Reviewerly/exHarmony huggingface dataset:
Impact of Training Parameters on Characteristics of Codebooks Obtained Using Autoencoder Networks

Autoencoder neural networks are used by many researchers to simulate digital communication systems and optimize signal codebooks and reception algorithms. Achieved results heavily depend on network training parameters but existing publications usually give little detail about them. In this paper, we present the systematic analysis of the influence of training parameters on the mean value and variance of results. As an example, we use the autoencoder network intended for design of codebooks for noncoherent communication over additive white Gaussian noise channel. For codebook quality measure, the maximum pairwise correlation between signals is used. The results are obtained and analyzed for two codebooks with different spectral efficiency. The influence is studied for such parameters as signal-to-noise ratio for training, learning rate behavior, batch size, and the number of iterations. Recommendations on the optimal choice of training parameters to obtain acceptable results with minimal computational cost are given.
A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.
Robust algorithmic binding to arbitrary fragment of program code

When solving a task, a programmer actively interacts with a finite set of code fragments. The information about their locations is important for quick navigation, for other developers, and as a kind of documentation. Integrated development environments (IDEs) provide tools for marking code fragments with labels, displaying lists of labels, and using these labels for quick navigation. However, they often lose the correspondence between the label and the marked place when the code is edited, in particular when changes are made outside the IDE. In previous works, the authors propose a tool to be integrated into various IDEs for “binding” to large syntactic entities of a program and building a markup that is robust to code editing. The description of the marked element is built on the basis of the abstract syntax tree (AST) of the program. Later it is used to algorithmically search for the element in an edited code. The search has a success rate from 99 to 100%. This article aims at robust algorithmic binding to an arbitrary section of the code. For binding to a single-line code fragment, we propose an extension of the model describing the marked fragment, and an additional search algorithm. We also propose an algorithm for embedding nodes corresponding to multi-line fragments in an AST. We show that the correctness of the AST is not violated by these embeddings. Bindings to randomly selected lines were made in the code of three large C# projects. Manual check of these lines search results in the edited code has confirmed that the bindings are robust to code editing.
Research on the Construction of Emergency Network Public Opinion Emotional Dictionary Based on Emotional Feature Extraction Algorithm

How to strengthen emergency management and improve the ability to prevent and respond to emergencies is an important part of building a harmonious socialist society. This paper proposes a domain emotion dictionary construction method for network public opinion analysis of public emergencies. Using the advantages of corpus and semantic knowledge base, this paper extracts the seed words based on the large-scale network public opinion corpus and combined with the existing emotion dictionary, trains the word vector through the word2vec model in deep learning, expands the emotion words, and obtains the candidate emotion words according to the semantic similarity calculation, So as to generate a domain emotion dictionary. The accuracy rate of emotion discrimination by the emotion dictionary constructed in this paper is 0.86, the recall rate is 0.92. Through the verification of accuracy and recall rate, the construction method proposed in this paper has good accuracy and reliability. Because of the great differences in different experiences and situations of different groups, there will be great differences in views and perspectives on the same event. The key to prevent the public from blindly following the crowd should be to reach groups close to emotional distance, and targeted prevention and control of public opinion can be conducted according to different characteristics of different groups.
Design of unmanned interaction system based on visual cognition

The stability of unmanned vehicle is related to the safety of the vehicle itself. In the process of unmanned vehicle control, there will be collision phenomenon in the process of meeting the vehicle. To solve the above problem, the design of unmanned interaction system based on visual cognition is proposed. The hardware structure of the system is designed based on 80C51 single chip microcomputer, including ARM processor, GPS receiving module, driving record signal collecting module, etc. The PID controller design based on neural network is optimized, and the design of unmanned interactive system based on visual cognition is completed. Experimental results show that the designed system can identify the surrounding environment in real time, make corresponding decisions, let the vehicle avoid the wrong vehicle operation, and save Oil consumption.
Comparative Analysis of the Formation of the Competence “Teaching in Supplementary General Education Programmes” аmong Teachers in the Field of Children’s Supplementary Education

