Reviewerly/exHarmony huggingface dataset:
VR cycling system with Cross-platform. Multi-scene and Highly Immersive

With the promotion of national fitness and the development of socialization of sport, physical and mental health has gradually become an important demand of the Chinese people in the new era in pursuit of a better life. As a simple and efficient way of fitness and entertainment, VR spinning bikes are not restricted by the weather, time, place, and air pollution, and are slightly loved by the public. However, the current virtual cycling system based on dynamic cycling still has problems such as a single riding scene, inconsistent visual somatosensory, and poor portability. In response to the lack of interactivity and presence of traditional virtual cyclingsystems, this article has developed a cross-platform Multi-scenario VR cycling system with high immersion. The system has multiple riding environments for users to choose personally. The core solves the problem of inconsistent VR riding body sensation, improves the user's immersion and reduces the user's dizziness In addition, the system can run on a varietyof platforms to meet the needs of users' personalized riding.
Application Analysis of Vector Model and GIS System Data in the Substation Engineering Entire Life-cycle

With the further popularization of 3D design in substation engineering, the previous works provide lots of data foundations for digital operation and maintenance. Different from the editing requirements of the model in the design stage, higher requirements are put forward for the corresponding speed of the model and the bearing capacity of the graphics platform in the operation and maintenance stage. At present, the application methods of model library are mainly based on geographic information data (GIS) and vector graphics platform. In this paper, the model data are analysed and compared from the dimensions of analytic speed, graphic effect, operation efficiency, integrity and storage space, etc. The graph platform based on geographic information data can better support the application of the model in operation and maintenance stage.
Revealed Preference Argumentation and Applications in Consumer Behaviour Analyses

Consumer preference studies in economics rest heavily on the behavioural interpretation of preference especially in the form of Revealed Preference Theory (RPT). Viewing purchasing decisions as a species of human reasoning, in this paper we are interested in generalising behaviourism to preference-based argumentation where existing frameworks are universally governed by the opposing mentalistic interpretation of preference. Concretely we re-construct and unify two main approaches to RPT then develop a so-called Revealed Preference Argumentation (RPA) framework which identifies preference as observed reasoning behaviour of an agent. We show that RPA subsumes RPT, by showing that key RPT-based consumer analyses can be translated to and solved as RPA computational tasks. It is argued that RPA may pave the way for future applications of argumentation to behavioural economics.
Item-Share Propagation Link Applying for Recommendation

The growth of social networks and various web services (e-commerce, e-learning, e-health, etc.) urgently requires recommendation techniques to satisfy users. Providing high-quality recommendations with a minimum of common feedback is a major challenge for a new recommendation algorithm. In this paper, we use more informative modelling with a weighted bipartite network around the item entity. The idea is to exploit the item-user connectivity to extract hidden information. The problem is to predict non-existent links based on existing links by double projection forward and backward. The results are promising with the implementation of real data sets.
How Intuitive Is It? Comparing Metrics for Attitudes in Argumentation with a Human Baseline

It is often interesting to know how similar two persons argue, e.g. when comparing the attitudes of voters and political parties, or when building an argumentation-based recommender system. Those applications need a distance function, which should give intuitive results. In this paper, we present seven functions which calculate how similar the attitudes of two agents are in an argumentation. We evaluate how good those functions match the results of a human baseline which we determined in a previous work. As it turns out, variants of the p-metric, Cosine, and Soergel distance best agree with human intuition.
Discussion of Intelligent Electric Wheelchairs for Caregivers and Care Recipients

In order to reduce the burden on caregivers, we developed an intelligent electric wheelchair. We held workshops with caregivers, asked them regarding the problems in caregiving, and developed problem-solving methods. In the workshop, caregivers’ physical fitness and psychology of the older adults were found to be problems and a solution was proposed. We implemented a cooperative operation function for multiple electric wheelchairs based on the workshop and demonstrated it at a nursing home. By listening to older adults, we obtained feedback on the automatic driving electric wheelchair. From the results of this study, we discovered the issues and solutions to be applied to the intelligent electric wheelchair.
Construction of Examination Questions Database of Medical Information Retrieval Course Based on Multivariate Collaborative Filtering Recommendation Algorithm

