Reviewerly/exHarmony huggingface dataset:
Research on Two Types of Estimation Algorithms in Carrier Tracking Loop

Abstract This paper analyzes two estimation algorithms in the carrier loop, i.e. maximum likelihood estimation (MLE) and modern filtering based on Bayesian recursion. The limitations of the MLE algorithm are pointed out by establishing the MLE model. In addition, several modern filters based on Bayesian recursion are analyzed. In this process, the article draws two inferences affecting high dynamics and low signal-to-noise ratio (SNR) carrier tracking from two aspects of state equation and measurement equation. Thus, the optimal algorithm using unscented Kalman filter (UKF) is proposed by the inferences mentioned. at the end, simulation results demonstrate the combined strong tracking algorithm has advantages in case of high dynamic and low SNR.
Companionship Games: A Framework for Emotionally Engaging and Empathetic Interactive Characters

1 in 2 people are diagnosed with cancer in their lifetime, and for those living with cancer loneliness and isolation are significant problems. This paper discusses the development of a virtual cancer support group, using the support group format to offer a companionship game to breast cancer patients. Seven characters populate this support group, designed as empathetic virtual agents. Interviews and playtests will assess the effectiveness of the design practice developed, and these learnings will be used to create a design framework for emotionally engaging and empathetic interactive characters.
Speculative Execution Attacks and Cloud Security

Speculative execution attacks, such as Meltdown, Spectre, and Foreshadow, exploit performance optimization features in modern processors and micro-architectural side channels to breach the confidentiality of computer systems. Although few incidences have been reported in the wild, their very existence imperils the promise of strong security isolation of cloud computing. This panel will invite experts from both academia and industry to discuss the impacts of speculative execution attacks on the cloud industry. The aim of the panel is to understand the state of the art of academic research and industry practice, and to explore future academic research directions and industry best practices.
A Progressive Learning Framework for Unconstrained Face Recognition

The carefully designed backbone network, the increase of training data and the improved training skills have boosted the performance of modern face recognition systems. However, in some deployment cases which aim at model compactness and energy efficiency, some of the existing systems may fail due to the high complexity. Lightweight Face Recognition Challenge is proposed in order to make some progress in this direction and establishes a new comprehensive benchmark. In this challenge, we have designed a light weight backbone architecture and all the parameters are trained in a progressive way. Finally we achieve the 5th in track 1 and the 4th in track 3.
Fast Two-Cycle Level Set Tracking With Interactive Superpixel Segmentation and Its Application in Image Retrieval

In this paper, a fast two-cycle level set contour tracking algorithm based on interactive superpixel segmentation is proposed. Then, image retrieval is carried out based on the contour tracking results. The method of contour tracking is divided into three stages: superpixel segmentation, interactive feature extraction, and foreground tracking. In the first phase, the image is preprocessed by a superpixel segmentation method. In the second stage, the feature pools are defined and established interactively according to the complexity of the image. In the last stage, based on the first two stages, a fast two-cycle level set method is used to track the contour of the target object. Finally, the results of the contour tracking are applied to an image matting of the target foreground, and then the foreground image retrieval is implemented. The results of experimental comparisons demonstrate the effectiveness of the proposed method.
User Behavior Tracking for Education Assisting System by Using an RGB-D Sensor

It is difficult to track multiple people effectively for a long time in a complex environment because people’s clothes and body shapes may be similar, and their postures may be constantly changing. This paper proposes a novel method for multiple people tracking in crowded places where people can be partially or completely occluded. The people are detected by the deep learning method ConvNet from the color image first, and detection results are integrated with the depth information so that the accurate human areas can be extracted. The accurate personal color information can be extracted then without any background color information. multiple people tracking is proceeded by using particle filter based on the color information. the effectiveness of the proposed method is verified through experiments of tracking multiple children in a classroom.
A new method for fringe order error correction in fringe projection profilometry

