Miracle-dz/UniFAR-FTUs huggingface dataset:
Content Unification in iTV to Enhance User Experience: The UltraTV Project

Recent changes in TV viewers' consumption habits are pushing to a point where industry content providers and producers must create new technological solutions to retain customers. To cope with these, the UltraTV project consortium developed an iTV concept, with a focus on the unification of content from different sources. This brings together traditional TV along with Over-the-Top content, aiming to provide an integrated solution that could foster the audiovisual consumption and ease the discovery of content. This paper presents the implemented solution and reports on the results of its evaluation using a Field Trial. Results provide valuable insights for a market-oriented version of the UltraTV concept, proving the feasibility and user demand for a profile-based content unification solution for future iTV solutions.
Using mobility data as proxy for measuring urban vitality

In this paper, we propose a computational approach to Jane Jacobs' concept of diversity and vitality, analyzing new forms of spatial data to obtain quantitative measurements of urban qualities frequently employed to evaluate places. We use smart card data collected from public transport to calculate a diversity value for each research unit. Diversity is composed of three dynamic attributes: intensity, variability, and consistency, each measuring different temporal variations of mobility flows. We then apply a regression model to establish the relationship between diversity and vitality, using Twitter data as a proxy for human activity in urban space. Final results (also validated using data sourced from OpenStreetMap) unveil which are the most vibrant areas in London.
Physical characteristics of nanoscale titanium-aluminum alloy powder during 3D printing laser sintering process — A molecular dynamics study

In the study, the molecular dynamics simulation method is utilized to investigate the physical characteristics of nanoscale titanium aluminum alloy powder during 3D printing (additive manufacturing) powder bed fusion laser sintering process. The radius of gyration, neck width and root mean square of different powder size of nanoscale titanium aluminum alloy powder during 3D printing laser sintering process under different heating rate is analyzed and discussed. The physical characteristics of nanoscale solid/hollow spherical titanium aluminum binary alloy powder are simulated, and the neck width and internal lattice changes are also observed. It is found that the phenomena of spontaneous solid-state sintering is occurred at room temperature whatever solid or hollow spherical titanium-aluminum alloy nanoscale powder. It is observed that the temperature of solid state diffusion decrease with heating rate increasing, but the temperature of that increase with powder size decreasing. It is found that the coalescence temperature of nanoscale solid titanium aluminum alloy powder is range of between 940K and 1700K, and the melting temperature of that is range of between 1600K and 1770K. It is also observed that the coalescence temperature of nanoscale hollow coalescence temperature is range of between 1050K and 1580K, and the melting temperature of that is range of between 1450K and 1630K.
Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms

Deep neural networks such as Convolutional Neural Networks (CNNs) have achieved several significant milestones in visual data analytics. Benefited from transfer learning, many researchers use pre-trained CNN models to accelerate the training process. However, there is still uncertainty about the deep learning models, structures, and applications. For instance, the diversity of the datasets may affect the performance of each pre-trained model. Therefore, in this paper, we proposed a new approach based on genetic algorithms to select or regenerate the best pre-trained CNN models for different visual datasets. A new genetic encoding model is presented which denotes different pre-trained models in our population. During the evolutionary process, the optimal genetic code that represents the best model is selected, or new competitive individuals are generated using the genetic operations. The experimental results illustrate the effectiveness of the proposed framework which outperforms several existing approaches in visual data classification.
Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow

Micro-expression is a kind of brief facial movements which could not be controlled by nervous system. Micro-expression indicates that a person is hiding his truly emotion consciously. Micro-expression analysis has various potential applications in public security and clinical medicine. Researches are focused on the automatic micro-expression recognition, because it is hard to recognize the micro-expression by the naked eye. This research proposes a novel algorithm for automatic micro-expression analysis which combines a deep multi-task convolutional network for detecting the facial landmarks and a fused deep convolutional network for estimating the optical flow features of the micro-expression. Firstly, the deep multi-task convolutional network is employed to detect facial landmarks with the manifold related tasks for dividing the facial region. Furthermore, a fused convolutional network is applied for extracting the optical flow features from the facial regions which contain the muscle changes when the micro-expression presents. Finally, a revised optical flow feature is applied for refining the information of the features and a Support Vector Machine classifier is adopted for recognizing and detecting the micro-expression. The result of experiments on two spontaneous micro-expression database proves that our method achieved competitive performance in micro-expression recognition and detection.
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

