Miracle-dz/UniFAR-FTUs huggingface dataset:
DISCA: Decentralized Infrastructure for Secure Community Attribute certifying

Inter-domain routing is the cornerstone of the modern Internet, and the security of inter-domain routing is very important to the reliability and security of Internet basic services. However, BGP protocol, as a current standard inter-domain routing protocol, lacks security considerations at the beginning of its design and does not authenticate routing messages. Because the BGP Community attribute is widely used, researchers have found a variety of new routing attacks using the Community attribute. This kind of attack is more covert and flexible, the detection mechanism is difficult to detect its existence, and the current trusted verification scheme can not completely defend against this kind of attack. In order to solve the above problems, this paper proposes a BGP Community attribute authentication scheme based on blockchain. This scheme authenticates the use of BGP Community attributes based on the blockchain smart contract for the first time. Based on the routing source authentication provided by the routing source authentication mechanism, this scheme further puts forward the concept of "the right to know about using". Through the agent authentication mechanism, it can effectively resist a variety of new routing attacks without changing the existing BGP routing protocol.
Fake news and COVID-19 vaccination: a comparative study

COVID-19 pandemic has changed almost every aspect of people's lives around the world. Along with non-pharmaceutical interventions such as physical distancing, vaccination is one of the proposed solutions to control the spread of this pandemic. However, so much fake information is spread on social media websites about the vaccination. In this paper, we study the problem of fake news detection on Twitter network. After collecting a dataset and pre-processing, a set of features are extracted from the tweets. This includes the tweet's length and its keywords, number of followers, sentiment, and readability scores. In the next phase, six well-known classifiers are executed on this data, and the best result with the highest accuracy is chosen for the community detection process to study and track the evolution of fake news campaigns. For the analysis, we considered multiple criteria such as the number of communities, their sizes, leaders, and topics. The results of this research can help decision-makers to understand the underlying and formation of fake news campaigns.
Smart Retail Adaptation Framework for Traditional Retailers: A Systematical Review of Literature

The application of smart retail in traditional retail requires adaptation, rather than adoption to make it affordable both in terms of cost and capabilities. This study proposed a framework of smart retail adaptation in traditional retailers drawn from a systematic literature review. The adaptation processes consist of three stages, that are visioning, design, and implementation and evaluation, which are iterative. The iterative processes ensure that technological innovations are continuously evaluated to avoid the risk of out-of-use and out-of-date. Eventually, the framework is useful in implementing adaptation stage-specific strategies to promote smart retail adaptation for traditional retailers, particularly in developing countries.
Early-Stage Depression Detector Using IoMT

Psychological stress influences the activity of normal bodily physiological function of an individual. Stress has its own consequences in every different individual. Early-Stage Stress Detector using IoMT is proposed to monitor stress levels through monitoring Cortisol level, temperature, movement rate and perspiration rate at the time of stress. This device is developed using the IOMT- Internet of Medical Things. For implementing this device, we use Mamdani type fuzzy logic controller, which is a neural network approach with more than 200 rules as the model. In order to real time monitor the stress levels of a person, the validated data is sent and stored in the cloud (Thingspeak platform), thereby periodic monitoring and health risks are reduced at the earliest. Early-Stage Depression Detector classifies the stress into three types, Low stress, Normal stress and High stress. This classification is done according to the observance of the changes in the physical state of the body. This idea is mainly focused on the mental health monitoring of students, by their parent or guardian. Even though this method is real time, it consumes low energy. The proposed system is at high advantage of accuracy, low systematic complications and cost affordable.
Identification of a New Pathogenicity Island Within the Large pAH187_270 Plasmid Involved in Bacillus cereus Virulence

