Reviewerly/exHarmony huggingface dataset:
Conditional Independence Test for Categorical Data Using Poisson Log-Linear Model

We demonstrate how to test for conditional independence of two variables with categorical data using Poisson log-linear models. The size of the conditioning set of variables can vary from 0 (simple independence) up to many variables. We also provide a function in R for performing the test. Instead of calculating all possible tables with for loop we perform the test using the loglinear models and thus speeding up the process. Time comparison simulation studies are presented.
Interactive virtual reality orchestral music

The authors developed a VR orchestral application for interactive music experience, allowing virtual musical instruments in an orchestral piece to be repositioned spatially, dynamically and interactively in VR space. This can be done for changing environments where 3D audio technology is used to restructure traditional orchestral pieces into a new music art form. User experience surveys were undertaken on two kinds of users, with the results showing that the VR orchestral system developed in this paper could bring some special advantages in the musical experience.
Research of magnetic system applied in autonomous MegaSumo robots

Research of magnetic system applied in autonomous MegaSumo robots is an important issue in the best adaptation of a fighting robot. Only this type of robots (dimensions 200x200 mm, weight up to 3000g) are obliged to use it. It is caused by the driving of robots on the metal rings (dohyo). To get the best movement characteristics, magnets are attached to the robot's floor. The quantity of magnets results in high or low traction during the fight. High magnetising power does not allow the opponent to get under the robot and push it out of the ring. In the article, simulation research was carried out on three different magnetic systems that can be used in a robot. After the simulations, each system was built in reality. In order to check the actual attractive forces of the system, tests were carried out on the strength machine. This allowed comparing the results from simulations and real tests. The best layout was used in the sumo robot. It had the greatest strength of attraction for a metal dohyo.
Ishara-Bochon: The First Multipurpose Open Access Dataset for Bangla Sign Language Isolated Digits

Ishara-Bochon, the first multipurpose comprehensive open access isolated digits dataset for Bangladeshi Sign Language (BdSL) is introduced in this article. Sign language digits is a major part of communication for deaf and hearing impaired people. It is important in their everyday stuff, such as – in routine accounting and to interact with general people. Ishara-Bachon dataset contains 100 sets of 10 Bangla basic sign digits (0, 1, 2 . . . 9), collected from different deaf and general volunteers from different institutes. After discarding maximum errors and performing different preprocessing methods, 1000 images of Bangla sign language isolated digits were included in the first dataset. After collectiong raw data, some effective preprocessing methods were performed for making those data useable for computer vision model and any other application development purposes.
Compressed Multiple Pattern Matching

Given $d$ strings over the alphabet $\{0,1,\ldots,\sigma{-}1\}$, the classical Aho--Corasick data structure allows us to find all $occ$ occurrences of the strings in any text $T$ in $O(|T| + occ)$ time using $O(m\log m)$ bits of space, where $m$ is the number of edges in the trie containing the strings. Fix any constant $\varepsilon \in (0, 2)$. We describe a compressed solution for the problem that, provided $\sigma \le m^\delta$ for a constant $\delta < 1$, works in $O(|T| \frac{1}{\varepsilon} \log\frac{1}{\varepsilon} + occ)$ time, which is $O(|T| + occ)$ since $\varepsilon$ is constant, and occupies $mH_k + 1.443 m + \varepsilon m + O(d\log\frac{m}{d})$ bits of space, for all $0 \le k \le \max\{0,\alpha\log_\sigma m - 2\}$ simultaneously, where $\alpha \in (0,1)$ is an arbitrary constant and $H_k$ is the $k$th-order empirical entropy of the trie. Hence, we reduce the $3.443m$ term in the space bounds of previously best succinct solutions to $(1.443 + \varepsilon)m$, thus solving an open problem posed by Belazzougui. Further, we notice that $L = \log\binom{\sigma (m+1)}{m} - O(\log(\sigma m))$ is a worst-case space lower bound for any solution of the problem and, for $d = o(m)$ and constant $\varepsilon$, our approach allows to achieve $L + \varepsilon m$ bits of space, which gives an evidence that, for $d = o(m)$, the space of our data structure is theoretically optimal up to the $\varepsilon m$ additive term and it is hardly possible to eliminate the term $1.443m$. In addition, we refine the space analysis of previous works by proposing a more appropriate definition for $H_k$. We also simplify the construction for practice adapting the fixed block compression boosting technique, then implement our data structure, and conduct a number of experiments showing that it is comparable to the state of the art in terms of time and is superior in space.
Material Classification Using Color and Texture Features

