Reviewerly/exHarmony huggingface dataset:
Global Sensitivity Analysis for Fuzzy RDF Data

The resource description framework (RDF) was adopted by the World Wide Web (W3C) as an essential semantic web standard and the RDF scheme. It accords the hard semantics in the description and wields the crisp metadata. However, it usually produces vague or ambiguous information. Consequently, fuzzy RDF helps deal with such special data by transforming the crisp values into a fuzzy set. A method for analyzing fuzzy RDF data is proposed in this paper. To this end, first, we decompose the RDF into fuzzy RDF variables. Second, we are designing a model for global sensitivity analysis based on the decomposition of fuzzy RDF. It figures out the ambiguities of fuzzy RDF data. The proposed global sensitivity analysis model provides the importance of fuzzy RDF data by considering the response function’s structure and reselects it to a certain degree. A practical tool for sensitivity analysis of fuzzy RDF data has also been implemented based on the proposed model.
SuperPruner: Automatic Neural Network Pruning via Super Network

Most network pruning methods rely on rule-of-thumb for human experts to prune the unimportant channels. This is time-consuming and can lead to suboptimal pruning. In this paper, we propose an effective SuperPruner algorithm, which aims to find optimal pruned structure instead of pruning unimportant channels. We first train a VerifyNet, a kind of super network, which is able to roughly evaluate the performance of any given network structure. The particle swarm optimization algorithm is then used to search for optimal network structure. Lastly, the weights in the VerifyNet are used as the initial weights of the optimal pruned structure to make fine-tuning. VerifyNet is a network performance evaluation; our algorithm can quickly prune the network under any hardware constraints. Our algorithm can be applied in multiple fields such as object recognition and semantic segmentation. Extensive experiment results demonstrate the effectiveness of SuperPruner. For example, on CIFAR-10, the pruned VGG16 achieves 93.18% Top-1 accuracy and reduces 74.19% of FLOPs and 89.25% of parameters. Compared with state-of-the-art methods, our algorithm can achieve higher pruned ratio with less accuracy cost.
Applying Vector Symbolic Architecture and Semiotic Approach to Visual Dialog

The multi-modal tasks have started to play a significant role in the research on Artificial Intelligence. A particular example of that domain is visual-linguistic tasks, such as Visual Question Answering and its extension, Visual Dialog. In this paper, we concentrate on the Visual Dialog task and dataset. The task involves two agents. The first agent does not see an image and asks questions about the image content. The second agent sees this image and answers questions. The symbol grounding problem, or how symbols obtain their meanings, plays a crucial role in such tasks. We approach that problem from the semiotic point of view and propose the Vector Semiotic Architecture for Visual Dialog. The Vector Semiotic Architecture is a combination of the Sign-Based World Model and Vector Symbolic Architecture. The Sign-Based World Model represents agent knowledge on the high level of abstraction and allows uniform representation of different aspects of knowledge, forming a hierarchical representation of that knowledge in the form of a special kind of semantic network. The Vector Symbolic Architecture represents the computational level and allows to operate with symbols as with numerical vectors using simple element-wise operations. That combination enables grounding object representation from any level of abstraction to the sensory agent input.
An ontology for Chinese government archives knowledge representation and reasoning

Government archives comprise a series of documents or important information resources produced or used by government departments to meet the needs of economic and social governance, fulfill government functions, and perform official activities.They have inestimable value in economic development, social operations, and strategy.However, due to the lack of effective application of knowledge management technology, government information resources are locked in archive records, making it difficult to achieve knowledge organization and interoperability.Based on the theories and methods of the semantic web and semantic ontology construction, we summarize the relevant domain knowledge of government archives and construct the Chinese Government Archive Ontology (GAO) with the collection of government archives as the data drive.The ontology model implements the semantic interoperability between government archives and performs knowledge representation and reasoning on their content.We define the classes and properties of GAO, describe its creation process in detail, and demonstrate its applicability in practice through SPARQL query and event logical representation based on the 5W1H framework.GAO is capable of realizing linked data and discovering the knowledge hidden behind the policies of various agencies and departments, which can serve scientific decision-making and the economic society, regulate institutional powers, and further promote the research and practice of archival science in the digital age.
Analyzing Motion of Touching Screen for Inferring User Characteristics

