Miracle-dz/UniFAR-FTUs huggingface dataset:
A Competitiveness-driven and Secure Incentive Mechanism for Competitive Organizations Data Sharing: A Contract Theoretic Approach

In the era of big data and artificial intelligence, data sharing is desirable for vigorous development of data-driven intelligent services. Although data sharing is supported to a certain extent by current mechanisms and technologies, organizations especially with potential competitive relationships might refuse to share their data. One reason is that data holders worry that data sharing improves competitors’ competitiveness. The other reason is that data sharing suffers huge privacy security risk. To address these problems, in this paper, the concept of competitiveness is introduced as a data sharing transaction driving force to eliminate the competitiveness worry of data holders while differential privacy is adopt to protect their privacy. As there is an information asymmetry between data sharers and data demanders, a contract theoretic approach is proposed to motivate data holders to share data with privacy protection, which is expected to achieve a target of win-win and data sharing security. By designing optimal contracts, the data demander can decide rationally how to pay the data holders given the privacy parameter. Moreover, data holders can choose the contract that maximize their utilities. Numerical results substantiate the effectiveness of the the proposed scheme.
A Robust Parameter-Free Thresholding Method for Image Segmentation

In this paper, we presented a new parameter-free thresholding method for image segmentation. In separating an image into two classes, the method employs an objective function that not only maximizes the between-class variance but also the distance between the mean of each class and the global mean of the image. The design of the objective function aims to circumvent the challenge that many existing techniques encounter when the underlying two classes have very different sizes or variances. The advantages of the new method are twofold. First, it is parameter-free, meaning that it can generate consistent results. Second, the new method has a simple form that makes it easy to adapt to different applications. We tested and compared the new method with the standard Otsu method, the maximum entropy method, and the 2D Otsu method on the simulated and real biomedical and photographic images and found that the new method can achieve a more accurate and robust performance.
A Phase-shift Single Ful1-bridge (PSSB) Converter with Three Shared Leading-legs

This paper proposes a novel PSSB converter with three shared leading-legs. The ZVS of all the primary-side switches can be realized with wide conversion range and full load range. Moreover, the effective duty cycle has nothing to do with the dead-time. Thus, the PSSB would be useful for high switching frequency application. A 100W PSSB converter has been made with GaN transistors and planar transformers to verify the characteristics of PSSB. The experimental results show that the PSSB converter can achieve a peak efficiency of 93.8% when the switching frequency is 300kHz.
An Incremental Community Detection Method in Social Big Data

Community detection methods are meaningful and challenging especially in the era of social big data. Generally, the data could be modeled as the streaming graph where two types of machine learning algorithms could be applied. The one is the time slice based method which is time consuming, because the algorithms need repeatedly run in each time slice. The other is the incremental method which might cause the lower community detection accuracy, because the algorithm needs frequently update the results for a new data point. In this paper, an incremental community detection method is proposed to balance the above two limitations. The experimental results demonstrate that the proposed method is much more efficient compared with the time slices based method, and it is much more effective compared with other incremental community detection methods.
Function Points Method in Game Casual Context

Function Points (FP) method has been trusty and proven to be effective to estimate the effort of thousands of software development projects. Nowadays, the mobile game development—especially in a casual genre—is growing. Therefore, the researcher is excited to test the FP method whether it can also be applied to estimating the game development effort. FP method usually consists of 3 main stages: weighing 5 parameters of Unadjusted FP, calculating 14 complexity factors, and calculating Adjusted FP. Survey to 8 casual games was conducted and the value of effort for each one of them has been estimated. So, we conclude that FP is not suitable to forecast the effort of mobile casual games. There are 376 percent deviation between effort estimations and actual effort. The authors suspect, for mobile game development, the game’s point of view needs to be re-adjusted to 5 input parameters, complexity factors, conversion of programming languages, and productivity rate.
Terahertz Imaging and Sensing Applications With Silicon-Based Technologies

Traditional terahertz (THz) equipment faces major obstacles in providing the system cost and compactness necessary for widespread deployment of THz applications. Because of this, the field of THz integrated circuit (THz IC) design in CMOS and SiGe HBT technologies has surged in the last decade. An interplay of advances in silicon process technology, design technique, and microelectronic packaging promises to narrow the gap between the requirements and the reality of system cost and performance of THz components. Furthermore, the scalability, reconfigurability, and signal processing features of silicon technology have initiated research in complex THz ICs that expand the functionality of THz systems; this has enabled new applications, methods, and algorithms. This paper reviews the progress in THz IC research and investigates several realizations of THz imaging and sensing applications with silicon-based components regarding their motivation, system performance, and challenges. THz computed tomography, broadband multicolor imaging, high-resolution FMCW radar imaging, subwavelength resolution near-field imaging, and compressed sensing are presented.
Channel Constrained Multiple Selective Retransmissions for OFDM System: BER and Throughput Analysis

