Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Immortality in Wireless Rechargeable Sensor Networks Using Local Learning

Wireless rechargeable sensor networks have been studied in depth after the breakthroughs in wireless power transfer. In this paper, we propose an efficient decentralized recharging mechanism based on the use of a mobile energy charger (MC), restoring the energy supply to nodes in need.Our strategy is composed of an initialization phase, during which the MC moves in the network to learn the sensors’ location and to make an estimate of their consumption rate, followed by charging rounds where the charging schedule is dynamically computed based on the most up-to-date information collected.Our strategy is fully decentralized, uses only local communication between the MC and neighbouring nodes, operates under dynamic network conditions without any a-priori knowledge of the network, is based on simple computations by the MC with little memory requirements, and is highly effective in keeping the network perpetually operating and achieving immortality even in large networks.
Optimizing the Routing in Ad Hoc Networks With Option-Critic

The mobile Ad Hoc networks require routing algorithms with fast convergence speed, strong dynamic adaptability, and high information fault tolerance due to the characteristics of strong node mobility and dynamic change of topology. However, most of the existing routing algorithms fail to solve the problems caused by the dynamically changing characteristics of mobile Ad Hoc networks. To deal with this problem, this paper proposes an intelligent routing algorithm which can dynamically optimize network routing. Especially, we adopt option-critic architecture based on hierarchical reinforcement learning to train agents. The proposed algorithm can optimize routing with few a priori conditions. Simulation results show that the proposed algorithm has better convergence and effectiveness than the existing Q-learning algorithm, and provides better routing configuration, thereby improving network performance.
A mathematical programming model for the management of carriages in virtually coupled trains

This work proposes a model for exploiting some possibilities that can derive from the concept of virtual coupling in railways, in which trains can be dynamically grouped to form single convoys. In particular, in the considered scenario, convoys running on a railway line are formed by virtually coupling train carriages, and the capacity of each convoy can change dynamically by leaving or adding carriages at each station. In the paper, an original optimization model based on a mathematical programming formulation is proposed for a passenger railway line, with the aim of determining the optimal time-variant capacity of trains that satisfies the transport demand and avoids trains with unnecessary capacity. More in detail, the proposed system determines, for each link of the line, the optimal number of carriages that is needed, allowing maneuvers like the parking of unused carriages and the exchange of them among the trains. The paper presents also an example with the results derived from the implementation of the model for a simple case, done with the aim of testing and validating the model.
All Digital Ambient Backscatter System

This work presents an all digital ambient backscatter system which operates with ambient FM sources. Moreover, the system uses commercial modules to perform backscatter and has the ability of using Wi-Fi and Bluetooth signals. The backscatter module presented here uses an ESP32 MCU and a telescopic monopole as an antenna. The variation of a digital IO pin at 5 kHz causes an impedance variation in the connected antenna reflecting the incoming FM waves with an ASK modulation and can be demodulated by a low-cost RTL-SDR USB dongle. In this work an all digital backscatter communication system was shown by using an ambient FM signal from a station at a distance of 3 Km which was decoded with success. With this system it is possible to reduce the complexity of a low power communication system, the number of sensors batteries and the number of dedicated transmitters, that proves to be a low-cost and environmentally friendly solution.
Scalable Generation of Statistical Evidence for the Safety of Automated Vehicles by the Use of Importance Sampling

Simulation-based validation methods exhibit properties which should allow them to contribute to the release of highly automated driving functions (HADF). Monte-Carlo (MC) simulations make it possible to generate statistical statements about a HADF. However, the fact that accidents happen very rarely in the real world can strongly enlarge the number of MC simulation runs which are needed to obtain statistically stable results. In the past, importance sampling (IS) was used to reduce these simulation costs. This paper considers some of the still existent limitations of IS: The definition of proper safety metrics needed to find good IS distributions and the application of IS to high dimensional parameter spaces. The approach in this paper was able to reduce an IS optimization of 2700 dimensions to 17 optimizations of 9 dimensions. The number of MC runs could be reduced by a factor of 350.
Design of a Transmitarray Antenna with Triple Rectangular Rings at 29 GHz

Antenna with high gain and steerable beam is a challenging requirement for point to point wireless communications. Antennas in array configurations can provide high gain characteristic and beam focus. Transmitarray antenna in the last decade is getting high interest for researchers to provide high gain antenna with space-feed mechanism for steering beam feature by accommodating phase distributions of unit cell element in array form. This paper proposes a design of transmitarray in 29 GHz frequency. The unit cell consists of triple rectangular loops. The simulated and fabricated results confirmed that the gain value obtained is 23.6 dB.
CNN Based Road Course Estimation on Automotive Radar Data with Various Gridmaps