Due to advances in technology, today's users can enjoy a variety of services more comfortably on a larger screen than in the past. However, due to the large size, it is difficult for the users to touch all of the display with one hand. Moreover, most smartphone applications have inflexible forms that fill the entire screen, and the placements of buttons are usually fixed. To overcome such limitations, we focus on diverse sensor data obtained while touching the screen and propose the analysis method to infer user characteristics. The proposed method utilizes different sensor data from not only a touchscreen but also a gyroscope and an accelerometer in combination. Moreover, the proposed method utilizes the unsupervised clustering algorithm to quantify how much of the screen the user could reach. We conducted experiments involving users to evaluate our analysis method. The results show that, by using the proposed method, it is possible to know whether a user is right-handed or left-handed. Furthermore, the results verify that our analysis method is able to distinguish the natural, reach, and unreachable zones on the screen well.
Zero-Shot Learning with Missing Attributes using Semantic Correlations

Zero-shot learning (ZSL) aims to recognize instances belonging to unseen categories which are not available at training time. Previous ZSL models learn a projection function from the visual feature space to a semantic space which contains a description of the categories. The semantic attributes are often correlated with each other at the semantic space and it is not appropriate to learn them independently. Existing ZSL methods are designed to work on complete descriptions of the semantic attributes. However, because these attributes are human-designed values, they might be incomplete or contains noisy values which may affect the recognition performance of many existing ZSL models. This paper proposes a novel zero-shot learning approach (ZSL-MSA) to handle missing and noisy semantic attributes during the training process. Significantly, the proposed method learns a supplementary attribute matrix by exploiting the attribute correlation. The proposed method also learns the relevant feature coefficients in the projection matrix to identify the correlated attribute space. Th proposed method also adopts l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization norm to select the relevant sparse features. A constrained optimization function is formulated and solved using the accelerated proximal gradient method. Extensive experiments on three benchmark datasets using ZSL and generalized ZSL demonstrate the effectiveness of the proposed method.
Hybrid and non-uniform quantization methods using retro synthesis data for efficient inference

Existing quantization aware training methods and post-training quantization methods attempt to compensate the quantization loss by leveraging on training data. Hence, these methods are not effective for privacy constraint applications as they are tightly coupled with training data. In contrast, this paper proposes a data-independent post-training quantization scheme that eliminates the need for training data. This is achieved by generating a faux dataset, hereafter referred to as ‘Retro-Synthesis Data’, from the FP32 model layer statistics and further using it for quantization. This approach outperformed state-of-the-art methods including, but not limited to, ZeroQ and DFQ on models with and without Batch-Normalization layers for 8, 6, and 4 bit precisions on ImageNet and CIFAR-10 datasets. We also introduced two futuristic variants of post-training quantization methods namely ‘Hybrid Quantization’ and ‘Non-Uniform Quantization’. The Hybrid Quantization scheme determines the sensitivity of each layer for per-tensor & per-channel quantization, and thereby generates hybrid quantized models that are ‘10 to 20%’ more efficient in inference time while achieving the same or better accuracy as compared to per-channel quantization scheme. Also, this method outperformed FP32 accuracy when applied for ResNet-18, and ResNet-SO models on the ImageNet dataset. In the proposed Non-Uniform Quantization scheme, the weights are grouped into different clusters and these clusters are assigned with a varied number of quantization steps depending on the number of weights and their ranges in the respective cluster. This method resulted in ‘1%’ accuracy improvement against state-of-the-art methods on the ImageNet dataset.
<scp>AGRMTS</scp>: A virtual aircraft maintenance training system using gesture recognition based on <scp>PSO‐BPNN</scp> model

Abstract The quality and efficiency of aircraft maintenance are the key to ensure flight safety and on‐time rate, which mainly depend on the techniques and experience of maintenance engineer. Generally, exercises on physical prototypes are used to improve the maintenance capability of engineers, but this will waste a lot of consumables and easily cause safety accidents. With the development of computer technology, maintenance training in a virtual environment has become an advanced and reliable solution. In this paper, a virtual training system of aircraft maintenance based on gesture recognition interaction is established. Leap Motion is used as a sensor to construct a hybrid machine learning gesture recognition model, so as to obtain natural human–computer interaction experience. In the recognition model, the initial weight matrix and the number of hidden layer nodes in the back propagation neural network are jointly optimized by the Particle Swarm Optimization algorithm with self‐adaption inertial weight. This optimization algorithm achieved a recognition rate of 81.26% in the dynamic gesture database constructed in this paper, which is higher than other available algorithms. A preliminary usability evaluation in university classrooms shows that the teaching system in this paper can achieve a better interactive experience.
Simulation of Speckle Noise Using Image Processing Techniques

