Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Convolutional Neural Network-Based Illumination Estimation

In the image processing pipelines of digital cameras, one of the first steps is to achieve invariance in terms of scene illumination, namely computational color constancy. Usually, this is done in two successive steps which are illumination estimation and chromatic adaptation. The illumination estimation aims at estimating a three-dimensional vector from image pixels. This vector represents the scene illumination, and it is used in the chromatic adaptation step, which aims at eliminating the bias in image colors caused by the color of the illumination. An accurate illumination estimation is crucial for successful computational color constancy. However, this is an ill-posed problem, and many methods try to comprehend it with different assumptions. In this paper, an iterative method for estimating the scene illumination color is proposed. The method calculates the illumination vector by a series of intermediate illumination estimations and chromatic adaptations of an input image using a convolutional neural network. The network has been trained to iteratively compute intermediate incremental illumination estimates from the original image. Incremental illumination estimates are combined by per element multiplication to obtain the final illumination estimation. The approach is aimed to reduce large estimation errors usually occurring with highly saturated light sources. Experimental results show that the proposed method outperforms the vast majority of illumination estimation methods in terms of median angular error. Moreover, in terms of worst-performing samples, i.e., the samples for which a method errs the most, the proposed method outperforms all other methods by a margin of more than 18% with respect to the mean of estimation errors in the third quartile.
Opinion dynamics in discrete-time networks with trust-mistrust interactions

We consider a general version of opinion dynamics in discrete-time multi-agent networks when interactions among agents are either cooperative (trust) or antagonistic (mistrust). In particular, we study cluster consensus problem wherein agents divide into disjoint sets with non-identical opinions. We present a distributed synchronous update rule to achieve cluster consensus over structurally balanced networks and extend it for asynchronous and time-varying systems. We show that structural balance network is a sufficient condition to achieve cluster consensus but not a necessary condition. We give sufficient conditions for the system stability and propose a control mechanism to achieve cluster consensus in structurally unbalanced networks.
Wall Flames and Implications for Upward Flame Spread

Abstract New concepts are addressed for predicting the flame spread on materials from laboratory measurements. It focuses on heat transfer which precipitates and precedes upward flame spread on a vertical surface. Six materials have been featured in this study as well as in past related studies. Their flame spread properties are presented. In this particular study heat transfer and flame height results are presented for wall samples burned at varying levels of external irradiance. Also complementary results are presented for methane line burner wall fires. An approximate theoretical analysis is included to serve as a guide to identifying the important variables and their relationship for correlation purposes. Experimental results yield flame height proportional to energy release rate to the 2/3 power, and wall heat flux distributions are roughly correlated in terms of distance divided by flame height. These correlations appear to at least hold for the scale of these experiments: flame heights of 0.3 to 1.
Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student’s answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the “Natural Language Processing” course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.
Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this article, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.
Blockchain Enabled Data Trading with User Consent

Data trading is ubiquitous in today’s Internet. User data is often sensitive in nature, for example, medical records and sensitive health related information. Present data trade often does not capture the owner’s consent or allow tracking of the sale of her data. Even during a sale, fairness is not guaranteed and both the data owner and the data buyer remains at risk of being cheated. In this paper, we propose an end-to-end protocol to trade sensitive information addressing the key issues of consent, traceability and fairness. We use blockchain as the arbitrator and tamper-resistant log. We implement a prototype of our protocol on Ethereum and show that our proposed solution is practical.
Object Pose Estimation Incorporating Projection Loss and Discriminative Refinement

The accurate estimation of three-dimensional (3D) object pose is important in a wide range of applications, such as robotics and augmented reality. The key to estimate object poses is matching feature points in the captured image with predefined ones of the 3D model of the object. Existing learning-based pose estimation systems utilize a voting strategy to estimate the feature points in a vector space for improving the accuracy of the estimated pose. However, the loss function of such approaches only takes account of the direction of the vector, resulting in an error-prone localization of feature points. Therefore, this paper considers a projection loss function dealing with the error of the vector field and incorporates a refinement network to revise the predicted pose to obtain a good final output. Experimental results show that the proposed methods outperform the state-of-the-art methods in ADD(-S) metric on the LINEMOD and Occlusion LINEMOD datasets. Moreover, the proposed method can be applied to real-world practical scenarios in real time to simultaneously estimate the poses of multiple objects.
A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.
Identification Method of Electrical Load for Electrical Appliances Based on K-Means ++ and GCN

Most cluster identification studies regarding consumer electricity load is faced with problems of erroneous clustering method similarity, low clustering quality and poor identification accuracy. To solve these problems, this paper utilizes the elbow method, k- Means ++, entropy weight method and a graph convolutional neural network to provide a means for cluster identification based on electrical appliance power data collected via smart sockets. In this article, elbow and entropy weight methods were used to achieve the adaptive clustering algorithm. To obtain the electrical appliance load curves, Euclidean and dynamic time warping (DTW) distances were integrated and the similarity measurement method was used to improve the k-means ++ algorithm, which was then applied to data collected via smart socket clustering. Next, clustering results were input into the graph convolutional neural network (GCN) for identification purposes and appliance type information was obtained. Finally, experiments were conducted using electrical load data from 20 commercial users. The method used was a combination of the k-means algorithm and long short-term memory network (LSTM). The results show that under optimal K value conditions (as determined by the elbow score), the methods used in this paper have improved clustering quality and recognition accuracy, when compared to LSTM.
Self-Interference-Resistant IEEE 802.11ad-Based Joint Communication and Automotive Long Range Radar

