Reviewerly/exHarmony huggingface dataset:
AN EVALUATION OF SOME FACTORS AFFECTING ACCURACY OF THE VIETNAMESE KEYWORD SPOTTING SYSTEM

Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m. The obtained results show that the noise trained models are better performance than clean trained models in any (clean or noise) testing environment. The results in this far-field experiment suggest to us how to choose the suitable distance of the recording microphones to the speaker so that there is no redundancy of data with the contexts considered to be the same.
Random Walk-steered Majority Undersampling

This paper proposes Random Walk-steered Majority Undersampling (RWMaU), an undersampling approach to address the class imbalance problem for binary classifiers. RWMaU is focused to find the majority points which lie at the overlapped region of the minority and the majority classes. Such points meddle with the learning and detection of the minority points. RWMaU uses random walks to mark the majority points satisfying the above characteristic in a non-parametric fashion. For each majority point, a proximity score is calculated on the basis of - their visit frequencies and the order of visits of the majority points in the random walks. This score is used to perceive the closeness of the majority class points to the minority class. The majority points lying close to the minority class are subsequently undersampled. Empirical evaluations on 21 datasets using 3 classifiers demonstrate substantial improvement in performance of RWMaU over existing methods for addressing class imbalance and show that it is an efficient and effective way to address class imbalance in binary classification problems.
Research on Data Consistency Detection Method Based on Interactive Matching

Data consistency detection is a research hotspot in the field of data mining. Traditional data mining algorithms assume that the data is static. The current data classification models can handle static data well, but most of the data has the characteristics of huge scale and diverse changes. The performance of the classification model will be greatly reduced with changing data, so how to effectively detect the changes of the data is a wideconcern in the academic fields. By using decision tree as the background, and If-then rule as the core content of the data set, this paper discusses the feasibility of using rule interactive matching to detect the consistency. Further, a data consistency detection method based on interactive matching (IM-CC detection method) is given, and then several common UCI data sets are combined to analyze the characteristics and performance of the algorithm. Experimental results show that IM-CC has good interpretability and operability, can concisely integrate decision-making awareness into the decision-making process. So it has a wide range of application prospects.
Efficient Clustering of Transactional Data for Privacy-Preserving Data Publishing

Transactional data is set-valued data which is generated from retail store, healthcare, etc. The data needs to be published to extract useful information from the data. The data contain some sensitive information about the individual, and if leaked, then it will cause serious implications to the privacy of an individual. Therefore, it is required to protect the user’s privacy on the published data while ensuring the data should remain useful for analysis purpose. The paper proposes efficient clustering method using ant-colony-based clustering algorithm to bring similar transactions in same equivalence class/cluster. Finally, we can achieve privacy with minimal information loss. The approach has been tested on INFORMS dataset and compared with the Disassociation. The result shows that the more information is preserved as compared to Disassociation approach.
On the modal interpretation of the connective of realisation

The connective of realisation associates propositions with names of contexts, at which they are said to be realised. Realisation is usually understood as relativised truth-connective, thus under most accounts it distributes over all Boolean connectives. Nonetheless, there are good reasons to consider weaker kinds of realisation, which lack some distributive laws. Three kinds of semantics for such weak realisation were provided: many-valued, set-theoretic and relational. The paper addresses the problem of Jarmużek's interpretation of the connective of realisation in relational models. It is argued that Jarmużek truth condition for the connective of realisation is tantamount to imposing symmetry on the relation of accessibility in a model. Moreover, a simplified semantics as well as axiom system for Jarmużek system are presented. Finally, a unification with Tkaczyk's – most general -- results is provided by proving completeness result for the axiom system with respect to Tkaczyk-style set-theoretic semantics.
Efficient Multi-client Order-Revealing Encryption and Its Applications

Order-revealing encryption (ORE) is a cryptographic primitive that enables ciphertext comparison while leaking nothing about the underlying plaintext beyond their lexicographic ordering. However, how to achieve efficient and secure ciphertext comparison for multi-user settings is still a challenging problem. In this work, we propose an efficient multi-client order-revealing encryption scheme (named m-ORE) by introducing a new token-based comparison method. Specifically, data owner is enabled to delegate token generation ability to some authorized users without revealing his secret key, and then each authorized user can perform comparison on ciphertexts from multiple data owners by generating the associated comparison tokens. Benefiting from our new method, m-ORE can not only reduce ciphertext size but also improve comparison efficiency, compared with the state-of-the-art (Cash et al. Asiacrypt 2018). Further, we present a non-interactive multi-client range query scheme by extending m-ORE. Finally, we show a formal security analysis and implement our scheme. The evaluation result demonstrates that m-ORE outperforms the scheme by Cash et al. in terms of both query and storage cost while achieving the same level of security.
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More

