Miracle-dz/UniFAR-FTUs huggingface dataset:
A proposed Adaptive Pre-Encryption Crypto-Ransomware Early Detection Model

Crypto-ransomware is a malware that uses the system’s cryptography functions to encrypt user data. The irreversible effect of crypto-ransomware makes it challenging to survive the attack compared to other malware categories. When a crypto-ransomware attack encrypts user files, it becomes difficult to access these files without having the decryption key. Due to the availability of ransomware development tool kits like Ransomware as a Service (RaaS), many ransomware variants are being developed. This contributes to the rise of ransomware attacks witnessed nowadays. However, the conventional approaches employed by malware detection solutions are not suitable to detect ransomware. This is because ransomware needs to be detected as early as before the encryption takes place. These attacks can effectively be handled only if detected during the pre-encryption phase. Early detection of ransomware attacks is challenging due to the limited amount of data available before encryption. An adaptive pre-encryption model is proposed in this paper which is expected to deal with the population concept drift of crypto-ransomware given the limited amount of data collected during the pre-encryption phase of the attack lifecycle. With such adaptability, the model can maintain up-to-date knowledge about the attack behavior and identify the polymorphic ransomware that continuously changes its behavior.
Hybrid EEG-EOG-based BCI system for Vehicle Control

Brain-Computer Interfaces (BCI) has become a medium of communication and interaction for disabled people. Electroencephalography (EEG) signals are one of the most widely used for such BCI systems. Over the past decade or so, electrooculography (EOG) signals have shown tremendous potential to complement EEG based BCI systems. In this paper, we investigate the possibility of a hybrid BCI system, combining the EEG and EOG signals, for remotely controlling a vehicle, such as a wheelchair, using machine learning technique. Motor Imagery (MI) EEG signals and EOG signals are combined to design this robust and computationally faster system. The proposed system is trained and tested on 13 in-house subject's data and it is able to achieve an average accuracy of 87.3%, where, 3 of the subjects produced more than 90% accuracy.
Comparison of Fake News Detection using Machine Learning and Deep Learning Techniques

Fake news has spread widely on the Web in recent years due to the massive amount of information exchanged on digital media. This has motivates our study to determine the best-performing model among two Machine Learning models: Naïve Bayes (NB), Support Vector Machine (SVM), and three Deep Learning models: Long Short-Term Memory (LSTM), Neural Network with Keras (NN-Keras), and Neural Network with TensorFlow (NN-TF). We examined five models using two different English language news datasets. The performance of the models was evaluated using four metrics; accuracy, precision, recall and F1-score. The obtained results showed that deep learning models had achieved better accuracy than traditional ML models. The LSTM model has outperformed all other models examined. It achieved an average accuracy of 94.21%. The NN-Keras has also produced a good performance with an average accuracy of 92.99%. The words’ order carries critical information and plays a significant role in the fake news classification, where our LSTM makes a prediction based on this.
MEC-Assisted FoV-Aware and QoE-Driven Adaptive 360° Video Streaming for Virtual Reality

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users’ Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users’ QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.
Edge Computing Based Privacy-Preserving Data Aggregation Scheme in Smart Grid

Smart grid is a highly intelligent power system integrating advanced communication technology, sensor measurement and automatic control technology, which is gradually replacing the traditional power grid. However, the smart grid faces challenges of balancing privacy, efficiency and functionality when processing massive amount of data. In this paper, a smart grid model based on edge computing paradigm is established, and then an efficient privacy-preserving multidimensional data aggregation scheme is proposed. This scheme adopts an improved identity-based signature algorithm and Paillier homomorphic cryptosystem to protect the privacy of users. In addition, super-increasing sequence is used in the proposed scheme to enable smart meters to report multiple types of data in a single reporting message, so that the Service Center (SC) can perform one-way analysis of variance on the data to provide users with more personalized services. Also the security analysis indicates that the proposed scheme works in protecting user’s electricity consumption privacy. Finally, performance analyses indicate that this scheme can effectively reduce the computational overhead.
Federated Deep Payload Classification for Industrial Internet with Cloud-Edge Architecture

