Reviewerly/exHarmony huggingface dataset:
Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.
Breast Cancer Predictive Analytics Using Supervised Machine Learning Techniques

Breast cancer unarguably has been the very prominent disease amongst women as well as the next most dangerous after lung cancer.Early diagnosis and prevention is of paramount importance.Several methods such as micro-array analysis and network analysis have been proffered but they are somewhat expensive and time consuming.There is a need to develop an automated system based on Machine learning techniques to detect breast cancer early.Benign and Malignant tumors were classified using Logistic Regression (LRO), Bayes Network (BNK), Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Naive Bayes (NBS) and Instance Based Learner (IBK) algorithms, which were implemented in Waikato Environment for Knowledge Analysis (WEKA).The breast cancer database for this study was collected from the University of Wisconsin Hospitals, published on California College, Irvive (UCI) website.The five most critical performance metrics when selecting an algorithm in model building in the health related domain are Area Under the ROC Curve (AUC), Receiver Operating Characteristics Curve (ROC), Mean Absolute Error (MAE), Accuracy and Kappa Statistic.In relation to the results of Accuracy, Precision and Kappa Statistic which were evaluated and compared, BNK has best predictive accuracy of 97.14%, followed by SMO with 96.71%, then LOR with 96.57%.On the other hand, LOR has the highest AUC of 99.3%, followed by BNK with 99.2%, then SMO with 96.5%.Beyond accuracy, AUC should be keenly considered in algorithm selection and model building.Therefore, Logistic Regression should be chosen as the best classifier instead of Bayes network for breast cancer optimal prediction.
A Blockchain Based Searchable Encryption Scheme for Multiple Cloud Storage

Searchable encryption is a necessary and important service for cloud storage since it can realize the function of information retrieval on encrypted cloud data while protecting privacy. However, the typical searchable encryption models consisting of only a single cloud service provider can not prevent malicious behavior. In this paper, a new searchable encryption scheme in multi-cloud was proposed based on blockchain. We first define a system model based blockchain in multiple clouds and combine multiple cloud service providers together to store data together through a consortium chain. And then we store the encrypted document and document index in the IPFS, at the same time the hash value of the documents and the IPFS address of the document are stored in blockchains. Our scheme can provide a sorting retrieval scheme based on multi-keywords over the outsourced encrypted data and a validation scheme to detect file integrity. The theoretical analysis and experiments on real-world data show the security and high performance of our scheme.
Improved Algorithms for Edit Distance and LCS: Beyond Worst Case

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Improved Algorithms for Edit Distance and LCS: Beyond Worst CaseMahdi Boroujeni, Masoud Seddighin, and Saeed SeddighinMahdi Boroujeni, Masoud Seddighin, and Saeed Seddighinpp.1601 - 1620Chapter DOI:https://doi.org/10.1137/1.9781611975994.99PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Edit distance and longest common subsequence are among the most fundamental problems in combinatorial optimization. Recent developments have proven strong lower bounds against subquadratic time solutions for both problems. Moreover, the best approximation factors for subquadratic time solutions have been limited to 3 for edit distance and super constant for longest common subsequence. Improved approximation algorithms for these problems1 are some of the biggest open questions in combinatorial optimization. In this work, we present improved algorithms for both edit distance and longest common subsequence. The running times are truly subquadratic, though we obtain 1 + o(1) approximate solutions for both problems if the input satisfies a mild condition. In this setting, first, an adversary chooses one of the input strings. Next, this string is perturbed by a random procedure, and then the adversary chooses the second string after observing the perturbed one. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Framework and algorithms for identifying honest blocks in blockchain

Blockchain technology gains more and more attention in the past decades and has been applied in many areas. The main bottleneck for the development and application of blockchain is its limited scalability. Blockchain with directed acyclic graph structure (BlockDAG) is proposed in order to alleviate the scalability problem. One of the key technical problems in BlockDAG is the identification of honest blocks which are very important for establishing a stable and invulnerable total order of all the blocks. The stability and security of BlockDAG largely depends on the precision of honest block identification. This paper presents a novel universal framework based on graph theory, called MaxCord, for identifying the honest blocks in BlockDAG. By introducing the concept of discord, the honest block identification is modelled as a generalized maximum independent set problem. Several algorithms are developed, including exact, greedy and iterative filtering algorithms. The extensive comparisons between proposed algorithms and the existing method were conducted on the simulated BlockDAG data to show that the proposed iterative filtering algorithm identifies the honest blocks both efficiently and effectively. The proposed MaxCord framework and algorithms can set the solid foundation for the BlockDAG technology.
Timescales of Boolean satisfiability solver using continuous-time dynamical system

Various physical systems for solving combinatorial optimization problems have been proposed, and among them, the dynamical system recently proposed in [Ercsey-Ravasz & Toroczkai, 2011] to solve the satisfiability problem is also expected to perform well in a physical realization. This system improves the assignment by the gradient descent with respect to a target function, which is also changing in time. These two parts have their own timescales, and their balance can be considered to play an important role in finding a solution. In this paper, we develop a variant of the system, where the balance is explicitly represented by a parameter. Using the developed system, we propose a natural time measure for evaluation and show that with an appropriate choice of the relative timescale we can maximize the performance with respect to the proposed measure.
A novel 4D chaotic system with nonhyperbolic hyperbolic shape equilibrium points: Analysis, circuit implementation and color image encryption

