Miracle-dz/UniFAR-FTUs huggingface dataset:
A Selfish Attack on Chainweb Blockchain

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker’s extra gain increases monotonically with its computational power.
Trajectory User Linking in C-ITS Data Analysis

Vehicles in an Intelligent Transport Network exchange a lot of messages. Every message sent is generated with an identifier of the transmitting vehicle. To respect the user privacy, an identifier is kept only over a specified time interval. The need that arises is, given that multiple identifiers are assigned to a vehicle, are we able to group the identifiers and detect those which belong to the same vehicle? We solved this Trajectory-User Linking problem by chaining anonymous trajectories to potential vehicles by considering similarity in movement patterns. Our method managed to link trajectory segments to their common vehicles which we validated through map matching of the trajectories using QGIS.
Optimal Functional Split for Processing Sharing based CoMP for Mixed eMBB and uRLLC Traffic

In this paper, we conceive the first work of optimal functional split mode (FSM) selection for striking a balance between enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (uRLLC) in a cloudradio access network (C-RAN) for 5G new radio (NR). As a benefit of processing sharing (PS) and of coordinated multipoint transmission (CoMP) techniques, the user equipment (UE) can be served with more flexible transmission and sustaining eMBB/uRLLC service quality. Therefore, we aim at minimizing delay-outage probability (DOP) for uRLLC services by guaranteeing the quality of eMBB traffic. By employing Newton’s method and convexity property, we propose an optimal FSM decision (OFD) scheme to achieve the optimal centralized/distributed control mode (CM/DM) assignment. Simulation results demonstrate the convergence and optimality of the proposed OFD algorithm compared to different parameters and schemes.
Cooperative path planning of a UAV swarm to meet temporal-spatial user demands

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing demands locally. It is necessary for different UAVs to cooperate with each other for servicing many users, and how to determine their cooperative path planning to best meet many users’ spatio-temporally distributed demands is an important question. This paper is the first to design and analyze cooperative path-planning algorithms of a UAV swarm for optimally servicing many spatial locations with dynamic user arrivals and waiting deadlines in the time horizon. For each UAV, it needs to decide whether to wait at the current location or chase a newly released demand in another location, under upper coordination with the other UAVs in the swarm. For each UAV’s routing problem even without coordinating with the rest UAVs, it follows dynamic programming structure and is difficult to solve directly given many user demands. We manage to simplify and propose an optimal algorithm of fast computation time (only polynomial with respect to both the numbers of user locations and user demands) for returning the UAV’s optimal path-planning. When a large number $|K|$ of UAVs are coordinating, the dynamic programming simplification becomes intractable. Alternatively, we present an iterative cooperation algorithm with approximation ratio 1 - $\left(1 - \frac{1}{|k|}\right)^{|K|}$) in the worst case, which is proved to obviously outperform the traditional idea of partitioning UAVs to serve different user/location clusters separately. Finally, we conduct simulation experiments to show that our algorithm’s average performance is close to the optimum.
Alternate-Jamming-Aided Wireless Physical-Layer Surveillance: Protocol Design and Performance Analysis

In this article, we develop an alternate-jamming-aided wireless physical-layer surveillance protocol where two devices (M1 and M2) work cooperatively to eavesdrop on and intervene in a suspicious transmission link from a source (S) to a destination (D). Unlike existing approaches which rely on the use of a multi-antenna full-duplex radio as the monitor, in our protocol, M1 and M2 are both single-antenna nodes operating in half-duplex mode, which alternately perform proactive eavesdropping and jamming to mimic the behavior of a full-duplex monitor. Within any time slot, M1 sends a jamming signal to deteriorate the signal reception at D and M2 eavesdrops on the transmission from S. During the next slot, M1 overhears the signal sent from S, and M2 forwards its received signal during the previous slot to realize jamming. In this manner, the jamming signal received at M1 can be perfectly removed after self-interference cancellation, and the signals from S during the two consecutive slots are jointly decoded with high reliability, thus enabling successful surveillance. On the other hand, the detection performance at D is heavily degraded due to the injection of the jamming signal, thereby preventing information leakage from S to D. The performance of the proposed protocol is analyzed in terms of the eavesdropping non-outage probability, the surveillance success probability, as well as the symbol error probability. Theoretical analysis and simulation results demonstrate the superiority of our design compared to competing solutions in the literature.
Feasibility Study of The IoT-Connectivity Deployment for AMI Service: A Case Study in Surabaya City

