Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence Improvement of Q-learning Based on a Personalized Recommendation System

Reinforcement Learning (RL) – a research branch of artificial intelligence – exploits the concept of reward/penalty from human learning so that the feedback information of the environment can be used for self-learning without previous knowledge. Although RL has been applied to many research fields, there are difficulties when implementing it in some real world applications. One difficulty is the tradeoff between exploration and exploitation for the agent of RL in choosing a proper action. An improper action may lead to a learning failure or an increase in learning cost. Another difficulty is that the learning agent of RL needs to interact with the environment to attain a real-time reward/penalty; however, the learning time spent in the interaction process may be too long. In order to overcome the aforementioned difficulties, this paper proposes an approach that employs a personalized recommendation system to provide a feedforward candidate action for RL to implement self-adaptive learning through teaching. A real visual tracking experiment using a pan-tilt camera system is conducted to assess the performance of the proposed approach. Experimental results show that the proposed personalized recommendation system-based approach is able to improve the effectiveness and practicality of RL.
Machine Learning for Power Control in D2D Communication Based on Cellular Channel Gains

We consider a mobile network with users seeking to engage in a device-to-device (D2D) communication. Two D2D users (DUEs), a transmitter and a receiver, compose one D2D pair. We assume that the D2D pairs reuse a single communication channel to increase the spectral efficiency. Thus, a power control is needed to manage interference among the D2D pairs and to maximize capacity. We address the problem of D2D power control in the case when only standard cellular channel gains between the DUEs and base stations (BSs) are known while channel gains among DUEs are not available at all. We exploit supervised machine learning to determine transmission powers for individual D2D pairs. We show that the cellular channel gains can, in fact, be exploited to predict the transmission power setting for D2D pairs and, still, close-to-optimum sum capacity of the D2D pairs is reached. Moreover, even if our proposed power control requires no knowledge of the channel gains among DUEs and, thus, introduces no additional signalling, the sum capacity can be increased by 16% to 41.9% with respect to no power control, as demonstrated via simulations.
High-speed Target Tracking of 3D Object by Monocular camera

Tracking a high-speed moving target is a fundamental task in many robotic applications. There are numbers of high-speed vision systems have been developed for high-speed visual feedback [1]. In this study, we somehow focus on high-speed target tracking of both 3D position and orientation using only a monocular camera. This is a light-wight solution for many fields. We modify the pixel-wise posteriors 3D (PWP3D [2]) algorithm for high-speed target tracking and pose estimation. Experiment result show that the tracking accuracy against changes in the environment (e.g., partial occlusion) was improved compared with the original PWP3D. As a real-time application, we demonstrate our method is capable of tracking high-speed flying object.
Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation

There is a cold-start problem in the recommendation system field, which is how to profile new users and new items. The popular recommendation algorithm is an important solution to the cold-start problem. In this paper, we propose a new joint deep network model with auxiliary semantic learning for the popular recommendation algorithm (DMPRA). First, we define the items with a large quantity of review data and high ratings as the popular recommended items. Second, we introduce text analysis into the popular recommendation algorithm. We use the optimized CharCNN networks to learn the auxiliary semantic vectors from the users’ reviews. Then, we use the Factorization Machine (FM) component and deep component to learn the corresponding vector representations of the items’ attribute features. We use convolution to simulate the interaction of hidden latent vectors. This method can make the vectors interact more satisfactorily than traditional interactive representation methods. Finally, we provide the users with a reasonable popular recommendation list. The experimental results show that our algorithm can improve the AUC (area under the ROC curve) and Logloss (cross-entropy) of the popular items’ prediction. In addition, we provide relevant explanations for some useful phenomena.
Reconfigurable Space- Time-Coding Through Time-Modulated Array

In this work, the synthesis of multi-beam patterns in time-modulated linear arrays (TMLAs) for multi- point-to-point communication purposes is addressed. The harmonic radiations unavoidably generated by the periodic pulse trains modeling the excitations of the array elements are exploited as independent channels for receiving multiple signals with different directions of arrival, jointly suppressing the undesired interferences. A stochastic algorithm is employed to determine the optimal descriptors of the pulse sequences which allow for the maximization of the signal-to-interference- plus-noise ratio (SINR) at the receiver by means of suitable pattern shaping and nulling.
Layered Division Multiplexing With Co-Located Multiple-Input Multiple-Output Schemes

The most recent standard for broadcast services, Advanced Television System Committee—Third Generation (ATSC 3.0), has adopted co-located multi-antenna schemes and Layered Division Multiplexing (LDM) in order to increase the capacity and reliability compared to former Digital Terrestrial Television (DTT) systems. ATSC 3.0 has adopted both technologies separately, but no combination of them is planned yet. Compared to baseline LDM case, use of several antennas allows for diverse parametrization for each layer. This paper analyzes the potential combination of co-located Multiple-Input–Multiple-Output (MIMO) schemes with LDM. A trade-off analysis between complexity constraints and performance benefits is evaluated.
Design of a High-gain Dual-frequency Omnidirectional Antenna

