Reviewerly/exHarmony huggingface dataset:
Automated Cyberbullying Detection in Social Media Using an SVM Activated Stacked Convolution LSTM Network

Cyberbullying is becoming a huge problem on social media platforms. New statistics shows that more than a fourth of Norwegiankids report that they have been cyberbullied once or more duringthe last year. In the most recent years, it has become popularto utilize Neural Networks in order to automate the detection ofcyberbullying. These Neural Networks are often based on using Long-Short-Term-Memory layers solely or in combination withother types of layers. In this thesis we present a new Neural Networkdesign that can be used to detect traces of cyberbullying intextual media. The design is based on existing designs that combinesthe power of Convolutional layers with Long-Short-Term-Memorylayers. In addition, our design features the usage of stacked corelayers, which our research shows to increases the performance ofthe Neural Network. The design also features a new kind of activationmechanism, which is referred to as "Support-Vector-Machinelike activation". The "SupportVector-Machine like activation" isachieved by applying L2 weight regularization and utilizing a linearactivation function in the activation layer together with using aHinge loss function. Our experiments show that both the stackingof the layers and the "Support-Vector-Machine like activation"increasesthe performance of the Neural Network over traditionalState-Of-The-Art designs.
A Technical Review on Image Super-Resolution Techniques

Conversion from multiple low resolution (LR) images to high resolution (HR) image is done by using super-resolution techniques. Anyone can achieve more information in detail from high-resolution images, which helps further for many satellite image applications. This growing technology interest in the reconstruction of imagery leads to several methodologies in the field of advanced digital color image processing. The study presents and describes the various conventional algorithms of image SR reconstruction used to date by researchers. In this review paper, we have shown different types of super-resolution techniques starting from image noisy to remotely sensed imagery for super-resolution mapping at a sub-pixel level with their characteristics and major limitations. A precise comparative study was done on different domains like spatial and frequency as shown separately in tabular form.
Aesthetic-Aware Text to Image Synthesis

Synthesizing an image from natural language description is an important task to many applications such as photo-editing, art generation, and computer aided-design. However, to synthesize an appealing image from the text, image aesthetics criteria should be maintained. In this study, we propose a new framework which first generates a set of mask maps from the input text via mask map generator (MG), and then we compute and rank the image aesthetics score for all generated mask maps via Pre-IG Aesthetic Ranking that contains two composition rules, i.e., the rule of thirds along with the rule of formal balance. At the next stage, we feed the subset of the mask maps, which are the highest, lowest, and the average aesthetic scores, to image generator (IG). The photorealistic images are ranked at the second round through, namely Post-IG Aesthetic Ranking, to determine the lowest aesthetic score and return the most appealing generated image. The experiments on COCO-stuff dataset demonstrate that our framework yields better results compared to previous text-to- image models.
Carrot and stick approaches when managing technical debt

When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, decrease the developers' morale, and compromise the overall quality of the software. However, even if researchers and practitioners working in today's software development industry are quite familiar with the concept of technical debt and its related negative consequences, there has been no empirical research focusing specifically on how software managers actively communicate and manage the need to keep the level of technical debt as low as possible.
Image Enhancement based on Whale Optimization Algorithm

The digital image technology is the most widely used in various fields, especially in medical imaging. Digital image technology can effectively improve the efficiency of medical image processing and ensure the accuracy of clinical diagnosis. This paper studies Whale Optimization Algorithm (WOA) to optimize the optimal value of Gamma correction in image enhancement. In the process of searching for the optimal solution, the WOA is an efficient algorithm, which has the characteristics of fast convergence and high precision. In order to present the detailed information of medical image, WOA is applied to search the optimal value of Gamma correction.
Fine-Grained Access Control-Enabled Logging Method on ARM TrustZone

