Reviewerly/exHarmony huggingface dataset:
A Steganographic Analysis using Batch Steganography

Steganography is a scheme to conceal a secret file inside another means of information, also known as a cover, which can be retrieved at the destination. It is an approach that prevents unauthorized users from not only having access to important data but also prevents the attackers from knowing about the transmission of it. The fundamental target of this work is to introduce a robust steganographic method to send data to the destination without being noticed. Here, the secret message is compressed and encrypted using Lempel-Ziv-Welch [1] technique. Conventionally, a single image would be the cover image, but it has shortcomings. So, we implement Batch Steganography [2] with sequential hashing. It is the process of segregating the secret data and embedding it to multiple cover images using the Least Significant Bit method. The decoding and retrieving process is discussed as well.
STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user's multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.
Research on Electronic Document Management System Based on Cloud Computing

With the development of information technology, cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion, popularization and vigorous development of e-government and e-commerce, the number of documents in electronic form is getting larger and larger.Electronic document is an indispensable main tool and real record of e-government and business activities.How to scientifically and effectively manage electronic documents?This is an important issue faced by governments and enterprises in improving management efficiency, protecting state secrets or business secrets, and reducing management costs.This paper discusses the application of cloud computing technology in the construction of electronic file management system, proposes an architecture of electronic file management system based on cloud computing, and makes a more detailed discussion on key technologies and implementation.The electronic file management system is built on the cloud architecture to enable users to upload, download, share, set security roles, audit, and retrieve files based on multiple modes.An electronic file management system based on cloud computing can make full use of cloud storage, cloud security, and cloud computing technologies to achieve unified, reliable, and secure management of electronic files.
Vision Based Detection of Driver Cell Phone Usage and Food Consumption

Distracted driving is a problem which yearly causes a large amount of road traffic crashes with high rates of fatalities and injured persons. Recently, car manufacturers started to integrate driver monitoring systems to detect visual distraction. This paper proposes a method to extend such systems by driver posture classification to detect driver cell phone usage and food consumption. Such an extension can be beneficial since systems that focus on the detection of visual distraction mainly rely on head pose and gaze information. Thus, distraction caused by cell phone usage or food consumption can not be detected by these systems when the driver is looking to the road ahead. To robustly detect those types of manual and cognitive distraction, different deep learning models were trained and evaluated based on a new image dataset which was captured by two infrared cameras to ensure that a large range of head angles can be covered by the system. Separate Convolutional Neural Networks (CNNs) were trained and evaluated for the dataset of the left and the right camera to optimize the classification accuracy. The trained CNNs revealed a competitive test accuracy of 92.88% and 90.36% for the left and the right camera, respectively. In inference mode, the models achieve a frame rate of 44Hz and 28Hz for the left and the right camera, respectively. The combination of the classification output of both networks revealed a test accuracy of 92.54%.
Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders

Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).
Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines

The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning problems are continuous. While binarization of continuous data through brute-force thresholding has yielded promising accuracy, such an approach is computationally expensive and hinders extrapolation. In this paper, we address these limitations by standardizing features to support scale shifts in the transition from training data to real-world operation, typical for e.g. forecasting. For scalability, we employ sampling to reduce the number of binarization thresholds, relying on stratification to minimize loss of accuracy. We evaluate the approach empirically using two artificial datasets before we apply the resulting TM to forecast dengue outbreaks in the Philippines using the spatiotemporal properties of the data. Our results show that the loss of accuracy due to threshold sampling is insignificant. Furthermore, the dengue outbreak forecasts made by the TM are more accurate than those obtained by Support Vector Machines (SVMs), Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting precision and Fl-score.
Sign Language Recognition Based on Notations and Neural Networks

Automated translation from sign languages used by the hearing-impaired people worldwide is an important but so far unresolved task ensuring universal communication in the society. In the paper, we propose an original approach to recognizing gestures of the Russian Sign Language ​​based on the combined use of the linguistic Hamburg System of Notations (HamNoSys) and OpenPose library for tracking human movements. Our software based on the specially constructed and trained artificial neural network (ANN) model performs recognition of the two main components commonly identified in gestures: handshape and location (while the hand orientation, the movement and the non-manual component are so far not considered). The recognition accuracy obtained in the experimental validation with the standard Leap Motion SDK hand tracking algorithm was 100% for adult signers and about 76% for the children. Details of the software architecture and the image recognition process with skeletal data are provided.
GANs Can Play Lottery Tickets Too

Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression techniques normally leads to unsatisfactory results, due to the notorious training instability of GANs. In parallel, the lottery ticket hypothesis shows prevailing success on discriminative models, in locating sparse matching subnetworks capable of training in isolation to full model performance. In this work, we for the first time study the existence of such trainable matching subnetworks in deep GANs. For a range of GANs, we certainly find matching subnetworks at 67%-74% sparsity. We observe that with or without pruning discriminator has a minor effect on the existence and quality of matching subnetworks, while the initialization weights used in the discriminator plays a significant role. We then show the powerful transferability of these subnetworks to unseen tasks. Furthermore, extensive experimental results demonstrate that our found subnetworks substantially outperform previous state-of-the-art GAN compression approaches in both image generation (e.g. SNGAN) and image-to-image translation GANs (e.g. CycleGAN). Codes available at https://github.com/VITA-Group/GAN-LTH.
Deep learning based method for phase analysis from a single closed fringe pattern

Interferometry is a widely used optical measurement technique. We can estimate the physical parameters of the measured object by analyzing the phase of the fringe pattern obtained by interference imaging. However, when the measurement object has spherical surface, the interferogram always contains closed fringes which the traditional analysis methods are difficult to handle. Therefore, we use several common deep learning networks to learn the closed fringe patterns and their phases, evaluate and choose the appropriate network to build an end-to-end phase analysis system for a single closed fringe pattern. The experimental results show that the constructed deep learning network model has excellent phase recovery effect on simulation closed fringe patterns, and can estimate the curvature radius of the spherical surface accurately.
Generic UAV Autopilot Prototype Based on Adaptive Modified Incremental Backstepping

Unmanned Aerial Vehicles (UAVs) have demonstrated their effectiveness for a huge number of civilian and military applications. However, the widely used commercial autopilots and flight controllers have to be tuned for each particular airframe. This problem is investigated in this research which focuses on the design of a generic flight control system based on the Adaptive Modified Incremental Backstepping (AMIBKS) technique. In a lot of researches, the Modified Incremental Backstepping (MIBKS) controller is used to stabilize all state variables simultaneously with partial dependency on the model parameters. MIBKS relies on estimates of the UAV angular accelerations and instantaneous control deflections measurements. However, aerodynamic coefficients include uncertainty and can lead to unexpected behavior of the flight controller in some circumstances. For this reason, an adaptive controller is designed to compensate for the effect of the aerodynamic modeling error. To design a standalone UAV flight control system, a real-time flight simulation system of Hardware-in-Loop (HIL) is constructed to facilitate the development, validation, and verification steps of flight control algorithms. HIL is implemented by using the XPC target computer to run the UAV model under test. A 32-bit microcontroller is pre-programmed with the desired reference and is used to command the UAV control surfaces. Results from HIL and numerical simulations are presented and compared to validate the proposed generic controller adequacy and evaluate its usefulness as a rapid autopilot prototyping tool. It is shown that the AMIBKS controller is a model-independent flight controller with complete dependency on the accuracy of the used UAV onboard sensors.
Near-Parallel Binocular-Like Camera Pair for Multi-Drone Detection and 3D Localization

The paper presents the concept of using a pair of cameras that are setup near and almost parallel to each other to do detection and 3D localization of multiple drone at far distance away. These setup of the two cameras resemble the binocular cameras, as such the 3D positions of the detected drones can be estimated by epipolar-like geometry. For the special case of detecting the multiple drones at a distance, quick depth distance approximation based on similar triangles is also proposed. Sample field tests of detecting and tracking multiple drones at near and far distances in open areas are presented. The depth distances obtained are comparable to the actual GPS readings from the detected drones.
An intelligent learning framework for Industry 4.0 through automated planning

Abstract The fourth industrial revolution, also known as Industry 4.0, has already begun enabling technological innovations in various domains, especially in the manufacturing and service industry. The conditions of the disruptive new product‐service emergence and quickly changing work environments require cultivating a competitive workforce that is equipped with essential knowledge and skills in Industry 4.0. The work presented herein identifies key competencies required for engineers in Industry 4.0 from the extant literature and offers a mechanism to facilitate knowledge and skills acquisition. Specifically, an automated planning software tool, iCoursePlanner , is proposed to recommend an optimized pathway to attain the identified competencies through not only offline courses in a college but also Massive Open Online Courses. This tool is based on a mobile app system in a cloud environment integrated with an optimization model for automated planning. iCoursePlanner uses the initial knowledge and goal knowledge of a learner to suggest a customized course plan. Case studies are conducted for industrial and manufacturing engineering students at Iowa State University and Bradley University to demonstrate how it can be used to help a student to acquire key competencies for Industry 4.0. The originality of the proposed automated course planning tool is rooted in the personalized course plan for each student that considers individual educational background and goal knowledge.
A Knowledge-Based Pilot Study on Assessing the Music Influence