Classification of texture is a very tedious problem in computer vision and pattern recognition. In this problem, the material is assigned to particular class of texture using its properties. This paper used both color and texture features to improve the recognition performance of Flickr Material Database (FMD). Authors described method of combining Color features (RGB), Luminance and Texture features. Gray-Level Co-occurrence Matrix (GLCM) is used to extract Texture features. The classification using, K-Nearest Neighbors (KNN) classifier is discussed with the experimental results.
The Location Privacy Preserving Scheme Based on Hilbert Curve for Indoor LBS

Location-based service (LBS) brings great benefits to individuals and society, but it also exist serious threat to users’ privacy, because LBS supplier may leak users’ location-related information. It is important to protect users’ privacy while providing LBS. However, current privacy protection and location recommendation service have the problem of timeliness. To address the problem, a location privacy preserving scheme based on Hilbert curve for LBS is proposed. Firstly, a Hilbert curve is generated from given parameters for coordinate transformation to correspond to Hilbert coordinates. Secondly, the points of users are transmitted to the location service provider (LSP) through the randomly generated point of the fog server without using such methods as K anonymous, which satisfy the demand. Then the user’s point of interest (POI) could be obtained by the weighted KNN algorithm of LSP. Finally, the user’s POI would be transmitted back to the client. Simulation results show that the proposed scheme provide location privacy with timeliness.
Real-time structure aware color stippling

In computer graphics, stippling is a widely used non-photorealistic rendering technique. As the art of representing images with dots, one of the key problems is the placement of dots. In general, they should be distributed evenly, and with some randomness at the same time. Blue noise methods provide these characteristics and are used by state-of-the-art gray-scale algorithms to distribute dots. Color stippling, however, is more challenging as each channel should have even distribution at the same time. Existing approaches cast color stippling as a multi-class blue noise sampling problem and provide high quality results at the cost of a very long processing time. In this paper, we propose a real-time structure aware method for color stippling, based on samples generated from an incremental voronoi set. Our method can handle an arbitrary input color vector for stippling and produce significantly better results than previous methods, at real-time frame rate. We evaluate the perceptual quality of our stippling with a user study and its numerical performance by measuring the MSE between the reconstructed image from the stippling and the input image. As a result, the real time performance of our method makes interactive stippling editing possible, providing the artist with an effective tool to explore quickly a wide space of color image stippling.
Weakly-Supervised Spatial Context Networks

We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a representation of one image patch from another image patch, within the same image, conditioned on their real-valued relative spatial offset. Unlike auto-encoders, that aim to encode and reconstruct original image patches, our network aims to encode and reconstruct intermediate representations of the spatially offset patches. As such, the network learns a spatially conditioned contextual representation. By testing performance with various patch selection mechanisms we show that focusing on object-centric patches is important, and that using object proposal as a patch selection mechanism leads to the highest improvement in performance. Further, unlike auto-encoders, context encoders [21], or other forms of unsupervised feature learning, we illustrate that contextual supervision (with pre-trained model initialization) can improve on existing pre-trained model performance. We build our spatial context networks on top of standard VGG_19 and CNN_M architectures and, among other things, show that we can achieve improvements (with no additional explicit supervision) over the original ImageNet pre-trained VGG_19 and CNN_M models in object categorization and detection on VOC2007.
Learning to Quantify

Quantification (also known as "supervised prevalence estimation" [2], or "class prior estimation" [7]) is the task of estimating, given a set σ of unlabelled items and a set of classes C = c1, . . . , c |C| , the relative frequency (or "prevalence") p(ci ) of each class ci C, i.e., the fraction of items in σ that belong to ci . When each item belongs to exactly one class, since 0 ≤ p(ci ) ≤ 1 and Í ci C p(ci ) = 1, p is a distribution of the items in σ across the classes in C (the true distribution), and quantification thus amounts to estimating p (i.e., to computing a predicted distribution p?).
A High-Precision Position Detection Method for Mover Based on CTA

To improve the accuracy and robustness of linear motor mover position detection from the target image, this article constructs an aperiodic sinusoidal stripe image and introduces the chirp transform algorithm (CTA) to achieve a fast, high-precision measurement. First, three kinds of sinusoidal stripe images are generated by controlling the stripe period of the sinusoidal stripe image. Second, the gray-level co-occurrence matrix is used to optimize the aperiodic sinusoidal stripe image with strong robustness. Third, a line-scanning camera is used to scan the image information to acquire the one-dimensional (1-D) signal, thereby improving the real-time performance. Finally, the displacement between the 1-D signals is calculated by the CTA, and the moving distance of the mover can be acquired in accordance with the calibration coefficient of the measurement system. Simulation and experimental results show the effectiveness and feasibility of the mover position measurement method.
Representation learning for heterogeneous network with multiple link attributes