Most applications for the Internet of Things operate on embedded systems. In particular, embedded devices intended for smart healthcare, smart homes, and smart cars generate logs containing sensitive user information. These logs must be protected from malicious users while also being accessible for legitimate users to utilize them for providing customized services. Unfortunately, the existing logging system only supporting one-to-one encryption based on a server-client model, so there are limitations in building a decentralized logging infrastructure for the hyper-connected era. In this paper, we propose a new secure logging method that supports one-to-many encryption and extends existing logging systems to a decentralized logging infrastructure. In the proposed method, log publishers are able to encrypt generated logs and distribute them to cloud storage in real time and can ensure that only authorized log subscribers access the logs. For one-to-many encryption, we apply a key-policy attribute-based encryption scheme which is suitable for logging systems. For reliability and efficiency of logs, we apply a key-derivation process that cooperates with one-way hash functions within a trusted execution environment. In a real time logging scenario, the proposed method is 93% faster and occupies 83% less storage space than when an original attribute-based encryption scheme is applied. In addition, performance-tunable parameters can optimize our method for various environments.
Deep Learning Based Video Analytics For Person Tracking

As the assets of people are growing, security and surveillance have become a matter of great concern today. When a criminal activity takes place, the role of the witness plays a major role in nabbing the criminal. The witness usually states the gender of the criminal, the pattern of the criminal’s dress, facial features of the criminal, etc. Based on the identification marks provided by the witness, the criminal is searched for in the surveillance cameras. Surveillance cameras are ubiquitous and finding criminals from a huge volume of surveillance video frames is a tedious process. In order to automate the search process, proposed a novel smart methodology using deep learning. This method takes gender, shirt pattern, and spectacle status as input to find out the object as person from the video log. The performance of this method achieves an accuracy of 87% in identifying the person in the video frame.
Parallel Encryption of Noisy Images Based on Sequence Generator and Chaotic Measurement Matrix

With the rapid development of information technology in today’s society, the security of transmission and the storage capacity of hardware are increasingly required in the process of image transmission. Compressed sensing technology can achieve data sampling and compression at the rate far lower than that of the Nyquist sampling theorem and can effectively improve the efficiency of information transmission. Aiming at the problem of weak security of compressed sensing, this study combines the cryptographic characteristics of chaotic systems with compressed sensing technology. In the actual research process, the existing image encryption technology needs to be applied to the hardware. This paper focuses on the combination of image encryption based on compressed sensing and digital logic circuits. We propose a novel technology of parallel image encryption based on a sequence generator. It uses a three-dimensional chaotic map with multiple stability to generate a measurement matrix. This study also analyzes the effectiveness, reliability, and security of the parallel encryption algorithm for source noise pollution with different distribution characteristics. Simulation results show that parallel encryption technology can effectively improve the efficiency of information transmission and greatly enhance its security through key space expansion.
Realizing the Promise of Artificial Intelligence for Unmanned Aircraft Systems through Behavior Bounded Assurance

A key value proposition for incorporation of Artificial Intelligence (AI) and Machine Learning (ML) methods into aviation is that they offer means of understanding data in ways that allow hitherto unprecedented insights for decision making, whether by a human or a machine. When these techniques are applied to cyber-physical systems, such as unmanned aircraft systems (UAS), they can result in positive societal impacts (e.g., search and rescue). However, the advantages of such techniques must be balanced against appropriate safety and security requirements so that taken together the system can ensure an acceptable level of confidence and assurance in both civilian and military applications. To this end, there is a need for the capability to suitably characterize such techniques and assess how they can be integrated into a viable assurance framework that can maximize safety and security benefits while bounding the inherent risk of non-determinism arising from such these approaches. This paper focuses on assurance and behavior bounds for decision making systems from a) algorithmic functional performance; b) schedulability analysis and candidate scheduling paradigms; and c) processor architectures (including multi-core) to support minimized interference in general. We will place particular emphasis on machine learning approaches for control, navigation and guidance applications for unmanned systems. This paper will review available and emerging approaches (e.g., formal methods, modeling and simulation, real-time monitors/agents among others) to ensuring behavior assurance for unmanned systems engaged in missions of moderate-to-high complexity. The intent is to examine behavior assurance for advanced autonomous operations within a holistic life-cycle process.
Saliency Consistency-Based Image Re-Colorization for Color Blindness

