Reviewerly/exHarmony huggingface dataset:
Learning Invariant Representations with Kernel Warping

Invariance is an effective prior that has been extensively used to bias supervised learning with a \emph{given} representation of data. In order to learn invariant representations, wavelet and scattering based methods ``hard code'' invariance over the \emph{entire} sample space, hence restricted to a limited range of transformations. Kernels based on Haar integration also work only on a \emph{group} of transformations.In this work, we break this limitation by designing a new representation learning algorithm that incorporates invariances \emph{beyond transformation}.Our approach, which is based on warping the kernel in a data-dependent fashion, is computationally efficient using random features, and leads to a deep kernel through multiple layers. We apply it to convolutional kernel networks and demonstrate its stability.
A Tracking Window Adaptive Compressive Tracking Algorithm

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.
Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List

Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number U01DA038360 awarded to the Center for Substance Abuse Research (CESAR) at the University of Maryland, College Park.
Towards Hierarchical Probabilistic CTL Model Checking: Theoretical Foundations

This study proposes a hierarchical probabilistic computation tree logic, HpCTL, which is an extension of the standard probabilistic computation tree logic pCTL, as a theoretical basis for hierarchical probabilistic CTL model checking. Hierarchical probabilistic model checking is a new paradigm that can appropriately verify hierarchical randomized (or stochastic) systems. Furthermore, a probability-measure-independent translation from HpCTL into pCTL is defined, and a theorem for embedding HpCTL into pCTL is proved using this translation. Finally, the relative decidability of HpCTL with respect to pCTL is proved using this embedding theorem. These embedding and relative decidability results allow us to reuse the standard pCTL-based probabilistic model checking algorithms to verify hierarchical randomized systems that can be described using HpCTL.
Vector space of generalized radii of ellipses for quantitative analysis of blur patches and other referred apertures in the astigmatic eye

Ellipses are features of several structures in astigmatic eyes; they include retinal blur patches. How can one calculate change or averages or perform other quantitative analyses on such elliptical structures? The matrix A in the equation rTAr=1, commonly used to represent an ellipse, is positive definite; such matrices do not define vector spaces. They are unsuitable, therefore, for quantitative analysis of ellipses. This paper defines a generalized radius R of an ellipse that is positive or negative definite for locally diverging or converging rays, respectively, indefinite between line foci, and singular at foci. Generalized radii of ellipses constitute a vector space and are suitable for quantitative analysis of elliptical ocular structures.
Bayesian Learning for Policy Search in Trajectory Control of a Planar Manipulator

Application of learning algorithms to robotics and control problems with highly nonlinear dynamics to obtain a plausible control policy in a continuous state space is expected to greatly facilitate the design process. Recently, policy search methods such as policy gradient in Reinforcement Learning (RL) have succeeded in coping with such complex systems. Nevertheless, they are slow in convergence speed and are prone to get stuck in local optima. To alleviate this, a Bayesian inference method based on Markov Chain Monte Carlo (MCMC), utilizing a multiplicative reward function, is proposed. This study aims to compare eNAC, a popular gradient based RL method, with the proposed Bayesian learning method, where the objective is trajectory control of a complex model of a 2-DOF planar manipulator. The results obtained for the convergence speed of the proposed algorithm and time response performance, illustrate that the proposed MCMC algorithm is qualified for complex problems in robotics.
Detection of Student Teacher's Intention using Multimodal Features in a Virtual Classroom

The training program for high school teachers in Japan has less opportunity to practice teaching skills. As a new practice platform, we are running a project to develop a simulation platform of school environment with computer graphics animated virtual students for students’ teachers. In order to interact with virtual students and teachers, it is necessary to estimate the intention of the teacher’s behavior and utterance. However, it is difficult to detection the teacher’s intention at the classroom only by verbal information, such as whether to ask for a response or seek a response. In this paper, we propose an automatic detection model of teacher’s intention using multimodal features including linguistic, prosodic, and gestural features. For the linguistic features, we consider the models with and without lecture contents specific information. As a result, it became clear that estimating the intention of the teacher is better when using prosodic / non-verbal information together than using only verbal information. Also, the models with contents specific information perform better.
Avatar2Avatar: Augmenting the Mutual Visual Communication between Co-located Real and Virtual Environments

