Miracle-dz/UniFAR-FTUs huggingface dataset:
Image retrieval based on ResNet and ITQ

In recent years, more and more hash learning methods have been applied to solve largescale vision problems. It has been shown that learning hash function by using supervised information can boost hashing quality. The state-of-the-art image retrieval hashing methods based on visual features lacks of learning ability, the image expression ability is weak and the efficiency of largescale image retrieval is low. In this paper, we propose a new supervised hashing framework based on deep Residual Networks and Iterative Quantization hashing. Firstly, we exploit the learning abilities of deep residual network to mine the inherent hidden relationship of image content, extract deep feature descriptors, and increase the visual expression of images Secondly, Iterative Quantization Hashing is applied to learn from the high-dimensional image feature and map into low-dimensional hamming space and achieve compact Hash codes. Finally, image retrieval is accomplished in lowdimensional hamming space. Experimental results of MNIST, CIFAR-10, CIFAR-100 and Caltech 256 show that the expression ability of visual feature is effectively improved and the image retrieval performance is substantially boosted compared with other related methods.
Parallel Coordinates for Visual Analytics of Vehicle Navigation Area

In this paper, we present a parallel coordinates of visual analytics technique, by taking advantage of parallel coordinates and time series plots. An application of its performance is proved on datasets associated with vehicle. Its performance include data selection, dimension transfer, color transform, plot inter-conversion, dimension zoom, dimension control of combination of parallel coordinates, and replace, display, append of time series plots. Furthermore, parallel coordinates of visual analytics can visually describe the similarity dimensions datasets associated with vehicle navigation area, and time series plots may be as an auxiliary means to further promote understanding and analysis of the correlations between involving vehicle properties, as well as it can visually depict any of two properties, specifically, with time as the horizontal axis, It is demonstrated that these measures present a promoting visual understanding for user to find an important attribute of vehicles, involving possible accidents in a certain period or stage.
Definition and Efficient Construction of Encrypted k-anonymization Scheme

In this paper, we propose an encrypted k–anonymization scheme (EAS) to k–anonymize an encrypted database using a domain generalized hierarchy while maintaining the encryption state. Preparation of the domain generalized hierarchy is optional; the proposed EAS can generate domain generalized hierarchies using a Huffman code tree from a database encrypted with searchable encryption. As a result, the user can delegate k–anonymization processing to a third party organization such as the cloud while retaining the confidentiality of the database without preparing a generalized hierarchy. In addition, third-party organizations that are entrusted also have the advantage to eliminate possible of misconduct such as information leakage. In a standard computer experiment, we performed a generalization process, which is the major procedure for our EAS. The generalization process takes around 168 seconds only to achieve k–anonymity with k = 3 on 1,000,000 records consisting of 4 attributes. As a consequence, this high-speed performance means our EAS is applicable to not only batch processing but also real-time processing.
Nuclei counting in microscopy images with three dimensional generative adversarial networks

Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with near-infrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ’s 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D.
Reaction-diffusion Woodcuts

Woodcuts are a traditional form of engraving, where paint is rolled over the surface of a carved wood block which will be used as a printing surface over a sheet of paper, so only the non-carved parts will be printed in the paper. In this paper, we present an approach for computer simulated woodcuts using reaction-diffusion as the underlying mechanism. First, we preprocess the segmented input image to generate a parameter map, containing values for each pixel of the image. This parameter map will be used as an input to control the reaction-diffusion processing, allowing different areas of the image to have distinct appearances, such as spots or stripes with varied size or direction, or areas with plain black or white color. Reaction-diffusion is then performed resulting in the raw appearance of the final image. After reaction-diffusion, we apply a thresholding filter to generate the final woodcut black and white appearance. Our results show that the final images look qualitatively similar to some styles of woodcuts, and add yet another possibility of computer generated artistic expression.
Downlink Channel Estimation for Massive MIMO Systems Relying on Vector Approximate Message Passing

