Reviewerly/exHarmony huggingface dataset:
Usage of The Field Index in Machine Translation

The current article discusses the main problems of human and machine translations, as well as introduces a new lexical description in machine translation for faster and more accurate translation. The new method uses so-called field indicators or the Field Indices to facilitate the MT search engine for words by marking these words with special components creating a semantic field, and allowing the MT devices to search for the word according to its usage in the text. The Field Index system covers the semantic description of the following main spheres: scientific field, public or social field and humanitarian field. These three spheres contain subfields that usually mingle with the parent index through a dash, with the parent index being with the first one. The scientific field includes such aspects as geography, mathematics, chemistry, physics, economy, medicine, etc., with related subfields like diseases and biological terms (for medicine), names of drugs (as a separate filed), finance and accounting (as part of economy), etc. Applied Sciences Index contains miscellaneous words used by other subfields of the same scientific sphere: e.g., computer, telephone, function and many others, and plays a crucial part in distinguishing polysemantic words such as mouse (hardware), root (in mathematics), etc. The public or social field contains subfields that narrow the meaning of words to a specific one and includes aspects as art, agriculture, law, education, religion, housing utilities, time, transportation, people, etc.i+iT Ss the widest semantic field containing a lot of subfields specifying words that belong to such groups as colours, architecture, games, music, sport, etc. (for art index), clothing, beverages, food and production (for agriculture index). Notions of time, people, professions and terms for religion and items used in household are present in this group due to its wide usage within the society. The humanitarian sphere deals mostly with terms used in languages, literature, manuscripts and libraries. These subfields help identify polysemantic words between nations and languages, book titles and ordinary words and phrases, and literary styles (documentaries, fairy tales, dramas, etc.). In turn, these can be further defined as prose or a poem. All formulae proposed in the project consider the presence of the Field Indices and its position at the end of the description of the word. Depending on the target language, the translated version should be identical with the source following this very principle.
Multi-Objective Path Planning Based on Parallel Elite Particle Swarm Optimization Algorithm

The task mode of robots is mostly one machine corresponding to multiple targets, which is equivalent to a multi-point path planning problem under specific constraints. To solve this problem, a parallel elite particle swarm optimization (PSO) algorithm is proposed. Firstly, the elite selection mechanism is applied to PSO algorithm, which avoids the blindness of the algorithm and improves the convergence speed. At the same time, the improved artificial potential field gravity is combined with inertia factor in the PSO algorithm to construct an adaptive update strategy of particle position and velocity. This strategy enhances the global search ability of the PSO algorithm in the early stage of robot motion, and the local search ability of the PSO algorithm gradually increases with the decrease of gravity in the later stage, which solves the problem that the PSO algorithm is easy to fall into local optimal. Secondly, with the help of the adaptive elite PSO algorithm, the safety distance between any two target points is calculated to form the corresponding safety distance matrix. Then, the PSO algorithm is used to find the optimal order according to the safety distance matrix. Finally, multi-objective point path planning is implemented according to the optimal order, and the concept of parallel optimization is introduced to improve the operating efficiency of the program algorithm. The simulation results show that the algorithm is feasible and effective.
A Prototype Implementation of the NNEF Interpreter

The NNEF (Neural Network Exchange Format) is a de facto standard file format for the neural network description and exchange. In this work, we represent a simple implementation of the NNEF execution system, which is similar to the programming language interpreters. While the original NNEF file format focused on the data exchange format itself, our prototype implementation shows that the contents of an NNEF file can be directly executed by the underlying computing systems.
Considering User Agreement in Learning to Predict the Aesthetic Quality

How to robustly rank the aesthetic quality of given images has been a long-standing ill-posed topic. Such challenge stems mainly from the diverse subjective opinions of different observers about the varied types of content. There is a growing interest in estimating the user agreement by considering the standard deviation of the scores, instead of only predicting the mean aesthetic opinion score. Nevertheless, when comparing a pair of contents, few studies consider how confident are we regarding the difference in the aesthetic scores. In this paper, we thus propose (1) a re-adapted multi-task attention network to predict both the mean opinion score and the standard deviation in an end-to-end manner; (2) a brand-new confidence interval ranking loss that encourages the model to focus on image-pairs that are less certain about the difference of their aesthetic scores. With such loss, the model is encouraged to learn the uncertainty of the content that is relevant to the diversity of observers' opinions, i.e., user disagreement. Extensive experiments have demonstrated that the proposed multi-task aesthetic model achieves state-of-the-art performance on two different types of aesthetic datasets, i.e., AVA and TMGA.
Singapore