Objectives Bacillus cereus is responsible for food poisoning and rare but severe clinical infections. The pathogenicity of B. cereus strains varies from harmless to lethal strains. The objective of this study was to characterize three B. cereus isolates isolated from the same patient and identify their virulence potentials. Methods Three isolates of B. cereus were isolated from various blood samples from a patient who developed sepsis following a central venous catheter infection. The three isolates were compared by WGS, genotyping and SNP analysis. Furthermore, the isolates were compared by phenotypical analysis including bacterial growth, morphology, germination efficacy, toxin production, antibiotic susceptibility and virulence in an insect model of infection. Results According to WGS and genotyping, the 3 isolates were shown to be identical strains. However, the last recovered strain had lost the mega pAH187_270 plasmid. This last strain showed different phenotypes compared to the first isolated strain, such as germination delay, different antibiotic susceptibility and a decreased virulence capacity towards insects. A 50- kbp region of pAH187_270 plasmid was involved in the virulence potential and could thus be defined as a new pathogenicity island of B. cereus. Conclusions These new findings help in the understanding of B. cereus pathogenic potential and complexity and provide further hints into the role of large plasmids in the virulence of B. cereus strains. This may provide tools for a better assessment of the risks associated with B. cereus hospital contamination to improve hygiene procedure and patient health.
Maternal Fatty Acid Metabolism in Pregnancy and Its Consequences in the Feto-Placental Development

During pregnancy, maternal plasma fatty acids are critically required for cell growth and development, cell signaling, and the development of critical structural and functional aspects of the feto-placental unit. In addition, the fatty acids modulate the early stages of placental development by regulating angiogenesis in the first-trimester human placenta. Preferential transport of maternal plasma long-chain polyunsaturated fatty acids during the third trimester is critical for optimal fetal brain development. Maternal status such as obesity, diabetes, and dietary intakes may affect the functional changes in lipid metabolic processes in maternal-fetal lipid transport and metabolism. Fatty acids traverse the placental membranes via several plasma membrane fatty acid transport/binding proteins (FAT, FATP, p-FABPpm, and FFARs) and cytoplasmic fatty acid-binding proteins (FABPs). This review discusses the maternal metabolism of fatty acids and their effects on early placentation, placental fatty acid transport and metabolism, and their roles in feto-placental growth and development. The review also highlights how maternal fat metabolism modulates lipid processing, including transportation, esterification, and oxidation of fatty acids.
Degradation of Biodegradable Plastics in Incineration Ash

生分解性プラスチックの一般土壌中における分解については研究報告がなされているが, 焼却灰主体の埋立廃棄物中に埋設された場合の分解特性と微生物活性に関する知見はこれまでにない。 本研究では, PLA, PBSA, PHB/V, PBS, PCLの5種類の生分解性プラスチック素材を, 焼却灰主体の埋立廃棄物 (A: 焼却灰+不燃破砕ごみ, B: A+コンポスト) 中に埋設した場合の分解の様子, 面積消失率, SEM観察など物理的側面からの観察を行い, その分解要因を調査した。 その結果, 焼却灰中での分解特性として, 以下の点が明らかとなった。 (1) 焼却灰主体の埋立廃棄物中における分解型は, 微生物関与で生じている孔拡大型と, 焼却灰中のアルカリ成分による加水分解で生じるクラック発生型の2タイプみられた。 (2) 焼却灰主体の埋立廃棄物中において, 有機物の添加や温度上昇など, 微生物活性を高める操作を行うことによってBPの生分解が促進された。 (3) 有機物含有量の少ない焼却灰中においては, 一般土壌と同程度の分解速度を示すことがわかった。 (4) 生分解を受けたBPは, PBSA>PHB/V>PCL>PBSの順で分解が速かった。 (5) BPが生分解を受けない場合は, 焼却灰中のアルカリ成分によってクラックが生じた。 (6) 焼却灰主体の埋立廃棄物は, PBSAとPCLによって微生物活性が高められたと推察された。
Wavelet-Based Convolutional Neural Network for Parkinson's Disease Detection in Resting-State Electroencephalography