To reduce the pilot overhead of downlink channel estimation in massive multiple-input–multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli–Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the a-priori model are learned by invoking the expectation maximization algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems.
Bayesian Real-Time QRS Complex Detector for Healthcare System

An efficient algorithm for the heartbeat detection in the Internet of Things (IoT) health-care system remains a challenging issue due to incurred random variations. The QRS complex reflects the electrical activity within the heart during the ventricular contraction. Although recently many QRS complex detection methods have been proposed with different features, their real-time implementations in low-cost portable platforms are still problems due to limited hardware resources. As a result, it is difficult to provide the accuracy level required for medical applications. By contrast, this paper focuses on developing a new method based on the Bayesian framework to provide a real-time and accurate QRS complex detector. More specifically, we propose a new algorithm with two stages, i.e., variance-based detection (VBD) and maximum-likelihood estimation (MLE), to detect QRS complexes. Furthermore, simulations with the benchmark MIT-BIH arrhythmia and QT databases verify the advantage of being easily portable to different databases using the proposed approach.
An Experimental Evaluation of StatHand: A Free Application to Guide Students’ Statistical Decision Making

Quantitative research methods underpin psychological literacy and evidence-based practice in psychology. Despite this, many students struggle to identify appropriate statistics for different types of research questions and data types. StatHand (see https://stathand.net) is a free application that facilitates this statistical decision making process by prompting students to focus systematically on each structural characteristic of their research design. A total of 217 undergraduate psychology students were randomized to use one of four decision making aids: StatHand on an iPad, a familiar textbook, a familiar paper decision tree, or the textbook and decision tree combined. Participants were then asked to identify suitable statistics for five research scenarios. Students assigned to use StatHand demonstrated higher decision making accuracy than users of the three alternative aids (&dgr; = .50 to .64). StatHand users also experienced lower cognitive load, higher confidence in the accuracy of their decisions and greater satisfaction with their assigned aid than one or more of the other groups. However, it took the StatHand users longer to make their decisions. Finally, there was strong evidence to support the hypothesis that StatHand is instructionally efficient, and that its use requires less effort to promote higher performance relative to the other three aids (&dgr; = .49 to .70). StatHand can be incorporated into a variety of classroom learning activities, and educators are encouraged to consider how they can use it most effectively.
Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration

We propose a novel Gaussian Curvature (GC) based criterion to discard false point correspondences within the RANdom SAmple Matching (RANSAM) framework to improve the 3D registration. The RANSAM method is used to find a point pair correspondence match between two surfaces and GC is used to verify whether this point pair is a correct (similar curvatures) or false (dissimilar curvatures) match. The point pairs which pass the curvature test are used to compute a transformation which aligns the two overlapping surfaces. The results on shape alignment benchmarks show improved accuracy of the GRANSAM versus RANSAM and six other registration methods while maintaining efficiency.
Regression-Based Estimation of Individual Errors in Fingerprinting Localization

Practical location estimation is never ideal, and each location estimate is burdened with a certain level of error. In many use-case scenarios, knowing the magnitude of these errors can significantly improve the usability of the location estimates. The localization errors for different localization approaches are currently assessed using static performance benchmarks. These benchmarks typically provide aggregate metrics that statistically characterize the localization errors across the entire deployment environment. Due to the potentially dynamic nature and spatial heterogeneity of the environment, this characterization can be too generic to be really useful from the point of view of an individual location estimate. To address this issue for fingerprinting-based localization, we propose a regression-based procedure for estimating the individual (i.e., per-location estimate) localization errors. We use the received signal strength (RSS) values from various locations in an environment, as well as the observed localization errors in case the location estimates are generated using these RSS values, for training a number of contemporary regression models. Using the trained models, we are able to estimate the localization error of a location estimate at a new location using only RSS values collected at that location. Both by simulation and experimentally, we demonstrate the feasibility of the proposed procedure for Wi-Fi-based indoor and LoRa- and SigFox-based outdoor fingerprinting approaches. We do that by showing that the proposed procedure can, in the best-case scenario, yield more than 50% more accurate estimation than the reference procedure based on the average localization errors derived from the static performance benchmarks.
Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

