Reviewerly/exHarmony huggingface dataset:
Perceptual and Pixel-Wise Information for Visual Novelty Detection

Novelty detection is a process of identifying if new data does not belong to the previously observed patterns; this is important in several applications such as medical diagnosis and autonomous driving, where wrongly identifying a novel pattern could be catastrophic. We focus on the problem of novelty detection in images based on deep learning (DL) techniques. DL based models are trained through an optimization process in which a loss function is minimized. For visual novelty detection, this loss function must be able to capture in a scalar value the visual properties of the normal samples, usually the mean square error (MSE). While satisfactory performance has been obtained with this formulation, it is often complicated for a single loss function to capture all of the relevant information to discriminate novelty from normality. In this paper we propose novel reconstruction loss that combines pixel-wise and perceptual information. Its performance is experimentally evaluated using generative adversarial networks (GANs), and compared against autoencoders based on MSE, showing superior performance. Additionally, we present an application to novelty detection in X-Ray images, which is relevant for current work con COVID diagnosis.
A Survey on the Entity Linking in Knowledge Graph

With the rapid development of information technology, the amount of information is increasing exponentially. All kinds of text data are growing explosively. How to understand the meaning of these data quickly and accurately becomes extremely difficult and challenging. Entity linking is proposed for solving the above problem over all kinds of unstructured data. Entity linking is to link the mentions ( also called entity references) in a given text to the correct Wikipedia page without ambiguity. In this paper, we summarize the methods of entity embedding and the realization of each step of entity link in the application of machine learning.
Multipurpose Advanced Assistance Smart Device for Patient Care with Intuitive Intricate Control

In India, nearly 2.2% of population is suffering with regular disability as per the last Census data. Strokes, dementia, autism, spinal injuries, etc., are few common reasons of having disorder. Requirement of assistance for care is a must to disorder patients. Our design model varies with conventional ideas as it does not have bulk hardware setup which is not comfortable to patients and working duty staff. The proposed design deals with sensing movements of a patient’s body with an attached sensor, designed using PVDF sheets and silver ink. This sensor is flexible, small in size, and hence could be attached to any moving part of body like finger, toe, lower jaw, etc. It detects signal and assists the patient with the equipment such that they can communicate their thoughts with intuitive intricate control to reach the guardian. This model communicates between two NodeMCUs wirelessly thereby reducing the hardware. The assistance is displayed on the master screen, where the concerned person could see and help the patient.
INTERSCHOOL PROJECT ON ORGANIZATION OF TRAINING IN END-TO-END DIGITAL TECHNOLOGIES

An urgent problem of our time is the social order of business for the country's education system for high-quality pre-professional training of graduates in the field of industrial technologies at various levels of education. At the same time, the information society integrates digital technologies into all spheres of its existence, including education, in order to intensify processes. Accelerating education quality improvement is achieved through the use of digital tools in the transfer of best learning practices. The article considers one of the possible ways to organize training according to a unified innovative program simultaneously in several general education institutions. On the basis of the Basic General Education Program in informatics and technology, an original general education program in informatics for students of 8–11th grades and technology for students of 8th grade has been developed.
Comparison of Speech Recognition and Natural Language Understanding Frameworks for Detection of Dangers with Smart Wearables

Wearable IoT devices that can register and transmit human voice can be invaluable in personal situations, such as summoning assistance in emergency healthcare situations. Such applications would benefit greatly from automated voice analysis to detect and classify voice signals. In this paper, we compare selected Speech Recognition (SR) and Natural Language Understanding (NLU) frameworks for Cloud-based detection of voice-based assistance calls. We experimentally test several services for speech-to-text transcription and intention recognition available on selected large Cloud platforms. Finally, we evaluate the influence of the manner of speaking and ambient noise on the quality of recognition of emergency calls. Our results show that many services can correctly translate voice to text and provide a correct interpretation of caller intent. Still, speech artifacts (tone, accent, diction), which can differ even for each individual in various situations, significantly influences the performance of speech recognition.
Global Context-aware Representation for Implicit Discourse Relation Recognition