Bangladesh’s cybersecurity policies became well-known internationally in 2016 when the country’s central bank was the subject of one of the largest digital bank heists to date. This chapter contends that there is no alternative to promoting cooperation at both the regional and international level to ensure cybersecurity for Bangladesh. Bangladesh adopted its National Cyber Security Strategy in 2014 to face cyber threats, risks, and challenges to its national security. Bangladesh accepts the generally accepted understanding regarding the applicability of international law to cyberspace. Due to the easy accessibility of smartphones and the internet to the general public in the country, there is an increasing level of cybercrime in Bangladesh. Cybercrime has wider societal implications for Bangladesh. It is expected that after five or ten years, Bangladesh will be cited as one of the cyber secured countries in the world since the country is highly committed to secure its cyber space, which is manifested through the steps taken by the country.
Research of Neural Network Learning Rate Control Methods for the Automatic Signal Type Recognition

A neural network with dynamic parameters and algorithms for the learning rate hyperparameter control has been developed in the LabVIEW software environment in order to increase the efficiency of the signal type recognition algorithm and reduce the network training time. The learning rate hyperparameter control methods testing results are presented. As a result, the most effective method for this task was selected.
A Systematic Literature Review on Software Development Estimation Techniques

Project Failure is the key target undergoing in today’s scenario which is observed through the software project administrators. An ambiguity of the estimation is the major cause of this dilemma. According to tech project managers, project failure is the most common concern today. This issue arises as a result of measurement inaccuracy. When software's size and significance increase, its complexity makes it even more problematic to reliably estimate the cost of growth. This was the predicament of previous years. Software cost estimation (SCE) is the utmost significant subject in software design in modern times. Real calculation necessitates expense and commitment considerations when developing applications utilizing algorithmic or Artificial Intelligence (AI) techniques. In this paper, numerous predefined approaches for SCE are illuminated and their characteristics will be considered. This paper briefs numerous sessions of SCE models and methods. This study also claims that no particular procedure is superlative for all circumstances, software measures and metrics are also needed to enhance efficiency. This research paper focuses on Artificial Intelligence-based software cost estimation approaches, their implementation domain, and strategies for calculating software cost estimation. There are two kinds of SCE approaches: parametric/algorithmic models and non-parametric/non-algorithmic models. One of the most common algorithmic SCE models is the Constructive Cost Model (COCOMO). Basic COCOMO is ideal for fast, quick, and uneven estimates of the effort expected to manufacture software, but its accuracy is restricted because of the lack of variables to account for cost-driven differences. Non-algorithmic approaches for overcoming the shortcomings of algorithmic models include expert Judgment, machine learning (ML), and price to gain. In checked studies, ML models were implemented to address the shortcomings of parametric estimation models.
A cooperative communication scheme based on blockchain

Traditional cooperative communication is based on the complete trust between source and relay, and relay voluntarily helps source to forward information. However, in practice, relay may not be willing to forward information for source, and source may not trust relay due to the tampering or dropping of information. Existing incentive mechanisms that encourage relay to cooperative cannot establish trust foundation between source and relay, and cannot guarantee the security of data transmission. Blockchain naturally has properties of tamper-proof, transparent, and full traceability, it can effectively solve the trust problem and improve the security of data transmission. So we introduce blockchain into cooperative communication, propose a cooperative communication scheme based on blockchain, and design a set of detailed working procedure for this scheme. In the working procedure, we propose an optimal relay selection scheme and a consensus mechanism based on mobile terminal credit value (CM-MTCV) to encourage terminals to cooperation and achieve fast consensus. The simulation results show that our scheme can save 6.74% energy cost compared with the maximum-minimum scheme when the number of relay nodes is 8, the delay of CM-MTCV is 1/2000 of that of PoW, the transaction throughput of CM-MTCV is 2000 times of that of PoW.
EXISTING OPPORTUNITIES FOR DIGITAL ASSESSMENT OF UNIVERSITY STUDENTS’ COMPETENCIES

