Reviewerly/exHarmony huggingface dataset:
About using special data structures in coverage algorithms

The aim of this work is to increase the efficiency of methods and algorithms for solving the problem of finding coverage. Efficiency is understood as the minimum delay of the procedure that implements this method. To increase the efficiency of the “Columnization” method, a characteristic vector (CV) is introduced into the decision tree construction procedure, obtained by summing the units in columns / rows of the coverage table (CT); it characterizes the current state of the coverage table. The idea of this method is to gradually decompose CT into sub-tables using their reduction according to certain rules. We consider 3 ways to reduce the original table / current sub-tables in the methods: 1) "Border search over a concave set"; 2) "Using the properties of the coverage table"; 3) "The minimum column is the maximum row." In the latter method, CV was used for the first time, which made it possible to accelerate the coating finding procedure up to one and a half times. The complexity estimates for the considered coating methods are calculated; we have: S1 = O (n ^ 3); S2 = O (2 ^ n); S3 = O (n ^ 2), where n is the determining parameter of the coverage problem (number of columns), and the applicability limits of these methods are determined. It is shown that the use of CV in methods 1 and 2 is impractical.
Ad-Libbed Hand Motion Acknowledgment Framework Utilizing PCA

The Language of Sign is a strategy to correspondence for the people who are deaf & dumb. This Artical shows the linguistic communication Recognition framework equipped for recognizing twenty-six gestures from the Indian linguistic communication by utilizing MATLAB. The Proposed system have Multiple modules, first one is process data than hand segment than after extraction of feature after that, recognize the sign and then it will be converted into voice & text. Segmentation is finished by utilizing image processing. For example, some special components are removed. And some special features of eigen values and eigen vectors are used as an acknowledgment. The Principle Component Analysis is used to acknowledgment of gesture and then it will be changed into content and group of voice. Through the proposed system we have tried to limit correspondence between hard of hearing & dumb individuals and normal people.
Joint Estimation of Expertise and Reward Preferences From Human Demonstrations

When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from nonexpert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot would also be able to infer a human's true objectives even when the human fails to properly demonstrate these objectives due to a lack of expertise. In this article, we propose to jointly infer the expertise level and the objective function of a human given observations of their (possibly) nonoptimal demonstrations. Two inference approaches are proposed. In the first approach, inference is done over a finite discrete set of possible objective functions and expertise levels. In the second approach, the robot optimizes over the space of all possible hypotheses and finds the objective function and the expertise level that best explain the observed human behavior. We demonstrate our proposed approaches both in simulation and with real user data.
Online sports betting through the prism of software engineering

Online sports betting is a $50B industry that is heavily driven by software. The domain imposes significant demands on developers: the resulting solutions are large, complex, distributed, concurrent software systems with strict availability, real-time performance, scalability, reliability, and security requirements. This paper describes our experience with EmpireBet, a family of online sports betting platforms built and deployed over the past 15 years. The initial solution, implemented by four developers in a start-up, catered to users who connected to the system intermittently, for limited periods, via dial-up connections. Today’s system, engineered and maintained in 27 programming and markup languages by a team of 20 developers, is deployed in over 30 countries, integrated with over 50 third-party systems, and processes tens of millions daily transactions by over 680,000 players who are continuously using the system. This was accomplished via an an explicit focus on EmpireBet’s critical non-functional requirements; a modular, extensible architecture; a set of novel abstractions we introduced into the system; and several reusable libraries developed in the process.
An Efficient Training Framework for Reversible Neural Architectures

As machine learning models and dataset escalate in scales rapidly, the huge memory footprint impedes efficient training. Reversible operators can reduce memory consumption by discarding intermediate feature maps in forward computations and recover them via their inverse functions in the backward propagation. They save memory at the cost of computation overhead. However, current implementations of reversible layers mainly focus on saving memory usage with computation overhead neglected. In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint. By solving this problem, we can maximize the training throughput for reversible neural architectures. Our proposed framework fully automates this decision process, empowering researchers to develop and train reversible neural networks more efficiently.
Kernel difference maximisation‐based sparse representation for more accurate face recognition

Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited. According to the previous studies, more information can be obtained from samples in the feature space than those in the original space. The authors propose a novel kernel difference maximisation-based sparse representation method, and its remarkable performance in face recognition is demonstrated by the experiments. The proposed method converts all the samples into the feature space, and a test sample can be denoted as a representation with all the training samples’ linear combinations. Besides, a novel solution scheme for sparse representation is utilised to obtain the regularisation-based sparse solution. Finally, the classification of the test sample can be easily judged according to the representation result. The representation results of test samples from different classes obtained by their method are very different, making the classification of test samples easier. Besides, the proposed method is simpler than the related methods and does not require dictionary learning.
Preliminary Study on the Influence of Visual Cues, Transitional Environments and Tactile Augmentation on the Perception of Scale in VR

Virtual reality (VR) is being used more and more as a way to easily visualize and share ideas, as well as a step in product designing. The initial study presented in this paper is part of a project for getting the general public involved in the design of new busses for Northern Jutland in Denmark, by using VR visualization. An important part of VR visualizations is the correct understanding of scale. Studies show that the perception of scale in VR undergoes compression compared to the real world. In this paper we test how additional visual cues, transitional environments and tactile augmentation in a VR environment can help with the perception of scale. We show that familiar visual cues can help with the perception of scale, but do not remove the compression of perception. We can further mitigate the problem by introducing transitional environments and tactile augmentation, but transitional environments provide a better perception of scale than tactile augmentation.
Changes in Engineering Education in the Transition to a Digital Society

The main trend at now is intellectualization and digitalization in all activity areas. First of all, in the transition to the Industry 4.0 concept, this relates to the creation of management systems, including in education. This is actual for engineering education, including the learning of specialists in the control and computer systems sphere. To create the information and communication infrastructure to digital economy, we need specialists who are able to create, develop and implement projects in the information and communication technologies. Requirements for a modern engineer imply his social responsibility, solicitous attitude to ecosystems and adherence to the sustainable development principles. The accelerating digitalization of modern society is increasing the demand for engineers with creative thinking, which leads to the new education philosophy development - Smart Education. The article discusses the requirements for the modern education system, the main of which is to motivate the student so that he learns to formulate the problem in a professional language and solve it using new intelligent technologies. A feature of the proposed method is the use in the educational system to solve real problems of such a digital environment, which is being introduced in the industry.
Towards Amazon Fake Reviewers Detection

Online marketplaces such as Amazon allow people to share their experiences about purchased products using textual comments known as product reviews. These reviews have become a common tool that users rely on to get insights on the quality and functionality of products and services from online consumers. However, like any other online information, reviewers raise serious questions concerning the credibility and reliability, since anyone can post reviews, which might impact the reliability of the information. This paper tackles the phenomenon of Bulk reviewers. We first analyze a large dataset of reviews from Amazon aiming to spot bulk reviewers according to their behavior. We then apply a what-if analysis to assess the effect of bulk reviews on the online marketplaces using a metric called Net Promoter Score to measure the willingness of users to recommend products. Our Results reveal that bulk users (i.e., users that review multiple times) have same distribution of ratings as non-bulk users indicating that a bulk reviewer is not automatically a fake reviewer. Yet, we discover that bulk users do inflate NPS metric and thus contribute to overestimate the level of customer satisfaction.
A Heuristic Video Recommendation Algorithm based on Similarity Computation for Multiple Features Analysis

Objective:: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has increased greatly. In this way, mining and recommending videos for users based on their interests have become a very difficult problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multi-dimensional feature analysis and filtering is proposed. Methods:: Firstly, the video features are extracted from multiple dimensions such as user behavior and video tags. Then, the similarity analysis is carried out, and the video similarity degree is calculated by weighting, so as to obtain the similar video candidate set, and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results:: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion:: The proposed algorithm can effectively and greatly improve the accuracy, recall rate, coverage rate, running time and cache hit ratio.
Distant Supervision for E-commerce Query Segmentation via Attention Network

