Reviewerly/exHarmony huggingface dataset:
High Efficient Real-time Calculation Method of here Large FFT with High Overlapping Rate

Abstract In order to meet the needs of large number of overlapping samples in sequential fine feature extraction, this work proposes a frame by frame (segment) iterative algorithm based on the “sparse” feature of incremental data to achieve efficient real-time calculation of large FFT, which breaks through the hardware bottleneck of real-time computing platform such as DSP platform in high-resolution spectral analysis. The proposed algorithm makes use of the sparseness of incremental data to make the computation burden of segment iteration lighter than that of standard FFT, and has the potential of parallel computation. The simulation results show that the new algorithm is more efficient than the standard FFT when the number of segments is large.
Identification of Spatial Relationships in Arabic Handwritten Expressions Using Multiple Fusion Strategies

Automatic recognition of handwritten mathematical expressions in Arabic is a difficult problem, even if all the symbols that compose the expression are recognized correctly. The classification of spatial relations between pairs of adjacent symbols is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After features extraction, two types of fusion are compared for the final classification which are: feature-level fusion and decision-level fusion. In our proposed system, a support vector machine (SVM) classifier is employed. Experimental results show that our features give promising results. Moreover, using the decision-level fusion improve the classification accuracy.
An online/offline distributed attribute-based authentication scheme

Abstract Attribute-based authentication is an effective cryptography mechanism, which makes it possible for service providers to implement fine-grained access control on cloud resources. Although many attribute-based authentication schemes have been proposed, most of them only support single attribute authority and users have to perform a large amount of computation in the authentication phase. By extending the ciphertext policy attribute-based encryption scheme of Rouselakis et al, a distributed attribute-based authentication scheme was designed. The feature of the new scheme is to optimize the online computation efficiency of both service providers and users, i.e., by introducing the technique of online/offline attribute-based encryption, the online computational burden of semi trusted servers is greatly reduced. On the other hand, by introducing outsourcing decryption, users’ computation in the authentication stage is independent of the size of underlying access structures. Compared with previous schemes, the new scheme satisfies several ideal properties, that is, introducing distributed authorities, supporting outsourcing computation, satisfying anonymity and unlinkability, and so on.
Corporate Crisis-Response Match

When suspicions of corporate misconduct and crime emerge in the public, organizations respond in different ways. Their initial accounts can be defensive or accommodative, and they can reject responsibility or excuse wrongdoings (Bundy and Pfarrer 2015). This chapter compares initial corporate responses to later findings in fraud examinations by internal investigators. This chapter applies the concept of social approval to examine the match or mismatch between situational attributions found in examination reports and initial responses from the organizations. Thirteen investigation reports regarding corporate scandals in Canada, Denmark, Japan, New Zealand, Nigeria, Norway, Sweden, and the USA are subject to content analysis to interpret situational attributions by fraud examiners. Media reports are subject to content analysis to interpret initial responses from the organizations (Gamache and McNamara 2019; Greer and McLaughlin 2017). Research results indicate that 5 out of 13 organizations were in the match zone of conformity.
Reliability Learning for Interval Type-2 TSK Fuzzy Logic System with its Application to Medical Diagnosis

To apply intelligent model in serious practical applications like medical diagnosis, the reliability and interpretability of the model are very important to users. Among the existing intelligent models, type-2 fuzzy systems are distinctive in interpretability and modeling uncertainty. However, like most existing models, the reliability determination of fuzzy system for recognition task training is an unsolved problem. In this study, a method of constructing minimax probability interval type-2 TSK fuzzy logic system classifier (MP-IT2TSK-FLSC) based on reliability learning is proposed. The classifier can provide the lower limit of the correct classification of the model and is an important index to quantify the reliability of the model. Experimental results on medical datasets have demonstrated the advantages of this method, exhibiting remarkable interpretability and reliability of the proposed fuzzy classifier.
Scientific Dynasties Role in Sociocultural Development of a Region

The paper aims to identify the role of family values and dynasties’ scientific and intellectual potential in sociocultural development of a region. The phenomenon of scientific dynasties is considered as an example of inter-generational reproduction of sociocultural attitude, an individual’s socialization and adaptation. The author argues that the dynastic factor ensures continuity of generations in science development. Scientific originality of the study involves revealing a regional aspect of forming dynasties as a core of research teams. The findings are as follows: the author proves that scientific dynasties are a necessary human resource, which promotes growth of the scientific potential and ensures stability of regional scientific community.
Bi-Level Optimization Using Improved Bacteria Foraging Optimization Algorithm

