Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector.
The Effect of Personal Factors on Consumers’ Trust in Mobile Payment Systems in Australia

Trust is at the core of any payment method and its role is of vital importance in the adoption of a new payment system. Individuals need to trust in one another in order to validate any monetary system created. With the evolution of the payment methods, shifting from paper-based to electronic-based, building consumers’ trust has become a crucial element for the success of businesses involved in the mobile payment systems sector. In this sense, the aim of this study was to investigate the effects of socio-demographic characteristics of users (gender, age, income, education and ethnicity), or personal factors, on consumers’ trust, and their influence on mobile payment systems (MPS) adoption in Australia. A quantitative approach through a questionnaire survey was conducted to measure the data collected. The raw data of 200 participants were gathered using SurveyMonkey and the Excel spreadsheets were imported into SPSS (Statistical Package for the Social Sciences) system, where the sample homogeneity took place. The results of sample homogeneity showed minor differences between mean values in some responses, which may suggest that in some groups people with certain characteristics tend to trust more than others.
Clustered Block Diagonal Digital Precoding for BDMA Downlink Transmissions

Beam Division Multiple Access (BDMA) has recently been proposed for massive multiple-input multiple-output (MIMO) systems by simultaneously transmitting multiple users’ data streams via different beams. Meanwhile, the block hybrid precoding has been proposed to reduce the computational complexity. However, previous works mostly rely on a crucial condition that the number of RF chains must be not less than the number of data streams. In this paper, we propose a BDMA based hybrid block precoding system to break the ceiling on minimal required RF chains where the data streams are processed cluster-by-cluster. Then two digital precoding approaches are investigated to suppress the intra-cluster interference by separately considering the signal-to-interference-and-noise ratio (SINR) and signal-to-leakage-and-noise ratio (SLNR). To overcome the performance degradation arisen from block hybrid precoding scheme, a greedy user clustering algorithm is proposed to minimize the intercluster interference. Simulation results confirm the effectiveness of proposed block clustering precoding scheme compared to conventional hybrid beamforming scheme.
Enhanced Style Transfer in Real-Time with Histogram-Matched Instance Normalization

Since the neural networks are utilized to extract information from an image, Gatys et al. found that they could separate the content and style of images and reconstruct them to another image which called Style Transfer. Moreover, there are many feed-forward neural networks have been suggested to speeding up the original method to make Style Transfer become practical application. However, this takes a price: these feed-forward networks are unchangeable because of their fixed parameters which mean we cannot transfer arbitrary styles but only single one in real-time. Some coordinated approaches have been offered to relieve this dilemma. Such as a style-swap layer and an adaptive normalization layer (AdaIN) and soon. Its worth mentioning that we observed that the AdaIN layer only aligns the means and variance of the content feature maps with those of the style feature maps. Our method is aimed at presenting an operational approach that enables arbitrary style transfer in real-time, reserving more statistical information by histogram matching, providing more reliable texture clarity and more humane user control. We achieve performance more cheerful than existing approaches without adding calculation, complexity. And the speed comparable to the fastest Style Transfer method. Our method provides more flexible user control and trustworthy quality and stability
Model Checking CTLK Based on Knowledge-Oriented Petri Nets

We define Knowledge-oriented Petri Nets (KPN) to model and analyze multi-agent systems (MAS) especially from the perspective of privacy and security. A KPN is a special Petri net in which some places are used to represent knowledges. Consequently, KPN can formally represent not only the interaction/collaboration process of multiple agents but also their epistemic evolutions. These epistemic evolutions are closely related with the privacy and security of MAS. For those requirements of privacy or security, we use Computation Tree Logic of Knowledge (CTLK) to specify them. We define the similar reachability graphs of KPN and then construct the equivalence classes of knowledges for each agent. Based on them, we design the model checking algorithms to verify CTLK formulas efficiently. Examples of privacy protocols illustrate the usefulness of our model and method.
Enhanced Drowsiness Detection Using Deep Learning: An fNIRS Study

