Miracle-dz/UniFAR-FTUs huggingface dataset:
A Magnetic Field Sensor Utilizing Tellurite Fiber-Induced Sagnac Loop Based on Faraday Rotation Effect and Fresnel Reflection

According to Faraday rotation effect and Fresnel reflection theory, a novel magnetic field intensity sensor is theoretically proposed and experimentally demonstrated by introducing a 6.4-cm-long tellurite (76.5TeO2-6BiO3-11.5Li2O-6ZnO) no core fiber (TBLZ NCF) into the loop of Sagnac interferometer. The large Verdet constant of TBLZ NCF will bring about an apparent Faraday rotation angle in magnetic field sensing, and the large refractive index difference between TBLZ and silica fibers makes the Fresnel reflection more obvious. The designed sensor demonstrates a sensitivity of 13.51 pm/Gs with an accuracy of 1.47 Gs in the measuring range of 0–380 Gs. It has satisfactory linearity, repeatability, and stability, and does not require additional utilization of expensive magnetic field sensitive materials, exhibiting great application potential.
Gradients of Surface-Bound Laminin on Conducting Polymer Films for Potential Nerve Regeneration**Research supported by National Institute of Health, R01 NS087224.

Functional nerve recovery after the injury is crucial in the field of neural interfaces. Although numerous technologies have already been established to promote nerve regeneration, effective nerve guidance in long distances (over 10 mm) still remains challenging. Herein, we report a novel technique to fabricate long gradients of substrate-bound laminin protein on conducting polymer films of poly(3,4-ethylenedioxythiophene) (PEDOT). The developed methodology allows for creation of various gradient profiles of laminin on PEDOT films to modulate axonal regeneration. These bioactive and conductive platforms can potentially be employed to promote axonal regeneration in the long nerve gaps.
Evidential Association Classification for High-Dimensional Data

Association classification (AC) has p-roven to be a promising approach in data mining by integrating association mining and classification. The evidential association rule-based classification (EARC), as an extension of AC in belief function framework, is an effective classification model for addressing multiple uncertainties in real-world applications, but it encounters difficulties in dealing with high-dimensional data. To adapt the EARC to high-dimensional data, an improved evidential association classification method, called EARC-HD, is developed based on four stages: entropy-based fuzzy partition, evidential class association rule mining, rule prescreening and genetic rule selection. Comparing with EARC, an entropy-based fuzzy partition strategy is designed for deriving a series of fuzzy regions, with which some irrelevant attributes can be deleted. Moreover, the number of antecedent attributes is limited for effectively reducing the computational complexity in rule generation. To improve the efficiency of the classifier, the techniques of redundancy reduction and subgroup discovery are used for prescreening the mined rule set before a genetic rule selection process. Experimental results based on real-world datasets demonstrate the effectiveness of the proposed method in dealing with high-dimensional problems.
General, Efficient, and Real-Time Data Compaction Strategy for APT Forensic Analysis

The damage caused by Advanced Persistent Threat (APT) attacks to governments and large enterprises is gradually escalating. Once an attack event is detected, forensic analysis will use the dependencies between system audit logs to rapidly locate intrusion points and determine the impact of the attacks. Due to the high persistence of APT attacks, huge amounts of data will be stored to meet the needs of forensic analysis, which not only brings great storage overhead, but also sharply increases the computing costs. To compact data without affecting forensic analysis, several methods have been proposed. However, in real-world scenarios, we meet the problems of weak cross-platform capability, large data processing overhead, and poor real-time performance, rendering existing data compaction methods difficult to meet the usability and universality requirements jointly. To overcome these difficulties, this paper proposes a general, efficient, and real-time data compaction method at the system log level; it does not involve internal analysis of the program or depend on the specific operating system type, and it includes two strategies: 1) data compaction of maintaining global semantics (GS), which determines and deletes redundant events that do not affect global dependencies, and 2) data compaction based on suspicious semantics (SS). Given that the purpose of forensic analysis is to restore the attack chain, SS performs context analysis on the remaining events from GS and further deletes the parts that are not related to the attack. The results of the real-world experiments show that the compaction ratios of our method to system events are as high as <inline-formula> <tex-math notation="LaTeX">$4.36\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$13.18\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$7.86\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$26.99\times $ </tex-math></inline-formula> on GS and SS, respectively, which is better than state-of-the-art studies.
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks

Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover known ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
Novel Deep Reinforcement Algorithm With Adaptive Sampling Strategy for Continuous Portfolio Optimization

Quantitative trading targets favorable returns by determining patterns in historical data through statistical or mathematical approaches. With advances in artificial intelligence, many studies have indicated that deep reinforcement learning (RL) can perform well in quantitative trading by predicting price change trends in the financial market. However, most of the related frameworks display poor generalizability in the testing stage. Thus, we incorporated adversarial learning and a novel sampling strategy for RL portfolio management. The goal was to construct a portfolio comprising five assets from the constituents of the Dow Jones Industrial Average and to achieve excellent performance through our trading strategy. We used adversarial learning during the RL process to enhance the model’s robustness. Moreover, to improve the model’s computational efficiency, we introduced a novel sampling strategy to determine which data are worth learning by observing the learning condition. The experimental results revealed that the model with our sampling strategy had more favorable performance than the random learning strategy. The Sharpe ratio increased by 6 %–7 %, and profit increased by nearly 45 %. Thus, our proposed learning framework and the sampling strategy we employed are conducive to obtaining reliable trading rules.
A Load-Balanced Shortest-Path Routing and Spectrum Algorithm Considering Link Weight

With the rapid increase of transmission bandwidth requirements and much more differentiated services types, there has been an ever-growing research enthusiasm on routing and spectrum allocation (RSA) issue in elastic optical networks (EONs). Basing on the traditional RSA algorithm, this paper introduces the link weight into RSA algorithm, and forming the load-balanced shortest-path routing and spectrum allocation (LSPR) algorithm. Simulation results demonstrate that compared with two kinds of traditional RSA algorithm, the LSPR algorithm can reduce the bandwidth blocking probability (BBP) by approximate 20.14% and 15.30%, and improve the spectrum resource utilization (SRU) by about 20.26% and 10.67%, respectively.
CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Research into the production of relevant vaccines is progressively being advanced with the development of the Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax and Sanofi Pasteur vaccines. There is, however, a need for a computational intelligence solution approach to mediate the process of facilitating quick detection of the disease. Different computational intelligence methods, which comprise natural language processing, knowledge engineering, and deep learning, have been proposed in the literature to tackle the spread of coronavirus disease. More so, the application of deep learning models have demonstrated an impressive performance compared to other methods. This paper aims to advance the application of deep learning and image pre-processing techniques to characterise and detect novel coronavirus infection. Furthermore, the study proposes a framework named CovFrameNet. , which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement. The novelty of this study lies in the design of a CNN architecture that incorporates an enhanced image pre-processing mechanism. The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model. Results obtained revealed that the proposed model achieved an accuracy of 0.1, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. Thus, the study’s outcome showed that a CNN-based method with image pre-processing capability could be adopted for the pre-screening of suspected COVID-19 cases, and the confirmation of RT-PCR-based detected cases of COVID-19.
Self-Locating RFID Robot for Tag Localization in Retails

This paper presents a RFID-based mobile robot able of self-locating within an indoor scenario and to estimate the position of target UHF-RFID tags. To locate itself, the robot exploits a sensor-fusion method which combines data from an infrastructure of passive reference RFID tags arranged in known locations and data from rotary wheel encoders. Besides, during its motion it is able of measuring the target tag locations through a synthetic-array approach. The knowledge of the reader antenna trajectory is here achieved from the RFID-based sensor-fusion method which exhibits a localization error lower than 0.27 m for 20-m long paths in a real office environment. Then, the estimated trajectory is exploited for target tag localization with high accuracy by using the synthetic-array approach.
Harmonic Elimination of D.C to A.C Converters Using Embedded Design Techniques

