Reviewerly/exHarmony huggingface dataset:
A Novel Approach of Human Tracking and Counting Using Overhead ToF Camera

Human detection is a critical measure used for obtaining the information of flow density in a region, of which the data is conductive to the customer flow analysis for some businesses, avoiding human stampedes caused by excessive people simultaneously. Although previous research into human detection is generally based on a 2D camera, the data collected by it is vulnerable to pedestrian occlusion, complex background, shadow interference, and other uncontrollable factors. Meanwhile, human detection algorithms based on 3D are also studied currently by some researchers, however, these methods suffer from low efficiency and high error rate. Thus, a novel real-time human detection algorithm based on the ToF camera is proposed in this paper. Different from other detection algorithms running on the computer, our algorithm is implemented on an embedded 3D camera platform. This method is divided into two steps to go on: firstly, the novel detection algorithm determines the head position by finding the peak value for the region of interest (ROI). Secondly, combined with an improved Hungarian algorithm and a prediction of human movements using a Kalman filtering which facilitates the tracking rate significantly. The experimental results demonstrate that the accuracy of the novel algorithm in real-time people detecting and tracking is above 95%.
Interval Type-2 Fuzzy Sets in the Future: Scientific Projects for Development

The applications described in Chap. 3 are related to topics in the biological field, such as drug elimination, prostate cancer-related diseases, population growth, and epidemiological disease caused by HIV and COVID-19. In this chapter some scientific projects are suggested that may be useful for the development of research using an interval type-2 fuzzy set.
Discounted Semi-Markov Games and Algorithms for Solving Two Structured Classes

Two structured classes of zero-sum two-person finite (state and action spaces) semi-Markov games with discounted payoffs, namely, Additive Reward-Additive Transition and Action Independent Transition Time (AR-AT-AITT) and Additive Reward-Action Independent Transition and Additive Transition Time (AR-AIT-ATT) have been studied. We propose two practical situations of economic competition viz. petroleum game and groundwater game that suitably fit into such classes of games, respectively. Solution (value and pure stationary optimals) to such classes of games can be derived from optimal solution to appropriate bilinear programs with linear constraints. We present a stepwise generalized principal pivoting algorithm for solving the vertical linear complementarity problem (VLCP) obtained from such game problems. Moreover, a neural network dynamics is proposed for solving such structured classes. Examples are worked out to compare the usefulness of the above three algorithms.
Degenerate Gaussian Sum Filter for Onboard Satellite Navigation and Satellite Reentry

The extended Kalman filter (EKF) is the most popular onboard navigation filter for satellites orbiting around the earth. Indeed, the EKF is the simplest, fastest, and most stable nonlinear filter and is thus easy-to-implement and attractive for navigation practitioners. Moreover, even though the satellite dynamics model and the observation model are both nonlinear, the EKF often provides satisfactory navigation results since the modeling errors become considerably small due to the precise knowledge about the dynamics and observation processes. In this paper, using the simulation problem of satellite reentry, we first evaluate the filtering performance of the representative navigation filters including both Gaussian and non-Gaussian filters and show that the non-Gaussian filters such as the ensemble Kalman filter (EnKF) and the particle filter (bootstrap filter (BF)) are not suitable for this problem. We next show that the proposed filter called the degenerate Gaussian sum filter (DGSF) has a potential to outperform the comparison filters tested in this paper. Since the DGSF is a bank of the EKFs, we expect that the results obtained in this paper will contribute to the sophistication of the onboard satellite navigation filter currently based on the single EKF.
Different method for counting the number of Quadratic functions with prescribed spectra

In this correspondence we study a class of quadratic binary functions F 2,n from F 2 n to F 2, which are well-known to have plateaued Walsh spectrum; i.e., for each b ε F 2 n the Walsh transform f(b) satisfies |ƒˆ(b)| 2 ∈ {0,2 (n+s) } for some integer 0 ≤ s ≤ n — 1. For the type of integers n = q 1 q 2, where q 1 ; q 2 are two different odd primes, we determine possible values of s and present some enumeration results for counting the number of quadratic functions having those particular form of s .
Parametric Analysis of an Autonomous Sensor Tasking Engine for Spacecraft Tracking

The rapidly increasing space object population with growing capabilities demand that an autonomous tasking strategy be capable to maintain a space catalog. This work describes a parametric study of sub-optimal partially observed Markov decision process (POMDP) implemented to task a diverse, distributed sensor network to track possibly maneuvering, non-uniform resident space objects (RSO). The parametric analysis was performed by testing various combinations of metrics, reward functions, and decision algorithms. The presented work compares strengths and weaknesses of different solution realizations by quantifying the scenario-level performance based upon estimation theoretic statistics as well as behavioral metrics. As the future of space domain awareness trends towards autonomous tasking solutions, the goal of this work was to develop a methodology to evaluate an autonomous tasking strategy.
Splatty- A Unified Image Demosaicing and Rectification Method

