Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting the Hierarchical Community Structure Based on Advanced LPA

LPA is a classical community detection algorithm with linear time complexity. It can be applied to networks whose community structure is known. However, the randomness of it's initial nodes causes a poor stability. In this paper, we proposed a new method to improve the algorithm by reordering the initial nodes. The advanced algorithm uses ascending order instead of descending order based on degree. It can improve the stability of the original algorithm and detect the hierarchical community structure of the complex network. We tested the algorithm on Zachary's karate network, Dolphins social network and American football network. The experiments confirmed that compared to the classical algorithm the improved algorithm has better performance in terms of rationality and accuracy.
Students' Learning Experiences towards the Use of Assessments in a Virtual Learning Environment (VLE)

This study is about student learning experiences towards the use of assessments in a virtual learning environment. Students’ learning experiences are crucial in learning processes to achieve better learning outcomes. Students’ learning experiences can be assessed by using the Community of Inquiry (COI) Framework. Assessment can be defined as the evaluation for the students’ performance and abilities. This study involves the students to watch the pre-class lectures online. This study is quasi-experimental research which has divided the students into a control group and experimental group. The control group students watch the virtual micro-lectures with discussion while the experimental group students watch the virtual micro-lectures which integrated with assessments. Both groups watch the videos in different learning platform (Padlet/EDpuzzle) with different links. The data of the study are evaluated based on the Community of Inquiry Framework questionnaire and students’ quiz. The results are mixed in terms of the questionnaire and the quiz results. In the overall result, students in the experimental group have gained better learning experiences in teaching presence, social presence and cognitive presence compare to the students in the control group. This study is significant for educators to create more effective online assessments that can enhance the students’ learning.
Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.
Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning-based framework

Surgical instrument segmentation in laparoscopic image sequences can be utilized for a variety of applications during surgical procedures. Recent studies have shown that deep learning-based methods produce competitive results in surgical instrument segmentation. Difficulties, however, lie in the limited number of training datasets involving surgical instruments in laparoscopic image frames. Even though there are publicly available pixelwise training datasets along with trained models from the Robotic Instrument Segmentation challenge, we are not able to relate them to laparoscopic image frames from different surgical scenarios without any pre- or postprocessing. This is because they contain different instrument shapes, image backgrounds, and specular reflections, which implies laborious manual segmentation for training dataset generation. In this work, we propose a novel framework for semi-automated training dataset generation for the purpose of robust segmentation using deep learning. To generate training datasets in various surgical scenarios faster and more accurately, we utilize the publicly available trained model from the Robotic Instrument Segmentation challenge and then use the Watershed Segmentation-based method. For robust segmentation, we use a two-step approach: first, we obtain a coarse segmentation obtained from a deep convolutional neural network architecture, and then we refine the segmentation result via the GrabCut algorithm. Through experiments using four different laparoscopic image sequences, we demonstrate the ability of our proposed framework to provide robust segmentation quality.
A Flexible Wearable Antenna for Location Tracking Applications

A compact planar wearable antenna has been modelled, fabricated and measured using a flexible polyimide film with copper clad laminate. The design exploits the bowtie design principle and geometrical miniaturization techniques to achieve the dimensions of 0.2 λ × 0.13 λ for λ of the GPS L1 carrier. The antenna changes its polarization discrimination to suit a linearly or circularly polarized signal by varying its physical configuration. The antenna is intended for a wearable device. It is enclosed in a customised strap and interfaced with a GPS receiver to evaluate time-to-first-fix in practical outdoor conditions. The design is easy to integrate with general purpose GPS receivers and does not rely on a noisy ground plane shared with a digital circuitry. It has a radiation efficiency of 98% in modelled free space.
Robust concrete crack recognition based on improved image segmentation and machine learning

This paper presents an automatical crack recognition approach. Compared with the existing methods, it has a significant increase in robustness and efficiency when faced with widely varying field conditions. Inherent characteristics of crack images are exploited using proportional segmentation, combined with robust feature extraction to improve machine learning classifier performance. Experiments show that this method perform well in crack images recognition across different concrete conditions.
Optical compressive sensing technologies for space applications: instrumental concepts and performance analysis

This paper presents the results of a study aimed at investigating the potential of Compressive Sensing (CS) technologies for optical space instruments. Besides assessing the pros and cons for a wide set of proposed instrumental concepts for space applications, the study analyzed in further detail two CS-based instrument concepts, each targeting a specific application: an UV-VIS hyperspectral imager on orbiter for stellar spectro-photometry and a MIR camera for sky observation and real-time detection of Near Earth Objects (NEO). The proposed UV-VIS hyperspectral imager relies on a classical CS approach and addresses the CS reconstruction of the full image in order to implement slitless spectrophotometry of stars. The CS-based MIR camera for NEO detection instead explores a novel approach aiming at information extraction without a prior full reconstruction of the image. Besides outlining the optical design of the instruments, its key elements and a pros and cons analysis of the architecture, this paper presents the performance assessment of these instruments for typical application scenarios by means of simulated data. The results showed that, from the point of view of data reconstruction quality, a good performance can be achieved by the designed instruments in terms of compression ratio (CR) and image reconstruction. In terms of system budgets, the CS architecture offered only some marginal benefits with respect to their traditional counterparts, mainly due to the lack of a compression board. Most advantages are instead provided in terms of downlink requirements and memory buffer.
ESetStore: an erasure-coding based distributed storage system with fast data recovery