Anomnidirectional high-gain antenna is realized using a composite structure. Two groups of omnidirectional high-gain antenna elements operating on different frequency bands are placed on the same plane. Each group of antenna elements is composed of four directional dipole antennas, and the directional dipole antenna array produces an omnidirectional radiation pattern in space through reasonable spatial arrangement and feed network design. The designed antenna operates in the frequency rage 760 MHz–1100 MHz and 1380 MHz–1580 MHz. The VSWR is less than 2, the pattern roundness is less than 2.5 dB, and the realized-gain is greater than 5 dBi.
Novel Approaches to Realize Plasmonic Intrinsic and Extrinsic Optical Fiber Sensors with High Sensitivity

Authors recall two high sensitive, small size, and low-cost surface plasmon resonance (SPR) sensors based on plastic optical fibers (POFs): the first one, a D-shaped SPR intrinsic POF sensor, the second one an SPR extrinsic POF sensor. These plasmonic POF sensors, intrinsic and extrinsic, combined with different kinds of specific receptors can be directly connected to an online platform by a software interface, allowing storing, analyzing and displaying the sensor's data, to realize low-cost biochemical sensors for several applications. The simple experimental setup, used for both these SPR POF sensor configurations, was arranged to measure the light spectrum transmitted through the plasmonic platform, exploiting a halogen lamp and a spectrometer connected to a PC.
Multiband Monopole Antenna For Mobile Applications

In this paper, a multiband monopole antenna has been proposed for mobile applications. The monopole antenna has simple structure with a physical size of 15 cm × 7 cm. The antenna consists of monopole shape loaded by a set of folded arms with a varying length which lead to a better impedance matching result and multiband performance. The simulated results show that the proposed antenna provide multiband frequency operation of 0.8 GHz, 1.8 GHz 2.1 GHz, 2.6 GHz and 3.5 GHz which covers the range from 0 to 4 GHz. The antenna is designed to operate at sub-6 GHz which proposed as lower frequency band to deliver 5G in early stage. The designed antenna has been fabricated and measured to validate the simulated results. RF Coaxial U.FL Connector was used as the port connector. The measurement results agrees well with the simulated ones for all frequency bands.
Thermally Stable Optical Filtering Using Silicon-Based Comb-Like Asymmetric Grating for Sensing Applications

A silicon-based compact comb-like asymmetric grating is proposed as a thermally stable optical filter for label-free sensing applications. The device is designed and fabricated with a cavity section introduced between the two grating regions which are partially etched in the lateral direction to ensure the nonzero coupling of the fundamental and first-order modes in the propagation and counter-propagation direction. To demonstrate refractive index sensing, resonance shift in the device is measured with sodium chloride (NaCl) dissolved in DI water. In contrast to conventional Bragg grating where stopband lies in the transmission spectrum, the proposed device allows a single narrow passband transmission peak with a large Free Spectral Range (FSR) attributed to the engineered photonic bandgap of two modes present in the waveguide region. The device is deliberately designed such that slightly wide resonance peak is obtained that makes device operation thermally stable for a large temperature variation of ±15 K. A higher-order leaky mode with strong light-analyte interaction (due to long corrugation width) in the gratings governs high sensitivity of ≈352 nm/RIU for different concentrations of NaCl from 0% to 10% in the Deionized (DI) water with a small footprint area of $18~{\mu } {m}^{\textsf {2}}$ only. Proposed filter characteristics are well suited for multifunctional applications in integrated photonic devices.
Polarization-Insensitive Rectenna Arrays With Different Power Combining Strategies

This letter presents two 2 × 2 polarization-insensitive dual-polarized rectenna arrays with different power combining strategies for wireless power transmission applications. With a dual-polarized antenna array, the incident wave can be received and decomposed into the horizontally and vertically polarized components, which are equally redistributed by a hybrid coupler. By this means, the efficiency of the rectenna, in which the power is equally input to the two rectifiers in parallel, is independent of the incident wave's polarization. Based on the equal power routing mechanism, two rectenna arrays using the RF and dc combining strategies are designed and analyzed. Measurement results show that both rectenna arrays are insensitive to the incident wave's polarization, and have their own advantages and disadvantages.
Performance evaluation of temporal features for detection of mild cognitive impairment: An fNIRS study