The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supervised methods, especially those based on deep learning, are attractive for achieving better performance on the problem of query segmentation. However, the lack of labeled data is still a big challenge for training a deep segmentation network, and the problem of out-of-vocabulary (OOV) also adversely impacts the performance of query segmentation. Different from query segmentation task in an open domain, e-commerce scenario can provide external documents that are closely related to these queries. Thus, to deal with the two challenges, we employ the idea of distant supervision and design a novel method to find contexts in external documents and extract features from these contexts. In this work, we propose a BiLSTM-CRF-based model with an attention module to encode external features, such that external context information, which can be utilized naturally and effectively to help query segmentation. Experiments on two datasets show the effectiveness of our approach compared with several kinds of baselines.
Total Revenue Management (TRM)

Revenue management (RM) is the concept of systematically increasing the revenue streams in an organization. The concept utilizes systematic analytics that predict consumer behaviour at the micro-market levels and optimize product availability and price to maximize revenue growth (Wikipedia 2020). RM is mainly applied in industries like aviation or hospitality area with service offerings. RM is associated with earnings, profits and any sort of income by the use of smart and intelligent tools or algorithms. It means the strategic orientation of product or service in the market and the long-term planning of sales and sales activities (Gallego and Topaloglu 2019). This includes cost calculations such as break-even calculations, price floor calculations, capacity analyses and sales costs. The ultimate goal is to increase earnings (Landmann 2019). Major objectives in RM are:
Automated Estimation of Functional Size from Code

Determination of the size of a software project is challenging as well as crucial for both self-employed software developers and corporate businesses. That's why it is subjected to a lot of academic studies where it is discussed how to determine the size more accurately. Functional Size Measurement (FSM) is one the most popular measurement techniques for a software from the point of the delivered functionality. However, the aspects of know-how, the cost, time, and manual operation creates difficulties to apply FSM techniques. This study aims to solve these issues by automating the measurement process to approximate the functional size of a project using the COSMIC Functional Size Measurement. The end product of this study is called `Cosmic APP' that utilizes the sequence diagram of a software after reverse engineering it from the given code using a third-party tool called `SequenceDiagram'. The working principles, the estimation process, and the obtained results of `Cosmic APP' are described thoroughly in this paper.
Interpretability Derived Backdoor Attacks Detection in Deep Neural Networks: Work-in-Progress

Backdoor attacks to deep neural networks (DNNs) have received increasing attentions, particularly in applications from edge computing. The detection of backdoor attacks is a challenging task, due to the lack of transparency in DNN. In this paper, we design a novel method to detect backdoor attacks in deep neural networks, which is derived from the interpretability of a DNN. A comprehensive analysis of the critical path in DNN is conducted, based on which two indicators are proposed, including the correlation coefficient and the discrete degree. Conseqently, an efficient backdoor detection algorithm is proposed, which only needs a few runtime images to identify the backdoor attacks. Initial experiments indicated the efficiency.
Tackling Problems of Marker-Based Augmented Reality Under Water

Underwater sites are a harsh environment for augmented reality applications. Divers must battle poor visibility conditions, difficult navigation, and hard manipulation with devices under water. This chapter focuses on the problem of localizing a device under water using markers. It discusses various filters that enhance and improve underwater images and their impact on marker-based tracking. Then, it presents different combinations of ten image-improving algorithms and four marker-detecting algorithms and tests their performance in real situations. All solutions are designed to run real-time on mobile devices to provide a solid basis for augmented reality. The usability of this solution is evaluated on locations in the Mediterranean Sea. Results show that image improving algorithms with carefully chosen parameters can reduce the problems with underwater visibility and enhance the detection of markers. The best results are obtained with marker detecting algorithms specifically designed for marine environments.
Quantum entanglement, symmetric nonnegative quadratic polynomials and moment problems