With the rapid development of computer networks, the era of big data has come, and fields such as medical retrieval are flooded with massive amounts of data. It is becoming more and more difficult for users to select the knowledge they need from a large amount of knowledge. The personalized recommendation system can quickly recommend the knowledge that people need. Collaborative filtering recommendation algorithm is a hot area of personalized recommendation algorithm, which has been deeply studied and discussed by scholars at home and abroad. The user-based collaborative filtering recommendation algorithm selects neighbors by calculating user similarity, and then recommends neighbors. This is playing an increasingly important role in practical applications. In the current medical information retrieval course, the test question bank for assessment is mainly composed of a large number of test questions, and there is no effective distinction between the test questions under different users, different projects, and different responsibilities. Therefore, the test questions for different personnel are targeted to the medical retrieval course. It is particularly important. In order to solve this problem, this article is based on the existing recommendation system, and through reading a large amount of literature, conducting experimental analysis and other methods to study the role of multiple collaborative filtering recommendation algorithm in the construction of medical retrieval course test question bank. The research results show that the test question system of the medical retrieval course based on the multiple collaborative filtering algorithm is more intelligent, and different test questions can be given to different personnel, which greatly improves the intelligence and efficiency of the medical information retrieval test question database.
Image Enhancement Effects on the Forensic Facial Recognition System

In the field of forensic identification of human images, face recognition technology has become an important quantitative examination method for face similarity examination. Nevertheless, face image quality has its effects on recognition performance of face recognition systems. Image enhancement technology can improve image visual presentation quality, but whether it has effects on the state-of-the-art deep-learning based face recognition systems, especially in the application of the practical case scenarios of forensic identification of human images, is still unknown. In this paper, we studied the statistical effects of the four common used single-image super-resolution enhancement techniques on the recognition performance of the face recognition system by using the face image materials collected from 33 real cases of forensic human image identification. The results showed that not all image enhancement techniques had positive effects. Among them, the self-snake model based method could improve recognition performance, while the other methods had negative or none significant effects on the face recognition system.
Deep Learning Applications with Python

The applications of deep learning extend to many aspects of daily life and are not confined to the domains of computer science alone. From face recognition to the smart grid domain, deep learning has proved itself to be an effective tool in producing state-of-the-art results. This chapter discusses how the usage of a high-level language like Python and its compatibility with deep learning frameworks and its collection of utility libraries facilitates practitioners in the development process. The chapter further dwells into details of the implementation of deep learning techniques applied to different applications, namely, facial recognition, fingerprint recognition, character recognition, smart grids, and renewable energy, by providing a brief history of how the technology has influenced the domain and details regarding the common datasets used as well as a code implementation in Python thoroughly covering the different steps.
Intellect models used to search for data in stochastic information environment in Data Systems

IDEF models of the method of forming a semantic connection through calculations of the relevance on the basis of the area with the zone and attributes, and the calculation of the most cost-effective value of g, the method of calculating the weight of the data frequency and the frequency with inverse data, based on the model of intellectual semantic search, the creation of the ISIDR in the SIS and for software.*
From primary to dual affine variety codes over the Klein quartic

In Geil and Özbudak (Cryptogr Commun 11(2):237–257, 2019) a novel method was established to estimate the minimum distance of primary affine variety codes and a thorough treatment of the Klein quartic led to the discovery of a family of primary codes with good parameters, the duals of which were originally treated in Kolluru et al (Appl Algebra Eng Commun Comput 10(6):433-464, 2000)[Ex. 3.2, Ex. 4.1]. In the present work we translate the method from Geil and Özbudak (Cryptogr Commun 11(2):237–257, 2019) into a method for also dealing with dual codes and we demonstrate that for the considered family of dual affine variety codes from the Klein quartic our method produces much more accurate information than what was found in Kolluru et al (Appl Algebra Eng Commun Comput 10(6):433-464, 2000). Combining then our knowledge on both primary and dual codes we determine asymmetric quantum codes with desirable parameters.
Deep Learning for Character Recognition

The advances in technologies had led to the accessibility to the entire world at hand through the Internet and mobile phones. While computers and cameras have come to pockets as smart phones, a lot of vision-related applications have become easier. What a human visualizes has been able to be visualized by machines as well through advancements in machine vision algorithms. This chapter gives a detailed study about one such computer vision application – character recognition – with the aid of deep learning techniques. In particular, this chapter is about the implementation of widely used convolutional neural networks in offline character recognition which is used in document analysis, document recognition, scene text classification, localization, and recognition.
Value Based Collection in Intelligence Analysis

