Reviewerly/exHarmony huggingface dataset:
GLAMD: Global and Local Attention Mask Distillation for Object Detectors

Knowledge distillation (KD) is a well-known model compression strategy to improve models’ performance with fewer parameters. However, recent KD approaches for object detection have faced two limitations. First, they distill nearby foreground regions, ignoring potentially useful background information. Second, they only consider global contexts, thereby the student model can hardly learn local details from the teacher model. To overcome such challenging issues, we propose a novel knowledge distillation method, GLAMD, distilling both global and local knowledge from the teacher. We divide the feature maps into several patches and apply an attention mechanism for both the entire feature area and each patch to extract the global context as well as local details simultaneously. Our method outperforms the state-of-the-art methods with 40.8 AP on COCO2017 dataset, which is 3.4 AP higher than the student model (ResNet50 based Faster R-CNN) and 0.7 AP higher than the previous global attention-based distillation method.
Ensembled-based credit card fraud detection in online transactions

Fraud detection systems (FDS) are an important factor in e-commerce trading as online fraud grows and spreads quickly. The incidence of fraud involving online payments by credit cards has recently increased considerably, forcing all banks and e-commerce companies to carry out large-scale operational logs to create automatic methods to detect fraud. The controlled binary classification systems that are appropriately trained from pre-determined sampled data sets appear to be one of the most promising techniques for the detection of unlawful transactions, separating fraudulent and non-fraudulent cases. So the problems related to fraud detection are also classified as a novel challenge that is an assessment based on several advanced algorithms, such as random woodlands, Naive Bays (NB), and generative opponent networks. The problem is a classification problem. In this article, random forest and generative adversary networks are developed to further increase the accuracy of the detection rate of fraud via credit cards. Extensive experiments have shown that the technique offered exceeds current techniques.
Exploring Gradient-Based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn linear directions or individual channels that control semantic attributes in the image space. However, they often suffer from imperfect disentanglement, or are unable to obtain multi-directional controls. In this work, in light of the above challenges, we propose a novel approach that discovers nonlinear controls, which enables multi-directional manipulation as well as effective disentanglement, based on gradient information in the learned GAN latent space. More specifically, we first learn interpolation directions by following the gradients from classification networks trained separately on the attributes, and then navigate the latent space by exclusively controlling channels activated for the target attribute in the learned directions. Empirically, with small training data, our approach is able to gain fine-grained controls over a diverse set of bi-directional and multi-directional attributes, and we showcase its ability to achieve disentanglement significantly better than state-of-the-art methods both qualitatively and quantitatively. The source code is available at https://github.com/zikuncshelly/GradCtrl .
Corneal topography using dynamic point shifting method in quadrangular OLED´s prism

A practical implementation of the dynamic point shifting method as a corneal topographer is presented. A quadrangular OLED´s prism setup allows to change the target at will. This device has several advantages over the traditional commercial topographers using static target as those based in Placido disk. The target is displayed on the OLED screens as an image, by changing the image it is possible to change the spots position, but also, different targets can be used, for instance; B&W positive or negative, different colored targets, with square or radial arrays of spots, among other possibilities. Results of measurements obtained with calibration spheres as well as the first report of the qualitative evaluation of a corneal surface of a human volunteer using this device are presented.
Neutrosophic cognitive maps for analysis of social problems analysis

Investors have been flocking to the information technology (IT) industry recently. Any commercial or IT company's success is directly proportional to its employees' efforts. The workforce's contributions are crucial when it comes to the success of technology or any other business on the global stage. However, they will disregard their personal and social lives to achieve their aim. Workers in the private sector, particularly those in the information technology industry, suffer from mental health issues such as stress, depression, anxiety, and addiction. Using Neutrosophic Cognitive Maps (NCM), we evaluated the daily problem they face. The NCM decision-making system incorporates iterative tasks of resolving conflicts until the optimal answer is reached. The NCM uses neutrosophic graphs to model depending on the opinions of experts. Studying the issue with such vague data calls for an approach such as this, which eschews the use of statistics. For unlabeled data, NCM is the most effective method.
Exact Mapping of Quantum Circuit Partitions to Building Blocks of the SAQIP Architecture