Phase unwrapping is an essential step for 3D shape measurement based on fringe projection. Temporal phase unwrapping methods can be implemented by analyzing the multiple patterns that encode the fringe order information. They can retrieve the fringe orders on a pixel-by-pixel basis and are less prone to error propagation compared with spatial methods. However, fringe orders errors may still occur due to noise, reflectivity fluctuation and discontinuity of the object surface. Such errors may exhibit an impulsive nature and result in significant error to the recovered absolute phase map. This has been exploited by several methods to correct the fringe order errors, e.g., by filtering the fringe order sequences in a line-by-line manner. In this paper, a new method is proposed to correct the errors associated with fringe orders for the temporal phase unwrapping. The scheme first makes use of the low-rankness property of the fringe order map and sparse nature of the impulsive fringe order errors to more effectively remove the impulsive errors by applying robust principal component analysis (RPCA) algorithm. Then the smoothness of the two-dimensional unwrapped phase map is examined and the residual fringe order errors are detected based on a discontinuity measure of the phase map and corrected by comparing phase difference between two adjacent pixels in the unwrapped phase. The effectiveness of the proposed method is demonstrated via numerical experiments.
Better Pretrained Embedding with Convolutional Neural Networks for Morphological Stemming

Words are considered as independent entities without any direct relationship among morphologically related word. So, some rare words are poorly estimated and unknown words are represented only a few vectors. The process of stemming is to reduce different forms to a common morphological root. Word embedding is a good generalization to unseen words and that can capture general syntactic as well as semantic properties of word. Furthermore, deep learning approaches have become more and more prominent in NLP tasks and pre-trained embedding layers have been applied to improve the performance of neural network architectures for many NLP applications. However, word segmentation for Myanmar Language, like for most Asian Languages, is a vital task and widely-studied sequence labeling problem. Normally, stemming is considered as a separate process from segmentation. In this paper, new approach indicates segmentation boundaries when it performs stemming. This paper proposes several word representations from character and syllable level and they are corporate in convolutional neural network (CNN-based model) which jointly learns stemming and segmentation boundaries in parallel. It is also evaluated the performance of convolutional neural network that relies on different pre-trained embedding. According to the experimental results, the pre-trained embedding has a vast effect on the performance.
Using real-time image enhancement technique to improve prostate capsula detection

Plasmakinetic resection of the prostate (PKRP) and plasmakinetic enucleation of the prostate (PKEP) are two effective methods for treating benign prostate hyperplasia (BPH). During surgery, the doctors will trim away the extra prostate tissue using the boundary of the prostate capsula. When some doctors resect more, prostate capsula perforation will occur. At this time, accurate and real-time prostate capsula detection becomes extremely urgent. In addition, poor quality and low contrast images are the main problems in the medical field. After enhancement, the objective images are visibly more distinct when encountering image noise and clutter. Right now, popular object detection methods like YOLOv3 and Single Shot MultiBox Detector (SSD) can detect prostate capsule in real time, but the results are not ideal. We improve these methods using deep bilateral learning, which is a one real-time image enhancement technique, and the method increases the accuracy by 2 ~ 2.69 percent. We implement the experiments using YOLOv3, the SSD, YOLOv3 with deep bilateral learning, and the SSD with deep bilateral learning. The best detection results are provided by the SSD combined deep bilateral learning with an accuracy of 98.39 percent. The proposed method has the potential for specific practical uses in medical image diagnosis.
Dynamics in Foundations: What Does It Mean in the Practice of Mathematics?

The search for a synthesis between formalism and constructivism, and meditation on Gödel incompleteness, leads in a natural way to conceive mathematics as dynamic and plural, that is the result of a human achievement, rather than static and unique, that is given truth. This foundational attitude, called dynamic constructivism, has been adopted in the actual development of topology and revealed some deep structures that had remained hidden under other views. After motivations for and a brief introduction to dynamic constructivism, an overview is given of the changes it induces in the practice of mathematics and in its foundation, and of the new results it allows to obtain.
CCA Secure Public Key Encryption against After-the-Fact Leakage without NIZK Proofs

In leakage resilient cryptography, there is a seemingly inherent restraint on the ability of the adversary that it cannot get access to the leakage oracle after the challenge. Recently, a series of works made a breakthrough to consider a postchallenge leakage. They presented achievable public key encryption (PKE) schemes which are semantically secure against after-the-fact leakage in the split-state model. This model puts a more acceptable constraint on adversary’s ability that the adversary cannot query the leakage of secret states as a whole but the functions of several parts separately instead of prechallenge query only. To obtain security against chosen ciphertext attack (CCA) for PKE schemes against after-the-fact leakage attack (AFL), existing works followed the paradigm of “double encryption” which needs noninteractive zero knowledge (NIZK) proofs in the encryption algorithm. We present an alternative way to achieve AFL-CCA security via lossy trapdoor functions (LTFs) without NIZK proofs. First, we formalize the definition of LTFs secure against AFL (AFLR-LTFs) and all-but-one variants (ABO). Then, we show how to realize this primitive in the split-state model. This primitive can be used to construct AFLR-CCA secure PKE scheme in the same way as the method of “CCA from LTFs” in traditional sense.
Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View

