Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Pairwise Key Establishments for Flying Ad hoc Networks

This paper proposes a pairwise key establishment scheme which is immune to compromise of nodes in flying ad hoc networks. At network boot-up time, each node establishes initial keys with at most three-hop distant nodes. Then, any two nodes establishing a pairwise key get a partial key from nodes on the route between them, and generate a pairwise key by combining the partial keys. During the obtainment of partial keys, the nodes on the route employ the initial keys to send their partial key to both nodes securely. If a node one the route has no initial key for its partial key transmission, it can obtain such a key from its already known neighbors due to the small-world phenomenon.
Sidechains: Highlights and Challenges

The revolutionary concept of blockchain and its applications are spectacularly drawing more and more attention in today’s world. The crypto-currency platforms have been on the forefront of implementing blockchain technology. However, the capabilities of blockchains are not limited to monetary transactions. Introduction of smart contracts allowed development of various distributed applications. As the need for blockchains continues to grow, there is also a strong demand for larger, more scalable and more flexible "multipurpose" chains. Conventional blockchains (even the ones supporting smart contracts) cannot comprehensively cover these requirements. To address these issues, the concept of sidechains has been introduced. This paper provides a brief overview of sidechains, explains how they work, discusses their benefits and drawbacks; moreover, describes how they can be built using relevant software and tools.
Big data analytics in telecommunications: literature review and architecture recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics ( BDA ) architectures and infrastructures to telecommunications ( telecom ) industrial sector. Telecom companies are dealing with terabytes to petabytes of data on a daily basis. IoT applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts ( POC ) on a severely limited BDA technology stack ( as compared to the available technology stack ) , i.e., we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation ( called LambdaTel ) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines. We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe LambdaTel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.
Examining user's Trust Towards Healthcare Information on Facebook: Impacts of Information Type, Disease Type, and Social Consensus

The objective of this paper is to examine the impacts of information type, disease type, and social consensus on the user's trust towards health information on Facebook. Within a 2x2x2 between-subjects experimental setting, participants reviewed health information in each pertinent experimental condition and provided their perceptions of trust. Findings suggest that the impact of each factor depends on the level of the other factors. There were two-way interaction effects between information type and disease type, disease type and social consensus, and social consensus and information type. Also, there was a three-way interaction effect among information type, disease type, and social consensus. The research demonstrates that consumers of health information on Facebook make the trust decision based on their health care needs, degree of involvement, and how other people reacts to the information. This paper extends the application of dual cognition theory to the examination of user trust towards health information on Facebook.
Stock Market Forecasting Using Machine Learning Models

Stock market forecasting is a challenging problem. In order to cope with this problem, various techniques and methods have been proposed. In this study, the stock close values are tried to be forecasted as monthly and weekly. For this purpose, values of two traded stocks (Google, Amazon) are predicted using with models being processed. In addition, stock data were taken from two different indexes (Dow Jones Industrial Average (DJIA) and S&P 500) for a realistic assumption. Stock market data includes long term dependencies. For this reason, classical Recurrent Neural Networks (RNN)-based models could not effectively work for in such data. Therefore, Long Short-Term Memory (LSTM) Networks and Gated Recurrent Unit (GRU) based models were developed and their efficiencies were observed in this study. These models were also compared with traditional machine learning approaches and the obtained gains were calculated.With LSTM, the close prices of datasets were predicted more consistently than the other models. In particular, the LSTM model is more successful than the GRU model with similar error metrics in dealing with fluctuations in datasets. Besides, linear machine learning models are well behind the deep learning-based models in weekly prediction of datasets.
A Semantic Search Model Using Word Embedding, POS Tagging, and Named Entity Recognition

We are in trouble to remember the title of a movie. Even though we remember some scenes, it is difficult to recall words in the movie title. We also try to find the movie title with some web search systems, but in most cases, it is unsatisfactory. It is because most search systems available now do not work well for multiple word description. To overcome this problem, this study considers to use word embeddings, part-of-speech (POS) tagging, and named entity recognition (NER). Word embedding models have shown high potential in capturing a precise syntactic and semantic word relationships. POS tagging is a traditional technique to analyze a sentence to put each word a POS tag. NER model also provides a technique to analyze a sentence with named entity tag. This study evaluates the proposed system by conducting an experiment to search a movie title with several descriptive words for a specific movie. As an initial step, ontologies of 30 movies are developed. Then, it is asked to provide three, five, and seven words about any of the 30 movies. Finally, with these three, five, and seven words, it is tested if the proposed system can find the correct movie title. A total of 50 sample tests is repeated and the result shows that the hit ratio is about 50 to 80%. This hit ratio is, in fact, a very high one compared to those of Google and Naver in Korea.
SWORD: Towards a Soft and Open Radio Design for Rapid Development, Profiling, Validation and Testing