The study aims to identify the features of professional competencies formation within the generalised job function “Teaching in Supplementary General Education Programmes” among teachers holding different positions in the field of children’s supplementary education (CSE). The paper describes the approaches to solving the tasks to reach the stated aim. The work shows the results of a theoretical analysis of the extent to which the issue of formation and assessment of professional competencies in CSE teachers has been developed; defines the key concepts of the study, i.e. “generalised job function”, “job function”, “competence”, “competency”; substantiates research methods, describes a computer-oriented case; presents the results of assessing the level of professional competencies formation among CSE teachers holding different positions, which was conducted at the Municipal Autonomous Non-Standard Educational Institution “Palace of Children’s Creativity named after F. I. Avdeeva” of the Urban District “The City of Yakutsk” (PCC); identifies and describes professional deficits of its teaching staff holding different positions; makes recommendations on the development of professional competencies and the elimination of professional deficits. Scientific novelty of the study lies in developing and testing an assessment tool and identifying the features of professional competencies formation among teachers holding different positions. As a result of the study, the researchers have been able to identify the specificity of the professional level of the teaching staff at the PCC, their strengths and professional deficits, to determine the prospects for the development of the personnel potential at the PCC, taking into account the capabilities of the teaching staff themselves.
Voice Morphing Based on Formant Substitution and Phoneme Interpolation

Voice cloning is a major topic of speech sound processing and artificial intelligence. Its possible applications include customization of generated voice in robots, text-tospeech, it can also be used for making lines of speech of a given voice actor when he/she is unavailable. In this paper we describe a method to change the perceived speaker into a desired one by substituting the formants, interpolating between known phonemes when the measured formants don’t exactly coincide with them. The method enables voice cloning with a short sample of target voice.
Overcoming Barriers to Empirical Cyber Research

Empirical studies have the potential to both inform and transform cyber peace research. Empirical research can shed light on opaque phenomena, summarize and synthesize diverse stakeholder perspectives, and allow causal inferences about the impact of policymaking efforts. However, researchers embarking on empirical projects in the area of cyber peace generally, and cybersecurity specifically, face significant challenges – particularly related to data collection. In this chapter, we identify some of the key impediments to empirical cyber research and suggest how researchers and other interested stakeholders can overcome these barriers.
Are Two Binary Operators Necessary to Obtain a Finite Axiomatisation of Parallel Composition?

Bergstra and Klop have shown that bisimilarity has a finite equational axiomatisation over ACP/CCS extended with the binary left and communication merge operators. Moller proved that auxiliary operators are necessary to obtain a finite axiomatisation of bisimilarity over CCS, and Aceto et al. showed that this remains true when Hennessy’s merge is added to that language. These results raise the question of whether there is one auxiliary binary operator whose addition to CCS leads to a finite axiomatisation of bisimilarity. We contribute to answering this question in the simplified setting of the recursion-, relabelling-, and restriction-free fragment of CCS. We formulate three natural assumptions pertaining to the operational semantics of auxiliary operators and their relationship to parallel composition and prove that an auxiliary binary operator facilitating a finite axiomatisation of bisimilarity in the simplified setting cannot satisfy all three assumptions.
Self‐compressing object sequence for consistency maintenance in co‐editors

Abstract Co‐editors are a class of human‐centered collaborative systems that allow multiple geographically dispersed people to freely and concurrently edit shared documents at the same time over networks. The most important building co‐editors technique is consistency maintenance. Even though OT (operational transformation) is a widely adopted consistency maintenance technique, researchers and practitioners have persistently explored alternative techniques to OT. One of the representative techniques is AST (Address Space Transformation), which replaces OT's transformation components with basic manipulation on an additional object sequence. However, the additional object sequence causes an unbounded metadata overhead problem. To solve the problem, this work proposes an optimized AST algorithm called ASTO, which is equipped with a specially designed self‐compressing object sequence. ASTO can automatically compress the object sequence and keep the metadata overhead under control. The effectiveness and feasibility of ASTO are verified by simulation experiments and a publicly accessible prototype system.
Analysis and Implementation of Presenter Layer on MVC Architecture iOS Application Development∗

