Reviewerly/exHarmony huggingface dataset:
Detection of Vehicle Speeding Violation using Video Processing Techniques

Due to their excellent development skills and low cost, detection of car speeding violations using video processing techniques is known to be one of the better applications for Traffic Speed Control (TSC). These systems apply video processing techniques to camera outputs to obtain the required information. This work describes a method for measuring speed of the vehicle depending on motion data. This paper offers a new motion detection-based vehicle speed measurement method. Unlike the methods that are based on features which rely on the characteristics of vehicles such as license plates or windshields, the proposed methodology can find a vehicle&#x0027;s speed by evaluating its operating features for a specific areas of interest (ROI) with unique dimensions. This proficiency allows for real-time computation and outperforms feature-related methods. Vehicle identification, tracking, and measuring of speed are the three main modules in this system. The compound-off-Gaussian background subtraction procedure detects every moving object when it enter into its ROI. Then using morphological changes, the unique parts of these materials become integrated filled forms and some limited filtration functions produces the most likely objects to be a vehicle. The identified vehicles are then monitored by using the bulb tracking method and their displacement in successive frames is determined which will be used in the final speed calculation module. A photo of the car, its associated speed, and the detection time are all included in system releases. In comparison to existing speed measurement techniques, experimental findings show that the proposed technique has reasonable precision.
Classification of breast cancer with deep learning from noisy images using wavelet transform

In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PSNR values. The best PSNR values were carried out with the Gaussian noise type. After that, the denoised images were classified by Convolution Neural Network (CNN), one of the deep learning techniques. In this classification process, the proposed CNN model and the VggNet-16 model were used. According to the classification result, better results were obtained with the proposed CNN model than VggNet-16. The best performance (86.9%) was obtained from the data set created Gaussian noise with 0.3 noise intensity.
Task’s Choice: Pruning-Based Feature Sharing (PBFS) for Multi-Task Learning

In most of the existing multi-task learning (MTL) models, multiple tasks' public information is learned by sharing parameters across hidden layers, such as hard sharing, soft sharing, and hierarchical sharing. One promising approach is to introduce model pruning into information learning, such as sparse sharing, which is regarded as being outstanding in knowledge transferring. However, the above method performs inefficiently in conflict tasks, with inadequate learning of tasks' private information, or through suffering from negative transferring. In this paper, we propose a multi-task learning model (Pruning-Based Feature Sharing, PBFS) that merges a soft parameter sharing structure with model pruning and adds a prunable shared network among different task-specific subnets. In this way, each task can select parameters in a shared subnet, according to its requirements. Experiments are conducted on three benchmark public datasets and one synthetic dataset; the impact of the different subnets' sparsity and tasks' correlations to the model performance is analyzed. Results show that the proposed model's information sharing strategy is helpful to transfer learning and superior to the several comparison models.
Quantum algorithm for credit valuation adjustments

Quantum mechanics is well known to accelerate statistical sampling processes over classical techniques. In quantitative finance, statistical samplings arise broadly in many use cases. Here we focus on a particular one of such use cases, credit valuation adjustment (CVA), and identify opportunities and challenges towards quantum advantage for practical instances. To improve the depths of quantum circuits for solving such problem, we draw on various heuristics that indicate the potential for significant improvement over well-known techniques such as reversible logical circuit synthesis. In minimizing the resource requirements for amplitude amplification while maximizing the speedup gained from the quantum coherence of a noisy device, we adopt a recently developed Bayesian variant of quantum amplitude estimation using engineered likelihood functions (ELF). We perform numerical analyses to characterize the prospect of quantum speedup in concrete CVA instances over classical Monte Carlo simulations.
Hybrid algorithm based on the grey wolf optimizer and direct binary search for the efficient design of a mosaic-based device

We propose a novel hybrid algorithm based on the grey wolf optimizer (GWO) and direct binary search (DBS) for the design of mosaic-based devices. The DBS algorithm leads to the local optimum structure, and the design results are changed for each trial. Thus, we need to implement the DBS design hundreds of times to find a high-performance mosaic-based device, resulting in huge computer resources and calculation time. To resolve the problem and search for a better solution, the GWO is combined with the DBS. The average and best performances of the designed devices are better by 0.5 and 1.6 points when compared with those designed by the DBS.
Medical dataset classification for Kurdish short text over social media