The past decade has witnessed the rapid growth of data in large-scale distributed storage systems. Triplication, a reliability mechanism with 3x storage overhead and adopted by large-scale distributed storage systems, introduces heavy storage cost as data amount in storage systems keep growing. Consequently, erasure codes have been introduced in many storage systems because they can provide a higher storage efficiency and fault tolerance than data replication. However, erasure coding has many performance degradation factors in both I/O and computation operations, resulting in great performance degradation in large-scale erasure-coded storage systems. In this thesis, we investigate how to eliminate some key performance issues in I/O and computation operations for applying erasure coding in large-scale storage systems. We also propose a prototype named ESetStore to improve the recovery performance of erasure-coded storage systems. We introduce our studies as follows. First, we study the encoding and decoding performance of the erasure coding, which can be a key bottleneck with the state-of-the-art disk I/O throughput and network bandwidth. We propose a graphics processing unit (GPU)-based implementation of erasure coding named G-CRS, which employs the Cauchy Reed-Solomon (CRS) code, to improve the encoding and decoding performance. To maximize the coding performance of G-CRS by fully utilizing the GPU computational power, we designed and implemented a set of optimization strategies. Our evaluation results demonstrated that G-CRS is 10 times faster than most of the other coding libraries. Second, we investigate the performance degradation introduced by intensive I/O ii operations in recovery for large-scale erasure-coded storage systems. To improve the recovery performance, we propose a data placement algorithm named ESet. We define a configurable parameter named overlapping factor for system administrators to easily achieve desirable recovery I/O parallelism. Our simulation results show that ESet can significantly improve the data recovery performance without violating the reliability requirement by distributing data and code blocks across different failure domains. Third, we take a look at the performance of applying coding techniques to in-memory storage. A reliable in-memory cache for key-value stores named RMemcached is designed and proposed. This work can be served as a prelude of applying erasure coding to in-memory metadata storage. R-Memcached exploits coding techniques to achieve reliability, and can tolerate up to two node failures. Our experimental results show that R-Memcached can maintain very good latency and throughput performance even during the period of node failures. At last, we design and implement a prototype named ESetStore for erasure-coded storage systems. The ESetStore integrates our data placement algorithm ESet to bring fast data recovery for storage systems.
A Novel Weight-Bearing Lower Limb Exoskeleton Based on Motion Intention Prediction and Locomotion State Identification

A variable magnification ratio transmission structure powered by the electric actuators is proposed to improve the flexibility and portability of the exoskeleton under heavy load carrying condition. The parameters of connecting rod size and hanging position are optimized to ensure that the output torque of active joints can fully envelope the demand load area. The control strategy based on intrinsic sensing is designed to realize the automatic human motion intention prediction and flexible trajectory tracking. The newly developed split embedded connecting rod can accurately measure the human–robot interaction (HRI) force applied to the exoskeleton and extract the human motion intention without being affected by the differences in wearing status. The force tracking control based on the zero-force following is modified by feedforward compensation with extreme learning machine (ELM), which enhances the response speed to human motion intention and reduces the HRI force by 70.6%. Based on multi-sensor information, stacked autoencoder deep neural networks (DNNs) are utilized to realize the automatic locomotion transition and the corresponding control parameters’ switching. After optimization by a hybrid algorithm of genetic algorithm and particle swarm optimization (GA_PSO), the identification accuracy is enhanced from 96.2% to 99.7%. The adaptive neural-fuzzy inference system (ANFIS) is used to analyze the plantar pressure to achieve flexible switching between the swing phase and the stance phase. The experiments under various gait motion trajectories assisted by novel weight-bearing exoskeleton are carried out for evaluation, and the performance of the proposed control strategy based on motion intention prediction, locomotion mode identification, and gait phase switching is effectively verified.
DETECTION AND HANDLING OF DIFFERENT TYPES OF CONCEPT DRIFT IN NEWS RECOMMENDATION SYSTEMS