The vision, as we move to future wireless communication systems, embraces diverse qualities targeting significant enhancements from the spectrum, to user experience. Newly-defined air-interface features, such as large number of base station antennas and computationally complex physical layer approaches come with a non-trivial development effort, especially when scalability and flexibility need to be factored in. In addition, testing those features without commercial, off-the-shelf equipment has a high deployment, operational and maintenance cost. On one hand, industry-hardened solutions are inaccessible to the research community due to restrictive legal and financial licensing. On the other hand, research-grade real-time solutions are either lacking versatility, modularity and a complete protocol stack, or, for those that are full-stack and modular, only the most elementary transmission modes are on offer (e.g., very low number of base station antennas). Aiming to address these shortcomings towards an ideal research platform, this paper presents SWORD, a SoftWare Open Radio Design that is flexible, open for research, low-cost, scalable and software-driven, able to support advanced large and massive Multiple-Input Multiple-Output (MIMO) approaches. Starting with just a single-input single-output air-interface and commercial off-the-shelf equipment, we create a software-intensive baseband platform that, together with an acceleration/profiling framework, can serve as a research-grade base station for exploring advancements towards future wireless systems and beyond.
Design and Analysis of a Compliant Micro-gripper with LBL Type Displacement Amplifier

This paper presents the design and analysis of a compliant piezo-driven micro-gripper with large tip displacement supporting micro scale manipulations. An integrated lever-bridge-lever (LBL) type amplification mechanism is proposed to achieve a large tip displacement, which incorporates the merits of lever-type and bridge-type mechanism. An analytical model of the proposed micro-gripper is established to predict the output displacement based on elastic beam theory, which is also capable of estimating the gripping force. Numerical simulations based on Finite element analysis (FEA) are conducted to validate the performance of the proposed micro-gripper and the effectiveness of the modeling method. The results demonstrate that the proposed micro-gripper has a large gripping range for grasping micro-objects with different sizes and its characteristics is accurately predicted by the model, which has the tip displacement of 280 μm corresponding to the input stroke of 16 μm for a single jaw.
AFM Study of Temperature and pH Effects on BSA Structure and Adhesion

With the wide application of biomacromolecular materials such as proteins, it is increasingly important to explore the influence of external environment on protein structure and properties. In this paper, the effects of temperature and pH on the structure and adhesion of bovine serum albumin (BSA) were studied by atomic force microscopy (AFM). The experimental results showed that the spatial structure of protein changed gradually with the increase of temperature, which was manifested by a decrease in the height of the protein molecule. The diversification of BSA adhesion with temperature was measured. And the adhesion between BSA and silicon tips decreased when the temperature increased. By varying the pH of the environment, it was found that the protein molecule height reduced under strong acidic or alkaline conditions, accompanied by the decrease of protein adhesion. Temperature and pH are revealed to be effective factors in transforming the structure of the protein, besides affecting the mechanical properties of the protein.
On Palm Vein as a Contactless Identification Technology

Palm vein biometrics has received a lot of attention in recent years. This technology offers accuracy, robustness and is contactless, which makes it a promising option for clinical applications. It uses palm vascular patterns of individuals as identification metric to match the identity. As per observations, the vein structure beneath the palm surface has a more complicated pattern as compared to the back of the palm, the fingers or any other easily accessible vein networks in the body. Thus, the palm vein can provide more features to be used for authentication. This paper highlights the performance evaluation of various approaches adopting this authentication technique. The performance evaluation is based on standard metrics such as equal error rate and false acceptance rate. We compare different techniques based on existing published research and summarize their advantages and disadvantages. We finally suggest the use of deep learning algorithms in the decision-making process which promises to be most reliable for near future applications.
A Review of Incomplete Information Game about Games

The game research about games is divided into two aspects: complete information and incomplete information. With the maturity of complete information research, people pay more and more attention on the research of incomplete information. In this context, this paper summarizes the design method of incomplete information game system in the game, and expounds the common implementation strategies of incomplete information game system from the two perspectives of the opponent model and its own perspective.
An Extensible Access Control Architecture for Software Defined Networks based on X.812