Recently, advanced cyber attacks, which consist of a sequence of steps that involve many vulnerabilities and hosts, compromise the security of many well-protected businesses. This has led to the solutions that ubiquitously monitor system activities in each host (big data) as a series of events, and search for anomalies (abnormal behaviors) for triaging risky events. Since fighting against these attacks is a time-critical mission to prevent further damage, these solutions face challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale provenance data. To address these challenges, we propose a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomalies. To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies. We deployed our system in NEC Labs America comprising 150 hosts and evaluated it using 1.1TB of real system monitoring data (containing 3.3 billion events). Our evaluations on a broad set of attack behaviors and micro-benchmarks show that our system has a low detection latency (<2s) and a high system throughput (110,000 events/s; supporting ~4000 hosts), and is more efficient in memory utilization than the existing stream-based complex event processing systems.
Energy Optimized Robot Arm Path Planning Using Differential Evolution in Dynamic Environment

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the pre-defined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.
An exponential based simulated Kalman filter algorithm for data-driven PID tuning in liquid slosh controller

This paper presents an Exponent-based Simulated Kalman Filter (EbSKF) algorithm. SKF is a random based optimization algorithm inspired from a Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. The proposed EbSKF is strategized such that the Kalman gain is formulated based on an exponential function with respect to number of cost function evaluation (NFE). The Kalman gain is exponentially reduced when the NFE increases in which happens when the search operation is progress forward. The algorithm is tested to PID tuning for liquid slosh control application. The EbSKF is compared with the original SKF algorithm. In this study, 30 independent runs are performed to record different values of the best solutions generated by the algorithms of interest. The average of the minimum value is compared. It determines significant difference of the generated solution and it reflected the accuracy of the solution. Result of the statistical analysis on the accuracy, which is tested on the function of slosh control application is presented in a table form. The result shows that the proposed algorithm outperforms SKF significantly. The convergence plot for both EbSKF and original SKF is also presented and it confirms the statistical result.
Brief Mindfulness Training for Negative Affectivity: A Systematic Review and Meta-Analysis

Objective: Over the last 10 years, there has been a dramatic increase in published randomized controlled trials (RCTs) of brief mindfulness training (from single-session inductions to multisession interventions lasting up to 2 weeks), with some preliminary indications that these training programs may improve mental health outcomes, such as negative affectivity. This meta-analysis aimed to evaluate whether brief mindfulness training reliably reduces negative affectivity. Method: PubMed, PsycINFO, and the Mindfulness Research Monthly Newsletter were systematically searched for brief mindfulness intervention RCTs assessing negative affectivity outcomes (e.g., depression, rumination, anxiety, stress). Sixty-five RCTs, including 5,489 participants predominantly without experience in meditation (64.64% female, mean age = 24.62), qualified for the meta-analytic review. Results: The meta-analysis revealed a small but significant effect of brief mindfulness training on reducing negative affectivity compared to control programs (g = .21, p < .001). The overall effect size was significantly moderated by participant characteristics: community samples (g = .41, p < .001) produced larger training effects compared to student samples (g = .14, p = .001; Qbetween p = .03). No significant effect size differences were found between clinical and nonclinical samples. However, when accounting for publication bias, the overall effect size of brief mindfulness training programs on negative affectivity was significantly reduced (g = .04). Conclusions: Brief mindfulness training programs are increasingly popular approaches for reducing negative affectivity. This meta-analysis indicates that brief mindfulness training modestly reduces negative affectivity. Quantitative analyses indicated the presence of publication bias (i.e., unpublished null effect studies), highlighting the need to continue rigorous evaluation of brief mindfulness interventions.
Age‐related changes in cardiac electrophysiology and calcium handling in response to sympathetic nerve stimulation