Virtual Reality (VR) technology has the potential to support knowledge communication in several sectors. Still, when educators make use of immersive VR technology in favor of presenting their knowledge, their audience within the same room may not be able to see them anymore due to wearing head-mounted displays (HMDs). In this paper, we propose the Avatar2Avatar system and design, which augments the visual aspect during such a knowledge presentation. Avatar2Avatar enables users to see both a realistic representation of their respective counterpart and the virtual environment at the same time. We point out several design aspects of such a system and address design challenges and possibilities that arose during implementation. We specifically explore opportunities of a system design for integrating 2D video-avatars in existing room-scale VR setups. An additional user study indicates a positive impact concerning spatial presence when using Avatar2Avatar.
Optimal and Robust Solutions for Longitudinal Flight Control of a Canard-Configured High Performance Aircraft

The problem of longitudinal flight control for a canard-configured combat aircraft is considered in this paper. The problem is characterized by unstable dynamics, unavailability of full state vector for the feedback, and unknown but bounded disturbances affecting the system. The solutions are developed along the lines of H-infinity Measurement-Feedback Control and the Linear Quadratic Gaussian (LQG) Control. The H-infinity control approach yields a dynamic feedback law which solves the disturbance attenuation problem via measurement feedback. This dynamic measurement feedback law consists of a combination of an estimator and a full-information controller, thanks to the separation principle for H-infinity control. The LQG approach, on the other hand, uses the simpler and well-known separation principle for optimal control and leads to an estimator/regulator pair. The controllers and the estimators are simulated in a test case to compare the performance of robust (i.e., H-infinity) and optimal (i.e., LQG) designs. The robust and optimal solutions are also simulated in another test case to verify their disturbance rejection properties against the wind gusts, which primarily perturb the angle of attack. The similarities and differences in both approaches are highlighted and the results of both design approaches are compared.
Application of deep learning techniques for characterization of 3D radiological datasets: a pilot study for detection of intravenous contrast in breast MRI

Categorization of radiological images according to characteristics such as modality, scanner parameters, body part etc, is important for quality control, clinical efficiency and research. The metadata associated with images stored in the DICOM format reliably captures scanner settings such as tube current in CT or echo time (TE) in MRI. Other parameters such as image orientation, body part examined and presence of intravenous contrast, however, are not inherent to the scanner settings, and therefore require user input which is prone to human error. There is a general need for automated approaches that will appropriately categorize images, even with parameters that are not inherent to the scanner settings. These approaches should be able to process both planar 2D images and full 3D scans. In this work, we present a deep learning based approach for automatically detecting one such parameter: the presence or absence of intravenous contrast in 3D MRI scans. Contrast is manually injected by radiology staff during the imaging examination, and its presence cannot be automatically recorded in the DICOM header by the scanner. Our classifier is a convolutional neural network (CNN) based on the ResNet architecture. Our data consisted of 1000 breast MRI scans (500 scans with and 500 scans without intravenous contrast), used for training and testing a CNN on 80%/20% split, respectively. The labels for the scans were obtained from the series descriptions created by certified radiological technologists. Preliminary results of our classifier are very promising with an area under the ROC curve (AUC) of 0.98, sensitivity and specificity of 1.0 and 0.9 respectively (at the optimal ROC cut-off point), demonstrating potential usefulness in both clinical as well as research settings.
Top-Down Human Pose Estimation with Depth Images and Domain Adaptation

In this paper, a method for estimation of human pose is proposed, making use of ToF (Time of Flight) cameras.For this, a YOLO based object detection method was used, to develop a top-down method.In the first stage, a network was developed to detect people in the image.In the second stage, a network was developed to estimate the joints of each person, using the image result from the first stage.We show that a deep learning network trained from scratch with ToF images yields better results than taking a deep neural network pretrained on RGB data and retraining it with ToF data.We also show that a top-down detector, with a person detector and a joint detector works better than detecting the body joints over the entire image.
Digital Image Steganography by Using a Hash Based LSB (3-2-3) Technique