The Facebook application is used as a resource for collecting the comments of this dataset, The dataset consists of 6756 comments to create a Medical Kurdish Dataset (MKD). The samples are comments of users, which are gathered from different posts of pages (Medical, News, Economy, Education, and Sport). Six steps as a preprocessing technique are performed on the raw dataset to clean and remove noise in the comments by replacing characters. The comments (short text) are labeled for positive class (medical comment) and negative class (non-medical comment) as text classification. The percentage ratio of the negative class is 55% while the positive class is 45%.
An Eye Blink detection technique in video surveillance based on Eye Aspect Ratio

Detecting the open/closed state of the eyes is an important step in determining the state of the face in applications related to human facial expressions. This report deals with an eye open/closed detection technique based on the variation of the ratio rectangle surrounding the eye. The technique has been tested and proven effective in determining sleepiness based on images.
A Survey on Public Key Encryption with Keyword Search: Taxonomy and Methods

Given the many benefits that cloud computing brings, organizations tend to outsource most of their data to reduce a large portion of their costs, but concern about the privacy of data is a major obstacle to outsourcing sensitive data. To solve this problem, public key encryption with keyword search (PEKS) is suggested, which is a widely used method. Addressing this issue separately is beneficial because PEKS does not require a secure communication channel and key distribution. Therefore, at first glance, it seems that PEKS schemes should be used more in practical applications. Thus, reviewing and categorizing PEKS schemes are very important and necessary. In this article, we have tried to help reviewing the public key searchable encryption and categorizing these designs.
The Wormhole AI Training Processor

Deep-Learning Artificial Intelligence is a hot and growing field, requiring a challenging mix of performance, power efficiency, programmability, scalability, and low price. Wormhole is an AI training processor directly targeted at this field, designed for high performance (TOPS and TFLOPS), software enablement, extreme scalability, and high performance-per-watt-per-dollar.
A Hierarchical Stabilization Control Method for a Three-Axis Gimbal Based on Sea–Sky-Line Detection

Obtaining a stable video sequence for cameras on surface vehicles is always a challenging problem due to the severe disturbances in heavy sea environments. Aiming at this problem, this paper proposes a novel hierarchical stabilization method based on real-time sea-sky-line detection. More specifically, a hierarchical image stabilization control method that combines mechanical image stabilization with electronic image stabilization is adopted. With respect to the mechanical image stabilization method, a gimbal with three degrees of freedom (DOFs) and with a robust controller is utilized for the primary motion compensation. In addition, the electronic image stabilization method based on sea-sky-line detection in video sequences accomplishes motion estimation and compensation. The Canny algorithm and Hough transform are utilized to detect the sea-sky line. Noticeably, an image-clipping strategy based on prior information is implemented to ensure real-time performance, which can effectively improve the processing speed and reduce the equipment performance requirements. The experimental results indicate that the proposed method for mechanical and electronic stabilization can reduce the vibration by 74.2% and 42.1%, respectively.
Dynamic sample weighting for weakly supervised object detection

The framework based on Multiple Instance Learning (MIL) greatly improves the performance of Weakly Supervised Object Detection (WSOD), which enjoys a promising development. However, the detection results tend to be the most discriminative parts of the object, which is still an open problem. In this paper, we analyze the causes of the problem from the perspective of sample balance. Considering the inaccuracy of pseudo supervised information in WSOD, a Dynamic Sample Weighting strategy (DSW) is proposed to focus on samples which closely cover the object, making the detection results cover the object more comprehensively. The performance of DSW on PASCAL VOC 2007, PASCAL VOC 2012 and MS-COCO is significantly enhanced through the simple and effective method in this paper. Code will be made available.
Explaining Recommendations in E-Learning: Effects on Adolescents' Trust

In the scope of explainable artificial intelligence, explanation techniques are heavily studied to increase trust in recommender systems. However, studies on explaining recommendations typically target adults in e-commerce or media contexts; e-learning has received less research attention. To address these limits, we investigated how explanations affect adolescents' initial trust in an e-learning platform that recommends mathematics exercises with collaborative filtering. In a randomized controlled experiment with 37 adolescents, we compared real explanations with placebo and no explanations. Our results show that real explanations significantly increased initial trust when trust was measured as a multidimensional construct of competence, benevolence, integrity, intention to return, and perceived transparency. Yet, this result did not hold when trust was measured one-dimensionally. Furthermore, not all adolescents attached equal importance to explanations and trust scores were high overall. These findings underline the need to tailor explanations and suggest that dynamically learned factors may be more important than explanations for building initial trust. To conclude, we thus reflect upon the need for explanations and recommendations in e-learning in low-stakes and high-stakes situations.
Autistic sociality on Twitter: Enacted affordances and affiliation strategies