Automotive radar is a promising technology with regard to path planning, since radar systems offer a comparatively long range and are robust against bad weather conditions. In this paper, we use Convolutional Neural Networks (CNN) to determine the current road course from radar point clouds. For this purpose, we first transform the radar point cloud into various gridmaps, which then serve as an input for the CNN. The quality of the road course estimation is evaluated using a test dataset. Exemplary test results showed an average deviation of less than 91 cm at a range of 100 m between the ground truth and the estimated road course. These excellent results prove that CNN processing of radar measurements is an attractive option for reliable and precise road course estimation.
DESIV: Differential Fault Analysis of SIV-Rijndael256 with a Single Fault

This work analyzes the strength of NIST Lightweight Cryptography (LWC) Round 1 Candidate SIV-Rijndael256 in light of Differential Fault Attacks (DFA). It is observed that SIV-Rijndael256 presents an interesting case for DFA due to its large state-size which has been capitalized in this work to contribute to the state-of-the-art in DFA on AES-like ciphers. We first study the differential properties over three rounds of the cipher and then use it to mount a DFA that uses a classical strategy in conjunction with the properties of the key-schedule. The nonce-misuse property of SIV-Rijndael256 facilitates the use of DFA and is exploited to reduce the key-space from 2128 to just one using only a single random byte fault in the internal state. Moreover, the support for the release of unverified plaintexts which have been shown in literature as a vulnerability to DFA, makes the decryption also vulnerable to the same attack. Finally, the ability to uniquely retrieve the key using just a single fault makes the attack, reported here, an optimal DFA. To the best of our knowledge, this is the first differential fault analysis of any candidate in the NIST LWC competition.
Deep Learning for Cooperative Spectrum Sensing in Cognitive Radio

In this paper, we propose a collaborative spectrum sensing algorithm based on deep learning. Firstly, we use distributed secondary users (SUs) to receive sensing samples, which effectively avoids the channel’s fading and shadow effects. Then, we propose a detection framework based on deep learning and select the sample covariance matrix as the test statistic. To realize the detection framework, we put the sample covariance matrix into a convolutional neural network (CNN) and propose a new cooperative spectrum sensing algorithm named CSS-CNN algorithm. Besides, theoretical analysis for the proposed algorithms is given, and find the associated threshold. We also analyze the robustness of SNR and noise uncertainty for the CSS-CNN algorithm. Notably, the proposed algorithm requires neither any information about the signal nor noise power. Simulation results demonstrate that the proposed algorithm outperforms other popular sensing methods based on deep learning and conventional sensing methods.
Prediction of Stock Prices Using Markov Chain Monte Carlo

Financial sector investment is an activity that attract a lot of public interest. One of them is investing funds in purchase company’s shares. Stocks are proof of ownership of a company or business entity. Stocks are an attractive investment and quite challenging because they can provide large profits for investors if they predict correctly. Basically peoples buy stocks for long-term investment to get profit from dividend, but there are also investors who want to get benefit from buying and selling stock prices in the short-term periods. Predicting of stock prices become an attractive and challenges because it can be easy and hard based on fundamental and technical analysis. Apply Bayesian inference to create prediction model and Markov Chain Monte Carlo (MCMC) to generate predicted data. By mathematically predicting stock prices become more challenges, and spending much time to compute. But with parallel computing on Apache Spark requires less time.
Mining Biological Information from 3D Medulloblastoma Cancerous Gene Expression Data Using TimesVector Triclustering Method

Triclustering analysis is the development of clustering analysis and biclustering analysis. The purpose of triclustering study is to group three-dimensional data simultaneously. The three-dimensional data can be in the form of observations, attributes, and context. One of the approaches used in tricluster analysis, namely an approach based on sample patterns, is the TimesVector method. The TimesVector method aims to group data matrices that show the same or different patterns in three-dimensional data. The TimesVector method has a work step that starts with reducing the three-dimensional data matrix to a two-dimensional data matrix to minimize complexity in the grouping. In this method, the Spherical K-means algorithm will be used in cluster it. The next step is to identify the pattern of the groups generated in the Spherical K-means. The pattern referred to consists of three types, namely DEP (Differentiated Patterns), ODEP (Differentiated Patterns), and SEP (Differentiated Patterns). The TimesVector method was applied on gene expression data, namely medulloblastoma cancerous data carried out in 6 scenarios. Each scenario uses the same many clusters but different threshold values. The six scenarios’ results will be validated using the coverage value and the tricluster diffusion (TD) value. The application of the TimesVector method shows that using a threshold of 1.5 gives the most optimal results because it has a high coverage value and a low TD value. High-value coverage indicates the method’s ability to extract data, and a low TD value suggests that the resulting tricluster has a large volume and high coherence. The best tricluster results can be used by medical experts to perform further actions on medulloblastoma cancerous patients.
EEG Classification for Motor Imagery Mental Tasks Using Wavelet Signal Denoising