Quantum computing benefits from collective features of quantum states, such as superposition and entanglement, to efficiently address problems that are very hard to be solved on classical systems. The Scalable Architecture for Quantum Information Processor (SAQIP) architecture is a promising technology that is based on ion-traps and realizes a hybrid composed of a large number of full-custom building blocks (supposed to realize so-called Elementary Logic Units; ELUs) connected by a reconfigurable optical switch network. As with every architecture, corresponding design methods are required in order to properly map a given quantum functionality onto the respective device. However, since the corresponding complexity frequently made exact solutions for this task infeasible for past architectures, most of the existing mapping methods rely on heuristics and, hence, do not provide exact/optimal results. Considering the SAQIP architecture, however, this problem can be prevented. In fact, due to the building blocks of this architecture, any circuit to be mapped has to be partitioned into ELUs anyway. Since those are usually of moderate size, exact/optimal solutions for them are possible. In this work, we sketch an exact mapping method that can generate such optimal results. To this end, we propose a corresponding formulation in mixed-integer linear programming (MILP) that allows to cope with the (smaller, but still not non-trivial) complexity.
Detecting GPS-spoofing Attack on PMU Data with Phase Angle Unwrapping Technique and Low-Rank Approximation of Hankel Matrix

Introduction of PMUs to cyber-physical system provides accurate and fast data acquisition, while posing additional risk of being the victim of cyber attack. Both False Data Injection Attack (FDIA) and GPS-Spoofing Attack (GSA) or timing attack can provide malicious data to the cyber system, though these two attacks require different post-attack contingency plans. This difference in contingency plans has engendered the requirement of accurate detection of timing attack and its separation from conventional FDIA. In this article, we have developed a model to address this issue using low-rank approximation of Hankel matrix and phase angle unwrapping technique. Firstly, a method to distinguish timing attack and FDIA using unwrapped phase angle data is developed. Secondly, utilizing low rank Hankel matrix property to differentiate timing attack from FDIA is also presented. Finally, the proposed model is validated by numerical simulation on IEEE 13 node test feeder using simulated GPS-spoofing/timing attack and FDIA at the same time instances. The simulation result confirms the theory. The GSA or timing attack can increase the rank 1 approximation error of Hankel matrix with unwrapped phase angle data by 700 % for 3 sec delay in GPS time-stamp. The rank 1 approximation error increases with the time-delay caused by the GSA. On the other hand, FDIA doesnt show any significant change in the low rank approximation profile of Hankel matrix. Therefore, the proposed method is useful in differentiating GSA from FDIA.
Embedded System based Smart Gloves for Soldiers

The method which involves the conversion of those hand signals into respective voice signals that can be transmitted via wireless modules. The Hand Talk glove is a normal cloth driving glove fitted with electrode sensors. The sensors output a stream of data that varies with degree of bend made by the fingers. Electrode sensors are sensors that change in resistance depending on the amount of bend on the sensor. They convert the change in bend to electrical resistance—the more the bend, the more the resistance value. The output from the sensor is converted to digital and processed by using Arduino and then it responds in the voice using speaker. In this project we have used microcontroller, Wi-Fi module and voice kit to produce the voice signal. Arduino UNO, DAC, wireless speaker, tactile sensors, flex sensors, strength grant, and Voice IC (APR 9600) are the hardware components used. The importance of embedded systems is growing continuously. Exponentially increasing computing power (Moores law) connectivity and convergence of technology have resulted in hardware/software systems being embedded within everyday products and places. Already 90% of computing devices are in embedded systems and not in PCs. The growth rate of Embedded System is more than 10% per annum and it is forecasted there will be over 40 billion devices worldwide by 2020.The value added to the final product by embedded software is often orders of magnitude higher than the cost of the embedded devices themselves. The aim of this project is to develop smart gloves which are useful for our defense forces in the borders and in the surgical strikes.
Lightweight Hash Function Based on the New Mersenne Number Transform Family