In this paper, a deep-learning-based driver-drowsiness detection for brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) is investigated. The passive brain signals from drowsiness were acquired from 13 healthy subjects while driving a car simulator. The brain activities were measured with a continuous-wave fNIRS system, in which the prefrontal and dorsolateral prefrontal cortices were focused. Deep neural networks (DNN) were pursued to classify the drowsy and alert states. For training and testing the models, the convolutional neural networks (CNN) were used on color map images to determine the best suitable channels for brain activity detection in 0~1, 0~3, 0~5, and 0~10 second time windows. The average accuracies (i.e., 82.7, 89.4, 93.7, and 97.2% in the 0~1, 0~3, 0~5, and 0~10 sec time windows, respectively) using DNNs from the right dorsolateral prefrontal cortex were obtained. The CNN architecture resulted in an average accuracy of 99.3%, showing the model to be capable of differentiating the images of drowsy/non-drowsy states. The proposed approach is promising for detecting drowsiness and in accessing the brain location for a passive BCI.
A Dynamic Bypass Approach to Realize Power Efficient Network-on-Chip

High power consumption becomes the major bottleneck that prevents applying Network-on-Chips (NoCs) on future many-core systems. Power gating is an effective way to reduce the power consumption of a NoC. However, conventional power gating approaches cause significant packet latency increase as well as additional power consumption overhead due to the power gating mechanism. One comprehensive way to reduce these negative impacts is to bypass the powered-off routers in a NoC to transfer packets. Therefore, in this paper, we propose a dynamic bypass (D-bypass) approach, which is based on a reservation mechanism to allow different upstream routers to forward packets through the same powered-off router at different times. With this feature, our D-bypass power gating approach overcomes the drawbacks in related power gating approaches. Compared with a conventional NoC without power gating, our D-bypass approach causes only 2.55% performance penalty, which is less than 28.67%, 19.26%, 7.24%, and 6.69% penalties in related approaches. With small hardware overhead, our approach just consumes on average 22.23% of total power consumption in a NoC, which is slightly better compared to 27.06%, 23.89%, 26.45%, and 24.70% total power consumption in related approaches.
An RSA Based Authentication System for Smart IoT Environment

Authentication is the fundamental security service used in almost all remote applications. All such sensitive applications over an open network need authentication mechanism that should be delivered in a trusted way. In this paper, we design an RSA based authentication system for smart IoT environment over the air network using state-of-the-art industry standards. Our system provide security services including X.509 certificate, RSA based Public Key Infrastructure (PKI), challenge/response protocols with the help of proxy induced security service provider. We describe an innovative system model, protocol design, system architecture and evaluation against known threats. Also the implemented solution designed as an add on service for multiple other sensitive applications (smart city apps, cyber physical systems etc.) which needs the support of X.509 certificate based on hard tokens to populate other security services including confidentiality, integrity, non-repudiation, privacy and anonymity of the identities. The proposed scheme is evaluated against known vulnerabilities and given detail comparisons with popular known authentication schemes. The result shows that our proposed scheme mitigate all the known security risks and provide highest level assurance to smart gadgets.
Feature Engineering in Discrimination of Herbal Medicines from Different Geographical Origins with Electronic Nose

As pharmacists attach great significance to geographical origins of herbal medicines, cheap, nondestructive and convenient methods for discriminating herbal medicines originated from diverse regions are much in need. This work proposes a method of using electronic nose to discriminate herbal medicines from different origins. With 5 categories of herbal medicines and 3 to 4 geographical origins for each category, 8 pattern recognition algorithms prove the feasibility of the classification task and SVM, LDA and BP neural network have shown better classification accuracy. Additionally, feature engineering approaches are used to facilitate classification, showing that normalization based on each feature and each sensor and centralization prove to be better normalization approaches for classifiers; a proper degree of noise addition help classifiers get better generalization ability; finally, feature selection with SNR could lead to more efficient classifiers by selecting the most meaningful features and disregarding unnecessary features. This work provides insights for future herbal medicine evaluation based on electronic nose with better combinations of pattern recognition algorithms and feature engineering approaches for optimal classification performances.
Reliability Challenges with Self-Heating and Aging in FinFET Technology