Intelligence analysis is not a passive process, but proactive and reactive. Analysts have the opportunity to request collection of further intelligence to improve the analysis. The identification and selection of collection opportunities is a human led process requiring an understanding of the value of a collection to be balanced against the cost and risks of making the collection. Argumentation frameworks are a mathematical formalization to describe arguments and conflicts between such arguments. Argumentation frameworks can be used to represent intelligence analyses, with observations, hypotheses and assumptions represented as arguments and conflicts between these represented by attacks. A value of information can be defined over an argumentation framework based on the change in the result of evaluation of the framework, according to argumentation semantics, when arguments are added or removed. When accompanied by a representation of collection opportunities, the value of information can be used to assess which collection opportunity is most beneficial. In this paper we define a framework for calculating the net benefit over a closed set of potential collection opportunities. This framework is demonstrated against an example intelligence analysis on maritime situational awareness scenario.
Appropriation for Interdisciplinary Practice: The Case of Participatory Design in Brazilian Computer Science

Partner community participation in design enables the exploration of design spaces which could not be reached without partner knowledge and expertise, and also enables partner communities to exercise their personal power to influence technological change towards their own wants and needs. Participatory Design (PD) is an approach that since its inception seeks to build spaces of democratic technological change through practices with (rather than for) partner communities. From its origin in Scandinavia, the approach has been adopted by multiple academic communities around the world, and those adoptions have demanded changes to the underlying theories and methodologies to fit them into each different local reality, a process of appropriation which has transformed PD over the decades. As an example of a region which adopted and adapts PD, Latin America has specific epistemological and methodological challenges which emerge from historical and socioeconomic conditions, and in the context of the region, interdisciplinary practices have been discussed and applied in the context of partner participation in design. Considering the importance of academic communities for the advancement of PD towards meaningful changes to partner communities, this paper presents a systematic overview of PD in Brazilian Computer Science as an instance of such community. The study surveys articles from 2000 to 2020, seeking to expose how Brazilian PD operates in the region through the use of interdisciplinary referential. We expect that this contribution elucidates adaptation strategies of PD referential towards their appropriation by communities diverse from those the techniques were originally designed for, so that practitioners from other regions can discuss those strategies when adapting PD to the needs of their partners.
Latent Fibrations: Fibrations for Categories of Partial Maps

Latent fibrations are an adaptation, appropriate for categories of partial maps (as presented by restriction categories), of the usual notion of fibration. The paper initiates the development of the basic theory of latent fibrations and explores some key examples. Latent fibrations cover a wide variety of examples, some of which are partial versions of standard fibrations, and some of which are particular to partial map categories (particularly those that arise in computational settings). Latent fibrations with various special properties are identified: hyperconnected latent fibrations, in particular, are shown to support the construction of a fibrational dual; this is important to reverse differential programming and, more generally, in the theory of lenses.
Reform of Mathematics Teaching Mode from the Perspective of Artificial Intelligence and Big Data

With the rapid development of artificial intelligence and big data, the wide application of artificial intelligence and big data has promoted the reform process of traditional teaching mode. Traditional mathematics teaching makes students lack interest in mathematics, and the ability of applied mathematics in practice, what is more, the assessment of students is too single, so it is imperative to carry out mathematics teaching reform. In the teaching reform, artificial intelligence can stimulate students’ study interest, exercise students’ application ability, and enrich examination methods.
Efficient Lattice-Based Polynomial Evaluation and Batch ZK Arguments

In this paper we provide an efficient construction of a lattice-based polynomial argument and a polynomial batch-protocol, where the latter contains the polynomial argument as a building block. Our contribution is motivated by the discrete log based construction (EUROCRYPT’16), where in our case we employ different techniques to obtain a communication efficient lattice-based scheme. In the zero-knowledge polynomial batch-protocol, we prove the knowledge of an easy relation between two polynomials which also allows batching of several instances of the same relation. Our batch-protocol is applicable to an efficient lattice-based range proof construction which represents a useful application in cryptocurrencies. In contrast to the existing range proof (CRYPTO’19), our proof is more efficient for large number of batched instances.
Fault Prediction Using Deep Neural Network

