Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Resource Allocation for Energy Harvesting-Based Device-to-Device Communication

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme.
Measuring and Testing the Scalability of Cloud-based Software Services

Performance and scalability testing and measurements of cloud-based software services are critically important in the context of rapid growth of cloud computing and supporting the delivery of these services. Cloud-based software services performance aspects are interrelated, both elasticity and efficiency are depending on the delivery of a sufficient level of scalability performance. In this work, we focused on testing and measuring the scalability of cloud-based software services in technical terms. This paper uses technical scalability metrics that address both volume and quality scaling, that inspired by earlier technical metrics of elasticity. We show how our technical scalability metrics can be integrated into an earlier utility oriented metric of scalability. We demonstrate the application of the metrics using a practical example and discuss the importance of them.
Hybrid Traffic Identification Based on Sequential Pattern Mining

A novel identification method based on sequential pattern mining is proposed to detect different network traffic in encrypted hybrid data. After dimensionality reduction for traffic series, the data are cut into short sequences. By sequential pattern mining and pruning, the characteristics sequential sets, which are extracted from transaction database comprised of these sequences, are used to calculated and analyzed the identification results. With removing each traffic's characteristic away from the hybrid data, the final results are verified and concluded. The experiments show that the recall and precision of identification results are more than 60%.
About Strong String Stability of a Vehicle Chain with Time-Headway Control

This paper deals with the problem of string stability in a chain of acceleration-controlled vehicles, i.e. how input disturbances affect the distributed system for very long chains. There exist variants of string stability, like avoiding that a local disturbance gets amplified along the chain, or more strongly ensuring that the output vector's $p$-norm remains bounded for any bounded vector of input disturbances independently of the string length. They are all impossible to achieve with any linear controller if the vehicles only use relative information of few vehicles in front. Previous work has shown that adding absolute velocity into the controller, allows to at least avoid amplification of a local disturbance. In this paper, we consider the stronger definitions of string stability, under this same relaxation of using absolute velocity in the controller. We prove that the influence from input vector to output vector cannot be bounded independently of chain length in the most popular 2- norm sense, with any bounded stabilizing linear controller; while a proportional derivative (PD) controller can guarantee it in the practically relevant $\infty$-norm sense. Moreover, we identify the disturbance acting on the leader as the main issue for string stability.
Hate Speech Detection on Indonesian Instagram Comments using FastText Approach

Instagram is one of social media hype, potentially used to spread hatred. The objective of our study is to conduct hate speech detection on Instagram comments for Indonesian language. We used FastText as the classifier and word representation. The experiment results showed that FastText is better than Random Forest Decision Tree and Logistic Regression. The highest result achieved when FastText is combined with bigram feature with F-measure of 65.7%.
Efficient and Provably Secure Distributed Signing Protocol for Mobile Devices in Wireless Networks

Rapid advances in wireless communications, hardware/software, and Internet technologies have contributed to an exponential growth in the number of users accessing the Internet using mobile, wearable or other Internet of Things devices. Identity-based signature schemes have been widely applied to enforce user authorization and validate user messages in mobile wireless networks. However, the user’s private key used to generate signatures is prone to leakage because the key is being stored on the mobile device. Several (t, n) threshold secret sharing schemes have been proposed to address the issue. One limitation is that the private keys in most of those schemes have to be recovered on a single device when generating signatures, so that the user who holds the device can sign any message without the participation of other users. To address the recovery limitation, we propose an efficient and secure two-party distributed signing protocol for the identity-based signature scheme in the IEEE P1363 Standard, where two users can generate a valid signature without recovering the whole private key. We formally prove its security under a nonstandard assumption. We also implemented our proposed protocol using the MIRACL Cryptographic software development kit. The experimental results obtained show that the time it takes for two general Android devices to generate a signature is about 709.53 ms.
A Modified Round-Robin Load Balancing Algorithm Based on Content of Request