Payload classification is a kind of powerful deep packet inspection model built on the raw payloads of network traffic, and hence can remove the need of any configuration assumptions for network management and intrusion detection. While in the emerging industrial Internet, a majority of local industry owners are not willing to share their private payloads that possibly contain sensitive information and thus cause the classification model not always well trained due to the lack of sufficient training samples. In this paper, we address this privacy concern and propose a federated learning model for industrial payload classification. In particular, we consider a cloud-edge architecture for the industrial Internet topology, and assemble federated learning process by cloud-edge collaboration: each data owner has his own edge server for learning a local classification model and the industrial cloud takes the responsibility for aggregating local models to a federated one. We adopt a gradient-based deep convolutional neural network model as our local classifier and use the method of weighted gradient averaging for model aggregation. By this way, the data owners can avoid to disclose their private payload for model training, but instead share their local model’s gradients to keep the federated model able to learn local samples indirectly. At the end, we have conducted a large set of experiments with real-world industrial Internet traffic datasets, and have successfully confirmed the effectiveness of the proposed federated model for payload classification with privacy-preserved.
Real-time and Accurate RFID Tag Localization based on Multiple Feature Fusion

We propose a new radio frequency identification (RFID) localization approach that achieves both low latency and high accuracy by fusing multiple type of signal features. Existing RFID tag localization approaches either suffer from large localization latency (e.g., approaches based on phase measurements), or cannot provide high localization accuracy (e.g., approaches based on received signal strength (RSS)). We propose a two-step approach that fuses phase measurements and RSS measurements to resolve this dilemma. First, coarse-grained RSS measurements are utilized to Figure out a small bounding box that encloses the position of the target tag. Second, fine-grained phase measurements are used to refine the position estimation of the target tag in the bounding box. Experimental results show that the proposed fusion approach achieves centimeter-level localization accuracy with less than 10 feature measurements, reducing localization latency by more than one order of magnitude when compared to state-of-the-art solutions.
Compact High Efficiency Terahertz Filtering Antenna with Low Cross-Polarization Based on the Mixed-Mode Cavity

This paper investigates a D-band high-efficiency filtering antenna with low cross-polarization, which consists of a mixed-mode cavity, a TE440-mode cavity and a slot array. The mixed-mode cavity is deployed for achieving filtering power dividing. Hence, a compact size and high radiation efficiency are achieved for terahertz antenna applications. For verification, a 140-GHz 4×4 filtering slot array is fabricated and measured. The measured gain varies from 17.4 to 18.51 dBi within 16.72 FBW (125.7-148.63 GHz), and the peak aperture and radiation efficiencies are about 77.25% and 96.46%. In addition, the suppression level in stopband is higher than 39 dB.
Efficient Architecture Paradigm for Deep Learning Inference as a Service

Deep learning (DL) inference has been broadly used and shown excellent performance in many intelligent applications. Unfortunately, the high resource consumption and training efforts of sophisticated models present obstacles for regular users to enjoy it. Thus, Deep Learning Inference as a Service (DIaaS), offering online inference services on cloud, has earned great popularity among cloud tenants who can send their DIaaS inputs via RPCs across the internal network. However, such detached architecture paradigm is inappropriate to DIaaS because the high-dimensional inputs of DIaaS consume a lot of precious internal bandwidth and the service latency of DIaaS has to be low and stable. We therefore propose a novel architecture paradigm on cloud for DIaaS in order to address the above two problems without giving up the security and maintenance benefits. We first leverage the SGX technology, a strongly-protected user space enclave, to bring DIaaS computation to its input source as close as possible, i.e. co-locating a cloud tenant and its subscribed DIaaS in the same virtual machine. When the GPU acceleration is needed, we migrate this virtual machine to any available GPU host and transparently utilize the GPU via our backend computing stack installed on it. In this way the majority of internal bandwidth is saved compared to traditional paradigm. Furthermore, we greatly improve the efficiency of the proposed architecture paradigm, from the computation and I/O perspectives, by making the entire data flow more DL-oriented. Finally, we implement a prototype system and evaluate it in real-world scenarios. The experiments show that our locality-aware architecture achieves the average single CPU (GPU) based deep learning inference time 2.84X (4.87X) less than the traditional detached architecture on average.
Design and Numerical Analysis of Extreme Sensitivity Refractive Index Sensor Based on Cavity Resonance Mode With Strong Electric Field