The strategies of learning node representation in network, which try to reduce the dimension of adjacency matrix in network, have been drawn much attention recently. Existing approaches focus on preserving structure property and heterogeneity in a low dimensional space with simplex link. However, in the real world, link between each object containing rich attributes information will lead to treat links as multi-variables. In this paper, we split network into multiple aspects according to multiple link attributes and preserve structural and semantic properties for each aspect simultaneously. Then, we introduce an attention strategy, which is capable of combining each aspect with relevant weight, to extract more informative aspect for nodes. Results of experiment verified with link prediction task on a real world dataset show that our approach is able to capture the most contributory attribute of link, which outperforms the state of the art network representation learning techniques regarding each attribute equally.
New Quantum Codes From Matrix-Product Codes

In this paper, the construction of matrix-product codes with Euclidean dual-containing are studied. The critical issue is to determine the parameters of the matrix-product code and its dual code. Therefore, the necessary conditions for which the matrix-product code is Euclidean dual-containing are given. Consequently, new non-binary quantum codes are derived from those matrix-product codes by q-ary Steane’s construction, and some examples are given for q=4,5.
A Study of Deep Learning Methods for Mitotic Cell Detection Towards Breast Cancer Diagnosis

Advances in digital health records, computing and machine learning have led to the synergistic rise of machine learning techniques being applied to medical imaging tasks such as detection, diagnosis and discovery. Recent advances in computer vision and image processing have been applied to medical imaging yielding vast performance improvements over existing methods. Breast cancer is a leading cause of death among cancer patients in women. Mitotic count in biopsied breast tissue is an important biomarker for predicting breast cancer prognosis as per the Nottingham Grading System. In this work, we survey different deep learning based approaches to detect mitotic cells with the overall aim of assisting pathologists with the diagnosis of breast cancer.
Optimal 2-3 Chains for Scalar Multiplication

Using double-base chains to represent integers, in particular chains with bases 2 and 3, can be beneficial to the efficiency of scalar multiplication. However, finding an optimal 2-3 chain as long been thought to be more expensive than the scalar multiplication itself, complicating the use of 2-3 chains in practical applications where the scalar is used only a few time (as in the Diffie-Hellman key exchange). In the last few years, important progress has been made in obtaining the shortest possible double-base chain for a varying integer n. In 2008, Doche and Habsieger used a binary-tree based approach to get a (relatively close) approximation of the minimal chain. In 2015, Capuñay and Thériault presented the first deterministic polynomial-time algorithm to compute the minimal chain for a scalar, but the complexity of $$O((\log n)^{3+\epsilon })$$ is too high for use with a varying scalars. More recently, Bernstein, Chuengsatiansup, and Lange used a graph-based approach to obtain an algorithm with running time $$O((\log n)^{2.5+\epsilon })$$ . In this work, we adapt the algorithm of Capuñay and Thériault to obtain minimal chains in $$O((\log n)^2 \log \log n)$$ bit operations and $$O((\log n)^2)$$ bits of memory. This allows us to obtain minimal chains for 256-bits integers in the 0.280 ms range, making it useful to reduce scalar multiplication costs randomly-selected scalars. We also show how to extend the result to other types of double-base and triple-base chains (although the complexity for triple-base chains is cubic instead of quadratic). In the case of environments with restricted memory, our algorithm can be adapted to compute the minimal chain in $$O((\log n)^2(\log \log n)^2)$$ bit operations with only $$O(\log n(\log \log n)^2)$$ bits of memory.
EDUCATIONAL INTERVENTION FOR STUDENTS WITH ASD: EMOTION PRODUCTION AND RECOGNITION THROUGH THE BEE-BOT ROBOT

EDUCATIONAL INTERVENTION FOR STUDENTS WITH ASD: EMOTION PRODUCTION AND RECOGNITION THROUGH THE BEE-BOT ROBOT
Bitcoin Wallets and Transactions

In this chapter, you will be diving deep into bitcoin’s core RPC and learn about wallets and transactions. You will learn how to utilize legacy and SegWit’s bitcoin wallets. You will extract a wallet’s public and private keys.
Improved Security Notions for Proxy Re-Encryption to Enforce Access Control