Cluster web servers emerged as a reliable and leading resource in internet infrastructure with the growing demands for web-based applications. The key point is to increase utilization of cluster web servers through an effective and fair load balancing strategy. In this paper, we proposed a new load balancing algorithm, which considers the load and performance information of nodes and the type of request to solve the problem of uneven distribution of load in cluster servers. Compared the common Round-Robin algorithm, the discussed method achieves a relatively optimum load balancing result.
Moving Kinect-Based Gait Analysis with Increased Range

There are several systems that use one or several Kinect sensors for human gait analysis, particularly for diagnosis of patients. However, due to the limited depth sensing range of the Kinect—a sensor manufactured for video gaming—the depth measurement accuracy reduces with distance from the Kinect. In addition, self-occlusion of the subject limits the accuracy and utility of such systems. We overcome these limitations by using a two-Kinect gait analysis system and mechanically moving the Kinects in synchronization with the test subject and each other. This increases the practical measurement range of the Kinect-based system whilst maintaining the measurement accuracy. Results of the comparison of knee flexion, step length, and stride length with a software based method show that our moving Kinect system can accurately analyse these gait parameters.
Credit Card Fraud Detection Using Capsule Network

Credit card is now popular in daily life. Meanwhile, credit card fraud events occur more frequently, which result in massive financial losses. There are a number of fraud detection methods, but they do not deeply mine features of customer's transaction behavior so that their detection effectiveness is not too desirable. This paper focuses on two aspects of feature mining. Firstly, the features of credit card transactions are expanded in time dimension to characterize the distinct payment habits of legal users and criminals. Secondly, Capsule Network (CapsNet) is adopted to further dig some deep features on the base of the expanded features, and then a fraud detection model is trained to identify if a transaction is legal or fraud. Through experiments on a real transaction dataset, we demonstrate that the time dimension extension can improve the performance of fraud detection, and then CapsNet is further illustrated to be more advantageous in fraud detection compared with other models.
TDP: Temporal Dynamic Pooling — A New Method for Temporal Action Localization

Temporal action localization is a very important yet challenging task, since video in real applications is usually long, unconstrained and untrimmed, containing multiple action instances plus video content of complex background scenes. The task recognizes not only the action class but also the start frame and the end frame of each action instance. To address this issue, we propose a novel temporal dynamic pooling (TDP) network to determine the temporal boundaries of an action instance and to select key frames of a video through a sequential prediction of importance score, which is obtained by score prediction network realized based on residual learning. By pooling only a few key frames containing discriminative information, we could obtain the salient pooled vector for action recognition. We conduct experiments on a challenging temporal action localization dataset, THUMOS 2014. When setting Intersection-over-Union (IoU) threshold to 0.5 in evaluation, we significantly achieve superior performances compared with the most state-of-the-art methods by increasing mAP from 22.7% to 29.8%. Meanwhile, our TDP network demonstrates a very high efficiency with the ability to process 550 fps on a single CPU.
Load Sharing Techniques for Server Selection in Network Virtualization

Network Virtualization enables service providers to share the same physical network infrastructure in order to create virtual networks. Service providers who own their physical infrastructure tend to maximize the revenue and resource utilization while respecting the quality of service's constraints. For this, they choose Load Sharing techniques for server selection in the context of Network Virtualization where clients send their requests and receive responses without knowing which server has responded and which path is used. Equally Distributed, Round Robin, Weighted Round Robin and Equally Distributed combined with Round Robin are proposed as Load Sharing techniques on multiple servers. In this paper, we evaluate the performance of these techniques as a function of traffic intensity in terms of latency, jitter, packet loss ratio and connection to server failure. Then, we compare these performances with those of Traffic Concentration on the closest server which is adopted by service providers who don't own their infrastructure and want to lease it. Through simulation, we notice that both Traffic Concentration and Weighted Round Robin reduce the latency when the network is slightly loaded and at medium and heavy traffics respectively. However, Equally Distributed and Round Robin provide the best performance in terms of packet loss ratio. These two simulation results are proved by an analytical model based on the M/D/1/K waiting queue. Also, the combination of Equally Distributed with Round Robin minimizes the jitter values. Finally, Equally Distributed and Round Robin decrease the probability of connection failure.
Word Sense Disambiguation using Skip Gram Model to Create a Historical Dictionary for Arabic