Hybrid automatic repeat request (HARQ) retransmission is an effective method to combat time-variant channel fading. Conventional HARQ methods initiate retransmission of failed packet without exploiting channel knowledge. When a packet fails, retransmission of data corresponding to high gain sub-carriers of an orthogonal frequency division multiplexing (OFDM) modulation is not necessary and deteriorates the throughput of the communication system. In this paper, we propose accumulated channel norm constrained selective retransmission (CNSR) at physical layer (PHY) in conjunction with HARQ method, which enhances throughput of the transceiver without compromising latency. When a packet fails, medium access control (MAC) layer requests retransmission of the failed packet. The proposed CNSR method initiates retransmission of data at PHY layer transmitted over low signal-to-noise ratio sub-carriers using outdated channel information. The transceiver pair continues retransmission of the failed packet until target channel norm of the sub-carriers is achieved or maximum number of retransmissions are completed. We maximize throughput by optimizing threshold on channel norm for OFDM modulation. We also present an upper bound on bit error rate and throughput analysis of the proposed method. The proposed method achieves higher throughput as compared with the existing Chase combining with selective repeat and conventional Chase combining schemes with and without forward error correction codes.
The Semiotic Engine: Notes on the History of Algorithmic Images in Europe

A Klee painting named Angelus Novus shows an angel looking as though he is about to move away from something he is fixedly contemplating. His eyes are staring, his mouth is open, his wings are spread. This is how one pictures the angel of history. His face is turned toward the past. Where we perceive a chain of events, he sees one single catastrophe which keeps piling wreckage upon wreckage and hurls it in front of his feet. The angel would like to stay, awaken the dead, and make whole what has been smashed. But a storm is blowing in from Paradise; it has got caught in his wings with such violence that the angel can no longer close them. This storm irresistibly propels him into the future to which his back is turned …1
Schedule Reparability: Enhancing Time-Triggered Network Recovery Upon Link Failures

The time-triggered communication paradigm has been shown to satisfy temporal isolation while providing end to end delay guarantees through the synthesis of an offline schedule. However, this paradigm has severe flexibility limitations as any unpredicted change not anticipated by the schedule, such as a component failure, might result in a loss of frames. A typical solution is to use redundancy or replace and update the schedule offline anew. With the ever increase in size of networks and the need to reduce costs, supplementary solutions that enhance the reliability of such networks are also desired. In this paper, we introduce a repair algorithm capable of reacting to unpredicted link failures. The algorithm quickly modifies the schedule such that all frames are transmitted again within their timing guarantees. We found that the success of our algorithm increases significantly with the existence of empty slots spread over the schedule, an opposite approach compared to packing frames, commonly used in the literature. We propose a new ILP formulation that includes a maximization of frame and link intermissions to stretch empty slots over the schedule. Our results show that we can repair with 90% success rate within milliseconds to a valid schedule compared to a few minutes needed to re-schedule the whole network.
The Role of Noise in Determining Selective Ionic Conduction Through Nano-Pores

The problem of predicting selective transport of ions through nano-pores from their structure in the biological and nano-technological systems is addressed. We use a molecular dynamics simulation to provide insight into the key physical parameters of nano-pores and develop a self-consistent analytic theory describing ionic conduction and selectivity through these devices. We analyse the ion's dehydration and excess chemical potential, derive an expression for the conductivity of the nano-pore, and emphasize the role of fluctuations in its performance. The theory is verified by comparison of the predicted current-voltage characteristics with the molecular dynamics results and experimental data obtained for a graphene nano-pore and the KcsA biological channel.
An Energy Efficient Integration Model for Sensor Cloud Systems