Architectural pattern is the one of the most important parts in the development of a well-structured software, in this case, the code should be maintainable, readable, traceable, and modifiable. There are several architectural patterns that are available to be used by the developers. One of the most widely used is MVC (Model View Controller). The MVC pattern is also widely adopted in the development of applications in the iOS platform. However, according to the literature, the implementation of the MVC pattern violates the SOLID principles especially Single Responsibility Principle (SRP) - in this case of the implementation of the controller layer. In this paper, we propose the modification of the MVC architectural pattern in iOS application development in order to comply to the SOLID principle and reducing complexity of a particular module (layer). A comparative study was conducted by using a tool called coherent-swift (using LCOM4 as a method of measuring cohesion) to compare the level of cohesion as a measure of SRP. The result of the study shows that the modified MVC get a better cohesion level rather than the original one.
Power Spectrum, Cospectrum, and Coherence

The objective of this chapter is to present some important relations between the Fourier Transform and correlation functions. It turns out that the cross-correlation function and autocorrelation function have some useful relationships to Fourier Transform and power spectrum of the individual functions. As a result, cospectrum and coherence (normalized statistical correlation in frequency domain) can be defined.
WITHDRAWN: A semi-supervised method to generate Persian dataset for suggestion classification

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MP2

Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. \method has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.
Hardware and Software for Modern Computer Games

The article describes the technology for developing computer games. The computer games development model using the Unity 3D cross-platform environment has been created. A computer game for gaming PCs has been developed using the Unity 3D cross-platform environment. The recommendations for the development of modern computer games and their effective usage on gaming PCs are provided.
The Structure of the Production Expert System for the Selection of the Primary Temperature Measuring Detector

The paper discusses the component interaction scheme of a knowledge-based system focused on supporting the design of measuring channels for measuring thermometric characteristics in technological and production processes. The paper presents generalized structure of intelligent system, production model of knowledge representation, and method for selecting primary measuring transducer.
Developing Mixed Reality Applications with Platform for Situated Intelligence

Both industry and academic interest in mixed reality has skyrocketed in recent years. New headset devices for both virtual and augmented reality are increasingly available and affordable, and new APIs, tools, and frameworks enable developers and researchers to more easily create mixed reality applications. While many tools aim to make it easier to create and interact with content rendered to the head-set, these new devices are interesting not just from an output, but also from an input perspective-they contain powerful multimodal sensors that provide unique opportunities to drive forward research on egocentric perception and interaction. In this paper, we intro-duce Platform for Situated Intelligence-an existing open-source framework-to the mixed reality community. The framework was designed to help developers and researchers create and study real-time, interactive AI systems that process multimodal streaming data. Recent extensions to the framework include new capabilities and components designed specifically to support mixed reality sensory streams and scenarios.
How weight-sharing mechanisms affect the performance of deep Siamese networks

Siamese networks usually consist of two parallel networks and each network is made of a backbone and some fully connected layers. The entire Siamese structure is trained by a loss function, while the similarity of the latent spaces of the two networks is decisive at the inference time. The weights of the network can be shared in Siamese networks. Therefore, in this paper, four weight-sharing mechanisms are proposed, and their effects on the performance of Siamese networks are investigated. These methods are named "full weight sharing," "partial weight sharing in FC," "partial weight sharing in CNN," and "without weight sharing." In this paper, four network backbones are examined and trained by employing the four aforementioned weight-sharing methods; two losses (proxy anchor loss and contrastive loss) are utilized for training. According to the obtained results, "partial weight sharing in FC" combined with contrastive loss has the best result on all backbones.
Staple Food Price Information System in Bandar Lampung City Using JSON (Javascript Object Notation)