Electroencephalography (EEG) is not commonly used for Parkinson's Disease (PD) detection and diagnosis. However, it has been recently indicated in the literature that EEG may present unique biomarkers and features of the disease. In the current study, we introduce a Convolutional Neural Network (CNN) framework that exploits the wavelet domain of resting-state EEG in order to classify subjects into PD and Healthy Controls (HC). It was observed that PD exhibits a continuous uniform fading of the low wavelet scales as compared with HC. In addition, the proposed CNN approach was able to detect PD with a 4-fold as well as 10-fold cross-validation performance of up to 99.9% surpassing the-state-of-the-art deep learning-based architectures.
Abnormal Static and Dynamic Functional Connectivity in Left and Right Temporal Lobe Epilepsy

Objective Temporal lobe epilepsy (TLE) can be conceptualized as a network disease. However, the network characteristics in lateralization remain controversial. Methods In this study, resting-state functional MRI scans were acquired from 53 TLE patients [22 with left-side TLE (LTLE) and 31 with right-side TLE (RTLE)] and 37 matched healthy controls. We focused on the characteristics of static and dynamic functional connectivity, including static connectivity patterns and topological properties, as well as temporal properties of the dynamic connectivity state and the variability of the dynamic connectivity and network topological organization. Correlation analyses were conducted between abnormal static and dynamic properties and cognitive performances. Results The static functional connectivity analysis presented a significantly decreased cortical-cortical connectivity pattern and increased subcortical-cortical connectivity pattern in RTLE. The global-level network in RTLE showed a significant decrease in global efficiency. The dynamic functional connectivity analysis revealed that RTLE patients exhibited aberrant connectivity states, as well as increased variability in the subcortical-cortical connectivity. The global-level network in RTLE revealed increased variance in global efficiency and local efficiency. The static or dynamic functional connectivity in LTLE did not show any significant abnormalities. The altered dynamic properties were associated with worsening cognitive performance in language and conceptual thinking by the TLE patients. Conclusion Our findings demonstrated the presence of abnormalities in the static and dynamic functional connectivity of TLE patients. RTLE patients exhibited more pronounced aberrant connectivity patterns and topological properties, which might represent a mechanism for reconfiguration of brain networks in RTLE patients. These observations extended our understanding of the pathophysiological network mechanisms of TLE.
Estimation for Remaining Useful Life Based on a Complex Context Aggregation Model

Deep learning is wildly used in remaining useful life estimation of mechanical equipment. However, existing methods couldn't avoid losing useful information during the process of extracting feature. In order to extract rich feature from limited data, we proposed a prognostic model using residual network and dilated convolution to aggregat complex contextual information during training. Furthermore, time-frequency analysis is also utilized in our method to combine useful information in frequency and time domain. Experimental results represented that our method makes better results on remaining useful life estimation over other methods using deep learning.
DCCRN-SUBNET: A DCCRN and SUBNET Fusion Model for Speech Enhancement

Currently, most of the speech enhancement methods can’t address the performance degradation problem caused by low signal-to-noise ratios (SNR) and non-stationary noises. For better speech enhancement at the above scenarios, this paper proposes a two-stage method that fuses DCCRN and SubNet. Compared with the single stage-stage networks, two-stage networks have more powerful mapping capabilities. This paper uses complex-valued spectrogram as the training target. In the first stage, the DCCRN takes the magnitude and phase as input and estimates corresponding target of clean speech. By simulating the complex-valued operation, the DCCRN can train the complex target effectively. However, it is still difficult to handle the low SNR and non-stationary noises. This paper uses the SubNet as the second stage network for better speech enhancement. In the second stage, the SubNet further refines the magnitude of target frequency by exploiting the context frequencies. Its input is consisted of magnitude of target frequency and several context frequencies. The output is the estimation of the clean speech magnitude target for the corresponding frequency. The experimental results show that the proposed method obtains better performance than other baseline models in terms of PESQ, STOI and SI-SDR.
A Compact Size ISM Band Wearable Flexible Antenna for IoT Smart Watch Applications