Joint Energy-based Model (JEM) of [] shows that a standard softmax classifier can be reinterpreted as an energy-based model (EBM) for the joint distribution \(p(\boldsymbol{x}, y)\); the resulting model can be optimized to improve calibration, robustness and out-of-distribution detection, while generating samples rivaling the quality of recent GAN-based approaches. However, the softmax classifier that JEM exploits is inherently discriminative and its latent feature space is not well formulated as probabilistic distributions, which may hinder its potential for image generation and incur training instability. We hypothesize that generative classifiers, such as Linear Discriminant Analysis (LDA), might be more suitable for image generation since generative classifiers model the data generation process explicitly. This paper therefore investigates an LDA classifier for image classification and generation. In particular, the Max-Mahalanobis Classifier (MMC) [], a special case of LDA, fits our goal very well. We show that our Generative MMC (GMMC) can be trained discriminatively, generatively or jointly for image classification and generation. Extensive experiments on multiple datasets show that GMMC achieves state-of-the-art discriminative and generative performances, while outperforming JEM in calibration, adversarial robustness and out-of-distribution detection by a significant margin. Our source code is available at https://github.com/sndnyang/GMMC.
UMDSF: Unified Model With Dynamic-Static Features for Personalized Recommendation

Typically, existing works utilize static methods to extract the latent feature representation of user and item reviews, neglecting the time signals and behavior patterns hidden in the user-item interaction history, which may fail to capture users' instant interests and items' temporal attributes. Moreover, there is no framework that unifies recent behavior sequences and reviews. Therefore, in this paper, we first define dynamic and static features to describe users' short- and long-term preferences and items' temporal and inherent attributes. We then design feature extractors to capture these latent factors simultaneously from recent behavior sequences and reviews. Then, we propose a novel unified framework to extract and fuse these fine-grained characteristics, named unified model with dynamic-static features (UMDSF). Specifically, the proposed model extracts both temporal sequence and review features by two parallel feature extractors based on self-attention and a multi-head attention mechanism. Subsequently, an adaptive fusion module is utilized to combine the fine-grained representations for the downstream recommendation tasks. Extensive experiments on four real-world datasets demonstrate the superiority of UMDSF and additional ablation studies verify the effectiveness of the components designed in the proposed model.
A Method of Classification Twitter Posting Location for a Specific Space

Under the definition of Baseball Stadium as a specific space, locations from which tweets relating to relevant spaces had been posted were classified whether inside or outside the specific space. As the classification method, BERT being one of the natural language processing models was employed. Through the comparison between the features of tweets inside and outside a specific space, it was revealed that there were differences between them in terms the number of URLs and media including photographs provided in them and shown that classification accuracy would improve by combining the numbers of URLs and media provided in tweets and their contents. In addition, through the extraction and comparison of words affecting the results of classification using LIME, what types of words and information had impacts on the judgement of classification were visualized.
Improvised Threshold Based Task Scheduling

IT resources are delivered via the internet using cloud computing. The delivery of services is done on demand. Pay-as-you-go scheme is used in cloud computing. It is difficult to buy, own or to maintain physical data centers and servers with help of cloud computing, technology services like storage, databases and computing power can be accessed. These services can be accessed when needed from cloud provider like Amazon Web Services, Google Cloud, and Microsoft etc. Cloud is used for a variety of purposes in every organization without any discrimination. Since there is a huge demand for cloud-based services, there is a greater need for task scheduling and allocation of resources. This paper proposes Improvised Threshold based task scheduling algorithm which is compared with First come first serve (FCFS), Max-Min and Shortest Job Scheduling First (SJF) and is observed that the proposed method outperform the other baseline algorithms and it is more efficient than other algorithms in consumption of makes span time.
The Application of Centroid Tracking Algorithm in Video Action Recognition