High-quality data collection is crucial for mobile crowd sensing (MCS) with various applications like smart cities and emergency rescues, where various unmanned mobile terminals (MTs), e.g., driverless cars and unmanned aerial vehicles (UAVs), are equipped with different sensors that aid to collect data. However, they are limited with fixed carrying capacity, and thus, MT’s energy resource and sensing range are constrained. It is quite challenging to navigate a group of MTs to move around a target area to maximize their total amount of collected data with the limited energy reserve, while geographical fairness among those point-of-interests (PoIs) should also be maximized. It is even more challenging if fully distributed execution is enforced, where no central control is allowed at the backend. To this end, we propose to leverage emerging deep reinforcement learning (DRL) techniques for directing MT’s sensing and movement and to present a novel and highly efficient control algorithm, called energy-efficient distributed MCS (Edics). The proposed neural network integrates convolutional neural network (CNN) for feature extraction and then makes decision under the guidance of multi-agent deep deterministic policy gradient (DDPG) method in a fully distributed manner. We also propose two enhancements into Edics with $N$ -step return and prioritized experienced replay buffer. Finally, we evaluate Edics through extensive simulations and found the appropriate set of hyperparameters in terms of number of CNN hidden layers and neural units for all the fully connected layers. Compared with three commonly used baselines, results have shown its benefits.
Low Altitude, Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training

The low altitude, slow speed and small size object which we call LSS-object for short, such as small UAV (unmanned aerial vehicles) have become a hot issue of air defense security, which is difficult to detect and identify accurately from the image. In this paper, aiming at the problem of LSS-object detection under noise environment, the detection method based on deep learning is proposed. Firstly, a standard training dataset consisting 5 classes of typical objects is constructed. Then, the standard dataset is augmented with noise of different intensity. Finally, YOLO v3 algorithm is used to form a LSS-object detection system which can adapt to environment noise. The training and detection experiments were carried out on the GPU server. After only using the noise-free dataset for training, the mAP(mean Average Precision) of the noise-free test set detection reached 81.07%, but the mAP decreased to 20.68% when the noise variance was 0.03. After adopting the mixed training strategy of the dataset with noise variance of 0.01 and noise-free data, the mAP for the test set detection with noise variance of 0.03 was increased to 70.61%, and the mAP still reached 79.85% in noise-free test set detection. The experiment results show that the mixed training strategy can greatly improve the accuracy in the noisy images detection while maintaining a higher accuracy in noise-free images.
Neighbor-Aware Energy-Efficient Monitoring System for Energy Harvesting Internet of Things

In environmental monitoring systems, unnecessary transmissions can occur when an Internet of Things (IoT) device transmits its data without any consideration on neighbors’ transmissions. In this paper, we propose a neighbor-aware energy-efficient monitoring system (NA-EEMS) for energy harvesting IoT devices. In NA-EEMS, to exploit spatial correlation among IoT devices, geographically proximate IoT devices transmit their sensed data in a distributed manner by means of a constraint stochastic game. We devise a best response dynamics-based algorithm to obtain a multipolicy constrained Nash equilibrium. Evaluation results demonstrate that NA-EEMS can improve the network lifetime while preserving the monitoring probability above a desired level.
A Visualization Technique of a Music Emotion Represented in Dimensional Approach on Infinity Mirror Based LED Wall

In this paper, we propose a visualization method of a music emotion on LED wall. Emotion in music is recognized by a suggested algorithm using a dimensional approach. The algorithm overcomes some shortcoming of Thayer’s model in detecting emotion in a one-second music segment. Moreover, IRI color model is combined with Thayer’s model to determine LED light colors corresponding to 36 different music emotions. They are visualized through colors and animations on LED wall. The accuracy of music emotion visualization achieved to over 60%.
Permission-based Risk Signals for App Behaviour Characterization in Android Apps