Relatively simple structures are necessitated for achieving high-performance refractive index sensors. Herein, we propose a less sophisticated sensing platform based on a thin metal film resonance cavity. A super-strong electric field in an analyte medium is formed via multi-wave interference, and the effective refractive index of the cavity mode strongly depends on the refractive index of the tested medium. In a relatively ideal situation, the sensitivity and maximum figure of merit of our device can reach 1,456,700 nm/RIU and 1,234,500 /RIU, respectively, which are both much higher than those of most sensing methods. This sensor shows great potential for low-cost sensing with high performance in biological or chemical sensing. Moreover, the cavity resonance configuration with high quality and strong electric field is versatile and may facilitate conventional surface plasmon resonance sensors in enhancing the strength of the detection field. Moreover, it can be applied to integrated optics and high-resolution spectroscopy.
Learning Multi-Object Dense Descriptor for Autonomous Goal-Conditioned Grasping

In a goal-conditioned grasping task, a robot is asked to grasp the objects designated by a user. Existing methods for goal-conditioned grasping either can only handle relatively simple scenes or require extra user annotations. This letter proposes an autonomous method to enable the grasping of target object in a challenging yet general scene that contains multiple objects of different classes. It can effectively learn a dense descriptor and integrate it with a newly designed grasp affordance model. The proposed method is a self-supervised pipeline trained without any human supervision or robotic sampling. We validate our method via both simulated and real-world experiments while the training relies only on a variety of synthetic data, demonstrating a good generalization capability. Supplementary video demonstrations and material are available at https://vsislab.github. io/agcg/.
The Method of Disentangled and Interpretable Representations for Speech Enhancement

We study the problem of speech enhancement in monophonic and variety noisy conditions. Most speech enhancement models have successfully mapped noisy speech features to clean speech features through the deep network. However, analyzation of sequence data multi-scale information still has great challenges, which is important for the speech signal. In this paper, we present an end-to-end hierarchical model , combining factorized hierarchical autoencoder (FHVAE) and independently recurrent neural network (indRNN) structures, which learns disentangled and interpretable representations from speech data in unsupervision way. Compared to other end-to-end models, this encoder can be used to capture the features of speech linguistic and background noise, then encode them into latent variables at different levels. And speech enhancement is achieved by manipulating latent variables of noise conditions. The objective evaluation results demonstrate that the proposed model has better improvements than classic methods, and the training time is shorter than the others.
FluxInfer: Automatic Diagnosis of Performance Anomaly for Online Database System

The root cause diagnosis of performance anomaly for online database anomalies is challenging due to diverse types of database engines, different operational modes, and variable anomaly patterns. To relieve database operators from manual anomaly diagnosis and alarm storm, we propose FluxInfer, a framework to accurately and rapidly localize root cause related KPIs for database performance anomaly. It first constructs a Weighted Undirected Dependency Graph (WUDG) to represent the dependency relationships of anomalous KPIs accurately, and then applies a weighted PageRank algorithm to localize root cause related KPIs. The testbed evaluation experiments show that the AC@3, AC@5, and Avg@5 of FluxInfer are 0.90, 0.95, and 0.77, outperforming nine baselines by 64%, 60%, and 53% on average, respectively.
Connecting Networks for Two-Dimensional Butler Matrices Generating a Triangular Lattice of Beams

Connecting networks for two-dimensional beamforming matrices are discussed. They provide a suitable interconnection between the output ports of a Butler matrix and antenna array ports, transforming a square (and more generally, rectangular) lattice of beams into a triangular lattice of beams. This change in lattice improves the crossover between beams, reducing gain roll-off over the field of view for a given multiple fixed beam coverage. A general description of the concept is provided and numerical results are reported, which confirm a reduction in crossover level between 1 and 1.5 dB. The proposed solution is further validated through the design and test of a 4 × 4 open-ended waveguide array in K-band. The connecting network has a length of 70 mm, compared to 226 mm for the 2D Butler matrix, with a design frequency of 19.55 GHz. Good agreement is found between simulated and measured data. The desired triangular lattice of beams is confirmed with the measured multiple beams. This solution is of interest for multiple-beam millimeter-wave communication systems, considered for next generation of terrestrial and non-terrestrial networks.
Research on Resource Scheduling Algorithms for Satellite Earth Stations