This paper presents a compact size dual-band wearable antenna for the internet of things (IoT) smartwatch applications. The proposed antenna operates at 2.45GHz and 5.8GHz. The flexible material used for the substrate is Roger 3003C having a relative permittivity of 3, the loss tangent of 0.0019, and a thickness of about 1.58mm. Bending analysis along the (x & y)-axis are presented in this paper. The total radiation efficiency of the antenna is found to be more than 90%. The peak gain of the antenna is 1.03dB at 2.45 GHz and 4.29dB at 5.8 GHz. Specific absorption rate (SAR) values are also calculated as 0.95W/kg and 1.56W/kg over 1 gram. The simulation and measurement results indicate that the proposed design is a perfect candidate to be utilized for IoT wearable applications.
Mission specialist human-robot interaction in micro unmanned aerial systems

This research investigated the Mission Specialist role in micro unmanned aerial systems (mUAS) and was informed by human-robot interaction (HRI) and technology findings, resulting in the design of an interface that increased the individual performance of 26 untrained CBRN (chemical, biological, radiological, nuclear) responders during two field studies, and yielded formative observations for HRI in mUAS. Findings from the HRI literature suggested a Mission Specialist requires a role-specific interface that shares visual common ground with the Pilot role and allows active control of the unmanned aerial vehicle (UAV) payload camera. Current interaction technology prohibits this as responders view the same interface as the Pilot and give verbal directions for navigation and payload control. A review of interaction principles resulted in a synthesis of five design guidelines and a system architecture that were used to implement a Mission Specialist interface on an Apple® iPad. The Shared Roles Model was used to model the mUAS human-robot team using three formal role descriptions synthesized from the literature (Flight Director, Pilot, and Mission Specialist). The Mission Specialist interface was evaluated through two separate field studies involving 26 CBRN experts who did not have mUAS experience. The studies consisted of 52 mission trials to surveil, evaluate, and capture imagery of a chemical train derailment incident staged at Disaster City®. Results from the experimental study showed that when a Mission Specialist was able to actively control the UAV payload camera and verbally coordinate with the Pilot, greater role empowerment (confidence, comfort, and perceived best individual and team performance) was reported by a majority of participants for similar tasks; thus, a role-specific interface is preferred and should be used by untrained responders instead of viewing the same interface as the Pilot in mUAS. Formative observations made during this research suggested: i) establishing common ground in mUAS is both verbal and visual, ii) type of coordination (active or passive) preferred by the Mission Specialist is affected by command-level experience and perceived responsibility for the robot, and iii) a separate Pilot role is necessary regardless of preferred coordination type in mUAS. This research is of importance to HRI and CBRN researchers and practitioners, as well as those in the fields of robotics, human-computer interaction, and artificial intelligence, because it found that a human Pilot role is necessary for assistance and understanding, and that there are hidden dependencies in the human-robot team that affect Mission Specialist performance.
Achieving Quality Improvement ThroughUnderstanding And Evaluating InformationSystems Development Methodologies

Since the 70s literally hundreds of different methods and tools have appeared each claiming to ease the life of the developer and the user by achieving improved productivity without compromising the quality of the software artefact. These methodologies range from integrated collections of procedures to single technique, notations, 4GLs and tools for supporting the process at the various stages of the systems lifecycle [1, 2, 3, 4]. This paper discusses how an organisation wishing to improve their development practices embarks onto the time-consuming and expensive process of evaluation of methods and tools. The underlying complexity and application domain will themselves be decisive in the choice of methodology. The improvement process starts with the understanding phase. Here, we need to identify the important features of a methodology such as usability, portability, adaptability and functionality, and the nature of the problem(s) the methodology will apply to. We draw the distinction between problem, methodology (procedures, techniques) and tools and discuss their interrelationships [4, 7]. The evaluation phase starts with the specification of acceptance criteria and it involves the study of the features identified during the understanding phase against these criteria. Evaluations can be qualitative and quantitative.
A Low-complexity Variational Bayesian Learning Algorithm on Channel Estimation Using Group Sparse Structure