The evolution of the Arabic language from antiquity to the present days has given birth to several linguistic registers ascribed to the great periods of the history of the Arabic language. They can be classified as: Old Arabic, Classical Arabic and Modern Standard Arabic. In this work, we propose a method that aims to disambiguate words in Modern Standard Arabic. This method consists of measuring the semantic relation between the context of use of the ambiguous word and its sense definitions. Within the context of creating a historical dictionary for Arabic, and to disambiguate a word, we need to take into consideration the historical period in which the word appeared. This method disambiguates Arabic words takes into account that a word may have an old meaning but appears in a modern document.
A Pervasive Integration Platform of Low-Cost MEMS Sensors and Wireless Signals for Indoor Localization

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).
Pulsed-Laser Induced Single-Event Transients in InGaAs FinFETs on Bulk Silicon Substrates

The pulsed-laser single-event transient response of InGaAs FinFETs on bulk silicon substrates is investigated. Charge collection due to a source–drain shunt effect and drain-to-substrate junction charge collection contribute to the observed transients. The transient response of these silicon substrate devices is compared to that of InGaAs FinFETs on semi-insulating substrates. Faster transients with reduced peak currents and peak widths are observed on the silicon substrate devices. Simulations show hole collection by the silicon substrate. This reduces the amount of source-barrier lowering and bipolar-amplification relative to other III–V devices. Moreover, the reduced hole lifetime in the GaAs buffer layer also contributes to the relative reduction of the bipolar amplification in these devices.
Memory and Time Efficient 3D Neuron Morphology Tracing in Large-Scale Images

3D reconstruction of neuronal morphology is crucial to solving neuron-related problems in neuroscience as it is a key technique for investigating the connectivity and functionality of the neuron system. Many methods have been proposed to improve the accuracy of digital neuron reconstruction. However, the large amount of computer memory and computation time they require to process the large-scale images have posed a new challenge for us. To solve this problem, we introduce a novel Memory (and Time) Efficient Image Tracing (MEIT) framework. Evaluated on the Gold dataset, our proposed method achieves better or competitive performance compared to state-of-the-art neuron tracing methods in most cases while requiring less memory and time.
Ensuring Data Governace and Enhancing Data Security in a Private Cloud Environment

this paper proposes a new system of ensuring data governance and enhancing data security in private cloud environment. Security and Privacy concerns have been the major drawbacks in cloud computing environments. Security, reliability and privacy enhance users' flexibility in file accessibility. As easy-to-use cloud services penetrate personal computing, users expect to enjoy the same conveniences they have at home in the office. This paper proposes a workable security technique in a cloud computing environment that delivers Infrastructure as a Service (SaaS), and deployed in a Private Cloud to protect data and information stored and shared from potential intrusion, threats, theft, virus, and agents. This paper proposes a system that focuses on data encryption and CAPTCHA methods as regards to security solutions. This paper adopted the Structured Systems Analysis and Design Method (SSADM) for the development, and implementation of the new system. Java play framework is adopted for the implementation of the web based system. The database structure is handled be employing MySQL. The system was implemented and tested using the various test cases which were successful. The test result shows that the proposed method is efficient in data confidentiality, integrity and availability.
A Low-Profile Wideband Aperture-Fed Microstrip Antenna With Improved Radiation Patterns