Image demosaicing and rectification are key tasks that are frequently used in many computer vision systems. To date, however, their implementations have been plagued with large memory requirements and inconvenient dataflow, making it difficult to scale them to real-time, high resolution settings. This has motivated the development of joint demo-saicing and rectification algorithms that resolve the back-ward mapping dataflow for improved hardware implementation. Towards this purpose, we propose Splatty: an algorithmic solution to pipelined image stream demosaicing and rectification for memory bound applications requiring computational efficiency. We begin by introducing a polynomial Look-up-Table (LUT) compression scheme that can encode any arbitrarily complex lens model for rectification while keeping the remapping errors below 1E-10 pixels, and reducing the memory footprint to O(min(m, n)) from O(mn) for an m × n sized image. The core contribution leverages this LUT for a unified, forward-only splatting algorithm for simultaneous demosaicing and rectification. We demonstrate that merging these two steps into a single, forward-only splatting pass with interpolation, provides distinctive dataflow and performance efficiency benefits while maintaining quality standards when compared to state-of-the-art demosaicing and rectification algorithms.
Development of Powered Wheelchair Expanding Reach of User

The purpose of this study is to realize a powered wheelchair that expands the reach of a wheelchair user to improve his/her quality of life. In this research, it is mainly necessary to manufacture its carrosserie, to design control system, and to verify the integrated system. The carrosserie is designed to expand the reach of a rider by transforming itself. The physical model of the wheelchair is derived. The model is used to design the control system.
Do Libraries Need to Market Their Services and Resources?

Marketing concept is being widely used in all disciplines including library and Information Science domain for disseminating the services, resources and products to the target audiences. Thus, this study aims to define the marketing concept and how libraries can do marketing of their services, resources and products using marketing techniques and tools, such as 7Ps of the marketing mix. This study further pointed out the important internet resources available freely on the marketing of library services on the Internet which can be used by library and Information Science professionals with regard to getting the ideas how to market the library services and resources to the targeted users.
Visual Identification of Inconsistency in Pattern

The visual identification of inconsistencies in patterns is an area in computing that has been understudied. While pattern visualisation exposes the relationships among identified regularities, it is still very important to identify inconsistencies (irregularities) in identified patterns. The significance of identifying inconsistencies for example in the growth pattern of children of a particular age will enhance early intervention such as dietary modifications for stunted children. It is described in this chapter, the need to have a system that identifies inconsistencies in identified pattern of a dataset. Also, techniques that enable the visual identification of inconsistencies in patterns such as fault tolerance and colour coding are described. Two approaches are presented in this chapter for visualising inconsistencies in patterns namely; visualising inconsistencies in objects with many attribute values and visual comparison of an investigated dataset with a case control dataset. These approaches are associated with tools which were developed by the authors of this chapter: Firstly, ConTra which allows its users to mine and analyse the contradictions in attribute values whose data does not abide by the mutual exclusion rule of the dataset. Secondly, Datax which mines missing data; enables the visualisation of the missingness and the identification of the associated patterns. Finally, WellGrowth which explores Children’s growth dataset by comparing an investigated dataset (data obtained from a Primary Health Centre) with a case control dataset (data from the website of World Health Organisation). Instances of inconsistencies as discovered in the explored datasets are discussed.
Learning Design in the 21st Century

This chapter provides a review of multiple facets of current and promising trends for learning design technologies for the 21st century, with the intention to provide a centralized discussion of different avenues in learning design considerations. An attempt is made to cover multiple parallel trending areas, such as adaptive learning, digital storytelling, gamification, simulation technologies, augmented and virtual reality, cybernetics, the xAPI standard, mobile and ubiquitous learning, with a glimpse of how such trends are shaping or expected to shape the future of all those involved in designing and delivering learning or effecting human behavior and performance change. Moreover, a discussion on learning theories for the digital age is presented, as well as brief overviews on several areas of concern in learning design, such as in assessment and verification, cloud computing, data and data analysis, artificial intelligence, blockchain, open educational resources, etc.
AdaLSH: Adaptive LSH for Solving &lt;i&gt;c&lt;/i&gt;-Approximate Maximum Inner Product Search Problem