The need to implement systems for digital interaction between multiple stakeholders in the educational process is now more urgent than ever. The development and strategic priorities of higher education justify the need to develop technical solutions for assessing supra-professional competencies that are built into the educational process and meet the requirements of the highly competitive industry of educational technologies. This article presents the results of the technical capabilities analysis of university students’ assessing the competencies. The purpose of the paper is to analyze the existing possibilities of automating the process of evaluating the supra-professional (universal, key) competencies of students of higher educational institutions. As the key research methods, the authors conducted an expert survey and a series of focus groups on the problems of digital technologies for assessing competencies. Based on the results of the analysis, the authors identified a number of developed areas of existing tools for assessing competencies used on various platforms, in terms of functional variability and integration into individual plans and trajectories of student development. The obtained results will form the basis for the development of an online platform for the assessment and development of supra-professional competencies of university students.
A Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction

In human-computer interaction, head gestures play a significant role in improving smoothness and naturality. However, existing head gesture recognition algorithms have disadvantages in accuracy and generalization ability. To deal with these problems, this paper addresses a two-stream dynamic head gesture recognition method with the SlowFast pathway called 3DSFI (3D SlowFast Inception). The SlowFast pathway is designed to reduce parameters and computational costs. Meanwhile, its two-stream structure can efficiently capture motion features in videos and dense optical flows. Besides, Inception blocks of InceptionV3 are expanded by the 3D convolutional kernel into space-time and serve as a feature extractor. Finally, 3DSFI is applied to a robot Pepper in order to evaluate realistic performance. Experimental results show that the proposed method has higher accuracy and better generalization performance than the classical C3D (Convolutional 3D) and I3D (Inflated 3D ConvNet) methods.
ArtiVisual

ArtiVisual is a platform for generating new art-pieces based on an existing art style and comparing commonalities between paintings from different era. We combine an image generative network with established state-of-the-art visualisation techniques to deepen the users' understanding of art in general. With ArtiVisual we can generate images based on art- styles via an interactive timeline. Common features between art-styles are reflected on the generated art piece produced by the network after learning the subspace of each artist's specific features. Visualisations are presented to provide insight into commonalities between existing and generated images. The combination of a trained network and our visualisation techniques provides a rigid framework for thorough exploration and understanding of art datasets.
Hyperspectral Denoising Via Global Tensor Ring Decomposition and Local Unsupervised Deep Image Prior

Recently, unsupervised deep learning-based methods have shown an empirical success in hyperspectral images (HSIs) denoising, profiting from the strong representation ability of convolutional neural networks. However, these methods only can describe the local structure of the spatial dimension, which is restricted to very limited local receptive fields. To overcome this difficulty, a novel HSIs denoising model based on the deep image prior (DIP) framework is proposed by adding a tensor ring (TR) decomposition, which can enlarge the receptive field of the spatial dimension and capture global spectral correlation simultaneously. Unlike the previous DIP framework that directly minimizes the objective function, we develop an algorithm based on proximal alternating minimization to decouple the model into the classic DIP framework and TR cores least-squares problems, which are easy to solve. Experimental results verify that the proposed DIP- TR compares favorably with compared methods in terms of quality metrics and visual performance.
Sentence-State LSTMs For Sequence-to-Sequence Learning

Transformer is currently the dominant method for sequence to sequence problems. In contrast, RNNs have become less popular due to the lack of parallelization capabilities and the relatively lower performance. In this paper, we propose to use a parallelizable variant of bi-directional LSTMs (BiLSTMs), namely sentence-state LSTMs (S-LSTM), as an encoder for sequence-to-sequence tasks. The complexity of S-LSTM is only \(\mathcal {O}(n)\) as compared to \(\mathcal {O}(n^2)\) of Transformer. On four neural machine translation benchmarks, we empirically find that S-SLTM can achieve significantly better performances than BiLSTM and convolutional neural networks (CNNs). When compared to Transformer, our model gives competitive performance while being 1.6 times faster during inference.
Variance Weight Distribution Network Based Noise Sample Learning for Robust Person Re-identification

Person re-identification (re-ID) usually requires a large amount of well-labeled training data to learn generalized discriminative person feature representations. Most of current deep learning models assume that all training data are correctly labeled. However, noisy data commonly exists due to incorrect labeling and person detector errors or occlusions in large scale practical applications. Both types of noisy data can influence model training, while they are ignored by most re-ID models so far. In this paper, we propose a robust deep re-ID model, called variance weight distribution network (VWD-Net), to address this problem. Different from the traditional representations of each person image as a feature vector, the variance weight distribution network focuses on the following three aspects. 1) An improved Gaussian distribution and its variance are used to represent the uncertainty of person features. 2) A well-designed loss in the variance weight distribution network is used to delegate the distribution uncertainty with respect to the training data. 3) The noisy labels are rectified for further optimization on the model training performance. The large scale variance/uncertainty has been assigned to noisy samples and then rectifies their labels, in order to mitigate their negative impact on the training process. Extensive experiments on two benchmarks demonstrate the robustness and effectiveness of VWD-Net.
Low-light Image Enhancement Based on Conditional Generative Adversarial Network

