Miracle-dz/UniFAR-FTUs huggingface dataset:
See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition

Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (RL-GAN) to generate super image representation that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our RL-GAN, which improves the classification results significantly, with 10–15% gain on average, compared with benchmark solutions.
Effectiveness of “Maghzineh” Cognitive Video Game on Reading Performance of Students with Learning Disabilities in Reading

Individuals with learning disability in reading or dyslexia encounter many life difficulties including educational barriers in academic or social areas. Thus, they need support and therapy to overcome these difficulties. In this study, to assist this group of students, a serious cognitive game was played, and its effectiveness on reading performance of the participants was evaluated. A quasi-experimental research with pretest-posttest design with a control group was conducted to study the effectiveness of playing this game on reading performance of the students with learning disability. Based on teachers’ candidacy, some children who were suspected of having learning disability were screened and evaluated by the new version of Tehran-Stanford-Binet Intelligence Scale. 20 students with learning disability were selected and assigned randomly to two experimental and control groups of 10 students each. The experimental group was trained for 24 sessions with “Maghzineh” serious video game, individually for a month. The control group received no intervention during the training sessions of the experimental group. NEMA test of reading performance (Moradi and Noori, 1387) was used to measure reading performance. The obtained data was analyzed using Multiple Analysis of Covariance (MANCOVA). Based on the findings, there was a significant difference between the experimental and the control group in reading performance in students with learning disabilities.
Thai-English and English-Thai Translation Performance of Transformer Machine Translation

In this paper, the machine translation models were applied to the Thai-English and English-Thai machine translation task. We investigated three models of machine translation on Thai and English sentence pairs. The translation performance of the transformer model is better than that of the recurrent neural network and the traditional statistical machine translation models. We found that the BLEU scores of the transformer model were the highest in both Thai-English (44.22%) and English-Thai (46.48%) translations. Besides, the results were also analysed linguistically. In comparison with the three models, the errors about detailed description and wrong word ordering were mostly found in the SMT model, whereas wrong word choice and missing words were mostly found in the RNNs model. Although the transformer model could perform much better than others, three error categories – under-translation, over-translation, and incorrect lexical choice-were also found.
Simulated Annealing Approach for Clustering in Wireless Sensor Networks

Wireless sensor networks are characterized by the low energy of its elements and thus efficient protocols are always needed for better optimization. Many protocols were proposed using a hierarchical structure of cluster heads to route data from regular sensors to the base station. In this paper, a modified version of MR-LEACH protocol is proposed based on simulated annealing meta-heuristics (SA). A comparison of SA with peer protocols such as HEED is presented. Simulation results show that SA holds farther in terms of first dead node and residual energy.
Graph Filtering for Recommendation on Heterogeneous Information Networks

Various kinds of auxiliary data in web services have been proved to be valuable to handler data sparsity and cold-start problems of recommendation. However, it is challenging to develop effective approaches to model and utilize these various and complex information. Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to model auxiliary data for sparsity recommendation, named HIN based recommendation. But most of these HIN based methods rely on meta path-based similarity or graph embedding, which cannot fully mine global structure and semantic features of users and items. Besides, these methods, utilizing extended matrix factorization model or deep learning model, suffer expensive model-building problem and cannot treat personal latent factors carefully since their global objective functions. In this paper, we model both rate and auxiliary data through a unified graph and propose a graph filtering (GF) recommendation method on HINs. Distinct from traditional HIN based methods, GF uses a rate pair structure to represent user’s feedback information and predict the rating that says, “a predicted rating depends on its similar rating pairs. ” Concretely, we design a semantic and sign value-aware similarity measure based on SimRank, named Constrained SimRank, to weight rating pair similarities on the unified graph and compute the predicting rate score for an active user via weighted average of all similar ratings. Various semantics behind edges of the unified graph have different contributions for the prediction. Thus, an adaptive framework is proposed to learn the weights of different semantic edges and products an optimized predicted rating. Finally, experimental studies on various real-world datasets demonstrate that GF is effective to handler the sparsity issue of recommendation and outperforms the state-of-the-art techniques.
Fast Finding Optimal Redundancy to Satisfy Reliability Requirement for Safety-Critical Parallel Applications on Heterogeneous Distributed Automotive Systems