Maximum inner product search (MIPS) problem has gained much attention in a wide range of applications. In order to overcome the curse of dimensionality in high-dimensional spaces, most of existing methods first transform the MIPS problem into another approximate nearest neighbor search (ANNS) problem and then solve it by Locality Sensitive Hashing (LSH). However, due to the error incurred by the transmission and incomprehensive search strategies, these methods suffer from low precision and have loose probability guarantees. In this paper, we propose a novel search method named Adaptive-LSH (AdaLSH) to solve MIPS problem more efficiently and more precisely. AdaLSH examines objects in the descending order of both norms and (the probably correctly estimated) cosine angles with a query object in support of LSH with extendable windows. Such extendable windows bring not only efficiency in searching but also the probability guarantee of finding exact or approximate MIP objects. AdaLSH gives a better probability guarantee of success than those in conventional algorithms, bringing less running times on various datasets compared with them. In addition, AdaLSH can even support exact MIPS with probability guarantee.
Hubungan Antara Kebiasaan Belajar di Rumah dengan Prestasi Belajar Siswa MI Miftahul Huda Druju Kecamatan Sumbermanjing Wetan Malang Tahun Ajaran 2019/2020

Learning achievement is the result of a process to see the extent to which students can master learning after they participate in the learning process, students can obtain satisfactory learning achievement if during the learning process they have good study habits. Therefore, this study aims to determine the relationship between study habits at home and student achievement of class IV MI Miftahul Huda Druju, Sumbermanjing Wetan District Malang, Academic Year 2019/2020. The plan used in this study is correlational, because this study aims to determine the relationship between study habits at home and the learning achievement of grade IV students of MI Miftahul Huda Druju. The population in this study were all class IV students of MI Miftahul Huda Druju, Sumbermanjing Wetan Malang District in the second semester of the 2019/2020 academic year, totaling 32 students. While the data collection technique of this study used a questionnaire technique, and the data analysis technique used correlation. From this research, it shows that there is a significant relationship between study habits at home and student achievement of class IV MI Miftahul Huda Druju as evidenced by a hypothesis test that obtained a significant value of 0.000 less than 0.05, indicating that Ho is rejected and Ha is accepted, therefore it can be concluded there is a relationship between study habits at home and the learning achievement of grade IV students at MI Miftahul Huda Druju, Sumbermanjing Wetan District Malang, Academic Year 2019/2020.
Formation of a draft model of a national technology initiative specialist

The article indicates the draft model of an NTI specialist, built on the basis of the generalized opinion of experts. Education as an integrated process is focused on the intellectual, spiritual and moral, creative, physical and/or professional development of a person. Based on the analysis of personality theories, the main criteria of the personality model of an NTI specialist are determined. An algorithm for group expert assessment of the personality model of an NTI specialist is presented. Methods for registering indicators of the formation of personality substructures of NTI specialists are pedagogical and psychological diagnostics and expert assessment. The tendencies of positive changes in the personality development of NTI specialists are revealed.
Classification and Formalization of Interpretative Schemes

In our previous chapters, we underscored the role of interpretative arguments in the justification of the passage from a legal text to a legal rule (Hage, 1996, 214; Tarello, 1980), where the latter is intended as a normative premise under which an individual case can be “subsumed” or classified (Moreso & Chilovi, 2018). Legal interpretation can be compared to the common understanding and processing of utterances in ordinary conversation (Smolka & Pirker, 2016), in which semantic content is only a vehicle for getting to the “speaker’s meaning,” i.e., what is communicated – a richer content to which semantic “meaning and obvious background assumptions have both contributed” (Soames, 2008, 411; see also Butler, 2016; Carston, 2013; Horn, 1995; Miller, 1990).
Design and Implementation of Network Monitoring System Back End for 3T Regions with Parallel Processing

The increasing need for internet communication networks, various institutions and government agencies are demanded to be able to provide internet services in all regions in Indonesia, especially 3T regions. In providing this internet facility, we need a system that can monitor network devices that have been installed at various points so that the quality of the services provided can be maintained. The purpose of this final project is to design and implement a Network Monitoring System (NMS) that can monitor network devices, especially VSAT (Very Small Aperture Terminal), and provide data in the form of uptime, traffic, ping, signal quality factor, and availability that can help in handling problems that occur on the network so that the performance of these devices can still meet the standards and service level agreement (SLA) that has been determined. Design and implementation methods are carried out with the stages of problem identification, identification of engineering requirements, determination of design alternatives, implementation, and product testing. The testing results show that the Network Monitoring System that was designed meets the Telecommunication Management Network standard of ITU-T M.3400 and meets the design requirements that have been determined for systems that can monitor network devices. In addition, the testing results show that using multiprocessing as a parallel computation method can speed up program execution time 2 to 8 times compared to before using multiprocessing method.
Data Augmentation for Ancient Characters via Semi-MixFontGan