Meta-heuristics are mimicry of natural phenomenon in the form of computational frameworks and these are used to find robust and global solutions of complex problems. Bacteria Foraging System (BFS) is one of such newly developed model based on the life structure of single cell bacteria that can follow basic computational instructions like chemotaxis, reproduction, etc. and using these in sequence it can fight and survive in the complex chemical environments. In this work some new improvements has been experimented successfully for the reproduction part of BFS and tested for Capacitated Vehicle Routing Problems, formulated as Bi-Level Optimization Problem. Experimental results are showing its effectiveness for the searching of robust and global solutions.
Research on Text-Independent Writer Recognition with Background Image

In view of the issue that the writer fills in the information the given complex table, but the author may not be correctly identified as the writer for various reasons. In order to solve this, a text-independent writer recognition model is established in terms of automatic handwriting feature extraction from deep learning MobileNet. The automatic handwriting feature extraction based on MobileNet and the text-independent writer recognition model. Firstly, this paper is preprocessed by using different methods, in order to minimize background information and remain handwriting features to the full extent. Such preprocessing methods include the median filter, binarization, background subtraction, text detection, text segmentation, and image scaling. Then completing recognition by using MobileNet automatic handwriting feature extraction. It can effectively reduce human involvement and enhance the accuracy of recognition. Finally, comparing the established model with traditional classification algorithm and ensemble learning algorithm by experiments. The results show that the model obtained recognition accuracy of 91% or more in the manually collected data set and public data set HCL2000.
Analysis of credit-based scheduling algorithms in the cloud computing framework

Cloud Computing has done so many advancements. Cloud has been used immensely from the last 4–5 years. Cloud computing is based on distributed computing. Many organizations and individuals are harnessing cloud services so that they can access the software and hardware. Every task in cloud computing is different from the other. For efficient working of cloud services, there should be appropriate scheduling of the tasks. Many scheduling algorithms are deployed for the efficient working of the cloud services. The main focus of cloud service providers should be on user satisfaction. The cloud environment comprises of the hypervisor and it should be taken care that no virtual machine should be loaded with heavy tasks. It is very crucial to handle the entire load by deploying suitable scheduling algorithms along with the load balancing methods. Credit based scheduling works on assigning tasks to virtual machines by setting priorities but credit-based scheduling when incorporated with load balancing then load is distributed among servers, networks and computers. By comparing CBSA and CBSA_LB, users get aware regarding which suits them according to their requirements. The main motive of scheduling algorithm is to utilize each and every resource efficiently. In this paper, the study of credit-based scheduling in the cloud framework is depicted along with the comparison between CBSA and CBSA_LB. The results are evaluated based on the basis of total processing time, total processing cost, and make span time.
Identification of environments based on magnetic susceptibility and geochemical data using multivariate statistical analysis

Magnetic susceptibility and geochemical data have been widely used in environmental studies. In general, the data are analyzed only based on the correlation between variables. In this paper, the multivariate statistical analysis method is used intensively to identify environmental conditions based on magnetic susceptibility and geochemistry. The objects of this research are the two inlet rivers of Sentani lake, Papua, Indonesia, namely the Hubay and the Kamp Wolker River. In this study matrix correlation, principal component analysis (PCA) and the factor analysis technique were employed in the analysis task. Using the methods, we found the characteristic of geochemical elements and their correlations with magnetic susceptibilities, and dominant geochemical elements in the inlet rivers, which distinguishes the two rivers significantly.
Designing Communication System for Person with Locked in Syndrome Using Machine Learning Technique

Electrroculogram (EOG) based Human Computer Interaction (HCI) research was growing day by day due to need of new handheld assistive devices for the persons with Locked in Syndrome (LIS). For that particular disability only the solution was to develop communication devices to overcome their problem. So we conducted our experiment with ten subjects using Randomized Hough Transform (RHT) and Random Forest Tree Classifier (RFT). During the study, all the subjects were male subjects and their age was ranged from 30 to 45. At the end of the study we obtained the average maximum accuracy of 94.48% and mean minimum accuracy of 88.90% and mean average accuracy of 92.77% for RFT classifier. Best classification accuracy of individual subjects was obtained for the subject S4 with average mean classification accuracy of 93.96%. The minimal classification accuracy of individual subjects was attained by the subject S8 with average mean classification accuracy of 91.70%. The study concluded that individual performances were presented in between 93.96% to 91.70% and finalized that expanding the command signals to handle the exterior machines like wheelchair, TV Remote, mouse, game controller, mobile phone controller, alarm controller, keyboard and home Appliances were possible for disbaled person in usual way.
Striking a Balance in Unsupervised Fine-Grained Domain Adaptation Using Adversarial Learning