We present an end-to-end low-light image enhancement learning method. This learning is based on the conditional generative adversarial networks(GAN) and realizes low-light images enhancement. Specifically, our method uses a convolutional neural network containing residual structures as a generator and WGAN-GP as a discriminator to generate an effective low-light enhancement model under the constraints of GAN loss, Perceptual loss and Structural similarity loss. The model can retain the detailed information of the original image, improve the brightness of the image without generating noise interference, while the generated images are more natural and have higher quality. Extensive experimental results show that our method has reached the state-of-art in multiple objective evaluation indicators of image quality, and the visual appearance is superior.
Pose-aware monocular localization of occluded pedestrians in 3D scene space

Localization of pedestrians in 3D scene space from single RGB images is critical for various downstream applications. Current monocular approaches employ either the bounding box of pedestrians or the visible parts of their bodies for localization. Both approaches introduce additional error to the location estimation in the case of real-world scenarios – crowded environments with multiple occluded pedestrians. To overcome the limitation, this paper proposes a novel human pose-aware pedestrian localization framework to model poses of occluded pedestrians, where this enables accurate localization in image and ground space. This is done by proposing a light-weight neural network architecture, where this ensures a fast and accurate prediction of missing body parts for downstream applications. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the framework compared to state-of-the-art in predicting pedestrians missing body parts as well as pedestrian localization.
Encouraging University Employees to Effectively Implement the Main Educational Programs of Higher Education

In this study, we formulate proposals for group motivation of teaching staff, aimed at increasing the attractiveness of the main educational programs of higher education among various categories of applicants, as well as the safety of the contingent of students and employment of university graduates. The research topic is relevant both in terms of theoretical and practical understanding of new approaches to motivating university staff in solving problems that contribute to improving the quality and demand for basic educational services in the market of higher education.
Course experience report: full-class compiler collaboration

Compilers are large software systems. In course projects it is often a challenge for students to build a significant compiler on their own with features like memory management, closures, inheritance, and more. We report on our experience splitting a relatively large compiler, with several of these advanced features, among project groups in a graduate compilers course. In addition to allowing students to engage with a larger system than groups would have been able to build on their own, we also believe based on anecdotal feedback that this had positive effects on student morale and community. There were several concrete logistics and content decisions we made that were effective, along with other recommendations and refinements for when we run the course again.
Assessment of the quality of digital libraries based on the principles of information architecture. Research plan

Purpose The purpose of this paper is to present a detailed plan of research on the information architecture of digital library websites. Design/methodology/approach The research plan was prepared on the basis of a critical analysis of the scientific literature concerning the areas and criteria for the analysis and evaluation of the information architecture of digital resources. The evaluation criteria selected in individual areas of information architecture and the entire evaluation model were tested on the example of Europeana. The study proposes criteria for all areas of information architecture: service identity, organizational system, labeling system and navigation system. Findings The proposed research model containing areas and criteria for assessing usefulness from the point of view of information architecture may complement the methodology for assessing digital libraries. In a structured manner, it presents areas important for building good digital resources together with criteria for heuristic evaluation. This thesis is confirmed by a study conducted on the example of Europeana. Research limitations/implications For a more precise assessment of quality, you can add to the proposed criteria related to the information architecture the criteria proposed by Nielsen or other authors. These include, e.g. help users recognize, diagnose and recover from errors, result relevance, consistency of terminology and specific action conventions and an appropriate visual presentation. Practical implications The model can be used to assess the quality of websites of various digital libraries. Originality/value The methodology of assessing the quality of digital libraries is in the stage of formation and development. Such studies can apply evaluation criteria prepared to analyze all kinds of information systems and most of all web pages. At the beginning of the 21st century, concepts of using information architecture principles to design and evaluate digital libraries’ usability began to take shape. However, no detailed criteria for evaluating digital libraries based on the principles of information architecture have been presented.