Now-a-days, Internet becomes a big source for sharing information. When people share the data or information over the internet, they keep two important factors in their minds reliability and security. To achieve these factors, people are always trying to adopt new ideas. Image steganography is one of the famous method that is used to hide the secret data in the carrier image in the way that the carrier image having the secret data/information should not be detectable by human visual system (HVS). So we are going to design an approach for image steganography in which we will use the hash based technique with three Least Significant Bits method in the order of RGB 3-2-3 respectively. Our proposed approach will show the strong capability to read the information from text file and hide it in the image of any format like bmp, gif, jpg, jpeg and tiff without destroying the picture quality of stego image. No distortion in the image quality is detectable by Human Visual System. Experiments show that our proposed approach is providing better imperceptibility rate. So it enhances its security. Our proposed approach works efficiently as it is hash based. Every time it generates random hash key value. So it is not possible for any attacker or unauthorized person to get the hash key value. Hash Techniques improves its performance. Least Significant Bit Method makes it simple. Our proposed technique has also the high embedding capacity rate.
Unscented Kalman Filtering for Nonlinear Systems with Colored Measurement Noises and One-Step Randomly Delayed Measurements

Traditional unscented Kalman filtering (UKF) cannot solve the filtering problem for nonlinear systems with colored measurement noises and one-step randomly delayed measurements. To fix this problem, a new UKF algorithm is proposed in this paper. First, a system model with one-step randomly delayed measurements and colored measurement noises is established, wherein a first order Markov sequence model for whitening colored noises and an independently identical distributed Bernoulli variable for modeling one-step randomly delayed measurements is introduced. Second, an UKF is proposed for the above established models through unscented transformation by calculating the nonlinear states posterior mean and covariance based on the Bayesian filter framework. Specially, the proportional symmetric sampling method is used in the new UKF algorithm. Finally, the effectiveness and superiority of the proposed method is verified via simulation.
Key Generator to Encryption Images Based on Chaotic Maps

It is known that images differ from texts in many aspects, such as high repetition and correlation, local structure, capacitance characteristics and frequency. As a result, traditional encryption methods can not be applied to images. In this paper we present a method for designing a simple and efficient messy system using a difference in the output sequence. To meet the requirements of image encryption, we create a new coding system for linear and nonlinear structures based on the generation of a new key based on chaotic maps. The design uses a kind of chaotic maps including the Chebyshev 1D map, depending on the parameters, for a good random appearance. The output is a test in several measurements, including the complexity of the standard balance and time execution. The results show that any change in input will change the output and the time required for the application requires only parts of the minute. The output is passed through the exponential function [exp (X))] to obtain a wide range of input. The result is then set to machine words to fit the desired range of multicast used. The next step is the output of machine words that will be used as input to the array that we have structured to generate the key. The key is converted to a sequential element into an element with an ordinary image to produce the encrypted image. Furthermore, keystream succeeded by passing tests (NIST statistical package tests for random). Finally, the real-time image encoding of the corresponding algorithm was applied; preliminary results show that the proposed algorithm has good coding strength with added benefit that resists security analysis.
Classroom Action Research in Teaching English for Senior High School Students through Blended Learning in Kendari of Indonesia

his paper focuses on the problem of class teaching and solutions through classroom action research. The main problem in teaching, namely students are less motivated to learn and therefore their English language skills are still lacking. From these two problems, we carried out several activities ranging from observation, interviews, to teaching in the classroom with the blended learning model, then discussing what had been done to find out the root of the problem. This action research uses the design of “action research spiral structure” advocated by Stringer (2007) which consists of three levels, look, think and act. The results show progress in teaching where students’ summative grades are higher than the minimum requirement of completeness scores. This progress is obtained through transformation in teaching techniques by creating student-centered classes by involving them in class discussions, interacting, and integrating LMS Edmodo into the learning so that it becomes more interesting, interactive, and resourceful.
Roadway Image Preprocessing for Deep Learning-Based Driving Scene Understanding