A decline in cognition characterized by the deficit in memory, judgment, and language is known as mild cognitive impairment (MCI). This deficiency has great likelihood of developing Alzheimer’s disease (AD). Hence, the recognition of MCI or an early stage of AD is critical at earliest for evaluation and treatment. In this study, functional near-infrared spectroscopy (fNIRS) was used to measure the concentration changes of oxygenated hemoglobin (ΔHbO) from the pre-frontal cortex of six healthy controls (HC) and six MCI patients during a working memory task. Various temporal features of ΔHbO, such as mean, slope, kurtosis, skewness, and peak-values, were tested for distinguishing MCI patients from HCs. We further investigated the impact of intervention (i.e., acupuncture treatment) on MCI patients (i.e., MCI-1: before intervention, MCI-2: after intervention). Both groups went through fNIRS recording procedure with a gap of 45 days. Before the second fNIRS measurement, only the patient group underwent interventions twice per week for six weeks. The results showed that the mean ΔHbO response of MCI-1 was significantly less than (p < 0.05) that of HC but similar (p > 0.05) with MCI-2. Out of tested temporal features, signal mean and peak values of HC were significantly different (p = 0.0409, p = 0.0430) in comparison to MCI-1 but no significant difference (p = 0.4885, p = 0.4437) if MCI-2. Moreover, these fNIRS results were positively correlated with cognitive test scores. These preliminary results showed that fNIRS can be used for distinguishing MCI patients from HCs using mean hemodynamic response as well as temporal features.
Polyphonic Voicing Optimization for Automatic Music Completion

In this paper, we present a new algorithm for automatic music completion. We have proposed automatic music completion as the class of music composition assistance problems of generating a complete piece of music given fragments of musical ideas input by a user. These fragments include partial melodies in multiple voices or parts of the underlying harmony progression. Therefore, it is a generalization of common problems such as melody harmonization or harmony constrained melody generation, but also includes problems with constraints in multiple domains, i.e. multiple voices and harmony. We present a new polyphonic voicing model for automatically completing four-part chorales. It is based on a hidden Markov model and what we call correction factors. These factors are trainable functions that efficiently capture the context of a voicing in order to account for a multitude of music theoretical rules without having to resort to a rule-based system. We observed improvement over our old model with regards to metrics that are derived from music theory for polyphonic voicing, and also invite the reader to try our algorithm themselves at http://160.16. 202.131/music_completion_apsipa.
Activation of the Bradykinin System by Angiotensin-Converting Enzyme Inhibitors

The Bradykinin (BK) system has a very significant role in the regulation of blood pressure (BP). Hence, reduced activity of BK receptors mediated via decreased circulating endogenous kinin might explain the cause of high BP. This system also governs the activation of the angiotensin system at various axes in control of the physiological BP. The BK receptor antagonists can block the hypotensive action of angiotensin-converting enzyme inhibitors (ACEIs) in hypertensive and normotensive animals. The hypotensive action of BK is highly increased with ACEIs or kininase II inhibitor treatment. The development of specific BK agonists may provide a new direction to explore the experimental approach for examining the role of BK in hypertension.
An Ensemble of Activation Functions in AutoEncoder Applied to IoT Anomaly Detection

We propose an ensemble of activation functions for learning the latent representation of AutoEncoder (AE) to improve the accuracy of Internet of Things (IoT) botnet (anomaly) detection. The proposed model is called Ensemble Latent Representation (ELR) that combines the activation functions, i.e., hyperbolic Tangent (Tanh) and Rectified Linear Unit (Relu) in hidden layers of AE. This model enhances the advantages and mitigates the shortcomings of Tanh and Relu function. As a result, the latent representation learnt by AE is more robust, thereby improving the accuracy of classification algorithms for the IoT anomaly detection. To evaluate the effectiveness of the proposed model, we carried out intensive experiments on four IoT anomaly datasets using four popular classifiers, i.e., Support Vector Machine (SVM), Perceptron (PCT), Nearest Centroid (NCT), and Linear Regression (LR). The experimental results show that our proposed model significantly improves the accuracy of IoT anomaly detection up to 19.9% compared to the original one using area under the curve metric.
Performance Analysis of Optical Switches and Rack Placement in Mesh and Ring Topology in Data Center Network

Cloud services are basically supported by data center networks (DCN). It consists of a huge number of servers organized in racks, which work in parallel with data exchanged by a switch network. For example, a web search request may access an inverted index spreading across thousands of servers, among which petabytes of data are interactively processed and switches. With the expected growth in the Internet and data center traffic, the energy consumption of data centers is destined to drastically increase if the network energy efficiency is not improved. DCNs that are implementing optical switches are able to improve energy efficiency and overcome environmental and internal constraints. This research investigates the impact of adopting optical switches and distributing server racks and switches in DCNs. Then, we test the placement formulations against different network topologies and lastly, we analyze the performance of the overall network.
Abnormal SDN switches detection based on chaotic analysis of network traffic