The image noise is considered as one of the significant problems in scientific applications. The simulation of the speckle noise within a standard image is studied using the presented algorithm. Different speckle noise ratios were added, with per cent (0.01–0.06), to simulate noise within different images. This added noise based on the mathematical equations to simulate the behavior of this type of noise. The main work divided into two steps; the first step is the classification method which based on the minimum distance and it used to classify the tested image with different homogenous areas and compare it with the corresponding noise images in the same location. In the first step, the knowledge of understanding the behavior of speckle noise achieved. The second step is the statistical criteria namely mean and standard deviation which is used to calculate the speckle factor (SF) to know the effect of noise within the image. In this step, the effect of the noise is obvious by checking the statistical values of SF. The behavior of the speckle noise is well-described and recognize based on the presented algorithm and method.
Performance Comparison of Spider Monkey Optimization and Genetic Algorithm for Traveling Salesman Problem

This paper is made to compare the Genetic Algorithm (GA) and Spider Monkey Optimization (SMO) for Traveling Salesman Problem. The background of the selection of these two algorithms based on the resulting performance has been recognized in the literature. In this study, 33 experiment were conducted where each experiment on the dataset was carried out for 10 iterations and then the Average Tour Cost value was taken from all experiments carried out. Based on the Average Tour Cost test, GA has a better performance than SMO with a value of 30.91 and 30.94 (example in the Burma dataset14). However, in terms of testing a larger dataset, the results of ATC SMO values are better than GA with values of 46303,47 and 53592,29 (example in dataset d198). This paper also examines the comparison of the results of the Minimum Tour Cost between GA and SMO. Where SMO got 73% (Win 23 Dataset), GA 27% (Win 9 Dataset), and 3% had the same Minimum Tour Cost. From all these experiments, it can be concluded SMO is the best solution in solving TSP.
A Data Stratification Process for Instances Selection Applied to Co-training Semi-supervised Learning Algorithm

Machine Learning (ML) is a field focused on developing methods and algorithms that allow machines to learn from previous data and experiences. There are several ML techniques that can be broadly divided into three main approaches: unsupervised, semi-supervised and supervised. The semi-supervised learning has been proposed as an attempt to solve some weaknesses of both supervised and unsupervised techniques. There are several semi-supervised techniques and co-training is one of the most used methods. In its original form, the co-training algorithm selects instances with the best prediction rates without necessarily considering the class to which they belong, which can be a problem in domains where there are classes with low representativeness. This paper proposes the inclusion of a data stratification strategy in the Co-training algorithm in order to maintain the same representativeness of the classes initially labeled throughout the learning process. The Co-training algorithm proposed in this work (Co-FlexCon-CS) was adapted from the original Co-FlexCon-C method with the inclusion of a data stratification technique. This study evaluates all the variations of the Co-FlexCon-CS, using 30 datasets with different characteristics and four classifiers algorithms, comparing it to the original Co-training and the Co-FlexCon-C. Finally, a statistical analysis was performed using Friedman Test, and the critical difference diagrams were evaluated for all methods using a post-hoc Friedman Test (Nemenyi). The obtained results show that the proposed approach achieves better results than the standard Co-training, which does not use this strategy.
Comparison of Three Metaheuristic Algorithms for Optimization of Cyber Physical Systems

Although the paradigm of Cyber-Physical Systems (CPS) enable manufacturers to respond to challenges in the dynamic business environment, it relies on an effective resource allocation mechanism to optimize production processes. In the past years, the production processes optimization problem in CPS has been formulated and solved by applying discrete version of Particle Swarm Optimization (PSO) called DPSO and binary Differential Evolution (DE) called BDE. The DPSO algorithm is based on a transformation function that maps real values into discrete values. The BDE algorithm uses mutation mechanism that create mutant vectors based on logical operations on individuals in the population without relying on a transformation function. In this paper, another discrete version of Differential Evolution (DE) called DDE is proposed and applied to optimize production processes in CPS. We conduct experiments to compare these algorithms based on numerical results.
Improved Occluded Person Re-Identification with Multi-feature Fusion