Proxy Re-Encryption (PRE) allows a ciphertext encrypted under Alice’s public key to be transformed to an encryption under Bob’s public key without revealing either the plaintext or the decryption keys. PRE schemes have clear applications to cryptographic access control by allowing outsourced data to be selectively shared to users via re-encryption to appropriate keys. One concern for this application is that the server should not be able to perform unauthorised re-encryptions. We argue that current security notions do not adequately address this concern. We revisit existing definitions for PRE, starting by challenging the concept of unidirectionality, which states that re-encryption tokens from A to B cannot be used to re-encrypt from B to A. We strengthen this definition to reflect realistic scenarios in which adversaries may try to reverse a re-encryption by retaining information about prior ciphertexts and re-encryption tokens. We then strengthen the adversarial model to consider malicious adversaries that may collude with corrupt users and attempt to perform unauthorised re-encryptions; this models a malicious cloud service provider aiming to subvert the re-encryption process to leak sensitive data. Finally we revisit the notion of authenticated encryption for PRE. This currently assumes the same party who created the message also encrypted it, which is not necessarily the case in re-encryption. We thus introduce the notion of ciphertext origin authentication to determine who encrypted the message (initiated a re-encryption) and show how to fufil this requirement in practice.
Method on Human Activity Recognition Based on Convolutional Neural Network

In order to improve the accuracy of human activity recognition based on smart device, we proposed a recognition method based on convolutional neural network. We preprocess the raw acceleration data and input the processed data directly into the convolution neural network to do local feature analysis. After processing, we get the characteristic output result, which can be directly inputted into the SoftMax classifier, which can recognize five activity, such as walking, running, going downstairs, going upstairs and standing. By comparing the experimental results, the recognition rate of different experimenters is 84.8%, which proves that the method is effective.
Adjustable Real-time Style Transfer

Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.
Constructing Boolean Functions Using Blended Representations

In this paper, we study blended representations of Boolean functions, and construct the following two classes of Boolean functions. Two bounds on the r-order nonlinearity were given by Carlet in the IEEE TRANSACTIONS ON INFORMATION THEORY, vol. 54. In general, the second bound is better than the first bound. But it was unknown whether it is always better. Recently, Mesnager et al. constructed a class of Boolean functions where the second bound is strictly worse than the first bound, for r = 2. However, it is still an open problem for r ≥ 3. Using the blended representation, we construct a class of Boolean functions based on the trace function and show that the second bound can also be strictly worse than the first bound, for r = 3. The second class is based on the hidden weighted bit function, which seems to have the best cryptographic properties among all currently known functions.
Reviewing Students’ Vocabulary Mastery By Using Kahoot At Holmesglen Partnering With University of Muhammadiyah Tangerang

The present study investigated the use of Kahoot as the free game-based learning platform which provided the students with ongoing feedback on their learning progresses. Reviewing the students’ vocabulary was the focus of this study. Unlike summative test that tended to have formal assessment, Kahoot provided a learning with engagement and responsive feedback both students and teacher. In term of student’s vocabulary mastery, Kahoot was the learning model of this study. Blended learning was applied technically in this study so that the students’ vocabulary mastery was reviewed and tested through the application of Kahoot accessed by students’ mobile phones. The methodology of this study was based on a questionnaire survey that was conducted to determine the use of Kahoot to the students’ vocabulary mastery. More detail information was also added through observation, field note and interview. The 20 students of UMT (University of Muhammadiyah Tangerang) from Holmesglen English class were the population and samples of this study.&#x0D;
The Creation of One Truth: Single-Ledger Entries for Multiple Stakeholders Using Blockchain Technology to Address the Reconciliation Problem

Blockchain technology has evolved beyond its original uses as a methodology to record cryptocurrency transactions to support various business applications. Over the past few years, it has gained increased attention in the accounting profession. The current paper proposes a new conceptual framework to implement and apply blockchain technology in the accounting profession that is based on a single-ledger model. The paper addresses the reconciliation problem if more than one party is involved in a transaction. It defines the requirements to have an accounting system without reconciliation challenges. Following the paper approach, transaction details are recorded on the blockchain ledger before inserting the transaction details in the transacting parties’ ERP systems. This ledger is a single point of truth that can be viewed directly without the need for creating information silos in the ERP systems of transacting parties, hence addressing the reconciliation challenge. The presented framework creates one truth for all parties involved in a transaction eliminating much of the redundancy involved in current accounting systems.
Entropy global control for a chaos based pRNG

In this paper, we have attempted to resolve an important issue present in a previously proposed cryptographic pseudo-random number generator (pRNG), the issue being that for certain seeds values, the behavior of the system is not random. To solve this problem, he have added a global control system that gathers entropy and periodically alters the current internal state of the generator in order to prevent lack of randomness in the output values. The new system is empirically tested with an established randomness test battery, NIST, as well as through Monte-Carlo simulations. The results show that for almost all of the tested seeds, the output exhibits random properties.