Automotive system is the safety-critical and cost-critical heterogeneous distributed embedded system. Task assignment based on replication fault-tolerance strategy is commonly used to satisfy the reliability requirement of directed acyclic graph (DAG)-based parallel automotive application. However, automotive system development is redundancy constrained due to the limited hardwares and resources. Finding optimal redundancy under the reliability requirement saves the hardware and resource costs, and ensures the driving safety for the automotive industry, but the process is time-consuming by using traditional exhaustive method. In this paper, we propose the fast task assignment for optimal redundancy (FTAOR) algorithm to find minimum redundancy of DAG-based parallel automotive application under the established reliability requirement. Unlike exhaustive method, the verification time is reduced by using the FTAOR algorithm, which predicts and eliminates most meaningless computations effectively. Experimental results on parallel automotive applications at different heterogeneity show that the proposed FTAOR algorithm generates lower redundancy compared with the state-of-the-art heuristic algorithms and increases the task size to 150 with a shorter computation time compared with exhaustive method.
Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

The finite-time lag synchronization problem of memristive neural networks with multi-links (MNNLs) is discussed with the adaptive controllers, and the discrete and distributed time-varying delays are included in the network models. With the Lyapunov functional method and the properties of inequality, two different finite-time methods are adopted to prove and compare the effects of the two proposed theorems, and three corollaries are obtained and to be analyzed. Using the assumptions of Lipschitz continuous and boundedness of the activation functions, three lemmas are derived to deal with the difficulty caused by the memristive jumping properties and synchronization lag feature, which make the proof of the theorems simple and clear. Based on that whether time-varying delays are included, with the adaptive control strategy, we design delay-independent and delay-dependent adaptive controllers with different adaptive parameters for the MNNLs respectively. Finally, two numerical simulations are given to show the effectiveness and correctness of the obtained theoretical results.
Time Series Classification Based on Dictionary Learning and Sparse Representation

The high-dimensionality, shifting and distortion are considered as the main difficulties in time series classification. In this paper, a novel technique based on dictionary learning and sparse representation is developed to conquer these difficulties. Dictionary, which is a set of series or segments, is able to reconstruct signals sparsely. We use dictionary learning to reconstruct time series and introduce k-nearest neighbor classifier based on dynamic time warping to improve the quality of approximation of dictionary. Sparse coding is used to reconstruct an unseen series. The class label of an unseen series is determined by the reconstructive error. Extensive experiments were conducted to test the effectiveness of the proposed algorithm. Experimental results show that the proposed technique achieved significant improvements compared with some existing algorithms.
A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems

I-energy and its applications used in the smart grid are chosen as attractive research issues due to their widespread applications in the smart home. In this vein, the optimization of energy demand in smart home appliances, in a smart grid, is considered the main challenge faced by power companies during peak periods regarding its considerable effect on the global power system stabilities. Therefore, this paper aims to show explicitly the performance of smart grid on electricity and hydrogen applications with reused communication methods. To facilitate the understanding of the communication-reused methods in the smart grid, several components are identified and presented in terms of improvements, benefits and lack of features. In the smart grid, most innovation communications are being used as wireline and wireless bundle, where the main features are aggregated. Indeed, reducing consumers’ electricity bills in smart home electrical systems relies on increasing the relationship between the highest energy demand and annual energy use. The most appropriate research has been reviewed on optimization approaches discussing the smart home electric system. The examined methods are categorized into trustworthy and metaheuristic algorithms.
Fundamental Limit on Network Size Scaling of Oscillatory Neural Networks due to PrMnO3 based Oscillator Phase Noise

Oscillatory Neural Networks (ONNs) have been reported to solve graphical constraint optimization and pattern recognition problems in a time efficient manner [1], [2]. In particular, the vertex coloring problem can be solved using a capacitively coupled network of relaxation oscillators of the same frequency $(f_{i})$ (Fig. 1) [1]. The critical information for the accuracy is governed by the permutation (ordering on phase circle) of the oscillator settling phases. Compact device level oscillators have been proposed to enable densely packed arrays for large network sizes [3]–[6]. These are based on physics like insulator to metal transitions (IMT), volatile conductive filament and Joule heating. Hence, they are prone to phase variability during operation. The impact of phase variability in relaxation oscillators on the network solutions for ONNs has not yet been demonstrated. Here, we study the cycle to cycle variability (C2CV) in transient Joule heating based PrMnO3 (PMO) device [4]. Further, we demonstrate the effect of C2CV on the temporal distribution of the settling phases, i.e. the phase noise, in coupled oscillator networks. We show that the presence of phase noise in a finite output solution space (360°) puts a fundamental limit on the maximum size of the graphical problem for which the permutation of the output phases can be read-off reliably (Fig. 2).
Time-Series Analysis of Land Subsidence in Kunming

