Miracle-dz/UniFAR-FTUs huggingface dataset:
The EEG-based Lower Limb Exoskeleton System Optimization Strategy Based on Channel Selection

Compared with traditional exoskeleton robots, the brain-computer interface (BCI) based lower limb exoskeleton system can directly control the robot by recording the user's brain activity signals, such as the electroencephalogram (EEG). The brain activity signals contain the original and global motion intention, which is capable of operating the walking assistance exoskeleton device in a way that matches the operator's will more closely. In our previous work, an EEG-based lower limb exoskeleton system and a human-machine interaction paradigm that orient to the scenario of dynamic motion is designed. However, the EEG sensor configuration is a time-consuming process and limit the utility of the EEG based system. Besides, channel redundancy makes it difficult to explore experimental mechanisms of the designed paradigm. Therefore, in this paper, a heuristic channel selection optimization algorithm with the L1 norm is applied to the BCI system. Based on the data of four subjects, we verify the performance of the selected optimal channel subset and explore a universal optimal channel subset. Tested on another two new subjects, the universal optimal subset shows a promising prospect in improving the performance of the EEG-based exoskeleton system.
Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. It divides the joint extraction into two sub-tasks, first detecting entity spans and identifying entity relations type simultaneously. To consider the complete interaction between entities and relations, we propose a novel relation-aware attention mechanism to obtain the relation representation between two entity spans. Therefore, a complete graph is constructed based on all extracted entity spans where the nodes are entity spans and the edges are relation representation. Besides, we improve original GCN to utilize both adjacent node features and edge information when encoding node feature. Experiments are conducted on two public datasets and our model outperforms all baseline methods.
A Compact Control System and A Myoelectric Control Method for Multi-DOFs Prosthetic Hand

This paper introduces a compact control system for multi-DOFs prosthetic hand. Based on the optimization of sensors configuration and hardware structure of the control system, the power consumption and volume can meet the needs of the actual using. In addition, a myoelectric control method based on the principle of finite state machine (FSM) is integrated in the control system. The proposed method enables amputees to select one of the given gestures directly, and grasp objects. As the control method requires less electromyography electrodes comparing to the traditional myoelectric control method based on EMG pattern recognition, it's available for a wider range of amputees with less active muscles. To assess the performance of the proposed method, the experiments of grasping random objects by non-amputee subjects has been conducted with the 6-DOFs HIT-V prosthetic hand, and an amputee volunteer used this prosthetic hand for one day to demonstrate viability. The non-amputee experimental results indicate that subjects can select gesture correctly among seven gestures and accomplish the designed grasping tasks with a success rate up to 96.67% within a shorter time just using two electrodes, and amputee proved viability of proposed method in daily life.
Automatic Age Classification of Prospective Voters Using Deep Convolutional Neural Network.

Age estimation from images of the face, has gained more attention in recent as it is favorable in some realworld applications. In this work, we address the problem of Under-Age registration/voting in Nigeria Electoral system, which has been a major menace, hindering a free and fair election in the country since the assumption of the Democratic system of Government. To this end, a pretrained VGG-16 Deep Convolutional Neural Network while comparing two optimization algorithms without image preprocessing is employed to both extract features from image(s) of prospective voters and classify same under the established age classification group, as eligible or not to exercise their civil right. In light of this, a classification accuracy of 77.67% is achieved with the model.
An Improved Finite Control Set-MPC-Based Power Sharing Control Strategy for Islanded AC Microgrids

Hierarchical linear control scheme is widely used in ac microgrids. However, its transient response is slow and parameter tuning is time-consuming. Finite Control Set-Model Predictive Control (FCS-MPC) strategy has desired dynamic performance. Nevertheless, it requires an additional sensor to measure the inductor current. This article aims to mitigate these problems by introducing an improved FCS-MPC strategy for paralleled Voltage Source Inverters (VSIs). A capacitor current estimator is employed to reduce the extra current sensor in each VSI. The proposed control scheme consists of two loops: voltage reference generation loop and voltage tracking loop. The voltage reference generation loop achieves accurate load power sharing using virtual impedance-based droop control. Thus, communication is unnecessary among parallel VSIs. The voltage tracking loop utilizes a modified FCS-MPC block with capacitor current estimator to regulate the VSI output voltage. In order to verify the concept of the proposed control strategy, an ac microgrid consisting of two paralleled VSIs is implemented in dSPACE DS1202 hardware-in-the-loop platform. Then a single VSI hardware prototype is implemented and tested experimentally. The proposed method has the merits of good extensibility, low system cost and compact structure. Its steady-state performance is competitive with hierarchical linear control, while the transient response is significantly improved.
Attention-Based Sequence Learning Model for Arabic Diacritic Restoration

