Reviewerly/exHarmony huggingface dataset:
Walls Have Ears: Eavesdropping User Behaviors via Graphics-Interrupt-Based Side Channel

Graphics Processing Units (GPUs) are now playing a vital role in many devices and systems including computing devices, data centers, and clouds, making them the next target of side-channel attacks. Unlike those targeting CPUs, existing side-channel attacks on GPUs exploited vulnerabilities exposed by application interfaces like OpenGL and CUDA, which can be easily mitigated with software patches. In this paper, we investigate the lower-level and native interface between GPUs and CPUs, i.e., the graphics interrupts, and evaluate the side channel they expose. Being an intrinsic profile in the communication between a GPU and a CPU, the pattern of graphics interrupts typically differs from one GPU workload to another, allowing a spy process to monitor interrupt statistics as a robust side channel to infer behavior of other processes. We demonstrate the practicality of such side-channel exploitations in a variety of attacking scenarios ranging from previously explored tasks of fingerprinting the document opened and the application launched, to distinguishing processes that generate seemingly identical displays. Our attack relies on system-level footprints rather than API-level ones and does not require injecting any payload into the GPU resource space to cause contentions. We evaluate our attacks and demonstrate that they could achieve high accuracy in the assumed attack scenarios. We also present in-depth studies to further analyze the low-level rationale behind such effectiveness.
Circle detection and location based on binocular stereo vision

Abstract In this paper, a binocular stereo vision system is developed for recognition and localization of a spherical object. The system uses two cameras to acquire images of object, shows and processes the images from two cameras simultaneously. It then applies point Hough transform to recognize the object and get real-time coordinates through the two images. Experiments with the proposed method yielded good results, as the system can detect and position a spherical object successfully.
CNN-Based Medical Device Design for the Smart Pillbox With Face Recognition

This study aims to assist elders with chronic diseases living in healthcare centers or a big family. In the era of AI, designers are not only to design product forms and functions, but the computer vision technique of AI can be used to help them. Based on the convolutional neural network (CNN) technology (as a supervised learning), three different types of optimizers of MobileNet V2 for training the best model accuracy, which is applied to a gerontechnological pillbox device with face recognition. In addition, the MobileNet V2 model proposed is imported into the JETSON edge machine that can identify different faces and then emit different sounds with its corresponding name and the quantity of medications. The smart pillbox device can improve the compliance and safety of elderly people when taking medicine.
SRR-GAN: Super-Resolution based Recognition with GAN for Low-Resolved Text Images

Text images convey important information for various applications, while the recognition of low-resolution text images is a challenge. Most existing methods solve this problem using a cascaded scheme in two steps: image super-resolution and high-resolution text recognition. In this paper, we propose a novel framework, called SRR-GAN, which integrates text recognition with super-resolution via adversarial learning. By joint training of recognition and super-resolution models, more generic features for images of various quality can be learned, so as to yield high recognition performance for both high-resolution and low-resolution images. Experiments on natural scene and handwritten texts demonstrate that SRR-GAN outperforms the cascaded scheme on low-resolution images. The results show that SRR-GAN can improve recognition accuracies by 10%-20% relatively on five datasets of scene/handwritten texts. Meanwhile, SRR-GAN maintains high performance on high-resolution images.
A distance based multisample test for high-dimensional compositional data with applications to the human microbiome

Abstract Background Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. Results In this paper, we consider a general problem of testing for the compositional difference between K populations. Motivated by microbiome and metagenomics studies, where the data are often over-dispersed and high-dimensional, we formulate a well-posed hypothesis from a Bayesian point of view and suggest a nonparametric test based on inter-point distance to evaluate statistical significance. Unlike most existing tests for compositional data, our method does not rely on any data transformation, sparsity assumption or regularity conditions on the covariance matrix, but directly analyzes the compositions. Simulated data and two real data sets on the human microbiome are used to illustrate the promise of our method. Conclusions Our simulation studies and real data applications demonstrate that the proposed test is more sensitive to the compositional difference than the mean-based method, especially when the data are over-dispersed or zero-inflated. The proposed test is easy to implement and computationally efficient, facilitating its application to large-scale datasets.
deepKnit: Learning-based Generation of Machine Knitting Code