Abstract The price information system for Staple Food Prices is very helpful to the public by providing information on raw food prices for the Bandar Lampung markets. This information system allows the public to determine the price of food raw materials according to grouping: by product, by market and prices at a specific time. Investigation of information systems using data matrices and objects as alternative databases using JSON (JavaScript Object Notation), one of which applies information technology in the use of information systems with matrices and objects as others alternative databases. The results obtained are the monitoring of prices for the basic needs of the population to support the information system on the prices of basic foods. In general terms, how to monitor the price of community staples in support of the basic food price information system to capture the prices of basic community needs in each market. It is possible to use a database system with JSON in addition to an existing database, for example a relational database.
Explainable AI; Are we there yet?

Artificial intelligence (AI) and its models have made a significant impact when it comes to performing tasks that limit human capabilities; this not only reduces human efforts but also detaches human errors. The introduction of such models in various fields results in output which subsequently ameliorates laborious chores such as data processing and other prediction tasks. However, these models produce compound outputs which can be baffling for humans and makes these models vague in terms of providing the reason behind the output. The opaqueness in the internal functionality in the models makes them ‘black-box’ which not only raises a question in terms of the trust but also increases the chances of biasness in the model. When black-box models are applied in real-life problems where transparency is the biggest aspect, the end-user will always be in a dilemma whether to trust the decision produced by these models or not. In recent years, researchers have put a lot of effort into finding a tool that eclipses all these issues and came up with explainable artificial intelligence (XAI) that helps in justifying the output of such models, but with the advancement, there will be some counter challenges always. The extent of the paper is to feature the urgency of XAI to overshadow the downsides of these models and to inspect the impact of XAI in various sectors. Afterwards framework of XAI and challenges faced by XAI are also being discussed.
High-Quality Surface-Based 3D Reconstruction Using 2.5D Maps

Previous works on RGB-D reconstruction are based on voxels, points, or meshes, which are either too computationally expensive to represent high-resolution 3D models, or not convenient for model regularization to deal with noise and errors. In this paper, we propose a new method to reconstruct 3D models with more accurate geometric information and better texture. Our method uses abstract surfaces consisting of different points with similar information as units of the model. To reduce the complexity, we use 2.5D heightmaps to represent each surface in the reconstructed model, making it convenient to do regularization. Experiments demonstrate the effectiveness of our method.
The Virtual-Augmented Reality Simulator: Evaluating OST-HMD AR calibration algorithms in VR

When developing AR applications for high-precision domains such as surgery, we face a common problem: how can the system guarantee that the end-user will see a virtual object aligned with its real-world counterpart? Alignment, or registration, is a crucial feature of AR displays, but achieving accurate alignment between real and virtual objects is not trivial. With hundreds of calibration approaches available, we need better tools to understand how and when calibration algorithms fail as well as understand what can be done to improve alignment. This poster introduces a novel AR simulator in VR that facilitates experimentation with different calibration algorithms.
Currency Recognition For The Visually Impaired People

The objective of this research work is to identify the Indian banknotes using an android mobile application. According to a recent survey In India, nearly 40 million people are blind or visually impaired, including 1.6 million children. Owing to a number of factors, visually disabled people had trouble identifying and accepting various types of banknotes. This issue has prompted researchers to create an automated banknote recognition device that can be converted into a vision-based system. The main goal of this research is to dig deeper into the effects of area and orientation on machine learning and deep learning results using Indian currency. The main objective is to help visually impaired people by providing them a simple mobile application. Currency recognition systems based on image processing, on the other hand, are inadequate. This method uses image processing to automate and solidify the task of identifying Indian currencies. Here this system uses Indian Rupees as examples to illustrate this technique. In the phase of object recognition and matching assignments, color is an integral aspect that offers knowledge and important values. Many items can't be categorized accurately until they have color characteristics. So color and shape information are taken under consideration for the identification of the Indian currency notes.