Diacritic restoration is a fundamental issue in many languages such as Arabic, Greek and others. The bulk of online texts lack these markings. This makes these languages difficult for a human to interpret and computer process. In this work, we will automatically restore the diacritics for the Arabic language. Previous sequence-to-sequence based approaches such as Abandah et al., [1], and Belinkov and Glass [2] have been used. These use Recurrent Neural Network (RNN) involving Long- Short Term Memory (LSTM) models. Interestingly, these are language-independent, avoiding any morphological, syntactical analysis or any type of external resources. This approach leads to making this model agnostic to new languages. In this work, we extend the previous work with an alternate RNN architecture involving Gated-Recurrent Units (GRU). We augment the basic RNN model with attention-based mechanisms. Our GRUattentive model improved the Diacritic Error Rate (DER) by 0.19% and by 2.32% compared to the state of the art.
A Human-Aided Learning Approach to Optimal Control of Human-Robot Systems

Human-Robot Systems (HRS) play a significant role in many applications, including manufacturing, critical infrastructure, and civil applications. However, conventional HRSs require predetermined reference before the start of the mission, which restricts the flexibility of the HRSs. In this paper, we use a recursive least-square approach to design Human-Aided Learning (HAL) algorithm, which allows the robot to learn the reference via the human inputs and system outputs. With multiple learning iterations, the robot can determine the reference. After estimating the reference, the robot can execute the tasks independently. We also design a low-pass filter to remove the impact of a high-frequency noise introduced by the human inputs. Finally, we use simulation to evaluate the performance of the proposed HAL algorithm.
CNN-Based Sample Adaptive Offset Optimization in HEVC for Streaming Video

The high efficient video coding (HEVC) standard is crucial for video processing and communication. It is able to achieve about 50% bit-rate reduction with equivalent visual quality compared to H.264/AVC. In the in-loop filter part of HEVC, sample adaptive offset (SAO) is newly added in order to improve the coding efficiency as well as the subjective quality of encoded videos. In order to improve visual quality, we propose an approach by using convolutional neural networks (CNN) after de-blocking filter (DBF) to replace SAO, namely SAOCNN in this paper. Compared with SAO, the proposed SAOCNN is able to reduce the distortion of reconstructed pixels by learning residual feature. Further more, there will be no bit-rates by using the same SAOCNN model in both encoder and decoder sides. Experimental results show that our proposed approach achieves an average 2.04% BD-rate reduction as well as the average 0.12 BDPSNR increment for All intra configuration with sacrifice of encoding time rarely.
A CNN-based Coding Unit Partition in HEVC for Video Processing

The High Efficiency Video Coding (HEVC) standard is able to provide efficient video encoding for broadcast mobile applications. HEVC employs the Coding Tree Unit (CTU) to improve intra coding performance. The partition of each CTU is determined by calculating the Rate Distortion (RD) cost in an exhaustive search process manner, and the calculation results in high computation complexity. In this paper, we propose a deep learning approach for Coding Unit (CU) partition in HEVC intra coding. The proposed approach utilizes the Convolutional Neural Network (CNN) to predict CU partition during the CU search process. The input of CNN is a CU while the output is a partition flag, which refers to partition the current CU or not. The proposed approach employs Graphics Processing Unit (GPU) to accelerate the CNN computation. The experiment results demonstrate the proposed approach achieves 63.19% and 66.01% encoding time reduction on average with negligible perceptual quality loss.
Stemming algorithm for Kazakh Language using rule-based approach

This paper considers construction of Kazakh language words. Kazakh language is the type of Turkic languages or alternatively called – agglutinative language where words are formed by attaching the finite sequence of suffixes. Kazakh morphology investigation was the main work done in construction of rule-based stemming algorithm. Stemmer algorithm will be useful in development of sentiment analysis for Kazakh language, because of its rareness and competency. Stemming algorithm works with aim to cut ending combinations. The results checked by using Machine Learning algorithms using annotated dataset, which were retrieved from KazCorpus dataset [7].
On the Effectiveness of Application Permissions for Android Ransomware Detection

Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.
Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

In multi-label learning, instances can be associated with a set of class labels. The existing multi-label feature selection (MLFS) methods generally adopt either of these two strategies, namely, selecting a subset of features that is shared by all labels (common features) or exploring the most discriminative features for each label (label-specific features). However, both of them can play a key role in the discrimination of different labels. For example, common features can distinguish all labels, and label-specific features contribute to discriminating label’s differences. They are important for the discriminability of selected features. On the other hand, it is well-known that exploiting label correlations can advance the performance of MLFS, and label correlations are local and only shared by a data subset in most cases. How to effectively learn and exploit local label correlations in the selection process is significant. In this paper, to address these problems, we propose a novel MLFS framework. Specially, common and label-specific features are simultaneously considered by introducing both $l_{2,1}$ -norm and $l_{1}$ -norm regularizers, local label correlations are automatically learned with probability and learned correlation information is efficiently exploited to help feature selection by constraining label correlations on the output of labels. A comparative study with seven state-of-the-art methods manifests the efficacy of our framework.
Subjective and Objective Evaluation of Local Dimming Algorithms for HDR Images

In order to enhance viewing experiences, a number of backlight local dimming (BLD) algorithms have been developed to improve the image contrast ratio and provide power efficiency for modern displays. In order to evaluate which BLD algorithm performs best for HDR images rendering on dual-panel displays, this paper presents a comprehensive subjective and objective evaluation conducted with five BLD algorithms across a number of scenes. The subjective evaluation (N = 24) required participants to rank each BLD generated image based on which they thought was the most natural looking. The objective evaluation was undertaken via the use of a novel methodology to generate the images per BLD for comparison against the ground truth High Dynamic Range (HDR) image. Resulting images were compared with the ground truth using qualitative methods namely: HDR-VDP, puPSNR, puSSIM and puVIFP. The power-saving rate of each method was also calculated. The results demonstrate a strong correlation between objective and subjective evaluation. Furthermore, results show that BLD algorithms that consider the luminance balance between backlight and LCD images perform better than straightforward BLD methods.
Testing strength of the state-of-art image classification methods for hand drawn sketches

Classification of hand drawn sketches (images) reached a classification accuracy of % 77 with the latest state-of-the-art method, called Sketch-a-Net, in 2017. Most of the developed methods use image feature extractor techniques like HOG, BOVW, or CNN. In this paper, we tested the classification accuracy of hand drawn sketches with SVM and ANN, without using image feature extraction algorithms and compared the results with the findings of a number of important state-of-art researches. Our findings show that existing methods are reasonable to accept, even though the results of our experiments also produced some valuable results. We propose that our findings can serve as kind of `minimal milestone’ on future prediction experiments.
An Efficient Access Model of Massive Spatiotemporal Vehicle Trajectory Data in Smart City

Daily trajectory data scale of vehicle monitoring networks in smart cities is growing rapidly, reaching daily volumes of 1 billion. Accessing hyper massive spatiotemporal trajectory data (HMSTD) in transport, the Internet of Things, or other fields is difficult and limited based on the current spatiotemporal data index techniques. Therefore, we propose path-divided Hadoop Distributed File System (HDFS) data blocking (PDDB) based on the Apache Impala (PDDB-Impala) method to optimize the efficient access manner of HMSTD to enhance the efficiency of hyper data sharing. Moreover, PDDB parquet data partitioning rules are proposed. In experiments, 35,809 buses equipped with BD positioning sensors, creating 1.03 billion data records each day. The bus distribution in Shenzhen city is collected from 7:00 a.m. to 9:00 a.m. and 11:00 a.m. to 01:00 p.m. Moreover, PDDB-Impala achieves about 8 times, 9 times, 29 times, and 110 times higher performances than those in MongoDB or HBase for data scales of 1 billion, 10 billion, 50 billion, and 100 billion, the results of which outperform those of the equipartition in the Impala, MongoDB, and HBase methods.
Linear Protection Grid Optimized Image Stitching for Mobile Robots