A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.
Double measurement on cognitive learning apps for children with mentally retardation

Abstract There are four cognitive aspects revealed aim to develop learning application (apps) for children with mentally retardation (MR), namely perception, association, memory, and motoric. However, motoric aspects is less noticed in apps which had being developed. In this study, we aim to design the interaction of the apps which accommodate motoric aspect. Apps may be used as companion tool aside with formal learning. Apps is focused on motoric aspect, while another four aspects are adopted from existing apps. In order to reach high level of usability, interaction design (IxD) is measured iteratively using double measurements, cognitive walkthrough (CW) and heuristic evaluation (HE). Both method is covering each other. This way is suitable to verify previous measurement and strengthen usability. Result of the study stated that apps can properly used by students and teacher and relevant with user research. There is no major usability problems found either in CW or HE measurement. Moreover, HE method ensures end-user’s satisfaction is good design principles also.
Cultivating Online: Understanding Expert and Farmer Participation in an Agricultural Q&amp;A Community

Nowadays, due to the shortage of offline agricultural experts, hundreds of thousands of farmers in China seek online for cultivation advices. A key design issue here is to provide users with timely and high quality answers, which, for general Community-based Question and Answering (CQA) platforms such as Quora, has been extensively studied before. However, answering questions raised in agricultural CQA platforms requires domain knowledge and professional experience, and users are expected to behave differently. In this article, we conduct a case study on a agricultural CQA platform named Farm-Doctor. We obtain the whole knowledge repository of Farm-Doctor, we investigate the behavioral differences between experts and farmers, and we analyze the factors that influence the users’ answering behavior. Our results show that there exists obvious behavioral differences between experts and farmers, and differences also exist in the factors that influence user’s answering behavior. While recognition, i.e., whether answers are well received by the community, has a positive impact on the answering behavior of both experts and farmers, reciprocation, i.e., how their own questions are treated, works differently. Experts acknowledge the “effort” of the community, i.e., the number of answers they get for their own questions has a positive effect on the number of answers they provide whereas the quality of the answers does not. Farmers, on the other hand, care both the number and the quality of the answers. Our research provides valuable information for the community to motivate the users to answer questions and therefore helps farmers solving their cultivation problems, in hoping to improve their lives.
GridInSight

We demonstrate GridInSight, a suite of techniques that leverage low-cost, non-intrusive, and commodity smartphone and machine vision cameras to measure electricity grids. Specifically, we develop techniques to measure electricity grid frequency, phase (indoors), and phase (outdoors) across a mix of cameras with errors of 1-2%, 2-5%, and 3-10%, respectively. Further, we develop a novel technique and show an error of 8-15% for measuring voltage on a lightbulb that our system had not seen previously. The ability to cheaply and pervasively measure power quality with non-intrusive, off-the-shelf hardware can enable a wide range of applications for monitoring electricity grids, particularly in emerging economies.
The development of islamic learning media using macromedia flash on geometry

Abstract The aim of this study is to construct an Islamic learning media using Macromedia Flash on curved side geometry subject. The type of this research is a development research which refers to ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The result of this research indicates that based on the assessment of the media experts, the learning media have been valid and Based on the assessment of contain experts the learning media have been valid. The results of questionnaire responses of teachers, questionnaire responses of students, and interviews with students indicate that the learning media have been applicated by teachers and learners.
StickyTouch: An Adhesion Changeable Surface

We propose StickyTouch, a novel tactile display that represents adhesive information on a surface. Adhesion control can be achieved by a temperature sensitive adhesive sheet whose temperature is locally controlled by peltier devices arranged in a grid. We implemented a proof-of-concept prototype in which the adhesive size was 150 × 150 mm and proposed example applications of the display.
BARREIRAS PARA ADOÇÃO DE FERRAMENTAS EDUCACIONAIS BASEADAS EM REALIDADE AUMENTADA: UMA REVISÃO DA LITERATURA