The ancient documents provide people a way to understand history. However, the existing materials are suffering from unbalanced characters dataset, as well as intra-class multimodality fonts. As a result, humans and recognition systems are unable to identify these characters effectively. Based on these problems, we propose Semi-MixFontGan: a font generation method based on Semi-Supervised strategy that can learn from a small number of labeled font data to aggregate subclasses' information of categories and generate characters. In generating new samples from ancient books that have a small amount of labeled font data, the model can automatically learn the difference between them and generate font-consistent characters. The model is composed of two parts. In the first part, we propose a MixFont method to mix labeled and unlabeled and generated data. Then use a convolutional autoencoder to learn the font information. In the second part, the generator network can generate reasonable and realistic images by Font and Content Discriminator. Through this model, we can make the ancient book dataset more balanced. Experiments show that the generated characters by our model can get good visual effects and maintain font consistency with training data. With the augmented data, the accuracy of the recognition network has increased. Contribution-We propose a novel font generation method with semi-supervised learning to generate characters from small labeled font Kuzushiji dataset.
Text-Based Handwritten Recognition Through an Image Using Recurrent Neural Network

Neural computers' computational operations are employed in a strategic way that is entirely disparate from that of standard conventional computers. Neural computers are primarily computational train tutored there by if given a definite precise original state (data input), they whatsoever classify the input data should be rolled into one of the well known categorized classes or convicts that raw input data to yield into a specific worthwhile advantageous property is improved. In this system, we pass small patches of handwritten images to convolution neural networks, and it recognizes the text from the given input images. Neural computers implement data parallelism. The main objective of this paper is to propose the design of an expert knowledge-based neural network system for handwritten recognition that can effectively recognize the text from the given input image using recurrent neural network approach. This approach can be used as an optimal solution tool for handwritten recognition as it provides faster classification with more accuracy.
Learning from Family Mysteries

Given the importance and social significance of passing down family stories to each generation, why do important family stories not get told? How should designers of digital family storytelling platforms address missing or incomplete parts of narratives? Drawing from the results of an interview-based, practice-oriented inquiry, we argue that non-telling should be considered an important and integral part of family storytelling. Our findings show that non-telling is not simply silence. Non-telling allows family members to observe protective and discretionary values essential to the identity-making and relational goals of family storytelling. We also show ways that a person's reticence is situated and may change over time. In our discussion, we provide design strategies for family storytelling technologies to make room for silence and incorporate the values, purposes, and practices of non-telling.
CONFORMAL IMAGE REGISTRATION BASED ON CONSTRAINED OPTIMIZATION

Abstract Image registration is the process of finding an alignment between two or more images so that their appearances match. It has been widely studied and applied to several fields, including medical imaging and biology, where it is related to morphometrics. In this paper, we present a construction of conformal diffeomorphisms which is based on constrained optimization. We consider a set of different penalty terms that aim to enforce conformality, based on discretizations of the Cauchy–Riemann equations and geometric principles, and demonstrate them experimentally on a variety of images.
Imprecision of Semantic Meaning in a Natural Language

Most of linguistic computing models cause loss of information due to the approximation processes and imprecision in the results. The BoW scheme is a simple and popular scheme, but it suffers from numerous drawbacks. Traditionally, the Bag of Words (BoW) representation is used to model the documents in a vector space. It also fails to capture the semantics contained in the documents properly as automatic classification and clustering are carried out in the most common operations. We tried to use Fuzzy logic and its extension computing with words to analyze semantical meanings of a set of terms given in a natural language.
Deep Learning Techniques in Perception of Cancer Diagnosis

In this era of technology, for effective treatment of patients, clinical experts are getting great support from automated e-healthcare systems. Nowadays, one of the leading reasons of death is cancer. Some common cancers are breast cancer, prostate cancer, lung cancer, skin cancer, brain cancer, and so on. To save human lives from cancer, an effective and timely treatment is required. Many different types of image modalities like CT scan, ultrasound, x-ray, MRI can be used to determine the disease, but traditionally, this was purely dependent on the knowledge and experience of doctors. So, the death rate was quite high and increasing day by day. Machine learning and deep learning are providing robust solutions in this field. There are many deep learning techniques like RNN, CNN, DBN, autoencoders, generative adversarial networks which are providing robust solutions in cancer diagnosis and prognosis so that many human lives can be saved. The objective of this chapter is to give an insight into deep learning techniques in the field of a cancer diagnosis.