Modern knitting machines allow the manufacturing of various textile products with complex surface structures and patterns. However, programming these machines requires expert knowledge due to constraints of the process and the programming language. We present a long short-term memory (LSTM) based deep learning model that generates low-level code of novel knitting patterns based on high-level style specifications. To be processable by our model, we describe knitting instructions as one-dimensional sequences of tokens, which diverts from image-based approaches reported in previous research. We integrate our model into a design tool, that allows to assemble the atomic patterns to bigger swatches or garments. To evaluate our approach quantitatively, we formalize the requirements for patterns to be syntactically correct and valid to manufacture. Although our generated patterns look more random and seem to resemble less to human patterns, our evaluation shows that their knittability is orders of magnitudes better than randomly generated patterns.
IoT platform based on EOG to monitor and control a Smart Home Environment for Patients with Motor Disabilities

This paper presents an IoT platform based on electrooculography (EOG) to provide assistance, control and monitor a Smart Home Environment for Patients with Motor Disabilities in real time. The user can control the intelligent environment by selecting through a graphical interface (GUI) with predefined options such as control the doors, windows, lights, air conditioning, temperature and control the functions of a TV. To navigate in the GUI, a combination of three saccadic movements of the eye must be performed (e.g. move the eye to the left then right and left). The GUI is integrated in the gateway that connects the smart home with the cloud. The actions performed by the user can be controlled and monitored in real-time from the cloud. This method was evaluated with 12 users. Our designed IoT platform has an accuracy of 97.71%. All users were able to control the smart home environment using the sequence of instructions based on EOG.
Modeling the behavior of web crawlers on a web resource

Abstract In this paper, we present a study of web-crawlers behavior depending on the web resource. We provide a simulation model of web-crawler which is necessary for web robots detection techniques and dataset generation for methods based on machine learning. We analyze differences of behavior among humans, common crawlers, malicious crawlers and demonstrate that their models can be used for behavior analysis. We show that malicious crawlers behave similar to common crawlers and their behavior can be simulated to obtain necessary datasets and traffic patterns for the further detection and protection against unethical crawling. Our results and observations can be used as a basis of comprehensive intrusion detection and prevention system development.
Obstacle Avoidance using Fuzzy Controller for Unicycle Robot

Obstacle avoidance, researching goal, and the path planning are the most important tasks for an autonomous mobile robot which moves in an uncertain environment, this paper presents a method of a fuzzy controller to control the robot to avoid all fixed and mobile obstacles. The objective is the control and programming of a mobile robot with obstacle avoidance using fuzzy logic. This robot can be controlled by a human-machine interface or by a keyboard. Communication between the human-machine interface and Robot is done by using wireless communication. Ultrasonic sensors and infrared sensors will be used to measure the distances between the robot and the obstacles in different directions. This robot can avoid collision with obstacles by intelligent reasoning using fuzzy logic.
Inner-process visualization of hidden states in recurrent neural networks

In this paper, we introduce a visualization technique aimed to help machine learning experts to analyze the hidden states of layers in recurrent neural networks (RNNs). Our technique allows the user to visually inspect how hidden states store and process information throughout the feeding of an input sequence into the network. It can answer questions such as which parts of the input data had a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output. Our visualization comprises several components: our input visualization shows the input sequence and how it relates to the output (using color coding); hidden states are visualized by nonlinear projection to 2-D visualization space via t-SNE in order to understand the shape of the space of hidden states; time curves are employed to show the details of the evolution of hidden state configurations; and a time-multi-class heatmap matrix visualizes the evolution of expected predictions for multi-class classifiers. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory (LSTM) models applied to two widely used natural language processing (NLP) datasets.
Heterogeneous remote-sensing image matching method based on deep learning

Abstract In this paper, a deep learning matching method is proposed to address the difficulty in matching heterogeneous remote sensing images, which is caused by their differences in imaging modes, time phases, and resolutions. A heterogeneous image is inputted into the convolutional neural network (CNN) to extract deep features and, then, a graph neural network (GNN) is used for matching. Finally, correct matching points are retained while ensuring the effective elimination of mismatches. The algorithm adopted in this study was tested using multiple sets of heterogeneous remote-sensing images and compared with D2-Net+NN+RANSAC and Superpoint+SuperGlue algorithms. The results show that the algorithm used in this study possesses strong adaptability and robustness and is an optimal algorithm for the robust matching of remote-sensing images with different sources and large differences.
NCASH: Nonlinear Cellular Automata-based Hash function