The software-defined networking paradigm adds flexibility to network management as it allows the policy application in fined-grained flow level. However, the traditional definition of flow disregards user identification credentials. Thus, Identity Management in software-defined networking is a current challenge. In this paper, we propose an access control architecture for software-defined networking, based on ITU X.812 standard and implemented on AuthFlow authentication framework. The proposed architecture integrates AuthFlow with an attribute repository that maps network policies to user attributes. The proposal supports its integration with identity federation, and we evaluate it under a role-based access control model. The evaluated use case is a service differentiation policy according to the role of each user. The evaluation results demonstrate the correct application of the quality of service according to the role of the flow target user.
On Stability and Scalability Enhancement in WSN

Real-time applications integrated with WSN can be utilized in a hazardous area for long or short term, large or small scale, data monitoring or data collection. In such applications, stable network is required which will provide continuous services and less maintenance. So a higher stable clustering algorithm called Stability and Scalability Enhancement algorithm (SASE) is introduced. In SASE, probability and density conditions are used for clustering. In order to analyze the efficiency of the SASE algorithm, it is compared with the existing algorithms and the results show that SASE achieves 24% of higher stability compared to Mean Random PSO and 50% higher stable from other routing algorithms.
Polycrystalline In–Ga–O Thin-Film Transistors Coupled With a Nitrogen Doping Technique for High-Performance UV Detectors

In this article, thin-film transistors (TFTs) with a polycrystalline indium–gallium–oxide (poly IGO) active layer were fabricated for ultraviolet (UV) detection. To achieve better sensing performance, an <italic>in situ</italic> nitrogen doping (N-doping) technique was employed to passivate the excessive defect states in the bandgap. Under UV illumination, it is evident that the N-doped IGO TFT exhibited superior photoresponsivity and UV-to-visible rejection ratio (17.4 A/W and <inline-formula> <tex-math notation="LaTeX">${1.0}\,\,\times {10}^{{{6}}}$ </tex-math></inline-formula>) as compared to the undoped one (13.1 A/W and <inline-formula> <tex-math notation="LaTeX">${1.2}\times {10}^{{{5}}}$ </tex-math></inline-formula>). More importantly, a sharper responsivity cutoff at 340 nm (<inline-formula> <tex-math notation="LaTeX">${E}_{{\text {g}}}\sim ~3.65$ </tex-math></inline-formula> eV) was observed, suggesting that the N-doped phototransistor had better wavelength selectivity. Through X-ray photoelectron spectroscopy analysis, it was shown that N-doping reduced the subgap states and thus suppressed the visible light-induced ionization of oxygen vacancy. Furthermore, the nitrogen incorporation slightly reduced the effective bandgap, which enhanced the band-to-band electron transition and the UV responsivity.
Tibetan Location Name Recognition Based on BiLSTM-CRF Model

Tibetan location name recognition is an important research topic in Tibetan information processing. In this paper, we attempt to construct a neural network model based on BiLSTM-CRF framework for location name recognition. In the experiment, for the public dataset of Tibetan location name, the model obtains the context information of the current word in the BiLSTM layer, decodes the output of the BiLSTM layer in the CRF layer, and finally outputs the tag sequence. The experiment proves the validity of the framework and obtains good experimental results, and the results show that the model outperforms competitive the traditional method, and improved the F-measure without resorting to any language-specific knowledge or resources.
Trajectory Data Compression Using Hopfield Neural Network

Trajectory data can be used to predict traffic congestion and analyze crowd behavior patterns, which makes it more important in daily applications. For a long time, the volume of location data collected by various equipment will obtain an high speed increasing. Hence, it will be a enormous challenge to storage and transmit that big data. Due to the fact that traditional trajectory compression algorithms such as Douglas-Peucker algorithm are fast but not optimal algorithms. So, a kind of parallel algorithm utilizing Hopfield Neural Network is proposed in this paper. The proposed algorithm is an optimization algorithm with parallel computing capability, which satisfies fast processing to some extent. Finally, experience shows that the proposed algorithm can implement trajectory compression with fixed compression ratio and less accuracy loss compared with other existing algorithms.
PZT Based Multilayer Surface Acoustic Wave Device for High Frequency Applications

This paper presents a study on the integration of PZT thin film on SiO<inf>2</inf>/Si substrate, which offers a great deal of potential in high frequency applications. A finite element method (FEM) simulation analysis of the proposed PZT/SiO<inf>2</inf>/Si multilayer surface acoustic wave device is performed, and two propagation modes are studied i.e. the Rayleigh and Sezawa wave mode. The proposed structure is optimized by PZT (t<inf>PZT</inf>) and SiO<inf>2</inf> (t<inf>SiO2</inf>) layers thicknesses. The Rayleigh mode is optimized with k<sup>2</sup>=6.27% at t<inf>PZT</inf>/λ=0.4 and t<inf>SiO2</inf>/λ=0.025 and the Sezawa mode with k<sup>2</sup> =13.13% at t<inf>PZT</inf>/λ =0.2 and t<inf>SiO2</inf>/λ=0.025. The temperature coefficient of frequency of the Sezawa mode is 2.7 times lower than that of the Rayleigh mode. Which makes it a more suitable choice for temperature stable high frequency and high k<sup>2</sup> SAW devices.
A Compressive Sensing-Based Automatic Sleep-Stage Classification System With Radial Basis Function Neural Network

