Miracle-dz/UniFAR-FTUs huggingface dataset:
GRADI: Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences

Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the groupspecific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.
Stock Prediction using Deep Learning and Sentiment Analysis

Stock prediction has been a popular research topic and researchers have done a lot of work in this field. Due to its stochastic nature, predicting the future stock market remains a very difficult problem. This paper studies the application of attention-based LSTM deep neural network in future stock market movement prediction. We also build stock aggregate dataset and individual dataset including stock history data, financial tweets sentiment and technical indicators in the US stock market. The experiment studies the time sensitivity of finance tweet sentiment and methods of collective sentiment calculation. This paper also experiments on conventional LSTM and attention-based LSTM for performance comparison. We find the finance tweets that are posted from market closure to market open in the next day has more predictive power on next day stock movement. The weighted sentiment on max follower on StockTwits also outperforms other methods. In our experiment, the result on our individual stock dataset shows a similar pattern like normal distribution.
AI Recognition in Skin Pathologies Detection

Skin cancer is the most common type of cancer [1]. Between different malignant skin pathology melanoma is the most fleeting and mortality. Despite the superficial location of pathologies, only half of patients seek medical assistance on the early stages[2]. Treatment on the early (epidermal) stage provides a significantly higher chance of recovery. To assist a wide range of people in the early skin cancer detection, a software package was developed. The software based on deep convolutional neural networks technology. This complex allows to classify normal and malignant pathology on the uploaded photos. In clinical practice doctors use the ABCDE symptom's complex. This complex characterizes the observation of pigment spot asymmetry, border irregularities, color unevenness, diameter, and evolution [3]. The machine learning approach involves the computer evaluating similar factors when processing multiple images of different skin formations. The paper presents an algorithm for classification of skin lesions into pathology and norm using convolutional neural network architecture Xception with prior images segmentation. The upper classifying layers were frozen and new ones were added to classify skin diseases in the pre-trained neural network Xception. As a result, the classification of benign and malignant skin tumors provided at least 89% accuracy. At the moment, the result of research work is designed in form of application software that allows to download the image of pigmented skin spots from the camera. It is available on https://skincheckup.online
Digital Hybrid Current Mode Control with Asymmetric Slope Compensation for Three-Level Flying Capacitor Buck Converter

Due to high effective frequency and reduced voltage stress on the switching devices, the three level flying capacitor (3LFC) buck converter has potential to provide better efficiency and higher power density as compared to the traditional buck converter. However, the control implementation is challenging as a result of flying capacitor instability issue. In this paper, the digital hybrid current mode (HCM) control method which combines the average and peak current mode control techniques is modified and implemented to 3LFC buck converter. Flying capacitor voltage balancing is achieved by adjusting the slope compensation of the two switching pairs asymmetrically. The proposed asymmetric slope compensation technique for digital HCM control method is verified in simulations and experimentally with an ARM-based mixed-signal microcontroller on a 40 V input 13 V / 6 A output prototype.
Centralized Trust Scheme for Cluster Routing of Wireless Sensor Networks

With the increasing prevalence of Internet-of-Things (IoT) application, Wireless Sensor Networks (WSNs), as a layer in the IoT hierarchy which collects data from the environment, have received wide attention. An efficient category of routing protocols for WSN is cluster routing. However, secured messages routing has long been a concern, as traditional network security practices are inappropriate for WSNs due to their inherent hardware limitations. Hence, this paper introduces and explains a novel centralized statistics-based trust scheme with consideration on power budget, within the context of WSN cluster routing, aiming at detecting unreliable or potentially malicious sensor nodes. The proposed scheme is theoretically able to secure confidentiality, integrity and authenticity of messages in a WSN cluster routing setup, while the simulation results indicate that the computed trust values can correctly identify the node that causes message loss, which helps to improve messages availability.
Electromyography-Driven Progressive Assist-as-Needed Control for Lower Limb Exoskeleton

