustering models, so as to transform them into balanced clustering that simultaneously capture local and global data. We apply our global balanced regularization to spectral clustering (SC) and local learning (LL)-based clustering, respectively, and propose another two novel balanced clustering models: the local and global balanced SC (LGB-SC) and LGB-LL. Finding the optimal balanced partition is nondeterministic polynomial-time (NP)-hard in general. We adopt the method of augmented Lagrange multipliers to help optimize our model. Comprehensive experiments on several real world benchmarks demonstrate the advantage of our framework to yield balanced clusters while preserving good clustering quality. Our proposed LGB-SC and LGB-LL also outperform SC and LL as well as other classical clustering methods.
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning

Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we present a novel one-stop solution on aspect-based sentiment analysis. Specifically, we propose a novel multi-task neural learning framework to jointly tackle aspect extraction and sentiment prediction subtasks at the same time, and leverage attention mechanisms to learn the joint representation of aspect-sentiment relationship. We have conducted extensive comparative experiments on two benchmark datasets from SemEva1–2014. The experiment results demonstrate the effectiveness of our proposed solution. Especially, our multi-task model outperforms the state-of-the-art systems on aspect extraction subtask.
Deep Learning Single Logo Recognition with Data Enhancement by Shape Context

Recently, there emerges a new kind of Web AR APP for brand advertising, which displays the brand-related content when a smartphone with the APP sees the brand's logo. However, the hand-crafted features used in these APPs usually lead to low recognition performance when used in a real environment. This is either because the target logo has less sharp edges and corner features which the hand-crafted schemes heavily depend on, or because some logos have larger intra-class variations and appear with different backgrounds. Some apps deal with this problem by just adding a button for the user to skip the phase of logo recognition. But this dramatically reduces the attractiveness of the apps as well as the advertisement effect. We promote an elegant solution to tackle the challenge of brand logo recognition. Thus, we propose a new CNN-based logo recognition scheme with a novel data enhancement strategy bias towards a single logo. We adapted the well-known CNN LeNet-5 to learn only two classes: the target logo and its best accompanying logo. Specifically, we utilized shape context as a measurement for characterizing the similarity between the target logo and other logos. Based on the similarity, the best accompanying dataset is generated for training the target logo. The experimental results show that the recall of our scheme surpasses hand-crafted feature based methods such as SIFT by 10% on real images while maintaining the same level of precision.
Cloud Service Provider Evaluation System Using Fuzzy Rough Set Technique

Cloud Service Provider (CSP) offers a wide variety of scalable, flexible, and cost-efficient services to users on demand and pay-per-utilization basis. However, vast diversity in available services leads to various challenges for a user to determine and select the best suitable service. Further, sometime user needs to hire required services from multiple service providers which leads to account, security, support, interface, and Service Level Agreements (SLAs) associated management difficulties. To avoid such problems having a Cloud Service Broker (CSB) which is aware of service offerings and user's Quality of Service (QoS) requirements will benefit both the service providers and users. We have proposed a cloud service brokerage architecture using fuzzy rough set technique, which facilitates ranking and selecting services, based on users QoS requirements and finally monitor the service execution. We have used fuzzy rough set technique for dimension reduction and weighted Euclidean distance to rank the service providers. To prioritize user QoS request, we suggest to use user assign weights. We also incorporate system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing technique based on the system response time. With the help of a case study, we have demonstrated the efficiency of the proposed technique. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.
Evolutionary Multi-objective Ensemble Learning for Multivariate Electricity Consumption Prediction

Energy consumption prediction typically corresponds to a multivariate time series prediction task where different channels in the multivariate time series represent energy consumption data and various auxiliary data related to energy consumption such as environmental factors. It is non-trivial to resolve this task, which requires finding the most appropriate prediction model and the most useful features (extracted from the raw data) to be used by the model. This work proposes an evolutionary multi-objective ensemble learning (EMOEL) technique which uses extreme learning machines (ELMs) as base predictors due to its highly recognized efficacy. EMOEL employs evolutionary multi-objective optimization to search for the optimal parameters of the model as well as the optimal features fed into the model subjected to two conflicting criteria, i.e., accuracy and diversity. It leads to a Pareto front composed of non-dominated optimal solutions where each solution depicts the number of hidden neurons in the ELM, the selected channels in the multivariate time series, the selected feature extraction methods and the selected time windows applied to the selected channels. The optimal solutions in the Pareto front stand for different end-to-end prediction models which may lead to different prediction results. To boost ultimate prediction accuracy, the models with respect to these optimal solutions are linearly combined with combination coefficients being optimized via an evolutionary algorithm. We evaluate the proposed method in comparison to some existing prediction techniques on an Australian University based dataset, which demonstrates the superiority of the proposed method.
Reducing SqueezeNet Storage Size with Depthwise Separable Convolutions

Current research in the field of convolutional neu-ral networks usually focuses on improving network accuracy, regardless of the network size and inference time. In this paper, we investigate the effects of storage space reduction in SqueezeNet as it relates to inference time when processing single test samples. In order to reduce the storage space, we suggest adjusting SqueezeNet’s Fire Modules to include Depthwise Separable Convolutions (DSC). The resulting network, referred to as SqueezeNet-DSC, is compared to different convolutional neural networks such as MobileNet, AlexNet, VGG19, and the original SqueezeNet itself. When analyzing the models, we consider accuracy, the number of parameters, parameter storage size and processing time of a single test sample on CIFAR−10 and CIFAR-100 databases. The SqueezeNet-DSC exhibited a considerable size reduction (37% the size of SqueezeNet), while experiencing a loss in network accuracy of 1,07% in CIFAR−10 and 3,06% in top 1 CIFAR−100.
The Effect of Psychological Stress on Salivary Testosterone in Puberty Children