There are many benchmark data sets for video action recognition, such as UCF-101, Activity Net and Kinetics data set. They are all the label scheme based on image classification, distributing each video or video clip of the data set a label. But there does not exist a data set which for complex scenarios, such as many different action scenes at same time. And the video pixels of the benchmark data set is small which is 224*224, and a person's actions will account for half of the screen. The model trained with this data set is not suitable for large pixel video. The standard video pixel taken by the camera is now 1920*1080, and the human motion area only takes up a small part of the screen, so the video classification model is not suitable for this situation. This paper provides a general method for the recognition for large-size pixel video and action video of multiple people with different actions at the same time. A network combine YOLOV5 object detection network, centroid tracking algorithm and C3D video action recognition network. It can recognize multiple person's actions in the video.
Location-based mobile learning system facilitating English learning

The central aim of this study was to determine the potential of the GPS-based MCL system for English learning in terms of its relationship with learning achievements. For this, the research enrolled 51 students of Moscow Aviation Institute, National Research University (Moscow, Russian Federation) aged 20 to 22, 35% were males, and 65% were females. The paper defined the predictors of self-efficacy, self-regulation, and academic success. The main research tool was represented by 7-point Likert scale questionnaire surveys. The calculation of Cronbach’s alphas allowed concluding that they were sufficiently reliable insofar as the values obtained for the questionnaires on respondents’ self-regulation, self-efficacy, the use of GPS-based MCL system, and this system’s perceived usefulness and simplicity equaled 0.78, 0.89, 0.84, and 0.84, respectively. Overall, the experiment outcomes uncovered that student self-regulation and self-efficacy do influence learning outcomes and can even predict future academic performance. There was a significant difference in the results of pre- and post-testing conducted before and after the implementation of the GPS-based MCL system with the upward trend observed. Alongside this, the mean scores for the perceived usefulness and ease of use of the proposed MCL system constituted 4.96 and 5.28 (on a 7-point scale).
Experimental Study on Three-Phase Induction Motor Performance Under Supply Voltage Unbalance for Star and Delta Connections

In this chapter, an experimental study is presented comparing the performance of an induction motor under supply voltage unbalance at no-load and full-load, for star and delta connections. Using a programmable voltage source, one of the phase voltages that supplies the motor is reduced gradually down to 80%. Then, a comparison is made regarding the motor phase voltage and current deviations in relation to the nominal values. Moreover, the phase current deviations are used to estimate the winding temperature rise in the slots. Finally, for both connection modes, the maximum output shaft power for nominal temperature rise under unbalanced supply voltage is compared to the NEMA derating curve.
Image Processing of Guqin Score Based on Mathematical Morphology

This paper first introduces the basic operations of mathematical morphology, such as erosion, expansion, opening and closing, and then introduces the hit miss transformation. Then it briefly describes the Chinese Guqin art and the music score collection of “Zheyin shiziqinpu”. Finally, the image processing of Guqin music score is carried out by using the method of mathematical morphology, Mathematical morphology has a good effect on Guqin score processing.
Development and Study of an Approach for Determining Incorrect Words of the Kazakh Language in Semi-structured Data

Research in the field of computer linguistics is relevant due to the rapid growth of information in natural languages on the Internet and social networks. Currently, there is an increase in the amount of information that humans and machines create in natural language. Information retrieval systems, dialog systems, machine translation, and automatic resume tools, spelling check modules analyze and process texts in natural languages. Thus, the range of automatic word processing systems is wide and covers a variety of tasks. One of the most important tasks of natural language processing (NLP) is to find errors in texts and including words, identify and correct incorrect words. The article provides an overview of semi-structured data, methods, and technologies for detecting incorrect words in natural languages. An approach for identifying incorrect words in the Kazakh language was developed and the features and capabilities of this approach were analyzed. A comparative analysis of texts on the Internet and social networks and of technologies that identify incorrect words in natural languages has been carried out.
Analysis and research on computer visualization in data science with bokeh and JavaScript

With the continuous development of data science, the data manifests an explosive growth in volume and dimension. In this historic background, the demand for data visualization is becoming increasingly urgent, because of which, many powerful instruments have been invented for handling the issues. Among them, the bokeh is a burgeoning, JavaScript-powered, open source python library which draws investigators, data scientists and developers. Present review mainly casts light on the application in field of data visualization with bokeh, including glossary, figures' type and widgets.
Complex Document Classification and Integration with Indexing