Quantum states are represented by positive semidefinite Hermitian operators with unit trace, known as density matrices. An important subset of quantum states is that of separable states, the complement of which is the subset of entangled states. We show that the problem of deciding whether a quantum state is entangled can be seen as a moment problem in real analysis. Only a small number of such moments are accessible experimentally, and so in practice the question of quantum entanglement of a many-body system (e.g, a system consisting of several atoms) can be reduced to a truncated moment problem. By considering quantum entanglement of n identical atoms we arrive at the truncated moment problem defined for symmetric measures over a product of n copies of unit balls in $${\mathbb {R}}^d$$ . We work with moments up to degree 2 only, since these are most readily available experimentally. We derive necessary and sufficient conditions for belonging to the moment cone, which can be expressed via a linear matrix inequality of size at most $$2d+2$$ , which is independent of n. The linear matrix inequalities can be converted into a set of explicit semialgebraic inequalities giving necessary and sufficient conditions for membership in the moment cone, and show that the two conditions approach each other in the limit of large n. The inequalities are derived via considering the dual cone of nonnegative polynomials, and its sum-of-squares relaxation. We show that the sum-of-squares relaxation of the dual cone is asymptotically exact, and using symmetry reduction techniques (Blekherman and Riener: Symmetric nonnegative forms and sums of squares. arXiv:1205.3102 , 2012; Gatermann and Parrilo: J Pure Appl Algebra 192(1–3):95–128. https://doi.org/10.1016/j.jpaa.2003.12.011 , 2004), it can be written as a small linear matrix inequality of size at most $$2d+2$$ , which is independent of n. For the cone of symmetric nonnegative polynomials with the relevant support we also prove an analogue of the half-degree principle for globally nonnegative symmetric polynomials (Riener: J Pure Appl Algebra 216(4): 850–856. https://doi.org/10.1016/j.jpaa.2011.08.012 , 2012; Timofte: J Math Anal Appl 284(1):174–190. https://doi.org/10.1016/S0022-247X(03)00301-9 , 2003).
Processing of Healthcare Data to Investigate the Correlations and the Anomalies

Fitness, safety and data monitoring wearables have become extremely prevalent. It is no wonder that sedentary lifestyle will lead to a variety of health and personal problems, including excess weight, the onset of chronic and acute illness, and perhaps low productivity at school, at work and even in everyday life. Monitoring fitness has been extensively pursued by the business and university researchers, focusing on sleep pattern, cardiac wellbeing, continuous activity tracking, overall wellness, recovery from illness & diseases, and many more. Researchers have used deep learning techniques; machine learning algorithms and a broad variety of statistical techniques to forecast and identify the trends, patterns, and deviations in the healthcare data collected through the wearable devices. The purpose of this study is to collect the data using Fitbit Inspire HR and Fitbit Versa. The information is extracted automatically using the Fitbit API. The study also explores and investigates any anomalies, trends and correlations inside the Fitbit healthcare data. The K-means algorithm is used to classify the data based on the parameters of the Fitbit healthcare data.
Parameter-Centric Analysis Grossly Exaggerates Certainty

The reason probability models are used is to characterize uncertainty in observables. Typically, certainty in the parameters of fitted models based on their parametric posterior distributions is much greater than the predictive uncertainty of new (unknown) observables. Consequently, when model results are reported, uncertainty in the observable should be reported and not uncertainty in the parameters of these models. If someone mistook the uncertainty in parameters for uncertainty in the observable itself, a large mistake would be made. This mistake is exceedingly common, and almost exclusive in some fields. Reported here are some possible measures of the over-certainty mistake made when parametric uncertainty is swapped with observable uncertainty.
Terrain Classification Algorithm for Lunar Rover Based on Visual Convolutional Neural Network

With the development of society, the lunar exploration capability marks the development of aerospace science and technology, and it has received more and more attention. We propose a method which combines vision-based convolutional neural networks with ensemble learning to classify the current terrain by the pictures taken by the onboard camera of the lunar rover. According to the classification result, the lunar rover can independently select a better path, avoiding the unnecessary trouble caused by the delay of the land-month communication. The overall accuracy of our classification is 80%, and some of them have higher precision. It is expected that the classification results will help the decision of path planning.
CREATION OF AN INTERACTIVE SYSTEM FOR TRAINING OPERATIONS WITH AN ELECTRONIC TACHEOMETER

The purpose of thе work is to create an interactive system that allows you to study how to work with an electronic total station, main functions and settings of the device, and improve the quality of the studied material. Such a system will be in demand among part-time students due to the unstable possibility of being present at the educational institution and studying the device physically.