This paper develops an efficient scheme to eliminate harmonics in D.C to A.C converters (inverters). The main advantages of the proposed method are its simplicity and high flexibility to adapt the pulse width according to the target. The proposed approach utilized embedded design techniques to construct a dual-processor system with a distributed memory model to be configured on Spartan-6E Field Programmable Gate Arrays (FPGAs) slice. The designed system is programmed to generate pulse width modulation (PWM) signals to drive PWM inverter suitable for Photovoltaic (PV) system. The duty cycle of the pulses produced by the designed system can be varied and optimized to reduce the distortion in the output waveform of the inverter circuit. The generated signals are applied on a prototype inverter circuit in real-time operation. The system performance is simulated using MATLAB -Simulink environment. From simulation results, it was found that the proposed system yields harmonics distortion as low as 3.85% at the inverter circuit output. The real-time inverter output matches very accurately the simulation results.
Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes’ forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.
Effect of gender in the onset and progression of Parkinson's disease

Parkinson's disease (PD) has been categorized as the second most common neurodegenerative disorder that affects people at the age of 50 and above, with an estimated increase in the prevalence worldwide to be more than double by 2030. In this study, we have investigated the effects of a patient's sex on Parkinson's disease at baseline scan using voxel-based morphometry (VBM). The analysis was conducted across 70 T1-weighted volumetric MRI scans obtained from Parkinson's progression markers initiative (PPMI) database consisting of 25 PD and 10 healthy control male subjects, and 25 PD and 10 healthy control female subjects. Essentially, a statistical test was performed across all voxels of the images to identify volume difference by comparing normalized-modulated images of Parkinson's disease subjects with normalized-modulated healthy control subjects. Although men are more prone to Parkinson's disease than women, Our study revealed many regions in the brain associated with reduced grey matter volume in PD female subjects and few regions affected in PD male subjects at baseline MRI scan.
Reconstruction of the Very Early Thalamo-Cortical Network with Combined EEG and MEG on Realistic Head Modeling

The early primary somatosensory network remains a rarely investigated brain area due to the fast transitions among the involved cortical and subcortical regions. In this regard, a non-invasive and subject-specific method that quantifies the very early temporal interdependences in the thalamo-cortical/cortico-cortical pathway of the primary somatosensory network would be of particular significance. Combined electro- (EEG) and magneto- (MEG) encephalography (EMEG) source analysis has been shown to exploit the complementary content of the single modalities EEG and MEG based on subject-specific and realistic head modeling. The current study aims to investigate the connectivity of the very early primary somatosensory network using EMEG source analysis with functionally-based decomposition and time-variant effective connectivity. Three-time temporally determined components are chosen based on the combined somatosensory evoked responses to highlight the thalamo-cortical and cortico-cortical interactions. The results confirm that electrophysiological activity flows from the thalamic regions of the brain to the first tangentially oriented neurological somatosensory brain region (Broadman area 3b, thalamo-cortical connection) and then to the radially-oriented brain region (Broadman area 3a, cortico-cortical connection). We also present that the flows between the investigated components that do not fit to the flow of the neurological activity from the wrist to the somatosensory network, when increasing the confidence interval (CI). Overall, EMEG source analysis with realistic head modeling and a functionally-based connectivity estimator is able to capture very fast transitions on the early-involved somatosensory network.
Rectifier Integrated Multibeam Luneburg Lens Employing Artificial Dielectric as a Wireless Power Transfer Medium at Mm Wave Band

In this paper, a rectifier integrated Luneburg lens is designed at K band for wireless power transfer (WPT) applications. The lens consists of two metallic layers with a gap of 0.3 mm between them and has been made by employing the glide symmetry technique. A flare is tailored to match the outer impedance of the lens to the free space impedance. Five microstrip tapers are used at intervals of $18^{0}$ at the periphery of the lens to collect the energy from it. The rectifying circuits are co-designed and are integrated with these five tapered launchers so as to make the entire structure suitable for capturing the transmitted power from the solar power satellite wirelessly, and to convert it to the equivalent voltage. Finally, all the ports are connected with a common load for DC power combining, and the overall performance of the lens integrated rectifier as an energy harvesting system is reported in terms of its power conversion efficiency (PCE).
Logo Detection with Artificial Intelligent