The manual reading of electricity, water, gas, and fuel tank meters requires increased fieldwork efficiency and improved measurement accuracy and monitoring related to service users and reducing fraud potential. The implementation of measurement services needs to be supported by communication technology, which has low cost and reliability characteristics.AMI (Advanced Meter Infrastructure) is an integrated smart meter system, communication network, and data management system that enables two-way communication between utilities and customers. With AMI’s application, it is expected to increase efficiency in monitoring and detecting leaks to overcome losses. IoT is a concept where particular objects can transfer data over the network without requiring human-to-human or human-to-computer interaction. There is technology standardization, namely LPWAN.Planning is done by comparing LoRaWAN, Sigfox, and NB-IoT with the main topic of discussion regarding network connectivity for AMI service needs. Based on the planning results, LoRaWAN requires at least 31 gateways with an average signal level of -83.11 dBm, an average throughput of 21.88 kbps, and an average SNR -0.13 dB. Sigfox requires at least 32 gateways with an intermediate signal level of -81.28 dBm, an average throughput of 0.6 kbps, and an average SNR of 14.79 dB. NB-IoT requires at least 31 gateways with an average signal level of -57.38 dBm, an average throughput of 220.36 kbps, and an average SNR of 8.87 dB.
Model Predictive Torque Control for Multilevel Inverter fed Induction Machines Using Sorting Networks

Model Predictive Control is a promising technique for electric drive control, as it enables optimization for multiple parameters and offers reliable operation with non-linear systems. For induction machine drives it can be realized using separate cost functions for the torque and the stator flux. Although this eliminates the problem of calculating any weighting factor, the selection of the final voltage vector requires an additional sorting algorithm. By increasing the number of voltage levels or the prediction horizon, the sorting algorithm becomes more and more time-intensive, which can severely impair the performance of the control algorithm. This paper introduces a novel hybrid sorting algorithm consisting of two sorting networks and a merging step. As a case study, the described control method is applied for an induction machine with high rated frequency fed by a three-level inverter, while also discussing the implementation issues. Experimental results verify the operation of the devised sorting algorithm.
Deep Neural Network-Based Symbol Detection for Highly Dynamic Channels

In extreme communication environments, a highly dynamic channel (HDC) often arises with quite challenging fast time-varying and nonstationary characteristics. Different from existing studies, in this work, we investigate the most intractable HDC case when the coherence time of the channel is smaller than the symbol period. We propose a deep neural network (DNN)-based symbol detector using the long short-term memory (LSTM) neural network. Particularly, the sampling sequence of the received signal per symbol is used as the input data of each LSTM unit, which can take advantage of all received information and thus achieve better performance. Furthermore, a preprocessing unit using the basis expansion model (BEM) is designed to dramatically reduce the number of parameters while training the neural network, and the BEM-DNN-based detector achieves almost the same performance as the DNNbased detector. Finally, simulation results are achieved using the highly dynamic plasma sheath channel (HDPSC) data measured from realistic shock tube experiments. The results show that the proposed DNN-based method outperforms conventional methods and requires no prior channel estimation or knowledge of channel models.
A Joint SLNR-MMSE-based Adaptive Secure Transmission Technique for Broadband IoT Systems

Signal-to-leakage-and-noise ratio (SLNR)-based transmit precoding techniques are preferable options to perform secure transmission in a low to moderate signal-to-noise ratio (SNR) regime. The conventional SLNR-based techniques assume that a legitimate receiver can estimate and equalize its precoded virtual channels often exhibiting frequency selective property. However, in Internet-of-Things (IoT) systems, lowcost terminals do not always have such complex receivers. We hence propose a new secure transmission scheme for future broadband IoT systems, where the joint SLNR-minimum mean square error (MMSE)-based precoding is performed so that a legitimate terminal assuming a linear complexity receives secret information successfully. Moreover, the proposed scheme adaptively determines transmission rates according to ergodic capacities. Simulation results verify that the proposed method improves the transmission rates significantly while preventing active eavesdroppers from correct receiving.
Security Testing of Unmanned Flight System

