Miracle-dz/UniFAR-FTUs huggingface dataset:
The Not Yet Exploited Goldmine of OSINT: Opportunities, Open Challenges and Future Trends

The amount of data generated by the current interconnected world is immeasurable, and a large part of such data is publicly available, which means that it is accessible by any user, at any time, from anywhere in the Internet. In this respect, Open Source Intelligence (OSINT) is a type of intelligence that actually benefits from that open nature by collecting, processing and correlating points of the whole cyberspace to generate knowledge. In fact, recent advances in technology are causing OSINT to currently evolve at a dizzying rate, providing innovative data-driven and AI-powered applications for politics, economy or society, but also offering new lines of action against cyberthreats and cybercrime. The paper at hand describes the current state of OSINT and makes a comprehensive review of the paradigm, focusing on the services and techniques enhancing the cybersecurity field. On the one hand, we analyze the strong points of this methodology and propose numerous ways to apply it to cybersecurity. On the other hand, we cover the limitations when adopting it. Considering there is a lot left to explore in this ample field, we also enumerate some open challenges to be addressed in the future. Additionally, we study the role of OSINT in the public sphere of governments, which constitute an ideal landscape to exploit open data.
Salient FlowNet and Decoupled LSTM Network for Robust Visual Odometry

An end-to-end CNN-LSTM network with salient feature attention and context-guided feature selection mechanism for robust visual odometry (VO) on monocular image sequence is developed in this paper. Deep learning-based visual odometry methods have drawn significant concerns comparing with traditional methods. Existing learning-based VO methods usually ignored the redundant features would increase error accumulation, and their rotational and translational motion parameters coupling would enlarge trajectory drift. A scheme on enhancing the visual salient features and decoupling motion parameters to alleviate these problems is investigated in our approach. The classical FlowNet paralleled with a VGG-based salient feature model to reinforce perceptive fields is designed based on an attention mechanism to extract the prominent geometric features from successive monocular images. Furthermore, to reduce the coupling of different motion patterns, a motion decoupled dual Long Short-Term Memory (LSTM) scheme based on guided feature selection mechanism is designed to select guided features to separately regress rotational and translational parameters. Experiments on KITTI dataset show competitive performance of the proposed approach compared with state-of-the-art deep learning based visual odometry methods.
Mismatched Filtering of Doppler Tolerant Codes for Multi-code Sonar Systems

Sets of transmitted signals used for sonar sensing applications must have good correlation properties, conformance of the magnitude to a target profile, and Doppler tolerance. Achieving an optimal solution with all three constraints along with the receiver, transmitter and channel effects is a complex and non-convex problem. Whereas optimising signals for the first two constraints is relatively easy, a Doppler shift degrades the sidelobe levels considerably so that achieving low and flat sidelobe levels is not guaranteed with the use of a matched filter. An alternative is to design a mismatched filter for reducing the sidelobe levels beyond the limits achievable with a matched filter. Designing a mismatched filter which is longer in length than a matched filter offers greater flexibility in terms of the filter coefficients that can be tuned. Designing a signal may be a non-convex problem, but with the signal fixed, design of a mismatched filter is convex. We compare two different methods for designing mismatched filters for Doppler tolerant codes. Results from both our designs show flat sidelobes at least 2 dB lower than those of a matched filter.
Prediction and Analysis of Rumour's Impact on Social Media

Rumour, as an important form of social communication, has been run through the whole evolutionary history of mankind. People maliciously disseminate rumours in order to increase awareness, slander others or cause panic, etc. To eliminate this issue, many researchers resort to detecting rumours on social media. However, rumour detection is not sufficient to eliminate the negative impact, which also requires official institutions to provide the refutations. In practice, the number of rumours on social media is too large, there is no need to refute some rumours with little or no concern. Therefore, we need to evaluate the impact of the rumours in advance. In this paper, we devise a rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour. Extensive numerical experiments are carried out on the real rumour data that are collected from Toutiao.com, which demonstrate the effectiveness of our proposed RISM model.
Experimental Study on Ultrasonic Vibration assisted Electrochemical Grinding of Small Holes*

Electrochemical grinding, as a composite machining method, is currently widely used for drilling and milling of hard-to-machining materials. Based on the electrochemical grinding method, the ultrasonic vibration assists the hole enlargement process to improve the machining quality of small holes. In this paper, an experimental platform for ultrasonic assisted electrochemical grinding was set up, and a mathematical model for hole enlargement in ultrasonic assisted electrochemical grinding is established. The effects of ultrasonic vibration, electrical parameters and rotation speed of electrode on the machining quality of 304 stainless steel were studied.
Multi-step capacitor switching scheme for low-power SAR ADC

A new multi-step monotonic switching SAR ADC architecture was proposed. The paper describes a method to improve energy savings of conventional monotonic switching SAR ADC architecture by adding 2n voltage-steps per capacitor switching. Modified step-down switched capacitor DC-DC converter for generation of voltage steps between reference and ground was also described. Multi-step monotonic switching provides increased power efficiency of capacitive DAC in 10-bit SAR ADC of up to 66% compared to 1-step monotonic switching architecture without consideration of DC/DC conversion influence. Energy savings with considered influence of proposed switched-capacitor DC/DC converter are approximately 50%.
Energy Harvesting Rectenna Applying the theory of Yagi-Uda Antenna