Education is constantly changing in an attempt to add to the teachinglearning process aspects that contribute to quality education.In this context, this study aimed to characterize the scientific production on the barriers to the adoption of augmented reality educational tools.From a literature review, 44 studies were initially selected from the Science Direct and Scielo scientific bases.Barriers were classified into nine categories: technical, financial, content, learning, security, adherence, user interaction, visualization, and time period.Only 17 studies have measurable results.It is concluded that further studies are needed regarding the barriers to the adoption of this technology in the field of education.
Local Distortion Hiding in Financial Technology application: a case study with a benchmark data set

Data sharing has become an increasingly common procedure among financial institutions, but any organisation will most probably attempt to conceal some critical rules before exchange their information with others. This paper concentrates on protecting sensitive rules when we assume that binary decision trees will be the models to be induced by the shared data. The suggested heuristic hiding technique is preferred over other heuristic solutions such as output disturbance or encryption methods that restrict data usability, as the raw data itself can then more easily be offered for access by any third parties. In this article, we present a paradigm of using the Local Distortion Hiding (LDH) algorithm in a real-life financial data set to hide a sensitive rule.
Multitemporal Aerial Image Registration Using Semantic Features

A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.
Discovering Frequent High Average Utility Itemset Without Transaction Insertion

Data mining is a technique through which we can find interesting data and sequence from the available wide range of data source. Incremental high-average utility pattern mining (IHAUPM) algorithm is represented to manage the incremental database with transaction insertion. IHAUPM algorithm basically follows the comparison to the original database and newly inserted database itemset if itemset has High Average Utility Upper Bound Itemset (HAUUBI) in the initial database as well as new transaction database then the item always frequent. Second situation itemset has non-High Average Utility Upper Bound Itemset (non-HAUUBI) in the initial database as well as new transaction database then the item always not frequent. Otherwise, the itemset is recurring or not is identified by the given information. This new algorithm is represented in this paper to generate expected high utility frequent item set to form a new transaction database; this algorithm is much faster than the existing algorithm.
Methods for the Synthesis of Optimal Control of Deterministic Compound Dynamical Systems With Branch

This chapter states the result of the development of optimal control methods for deterministic discontinuous systems of optimal control problems for deterministic compound dynamical systems (CDS) with branching paths. The necessary conditions for optimality of the CDS branching paths are formulated in the form convenient for subsequent development of algorithms for the operational synthesis of these paths. The optimality conditions developed by the authors allow both preliminary and in real time (on-line) optimization of the CDS branching paths. The need for an operational synthesis of the CDS branching trajectory is caused by the inaccuracy of prior knowledge of information about the factors affecting CDS movement which are critical for the implementation of the CDS end-use. The developed conditions are universal for solving problems with any finite number of branches of a branching trajectory and are focused on the use of artificially intelligent systems which allow analyzing the structure of optimal control of CDS components as they move along the path branches.
Real-time Monitoring Method for Cybersickness using Physiological Signals

The potential for cybersickness remains a critical problem when engaged in Virtual Reality experiences. Cybersickness is difficult to resolve because, although there are commonly accepted symptoms and theories, there is still no consensus on how to overcome the problem. In this study, a method of real-time monitoring of physiological signals is proposed as an approach to measure the potential onset of cybersickness. An application called Cybatica which displays physiological data and a unique metric termed Onset of Cybersickness (OCS) has been developed.
Direct Planar Filter Synthesis Using Convolutional Neural Network

In this paper, we propose the first study on the synthesis of the filter geometry directly from the given frequency response using a convolutional neural network (CNN). By assuming the planar filter geometry as an image, the CNN can learn the relationship between the geometry and frequency response because the CNN is good at dealing with image matter. We also explain a way to generate an accurate and massive dataset. The massiveness is achieved by high-speed lookup-table-based cascaded ABCD matrix calculation. The accuracy is guaranteed by the consideration of junctions where the line widths are different. As a result, the pair of a randomly generated filter geometry and its filter response is calculated in 2.0 ms. After the CNN is trained with the dataset containing 100k pairs, the CNN can synthesize a filter in 2.9 ms on an nVidia RTX 2080 graphics card. We also introduce a CNN that can estimate the frequency response directly from the filter geometry in 1.9 ms.