Fault prediction is the major step in large industries where the complexity of the software is rising at an exponential rate. Assigning the proper damaging level of faults encountered in complex and large software, would help the developers to plan for fixing the faults. Traditional fault prediction studies mainly concentrate on designing hand-crafted features, which are input into machine learning classifiers to identify faulty modules. In this paper, we have developed a model based on deep learning techniques. Some of the extracted features from programs are used to train the Deep Neural Network and then other features are passed for testing. The proposed model is validated using open source Promise data repository. It is clear from the results that the performance of predicting faults by the proposed model is better than the existing models.
Systematic Modelling of Transport Processes across Interfaces

This paper discusses the implementation of interfacial transport in the systematic modelling methodology by Preisig (2012) and how to lecture the approach as well as its implications to engineering students. We first elaborate on the theoretical concept of interfaces before abstracting them into topologies that allow for comprehensible as well as precise mathematical assessments of the transport processes involved. Thereby, any interfacial transfer is rooted in its fundamental parts of transport towards, across and away from the interface. These steps can be assessed by only a handful of algebraic equations from an ontology, which covers the entirety of physical concepts, state derivatives, and conservation principles. Due to this, interfacial transport can rapidly be implemented in process models, significantly aiding subsequent model simplification as well as model fitting.
Tailoring the Environment—Cavity QED

It is possible to create electromagnetic environments where spontaneous emission can be enhanced or inhibited, or even becomes reversible. This is the domain of cavity QED. Following a review of its basic aspects we discuss the micromaser, where a tailored environment allows to produce strongly nonclassical radiation. We also consider the off-resonant situation where a single atom operates as a dispersive medium. We then transition to the quantization of LC electric circuits and superconducting qubits, which permit to extend cavity QED to the emerging area of many-body circuit QED and the possibility to reach extremely high atom–field couplings. The chapter concludes with a brief introduction to the Casimir effect.
Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varying the dataset numerical similarity. The focus is on convolutional layers with induced earlier learning through the use of striped forms for image classification. Which provided a higher performing accuracy in the first epoch, with improvements of between 3-5% in a well known benchmark model, and also ~10% in a color image dataset (MTARSI2), using a dissimilar model architecture. The proposed method is robust to limit optimization approaches like Glorot/Xavier and He initialization. Arguably the approach is within a new category of weight initialization methods, as a number sequence substitution of random numbers, without a tether to the dataset. When examined under the FGSM approach with transferred learning, the proposed method when used with higher distortions (numerically dissimilar datasets), is less compromised against the original cross-validation dataset, at ~31% accuracy instead of ~9%. This is an indication of higher retention of the original fitting in transferred learning.
CASTLE: Architecting Assured System-on-Chip Firmware Integrity

Modern System-on-Chip (SoC) designs include a large number of embedded microcontrollers that execute custom firmware. Firmware provides the flexibility of updating security features, i.e., it enables patching or in-field update, in response to an emerging security threat, bug, or changing requirements. Unfortunately, current firmware update mechanisms are complex, manual, and error-prone. In this paper we present CASTLE, an architectural framework to enable systematic and assured updates to SoC firmware. The main workhorse of CASTLE is a centralized, dedicated IP in the SoC that is responsible for receiving, authenticating, and installing a patch. The architecture works with off-chip firmware validation flows, e.g., cloud-based service for validating a proposed patch, and identifying compatibility constraints on other resident firmware in the SoC. The result is a comprehensive infrastructure that works seamlessly across architectures, vendors, and service providers, while meeting deployment and usability requirements. We demonstrate the application of proposed framework in addressing functional and security flaws of existing firmware patching mechanisms including firmware incompatibility, inadequate authentication, and time-of-check vs. time-of-use (TOCTOU) constraints.
TINJAUAN LITERATUR KONSEP GAMIFIKASI DALAM MENDUKUNG STRATEGI PEMASARAN ORGANISASI BISNIS

The marketing system that is developing by utilizing technology is increasingly shifting into a new era by involving game elements that are generally known by the younger generation, which has resulted in companies having many choices to win the competition. using the literature study method sourced from journals, books and websites. Researchers examine how the potential of gamification for the marketing of a product and service, and the results obtained are where gamification at this time is very suitable for use in digital marketing media to increase the effectiveness and efficiency of the company's marketing activities itself.