Image stitching can be used to in 3D reconstruction to obtain the comprehensive obstacle information, which plays an important role in the field of mobile robots. However, previous algorithms have two problems: 1. The linear structure of the image might have been corrupted. 2. Some inconsistency may exist in the transitional region of the stitched image. In order to solve above problems, in this paper, we propose a grid-based linear structure protection method, which applies the constraints to the lines extracted from the image to protect them from the distortion caused by the mesh deformation process, and resulting in a natural panorama with reduced distortion. This method helps to obtain a natural panoramic image with reduced distortion. At the same time, we use the neighbor weighted based boundary artifact removal approach to process the stitched image, which can avoid the stitching problem and can make the image look more natural. We conducted some experiments, and the results demonstrated that our method is more efficient and more natural as compared with some state-of-the-art methods.
Computing feature vectors of students for face recognition using Apache Spark

With a massive increase of images and videos in the internet, processing these data for face detection and recognition is a bottleneck in current situation. At least there is a lack of frameworks and tools to process lots of data efficiently and not time consuming. System proposed in this paper offers face recognition framework based on Apache Spark to analyze lots images and videos from Instagram. As a people need to be searched and recognized are university students, their feature vectors used for recognition initially computed and can be enlarged at any moment. Spark’s Resilient Distributed Databases gives ability to compute all the intermediate data directly in memory making computations faster meanwhile keeping resilience property.
Enhanced Two-Phase Virtual Machine Placement Scheme for Cloud Computing Datacenters

Over the years, Cloud Computing has offered many benefits such as providing services to end-users on demand. However, infrastructure as service providers is faced with the challenge of handling many end-users’ requests for virtual resources. In this regard, one of the key challenges of resource allocation is Virtual Machine Placement (VMP) problem. However, the dynamicity and uncertainty of Cloud platform and the unpredictable nature of the end-users’ requests have rendered the VMP problem more interesting. Recently, a two-phase Virtual Machine Placement scheme, combining the benefits of both online (dynamic) and offline (static) formulations were presented. The proposed scheme is based on a prediction-based triggering method used to determine when to trigger the VMP reconfiguration phase. However, the existing method leads to a less accurate prediction outcome, therefore, results in less optimal solution from the reconfiguration phase. This work proposes an enhanced two-phase Virtual Machine Placement strategy based on novel triggering method. The proposed triggering method considers Damped trend exponential smoothing method. An experimental evaluation is performed against the previous approach, considering 160 scenarios. The experimental results show that the proposed work achieved a minimum cost function when compared with the benchmark approach.
A Secure Framework for Authentication and Encryption Using Improved ECC for IoT-Based Medical Sensor Data

Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors. This has attracted attention for a secure communication framework to access e-health data on mobile devices. The wearable sensor device is attached to the patient’s body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc. In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server. The patient’s biometrics information has been added as a parameter in addition to the user name and password. The authentication scheme is coined with the SHA-512 algorithm that ensures integrity. To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC). Whereas in improved ECC, an additional key (secret key) is generated to enhance the system’s security. In this way, the intricacy of the two phases is augmented. The computational cost of the scheme in the proposed framework is <inline-formula> <tex-math notation="LaTeX">$4H+Ec+Dc$ </tex-math></inline-formula> which is less than the existing schemes. The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm. The obtained encryption and decryption time are <inline-formula> <tex-math notation="LaTeX">$1.032~\mu s$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.004~\mu s$ </tex-math></inline-formula> respectively. The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest–Shamir–Adleman (RSA)and ECC algorithms.
A Hierarchical Searching Approach for Laser SLAM Re-localization on Mobile Robot Platform

Simultaneous localization and mapping (SLAM) is an important issue for mobile robot system. Re-localization, as a part of close loop checking of SLAM, focuses on localize the position of a robot agent when it is restarted. Traditional localization methods couldn't reach both a high accuracy and a little time cost. In this paper, we proposed a newly hierarchical searching approach for laser SLAM re-localization. We do hierarchically searching to match an input laser frame with the whole 2-D map, instead of comparing it with each frame in the map. By using such approach, we could accelerate re-localization speed, so the performance of SLAM is improved. Our SLAM system is well organized on a mobile robot platform. After field test, our approach reaches a average precision (AP) of 90.6% on a $20\mathrm{m}\times 20\mathrm{m}$ map area, and the time cost is averagely 30ms. The result shows our hierarchical searching approach is effective for re-localization problems on laser SLAM.