In this paper, a road lane is detected as a reference point in a road driving image, and the objects that are likely to affect the driving situation are detected depending on a reference point, and then the remaining objects are excluded from the road driving situation recognition. In addition, we propose a method of mapping to Bird's Eye View as a normalization method to uniformly express the positions of objects irregularly appearing on each road driving image based on these reference points. As a result, the accuracy of 97.66% was obtained as a result of the experiment on the road driving situation recognition by the proposed method.
Cloud Computing-Positive Impacts and Challenges in Business Perspective

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Distributed Algorithm for Building Space Topology Matching

Nowadays, distributed systems turn to be increasingly competitive with centralized systems, especially in the construction field. Thus, it is indispensable to design new effective distributed algorithms. In this work, a distributed algorithm focusing on the building space topology matching problem is proposed. In distributed control architectures, each floor is equipped with several Computing Process Nodes (CPNs) which play roles as controllers in the system. Our goal is to make every CPN in the building system acquire its own position on a designed CAD drawing. To achieve this, we utilize the geographic relationships among nodes. In the algorithm, each node compares its local topology features to the ones in the drawing and communicates with their neighborhoods. We prove that the topology matching problem can be solved by using this algorithm and derive the upper bound of the number of iterations. The experiments show that this algorithm works successfully in real designed buildings.
Flocking Model for Self-Organized Swarms

The algorithms of self-organized swarm control refer first to two basic behaviors, these are aggregation and flocking. The present work focuses its research on coordinated movement behavior that is defined as the ability of a group of individuals (usually composed of hundreds or thousands) to move and maneuver in a coordinated manner as if they were a single structure. Such behavior refers us to studies carried out in the field of trajectory control and aggregation behavior, both being keys for the development of a coordinated movement control algorithm. Therefore, control of the system starts from the combination of these studies. The control is a leading robot model which can be designated for any unit according to the assigned task. On the other hand, all robots except the leader keep the group attached and keeping a safe distance. The simulations of the system were developed for three units and later for twelve, observing the cohesion and uniformity of the swarm in movement.
E-PORTFOLIO IN THE SYSTEM OF PREPARATION OF STUDENTS OF BACCALAUREATE FOR FUTURE PROFESSIONAL ACTIVITY

Industry 4.0 imposes certain requirements on the openness of academic results presented in the electronic portfolio — an alternative to academic diploma, which allows the employer to see how the co-seeker implements the lifelong education society slogan in his life. The article presents a study on the use of the undergraduate student’s electronic portfolio, which ensures the productivity of the educational process at the university. The creation of an electronic portfolio provides a high-quality accumulation of information necessary for the timely recording of real changes and achievements of an undergraduate student. Maintaining an electronic portfolio is a creative work that allows the undergraduate student to realistically present their educational level, see the reserves, determine the vector of professional and personal self-improvement and master digital technologies, and let the employer remotely select applicants to make a decision upon inviting him for an interview. In the conditions of the fourth industrial revolution, the country’s position in the global world economy will be determined not only by the degree of development of the digital economy, but also by the quality of human capital and education.
Choosing a password breaking strategy with imposed time restrictions

The aim of the article is to present the password breaking methodology in case when an attacker (forensic investigator, court expert, pen tester) has imposed time restrictions. This is a typical situation during many legal investigations where computers are seized by legal authorities but they are protected by passwords. At the beginning, the current state of law in that matter is presented, along with good practices in seizing the evidence. Then, the ways of storing static passwords in information systems are showed, after which various classes of password breaking methods are reviewed (dictionary, brute-force, rule, combinator, mask, hybrid, etc.). The most popular tools supporting this process are listed as well. The main part of the paper presents the original strategy of conducting an attack on a single hashed password with time constraints. Costs as well as economic efficiency for four different hardware solutions (laptop, gaming computer, rig with 6 GPU’s, cloud computing) are discussed. The calculations are shown on the example of two real attacks carried out by the author in the real legal cases. Keywords: passwords, breaking passwords, hash functions, computer forensics.