Pedestrians are often occluded by various obstacles in public places, which is a big challenge for person re-identification. To address this issue, we propose an improved occluded person re-Identification with the feature fusion network. The new network integrates spatial attention and pose estimation (SAPE) to learn representative, robust, and discriminative features. Specifically, the spatial attention mechanism anchors the regions of interest to the unoccluded spatial semantic information. It digs out the visual knowledge that is helpful for recognition from the global structural pattern. Then, we explicitly partition the attention-aware global feature into parts and improve the recognition granularity by matching local features. On this basis, we improve a pose estimation model to extract the information of the key points and feature fusion with the attention-aware feature to eliminate the influence of occlusion on the re-identification result. We test and verify the effectiveness of the SAPE on Occluded-REID, Occluded-DukeMTMC and Partial-REID. The experiment results show that the proposed method has achieved competitive performance to the state-of-the-art.
Breast cancer diagnosis based on guided Water Strider Algorithm

Breast cancer is one of the main cancers that effect of the women's health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.
A Study on Diacritic Restoration Problem in Vietnamese Text using Deep Learning based Models

Diacritic restoration is a challenging problem in natural language processing (NLP). With diacritic restoration, one can text faster and easier. Diacritic restoration is also helpful in making use of diacritic-missing texts, which are normally discarded in many NLP applications. This paper deals with the diacritic restoration problem for Vietnamese text. Three state- of-the-art deep learning models including Gated Recurrent Unit, Bidirectional Long-short Term Memory and Bidirectional Gated Recurrent Unit have been examined for the problem and the last one turned out to be the best among them. Besides deep learning models, it was found in this paper that word tokenization, which is the final pre-processing step applied on the data before feeding it to deep learning models also have influences on the final accuracy. Between two examined word tokenization methods: morpheme-based tokenization and phrase-based tokenization, the former yield better results regardless of the applied deep learning models. The experimental results show that the combination of morpheme-based tokenization and Bidirectional-GRU achieve the best performance of diacritic restoration with the Bleu-score of 88.06%.
HISTORICAL DEVELOPMENT AND CURRENT STATE OF STUDENTS’ SELF-DETERMINATION SUPPORTING SYSTEM

One of the tasks of the modern educational process is to support students’ selfdetermination in personal, social and professional spheres. The successful implementation of this task depends on the professional skills of teachers and ensures the formation of students' worldview, a strategy for their development and self-realization in accordance with social, moral and ethical norms. However, in practice, the readiness of teachers to complete this task is not sufficient for its successful implementation. This article is an attempt to systematize the historical background for the development of training teachers to support students’ self-determination, to identify the teachers’ functions to support students’ self-determination, to determine the readiness of teachers to support students’ self-determination. The methods of comparative analysis and synthesis of scientific knowledge were used. The results of this study can be used to plan activities to estimate the teachers’ readiness for students’ self-determination support, for programs of additional education and teachers’ advanced training.
Deep Video Compression using Compressed P-Frame Resampling

The Convolutional Neural Network has emerged as one of the major players in the field of deep video compression. Many deep learning models relying on convolutional layers have outperformed the state-of-the-art compression standards by a huge margin. Although their work is still at infancy level, they seem to be the future of video coding. The proposed approach uses a frame resampling based video compression approach using Temporal 3-D CNN based encoder and Y-style CNN based decoder concatenated with High Fidelity GAN based entropy coding for frame compression. The proposed architecture employs frame downsampling method over the residual frame to control the bitrate of the compressed data and is trained through a simplified stagewise training procedure. The extensive experiments are conducted with different datasets and different colorspaces. The study shows that the proposed model outperforms the H.265 by 0.255 dB in terms of PSNR and nearly 0.1 in terms of MS-SSIM.
Quantifying Explanations of Neural Networks in E-Commerce Based on LRP

Neural networks are a popular tool in e-commerce, in particular for product recommendations. To build reliable recommender systems, it is crucial to understand how exactly recommendations come about. Unfortunately, neural networks work as black boxes that do not provide explanations of how the recommendations are made.