The introduction of FinFET technology as an effective solution to continue technology scaling has pushed self-heating effects to the forefront of reliability challenges, especially at the 14nm technology node and below. Due to limited silicon volume for heat dissipation, elevated temperatures across the transistors channel can be generated during operation. This results in a considerable degradation of the key properties of transistors like decreased drain and increased leakage current. In addition, excessive temperatures considerably accelerate aging phenomena in transistors such as Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI), which shorten the lifetime of circuits. In this work, we discuss how self-heating effects in FinFET transistors can prolong the delay of circuits leading to reliability problems. We evaluate self-heating in an entire SRAM block consisting of SRAM cells, pre-charging circuit, sense amplifiers and an output latch. When it comes to reliability and lifetime, we demonstrate how self-heating effects can result in larger aging-induced degradations which, in turn, enforce designers to include wider and wider safety margins to sustain reliability. Lastly, we provide an outlook of self-heating and reliability concerns in Negative Capacitance Field Effect Transistors (NCFET).
MS-Pointer Network: Abstractive Text Summary Based on Multi-Head Self-Attention

Abstractive text summarization plays an important role in the field of natural language processing. However, the abstractive text summary adopts deep learning research method to predict words often appears semantic inaccuracy and repetition and so on. at the present stage, in order to solve the problem that semantic inaccuracy, we propose an MS-Pointer Network that based on the multi-head self-attention mechanism, which a multi-head self-attention mechanism is introduced in the basic encoder-decoder model. Since multi-head self-attention can combine input words into the encoder-decoder arbitrarily, and given a higher weight of these words that combination of the semantics, thereby achieving the purpose of enhancing the semantic features of the text, so that the abstractive text summary is more semantically structured, And the multi-head self-attention mechanism add the position information of the input text, which can enhance the semantic representation of the text. At the same time, in order to solve the problem of out of vocabulary, a pointer network is introduced on the seqtoseq with a multi-head attention mechanism. The model is referred to as MS-Pointer Network. We used CNN/Daily Mail and Gigaword datasets to validate our model, and uses the ROUGE metric to measure model. Experiments have shown that abstractive text summaries generated using the multi-head self-attention mechanism outperforming current open state-of-the-art two points averagely.
A Vulnerability Factor for ECC-protected Memory

Fault injection studies and vulnerability analyses have been used to estimate the reliability of data structures in memory. We survey these metrics and look at their adequacy to describe the data stored in ECC-protected memory. We also introduce FEA, a new metric improving on the memory derating factor by ignoring a class of false errors. We measure all metrics using simulations and compare them to the outcomes of injecting errors in real runs. This in-depth study reveals that FEA provides more accurate results than any state-of-the-art vulnerability metric. Furthermore, FEA gives an upper bound on the failure probability due to an error in memory, making this metric a tool of choice to quantify memory vulnerability. Finally, we show that ignoring these false errors reduces the failure rate on average by 12.75% and up to over 45%.
Decentralized Collaborative Flow Monitoring in Distributed SDN Control-Planes

The Software-defined Networking paradigm became widely accepted in academia as well as the industry in the last decade. The logically centralized control-plane provides simple mechanisms to support efficient monitoring as proposed in a plethora of works recently. However, existing works simplify the control-plane to a single entity whereas it is proven that the control-plane must be implemented physically distributed. To this end, we lately proposed DistTM, a system to increase the efficiency while monitoring flows in distributed control-planes by eliminating redundant measurements of shared flows. The system is based on a centralized coordinator that introduces a single-point-of-failure and is vulnerable to network partitioning. To overcome this shortage, we propose a distributed algorithm to assign redundant measurement responsibilities to single controllers and share information among controllers in the work in hand. In an evaluation, we show that we decrease the number of measurements strongly and improve the load fairness among controllers through the collaboration of networks.
The QoE Driven Transmission Optimization Based on Cognitive Air Interface Match for Self-Organized Wireless Body Area Network