To address the increase in volume of data streams online users interact with, there are a growing number of tools and models to summarize and extract information. These tools use prediction models to personalize and extract useful information. However, data streams are highly prone to the phenomena of concept drift, in which the data distribution changes over time. To maintain the performance level of these models, models should adapt to handle the existence of adrift. In this work, we present the Incremental Knowledge Concept Drift (IKCD) algorithm, an adaptive unsupervised learning algorithm for recommendation systems in news data stream. Data modelling in IKCD uses k-means clustering to determine the occurrence of a drift while avoiding the dependency on the availability of data labels. Once a drift is detected, new retraining data is composed from the old and new concept. IKCD is tested using synthetic and real benchmark datasets from various domains, which demonstrate the different drift types and with different rate of change. Experimental results illustrate an enhanced performance with respect to (a) reducing model sensitivity to noise, (b) reducing model rebuilding frequency up to 50% in case of re-occurring drift and (c) increasing accuracy of the model by about 10% with respect the accuracy of confidence distribution batch detection algorithm.
Solving Coupling Security Problem for Sustainable Sensor-Cloud Systems Based on Fog Computing

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.
SurfOpt: A New Surface Method for Optimizing the Classification of Imbalanced Dataset

Imbalanced classes constitute very complex machine learning classification problems, particularly if there are not many examples for training, in which case most algorithms fail to learn discriminant characteristics, and tend to completely ignore the minority class in favour of the model overall accuracy. Datasets with imbalanced classes are common in several machine learning applications, such as sales forecasting and fraud detection. Current strategies for dealing with imbalanced classes rely on manipulation of the datasets as a means of improving classification performance. Instead of optimizing classification boundaries based on some measure of distance to points, this work directly optimizes the decision surface, essentially turning a classification problem into a regression problem. We demonstrate that our approach is competitive in comparison to other classification algorithms for imbalanced classes, in addition to achieving different properties.
Permissioned Blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for smart grid network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users’ validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.
The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media

Automatic identification of conversations related to DDoS events in social networking logs helps the organizations act proactively through early detection of negative and positive sentiments in cyberspace. In this article, the authors describe the novel application of a deep learning method to the automatic identification of negative and positive sentiments in large volumes of social networking texts. The authors present classifiers based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to address this problem domain. The improved CNN and LSTM architecture outperform the classification techniques that are common in this domain including classic CNN and classic LSTM in terms of classification performance, which is measured by recall, precision, f-measure, train loss, train accuracy, test loss, and test accuracy. In order to predict the occurrence probability of the DDoS events the next day, the negative and positive sentiments in social networking texts are used. To verify the efficacy of the proposed method experiments is conducted on Twitter data.
Android Malware Detection using Sequential Convolutional Neural Networks

As the largest Operating System in the smart phone market, Android is gaining more adherents in recent years, which makes it increasingly important to detect Android malware correctly and efficiently. In this paper, we proposed a way to detect it with sequential convolutional neural networks. In the pre-processing work, Android package files were dissected into Dalvik operation codes. These codes were treated as text sequence in our designed model. This model was trained on 3000 samples, and tested on two large datasets. One with 10,174 samples where our model reached an accuracy of 90% in 1.2 seconds and the other with 20,083 samples where its accuracy was 88% and cost 2.4 seconds.
A Study on HL7 Standard Message for Healthcare System Based on ISO/IEEE 11073

We studied the standard of HL7 message for healthcare system which can support diagnosis for healthcare provider and user. We studied the connection of standard medical devices, i.e., continua health alliance under the Android 4.x Bluetooth HDP (Health Device Profile) environments based on ISO/IEEE 11073 which is standard of information exchange among health device agent and service. And also we studied smart healthcare system using smart phone depending upon current trend. This system was studied on the environment of ISO/IEEE 11073 and HL7 standard.
Implementation of an Actuator Placement, Switching Algorithm for Active Vibration Control in Flexible Structures

The recent years have seen the innovative system integration of a great many actuator technologies, such as point force actuators for space vehicle applications and the use of single fire actuators; such as pyrocharges to guide a free falling bomb to it’s target. The inherent limitations of these developments, such as nonlinear behavior under extreme environments and/or prolonged/repeated usage leading to a relaxation time component between firing of actuators and inherent system power limitations, have resulted in greater need for sophisticated control algorithms that allow for optimal switching between various actuators in any given embedded configuration so as to achieve the best possible performance of the system. The objective of this investigation is to offer a proof of concept experimental verification of a real time control algorithm, which switches between online piezoelectric actuators, employed for vibration control in an aluminum beam with fixed boundary conditions. In this investigation at a given interval of time, only one actuator is activated and the rest are kept dormant. The reason is to demonstrate the better vibration alleviation characteristics realized in switching between actuators depending on the state of the system, over the use of a single actuator that is always in fire mode. This effect is particularly pronounced in controlling systems affected by spatiotemporal disturbances. The algorithm can be easily adapted for various design configurations or system requirements. The optimality of switching is with respect to the minimal cost of an LQR performance index that corresponds to each actuator. Computer simulations with repeatable disturbance profiles, revealed that this algorithm offered better performance over the non-switched case. Performance measures employed were