The rapid development of intelligent information systems and widely used unmanned systems will change the rules and patterns of warfare once again. Security concerns lie behind the development and widespread use of technology. Vulnerabilities in the software and hardware of unmanned flight system and malicious attacks from outside will greatly threaten the security of the system, such as being induced and hijacked by external attacks in various ways. This paper will carry out the security test of GPS spoofing and traffic hijacking for the industrial UAV firstly, and put forward corresponding improvement suggestions. In future work, we will explore more complex unmanned systems security threats, and do in-depth research on the security of unmanned systems.
MSCAN: Multi-scale Channel Attention for Fundus Retinal Vessel Segmentation

Retinal vessel segmentation is an essential step for early diagnosis of eye-related diseases such as diabetes and hypertension. However, due to the extremely complex morphological of vessels and the influence of lesion areas, how to segment retinal vessels especially the tiny capillary accurately and efficiently is of great significance. The previous methods have poor generalization performance in the segmentation of vessels with lesions and they are not sensitive to distinguish vessels with similar texture structures. So we propose a Multiscale Channel Attention Network based on the encoder-decoder architecture for fundus retinal vessel segmentation. Firstly, the redesigned encoder extracts the multi-scale structure information of retinal vessels by fusing multi-scale features, which weakens the influence of complex morphology on segmentation performance. Secondly, the channel attention module is fused in the decoding part to enhance the modeling ability of the network to the vessel area and reduce the interference of the lesion area. We test our approach on two public datasets, the Digital Retinal Images for Vessel Extraction (DRIVE) and the Child Heart and Health Study (CHASE-DB1) dataset. The results show that our method improves upon previous proposed methods for the segmentation of fundus retinal vessel especially the capillaries with lesion area.
Ternary Limited-Weight Codes and Quaternary Transition-Signaling for Low-Power Bus Encoding

This paper presents a new bus encoding scheme by ternary limited-weight codes (ternary LWC) and quaternary transition-signaling for low-power, high-performance data transmission. In this technique, digit ‘0’ indicates no transition, whereas ‘1’ and ‘2’ indicate two different types of transitions within a clock cycle. The number of ternary LWC code words as well as their length and weight affect the overall performance of the bus system. On the one hand, it is preferred to have fewer ‘1's and ‘2's in order to decrease the switching activity factor. On the other hand, shorter code words reduce the amount of redundancy. The mathematical analyses reveal that 64 4-digit ternary LWC code words with the maximum weight of three is the optimal solution, considering other criteria. Furthermore, our estimations show promising results, including 22.4% and 50.3% average reductions in switching activity and voltage swing, respectively. Therefore, the proposed bus encoding technique can reduce dynamic power up to 80.8%. In addition, it increases data transmission rate by the factor of 1.5.
Adversity-based Social Circles Inference via Context-Aware Mobility

The ubiquity of mobile devices use has generated huge volumes of location-aware contextual data, providing opportunities enriching various location-based social network (LBSN) applications – e.g., trip recommendation, ride-sharing allocation and taxi demand prediction etc. Trajectory-based social circle inference (TSCI), which aims at inferring the social relationships among users based on the human mobility data, has received great attention in recent years due to its importance in many LBSN applications. However, existing solutions suffer from three key challenges, including (1) lack of modeling contextual feature in user check-ins; (2) cannot capture the structural information in user motion patterns; (3) and fail to consider the underlying mobility distribution. In this paper, we propose a novel framework ASCI-CAM (Adversity-based Social Circles Inference via Context-Aware Mobility) to address the above challenges. ASCI-CAM is a graph-based model taking into account the contextual information associated with check-ins which, combined with an attentive auto-encoder, allows for semantic trajectory representation. We regularize the learned trajectory embedding with an adversarial learning procedure, which allows us to better understand the user mobility patterns and personalized trajectory distribution. Our extensive experiments on real-world mobility datasets demonstrate that our model achieves significant improvement over the state-of-the-art baselines.
ETSI Multi-Access Edge Computing for Dynamic Adaptive Streaming in Information Centric Networks

Using Information Centric Networks (ICNs) instead of IP networks will improve Quality of Experience (QoE) by enabling efficient and scalable Content Delivery Networks (CDNs). However, Information Centric based CDNs still face many challenges. The rate adaptation algorithms used in IP based CDNs can for instance lead to inaccurate estimations of Round-Trip Times (RTTs) in ICN settings. Furthermore, the network storage feature of ICN can introduce major oscillations in adaptive streaming. In this paper, we use the ETSI Multi Access Edge Computing (MEC) to tackle these issues. An overall system view (which includes the MEC server) and a novel rate adaptation algorithm are proposed. The proposed rate adaptation algorithm is validated with simulations and the results show that it significantly improves users’ QoE due to higher accuracy and stability in rate estimation.
Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