Identify implicit discourse relations requires an in-depth understanding of the discourse. We argue that discourse relation recognition should not be isolated from the discourse. However, previous works mostly treat two arguments as independent sentences or encode sentences within a synthetic paragraph context only containing annotated arguments. The overall discourse-level content and structure sequence are partly missed. In this paper, we propose a global context-aware model to leveraging discourse context to improve argument representation. Besides, we integrate a multi-view interaction representation between two arguments themselves to the global context-aware representation to boost implicit discourse recognition performance. Experiments show that our model achieves state-of-the-art results on the benchmark PDTB corpus.
3D Anchor Generating Network:3D Object Detection via Learned Anchor

In both 2D and 3D object detection algorithms, the reference boxes obtained by the anchor mechanism lay the foundation for the next detection tasks of the network. Most of the existing anchor mechanisms are designed by hand to generate a dense array of anchors. The size of the anchors obtained in this way is single and the anchors are densely distributed in the image space. It has poor robustness and a large number of redundant anchors. In views of these defects, we propose the 3D anchor generating network. It predicts the 3D anchors by learning the semantic features of the picture, in which the anchors are sparsely distributed around the objects in the image with different sizes in different positions of the image. We applied it to TLNet’s baseline monocular network for 3D object detection on KITTI dataset, the result showed a significant improvement on the performance of 3D object detection algorithms.
Chemostratigraphy and its application in integrated reservoir correlation

The present chapter aims to provide an overview of the technique of chemostratigraphy, including information on methodologies that are generally poorly documented. This may seem surprising given the popularity of the technique, but is probably best explained by the existence of only two textbooks on the subject. Academic papers may explain the details of correlation schemes, but rarely provide comprehensive information on analytical techniques, processes by which element:mineral links were established, how key/index elements and ratios were chosen, and on what basis the boundaries were placed. Such information is often lacking because editors of scientific journals generally expect authors to concentrate on “the final result” rather than providing detailed information on methodologies.
IBM Crew Pairing and Rostering Optimization (C-PRO) Technology with MDP for Optimization Flow Orchestration

We created the IBM Crew Pairing and Rostering Optimization (C-PRO) solution for air crew scheduling. It was deployed at El Al in 2013 and at Aeroflot in 2020. The core of the system is an optimization flow, which models the problem using mixed integer linear programming (MILP) with millions of integer variables. The solution is derived iteratively using heuristics. Most recently, we applied Markov Decision Process (MDP) in place of the heuristics orchestrator and realized a 30% improvement in performance.
Digital Content Directive And Copyright-related Aspects

JIPITEC 12 (2021) 2 - This article deals with the difficult relationship between the Digital Content Directive and copyright principles. Applying the objective con¬sumer expectation test as laid down in Art 8 DCD, typical copyright restrictions such as those related to the exhaustion principle or limiting the use to a nar¬row circle of users are examined. Moreover, the trian¬gle between rightholders, traders, and consumers as reflected by licenses are scrutinized.
Deep Architectures in Visual Transfer Learning

Deep learning is a recent form of machine learning that depends on structural compositional models that can represent mapping functions that otherwise require an exponentially larger size flat models. The most successful realization of such learning paradigm is deep neural networks that have in recent years achieved state-of-the-art performance in tasks related, in particular, to visual data, audio data, and natural language processing. The main drawback of such methods is that, albeit their superhuman performance, that performance is achieved solely on very specific individual tasks. Intelligence must however, be broader and more general. So the next big research program is to build machines that are smart in a more general sense over multiple tasks and domains. One way to achieve that is through transfer learning. Transfer learning refers to a broad set of techniques, all aimed towards the reuse of knowledge gained from solving some problem towards the solution of some other problem. In this paper we study the effectiveness of known deep architectures in transfer learning in visual tasks. We consider two of the VGG family, namely, VGG16 and VGG19, the Xception architecture, DenseNet121, and finally the ResNet50 architecture. They are already pretrained on the ImageNet dataset, we tune and test their transfer performance on the Caltech-256 image set. Four of these architectures have shown good training/validation performance on the latter dataset. In addition, DenseNet121 and Xception have shown exceptional superior performance over the VGG variants and ResNet50, though thay are an order of magnitude less in size. However, they are an order of magnitude deeper confirming the lasting conjecture that deep compositional architectures are exponentially more representative and expressive than flatter networks.
Pragmatic Augmentation Algorithms for Deep Learning-Based Cloud and Cloud Shadow Detection in Remote Sensing Imagery