This paper proposes a novel information-centric prediction-based integration model for WSNs and the sensor cloud, which exploits the trade-off between the data accuracy requirement of applications, sensing data prediction quality, and the energy efficiency of sensors, to reduce the workloads and energy consumptions of resource-constrained sensors. The model decouples information producers (IPDs) (i.e., physical sensors) from information providers (IPVs), which are implemented as IPDs’ virtual sensors in the sensor cloud, to enable IPVs to provide sensing services when IPDs sleep. In the model, we design an efficient interactive sensing data prediction scheme for IPDs and IPVs to predict and control the accuracy of the sensing data prediction of IPVs using internal temporal information correlation, community detection, and external information correlation among sensors. According to the data accuracy requirement of applications, the model controls: 1) the number of IPDs required to be active and 2) when an active IPD transmits sensing data to the sensor cloud, to maintain the quality of sensing data meeting the requirement. Through extensive experiments with data collected from the real-world IntelLab sensor deployment, we show that the model achieves significant improvements in terms of data transmission suppression ratio, energy efficiency, and response latency compared with the existing schemes.
An Area Efficient True Random Number Generator Based on Modified Ring Oscillators

True Random Number Generators (TRNGs) are used in but not limited to neural networks, Monte Carlo calculations and cryptography. Ring oscillator based designs are most common way of implementing random number generators on field-programmable gate arrays (FPGAs). This paper presents new method of implementation of two fully digital modified ring oscillator methods which utilize ring oscillators’ wake-up and shut-down time uncertainties. With the added uncertainties, overall entropy is increased, and fulfilling binary random data is obtained using fewer ring oscillators. This makes TRNG area efficient by reducing the number of gates. This method is based on two existing modified ring oscillator models. The circuit is implemented in the FPGA and tested it on National Institute of Standards and Technology (NIST) 800-22 test suite without using any post processing methods. It was shown that wake-up model uses 25%, and sleep model uses 50% fewer ring oscillators compared to classical ring oscillator based TRNGs.
Prototyping a Secure, Hierarchical Internet of Things System

It is sometimes useful to be able to prototype Internet of Things protocols and technologies. Our aim for this project was to have edge (sensor) devices being measured by Arduinos and relaying data through a "fog computing" layer implemented with Raspberry Pis to aggregating services run in the cloud. The implementation was done as a part of a Horizon2020-funded project called mF2C, and demonstrated the use of MQTT relaying JSON messages over BlueTooth and WiFi and over more traditional Internet connections to the cloud services. Particularly useful were the ability to rapidly prototype the technologies using inexpensive devices, and the interest the results garnered both in the wider organisation and in the public through our outreach activities.
Visual Servoing Design and Control for Agriculture Robot; a Review

At the beginning of its application, arm-robot manipulator worked blindly in completing the assigned task, and along with the improvement of camera technology as it is getting cheaper and smaller, the application of visual sensor lead to visual servoing control which utilized target detection as the input to control/move the end-effector of an arm-robot. The application of camera in arm-robot manipulator is divided into eye-in-hand where a camera is mounted to the robot, and eye-to-hand where the camera is fixed elsewhere. Visual control involves kinematics modeling and target position in image plane provided by image processing to ensure the motion accuracy in grasping and harvesting the fruit. The end-effector movement for harvesting relies heavily on the accuracy of target position detection that can be obtained by image processing method. This paper is a review study that discusses the design of visual servoing control of an arm-robot manipulator applied in agriculture starting from kinematics and inverse kinematics for controlling the motion and trajectory of the robot, and image processing for target detecting. An example of an image processing method is given by using Edge Detection method for detecting a dragon fruit simulated in SCILAB.
Artificial-Noise-Aided Optimal Beamforming in Layered Physical Layer Security

In this letter, we develop a novel layered physical layer security model to secure multiple messages simultaneously. It maintains a hierarchical security structure, in which each message transmitted from the base station has a security level, and each user in the network has a security clearance correspondingly. Focusing on the basic two-layer unicast system, we propose an artificial-noise-aided optimal beamforming scheme to maximize the higher level information security performance while adhering to the low-level information secrecy rate constraint. Due to the nonconvexity of the problem formulated, a successive convex approximation-based algorithm is proposed to earn a safe convex approximation of the original. Afterward, a low-complexity zero-forcing beamforming scheme is proposed to achieve high efficiency by reducing the computational complexity. Simulation results validate the superiority of our schemes on the promotion of the overall secrecy performance.
PTrack: Enhancing the Applicability of Pedestrian Tracking with Wearables