The vehicle suspension control unit serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. To effectively realize control strategies, it is essential to foreknowledge the road profile and the suspension system’s internal state variables. While the mentioned variables are not practically measurable using commercial sensors, it is necessary to estimate the desired variables by utilizing observer systems. Conventional means have mainly employed model-based approaches, in which model uncertainties and high computational cost pose limitations for practical implementation. Herein, we propose a data-driven deep learning method as an alternative because no explicit physical modeling is required, and evaluation is computationally cheap. We first propose a novel encoder-decoder structured recurrent neural network model with a two-phase attention mechanism to estimate the unknown road profile and four state variables of the vehicle suspension system. Based on a simulated data set, we assess the proposed model’s qualitative and quantitative results and demonstrate that our model can achieve highly accurate estimation results with fast computation time. Besides, we validate our black-box model’s reliability by comparing its interpretation with the suspension system’s actual physical characteristics. Furthermore, we compare the proposed model with existing baseline methods, and the results show that our proposed deep learning model significantly outperforms the baseline. Lastly, we experiment with our network’s autoregressive capability and demonstrate the feasibility of estimating a sequence of future values, which has not been presented in previous works.
Incremental Database Based on Distributed Ledger Technology for IDSs

Intrusion Detection Systems (IDS) is an important technology for cyber security, as it can mitigate both inner and outer threats in networks. However, a critical problem in IDSs is that the detection capacity is gradually decaying with the emergence of unknown attacks. To constantly retrain IDSs with a more extensive database is critical to make IDSs adaptive with the ever-changing network environment, but the security institutes usually lack the motivation to persistently update and maintain the database for public. Thus, in this paper, a blockchain-based database (bc-DB) is proposed, which is multilaterally maintained by the security institutes and universities using Data Coins (DCoins) as the incentives. In addition, a Lifetime Learning IDS (LL-IDS) is further designed as the supplement of the bc-DB for common IDS users. After being retrained by the latest bc-DB, the LL-IDS can detect the newly discovered attacks while uploading the suspect network packets to the database. Simulation experiments show that the proposed LL-IDS with the bc-DB are secure and effectiveness in attacks detection.
Does Blockage Correlation Matter in the Performance of mmWave Cellular Networks?

Past literature on the analysis of mmWave cellular network’s performance in the presence of random blockages has assumed independent blocking of each link. In reality, a large blockage can block multiple links resulting in a correlation in blocking of these links. This paper focuses on deriving the impact of this correlation on the SINR performance of a mmWave cellular system. In particular, we consider the conditional probability of having line-of-sight (LoS) and non-LoS (NLoS) interferers conditioned on serving link being LOS or NLoS. We derive the distribution of the serving base-station (BS) distance from a typical user. We characterize the sum interference at the user by considering the correlation between blockage of serving and interfering links and derive the SINR coverage probability. Via numerical simulations, we show that including blockage correlation in the analysis is required in accurate characterization of the system performance, in particular when the blockages tend to be large.
Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders

Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby ZigBee devices, LTE-U UEs or even microwave ovens could emit RF signals over the frequency partially overlapping with the WiFi band. To combat such CTI, existing solutions have proposed several signal processing algorithms for error recovery or interference cancellation. However, most of those approaches need knowledge about the physical layer structure of CTI, which cannot be applied to denoise the unstructured interference from unknown electronics, e.g., microwave ovens. To overcome this deficiency, we present a CTI suppression framework based on Denoising AutoEncoder (DAE). The DAE is developed to learn the patterns of interference with unknown structures and passively suppress CTI with the zero cost. To avoid the expansive human cost of data collection, we propose a systematic way to synthesize corrupted WiFi signals for model training. Our experiments verify that the model trained with synthesized data can effectively reconstruct real corrupted WiFi signals and improve the decoding success probability.
Preparation and Characterization of KNbO3 Ceramics

The processing and characterization of electrical properties of potassium niobate ceramic, KNbO3 (KN) have been studied. The difficulty of obtaining dense samples by conventional methods limits the knowledge on electrical properties of this material. In this paper, a complete route for processing of KN with density over 94% is described. Piezoelectric and dielectric data are presented. It is observed that the major problem concerning the density is related to incorrect stoichiometry, which is believed to be a more critical issue for this system compared with other electro-ceramics.