Energy harvesting that converts electromagnetic waves for wireless communication such as LTE and WiFi into energy using wireless power transmission (WPT) technology is expected as an IoT power supplies. However, since radio waves existing in the air are weak, it is difficult to obtain energy that can turn on the LED at a distance from the wave source. On the other hand, in the Yagi-Uda antenna, since the parasitic element acts as a director, current can flow also to the element separated from the feed element. So, in this research, we applied the theory of Yagi-Uda antenna to wireless power transmission and examined to realize energy harvesting. As a result, by constructing a linear array antenna with half-wave length dipoles, the array element works as a director and radio waves propagate. Using the array antenna, LED lights at a longer distance than in the case of one conventional dipole. Through experiments and analysis using kQ products, it has been clarified that lighting can be performed, and further the distance can be increased by increasing the number of elements with which the LED is turned on.
Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with their many variations that can effectively forecast. However, with the recent advancement in the computational capacity of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms have been developed to forecast time series data. This article compares different methodologies such as ARIMA, Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) and WaveNets for estimating the future price of Bitcoin.
Optimal Zero-Voltage-Switching Method and Variable on-Time Control for Predictive Boundary Conduction Mode Boost PFC Converter

This article presents a fully zero-voltage-switching (ZVS) method and an optimal variable on-time (VOT) control for the predictive boundary conduction mode (BCM) boost power factor correction (PFC) converter. The operation analysis of the BCM boost PFC converter, which considers the parasitic capacitances, is performed. Based on the analysis, a novel Taylor approximation method to derive the equations of switching off-time for achieving fully ZVS is proposed. Then, an inductor current approximation method is applied to derive the VOT equation in order to reduce the input current total harmonic distortion (THD). A calculation method for selecting the accurate parasitic capacitance is proposed. Most importantly, the derived equations for achieving ZVS and VOT control are simple and take short calculation time in a DSP controller, and, thus, they can be applied to the predictive control. Experimental results demonstrate that the fully ZVS operation can be achieved by the proposed ZVS method. The input current THD is indeed improved by the optimal VOT control.
Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network

Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the trade-off between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.
A Lightweight Architecture For Driver Status Monitoring Via Convolutional Neural Networks

Nowadays, a significant portion of road accidents is attributed to dirver’s inattention or dangerous behavior such as call or smoke. Monitoring the driver’s status through computer vision techniques to detect driving errors in real time to avoid vehicle collisions is one of the most important issues in machine vision and Advanced Driver Assistant System(ADAS). In this paper, we propose a novel facial landmark model and a lightweight architecture of Driver Status Monitoring(DSM) by integrating several deep learning models in order to monitor the driver’s inattention caused by drowsiness, fatigue and distraction in real time. With inverted residual block and multi-scale feature map, the proposed facial landmark model achieve 4.90% normalized mean error on common set of 300W dataset. The proposed detection model achieved a mean average precise(mAP@0.5) of 90% on our validation dataset. Several results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development.
A Decision-Making Model Based on Basal Ganglia Account of Action Prediction

Reinforcement learning offers robotics a framework for the design of sophisticated behaviors. The theory of it is deeply rooted in psychological and neuroscientific perspectives. The basal ganglia, thalamus and cerebral cortex constitute an important neural decision circuit involving in the behavioral choice of reinforcement learning. However, the influence of the changes of various nucleuses in the basal ganglia on decision-making is still unclear. Considering the complexity and difficulty of brain experiments on both animals and humans, it is necessary to establish a neural computing model. This paper builds a decision-making model based on physiology and anatomy of basal ganglia. The feasibility of the model was verified by comparing the behavior data from experiments and simulated by the model as well as readiness potentials and discharges rate of nucleus. Moreover, the simulation data were then used to further analyze the effects of conflict, dopamine, and subthalamic nucleus changes on behavioral accuracy and response. The results show this model opens a window for exploring specific neural mechanisms associated with basal ganglia-related decisions.
Reduction of Correlation Coefficient in Wideband MIMO Antennas using Partially Reflective Surface

This work presents a technique to reduce the Correlation Coefficient (CC) of a wideband Multiple Input Multiple Output (MIMO) antenna using wideband partially reflective surfaces (PRS). The proposed design of the PRS acts as a superstrate over the MIMO antenna forming a wideband Fabry-Perot Cavity (FPC). The introduction of the cavity reduces the beamwidth of antenna elements, which in turn reduces the overlap between the beams and reduces the CC of the MIMO antenna system. A wideband parasitic patch antenna operating from 9 GHz to 10.4 GHz is used to validate the effectiveness of proposed design. Simulated far-field results have been used to calculate the CC for which a 35-63% reduction is achieved with a bandwidth of 12.5%.
Adaptive Bivariate Function Generation based on Chebyshev-Polynomials