A low-profile wideband aperture-fed microstrip patch antenna (MPA) under radiation of TM<sub>12</sub> and TM<sub>30</sub> modes is proposed with improved radiation patterns. Initially, the radiation characteristic of a rectangular MPA operating in TM<sub>30</sub> mode is theoretically investigated. The results demonstrate that its E-plane sidelobe could be gradually reduced by decreasing the resonant frequency of TM<sub>30</sub> mode (<inline-formula> <tex-math notation="LaTeX">${f} _{30}$ </tex-math></inline-formula>). As such, a pair of parallel slots is etched out on the radiating patch so as to significantly decrease the <inline-formula> <tex-math notation="LaTeX">${f} _{30}$ </tex-math></inline-formula>. After that, the MPA under an operation of TM<sub>12</sub> mode is further studied. It illustrates that its radiation pattern has the maximum off broadside direction and can be transformed into the broadside radiation by enlarging the length of the parallel slots. In addition, the width of the radiating patch is progressively enlarged in order to reallocate the dual modes in proximity to each other. With these arrangements, a wide bandwidth with improved radiation characteristics is obtained for the antenna under dual resonance. Finally, a prototype aperture-coupled MPA is fabricated and measured. The simulated and measured results agree well with each other, thus demonstrating that its impedance bandwidth is dramatically extended to about 14.8% with a low-profile property of about 0.048 free-space wavelength in thickness. Besides, the antenna has achieved a stable gain varied from 7.0 to 9.9 dBi over the operating band. In particular, the E-plane sidelobe level of the proposed antenna has been successfully suppressed.
Global maximum power point tracking of a PV system MPPT control under partial shading

The aim of this paper is to control a photovoltaic (PV) generation system under partial shading conditions connected to the grid. The PV generator is connected to the grid through a Grid Side Converter (GSC). In fact, the Maximum Power Point Tracking (MPPT) is achieved by using the intelligent algorithm for finding the Global Maximum Power Point (GMPP). This algorithm combines a traditional MPPT technique based on Perturb and Observe (P& O) with the Artificial Neural Network (ANN) technique. A hybrid MPPT algorithm is based on the ANN and used to predict the global maximum power point region. Consequently, the (perturb and observe) technique searches for the Maximum Power Point (MPP) in the predicted region. The GSC is controlled in system a way to guarantee a smooth DC voltage with a unity power factor at the grid side. Simulation results show the effectiveness of the control strategies applied to the studied system.
One Software Solution for Safe Data Transmission via Broadr-Reach Interface

In order to see how autonomous cars take over our streets, one of the most important challenges for engineers are transmission, storage and manipulation of data. From the safety standpoint, data security in car networks has to be on much higher level in the automotive industry than it is in the consumer industry. It is necessary to provide additional safety barriers in order to secure data from malicious manipulation. This paper presents the design and implementation of safe data transmission from embedded board to the PC. Data encryption is necessary in order to secure safe communication. Data transmission is done via the BroadR-Reach interface and IPv6 protocol.
Adaptable iISS Small-Gain Formulation and its Application to Observer-Based Output Feedback Design

For a popular class of nonlinear systems, an observer can be constructed by rendering estimation error dynamics linear in unmeasured state variables. However, measurement disturbance violates the structure of the error dynamics and brings in undesirable terms in which the estimation error and the plant state are often coupled and multiplied by the measurement disturbance. This is why local analysis and semi-global design have been performed in many cases. This paper introduces new flexibility into the iISS small-gain framework to accommodate such terms. It is demonstrated how the flexible framework enables global analysis of observer-based output feedback control systems to achieve convergence and boundedness in the presence of measurement disturbance.
Using Browser Cookies for Event Monitoring and User Verification of an Account

In a server-based system where end users use their own account to get connection to the server and possibly other accounts, encapsulation and protection of data of the accounts is a big factor. The administrator must be aware of data security of an account, so that it is not passed to any third party. Another job of administrator is to monitor the identity of the user of an account, which unfortunately is not taken care of most of the times. A third job that an administrator should take care of is monitoring the events in an account - such as devices used to open the website or whether more than one device is using a single account at the same time. In this paper, we have given a concept of using browser cookies effectively in order to monitor the identity of user and the events in an account. In this context we have managed to give solution for three different problems - a) Possibility and technology approach of how to control who is using login/password system; b) The login/password information is not passed to other person for use without the notice of administrator team; c) Ability to identify simultaneous use of login/password. We have used cookies for 2 different parameters - a) Protocol id: A unique id for each and every protocol (i.e., browser + device system); and b) Client id: Varies whenever the user changes his protocol, even if he opens the same account. Our system has efficiency to give solutions of all the said problems.