A knowledge-driven approach is proposed for assessing the music influence on university students.The proposed method of modeling and conducting the interactive pilot study can be useful to convey other surveys, interviews, and experiments created in various phases of the user interface (UI) design processes, as part of a general human-computer interaction (HCI) methodology.Benefiting from existing semantic Web and linked data standards, best practices, and tools, a microservice-oriented system is developed as a testbed platform able to generate playlists in a smart way according to users' music preferences.This novel approach could bring also benefits for user interface adaptation by using semantic Web techniques.Statistical analysis based on the ANOVA method and post-experiment survey data led to the conclusion that music listened has a significant impact on students' cognitive abilities in various contexts.All obtained results were semantically enhanced by using different conceptual models in order to create a knowledge graph providing support for automated reasoning.Also, a knowledge-based persona Web smart editor was implemented in order to include music preferences for certain categories of the potential users operating a specific interactive system.
Distributed Noise Covariance Matrices Estimation in Sensor Networks

Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, the distributed auto-covariance least squares (D-ALS) algorithm is proposed based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of DALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.
I/O lower bounds for auto-tuning of convolutions in CNNs

Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous practical applications. Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on data movement (i.e., memory access). This work provides comprehensive analysis and methodologies to minimize the communication for the convolution in CNNs. With an in-depth analysis of the recent I/O complexity theory under the red-blue game model, we develop a general I/O lower bound theory for a composite algorithm which consists of several different sub-computations. Based on the proposed theory, we establish the data movement lower bound results for two main convolution algorithms in CNNs, namely the direct convolution and Winograd algorithm, which represents the direct and indirect implementations of a convolution respectively. Next, derived from I/O lower bound results, we design the near I/O-optimal dataflow strategies for the two main convolution algorithms by fully exploiting the data reuse. Furthermore, in order to push the envelope of performance of the near I/O-optimal dataflow strategies further, an aggressive design of auto-tuning based on I/O lower bounds, is proposed to search an optimal parameter configuration for the direct convolution and Winograd algorithm on GPU, such as the number of threads and the size of shared memory used in each thread block. Finally, experiment evaluation results on the direct convolution and Winograd algorithm show that our dataflow strategies with the auto-tuning approach can achieve about 3.32× performance speedup on average over cuDNN. In addition, compared with TVM, which represents the state-of-the-art technique for auto-tuning, not only our auto-tuning method based on I/O lower bounds can find the optimal parameter configuration faster, but also our solution has higher performance than the optimal solution provided by TVM.
Progressive Attentional Learning for Underwater Image Super-Resolution

Visual perception plays an important role when underwater robots carry out missions under the sea. However, the quality of images captured by visual sensors is often affected by underwater environment conditions. Image super-resolution is an effective way to enhance the resolution of underwater images. In this paper, we propose a novel method for underwater image super-resolution. The proposed method uses CNNs with channel-wise attention to learn a mapping from low-resolution images to high-resolution images. And a progressive training strategy is used to deal with large scaling factors (e.g. 4x and 8x) of super-resolution. We name our method as Progressive Attentional Learning (PAL). Experiments on a recently published underwater image super-resolution dataset, USR-248 [11], show the superiority of our method over other state-of-the-art methods.
Thermal Face Recognition using Artificial Neural Network

Face recognition is the process of identifying the human face with the help of computerized system mostly for the purpose of security reasons. As the face is very important characteristic of human body through which one can uniquely identify the person. So focusing the face for authentication or identification is at most important for variety of its applications. It is the problem of pattern recognition which can be easily and efficiently solved with artificial neural networks (ANN). Pattern recognition is the process through which particular patterns can be identified, recognized based on the relatedness and similarities between the patterns. Face recognition is having number of applications such as authentication at various offices, organizations or other sectors where security is of utmost important. The proposed methodology for face recognition made use of visible, thermal and fused image database. Thermal imaging detects the heat of the object or face. The artificial neural network is a very efficient technology to enhance the results. Here backpropagation and Levenberg-Marquardt algorithm were compared by using the same database. The Backpropagation algorithm achieved the accuracy of 92.86% and for Levenberg-Marquardt accuracy is 83.92%.
A novel semi-supervised method for classification of power quality disturbance using generative adversarial network

The existing methods for classification of power quality disturbance signals (PQDs) have the problems that the process of signal feature selection is tedious and imprecise, the accuracy of classification has no guiding significance for feature extraction, and lack of adequate labelled training data. To solve these problems, this paper proposes a new semi-supervised method for classification of PQDs based on generative adversarial network (GAN). Firstly, a GAN model is designed which we call it PQDGAN. After the unsupervised pre-training with unlabeled training data, the trained discriminator is extracted alone and conduct supervised training with a small amount of labelled training data. Finally, the discriminator became a classifier with high accuracy. This model can achieve the step of feature extraction and selection efficiently. In addition, only a small amount of labelled training data is used, which greatly reduces the dependence of classification model on labelled data. Experiments show that this method has high classification accuracy, less computations and strong robustness. It is a new semi-supervised method for classification of PQDs.