This paper introduces a progressive assist-as-needed (pAAN) controller into our custom-made lower limb exoskeleton system. This control strategy can enhance the active participation of subjects. The controller can dynamically estimate a subject’s input (voluntary joint torque) based on electromyography (EMG) without calibrations. The EMG-torque relationship learning is unsupervised. The zero-error estimation of the subject’s input is guaranteed by a progressive learning strategy. The adaptive controller adjusts the control inputs of motors to complete predefined trajectories. Online torque estimation and adaptive motion control are both realised in the pAAN controller. Additionally, some practical problems of EMG application, caused by time-varying property of EMG signals and electrode displacement, would be avoided. From the simulation and experimental studies, our pAAN controller can predict the subject’s input well, and the exoskeleton helps subjects move precisely. Active participation of subjects is achieved during training.
Screening Currents and Hysteresis Losses in the REBCO Insert of the 32 T All-Superconducting Magnet Using T-A Homogenous Model

The 32 T all-superconducting magnet of the National High Magnetic Field Laboratory (NHMFL) was successfully tested in December 2017 and it is expected to be soon available for users. This all-superconducting magnet, comprised of a high-temperature superconducting (HTS) insert and a low-temperature superconducting (LTS) outsert, is the first superconducting magnet reaching more than 30 T. One of the challenges facing this new magnet technology is the estimation of the screening currents, and the corresponding hysteresis losses in the two HTS coils. These coils are made of more than 20,000 turns of insulated REBCO conductor connected in series. The modelling of such system represents a significant challenge due to the huge computational load imposed by the size of the system. Up to now, only medium size magnets (made of units of thousands of turns/tapes) have been successfully modelled with methods based on the well-known H formulation of the Maxwell's equations. In the present work, a new model based on the T-A formulation and a homogeneous technique is proposed. This new approach greatly reduces the computational load and allows performing real-time simulations of large-scale HTS magnets on personal computers.
Using Deep Learning for Food and Beverage Image Recognition

Recently, deep learning achieved the state of the art in the field of food image recognition. In this paper we describe our deep learning contributions to the field: NutriNet, a novel deep learning architecture, and a pixel-level classification solution for images of fake food. NutriNet was trained on a food image dataset of a larger size and containing more food classes than previous works, and was the first to recognize beverage images. Our work on fake-food image recognition includes the first automatic system for recognizing images of fake food, while the visual similarity of fake and real food makes it useful for fake-food experiments as well as real food recognition.
Multilevel Event Detection, Storyline Generation, and Summarization for Tweet Streams

Users acting as real-time sensors post information about current events on various social media sites like Twitter, Facebook, Instagram, and so on. This generates a huge amount of data requiring significant effort to process and filter it to detect events/topics. It becomes more challenging when data are generated as a tweet stream because of its speed, presence of noise, slangs, phrases, abbreviations, and so on. In recent years, many approaches have been proposed either for detecting small- or large-scale events, individually. There is a lack of a complete solution that provides analysis from different perspectives. We propose a novel approach Mythos that detects events, subevents within an event, and generates abstract summary and storyline to provide different perspectives for an event. There are three modules in Mythos. Online incremental clustering algorithm identifies small-scale events in the form of small clusters, the event hierarchy generator generates bigger events in the form of hierarchies, and the summarization module produces summary of events/subevents. The summarization module uses a long short-term memory (LSTM)-based learning model to generate summaries at different levels—from the most abstracted to the most detailed. The summaries at different levels are used to generate a storyline for the event. Our experimental analysis on a variety of twitter data sets from different domains compares Mythos against the known existing approaches for event detection and summarization. It outperforms baseline approaches for both. The generated summaries are evaluated against summaries provided by external reference sources like Guardian and Wikipedia.
Classifying Genuine Face images from Disguised Face Images