The family of new Mersenne number transform (NMNT)-based algorithms – namely NMNT, odd new Mersenne number transform (ONMNT) and odd-squared new Mersenne number transform (O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMNT) – have shown promising results in cryptographic applications. Parameterised implementation, strong diffusion performance and simple arithmetic operations are critical advantages of NMNT-based cryptographic ciphers. Lightweight cryptography plays a critical role in offering security for resource-constrained devices, especially in the internet of things(IoT) applications. One of the key challenges of lightweight cryptography is to strike an optimal balance between security and resource requirements. This paper introduces a suite of novel lightweight cryptographic hash functions based on NMNT, ONMNT and O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMNT which offers enough security with limited resources. This research focuses solely on software implementation, and the proposed hash functions will be used with a lightweight cipher to offer a complete crypto package.Among the three lightweight ciphers, O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NMNT-Hash showed the best performance in terms of uniform distribution, collision resistance and diffusion percentage with fewer internal components. All three hash functions provided substantial protection against collision attacks, which is the most desirable property.
Readability of Non-Text Images on the World Wide Web (WWW)

The World Wide Web associated the world in a manner that was unrealistic previously and made it a lot more straightforward for users to get data, share and impart. But, irrelevant non-text images on the web pages equally specify poor readability, disrupting the people from the emphasis of the reading. The main goal of this paper is to evaluate the impact of irrelevant or low-quality non-text images on the readability of the webpage. An automatic has been proposed to compute the relevancy of the non-text images. This methodology merges different approaches to extract information from non-text images and read text from websites in order to find relevancy between them. This technique was used to analyze fifty different educational websites to automatically find the relevancy of their non-text images. A user study has been carried out to evaluate the proposed methodology with different types of questions. The results agree with the fact that the relevant non-text images enhance the readability of the web page. This methodology will help web designers to improve readability by considering only the relevant content of a web page, without relying on expert judgment.
Analysis of professional competencies in the characteristics of the teacher of the future: global challenges of our time

The teacher's professional competences must correspond to socio-cultural realities, therefore, their forecasting is carried out in educational strategies. The key trends of the modern scientific outlook paradigm define the principles of pragmatism and dynamism as fundamental priorities that determine the components of the professionalism of the future teacher. The purpose of scientific research is to determine the content and form of formation of professional skills, which will determine the level of professionalism in the educational system in the near and distant future. The tasks of the article are actualization of perspectives of synergistic interaction of pedagogical hard skills &amp; soft skills and their relationship with the socio-cultural space (efficiency and transformation of professional qualities of a teacher). Methodological approaches that ensure the realization of the goal and objectives of the research are based on general scientific, scientific-pedagogical and philosophical methods focused on forecasting and modeling. The research was carried out taking into account the experience of current modern pedagogical competencies and forecasting prospective vectors of the development of pedagogical skills. Thus, as a result of the conducted scientific research, the theoretical and methodological principles of the perspective of the formation of pedagogical competences were determined: the effectiveness of pedagogical activity, constant updating of pedagogical skills, synergism between fundamental (traditional) and flexible (innovative) competencies of the teacher of the future; sociocultural relevance of pedagogical professionalism.
Semantic Segmentation Using Neural Ordinary Differential Equations

The idea of neural Ordinary Differential Equations (ODE) is to approximate the derivative of a function (data model) instead of the function itself. In residual networks, instead of having a discrete sequence of hidden layers, the derivative of the continuous dynamics of hidden state can be parameterized by an ODE. It has been shown that this type of neural network is able to produce the same results as an equivalent residual network for image classification. In this paper, we design a novel neural ODE for the semantic segmentation task. We start by a baseline network that consists of residual modules, then we use the modules to build our neural ODE network. We show that our neural ODE is able to achieve the state-of-the-art results using 57% less memory for training, 42% less memory for testing, and 68% less number of parameters. We evaluate our model on the Cityscapes, CamVid, LIP, and PASCAL-Context datasets.
SOS! Self-supervised Learning over Sets of Handled Objects in Egocentric Action Recognition

Learning an egocentric action recognition model from video data is challenging due to distractors in the background, e.g., irrelevant objects. Further integrating object information into an action model is hence beneficial. Existing methods often leverage a generic object detector to identify and represent the objects in the scene. However, several important issues remain. Object class annotations of good quality for the target domain (dataset) are still required for learning good object representation. Moreover, previous methods deeply couple existing action models with object representations, and thus need to retrain them jointly, leading to costly and inflexible integration. To overcome both limitations, we introduce Self-Supervised Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact (OIC) representation model from video object regions detected by an off-the-shelf hand-object contact detector. Instead of augmenting object regions individually as in conventional self-supervised learning, we view the action process as a means of natural data transformations with unique spatiotemporal continuity and exploit the inherent relationships among per-video object sets. Extensive experiments on two datasets, EPIC-KITCHENS-100 and EGTEA, show that our OIC significantly boosts the performance of multiple state-of-the-art video classification models.
Feature Selection Using Correlation Matrix on Metagenomic Data with Pearson Enhancing Inflammatory Bowel Disease Prediction

