Reviewerly/exHarmony huggingface dataset:
Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP’s preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.
The design and realization of the management system of college physical education under the network environment

Abstract With the expansion of the scale of institutions of higher learning, the requirements on the management of physical education gradually improve. In order to improve the management level of physical education in schools, it is urgent to develop a physical education management system in colleges and universities, so as to solve the needs of each college for physical education management and improve the management level. This system adopts the design idea of software engineering, and conducts detailed and detailed research on the system from preliminary material collection, demand analysis, feasibility study, overall design, detailed design, system coding and system test. In the system development design, the application of the current popular ASP. NET development framework, the database aspect used Microsoft’s SQL Server 2008, at the same time added some JavaScript scripts and JQuery framework technology, and finally realized the university physical education management system based on B/S architecture. This paper describes the demand analysis of the physical education administration system, and expounds the design and implementation process of each functional module. The main functional modules of the system include student information management module, teacher information module, teaching plan management module, teaching affairs arrangement management module, student course selection management module, class management module, student performance management module, teacher teaching quality evaluation management module and system management module.
Developing an Electronic Device to Teach English as a Foreign Language: Educational Toy for Pre-Kindergarten Children

This Educational toys are important in early child development and motivate children to learn language. Numerous digital toys are now available in the children’s educational toys market to help children learn foreign languages. However, a number of such technologies are expensive and the quality of usability is not at an acceptable level. The aim of the study is to develop a cost-effective, sustainable and danger-free educational toy for pre-kindergarten children aged between 4-5 years old to teach English as a second language in developing countries. This toy has been developed with Raspberry Pi and makes use of the RFID technology. The experiment in this research was conducted with 20 pre-kindergarten children for four weeks under the supervision of 14 teachers, using five games of the developed toy testing the pre-kindergarten children’s learning of alphabet, words, colors, shapes, and numbers. A design-based research model with an innovation design method utilized during the development stage of the toy. Quantitative and qualitative methods were used to evaluate the developed toy. The results of the experiment showed that the developed toy is user-friendly, and the pre-kindergarten children were enthusiastic about playing with it and learning was enjoyable. Also, the results showed that the developed toy can be suitable for use as an educational toy for pre-kindergarten children to teach English as a second language. The educational toy developed by the researchers will be beneficial for those who wish to teach English to pre-kindergarten children such as administrators, teachers and parents in developing countries.
A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary

Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.
New Fair Multiparty Quantum Key Agreement Secure against Collusive Attacks

Abstract Fairness is an important standard needed to be considered in a secure quantum key agreement (QKA) protocol. However, it found that most of the quantum key agreement protocols in the travelling model are not fair, i.e., some of the dishonest participants can collaborate to predetermine the final key without being detected. Thus, how to construct a fair and secure key agreement protocol has obtained much attention. In this paper, a new fair multiparty QKA protocol that can resist the collusive attack is proposed. More specifically, we show that in a client-server scenario, it is possible for the clients to share a key and reveal nothing about what key has been agreed upon to the server. The server prepares quantum states for clients to encode messages to avoid the participants’ collusive attack. This construction improves on previous work, which requires either preparing multiple quantum resources by clients or two-way quantum communication. It is proven that the protocol does not reveal to any eavesdropper, including the server, what key has been agreed upon, and the dishonest participants can be prevented from collaborating to predetermine the final key.
HEURISTIC METHODS FOR THE DESIGN OF CRYPTOGRAPHIC BOOLEAN FUNCTIONS

In this article, heuristic methods of hill climbing for cryptographic Boolean functions satisfying the required properties of balance, nonlinearity, autocorrelation, and other stability indicators are considered. A technique for estimating the computational efficiency of gradient search methods, based on the construction of selective (empirical) distribution functions characterizing the probability of the formation of Boolean functions with indices of stability not lower than required, is proposed. As an indicator of computational efficiency, an average number of attempts is proposed to be performed using a heuristic method to form a cryptographic Boolean function with the required properties. Comparative assessments of the effectiveness of the heuristic methods are considered. The results of investigations of the cryptographic properties of the formed Boolean functions in comparison with the best known assessments are given. On the basis of the conducted research, it can be concluded that the functions constructed in accordance with the developed method have high persistence indexes and exceed the known functions by these indicators.
Forbidden knowledge in machine learning reflections on the limits of research and publication