Detecting fake or disguised face images become much more challenging due to the significant advancements made in machine learning, computer vision, and image processing techniques. In addition, due to the rise of various DeepFakes, fake images can be maliciously used to attack individuals and deter true information. Therefore, it is crucial to building a classifier that accurately distinguishes an individual from different or similar persons. In this preliminary work, we aim to detect a target person’s face from different similar individuals, Doppelgangers, leveraging the dataset from Disguised Faces in the Wild (DFW) 2018. We use well-known off-the-shelf face detection classifiers, such as ShallowNet, VGG-16, and Xception to evaluate the classification performance. In order to further improve the detection performance, we apply data augmentation. Our preliminary result shows that the Xception model can classify one from different individuals with a 62% accuracy.
Parallel Training via Computation Graph Transformation

Parallel training can speed up the convergence of machine learning models via splitting the workload into multiple accelerators by the wide array of possible parallel paradigms (e.g., data parallelism, model parallelism, attribute parallelism, and pipelining parallelism). However, most machine learning frameworks lack sufficient support for these flexible and sometimes complex parallel training schemes (e.g., TensorFlow does not provide convenient APIs for any paradigm other than data parallelism), and the engineering effort to support all parallelisms in all machine learning frameworks seems gigantic. In this paper, we demonstrate that most parallel training designs/paradigms can be abstracted as a computation graph transformation problem, so that they are realized via computation graph duplication, splitting, augmentation, and assignment to different accelerators, which are then connected by send/recv channels for tensor communications. Furthermore, conducting such computation graph transformations in a por table I R allows the engineering efforts of parallel training to be widely applied across machine learning frameworks. We propose an extensible parallel training search space which describes parallel training schemes in a declarative fashion. We then implement a computation graph transformation compiler that can instantiate the parallel schemes into explicit execution plans, which are readily executable on modern machine learning frameworks (such as TensorFlow). We maximize code reuse by handling parallel configurations and computation graph transformations in extended ONNX, which can be ported to machine learning frameworks by adapting their existing ONNX frontend/backend implementations. Our design reflects a few good themes in machine learning frameworks, including code reuse via powerful IR (as in MLIR) and separation of declaration and realization (as in Halide/TVM).
Automated Machine Learning for EEG-Based Classification of Parkinson’s Disease Patients

The treatment of Parkinson’s Disease (PD) with Deep Brain Stimulation (DBS) can provide a constant level of motor functioning. Several patients, however, may suffer from postoperative cognitive deterioration. The DBS screening therefore includes an assessment of cognitive functioning prior to DBS surgery. However, these assessments may be influenced by factors such as fatigue or motivation and there is a need for novel biomarkers of cognitive dysfunction to complement the DBS screening. Electroencephalography (EEG) has been previously suggested to identify potential cognitive impairment in PD patients and may have utility during the DBS screening. A limited set of biomarkers (features) from the EEG has been identified for this purpose. Finding new biomarkers is time-consuming and there is no driving hypothesis on which new biomarkers may be important. Based on EEG time series of 40 DBS candidates, this research focuses on automated machine learning techniques to develop EEG-based algorithms for the evaluation of the cognitive function of PD patients. The automated pipeline consists of feature extraction, feature selection, modelling algorithm and optimization. With this approach we extract 794 features from each of the 21 EEG channels which results in a massive feature space. From this feature space the most significant features are selected and used for modelling. The hyperparameters of the model are optimized by a Bayesian technique as part of the automated approach. Aside from the automatically computed features, we also explore the use of features commonly used during clinical evaluation of the EEG, with the result that the model based on automatically computed features achieves a significant higher accuracy (84.0%). The newly identified features are potentially new biomarkers. We used the knowledge gathered from our automated approach to build a hand-crafted model resulting in an accuracy of 91.0%.
Privacy-preserving Heterogeneous Federated Transfer Learning