BACKGROUND: Psychological stress is a condition that is experienced by many adolescents which affect the Hypothalamic-Pituitary-Gonadal axis. Testosterone is known as a sex steroid hormone that is susceptible to acute stress and can be measured through saliva. Disruption of the reproductive system can affect the sexual maturation process. AIM: To understand the difference in salivary testosterone levels in puberty children before and after given a stressor. METHODS: A quasi-experimental intervention study was conducted at Antonius Bangun Mulia junior high school, Medan, North Sumatera, in July-October 2017. Subjects were students aged 12-14 years with sexual maturity G2 for boys and M2 for girls. Psychological stress intervention was generated by the Wechsler intelligence scale for children fourth edition (WISC IV). Saliva was collected before and after the intervention. The analysis was done with Wilcoxon test and a P value < 0.05 was considered significant. RESULTS: Forty-two subjects of 24 male students and 18 female students with sexual maturation Tanner II (54.8%) and Tanner III (45.2%). This study obtained that there was a statistically significant difference in salivary testosterone levels before and after the subject was given a stressor (P = 0.015, CI 95%). This difference also was seen within sexual maturation Tanner II (P = 0.045, CI 95%). No difference was observed in testosterone levels based on gender, male students (P = 0.065, CI 95%) and female students (P = 0.112, CI 95%). CONCLUSION: Stress can affect salivary testosterone levels. There was a statistically significant difference in salivary testosterone levels before and after psychological stress in puberty children.
A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention

Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.
Learning Stable Movement Primitives by Finding a Suitable Fuzzy Lyapunov Function from Kinesthetic Demonstrations

Transferring skills to roUots through human demonstrations is an interesting problem. Locally generated demonstrations of reaching motion, given by a human teacher are generally encoded in a dynamical model. Stability of this encoding system demands great attention while learning the model parameters. In that context, we present a new architecture of dynamical system to learn movement primitives from multiple demonstrations exploiting a fuzzy Lyapunov function (FLF). We assume that there exists a natural Lyapunov function (LF) that associates the demonstrations. The proposed FLF tries to approximate that LF. First, the dynamics of the demonstrations are encoded in a regressive model, learnt using Gaussian mixture regression with EM algorithm. Then the FLF is searched involving the learnt dynamics in an optimization process. The FLF in turn helps to learn a fuzzy controller. Our architecture is new in a sense that it combines the probabilistic model with a fuzzy controller to create a globally asymptotically stable motion model. The proposed algorithm can simultaneously learn position and orientation profiles in a single model. The algorithm is experimentally validated on a commercially available manipulator and also compared with a state-of-the-art technique.
A Low-Latency SC Polar Decoder Based on The Sequential Logic Optimization

Recently, polar code has been identified as one of the channel coding schemes in the 5G wireless communication system. One of the challenges in the hardware design of successive cancellation (SC) polar decoder is to reduce the latency. To achieve this goal, in this paper, we first propose the general sequential logic laws (SLL) of SC decoding. The SLL reflects the timing switch relation between the $f$ and 9 operations at various decoding stages. Guided by the SLL, we design a new low-latency SC decoding architecture. It is a novel reformulation for the last two stages of SC decoding so that four bits can be de decoded simultaneously. A polar SC decoder with code length $N$ = 212 is implemented in the Stratix V FPGA to verify the proposed architecture. As a result, 25% decoding latency reduction can be achieved with respect to the already-known mainstream SC decoders.
Twin Neural Networks for Efficient EEG Signal Classification

Classification of ElectroEncephaloGram (EEG) signals has found several applications in developing Brain Computer Interfaces (BCIs), as well as other clinical and nonclinical applications based on EEG signals. Processing of EEG signals in this context is challenged by its non-stationarity, high dimensionality and the problem of class imbalance for training classifiers, particularly in case of multi-class classification. Our recent work demonstrated the utility of Twin Support Vector Machine (TWSVM) classifiers for robust classification of imbalanced datasets, specifically EEG signal classification. However, the architecture of the TWSVM is not scalable for large datasets as it involves computing the kernel and matrix inversion operations. In this paper, we present an application of the recently proposed neural network architecture for the Twin SVM, the Twin Neural Network (Twin NN), for robust classification of EEG signals. Results on datasets from BCI competitions illustrate the improved generalization and scalability of the Twin NN for the binary and multi-class classification tasks.
A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.
Terrain Classification for Autonomous Vehicles Using Bat-Inspired Echolocation

Many types of bats use echolocation to sense their environment. Despite often have little or no visual acuity, they are able to acquire very detailed views of their surroundings through emission, receipt, and analysis of acoustic pulses. In this study autonomous navigation was examined with respect to classification of nearby terrain. The goal of this effort was to demonstrate that a bat-inspired acoustic sensor could be built, and when trained using advanced signal filtering and machine learning techniques, could be used to accurately classify terrain types for a small mobile robot. A dual channel in-air sonar was constructed using two common piezoelectric transmitter elements with 25 kHz and 32 kHz nominal center frequencies, and echo data was collected from grass, concrete, sand, and gravel terrain substrates. Higher dimension time, frequency, and time-frequency PCA scores were used to discriminate between terrain substrates. These features were used to train a support vector machine (SVM) to classify the terrain types. The SVM-based classifier was able to classify terrain types at a greater than 97% success rate using the constructed bat-inspired echolocation sensor.