Fine-grained domain adaptation is an emerging yet very challenging task in representation learning. In this paper, we analyze a possible reason for the poor performance in fine-grained domain adaptation, which is the difficulty in striking a balance between distribution alignment and fine-grained variations elimination. Furthermore, we propose an adversarial fine-grained domain adaptation framework as a step towards alleviating the underlying conflict between fine-grained variations elimination and domain adaptation. Specifically, our adversarial framework consists of two key modules: a joint label predictor for conditional distribution alignment and a rectifier for fine-grained variations elimination. The key balance can be achieved through the adversarial learning. Besides, experiments on domain adaptation benchmark and fine-grained dataset validate the effectiveness of our framework and show that our framework consistently outperforms the state-of-the-art methods including RTN, MADA, Multi-Task, and DASA.
On Defining Rules for Cancer Data Fabrication

Data is essential for machine learning projects, and data accuracy is crucial for being able to trust the results obtained from the associated machine learning models. Previously, we have developed machine learning models for predicting the treatment outcome for breast cancer patients that have undergone chemotherapy, and developed a monitoring system for their treatment timeline showing interactively the options and associated predictions. Available cancer datasets, such as the one used earlier, are often too small to obtain significant results, and make it difficult to explore ways to improve the predictive capability of the models further. In this paper, we explore an alternative to enhance our datasets through synthetic data generation. From our original dataset, we extract rules to generate fabricated data that capture the different characteristics inherent in the dataset. Additional rules can be used to capture general medical knowledge. We show how to formulate rules for our cancer treatment data, and use the IBM solver to obtain a corresponding synthetic dataset. We discuss challenges for future work.
reFrame: An Alternate Paradigm for Augmented Reality

“reFrame” is an optically see-through Augmented Reality (AR) platform capable of displaying parallax-free images superimposed over physical objects and the scenery behind it. It uses head-tracking technology and off-axis perspective projection to simulate the motion parallax perspective of a virtual 3D scene in relation to a user's position in space. This perspective corrected scene is then rendered on an optically see-through display, practically turning into a parallax-free and scalable general-purpose Heads Up Display (HUD). reFrame combines established and affordable technologies to offer an extremely accessible alternative to available mixed-reality systems, as well as a medium to explore the practical and creative possibilities of spatial augmented reality. It provides an opportunity to focus on subject-centered and attention-based embodied interaction paradigms that are less explored in other forms of Mixed Reality (MR). This paper is offered as a proof of concept and a starting point for further research and conversation.
Collaborative Virtual Reality Training Experience for Engineering Land Surveying

Training students to perform accurate and efficient land surveys through hands-on laboratory experience takes time, space, and expensive equipment. To alleviate demands on these resources, we designed and implemented an immersive virtual reality training simulator that closely replicates both the equipment and social experience of land surveying. Our pilot study of 85 undergraduate engineering students shows high levels of student enthusiasm for the approach, which, by virtue of the recent affordability of virtual reality equipment, is now feasible for a multitude of use cases throughout the engineering curriculum.
Research on Chinese Translation of Fuzzy Semantics of English Modal Verbs Based on Quantification

This paper presents a significant order of magnitude for the English modal verbs might, may, should/ought, Will, Would, could, must, can. In fact, the semantics of “possibility” is also fuzzy, and the boundaries between “possible”, “probable” and “certain” are unclear. Based on their semantic meaning, this paper assigns magnitude to eight English possible modal verbs. In order to study their Chinese translation the Words indicating possibility in Chinese are also given magnitude in the semantics of Chinese. This provides a possibility for comparative research on translation. Furthermore, this paper uses the quantization tool to deal With fuzzy semantics, respectively calculates the true value of the Words in the English-Chinese possibility semantics in the previous text, displays the true value comparison, and compares and analyzes the semantic truth values in the translation to find the Words With closer truth values to verify the validity of the translation criteria “accuracy”.
Identifying Loners from Their Project Collaboration Records - A Graph-Based Approach