The Fourth Industrial Revolution has brought up a vast amount of new innovative implementations. These can be used for numerous areas to make wealth and to improve human ways of living. Our point of view is to consider medical problems for enhancing prediction first. In this study, we like to rise a question about whether if we could enhancing Inflammatory Bowel Disease (IBD) prediction for early detect related sickness by feature selection on metagenomic data. Over the last few years, the prediction has been a challenge. Because of rare information and lacking data, the problem is not well considered enough. To bring back the subject, in this work, we propose a new way of enhancing Inflammatory Bowel Disease (IBD) prediction by using the Correlation Matrix with the Pearson on Metagenomic Data. Our implications have the purpose of finding out whether we could do predictions better using a specific amount of features selected by Pearson correlation coefficient. The result with the proposed method is quite promising, when we address some high correlation features out, the model can predict better comparing to randomly select features.
Design of an Extended Reality Collaboration Architecture for Mixed Immersive and Multi-surface Interaction

EXtended Reality (XR) is a rapidly developing paradigm for computer entertainment, and is also increasingly used for simulation, training, data analysis, and other non-entertainment purposes, often employing head-worn XR devices like the Microsoft HoloLens. In XR, integration with the physical world should also include integration with commonly used digital devices. This paper proposes an architecture to integrate head-worn displays with touchscreen devices, such as phones, tablets, or large tabletop or wall displays. The architecture emerged through the iterative development of a prototype for collaborative analysis and decision-making for the maritime domain. However, our architecture can flexibly support a range of domains and purposes. XR designers, XR entertainment researchers, and game developers can benefit from our architecture to propose new ways of gaming, considering multiple devices as user interfaces in an immersive collaborative environment.
Application of blockchain technology in railway information sharing research

As new information technology, blockchain has produced a new computing model and penetrated various industries, realizing the transformation from information Internet to value Internet and triggering a new round of technological innovation and revolution. Blockchain has great potential and a wide range of applications. Currently, its application fields are constantly expanding and there are application scenarios in many fields. It has also been widely used in the railway industry. For example, information sharing of data generated in railway construction and security guarantee of data flow are important development directions of railway construction in the future. All these require data exchange between the railway and other industries, but data exchange is difficult in the following aspects: (1) The traditional data exchange process requires the exchange service manager, who can access, utilize and control all exchanged data. No matter which party chooses to assign the role to the manager, other business parties will lose the effective supervision and management of exchanged data; (2) There is business competition between the railway and other transportation industries. The mining and analysis of exchange data by competitors make all parties face pressure. How establish an effective trust mechanism is a difficult problem. (3) Through the traditional centralized database to achieve data sharing, hackers only need to attack the exchange database to obtain data, which increases the risk of data leakage. In this paper, the Byzantine redundant error algorithm (PBFT) is used to ensure the safety and accuracy of the system.
360-Attack: Distortion-Aware Perturbations from Perspective-Views

The application of deep neural networks (DNNs) on 360-degree images has achieved remarkable progress in the recent years. However, DNNs have been demonstrated to be vulnerable to well-crafted adversarial examples, which may trigger severe safety problems in the real-world applications based on 360-degree images. In this paper, we propose an adversarial attack targeting spherical images, called 360-attactk, that transfers adversarial perturbations from perspective-view (PV) images to a final adversarial spherical image. Given a target spherical image, we first represent it with a set of planar PV images, and then perform 2D attacks on them to obtain adversarial PV images. Considering the issue of the projective distortion between spherical and PV images, we propose a distortion-aware attack to reduce the negative impact of distortion on attack. Moreover, to reconstruct the final adversarial spherical image with high aggressiveness, we calculate the spherical saliency map with a novel spherical spectrum method and next propose a saliency-aware fusion strategy that merges multiple inverse perspective projections for the same position on the spherical image. Extensive experimental results show that 360-attack is effective for disturbing spherical images in the black-box setting. Our attack also proves the presence of adversarial transferability from Z2 to SO(3) groups.