While there is an increasing focus on the use of online networks among autistic users, how autistic adults communicate in social networking sites remains underexplored. The article puts forward an argument for combining systematic observation of digital practices with analysis of evaluative language in order to provide a situated account of ‘autistic sociality’ in social media. Drawing on practice-based theories of social media affordances and discourse analysis research on online self-presentation and affiliation we show how autistic Twitter users rely on association, content persistence and editability in order to signal social engagement through different forms of interaction and alignment. We discuss how the proposed framework can provide a new perspective on communicative practices of autistic social media users and advance development of inclusive digital networking platforms.
Exponential sine chaotification model for enhancing chaos and its hardware implementation

Chaotic systems have been intensively studied for their roles in many applications, such as cryptography, secure communications, nonlinear controls, etc. However, the limited complexity of existing chaotic systems weakens chaos-based practical applications. Designing chaotic maps with high complexity is attractive. This paper proposes the exponential sine chaotification model (ESCM), a method of using the exponential sine function as a nonlinear transform model, to enhance the complexity of chaotic maps. To verify the performance of the ESCM, we firstly demonstrated it through theoretical analysis. Then, to exhibit the high efficiency and usability of ESCM, we applied ESCM to one-dimensional (1D) and multi-dimensional (MD) chaotic systems. The effects were examined by the Lyapunov exponent and it was found that enhanced chaotic maps have much more complicated dynamic behaviors compared to their originals. To validate the simplicity of ESCM in hardware implementation, we simulated three enhanced chaotic maps using a digital signal processor (DSP). To explore the ESCM in practical application, we applied ESCM to image encryption. The results verified that the ESCM can make previous chaos maps competitive for usage in image encryption.
Service Oriented Architecture(SOA): A Literature Review on the Maintainability, Approaches and Design Process

The majority of software efficiency is likely to be supplied and absorbed as services in the future. Undoubtedly, they might be implemented as closely connected technologies, however, the site of use, whether it&#x2019;s a browser, a machine, or some other point, will all utilise a service-based architecture. Information technology systems have expanded tremendously over the previous years, forcing businesses with more sophisticated software structures to manage. Information technology teams must continue to adapt rapidly to new business demands, decrease information technology costs to the company, and incorporate and absorb business development customers and clients in a smooth manner. Nevertheless, a comprehensive answer leaves much to be desired. This paper aims to discuss Service Oriented Architecture(SOA) which are now being pushed as its next incremental milestone in helping information technology businesses address their ever-increasingly complicated issues. It also tries to come up with a description of service based on how it is used in practice.
Integrating Handcrafted Features with Deep Representations for Smartphone Authentication

Recent research demonstrates the potential of touch dynamics as a usable and privacy-preserving scheme for smartphone authentication. Most existing approaches rely on handcrafted features since deep models may be vulnerable to behavioral uncertainty due to the lack of consistent semantic information. Toward this end, we propose an approach to integrating handcrafted features into two phases of the deep learning process. On one hand, we present three fine-grained behavior representations by encoding semantic handcrafted features into the raw touch actions. On the other hand, we devise a deep Feature Regularization Net (FRN) architecture to combine the complementary information in both handcrafted and deep features. FRN involves handcrafted features as regularization to guide the learning process of deep features and selectively fuses these two feature types through a feature re-weighting mechanism. Experimental findings demonstrate that FRN outperforms the existing handcrafted or deep features even with smaller training and template sets. The framework also works for SOTA deep models and further boosts the accuracy. Results show that our approach is more reliable to alleviate behavioral variability and is competitively robust to statistical attacks compared with the most effective handcrafted features, suggesting a promising mechanism to improve the effectiveness and usability of behavioral authentication for multi-touch enabled mobile devices.
Video analytics-based real-time intelligent crossing detection system (RICDS): Killer app for edge computing