Kunming is located in the northeastern margin of the Tibetan Plateau, covering the Quaternary sediments. With the development of the city, Kunming has had a large area of land subsidence in recent years. In this paper, time-series analysis of the land subsidence in Kunming was assessed by using permanent scatterers interferometric synthetic aperture radar (PS-InSAR) based on 28 single look complex Sentinel-1A images from 2014 to 2016. The result showed that the four subsidence areas were Xiaobanqiao-Guangwei village, Dongjuxin village -Yulong village, Dianchi Convention and Exhibition Center, and Palm Tree Camp. The most serious area of land subsidence was Xiaobanqiao-Guangwei Village, and its subsidence rate is 30mm/a. The largest settlement area is Dianchi Convention and Exhibition Center. Compared with other research results, the Xiaobaoqiao -Guangwei village and Dongju New Village-Yulong Village continue to subside and two new subsidence areas hve formed in Palm Tree Camp and Dianchi Convention and Exhibition Center. In Kunming, regional tectonic movement and its' special geological conditions, long-term groundwater exploitation, urban construction have led to land subsidence.
Medicine as an institution of social control

The increasing function of medicine as a form of social control is seen in a broad historical and theoretical context. The process by means of which that function is accomplished - medicalization - is viewed as a fourfold phenomenon including (1) the expansion of medicine into any area deemed relevant to its good practice, (2) the retention of absolute control over certain technical procedures, (3) the retention of near absolute access to certain 'taboo' areas, and (4) the expansion of what in medicine is deemed relevant to the good practice of life. In conclusion, the moral and value implications of that process are discussed.
A New Method to Achieve Single-Polarization Guidance in Hollow-Core Negative- Curvature Fibers

We propose a new method to achieve single-polarization guidance in a hollow-core negative-curvature fiber. Simulation results show that coating a layer of high-index material with a suitable thickness on the inner surface of one vertical cladding tube in a hollow-core negative-curvature fiber can effectively suppress <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-polarized fundamental mode. Silicon is chosen as the high-index material in this paper. In the five-nested-tube silicon-coated negative-curvature fiber, the single-polarization region is located in the wavelength range of 1512–1587 nm with a broad bandwidth of 75 nm. The polarization extinction ratio is up to 1732 at the wavelength of 1550 nm. Moreover, the mode evolutions of <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-polarized fundamental modes in the five-nested-tube silicon-coated negative-curvature fiber are investigated to explain why <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-polarized fundamental mode is suppressed. Compared with the traditional method of using different tube thicknesses to achieve single-polarization guidance in a negative-curvature fiber, this method possesses the advantages of broad single-polarization bandwidth and high error tolerance.
A 4GS/s 8-bit SAR ADC with an Energy-Efficient Time-Interleaved Architecture in 130nm CMOS

The design, implementation and characterization of an 8-bit, 4 GS/s, time-interleaved (TI) successive approximation register (SAR) analog-to-digital converter (ADC) with a non-buffered hierarchical demultiplexing sampling architecture are presented in this work. The core of the ADC is composed of an arrangement of 32 asynchronous SAR ADCs ranked in a $4\times 8$ hierarchy. The proposed fully dynamic SAR ADC features a noise-configurable comparator, configurable asynchronous clock and background DC offset calibration. The non-buffered input signal circuit includes an input matching network for tracking bandwidth enhancement. The design also has a programmable delay cell to adjust the clock sampling phases mismatch, and a 32 Gb/s low-voltage differential signaling (LVDS) interface. The prototype is fabricated in a $0.13 \mu \mathrm{m}$ CMOS process. The TI-ADC achieves 7.09 bit of peak ENOB, 1.3 GHz input bandwidth and 93 mW of power consumption at 1.2 V.
A Reinforcement Learning Approach to Handle Radio Link Failure in Elevator Scenario

Metallic frame of elevators attenuate the radio signals severely causing Radio Link Failure (RLF) at Mobile Device (MD). RLF results in connection interruption causing degradation in user experience. To overcome this problem, we propose a reinforcement learning based technique called Intelligent Elevator Detection and Network Adaptation (IE-DNA) algorithm, that can be implemented on MD. The IE-DNA algorithm has the capability to handle frequent RLF, detection of elevator movement and intelligent handover among the Cellular (i.e., 3G/4G/5G) or Wi-Fi networks. Using IE-DNA algorithm, MD can upload and store radio link statistics, experienced by the user, to a cloud server. Further, uploaded data can be utilized by any mobile user to know the radio link condition a-priory for a specific elevator, when visiting later in time and handle it by selecting a network that wouldn't cause interruption during elevator movement. Based on real data-set for Cellular/Wi-Fi collected by Samsung Galaxy S8 device, we conduct extensive experiments using real elevator environment to compute performance of the schemes with respect to the state of art.
High performance and colossal permittivity of (Nb+La) co-doped rutile TiO2 ceramics induced by composition regulation