In order to fully utilize the advantages of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) must be obtained. In this paper, we focus on the downlink channel estimation in a frequency division duplex (FDD) multi-user massive MIMO system where spatial channels of adjacent users share group structure. Based on this property, we propose a low-complexity variational Bayesian inference algorithm by dividing the spatial channel support set into a public part and a unique part. Then, we introduce the generalized approximate message passing (GAMP) algorithm in the parameter update process to avoid complex matrix inversion, which greatly reduces the computational complexity. The simulation results show that our method has better estimation performance and lower computational complexity compared with the existing counterparts.
Performance Analysis of Adaptive Filter and Machine Learning Algorithms for Heart Rate Estimation Using PPG Signal

Photoplethysmography (PPG) signal provide advanced and simple ways for estimating heart rate (HR) information as an unremarkable system on wearable devices. In this paper, we analyze the performance of adaptive filter and machine learning (ML) algorithms for estimation of HR during physical activity. Three cascades recursive least square (RLS) and cascades normalized least mean square (NLMS) adaptive filters are developed and combined using convex combination scheme to reduce motion artifacts (MA) from the recorded PPG signal. Then, ML based spectral tracking algorithms is applied, to locate the spectral peak corresponding to HR. Four different supervised ML algorithms (Support Vector Machine, Decision Tree, K- Nearest Neighbor and Logistic Regression) are examined to track the spectral peaks and the decision tree out performs all three algorithms with an accuracy of 98.96%. Experimental results on the PPG datasets including 23 subjects used in the 2015 IEEE signal processing cup showed that the proposed approach has a very good performance by achieving an average absolute error (AAE) of 1.98 beats per minute (BPM) and the personal correlation coefficient of 0.9899. AAE result proved that the proposed method provides accurate HR estimation performance in comparison with other existing works.
Flexible Ultra-Wideband Antenna for 5G and Beyond Wearable Applications

Millimeter wave frequencies are front running contenders for 5G wireless communications. A wideband antenna that can provide coverage in whole of these frequencies is a well sought off challenge. This paper presents design of a quasi-Yagi antenna consisting of three driven arms and three pairs of spiral directors that efficiently meets this requirement. To satisfy the flexibility demand of wearable applications, the antenna employs liquid crystal polymer LCP as the substrate. The antenna has a small size of 5×8×0.202 mm3 while covering 24-71 GHz band.The proposed antenna achieves peak gain of 2.85, 6.33 and 8.52 dBi, respectively, and an efficiency of more than 70% in the desired frequency bands. The symmetric structure of the antenna also makes the fabrication easier. The ultra-wideband, radiation characteristics and flexibility of usage makes it a promising candidate for 5G and Beyond wearable applications.
Effects of Particle Swarm Optimization and Genetic Algorithm Control Parameters on Overcurrent Relay Selectivity and Speed

Distribution systems continue to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to solve overcurrent relay coordination problems; however, the implementation of these methods is time-consuming. Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this paper presents a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on particle swarm optimizer and genetic algorithms performance, alternatively on the behaviour of overcurrent relays. Optimization algorithms aim to minimise overcurrent relay time multiplier settings and accomplish optimal protection coordination. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that 30% crossover, 2% mutation, and smaller population size yield faster convergence rate and optimise fitness function, which improves genetic algorithms performance. Sensitivity analysis results are verified by comparing the performance of particle swarm optimization with the genetic algorithms which show the former parameter setting outperforms the latter. The relay operational speed is reduced by 15% for particle swarm optimization and system selectivity is maximised. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and efficiency.