Identification of clouds and their shadows are two major preprocessing steps for providing an effective interpretation of remotely sensed optical images. Although deep learning-based methods have proved to deliver competent performance for detecting clouds and cloud shadows, improving their generalization ability for reliable results requires a large number of training images and accurate ground truths. As creating ground truth of cloud and cloud shadow is expensive, a practical way to generate more images and their ground truths is to use data augmentation methods. We propose two new data augmentation approaches (one for generating synthetic clouds in scenes and the other one for creating cloud shadows with different levels of shade) so as to achieve natural-looking images with little or no effort required for creating their ground truths. Our experiments show that while each of these approaches is capable of boosting cloud and cloud shadow segmentation individually, the fusion of them improves detecting cloud and shadow in a simultaneous learning pipeline, leading to outperforming the state-of-the-art results.
High-Performance Automated Image Tracking for Astronomical Observations

Many astronomical observations require extended image exposures in order to improve signal to noise ratio and to provide adequate data for quantitative analysis of image contents. Maintaining accurate pointing of a telescope over the desired observation time is challenging. We consider the problem of correcting for changes in the field of view by dividing the overall observation into a series of image frames, analyzing the individual frames to estimate the changes in the field of view between frames, and performing a corrected accumulation of image frames to form the final observation. We present an automated image tracking algorithm tailored for execution on resource-constrained platforms. We show the results of extensive algorithm performance characterization studies. Finally we describe an implementation of the algorithm for a radiation-tolerant FPGA.
The application prospect of knowledge graph in IPO approvals

On March 1, 2020, China began to implement the newly revised Securities Law, which clarified the full implementation of the registration system. The IPO registration system with information disclosure as the core is an important reform of China's capital market. How to effectively urge issuers and securities intermediaries to disclose information in accordance with laws and regulations and ensure the high quality of information disclosure is the focus and difficulty of registration system supervision. Knowledge graph can fully explore the correlation between entities, and it has a high application value in the formalized approval of IPO information disclosure.
Design and preliminary realization of component installation scheme generation system in virtual EAST

• The scheme generation system of immersive virtual EAST component installation is established to provide a reference for researchers to access the actual installation process, reduce the conflict of component positions, and improve the installation efficiency of the components. • The detailed steps of reconstructing EAST model is introduced. • Advanced level of detail algorithm is used in the system to improve the rendering efficiency. In the experimental advanced superconducting tokamak (EAST) experiment, the properties, states of plasma and the engineering parameters of the EAST device are measured and analyzed by various diagnostic methods. The approaches of diagnosis and measurement are constantly updated, and the diagnostic systems should be updated accordingly. However, position conflicts would inevitably occur due to the traditional diagnostic marker from diagnosis personnel according to the installation location of the part drawing, leading to modification after the rediscussion with other diagnosis personnel. In addition, the confined environments of EAST result in inconvenient installation. Therefore, the technology of scheme generation of an immersive component installation should be urgently pursued. Aimed at the actual requirements of physical experiments, the digital technology of component installation and design process based on multiuser and multidetail level, as well as the technology of virtual installation and cooperative discussion based on hierarchical collision detection are proposed. Taking the electromagnetic measurement diagnosis system as an example, the scheme generation system of immersive virtual EAST component installation is established to provide a reference for researchers to access the actual installation process, reduce the conflict of component positions, and improve the installation efficiency of the components. Preliminary realization and design of component installation scheme generation system are described.
Research on LED multi-sector stable display model in variable rotational speed