For patients with color vision defects, owing to the destruction of cone cells and loss of function, some color information is lost, hence changing the originally transmitted information by the image. The purpose of the traditional method of correction is to help patients distinguish between colors, but it does not take into account the problem of the saliency of the image. In this paper, we propose a saliency consistency-based image re-colorization for color blindness. We use image retrieval methods to select a large number of images and use co-saliency methods to detect salient areas of standard color images and color-blind simulated images. According to the detection results, an image with the same detection result was selected as the reference image. We grayscale the significantly changed image, recolor the grayscale image using the reference image, and the color scheme of the recolored image is similar to the reference image. The color matching scheme of the reference image makes the significance of the image basically unchanged in standard vision and color vision defects, thereby making the color blindness patient's perception of the image close to standard vision. We invite green blind patients to evaluate CVD simulation images with different recoloring methods subjectively. In addition, we use different evaluation criteria to evaluate the experimental results objectively. In the subjective evaluation and objective evaluation, the method proposed in this paper has achieved good results, which validates the effectiveness of our method.
Transfer2Depth: Dual Attention Network With Transfer Learning for Monocular Depth Estimation

Monocular depth estimation poses a fundamental problem in many tasks. Although recent convolutional neural network-based methods can achieve high accuracy with very deep networks and complex architectures to exploit different cues and features, doing so not only increases the vulnerability of the model, but also increases the difficulty of convergence. Moreover, recent depth estimation methods for indoor environments are impractical for outdoor environments. In this work, we aim to develop a simple deep network structure to improve model effectiveness for depth estimation. We apply a dual attention module that can be inserted into any type of network to improve the power of representation, and additionally propose a training strategy which combines transfer learning and ordinal regression to improve training convergence. Even with a simple end-to-end encoder-decoder type of network architecture, we are able to achieve state-of-the-art performance on two of the biggest datasets for indoor and outdoor depth estimation: NYU Depth v2 and KITTI.
Identification of Relevant Interactive Teaching Methods for the Development of Universal Competencies of Future Engineers

The article summarizes the results of the authors’ research of relevant interactive teaching methods aimed at developing the universal competencies of future engineers. The importance of universal competences in the process of becoming a specialist is associated with their stable and unquenchable character. The authors note that the bulk of the available work in this area is related to the classification and description of a sufficiently large number of methods of interactive learning, or reflect the results of their use in the process of professional training of various specialists in higher education. At the same time, the question of the optimal number and combination of interactive teaching methods has not been studied in relation to the modern tasks of training future engineers in the context of the transition to the Federal State Educational Standard of Higher Education 3++. The article presents the result of an expert survey of specialists employed in the engineering personnel training system of 5 universities in Yekaterinburg (n = 40). Using an expert survey, the authors identified a set of relevant methods of interactive training for the development of universal competencies for future engineers according to the Federal State Educational Standard 3++. The analysis of the results of the survey showed that there are significant differences in these sets, which, in turn, poses a further research task for the authors – to study the influence of the methods indicated by experts on the process of developing universal competencies of future engineers.
Virtual and Augmented Reality Applications in Science and Engineering

The articles in this special section focus on virtual and augmented reality applications in science and engineering. There has been an explosive growth in visually augmenting the spaces around us by creating environments where visual, aural, and kinesthetic immersive experiences afforded by virtual and augmented reality (AR) powerfully engage us in a way no other medium can. Virtual reality (VR) recreates the sensory world around us entirely through computer-generated signals of sight, sound, touch (and in some cases smell and taste). AR overlays the computer-generated sensory signals on the real world allowing the user to experience a rich juxtaposition of the virtual and the real worlds simultaneously. Together, these technologies are transforming the way people from all walks of life—scientists, engineers, educators, industrial workers, health care professionals, artists, and everyday people— see and use the information that matters most to them, in an intuitive embodied way. Just as mobile technology has revolutionized how we communicate with each other and with our digital worlds, ubiquitous VR and AR will fundamentally alter how our society creates, inspires, engages, and learns fromthe information-rich and enriched cyberspaces around us. While affordable consumer- quality VR and AR hardware is becoming available, significant work is needed to adopt VR and AR for important and difficult scientific and societal applications.
Learning the Game of <i>Go</i> by Scalable Network Without Prior Knowledge of Komi

AlphaGo trains a value network to predict the win rate of the current state with 7.5 komi on a 19 × 19 board. The komi of most rectangular boards is unknown, so we do not know who the winner is at the end of the game. We need to use the human experience to guess a komi and then train the value network with this komi. Therefore, the accuracy of the value network is related to the accuracy of the guess. This article uses the board value network to calculate the score of the current state and tries to maximize the score. Then, we do not need to guess the komi. We also modify the network structure to support the board with arbitrary board size as input. Furthermore, we can transfer knowledge of the small board to the large board. We propose an algorithm that can adapt to the bonus rule. We have experimentally proved that our method is effective on a small board and has the ability to transfer knowledge to the large board. In order to better understand the learning process, we visualize the policy and score of some major branches. Finally, we show the solution that our program obtained on 6 × 6, 6 × 7, and 7 × 8 boards.
MIRD-Net for Medical Image Segmentation

Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. Although fully convolutional neural networks such as U-Net has demonstrated impressive performance on medical image segmentation, distinguishing subtle features between different categories after pooling layers is still a difficult task, which affects the segmentation accuracy. In this paper, we propose a Mini-Inception-Residual-Dense (MIRD) network named MIRD-Net to deal with this problem. The key point of our proposed MIRD-Net is MIRD Block. It takes advantage of Inception, Residual Block (RB) and Dense Block (DB), aiming to make the network obtain more features to help improve the segmentation accuracy. There is no pooling layer in MIRD-Net. Such a design avoids loss of information during forward propagation. Experimental results show that our framework significantly outperforms U-Net in six different image segmentation tasks and its parameters are only about 1/50 of U-Net.
PV Power Forecasting with Holt-Winters Method

Due to global warming issue and growing of energy demand, photovoltaic (PV) power plant is a desirable alternative to electrical power generations. An accurate PV power forecasting is essential to alleviate its negative impacts and to improve its performance. This paper proposed a short-term PV power forecasting technique based on Holt-Winters method. Its property and principle are presented. There are two main parameters, i.e. the number of days (N) and the weight index (α). The values of these two parameters affect the forecasting accuracy, and a set of optimal values for both parameters used for all season is proposed. A real PV generated power profile is collected from the eastern part of Thailand and used in this study. The performance of the proposed forecasting technique is carried out by MATLAB program. The simulation results indicate that the proposed method provides acceptable short-term forecasting results. The proposed technique can be applied in energy management and electrical power smoothing applications.
Vehicle Path Planning Fusion Algorithm Based on Road Network

To solve the problem of global path planning and local path planning for small-area unstructured road region, a vehicle path fusion algorithm is proposed. According to the small regional global optimal road network based on Floyd algorithm, the A* algorithm plans a feasible path. Based on the Floyd path matrix, the path is recursively iterated to be optimal. The improved artificial potential field method, according to the obstacle information, plans an obstacle avoidance path. Based on the cubic polynomial curve, the path point set is fitted to generate a smooth driving track. Based on Matlab/Simulink simulation, the results prove that the designed algorithm meets the real-time requirements. The actual vehicle experiment results show that the simulation results are accurate, and prove the effectiveness of the path planning fusion algorithm and its robustness against environmental noise.
Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms

The bootstrap method is one of the major developments in statistics in the 20th century for computing confidence intervals directly from data. However, the bootstrap method is traditionally approximated with a randomized algorithm, which can sometimes produce inaccurate confidence intervals. In “Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms,” Bertsimas and Sturt present a new perspective of the bootstrap method through the lens of counting integer points in a polyhedron. Through this perspective, the authors develop the first computational complexity results and efficient deterministic approximation algorithm (fully polynomial time approximation scheme) for bootstrap confidence intervals, which unlike traditional methods, has guaranteed bounds on its error. In experiments on real and synthetic data sets from clinical trials, the proposed deterministic algorithms quickly produce reliable confidence intervals, which are significantly more accurate than those from randomization.
Supervised dimensionality reduction of proportional data using mixture estimation

In this paper, an effective novel approach for dimensionality reduction of labeled proportional data is proposed. By avoiding formulating an eigenvalue problem and constructing a neighborhood graph, the introduced method mitigates some of the major problems from which the well-known algorithms in this category suffer. These disadvantages include problem handling multi-modal or sparse data as well as curse of dimensionality. The devised method transfers the data from high-dimensional space into low-dimensional space using a linear transform which is optimized using an information theoretic measure. To find this projection, a novel approach has been adopted in which projected data are transfered into the low-dimensional space first, and a mixture of distributions is estimated using the projected data for each class separately. In the next step, the distance between the estimated distributions is used as a measure of separation for data classes, and a heuristic search is carried on to find the optimal projection. The effectiveness of the proposed algorithm is demonstrated using different datasets in different scenarios in comparison with other well-known algorithms in the literature.