In this paper, we design and implement a video analytics based real-time intelligent crossing detection system (RICDS) as a novel safety service for the smart city. This system spans across the edge device and visualization unit, with reduced latency. To enhance object tracking in the proposed RICDS on the edge devices, which has limited computational resources, we first design an adaptive queue management-based object tracking (QMOT) scheme. This scheme adaptively controls the maximum queue size to meet the target delay requirement for the RICDS while the image frames come into the proposed queue to wait for object detection and tracking. In this sense, the maximum queue size is calculated by the target delay requirement and weighted-average of processing time, so that some redundant frames for object detection are automatically dropped due to the overflow of the maximum queue size. Moreover, we introduce the real-world-real-time (RR) tracking scheme that lightly tracks multiple objects (i.e., pedestrians in this scenario) by predicting the future position of the object and assigning unique IDs to them. Specifically, to predict the future positions of multiple objects in a lightweight manner, the possible movement boundary of every single object is determined by considering both the movement directions and movement speeds of each object to reduce search space for mapping IDs to the objects. Here, the movement directions and movement speeds are also calculated based on the real-world coordinate system after calibrating of image frame's coordinate system into the real-world one. Finally, we implement the proposed RICDS on various commercial edge devices and a visualization unit (i.e., the LED panel). Moreover, the GUI application at the remote server is provided for (i) monitoring the result of the detection and tracking and (ii) dynamically changing the configuration settings. The measurement-based experiments show that the proposed tracking scheme achieves a latency reduction of about 33%–62% compared to the benchmark schemes on the NVIDIA Xavier while exhibiting similar multi object tracking accuracy performance compared to the benchmark schemes.
Zoom gesture analysis for age-inappropriate internet content filtering

The Internet contains large amounts of adult content. With only a few taps, or mis-taps, an under-aged user can be exposed to age-inappropriate content. Currently, this can be avoided by creating age-restricted profiles or restricting users to child-friendly applications (apps). However, these existing measures are time-consuming, laborious, and require a higher level of technical literacy than many parents can afford. We believe a better solution is to use a browser or an app that automatically detects the user’s age then applies any appropriate content filters. For such a browser/app to be developed, we must learn that age estimation can indeed be performed with an acceptable rate of error. To that end, we created an Android app that collects biometric touchscreen data from elementary school, middle school, high school, and university students. Touch samples were collected from participants aged 5 to 61 on both smartphones and tablets. We focused exclusively on zoom-in and zoom-out touchscreen data samples. We made this decision because we found the zoom gesture to be rich with data and highly used among the most popular applications. Furthermore, we identify a niche within the current research landscape: no other machine learning experiments have leveraged the benefits of the zoom gesture for age estimation. We collected a total of 41,911 zoom data samples. From each zoom sample, 90 features were extracted. Those features were then used to train and test on six regressors and six classifiers to build a method that can accurately estimate the user’s age from their touchscreen behavior. The regressors performed with the best mean absolute errors (MAEs) of 2.27 and 2.54 years for smartphones and tablets, respectively. The classifiers performed with the best accuracies of 90% and 91% for smartphones and tablets, respectively. Given these results, it is our belief that not only is touch-based age estimation viable, but developing a child-safe browser or a parental control app with this underlying technology is a worthwhile endeavor. • Age estimation using zoom-in and zoom-out touchscreen gesture data. • We envision a web browser with a feature to filter age-inappropriate Internet content. • Developing a child-safe browser based on our method shows significant promise. • Classification between “child,” “between,” and “adult” result in accuracies over 90%. • Regression methods to estimate age result in mean absolute errors as low as 2.2 years.
Cicero: A Declarative Grammar for Responsive Visualization

Designing responsive visualizations can be cast as applying transformations to a source view to render it suitable for a different screen size. However, designing responsive visualizations is often tedious as authors must manually apply and reason about candidate transformations. We present Cicero, a declarative grammar for concisely specifying responsive visualization transformations which paves the way for more intelligent responsive visualization authoring tools. Cicero's flexible specifier syntax allows authors to select visualization elements to transform, independent of the source view's structure. Cicero encodes a concise set of actions to encode a diverse set of transformations in both desktop-first and mobile-first design processes. Authors can ultimately reuse design-agnostic transformations across different visualizations. To demonstrate the utility of Cicero, we develop a compiler to an extended version of Vega-Lite, and provide principles for our compiler. We further discuss the incorporation of Cicero into responsive visualization authoring tools, such as a design recommender.