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.
Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn a prediction model for the target domain given labeled source data and unlabeled target data. Impressive progress has been made by adversarial learning-based methods that align distributions across domains through deceiving a domain discriminator network. However, these methods only try to align two domains and neglect the boundaries between classes, which may lead to false alignment and poor generalization performance. In contrast, consistency-enforcing methods exploit the target data posterior distribution to make the target features far away from decision boundaries. Despite their efficacy, these approaches require additional intensity augmentation to align distributions when encountering datasets with large domain discrepancy. To solve the above problems, we propose a novel UDA method that unifies the adversarial learning-based method and consistency-enforcing method together to take both domain alignment and boundaries between classes into consideration. In addition to the supervised classification on the source domain and the adversarial domain adaptation, we introduce interpolation consistency into the UDA task. To be specific, we first construct robust and informative pseudo labels for target samples, and then we encourage the prediction at an interpolation of unlabeled target samples to be consistent with the interpolation of the pseudo labels of these samples. The extensive empirical results demonstrate that our method achieves state-of-the-art results on both digit classification and object recognition tasks.
Intelligent Monitoring of Data Center Physical Infrastructure

This paper presents a new intelligent monitoring and event management method for data center physical infrastructure based on multilayer node event processing. The priorities of highly classified data centers are not only the maintenance of the highest level of reliability and availability of the operation, but also fast, specific event identification and energy consumption monitoring, which altogether improve the level of energy efficiency. The new method, using a tree node for each device, improves information about the events in a specific node. Its major advantage is that in the case of a large number of nodes, it considerably reduces the number of identified events (alarms/alerts) that might be the main cause. Fast event processing lowers data center physical infrastructure operating costs, improving at the same time the level of energy efficiency. The experimental results show that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data center physical infrastructure.
Anomalies and Vector Space Search: Tools for S-Box Analysis

S-boxes are functions with an input so small that the simplest way to specify them is their lookup table (LUT). How can we quantify the distance between the behavior of a given S-box and that of an S-box picked uniformly at random? To answer this question, we introduce various "anomalies". These real numbers are such that a property with an anomaly equal to a should be found roughly once in a set of $$2^{a}$$ random S-boxes. First, we present statistical anomalies based on the distribution of the coefficients in the difference distribution table, linear approximation table, and for the first time, the boomerang connectivity table. We then count the number of S-boxes that have block-cipher like structures to estimate the anomaly associated to those. In order to recover these structures, we show that the most general tool for decomposing S-boxes is an algorithm efficiently listing all the vector spaces of a given dimension contained in a given set, and we present such an algorithm. Combining these approaches, we conclude that all permutations that are actually picked uniformly at random always have essentially the same cryptographic properties and the same lack of structure.
Automated Library System Using SMS Based Pick and Place Robot

Library management system aims to automate the library processes throughout a collection of actions as book loan, catalogue, indexing, and recording.This paper goes one-step further in the automation of library systems by using the IoT and robot for more precise and reliable automation.In this proposed work, Pick and place robot has been integrated with GSM technology, Radio Frequency Identification (RFID) equipment, and sensory technology to enhance the robot functionality.The augmented robot, in this work, is used to automate the process of picking the library books and sending them to the borrower table.The user can control the robot movement inside library remotely using SMS commands.In this paper, redesigning the library ground was implemented to let the robot moves freely between the shelves.The experimental results showed the ability of robot to collect books with different thickness and weights from different shelf levels at different distances with accuracy up to 97.33%.
Cognitive competence and electronic learning

This article investigates cognitive psychology and electronic learning and shows experimental review about cognitive development which were carried out by modern scientists.Authors studied the historical background of the term 'cognition' and outlined that e-learning is a key factor to develop students' cognitive competence.They also gave more information about distance education and universities that provide online education worldwide.It was concluded that cognitive development is more effective at a younger age than in adult education because the cognitive development process occurs very slowly in adult education.Additionally, online education should serve the society in a beneficial way since there are myriad online providers in open access.
An efficient architecture for medical high-resolution images transmission in mobile telemedicine systems

The medical high-resolution image is very important in image processing and computer vision applications, which plays a critical role in image-guided diagnosis, clinical trials, consultation, and case discussion. How to efficiently access medical high-resolution images in mobile telemedicine systems is becoming a big challenge. Therefore, this work proposes an efficient pyramid architecture for optimizing medical high-resolution images transmission and rendering. The proposed architecture consists of three core schemes: (1) unbalance pyramid scheme based on geometric relationship, (2) indexing scheme based on hash table and lattice partitioning and (3) query scheme based on similar matching. Then, we design the responsive service components: generating service, indexing service, and query service. Finally, these services are combined into a prototype system that enables efficient transmission and rendering of medical high-resolution images. The result shows that the unbalance pyramid scheme can quickly generate the pyramid structure and the corresponding image files. The indexing scheme can create the index structure and the index file in real-time. The query scheme can not only match the best layer to which the image block belongs in real-time, but also can accurately capture the query image block. The prototype system based on proposed architecture is fully compliant with the DICOM standard, which can be seamlessly integrated with other existing medical systems or mobile applications, and used in various scenarios such as diagnosis, research, and education.
Multi-Level Metric Learning Network for Fine-Grained Classification