Banerjee, Tapadyoti Roy Chowdhury, DipanwitaIn this paper, we propose a new keyed one-way double-block-length hash function by using Non-linear Cellular Automata (CA), named as NCASH. The structure of NCASH mainly follows the Wide-Pipe construction, which is a modified Merkle Damgård construction along with the concept of Hirose double-block-length one-way compression function. Our design exploits the random evolution of the CA, as well as a simple regular structure to construct the compression function. The analysis shows that NCASH family is secure against the related known attacks.
Low-Level Sensor Fusion for 3D Vehicle Detection Using Radar Range-Azimuth Heatmap and Monocular Image

Robust and accurate object detection on roads with various objects is essential for automated driving. The radar has been employed in commercial advanced driver assistance systems (ADAS) for a decade due to its low-cost and high-reliability advantages. However, the radar has been used only in limited driving conditions such as highways to detect a few forwarding vehicles because of the limited performance of radar due to low resolution or poor classification. We propose a learning-based detection network using radar range-azimuth heatmap and monocular image in order to fully exploit the radar in complex road environments. We show that radar-image fusion can overcome the inherent weakness of the radar by leveraging camera information. Our proposed network has a two-stage architecture that combines radar and image feature representations rather than fusing each sensor's prediction results to improve detection performance over a single sensor. To demonstrate the effectiveness of the proposed method, we collected radar, camera, and LiDAR data in various driving environments in terms of vehicle speed, lighting conditions, and traffic volume. Experimental results show that the proposed fusion method outperforms the radar-only and the image-only method.
A Cloud-based Architecture for Automated Grading of Computer-Aided Design Student Work Using Deep Learning

The practical benefits of deep convolutional networks are increasingly proven by successful implementations of deep learning models in various image processing applications. A model is proposed to evaluate the realize the benefits of deep convolutional neural networks to grade computer aided student drawings. Such model would assist in full automating the educators task or serving as a decision support system. Given the shared fundamental skills at least for fundamental drawing skills, the model has the potential to be used as Cloud service to be utilized by various distributed users. This would only serve to support and improve the model's performance given the additional training it could provide. A cloud software as a service (SaaS) architecture is introduced. The neural network model for the image recognition problem is introduced and evaluated. Experimental results using student drawings prove the model's capability to automatically generate accurate grades for the computer aided designs.
Mechanism of Parked Domains Recognition Based on Authoritative DNS Servers

At present, there are a large number of parked domains, which seriously affect online users when surfing. To identify parked domains effectively, a new technique was proposed based on authoritative Domain Name Server (DNS). In this way, suspected authoritative DNS servers of typosquatting domains were extracted, which commonly used in domain parking service. Then these DNS servers were clustered by semi-supervised clustering method, to identify whether they were associated with domain parking service. When detecting a parked domain, we can identify it by judging whether its authoritative DNS applied in domain parking service and whether its mapping IP addresses concluded in the set od IP addresses of parking web servers. With existing detecting method by using webpage's features to analyze the accuracy of the proposed method, the experimental results show the proposed method achieves a high accuracy rate of 92.8%, avoids crawling the webpages, has a good performance on parked domains detection in real time.
Computer Processing of an Image

In this chapter, the authors have briefed about images, digital images, how the digital images can be processed. Image types like binary image, grayscale image, color image, and indexed image and various image formats are explained. It highlights the various fields where digital image processing can be used. This chapter introduces a variety of concepts related to digital image formation in a human eye. The mechanism of the human visual system is discussed. The authors illustrate the steps of image processing. Explanation on different elements of digital image processing systems like image acquisition, and others are also provided. The components required for capturing and processing the image are discussed. Concepts of image sampling, quantization, image representation are discussed. It portrays the operations of the image during sampling and quantization and the two operations of sampling which is oversampling and under-sampling. Readers can appreciate the key difference between oversampling and under-sampling applied to digital images.
ELECTRONIC TESTS IN GAMIFICATION DISCOURSE