Identification of lonely students is important because loneliness may lead to sickness, depression, and even suicide for college students. Loneliness scales are the general instruments used to identify loners, but it usually fails when loners try to conceal their real conditions in the questionnaires. In this paper, we propose a framework for the identification of loners based on their project collaboration records, a relatively more objective data source than student's self-reports. Considering that collaborative relationships among students are highly informative for the identification of loners, we employ Graph Neural Networks to model the complex patterns of student interactions. Furthermore, we propose a Graph-based Over-sampling Technique (GOT) to address the class-imbalanced problem for graph-structured data. Experiments on a real-world dataset show that our proposed method can identify loners with high accuracy.
Extracurricular Activities of Students as An Object of Project Management in A Comprehensive School

The purpose of the study is to reveal the essence of extracurricular activities of students within the federal state standards as an object of project management in an educational organization of general education. The place of extracurricular activities is determined as a full and compulsory component of the main educational program. The essence of extracurricular activities, its relationship with the planned educational results, as well as the difference from continuing education, are revealed. The results of the study of educational programs and examples of the design of plans of extracurricular activities by the teaching teams of schools are presented. The algorithm for designing a plan for extracurricular activities is described. The initial, conceptual and design stages are highlighted and characterized. The scientific novelty consists in substantiating the content of the concept of extracurricular activities of a school, including the presentation system on goals, design strategies, principles for selecting content, formats and implementation procedures.
Summary of continuous action recognition

Abstract In the field of human-computer interaction, it is very important for computers to understand human behaviors, so human action recognition is of great significance. But in the current action recognition work, most of them are for the segmented action data. Compared with this, there is less research on continuous action recognition. Therefore, this paper summarizes the research on continuous action recognition, and elaborates on action feature extraction, action classification, and continuous action segmentation.
Preliminary Experiment for Location Tracking from Camera Image Using CNN

We will find a way to identify the location of the robot until it reaches the destination. When using robots in a factory or a shopping mall, it is important to confirm the current location of the robot. In contrast with using in a factory, it is difficult to run a given course in a shopping mall because it needs to avoid obstacles such as people or carts. Then, we postulate scenes that auxiliary such as a line trace cannot be used. As a method, we analyze camera images by using deep learning. Specifically, a camera on the robot takes many pictures while moving. After that, the robot identifies its own location with using the knowledge from the deep learning of pictures. Not using whole pictures, but the images which are trimmed around feature points are used. As a result, the location and the direction are determined with a maximum accuracy rate of 85.7%.
Improving Policy Generalization for Teacher-Student Reinforcement Learning

Teacher-student reinforcement learning is a popular approach that aims to accelerate the learning of new agents with advice from trained agents. In these methods, budgets are introduces to limit the amount of advice to prevent over-advising. However, existing budget-based methods tend to use up budgets in the early training stage to help students learn initial policies fast. As a result, initial policies are some kind solidified, which is not beneficial for improving policy generalization. In this paper, to overcome advising intensively in the early training stage, we enable advising in the entire training stage in a decreasing way. Specifically, we integrate advice into reward signals and propose an advice-based extra reward method, and integrate advice into exploration strategies and propose an advice-based modified epsilon method. Experimental results show that the proposed methods can effectively improve the policy performance on general tasks, without loss of learning speed.
Camera-Specific Simulation Method of Fish-Eye Image

Simulation tools are widely used in intelligent vehicle systems and robot auto-control systems. They considerably contribute in the cost reduction of the validation and testing stages of the new intelligent functionalities. As one of the common parts of these systems, visual sensors need to be simulated physically, in order to enable the implementation of the computer vision-based perception techniques in the virtual environment. Fish-eye cameras are often used in this techniques to provide an omnidirectional field of view, and thus need to be simulated. The existing methods cannot simulate the output image of specific real fish-eye cameras. In this paper, we present a post-processing fisheye camera simulation method. According to the stereographic projection model, a mapping is established between the output image and a cube-map rendered with the graphic engine. In order to simulate the output of a specific camera, the distortion of the real camera is measured and added to the simulated image. Experimental result shows that our simulation method can give output close enough to the image captured by a specific fish-eye camera. The practicality of our method is validated in an actual application.