In this paper an adaptive hardware architecture for high-performance bivariate numerical function approximation is presented. Orthogonal Chebyshev-Polynomials are exploited that cover incremental accuracy refinements. Additionally, switching between two numeric functions is easily deployable by changing the set of polynomial coefficients. For evaluation, different configurations of the proposed hardware function generator are implemented and analyzed considering three bivariate numeric functions. The resulting performance highlights this approach to be a powerful extension for bivariate function approximation.
An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and $F_{1.5} $ score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward.
Deep Convolutional Neural Network Combined with Concatenated Spectrogram for Environmental Sound Classification

Environmental sound classification (ESC) is an important but challenging issue. In this paper, we propose a new deep convolutional neural network, which uses concatenated spectrogram as input features, for ESC task. This concatenated spectrogram feature we adopt can increase the richness of features compared with single spectrogram. It is generated by concatenating two regular spectrograms, the Log-Mel spectrogram and the Log-Gammatone spectrogram. The network we propose uses convolutional blocks to extract and derive high-level feature images from concatenated spectrogram, and each block is composed of three convolutional layers and a pooling layer. In order to keep depth of the network and reduce numbers of parameters, we use filter with a small receptive field in each convolutional layer. Besides, we use the average pooling to keep more information. Our method was tested on ESC-50 and UrbanSound8K and achieved classification accuracy of 83.8% and 80.3%, respectively. The experimental results show that the proposed method is suitable for ESC task.
A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness.
Nighttime Sleep Duration Prediction for Inpatient Rehabilitation Using Similar Actigraphy Sequences

Actigraphs are wearable sensors used to collect activity and sleep time series data from healthy and unhealthy populations. Unhealthy populations, such as individuals undergoing inpatient rehabilitation, typically exhibit abnormal daytime physical activity and nighttime sleeping patterns due to their injury and drastic changes in their activities of daily living. Consequently, Actigraph data collected from patients attending inpatient rehabilitation are often noisy and can be difficult to reliably draw conclusions from. In this paper, we apply machine learning to analyze such highly variable Actigraph data. We collected 24-hour, minute-by-minute Actigraph data from 17 patients receiving inpatient therapy post-stroke or post-traumatic brain injury. Our approach utilizes similarities among historical sequences of data to train machine learning algorithms to predict nighttime sleep duration. By tuning parameters related to our regression algorithm, we obtained a normalized root mean square error of 14.40%. Our approach is suitable for point of care and remote monitoring to detect changes in sleep for individuals recovering from stroke and traumatic brain injuries.
Effective treatment of relapsing experimental autoimmune encephalomyelitis with pseudoautologous bone marrow transplantation.

: Several human and experimental autoimmune diseases, including the current model for multiple sclerosis (MS), ie relapsing experimental autoimmune encephalomyelitis (R-EAE), have been reported to respond with complete remission to lympho- and myeloablation and rescue with transplantation of bone marrow (BMT) from healthy donors. Since treatment with autologous BM was as effective in our arthritis model as treatment with allogeneic BM from healthy rats, and in view of the much lower risk of autologous BMT in clinical practice, we have investigated treatment of R-EAE with pseudo-autologous (PSA) BM. PSABM is harvested from animals in the same active stage of R-EAE as the syngeneic recipients which are to be treated. Our results show that PSABM is as effective as syngeneic BM, except for a higher incidence of artificially induced relapses. We also demonstrated that lymphocytes present in the autologous BM graft may contribute to the occurrence of relapses post-transplantation. The implications of our results for a possible clinical application in the treatment of MS patients are discussed. If such treatment is envisaged, the use of T cell-depleted autografts seems mandatory, particularly as human BM contains 10 times more lymphocytes than rat BM.
Assessment of buccal and lingual alveolar bone width in the posterior region at dentate and edentulous sites: A cone-beam computed tomography study

Background: Alveolar bone dimensions form an important prognostic factor in determining the success of implant treatment outcome. The present study evaluated the buccal and lingual bone width in posterior dentate and edentulous sites using cone-beam computed tomography (CBCT). Materials and Methods: The study included 100 patients, divided equally into two groups, Group A (males) and Group B (females) indicated for implant therapy. CBCT scans were evaluated for the assessment of the thickness of buccal and lingual bone width at four levels, i.e., crestal bone width (CBW), mid root bone width, middle of alveolar bone housing bone width , and most apical portion bone width (APBW). Bone width was measured at three levels in the edentulous region as CBW, bone width 5 mm from crest (CBW-1), and 10 mm from crest (CBW-2). Results: Gradual increase in bone width was observed from crestal bone at buccal and lingual level (CBW-B and CBW-L) from 1.10 ± 0.29 mm and 1.21 ± 0.34 mm to APBW at buccal and APBW lingual side from 2.82 ± 0.51 mm and 3.43 ± 0.42 mm, respectively. For both the groups, the differences in bone width at three levels were statistically significant, with CBW being significantly higher for Group A than Group B. Conclusion: At edentulous sites, CBW was lesser as compared to the apical levels. The bone width on buccal and lingual sides of dentate sites at the coronal level is minimal compared to the apical level, which has definite implications for implant therapeutics.