This study presents an automatic sleep-stage classification system based on utilizing compressive sensing (CS) for data reduction. The amount of electroencephalogram (EEG) signal data required for sleep-stage classification can be significantly reduced by applying CS at the cost of distortion in the reconstructed EEG signal. A neural network trained on features extracted from the reconstructed EEG signal was implemented as a classifier to avoid compromising the accuracy of classification in the presence of distortion in the reconstructed EEG signal. A radial basis function (RBF) neural network that uses a simple Manhattan distance function instead of complicated computation, such as vector multiplication matrix (VMM), was selected to reduce the hardware complexity. A classification method that utilizes information from the previous sleep stage based on the unique nature of human sleep was also presented and implemented on the proposed RBF classifier for further improvement of the classification accuracy. The classification system has been evaluated using EEG data from eight subjects in the Cyclic Alternating Pattern (CAP) sleep dataset. The measurement results showed that the classification system achieved high classification accuracy comparable to the previously reported sleep-stage classifiers that do not utilize signal compression.
Motion Prediction for Teleoperating Autonomous Vehicles using a PID Control Model

Teleoperating autonomous vehicles is challenging due to latency and bandwidth constraints. In order to increase operator safety and situation awareness, techniques similar to motion planning for control of autonomous cars in dynamic environments have been adapted for aerial vehicles in this study. An overview of a novel concept based on reconstruction of the environment, user handling, and predictive modeling will be given. The working principle of predictive motion for teleoperating vehicles is explained and key metrics are introduced to compare changes of model parameters. A proportional-integral-derivative (PID) control model has been developed and integrated into the concept. The concept has been evaluated based on flight simulations as well as with actual test flights. The sensitivity of the PID parameters and the impact of the correct estimation of the predicted latency were investigated. The concept has been successfully been demonstrated with a DJI M600 hexacopter. The analysis indicates a high sensitivity for the P-component and low sensitivity for I and D components for an accurate prediction. Latency analysis shows that underestimation of the real latency does not have as high an impact as overestimating it and that the model fits best for latencies below 250 ms. Furthermore, the implemented model lacks the prediction accuracy in the acceleration phase and a representative inertial model. The here presented model is a novel approach to handle the predicted motion of teleoperated vehicles and shows promising results in accuracy and parameter sensitivity.
Modeling of Amplitude-phase Coupling Effect of Mixing Electronic Attenuator

The amplitude-phase coupling (APC) effect of the mixing electronic attenuator (MEA) is the main factor that limits its application in microwave interference cancellation system. The APC effect will cause the MEA to have an attenuation-dependent phase. In this paper, a mathematical model of MEA taking into account the APC effect is established by means of RF circuit equivalence. Simulations and experiments are also carried out to analyze the APC effect. The simulation and experiment results both verify the correctness of the proposed model. It is interesting to find that the phase deviation of output signal decreases oppositely with the increase of the input signal power.
FTIR Analysis of Partial Discharge-initiated Polyimide Nanocomposites Degradation and Insulation Durability under High Frequency Ac Voltage Stress

For the sake of investigating the contribution of inorganic nanoparticles in reduction of partial discharge (PD) activity, and insulation durability, PI/TiO2 nanocomposite thin films were fabricated with weight percentage of 2 and 3 via in-situ polymerization. PD aging tests were carried out for both the pure and PI/TiO2 nanocomposite films to collect partial discharge data until the insulation breakdown occurred in order to analyze the nanoparticles impact on partial discharge characteristics parameters and insulation durability i.e. PD number per cycle and per second and maximum PD amplitudes. FTIR analysis were conducted for both aged and unaged pure polyimide (PI) and PI/TiO2 nanocomposite samples for investigating bonding structure and the interactions happening between nanoparticles and polyimide matrix at the interface. Based on bonding structure analysis, it was noticed that incorporation of nanoparticles into polymer matrix introduces abundant amount of hydrogen bonds between TiO2 nanoparticles and -OH groups, which offers hitch to partial discharge occurrence resulting greater insulation lifetime and durability. Besides, less degradation had been observed in aged PI/TiO2 nanocomposites as compare with aged unfilled PI.