Brain-Computer Interfaces (BCIs) are an approach that enables humans to interact with their surroundings by brain generated control signals. Electroencephalographic (EEG) signals that records electrical activity through the scalp might contain superfluous artifacts suppressing some valuable information. Thus the EEG signal denoising is an important stage of the EEG data analyses. The paper presents an experimental comparison of several classification approaches for 2-class motor imagery EEG data classification and explores the influence of wavelet signal denoising on the classification accuracy.
An Energy-Efficient Train Control Approach Based on Deep Q-Network Methodology

Application of the automatic train operation can efficiently reduce the energy consumption under the premise of ensuring safety and punctuality. Most of the traditional research mainly focused on solving the driving strategy with the given model or fixed parameters. To flexibly respond to the dynamic changes during the train operation, this paper proposes a data-driven based approach, specifically a reinforcement learning (RL) approach, which can optimize the train control strategy without using the prior knowledge of train dynamics and the predesigned velocity profile. The optimization objective is to minimize the energy consumption under the constraints of punctual arrival and speed restrictions. Numerous driving experiences are used to train the deep Q-network (DQN) until it approximates the optimal action-value function. The trained neural network can output the values of all actions with arbitrary input state such that the energy-efficient control strategy can be obtained according to action values. Based on the real-world data, some cases are conducted to illustrate the effectiveness of the proposed approach.
Conflict-Free Replicated Relations for Multi-Synchronous Database Management at Edge

In a cloud-edge environment, edge devices may not always be connected to the network. Still, applications may need to access the data on edge devices even when they are not connected. With support for multi-synchronous access, data on an edge device are kept synchronous with the data in the cloud as long as the device is online. When the device is offline, the application can still access the data on the device, asynchronously with concurrent data updates either in the cloud or on other edge devices. Conflict-free Replicated Data Types (CRDTs) emerged as a technology for multi-synchronous data access. CRDTs guarantee that when all sites have applied the same set of updates, the replicated data converge. However, CRDTs have not been successfully applied to relational databases (RDBs) for multi-synchronous access. In this paper, we present Conflict-free Replicated Relations (CRRs) that apply CRDTs to RDBs for support of multi-synchronous data access. With CRR, existing RDB applications, with very little modification, can be enhanced with multi-synchronous access. We also present a prototype implementation of CRR with some preliminary performance results.
Area of Mangrove Forests Calculated by Color Image Segmentation using K-Means Clustering and Region Growing)

The calculation of the area of mangrove forests by conventional methods requires much time and energy. In this study, a tool for calculating the area of mangrove forests in Southeast Sulawesi Province, Indonesia, using satellite imagery is developed on the basis of two segmentation methods, k-means clustering and region growing. We then compare those two methods to obtain the optimal method to calculate the area of mangrove forests. Before this research, there were no researchers who calculated the area of mangrove forests in Southeast Sulawesi using both methods. We constructed a calculation algorithm using Matlab, which includes different stages of digital image processing. The area of mangrove forests is calculated on the basis of the number of pixels with an area density of 900 m2/pixel. The accuracy of the two segmentation methods is compared for identical areas obtained by the National Institute of Aviation and Space in Indonesia (LAPAN), i.e., the area obtained by LAPAN is used as a reference in calculating the accuracy. The accuracy of the region growing segmentation method is 33.33%, whereas that by the k-means clustering segmentation method under optimum conditions is 59.26% in the application of 12 clusters.
Distance Aware Radio Planning for SDN Based 5G-Satellite Network

Satellite component will inevitably hold a substantial role in the emerging 5G-Satellite network context, performing ubiquitous coverage, content distribution in addition to spectrum utilization improvement. In this paper, we introduce Software Defined Network (SDN) solution in order to address satellite radio planning strategy according to satellite movement around the earth. Indeed, continuous satellite movement, may exhibit significant spatial inhomogeneity among different areas belonging to the same satellite footprint, leading to high propagation delay in addition to disadvantageous propagation conditions. SDN for next generation networks, is introduced to propose a link adaptation process aware in advance radio channel conditions. So, based on link budget characteristics of the area covered by the satellite footprint, the SDN controller will determine the appropriate radio planning, which could be in term of ground base stations deployment or in term of bandwidth allocation for each spot-beam. This deployment depends directly on the physical bit rate and hence on the radio conditions in term of SINR (Signal to Noise Interference ratio) and propagation delay. Our proposal was tested for different radio environments in order to overcome the satellite link variation.
Key Aspects in the design of silicone/graphene-based strain sensors for structural monitoring