The application of fine-grained image classification can be problematic due to subtle differences between classes.The existing global feature-based methods have worse accuracies than regional feature-based methods, because regional feature-based methods focus on the determination of differentiated features within local regions.To learn more discriminative global features, in this paper, we proposed the use of L2 normalization to tackle a neglected conflict between the widely used metric loss (triplet loss) and classification loss (softmax loss) in global feature-based methods.Furthermore, a multi-level metric learning network (MMLN) is proposed for fine-grained image classification based on global features.In the MMLN, multi-level metric learning objectives and classification objectives are present at multiple high-level layers.The multi-level metric learning objectives work together to supervise the network in order to learn highly discriminative features.In addition, a new probability aggregation strategy (PAS) is proposed to produce a fused prediction by combining the multi-level predictive probabilities.Experiments were conducted on three standard fine-grained classification datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft).Results demonstrated that our MMLN achieved accuracies of 88.0%, 94.6% and 92.4% respectively and outperformed state-of-the-art methods, substantially improving fine-grained classification tasks.Besides, gradient-weighted class activation mapping (Grad-CAM) shows that the MMLN is able to pay more attention to the discriminative local regions due to the application of multi-level metric learning.
Axiological portrait of information society

The urgency of the issue is determined by an extent social discussion about the advantages and disadvantages of information process itself and application to the diverse social spheres, its problems and threatens. The main range of the problems is connected with the constant transformation and social variability, consequently with a change of worldview’s field of a person. The advantages of information society are traditionally connected with the constant development, creativity, freedom and discoveries of new ways of personal realization and communication possibilities. According to a principle of post-non-classical methodology, the authors study a problem of the value transformations in conditions of information society and determine the crucial contexts of infosociogenesis: a context of networked coordination, ethical context and symbolic context (namely, virtualization and mythologizing of information space). The realities of modern society form various even diametrically opposed values that lead to complete devaluation of each value and deprive a worldview of its principle of consolidation are considered in the issue. The authors describe the mechanisms of pe rson’s networked coordination and demonstrate that their action (constructive and destructive) facilitates in the general process of experience enrichment which is gained via a multiplicity and simultaneity of virtual practices in different spheres (simuclarization) and often mistakenly presented as a real experience of a person. The ethical transformations of information society are considered to be closely connected with an issue of personal social stability which is not often aware of the freedom limits in information space. The symbolic context of infosociogenesis in the form of virtualization and mythologizing is characterized by the creation and social inquiry of cultural forms which are produced by virtual patterns of the reality (simulacrums).
SPCM: Image quality assessment based on symmetry phase congruency

Phase congruency (PC) algorithm is a frequency based algorithm. Instead of processing image spatially, the PC algorithm calculates the phase and amplitude of individual frequency components in the frequency domain. As one of the successful algorithm of image feature detection, PC has some advantages in the image quality assessment, however it has some inherent limitations. This paper studies the applications of symmetry phase in image quality assessment (IQA) and proposes a metric based on symmetry phase congruency (SPC). Symmetry phase congruency overcomes the limitations of phase congruency during the feature detection of image. This paper proposes a new IQA metric which is named as the symmetry phase congruency metric (SPCM). The sign responses of neighboring pixels are used to find the location of symmetry phases and then the symmetry phase congruency is used to detect image features and assess image quality. The experimental results show that SPCM is more sensitive to structural features of image and more robust to noises, and SPCM can achieve a higher consistency with the subjective evaluation of image quality.
A Fine-Grained Video Encryption Service Based on the Cloud-Fog-Local Architecture for Public and Private Videos

With the advancement of cloud computing and fog computing, more and more services and data are being moved from local servers to the fog and cloud for processing and storage. Videos are an important part of this movement. However, security issues involved in video moving have drawn wide attention. Although many video-encryption algorithms have been developed to protect local videos, these algorithms fail to solve the new problems faced on the media cloud, such as how to provide a video encryption service to devices with low computing power, how to meet the different encryption requirements for different type of videos, and how to ensure massive video encryption efficiency. To solve these three problems, we propose a cloud-fog-local video encryption framework which consists of a three-layer service model and corresponding key management strategies, a fine-grain video encryption algorithm based on the network abstract layer unit (NALU), and a massive video encryption framework based on Spark. The experiment proves that our proposed solution can meet the different encryption requirements for public videos and private videos. Moreover, in the experiment environment, our encryption algorithm for public videos reaches a speed of 1708 Mbps, and can provide a real-time encryption service for at least 42 channels of 4K-resolution videos.