Donor-acceptor co-doped rutile TiO<inf>2</inf> ceramics with colossal permittivity have been paid more attention recent years. However, the application of the ceramics in high-energy-density storage devices was limited by the low breakdown strength of co-doped TiO<inf>2</inf> colossal permittivity ceramics, therefore the dielectric properties of Nb<inf>x</inf>La<inf>0.004</inf>Ti<inf>0996-x</inf>O<inf>2</inf> (x:0.004=1:1, 1.02:1, 1.04:1, 1.06:1, 1.08:1) ceramics were investigated. Colossal permittivity with low dielectric loss and high dielectric strength was found in Nb<inf>x</inf>La<inf>0.004</inf>Ti<inf>0.996-x</inf>O<inf>2</inf> (x:0.004=1.04:1) ceramics. The enhancement dielectric properties with larger Nb<sup>5+</sup> doping concentration in co-doped ceramics provide the colossal permittivity mechanism in (Nb+La) co-doped TiO<inf>2</inf> ceramics could be attribute to the electron-pinned defect-dipole effect. Furthermore, finding the proper donor concentration is a feasible way to enhance the dielectric strength to satisfy the condition for application in high-energy-density storage devices.
Anomaly Subgraph Mining in Large-Scale Social Networks

Anomaly detection is a relevant research field in online social networks (OSNs), which detects the abnormal individuals and activities to protect other regular activities of the whole network. Current detection approaches can be divided into two types: structure-based automatic detection and experience-based manual detection. However, the first approach may have inaccurate detection results and neglect some abnormal nodes; The second one needs a great deal of experts' experience and needs such high labor costs. In this paper, we design a training-based graph pattern matching systems (T-GPM) to detect abnormal individuals in large-scale social networks. T-GPM first mines the feature patterns by training some existed abnormal samples. Then, by applying the feature patterns to the original social networks, T-GPM can find out all the similar abnormal nodes or subgraphs. Our experiments show that T-GPM can get essential features of the abnormal sample without extra experts' experience. Meanwhile, the accuracy of T-GPM increases with the number of iterations, and it achieves the final accuracy rate of more than 90%. Finally, the entire system has only a small amount of labor cost for subgraph filtering in the predicting phase.
Modeling of Real-Time Multimedia Streaming in Wi-Fi Networks With Periodic Reservations

An important problem of modern Wi-Fis is the interferences caused by hidden stations active in the same area, or in multihop communications. All these issues significantly degrade the efficiency of the random channel access methods. Recent standardization and research activities are focused on solving coordination problems between various Wi-Fi devices. For example, the ongoing development of Wi-Fi 7 includes a coordinated schedule between the access points as a candidate solution. Consequently, Wi-Fi has many deterministic channel access mechanisms, which schedule channel time in a periodic manner well in advance and, thus, are utilized for streaming QoS sensitive data. However, both random traffic intensity and error-prone nature of the wireless channel complicate choosing such reservation parameters, i.e., the duration and the period of the reserved time intervals, that satisfy QoS requirements while minimizing channel time consumption. This paper introduces a general mathematical framework to solve the problem of choosing appropriate reservations parameters. The comparison of the analytical and simulation results show the high accuracy of the proposed framework. Finally, the paper gives an example of how to use the developed framework to maximize the network capacity.
Spare Sensing Matrix Construction for Compressed Sensing

Compressed sensing (CS) enables new sampling theories to provide revolutionary approaches for data acquisition. Unlike traditional methods, it allows a parse or compressible signal to be reconstructed from much fewer measurements. To guarantee exact recovery from compressed measurements, a specific matrix satisfying the Restricted Isometry Property (RIP) is necessary in the sensing procedure. In this paper, we first propose matrix, which only contains 0 and 1. Then we propose spare sensing matrix (SSM) based on the binary matrix. Additionally, we propose a RIP testing method to prove that SSM satisfy RIP. However, the size of SSM is constrained by sum of elements in every column and the maximum inner product of any two different column of binary matrix. To solve the problem, we propose two methods to increase the number of column and the number of row for SSM correspondingly. Simulation experiments show that SSM performs almost the same as Gauss sensing matrix. While the performance of improved SSM is much better than SSM and Gauss sensing matrix.