A logo detector is a device that detects illegal produced or counterfeit brand products. The focus of this research is to detect the product logo and consider the likeness of the sample product logo. Building the detector’s product logo detector, we used the image detection process with a darknet framework and YOLO algorithm. Through this process, the logo of the copyright products is being set as sample product data for having a dataset. Furthermore, OpenCV image classification by DNN module is used in Python language to read our dataset and work in Windows OS platform, to create a Graphical User Interface (GUI) simply including the creating a function to support the various application. The YOLO algorithm will be the main variable in this research. With this precision, we can detect the fake logo with 97% confidence scores and for the authentic logo with 99% confidence scores.
Rotation Aware Object Detection Model with Applications to Weapons Spotting in Surveillance Videos

Detection of weapons automatically is very important for improving the security and prosperity of people, in any case, it is a troublesome undertaking due to huge assortment of size, shape and presence of weapons. View point varieties what’s more, impediment likewise are the reasons which makes this errand more troublesome. Further, the present detection algorithms of objects process rectangular areas, anyway a thin and long rifle may truly cover simply a little part of zone and the rest may contain unessential subtleties. To beat this issue, we propose a deep learning based model for detection of weapons with orientation, which not only gives rotation aware bound box but also improves the detection performance. The proposed model provides orientation with the help of angle classification by dividing angle into eight different classes. To train our model for weapon recognition another new dataset containing of around 6400 pictures is assembled from the web and afterward manually annotated that. We also provide three standard horizontal annotation format of our dataset as ground truth along with oriented ground truth for further exploration in future. The proposed model is assessed on this dataset, also, the near investigation with off-the rack object indicators yields predominant execution of proposed model, estimated with the standard assessment procedures. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF.
Impact of Co-Worker Support and Supervisor Support Among the Middle and Senior Management in the Manufacturing Industry

The purpose of the this research work was to evaluate the impact of social support (through its two dimensions: co-worker support and supervisor support) on the reduction of the burnout syndrome (considering its three dimensions: emotional exhaustion, professional efficacy, and cynicism) from two proposed models that show clear differences. Social support was assessed using the Job Content Questionnaire instrument, and the burnout syndrome was measured through the Maslach Burnout Inventory - General Survey. A total of 467 middle and senior managers from the Mexican manufacturing industry participated in the research. Structural Equation Modeling was used to determine the relationships between the proposed variables. The results of model one show that social support helps mitigate the effects of burnout syndrome in its three dimensions. On the other hand, model two features better fit indices and specifically shows that co-worker support has a direct negative relationship with the dimension of emotional exhaustion, while supervisor support directly and negatively affects cynicism. These results show that, although social support helps in the mitigation of burnout syndrome in general, each type of support separately has a direct impact on a specific dimension of the burnout syndrome. Clarifying these relationships will help in the proposal of occupational hazard management policies in the Mexican industrial sector.
Influence Maximization in Social Media: Network Embedding for Extracting Structural Feature Vector

In parallel with the development of online social networks, the number of active users in these media is increased, which mainly use these media as a tool to share their opinions and obtaining information. Propagation of influence on social networks arises from a common social behavior called "mouth-to-mouth" diffusion among society members. The Influence Maximization (IM) problem aims to select a minimum set of users in a social network to maximize the spread of influence. In this paper, we propose a method in order to solve the IM problem on social media that uses the network embedding concept to learn the feature vectors of nodes. In the first step of the proposed method, we extract a structural feature vector for each node by network embedding. Afterward, according to the similarity between the vectors, the seed set of influential nodes is selected in the second step. The investigation of the results obtained from applying the proposed method on the real datasets indicates its significant advantage against its alternatives. Specifically, the two properties of being submodular and monotonic in the proposed method, which lead to an optimal solution with the ratio of (1–1/e) approximation, make this method considered a tool with high potential in order to address the IM problem.