Network flow is susceptible to disruption through a software-defined network caused by malicious switches. The malicious behaviors such as dropping traffic, adding or delaying traffic are diverse. Once a switch is compromised by an attacker, the switch could be malfunctioning or configured incorrectly. In this paper, we propose a real-time method of detecting compromised SDN switches based on chaotic analysis of network traffic. An ARIMA model is used to predict the number of flows in every following three seconds. Then, by calculating the maximum Lyapunov exponent, the chaotic behavior of prediction error time-series is analyzed. Simulation findings indicate that 99.63% of traffic states can be accurately classified by the proposed algorithm.
Super-Resolution Algorithm of Satellite Cloud Image Based on WGAN-GP

The resolution of an image is an important indicator for measuring image quality. The higher the resolution, the more detailed information is contained in the image, which is more conducive to subsequent image analysis and other tasks. Improving the resolution of images has always been the unremitting pursuit of industry and academia. In the past, people used hardware devices to increase the resolution, which is a practical solution. However, there are many limitations in the method of improving the image resolution by hardware devices. We use software-based image super-resolution technology, which transforms low-resolution images into high-resolution images through a series of machine learning algorithms. The classic GAN algorithm is difficult to train a model, and the improved Wasserstein GAN algorithm can make the model training more stable. Based on SRGAN model, this algorithm replaces the classical GAN algorithm with the improved WGAN algorithm. We will use the FY-3D satellite’s Medium Resolution Spectral Imager Type II (MERSI-II) data, using super-resolution algorithms to make the reconstructed image significantly better visually. We conducted four sets of controlled experiments using four different improved methods. We will evaluate the image from three aspects: peak signal to noise ratio value, structural similarity value and visual effect. We applied the WGAN-GP algorithm to super-resolution tasks and achieved the desired results.
Characterizing an Academic Institution's Baseline Network for Anomaly Detection

The following paper details the research conducted for characterizing the University of Virginia's baseline network behavior. The paper lays out a methodology for extracting network traffic, processing the captured data, reading the traffic into the utilized programs, and generating time series graphs for characterization analysis. Furthermore, the paper details the study's initial results from various models in distinguishing the “pattern of life. ” Differing from previous studies, this study aims to characterize a large network accessed by an academic setting population through analysis of Bro logs (conn.logs and http.logs). Future work will focus on constructing wider time window comparisons of various features, in order to analyze how the baseline network behaves across each day, week, month, and conceivably year. The end result for this study is to develop a more accurate model in distinguishing the “pattern of life” of the University of Virginia's network traffic in order to detect network anomalies. This network characterization model would aid the University of Virginia and other large academic institutions in detecting network intrusions.
Constructing a Digital Signature Algorithm Based on the Difficulty of Some Expanded Root Problems

This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.
Adaptive Group Routing and Scheduling in Multicast Time-Sensitive Networks

Traditionally, industrial networks rely on field bus to support real-time applications. In the trend of industrial internet of things (IIoT), traffic load has increased dramatically, and industrial networks are moving towards time-sensitive networking (TSN), which is based on the widely used Ethernet. In TSN, the time-triggered mechanism is a promising technology for real-time control due to its ability to provide deterministic latency and jitter guarantees. However, this mechanism is mainly based on offline computed schedules, making it difficult to satisfy dynamic requirements in data transmission of IIoT. In this paper, we propose an adaptive group routing and scheduling (AGRS) approach in multicast time-sensitive networks. The framework of this approach contains three phases including pre-processing, schedule synthesis, and post-processing phases. While the pre-processing phase first prunes unnecessary links and adaptively groups qualified complete subgraphs of the network to obtain a simplified topology, the schedule synthesis phase then jointly considers routing and scheduling processes based on the simplified topology to improve schedulability. Finally, the post-processing phase handles the scheduling results of previous phases to generate a feasible schedule. Experiments show that our approach improves schedulability by 68% and reduces execution time by 74% for multicast time-sensitive networks compared with existing approaches.
Flexible and Low Profile Antenna with AMC for Wireless Body Area Network Applications

This letter presents a flexible antenna with artificial magnetic conductor (AMC). A CPW antenna is designed because its broadband characteristics with the relative bandwidth of 39.7% and 13.0% respectively. The proposed antenna operable at 2.45/5.8 GHz for wireless body area network applications and PDMS substrate is used makes the antenna thin and bendable. The AMC surface is integrated with the antenna to improve its radiation performance and to reduce the specific absorption rate (SAR). The 2 × 4 unit structure of AMC ensures a low profile with a thickness and a small size with the area of 50 * 100 mm2. The simulate result shows an AMC reflector increases the front-to-back ratio and antenna gain in free space. Moreover, the performance of the antenna with and without the AMC reflector under different crumpling conditions has been studied and presented, and the result shows the antenna performance is observed to be very robust against the effect of bending. The proposed wearable antenna structure can be used for wireless body area network (WBAN) wireless devices.