The IEEE 802.11ad based joint communication and radar sensing has attracted great attentions for vehicles in recent years. The existing studies all assume full duplex communications between the transmitter and radar receiver based on perfect self-interference cancellation. However, the self-interference may not be fully cancelled due to the limitation of self-interference cancellation capability in practical cases, which will significantly degrade the sensing capability of the radar function, especially for the detection range. In this paper, the imperfect self-interference cancellation is considered and a novel joint communication and automotive long range radar sensing design is proposed based on OFDM frame structure in 802.11ad standard. The received signal model in the frequency domain synchronized with the self-interference is derived, in which the target reflection signal suffers inter-carrier-interference (ICI) and inter-symbol-interference (ISI). However, we show that the ISI can be leveraged for enhancing radar parameter estimation. Based on the received signal model, a novel pilot signal design is first developed to combat the self-interference for accurate velocity and coarse range estimation. Then, a few self-interference-free OFDM symbols at the end of the data frame are utilized to achieve accurate range estimation. Simulation results show that the decimeter-per-second level velocity estimation and centimeter level range estimation can be achieved for up to 200-meter radar sensing.
A Dielectric Measurement-Based Design Approach for X-Band Applications on FR4 Substrate

This paper presents a design approach based on the dielectric measurement of the FR4 substrate material at X-band. The approach mainly aims to increase the design accuracy by measuring the frequency depended dielectric constant and tangent loss values of the substrate material to be used in the fabrication, and including the obtained data as input to the design. In accordance with this purpose, FR4 is measured by using waveguide method and its electrical characteristics are obtained at X-band frequencies. To minimize the effect of design complexity, a microstrip line is designed, fabricated and measured utilizing ordinary simulation and PCB tools. The same design is re-simulated with the approach proposed in this paper and comparisons of all results validate the accuracy improvement of the proposed approach.
Synthetic Aperture Radar Focusing Based on Back-Projection and Compressive Sensing

In this paper is presented a new methodology for synthetic aperture radar images focusing called bidimensional mixed compressive sensing back-projection (CS-BP-2D). Spatial compressibility of the radar images is exploited by constructing the sparsity basis using the backprojection focusing framework and solving the reconstruction problem by means of the orthogonal matching pursuit algorithm (OMP).
Dual-Band Antenna Array for 5.9 GHz DSRC and 28 GHz 5G Vehicle to Vehicle communication

The Dedicated Short Range Communications (DSRC) band (5.85-5.925 GHz) allocated for vehicle-to-vehicle (V2V) communication provides limited opportunities for high speed data transfer. Alternatively, FCC plans to allocate millimeter-wave spectrum for 5G V2V communication. In this paper, we present a novel dual-band dual linearly-polarized antenna array for both DSRC and 28 GHz communications. For each band, we optimized antenna gain and number of elements to maximize range and data rate. The designed array has dual linear polarization and is fed with a simple quarter wave transformer. Due to large available connector's size, a Wilkinson power divider is designed to combine adjacent elements. Infinite array simulation show that the array is well matched $(\mathbf{S}_{\mathbf{11}} < -\mathbf{10\ dB})$ from 5.85 to 6.48 GHz, and from 17.29 to 29 GHz. The realized gain at both frequency bands is near-theoretical.
Managing Multicast Membership for Software Defined Data Center Network

In this paper we design DCMA, a novel multicast membership management scheme for data center networks. Unlike traditional protocols like IGMP/MLD, DCMA leverages the characteristics of data center multicast application and the emerging software defined networking (SDN) technique to manage multicast members in an easier and better way. Multicast application master delivers the membership to the SDN controller, and the SDN controller assigns the group addresses in a coordinated way to minimize the forwarding table size in switches. In particular, by formulating the multicast group address allocation problem and capturing the relationship among forwarding entries in different switches, we design both a batch algorithm and an incremental algorithm to allocate the multicast group addresses. Evaluations based on real-world traces show that, DCMA can save almost half multicast forwarding entries in switches compared with random multicast address allocation.
Siamese Generative Adversarial Network for Change Detection Under Different Scales

Change detection methods based on low-resolution (LR) images with higher temporal resolution often lead to fuzzy results, while high-resolution images (HRIs) can provide more detailed information to solve this problem. However, it's hard to obtain two tiles of HRIs with high-quality for rapid change detection in actual production due to low temporal resolution and high cost. Therefore, it is necessary to explore a change detection method combing low- and high-resolution images to acquire urban change areas more accurately and quickly. In this paper, an end-to-end siamese generative adversarial network (SiamGAN) integrating a super resolution network and the siamese structure was proposed for change detection under different scales. The super-resolution network is used to reconstruct low-resolution images into high-resolution images, while the siamese structure is adopted as the classification network to detect changes. In the experiments, SiamGAN achieved an F1 of 76.06% and an IoU of 61.52% in the test set, which is respectively 5.68% and 6.92% higher than the CNN-based methods using LR images after bicubic interpolation. The results show that our proposed method can effectively overcome difference in scale between low- and high-resolution images and perform change detection more precisely and rapidly.
Stock Price Prediction by Using Hybrid Sequential Generative Adversarial Networks