The ability to accurately track pedestrians is valuable for various application designs. Although pedestrian tracking has been investigated extensively and owns a well-suited sensing platform, the proposed solutions are far from being mature yet. Pedestrian tracking contains step counting and stride estimation two components. Step counting already has commercial products, but the performance is still unreliable and less trustworthy in practice. Stride estimation even stays in the research stage without ready solutions released on the market. Such a non-negligible gap between the long-term research investigation and technique's actual usage exists due to a series of crucial applicability issues unsolved, including design's vulnerability to interfering activities, extracting purely body's movement from mixed sensor signals, and parameter training without user's intervention. In this paper, we deeply analyze human's gait cycles and obtain inspiring observations to address these issues. We incorporate our techniques into existing pedestrian tracking designs and implement a prototype, PTrack, on LG smartwatch. We find that PTrack effectively enhances the system applicability and achieves promising performance under very practical settings.
A Robust and Accurate Neural Predictive Model for Foreign Exchange Market Modelling and Forecasting

In this work, a robust and accurate neural predictive model based on a randomized neural learning scheme is developed for foreign exchange market modelling and forecasting purpose. In our predictive model, a dynamic single-hidden layer feedforward neural network (SLFN) is constructed with tapped-delay-memories applied at its input layer. A modified sigmoid function is designed and input weights and hidden biases are randomly assigned in such a way that highly coupled financial input patterns can be represented in the hidden feature space in a clearer way and sensitivities of the network’s hidden outputs to the changes in the financial input signals are enhanced. Also, a large number of hidden nodes in the hidden layer is used to improve the clarity of input patterns’ representation in the hidden feature space. Output weights of the network are optimized using regularised batch-learning type of least square method to improve robustness of the predictive model against external and internal disturbances. Simulation results show excellent performance of the developed model in both target deviation and directional performance measurements.
Robust  $H_{\infty}$  Control of Lurie Nonlinear Stochastic Network Control Systems With Multiple Additive Time-Varying Delay Components

In order to further solve the stability problem in the actual network control systems, this paper first simultaneously considers the controlled object system delay and network time delay in the Lurie nonlinear stochastic network control systems. Among them, the controlled object system delay is multi-time delay and the network time delay is two additive time-varying delay component. The proposing of the two additive time-varying delay components in network control systems has advantages in producing less conservatism over the previous one with a combined delay. By employing the method of improved free weighting matrix and constructing a Lyapunov–Krasovskii functional, less conservative robust $H_{\infty }$ stability criterion is established and the controller is designed for the system in this paper. Some numerical examples are given to demonstrate the applicability of the proposed method.
Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN

Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.
Writing in the Air with WiFi Signals for Virtual Reality Devices

Recently, handwriting recognition approaches has been widely applied to Human-Computer Interface (HCI) applications. The emergence of the novel mobile terminals urges a more man-machine friendly interface mode. The previous air-writing recognition approaches have been accomplished by virtue of cameras and sensors. However, the vision based approaches are susceptible to the light condition and sensor based methods have disadvantages in deployment and highcost. The latest researches have demonstrated that the pervasive wireless signals can be used to identify different gestures. In this paper, we attempt to utilize channel state information (CSI) derived from wireless signals to realize the device-free air-write recognition called Wri-Fi. Compared to the gesture recognition, the increased diversity and complexity of characters of the alphabet make it challenging. The Principle Component Analysis (PCA) is used for denoising effectively and the energy indicator derived from the Fast Fourier Transform (FFT) is to detect action continuously. The unique CSI waveform caused by unique writing patterns of 26 letters serve as feature space. Finally, the Hidden Markov model (HMM) is used for character modeling and classification. We conduct experiments in our laboratory and get the average accuracy of the Wri-Fi are 86.75 and 88.74 percent in two writing areas, respectively.
Development of Postures Estimation System for Walking Training

In this era of sub-health, walking has been proclaimed as the best sport because it can gradually enhance body function and relieve stress. However, bad walking postures during sports time could cause unnoticed bad effect on the spine, joints, and muscles. It is necessary to develop a system to monitor the walking postures and remind people of the bad postures. There are some limitations of traditional gait monitoring systems, such as the scant number of walking postures to be detected and the specific using environment. This paper presents a walking postures estimation system with inertial measurement unit (IMU) that is able to detect the walking postures correctly, meanwhile the device of the system is easy to be attached. This system uses random forest method with the features using acceleration data and gyro date collected by IMU to classify walking postures such as bow legs and knock knees. According to the classification results, the presented system is able to classify different walking postures of users correctly.