This paper studied the optimization of air interface (AI) allocation in self-organized wireless body area network (WBAN), for the quality of experience (QoE) of wireless data transmission between patients’ body base station and the medical surveillance network (MSN). To improve the spectrum efficiency, the partial overlapping channel (POC) is adopted for the cognitive AI, which could adjust its wireless channel according to the environment. In the same time, the heterogeneous QoE of patients is also taken into consideration to optimize the network. A two-layer game model and the corresponding learning algorithm have been proposed for the AIs’ channel choosing and body base stations’ AI choosing in a distributed way, to realize the optimized and stable match between AI and patient’s transmission demand. The theoretic analysis for the equilibrium of the game and the convergence of the proposed algorithm is carried out. Simulation experiment results shows that the proposed two-layer game model and learning algorithm could effectively improve the QoE of WBAN, along with the fairness.
An Anonymous Conditional Privacy-Preserving Authentication Scheme for VANETs

Vehicular ad hoc networks (VANETs) have been growing rapidly because it can improve traffic safety and efficiency in transportation. In VANETs, messages are broadcast in wireless environment, which is vulnerable to be attacked in many ways. Accordingly, it is essential to authenticate the legitimation of vehicles to guarantee the performance of services. In this paper, we propose an anonymous conditional privacy-preserving authentication scheme based on message authentication code (MAC) for VANETs. With verifiable secret sharing (VSS), vehicles can obtain a group key for message generation and authentication after a mutual authentication phase. Security analysis and performance evaluation show that the proposed scheme satisfies basic security and privacy-preserving requirements and has a better performance compared with some existing schemes in terms of computational cost and communication overhead.
Execution Latency and Energy Consumption Tradeoff in Mobile-Edge Computing Systems

In this paper, we consider a mobile-edge computing (MEC) system consisting of an MEC server and multiple mobile devices, in which each mobile device executes computation tasks with the help of the MEC server. We aim at minimizing the weighted sum of the execution latency and energy consumption by jointly allocating the CPU-cycles, the transmission power and bandwidth, as well as computation offloading decisions. To address this issue, we first decompose the original optimization problem into several subproblems by using Lagrange dual decomposition, which can be operated by mobile devices in a distributed manner. Although the MEC execution resource allocation subproblem is non-convex, we show that it can be decomposed into a two-stage optimization problem to derive the optimal solution by using Karush-Kuhn-Tucker conditions and exact line search algorithm. Moreover, it is shown that the internal problem can be equivalently transformed into a convex one, and the external problem is a single-variable optimization problem. Simulation results are presented to show that, there exists an inherent tradeoff between the execution latency and energy consumption of mobile devices, and the computation task can be processed with less energy consumption if certain execution latency is tolerable.
Registration Algorithm for Point Cloud Based on Normalized Cross-Correlation

We propose a registration algorithm based on neighborhood similarity for 3D point clouds collected by optical measurement and without prior information. The algorithm first applies the improved minimum spanning tree (Prim algorithm) to classify the point cloud in order to obtain the topology information of the data. Specifically, vectors among root nodes and child nodes are processed, and the points on nodes are classified into different levels according to their scanning angle to simplify data and preserve the most representative points. Then, through the perspective conversion between 2D and 3D and according to the corresponding point set obtained by previous classification, the fast normalized cross-correlation (a 2D matching criterion) is applied to determine the relationship between initial characteristic points. Finally, distance constraints remove the errors between point pairs and allow calculating the registration parameters. Experimental results show that the algorithm has high registration accuracy and is suitable for point cloud data obtained by laser and structured light acquisition.
Improved Ammonia Sensing by Solution Processed Dodecyl Benzene Sulfonic Acid Doped Polyaniline Nanorod Networks

Dodecyl benzene sulphonic acid (DBSA) doped polyaniline (PANI) nanorods have been synthesized by a templateless route via in-situ emulsion polymerization of aniline in aqueous DBSA solution. XRD patterns, UV-Visible Spectroscopy, and FTIR analysis confirm the formation of PANI and PANI-DBSA, whereas FESEM images revealed their elongated rod shaped morphology. The chloroform dispersion of synthesized PANI-DBSA was spin coated over prefabricated interdigitated Pt patterned glass substrate to realize porous gas sensing active layer. The sensor displays good response in 1–1200 ppm ammonia concentration range and can repeatedly detect 300 ppm of ammonia with response time of 6 s, recovery time of 37 s and relative response value of 9.57. Besides, its response was found to be linear for 1–50 ppm ammonia concentration with relative response per ppm value of 0.01675.