Ageing results in changes to cardiac electrophysiology, Ca2+ handling, and β‐adrenergic responsiveness. Sympathetic neurodegeneration also occurs with age, yet detailed action potential and Ca2+ handling responses to physiological sympathetic nerve stimulation (SNS) in the aged heart have not been assessed. Optical mapping in mouse hearts with intact sympathetic innervation revealed reduced responsiveness to SNS in the aged atria (assessed by heart rate) and aged ventricles (assessed by action potentials and Ca2+ transients). Sympathetic nerve density and noradrenaline content were reduced in aged ventricles, but noradrenaline content was preserved in aged atria. These results demonstrate that reduced responsiveness to SNS in the atria may be primarily due to decreased β‐adrenergic receptor responsiveness, whereas reduced responsiveness to SNS in the ventricles may be primarily due to neurodegeneration.
Airport Extraction via Complementary Saliency Analysis and Saliency-Oriented Active Contour Model

Automatic airport extraction in remote sensing images (RSIs) has been widely applied in military and civil applications. An efficient airport extraction framework for RSIs is constructed in this letter. In the first step, we put forward a two-way complementary saliency analysis (CSA) scheme that combines vision-oriented saliency and knowledge-oriented saliency for the airport position estimation. In the second step, we construct a saliency-oriented active contour model (SOACM) for airport contour tracking, where a saliency orientation term is incorporated into the level-set-based energy functions. Under the guidance of saliency feature representations obtained by CSA, the SOACM can acquire well-defined and highly precise object contours. Experimental results demonstrate that the proposed extraction framework shows good adaptability in remote sensing scenes, and uniformly achieves high detection rate and low false alarm rate. Compared with three state-of-the-art algorithms, our proposal can not only estimate the location of airport targets, but also extract detailed information of the airport contours.
Twickle: Growing Twitch Streamer's Communities Through Gamification of Word-of-Mouth Referrals

Twitch.tv has grown to be one of the largest streaming platforms worldwide, hosting over 2 million active streamers. Many of these streamers are using their Twitch stream to earn a living, turning their streams into a business. However, growing a community that supports this endeavor remains a central challenge amongst streamers. In this paper, we present Twickle: a web-based leaderboard tool that leverages the gamification of word-of-mouth referrals to grow a streamer's community. An initial feasibility study with four streamers reveals that Twickle increases the amount of new viewers and is appreciated by the Twitch community. We address design opportunities for Twickle and outline future research.
Estimating soil moisture content using unmanned aerial vehicles equipped with thermal infrared sensors

This study quantitatively analyzed the effects of land cover change and soil moisture content on regional thermal environments by using unmanned aerial vehicles equipped with thermal imaging sensors. Soil-related linear equations were incorporated into image-based change analysis to derive data on land cover change that were associated with soil moisture conditions. The objective of this study was to employ a payload comprising a thermal infrared sensor to detect land surface temperature and collect real-time soil moisture data in order to construct a model for estimating surface soil moisture content for various types of land cover.
A novel shot detection approach using 8-neighbors and key-colors

Shot is often used as basic unit for both analyzing and retrieving video. The approach for automatic shot detection, using the texture character of eight neighbors, is better than traditional one on efficiency. For improving the accurate rate, this method is combined with the color information by adding key colors extraction. At last the result of experiment is given to approve the validity of the method.
Infrared image change detection of substation equipment in power system using random forest

Early detection of equipment faults plays a crucial role in power system, and automatic change detection of working status of an equipment is an efficient tool for this purpose. In this study, we proposed a novel method to automatically detect temperature change in local region of a substation equipment in power system using bi-temporal infrared images. We considered the change detection as two-class classification problem, and a supervised machine learning algorithm — Random Forest (RF) — was used for forecasting change trend. Various features were extracted from two temporal images for change detection. The features we extracted include gray-level, weighted intensity mean, RGB, LBP, gray-level histogram, and texture originating from the grayscale images and color images of the bi-temporal infrared images of the substation equipment. Cross validation was used to evaluate the robustness of these extracted features. Due to the existence of environmental noise, there are isolated detection points in the change detection results. In order to remove these isolated noise points and improve detection accuracy, we performed a morphological filtering on the detection results. Evaluation indexes such as Dice Similarity Index (DSI), kappa coefficient were used to evaluate the detection performance. Four classical change detection methods i.e. Image Differencing, Image Ratioing, Change vector analysis (CVA) and Principal Component Analysis (PCA) were tested for comparison purpose. Experimental results demonstrated that the proposed algorithm outperformed significantly these classical methods.
A Minimum Barrier Distance Based Saliency Box for Object Proposals Generation