With the parallel growth of the Android operating system and mobile malware, one of the ways to stay protected from mobile malware is by observing the permissions requested. However, without careful consideration of these permissions, users run the risk of an installed app being malware, without any warning that might characterize its nature. We propose a permission-based risk signal using a taxonomy of sensitive permissions. Firstly, we analyse the risk of an app based on the permissions it requests, using a permission sensitivity index computed from a risky permission set. Secondly, we evaluate permission mismatch by checking what an app requires against what it requests. Thirdly, we evaluate security rules based on our metrics to evaluate corresponding risks. We evaluate these factors using datasets of benign and malicious apps (43580 apps) and our result demonstrates that the proposed framework can be used to improve risk signalling of Android apps with a 95% accuracy.
Naturalness-Aware Deep No-Reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) is a non-trivial task, because it is hard to find a pristine counterpart for an image in real applications, such as image selection, high quality image recommendation, etc. In recent years, deep learning-based NR-IQA methods emerged and achieved better performance than previous methods. In this paper, we present a novel deep neural networks-based multi-task learning approach for NR-IQA. Our proposed network is designed by a multi-task learning manner that consists of two tasks, namely, natural scene statistics (NSS) features prediction task and the quality score prediction task. NSS features prediction is an auxiliary task, which helps the quality score prediction task to learn better mapping between the input image and its quality score. The main contribution of this work is to integrate the NSS features prediction task to the deep learning-based image quality prediction task to improve the representation ability and generalization ability. To the best of our knowledge, it is the first attempt. We conduct the same database validation and cross database validation experiments on LIVE<xref ref-type="fn" rid="fn1"><sup>1</sup></xref>, TID2013<xref ref-type="fn" rid="fn2"><sup>2</sup></xref>, CSIQ<xref ref-type="fn" rid="fn3"><sup>3</sup></xref>, LIVE multiply distorted image quality database (LIVE MD)<xref ref-type="fn" rid="fn4"><sup>4</sup></xref>, CID2013<xref ref-type="fn" rid="fn5"><sup>5</sup></xref>, and LIVE in the wild image quality challenge (LIVE challenge)<xref ref-type="fn" rid="fn6"><sup>6</sup></xref> databases to verify the superiority and generalization ability of the proposed method. Experimental results confirm the superior performance of our method on the same database validation; our method especially achieves 0.984 and 0.986 on the LIVE image quality assessment database in terms of the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficient (SROCC), respectively. Also, experimental results from cross database validation verify the strong generalization ability of our method. Specifically, our method gains significant improvement up to 21.8% on unseen distortion types.
Analysis of Student Performance Data from a Computer Architecture and Organization Course

Some known problems in the literature are the lack of interest and difficulty ofstudents in Computer Architecture and Organization courses. We analyzed the students' performance in tests and semipresential learning activities in three editions of Introduction to Computer Architecture course from the Computer Engineering program of the Federal University of Pampa. We also analyzed the National High School Exam grades of these students, by knowledge area. The purpose of this study is to identify, through analytical processes using data from the course and grades of the NHSE, the most influential factors in the students’ first and final grades and subsidize the proposal of effective actions to solve the problem of high percentages of students’ failure.
Analytic Surface Detection in CAD Exported Models

3D models exported from CAD systems have certain specifics, that influence their subsequent processing. Typically, in contrast with scanned surface meshes, vertices of exported meshes lie almost exactly on analytic surfaces used in CAD modeling. On the other hand, the triangulation of exported models is usually dictated by the requirement of having the lowest possible number of primitives, which results in highly uneven sampling density and common appearance of extremely large and small triangle inner angles. For applications such as classification, categorization, automatic labeling or similarity based retrieval, it is often necessary to identify significant features of an exported model, such as planar, cylindrical, spherical or conical regions, and their properties. While this type of information is naturally available in the original CAD system, it is only rarely exported together with the surface model. In this paper, we discuss two means of identifying analytic regions in triangle meshes, taking into account the specifics of CAD-exported models, and provide a quantitative comparison of their performance.
Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.