The graphene as a material used in engineering applications has been on the rise in the last decade due to being a material with interesting electrical and structural properties. This possibility offers a very wide range of technological applications. Among the potential fields of application is in the development of new sensors for structural applications. Recently the combination of graphene with cross-linked polysilicones has been proposed. This compound has interesting electrical and visco-elastic properties that have allowed the development of highly sensitive pressure and strain sensors. For this reason, their use as strain sensors in structures through structural health monitoring systems opens up an interesting field of research. The aim of this work is to study the mechanical behavior, and the characteristics required of graphene / polysicone-based sensors to be applied effectively in structural health monitoring systems.
Joint Channel Estimation Techniques for Muti-User Massive MIMO Relay Networks

Due to their excellent performance, massive MIMO rely techniques has become more and more popular. In this paper, we develop a joint channel estimation method for a massive MIMO relay system consisting of multi-user and one relay. Firstly, the proposed method transmits interactive orthogonal channel training sequences at each user end. Then the relay amplifies received signals, and forwards the amplified signals to all users. Finally, the Tucker-2 tensor model is constructed at each user end, and the constructed Tucker-2 model is fitted to estimate all the channel matrices. The proposed method has higher channel estimation accuracy. In addition, the analytical expression of the CRB is derived for reference. Simulation results demonstrate the effectiveness of the proposed joint channel estimation method.
Unmasking Magnet, a Novel Malware on Facebook

We present the findings of our discovery of a novel Trojan malware on Facebook named Magnet. The new malware exploited the photo tagging feature of Facebook to reach a wider pool of victims, enabling it to spread faster than any existing Trojan malware. We provide in-depth technical details of the malware, its spreading mechanism and simulation results showing that Magnet can spread faster than traditional Trojan malware.
Improved Faster RCNN for Traffic Sign Detection*

With the increasing prevalence of autonomous driving, research on traffic sign detection (TSD) draws substantial attention recently. Existing studies usually adopt a twostep framework by first enhancing features of small Region of Interests and then for image analytic. However, the training process of this approach can be in-stable due to the lack of context information, which may restrict the quality of superresolution features. In this paper, we propose an efficient one-step learning-based solution to deal with the TSD problem. We first develop an improved Faster RCNN to detect small objects in traffic images. Then we introduce a new sampling method to optimize the proposed network by selecting highquality proposals. We also present a post-processing scheme to resample the hard false samples having significant contributions to network optimization. Moreover, we adopt Res2net as the backbone of the proposed network in order to obtain more discriminative features. We conduct extensive experiments on the Tsinghua-Tencent 100k dataset, and the results show that our method outperforms other algorithms in terms of accuracy and recall.
LMI-based Nonlinear State Observer for Vehicle Motion Tracking in Lane Change Manoeuvre

This paper provides a nonlinear observer for vehicle motion tracking system in lane change manoeuvre. Firstly, the vehicle body axis system is designed and the nonlinear vehicle motion dynamics is then studied. By using differential mean value theorem and introducing vehicle’s uncertainties, a nonlinear observer is designed and a estimation error system is developed. Applying the Lyapunov theory and Linear Matrix Inequality (LMI), the designed observer gains are obtained. Stability, effectiveness and potency of the theoretical results are confirmed in lane change manoeuvre by the simulation results.
Measuring the Effect of Elementary Descriptive Attributes on News Recommender Systems

Information overload and information obscuring are two most recent challenges in finding relevant information from the Internet. Moreover, specifically in the online news industry, using sensationalist and eye-catching headlines for click-baiting has become a common practice. Finding relevant information is becoming harder than ever before. A solution to overcome the challenge is by utilizing a news recommender system. In most of the news recommender systems, a certain number of data attributes is required to produce appropriate decisions. Unfortunately, this is not always the case, especially when users are not required to register to a news portal. In this case, valuable information that can be used to make decisions, such as users’ preferences, visit or reading histories, will not be available. Therefore, in this paper, we take advantage of the elementary descriptive attributes of news articles, namely titles and keywords. We compare how these attributes affect the decision results, namely the recommended related news. We collected news articles from a news portal, generated two sets of related news using different compositions of descriptive attributes, and compared them to the originally defined set. Our findings indicate that the combination of titles and keywords produces highly relevant news which achieved a mean rating value close to 3 (on a scale of 1 to 5). Whereas the original recommended related news only has a mean rating value around 1. The combination produces better results compared to the original recommended related news which is highly affected by news categories. Our findings also revealed that the presence of specific entities (such as a person, location) in the titles has a significant impact on the outcome. This work has a wide spectrum of potential applications in the future, for example for automatic news aggregation, combating spam, reading context understanding, and so on.