Object proposals generation plays an important role in computer vision. A good object proposals generation model should assign obviously high and low objectness score to the window that contains complete objects and incomplete objects, respectively. However, some existing methods such as local contrast based models usually fail to satisfy this requirement. In this letter, we propose MBDSal Box, a minimum barrier distance (MBD) based saliency box for locating object proposals. MBDSal Box consists of three components: item: First, a window saliency computation module that calculates the MBD saliency of each sliding window; second, a window refinement module that provides more accurate bounding boxes by a marker based watershed algorithm; third, a window scoring module which combines multiple features to compute the final objectness score. The experimental results carried on PASCAL VOC 2007 and Microsoft COCO 2014 datasets show that our model achieves better performance than the state-of-the-art models with competitive speed.
Particle-Size Analysis of Wood Fiber and Powder Based on Image Processing and Recognition

This article describes how to realize a wood fiber and powder automatic production and detection, using a mesh detection method based on image processing and particle-size analysis. With this method, the image will be transformed into an HSV color space, segmented based on S component with OSTU; calculated and analyzed with shape factor F, then rectangle fit, and finally the rectangle's length is converted into mesh. During wood flour mesh recognition, this article proposes the use of a rectangle fitting algorithm. In accordance with the actual demand for mesh recognition, a micro-nano wood flour mesh recognition system based on a modular approach has been designed and developed. This is as well as a mesh recognition design program based on wood flour features. The experiments and particle-size analysis results demonstrate that compared with traditional methods, the proposed approach is of higher accuracy and appropriate for extension.
Multilingual and skew license plate detection based on extremal regions

License Plate Detection (LPD) is an important component of many applications involving security and traffic surveillance. Despite current progress, lots of hurdles remain in the way of a robust LPD system. This is particularly true for the detection of license plates with different layouts and skew angles. In this paper, a novel LPD system is proposed for detecting English and Chinese license plates with large skew angles. The proposed system consists of three main stages: 1) a character proposal module to find candidate characters based on Extremal Regions (ERs); 2) feature extraction and classification relies on Convolutional Neural Network (CNN); 3) license plate detection by region linking. The new method much improves on the robustness of existing approaches by leaving out character segmentation. The performance of the proposed license plate localization algorithm is verified using different datasets of vehicle images, including a large field-captured dataset, a skew and tilt dataset, a 12 countries dataset and two benchmark datasets. For Chinese civilian vehicles, the accuracy of plate localization is over 98.3%.
A novel new idea of data hiding using GEMD technique

Data hiding is a technique about embedding secret message into multimedia, and transports information without causing any distrustful situation. In 2013, Kuo and Wang proposed GEMD (General Exploiting Modification direction). It can effectively achieve the balance of payload and image quality. However, the study of transmission security is slightly inadequate. In order to improve this shortcoming, we will propose a modified method which is used 1bit of pixel group to keep the channel safely or not in this paper. In other words, if it is an unsafe channel, the sender uses the encrypted key to encrypt secret message, in order to avoid other stealing or tampering message. The simulation results are effective about transmission security, moreover, payload and PSNR are 1bpp and 50 dB, respectively.
Small‐animal, whole‐body imaging with metamaterial‐inspired RF coil

Particular applications in preclinical magnetic resonance imaging require the entire body of an animal to be imaged with sufficient quality. This is usually performed by combining regions scanned with small coils with high sensitivity or long scans using large coils with low sensitivity. Here, a metamaterial‐inspired design employing a parallel array of wires operating on the principle of eigenmode hybridization was used to produce a small‐animal imaging coil. The coil field distribution responsible for the coil field of view and sensitivity was simulated in an electromagnetic simulation package and the coil geometrical parameters were optimized for whole‐body imaging. A prototype coil was then manufactured and assembled using brass telescopic tubes with copper plates as distributed capacitance. Its field distribution was measured experimentally using the B1+ mapping technique and was found to be in close correspondence with the simulated results. The coil field distribution was found to be suitable for large field of view small‐animal imaging and the coil image quality was compared with a commercially available coil by whole‐body scanning of living mice. Signal‐to‐noise measurements in living mice showed higher values than those of a commercially available coil with large receptive fields, and rivalled the performance of small receptive field and high‐sensitivity coils. The coil was deemed to be suitable for some whole‐body, small‐animal preclinical applications.