To solve the display distortion caused by the motor rotational speed changes in the long-term operation of the rotating LED multi-sector display system, the model for LED multi-sector stable display (MSSD) in variable rotational speed is proposed based on the principle of visual persistence. The stepping motor subdivision driving technology is applied to the rotating LED multi-sector display, and the equal precision frequency measurement method is used for accurate rotational speed measurement. Then the innovation adaptive kalman filter (IAKF) is researched to further enhance the robustness to eliminate noise pollution, and the relationship between the modification value caused by rotational speed and the LED linear array minimum refresh time is analysed. The rotating LED MSSD system based on LED linear array rotated by stepping motor is provided. The test results reveal the relationship between rotational speed and the LED linear array minimum refresh time, and show that the lighting position of the LED can be synchronized with the lighting state during the rotational speed fluctuation, and the error can be controlled to a low level via LED linear array minimum refresh time correction. The designed model can effectively eliminate the distortion of the rotating LED multi-sector display screen caused by the changes of the motor rotational speed, and has a very stable display effect.
Relationship-Aware Primal-Dual Graph Attention Network For Scene Graph Generation

The relationships and interactions between objects contain rich semantic information, which plays a crucial role in scene understanding. Existing methods do not attach great importance to the expression of relational features. To tackle this problem, we propose a novel Relationship-aware Primal-Dual Graph Attention Network (RPDGAT) to extract the comprehensive semantic features of objects and explore the sparse graph inference for scene graph generation. RPDGAT mines the inherent attributes and the relationships between objects by fusing multiple features, e.g. appearance, spatial, and category features. After feature extraction, we design a trainable relationship distance measure network to construct the robust and sparse graph structure for efficient graphical message passing. Moreover, it can preserve the contextual cues and neighboring dependency for objects and relationships from the interaction between primal and dual graphs. Extensive experimental results present the improved performance of our method over several state-of-the-art methods on the visual genome datasets.
Terra quantum at MIPT-QUANT 2020

Implementations of quantum information-processing systems require quantum algorithms and corresponding material tools to solve problems posited by modern information technologies. Terra Quantum offers state-of-the-art solutions that pave the way to a new level of efficiency in information processing. In this paper, we outline the research activities carried out by Terra Quantum experts who focus on quantum computing and quantum machine learning, sensors and metrology, quantum cryptography, random number generators, and algorithmic cooling.
Performance Improvement of Mobile Device Using Cloud Platform

The increased usage of mobile devices in day-to-day activities has led to the explosive growth of mobile applications. Due to the resource constraints of mobile devices, it is still a challenge to execute resource-intensive applications on mobile devices. Mobile cloud computing augments the capability of the mobile device by moving a resource-demanding task onto the cloud. In this article, the proposed method is to improve the performance of the mobile device by moving resource-intensive computation on the cloud. A face recognition system using a cloud platform is implemented to identify a person from a given image. The existing image available on the mobile device or the one captured from the camera of the mobile device is sent to the cloud server. The face detection and recognition happens on a cloud server and the result is displayed on the mobile device. The results indicate that the time taken to recognize the image is faster; the accuracy is higher using machine learning techniques with reduced energy consumption of the mobile device by offloading tasks onto the cloud.
Speaking System for Mute Peoples

This interacting device is a microcontroller based system which is basically outline for lessening the communication space between dumb and normal people. This system can be accordingly configured to work as a “smart device”. In this paper, Atmega 328 microcontroller, voice module, LCD display and flex sensors are utilize. The device considered is basically residing of a glove and a microcontroller based system. Data gloves are used to detect the hand motion and microcontroller based system will interpret those few manoeuvre into human place voice. The data glove is furnished with four flex sensors placed on the glove. This system is helpful for dumb people and their hand manoeuvre will be converted into speech signal because of the date glove worn on the hands.
Proof Nets for Classical Logic

We propose a new direct presentation of full (propositional) classical logic by means of proof nets, for which strong normalization and confluence hold true. Our proposal is based on the breaking of symmetry between propositional connectives, combined with a principle of focusing/defocusing.
Reusability-Driven Software Development

The last two chapters introduced the terminology of software engineering and software reuse as used in this book. Furthermore, principles of dependability and features of dependable software have been introduced. Application aspects of reusability based on these introductory elements have been discussed.
Segmentation of the Ventricle Membranes in Short‐Axis Sequences by Optical Flow Base on DLSRE Model

Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.
Research on the Application of Computer Network in my country’s Crime Investigation

Abstract With the development of information technology, more and more computers are used in our daily work and life, it has become an indispensable helper around us. Computer network technology has also effectively improved our country’s criminal investigation technology, this also makes our country gain more criminal investigation methods. This paper mainly analyzes and summarizes the computer network investigation technology.