Resource scheduling has always been a hot and difficult problem in systems engineering and management at home and abroad, and it is also very important in the research of satellite earth station system. Due to the limited resources in the satellite earth station system, the rationality of resource allocation directly affects the overall performance of the multi-satellite system. The resource scheduling problem of satellite earth station is a combinatorial optimization problem based on constraints.This paper summarizes the existing resource scheduling model and scheduling algorithm of satellite earth station system, and provides reference for the further research and development of this field.
Performance Evaluation of Wave Source Localization Method Using UAVs Based on the Maximum Likelihood Estimation

For temporal and spatial spectrum sharing between primary and secondary wireless communication systems, wave source localization is a promising technique to localize areas that wireless signals are radiated. In order to localize wave sources under NLOS (Non-Line-of-Sight) environments, we have proposed a wave source localization method using UAVs (Unmanned Aerial Vehicles) based on maximum likelihood estimation and evaluate the performance of the proposed method under a situation that DoAs (Direction of Arrivals) of received signals at each UAV follow a von-Mises distribution. In this paper, we evaluate the performance of the proposed method with a more practical simulation environment using a ray-tracing radio propagation simulator.
Blind Signal Separation Using Array Antenna with Modified Optimal-Stepsize CMA

The OS-CMA used for blind signal separation with array antenna has a problem that the stationary characteristics are not stable, although the convergence is extremely fast. This paper introduces a threshold into the stepsize of OS-CMA to realize the more stable stationary characteristics while maintaining the convergence speed. The computer simulation demonstrates the effectiveness of the proposed algorithm.
A Study on Beam Narrowing and Gain Enhancement of Patch Array Antenna Using FSS

This paper presents a study on beam narrowing and gain enhancement of a patch array antenna by loading Frequency Selective Surface (FSS) consisting of periodic arrangement of rectangular dipoles. The dipole element is designed so that its length get shorter towards the end of the antenna to equalize the phase distribution on the aperture surface. Antenna characteristics are evaluated by performing the finite-difference time domain (FDTD) analysis for the case when the main beam direction of the patch array antenna is the broadside and when the main beam direction is tilted from the broadside. From the simulated results, beam narrowing and gain enhancement are confirmed for the above-mentioned cases.
A Review of Digital Forensics Framework for Blockchain in Cryptocurrency Technology

A blockchain can be summarized as a decentralized ledger of all transactions across a peer-to-peer network. It is the primary technology behind the large number of diverse cryptocurrencies that are currently available in circulation. Since its introduction, blockchain technology has shown promising application prospects and attracted much attention from academia and industry. It also has become an obvious target for adversaries. In this paper, we conduct a review of the implementation of digital forensic investigation processes to blockchain and cryptocurrency. Based on our findings, we can conclude that digital forensics is still considered a new area for blockchain technology, especially in cryptocurrency.
On Selection of a Benchmark by Determining the Algorithms’ Qualities

The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.
Subject Independent Classification of Hand Gesture from sEMG using an Approximate Entropy Based Approach

Surface electromyogram (sEMG) signal is used as a convenient tool in prosthetics because of its accessibility and unobtrusiveness. However, because of the low signal quality, sEMG based prosthesis automation suffers from low accuracy. Besides, the force and neuromuscular stimulation related to a movement varies from person to person and results in poor generalizability. Most of the existing hand gesture classification methods propose subject-specific models that don’t have satisfactory accuracy across subject cases and hence lose their real-life usability. In this paper, we proposed a method based on approximate entropy (ApEn) that achieves high classification accuracy for both within and across subject cases. The method consists of a feature set combining ApEn with five time-domain (TD) and frequency domain (FD) features and it was tested on a publicly available sEMG dataset having six different hand movements of five subjects. A six-class linear discriminant analysis confirms that the method achieves above 96% accuracy and 70-80% accuracy within and across subject cases respectively. Because of high accuracy in subject independent tests, the method promises to perform well in automatic prosthetic devices in real-life scenarios.