In the current scenario, organizations come across a ton of digital documents which in turn increases the time and energy required to deal with them. This research work envisions providing an easy, efficient and cost-effective solution while dealing with tons of digital documents by all scale industries. The proposed solution aims at verification, validation and authentication of documents uploaded on any websites at that moment itself instead of waiting for a later manual process of authentication. Our model provides ease of business by merging these multiple documents of various forms into a single document composed of all the information. The proposed model assists the stakeholders to retrieve any data from the documents submitted by indexing and automation scanning without the need to open the document manually to search the required data from a particular document. By considering all the aforementioned requirements, this research work has created a system to address them accurately with a classification accuracy of around 98.5%.
Scale-aware and Anti-interference Convolutional Network for Crowd Counting

Crowd counting is a challenging vision task which aims to estimate the count and distribution of the crowd in a single image accurately. To this end, we propose a novel end - to-end trainable architecture called Scale-aware and Anti-interference Convolutional Network (SACN) to learn a mapping from the input image to the corresponding crowd density map, which concentrates on dealing with the scale variation and background interference of input images for the crowd counting problem. In specific, aiming to cope with the scale variation, we propose Scale-aware Feature Extraction Module (SFEM) to extract multiscale feature maps and learn corresponding pixel-level weight maps for assigning appropriate weights to features of different scales to generate scale-aware features. Futhermore, Regression and Classification Double-head Module (RCDM) is placed at the end of the network to resist the background interference, where the regression head is designed to perform the density map regression and the classification head provides the density map regression task with attention masks related to the foreground and background. In addition, we supervise the intermediate information to help optimize the network and alleviate the gradient vanishing phenomenon. Extensive experiments on three challenging datasets including ShanghaiTech, UCF-QNRF and JHU-CROWD++ were conducted to demonstrate the superiority of the proposed approach compared with the state-of-the-art.
ICO as a Modern Method for Financing High-Tech Projects

Attracting investments is one of the most significant problems faced by management at any stage of a business project’s life cycle. Besides traditional fundraising channels, relatively new methods of raising financing, such as crowdfunding and initial coin offerings (ICOs), have recently become more widespread. This route is most frequently used by teams implementing high-tech projects. Investor interest is heating up because of the rapid growth of cryptocurrencies and also because of the policies of a number of leading countries that support the circulation of cryptocurrencies and cryptocurrency transactions. This article surveys global experience with ICO and conducts a SWOT (strengths, weaknesses, opportunities, and threats) analysis of this mechanism for attracting investments. It also considers examples from the actual economic practice of recent years. These examples illustrate the capabilities and limitations of this method. Taking these into account, the article formulates several scenarios for the development of the technology used to create and use cryptocurrencies. It also assesses the prospects for carrying out an ICO based on issuing such currencies.
Component Based Development of Ontology-based Intelligent Tutoring Systems

The intelligent tutoring systems (ITSs) are e-learning systems that apply artificial intelligence techniques to achieve a behavior, similar to teacher’s behavior during tutoring process. A significant number of ITSs have been developed during the last decade. In this paper we present our results of the analysis of valuable scientific sources, discussed usage of ontologies for development of knowledge models for ITSs. We present a general methodology for development of lower-cost ITSs, containing reusable components, and general classification of tutoring strategies to ensure dynamic selection of the best strategy by the tutor.
Finnish Forest Data-Based Metsään.fi-services

Abstract This chapter introduces the Finnish forest data ecosystem and its role in DataBio project pilots. In these DataBio pilots, the main objective is to improve the use of the Finnish forest resource data. The Finnish forest data provides a foundation for the forest big data-based online and e-services. The technical solution elements for the introduced DataBio pilots are based on standardized XML data sets, X-Road data transfer protocols, open forest data application programming interfaces (APIs) and crowdsourcing applications. The Metsään.fi-services including the open forest data APIs and Wuudis-mobile application are the key components for the customer’s user interface. In the end of the chapter, the pilot-specific business benefits and key performance indicators are decribed showing clear positive impacts of the pilots. At the end of this chapter, visions for the future of public online services are discussed.
A Collision-Free Fast Planning Algorithm for Snake-Like Redundant Manipulators via Reinforcement Learning

This paper presents a planning algorithm via reinforcement learning to fast generate a collision-free path in the narrow and complex space for snake-like redundant manipulators. First, the snake-like redundant manipulator is introduced and its kinematic is analyzed. Second, the algorithm strategy is designed by combining the snake-inspired crawling motion and reinforcement learning. Finally, the simulations are conducted to test and verify the proposed algorithm. As a result, the snake-like redundant manipulator can explore the target position in the pipeline with a 93.33% success rate at an average planning time cost of 49.2s.