The article deals with peculiarities of modern electronic tests in the gamification discourse. Gamification allows teachers to motivate students and use effective approaches to the organization of the educational process. As a result of the study it was clarified that electronic tests have a number of advantages over paper tests, in particular, this is the presence of a larger number of test options offered to students; automatic check and scoring; convenience of passing the test for students; unlimited possibilities for the teacher to make amendments, changes and additions to electronic tests, etc. More than half of the respondents pointed out the importance of paper saving and the corresponding conservation of forests in the modern world. This indicates the development of students' environmental awareness. It was noted that the majority of respondents pass an electronic test more easily and comfortably, and, on the contrary, have a more serious attitude to a paper test. The advantages of using electronic tests in the eLearning system of Sumy State University are the availability of convenient templates for creating basic types of tests and their editing; automatic calculation by the system of students’ tests results; automatic change of the question and answer sequence options with each new test passing; ensuring the availability of tests or other educational materials for students; ability to use mobile devices for passing tests. The article defines the elements of gamification in electronic tests, among which there are clear rules for passing the test, which students should follow; test results appear on a computer or smartphone screen; there are elements of competition between students taking the test. The influence of modern technical means on gamification of the learning process was noted, in particular, almost a third of respondents noted that passing an electronic test would be more like a game when passing a test on a smartphone.
The Study of Two Novel Speech-Based Selection Techniques in Voice-User Interfaces

The voice-based technology is a promising alternative input modality to the conventional computer input devices, such as keyboards and mice.For mobility-impaired people who cannot use the traditional computer input devices, a voice-based interactive solution is highly desirable.Although some grid-based cursor control systems that use speech recognition show potentials, they come with limitations in processing time and difficulties in real-time applications.Since they are not natural enough to use, it is very slow to drill down the grid, and the point cursor selection range is prone to errors, and people need a more efficient and natural choice.To address these limitations, this paper proposes two novel speech-based target selection techniques: grid-based region selection (hereinafter referred to as "Region Selection") and grid-based target search (hereinafter referred to as "Target Search").Region Selection allows users to select targets directly using the selected area.Target Search permits users to move the cursor to a target in a specified direction.Experiments are designed and conducted to analyze the impacts of target size and target density on performance and processing time.The experimental results and comparison study with traditional solution show that both Region Selection and Target Search improve the participants' accuracy of target selection and reduce the processing time, especially for small targets.
Performance Comparison and Optimized Algorithm Classification

Abstract The current development of technology is quite rapidly not disengaged in a large data processor covering of all areas such as information technology, computer science, medicine, finance and other. This brings a large computing effect in identifying the processing of data. In data analysis for very large data, data processing is very much needed, in this study the authors propose data mining method as a solution to a large data processing problem, data mining is divided into several techniques including classification method techniques that aims to classify large amounts of data to be relevant data information. In this study the authors compared 5 algorithms in the classification method to get better performance in classification problems. Researchers analyze and test 5 Algorithm classifications with 4 different datasets as a tool in the problem of large data classification. The results of this research show the method SVM is much better to be used 4 comparison methods in calculating the value of AUC by using 4 datasets of UCI Repository. The LSVT Dataset shows the highest AUC value with 0973, Ionsphere 0887, Sonar 0897, Heartstatlog 0868.
FORF-S: A Novel Classification Technique for Class Imbalance Problem

In recent years, the class imbalance problem that aims to correctly classify imbalanced data sets and improve the classification performance of minority instances has received attention. Such problem can be roughly described as one of the class(es) termed as minority class(es) contains much smaller instances than the others, also referred as majority class(es). To address this problem, a novel classification method called focused online random forest based on synthetic minority oversampling technique (FORF-SMOTE) is proposed in this paper and simply expressed as FORF-S, which constructs two online random forests respectively trained by original training dataset and new generated dataset, then further jointly constitute the model. Instead of oversampling the minority instances in data level, the algorithm in this paper is motivated by making the sampling strategies integrated into algorithm level to create classifiers, which can better identify the minority class. Moreover, the method is also compared with other state-of-the-art methods and the results have demonstrated that the proposed algorithm takes advantages of the aforementioned methods.
Artifactual Affordances in Playful Robotics

Programmable toys present interesting tools for the creation and facilitation of learning experiences through gaming and robotics. In order though for educators and game designers to use these tools to their full potential and provide pedagogical opportunities to support problem-solving activities, we should identify and characterise artifactual affordances in playful robotics. In problem-solving activities, supported by educational robotics artifacts, learners need to identify the artifactual affordances through their interaction with the artifact. This study aims to identify artifactual affordances through the analysis of 15 participants’ engagement in the CreaCube task. In this playful problem-solving activity, based on the use of modular robotics, players need to manipulate and assemble robotic cubes in order to create independently moving vehicles. This study aims at providing an initial analysis for the study of artifactual affordances in playful educational robotics.