Federated learning (FL) allows multiple parties to collaboratively train a machine learning model without sharing raw data. However, existing approaches are mainly designed for homogeneous feature spaces and fail to tackle covariate shift and feature heterogeneity without privacy leakage. In this paper, we propose a transfer learning approach to tackle the covariate shift of the overlapped homogeneous feature spaces, and bridge different data owners’ heterogeneous feature spaces with stringent privacy preservation in FL. We propose an end-to-end privacy-preserving multi-party learning approach with two variants based on homomorphic encryption and secret sharing techniques, respectively, to build a heterogeneous federated transfer learning (HFTL) framework. Finally, we not only demonstrate experimentally that the HFTL is secure, effective and highly scalable on five benchmark datasets, but also apply it into a real application of in-hospital mortality prediction from MIMIC-III dataset, where privacy is of significant concern.
Time-varying Item Feature Conditional Variational Autoencoder for Collaborative Filtering

The factors impacting on what users purchase can be classified as an internal factor which is time-invariant user’s unique taste and an external factor which is time-varying item characteristic. However, the current recommendation system has a limitation of making recommendations based only on the user’s history without taking into account the item features trends of the time, which prevents precise recommendations. In this paper, the recommendation system that reflects inter-items trends of time-based bin is proposed. We focus on creating a hybrid recommender system that could effectively combine time-varying content data with the users history data. Specifically, we use Conditional Variational Autoencoder (VAE) to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. In detail, the distributed representation per time bin can be extracted using LSTM. By applying a condition into VAE, a hybrid recommendation system can be created to reflect the item time-varying features. The proposed model in this paper differs from current studies in that it reflects the changing characteristics inherent of the products and utilizes it for recommendation. The Movielens 1M data and Amazon women’s clothing dataset are used for the evaluation of the proposed model.
Simulation and Experimental Analysis of Single-Stage TL-Shaped Electromagnetic Launcher

This article presents a new single-stage TL-shaped electromagnetic launcher (SS-TL-EL), which improves the configurations of the driving coil and armature on the basis of the traditional synchronous coil launcher. Compared to the traditional single-stage synchronous electromagnetic launcher (TSSSEL), it can achieve a higher launching efficiency. In this model, due to the configuration of the T-shaped driving coil and L-shaped armature; therefore, there are more parameters can be derived to optimize the model and improve the launching efficiency. The finite-element method is used to simulate the model, and the comparison between TSSSEL and SS-TL-EL is completed under the same inductance, capacitance, capacitance-voltage, and armature mass. The simulation results show that SS-TL-EL has the larger eddy current, radial magnetic field density, and the wider current distribution area. In order to verify this feasibility of the concept, an SS-TL-EL and a TSSSEL were established and tested, and their launching model is simulated and analyzed. The experimental results show that the launching effects of SS-TL-EL are in agreement with the simulation results in general. The comparison of experimental results indicates that SS-TL-EL has a better launching effect than the TSSSEL under the same conditions.
Automated Event Prioritization for Security Operation Center using Deep Learning

Despite their popularity, Security Operation Centers (SOCs) are facing increasing challenges and pressure due to the growing volume, velocity and variety of the IT infrastructure and security data observed on a daily basis. Due to the mixed performance of current technological solutions, e.g. IDS and SIEM, there is an over-reliance on manual analysis of the events by human security analysts. This creates huge backlogs and slow down considerably the resolution of critical security events. Obvious solutions include increasing accuracy and efficiency in the automation of crucial aspects of the SOC workflow, such as the event classification and prioritization. In the current paper, we present a new approach for SOC event classification by identifying a set of new features using graphical analysis and classifying using a deep neural network model. Experimental evaluation using real SOC event log data yields very encouraging results in terms of classification accuracy.
DeepBlue: Bi-layered LSTM for tweet popUlarity Estimation

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies about this problem focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this paper, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity; (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a unified LSTM model. We then propose a time aware mechanism to address the time interval irregularity issue in standard LSTM. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.