A significant application of machine learning in the financial field is stock price prediction. Investors can obtain a useful investment reference from the result of a stock prediction model, and for the whole financial system, it can optimize resource allocation. Stock future trend prediction is mainly divided into fundamental and technical analyses. Before the boom of machine learning, the ARIMA model was most used in stock price prediction. In recent years, according to the development of machine learning, the state-of-art algorithms of machine learning such as Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GANs) were used to predict stock price. In previous research, however, only one single model had been used for this task. In this work, we used the hybrid sequential GANs model, it set different Recurrent Neural Networks(RNN, LSTM, GRU) in the two components(Generator and Discriminator) of GANs. We designed three training strategies to train our model by using the data of the S&P 500. There are two evaluation methods in this work: different loss functions(RMSE, MAE) and the accuracy of classification on buy, hold, sell strategy. It is proved through experiments that hybrid sequential GANs has a better performance in the stock prediction than the previous single algorithm prediction research.
A survey on routing algorithms for opportunistic mobile social networks

Opportunistic Mobile Social Networks (OMSNs) are kind of Delay Tolerant Networks (DTNs) that leverage characteristics of Mobile Ad Hoc Networks (MANETs) and Social Networks, particularly the social features, to boost performance of routing algorithms. Users in OMSNs communicate to share and disseminate data to meet needs for variety of applications. Such networks have attracted tremendous attention lately due to the data transmission requirement from emerging applications such as IoT and smart city initiatives. Devices carried by human is the carrier of message transmission, so the social features of human can be used to improve the ability of data transmission. In this paper, we conduct a comparative survey on routing algorithms in OMSNs. We first analyze routing algorithms based on three social features. Since node selfishness is not really considered previously in aforementioned routing algorithms, but has significant impact on network performance, we treat node selfishness as another social feature, classify and elaborate routing algorithms based on incentive mechanism. To assess the impact of social features on routing algorithms, we conducted simulation for six routing algorithms and analyzed the simulation result. Finally, we conclude the paper with challenges on design of routing in OMSNs and point out some future research directions.
Unsupervised Metric for Large-Scale Cloud Mask Evaluation

Determining the efficacy of analytics at scale, especially when considering remote sensing imagery, provides unique challenges that the community has only recently been able to explore. The need for scaled analytics has become acute with the broad availability of geospatial platforms and the inability for classic modeling analytics to keep scale. In this paper, we discuss ongoing efforts to address this gap as it pertains to data correction. We illustrate the usage of a metric that takes into account the variation between scenes to create an unsupervised measurement of improved cloud masking performance. It is used to illustrate 46.7% relative improved performance of a deep learning based cloud mask on Sentinel-2 imagery as measured against the vendor provided cloud mask.
Packet Error Rate Based Validation Method for an OpenStreetMap Geometry-Based Channel Model

Repeatable system-level test methods for vehicle-to-everything communication (V2X) are crucial in improving road safety and supporting connected autonomous driving. They rely on geometrical channel models that provide realistic signal propagation delays, Doppler shifts and path loss. In this work we focus on the validation of an OpenStreetMap (OSM) geometry based stochastic channel model (GSCM) that has a high degree of automation and low computational complexity. In an urban intersection scenario we compare the time-variant power delay profile (PDP) and Doppler spectral density (DSD) obtained from a measurement campaign to the ones obtained by the OSM-GSCM. Furthermore, we measure packet error rates (PER) of commercial modem hardware by connecting a transmitter and a receiver to the AIT channel emulator, i.e. using a hardware-in-the-loop (HiL) setup. We compare the PER obtained when the channel emulator uses the OSM-GSCM as input and when it uses the measured impulse responses. The OSM-GSCM path loss shows minor differences to the measurements due to the 2D structure of the OSM-GSCM, while the PER, PDP and DSD exhibit a good match between OSM-GSCM and empirical measurement data.
5G Communication Signal Based Localization with a Single Base Station

With the growing demand for high accuracy indoor localization, the fifth generation (5G) wireless communication technology based localization attracts increasing attention. Aiming at the indoor localization with a single base station, this paper proposes a 5G signal based localization method by employing the estimates of the angle of arrival (AoA) and the time of flight (ToF). The proposed method takes advantage of the large bandwidth and subcarrier spacing of 5G signals to improve localization accuracy. First, the base station carries out frequency synchronization and channel estimation to obtain the channel state information (CSI) matrix. Second, the CSI matrix is utilized for joint estimation of the AoA and ToF for each path. Finally, the corresponding geometric localization method is applied. Simulation results show that this method can achieve a centimeter-level localization accuracy.