This paper presents a new 4D chaotic system by extending a 3D system to investigate chaotic systems 2 with hidden attractors further and explore their engineering applications in color image encryption. The system has hyperbolic shape equilibriums and the equilibriums are nonhyperbolic which is interesting and rarely mentioned before. The system has been investigated deeply by phase plot, time series, the largest Lyapunov exponent, bifurcation diagram, Poincaré map and 0–1 test. A coexisting hidden attractor has been found in this new system and the circuit simulation model of the system is built by Power Simulation (PSIM). Circuit simulation results are consistent with results of numerical simulations which verify its dynamic behavior further. Then, by combining the generalized Arnold transform with the new chaotic system, a new color image encryption algorithm has been proposed which has large key space and can encrypt either square images or nonsquare images with different size. Finally, the results of encryption experiment and security analysis prove the effectiveness of the algorithm.
A New Bitcoin Address Association Method Using a Two-Level Learner Model

Users in the Bitcoin system adopt a pseudonym-Bitcoin address as the transaction account, making Bitcoin address correlation analysis a challenging task. Under this circumstance, this paper provides a new Bitcoin address association scheme which makes address tracing possible in Bitcoin systems. The proposed scheme can be used to warn relevant institutions to study more secure encryption algorithms to protect users’ privacy. Specifically, the important features of a Bitcoin address are extracted. After that, to reduce the computational complexity, we transform the clustering problem of addresses into a binary classification problem in which we integrate the features of two Bitcoin addresses. A novel two-level learner model is then built to analyze if the two Bitcoin addresses are belonging to the same user. Finally we cluster the addresses belonging to the same user accordingly. Extensive experimental results show that the proposed method outperforms the other address association schemes, which use deep learning models or heuristics, and can achieve an increase by 6%–20% in precision and by 10% improvement in recall.
ARTIFICIAL INTELLIGENCE TO MASTER ENGLISH LISTENING SKILLS FOR NON-ENGLISH MAJOR STUDENTS

Technology plays an important role in people’s day-to-day life and various aspects of life including education also use technology. Artificial Intelligence is regarded as an example of the advancement of technology. The purpose of this study is to investigate students’ perceptions of using Artificial Intelligence mobile applications to improve English listening skills. It was under phenomenological qualitative research characterized by an interdisciplinary approach, mainly education and technology. The online interview was used to gain information from students’ on the specific theme of artificial intelligence mobile applications in improving English listening skills. The survey consists of seven questions which become category such as 1. The Knowledge of Artificial Intelligence; 2. The Knowledge of Tune In, Joox Music, VOA Learning English Listening every day, and Netflix; 3. The Use of the Applications; 4. The Reason to Use the Applications; 5. The Use of Applications in Improving English listening Skills; 6. The Influence of the Applications toward The Users; 7. The Most Effective and Efficient Applications to Improve English Listening Skills. This survey was conducted at the Faculty of Psychology in Universitas Sarjanawiyata took part in this study. Participants were three females and two males. The results showed that the most effective and efficient Artificial Intelligence mobile application to improve English listening skills is Netflix.
Approximately counting and sampling small witnesses using a colourful decision oracle

In this paper, we prove black box results for turning algorithms which decide whether or not a witness exists into algorithms to approximately count the number of witnesses, or to sample from the set of witnesses approximately uniformly, with essentially the same running time. We do so by extending the framework of Dell and Lapinskas (STOC 2018), which covers decision problems that can be expressed as edge detection in bipartite graphs given limited oracle access; our framework covers problems which can be expressed as edge detection in arbitrary k-hypergraphs given limited oracle access. (Simulating this oracle generally corresponds to invoking a decision algorithm.) This includes many key problems in both the fine-grained setting (such as k-SUM, k-OV and weighted k-Clique) and the parameterised setting (such as induced subgraphs of size k or weight-k solutions to CSPs). From an algorithmic standpoint, our results will make the development of new approximate counting algorithms substantially easier; indeed, it already yields a new state-of-the-art algorithm for approximately counting graph motifs, improving on Jerrum and Meeks (JCSS 2015) unless the input graph is very dense and the desired motif very small. Our k-hypergraph reduction framework generalises and strengthens results in the graph oracle literature due to Beame et al. (ITCS 2018) and Bhattacharya et al. (CoRR abs/1808.00691).
Software bug count prediction via AdaBoost.R-ET

Finding the bug count in software modules in order to utilized limited testing resources that could help the testers is a challenging task. Mostly the regression methods are used for finding the bug count. AdaBoost is the one of machine learning technique which has been used for both classification and regression problems. However, it cannot perform better if the weak learner cannot accomplish at least 50% accuracy when running on the skewed dataset. Extra Tree regression is computationally effective and delivers excellent prediction performance. In this paper, we present a new approach, AdaBoost.R-ET algorithm to predict the bug count in a software module. We used the extra tree regression as a weak learner of AdaBoost regression to provide a significant improvement over the model. An experimental study is performed on five projects from the PROMISE repository consisting of 15 different versions. To estimate the performance of the AdaBoost.R-ET algorithm, we compare this algorithm with varying models of AdaBoost. The outcomes show that the proposed model is better than the different variants of AdaBoost models.
Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities

We extend the core operation of non-parametric belief propagation (NBP), also known as multi-scale sequential Gibbs sampling, to approximate products of kernel density estimated beliefs that reside on some manifold. The original algorithm, though multidimensional, implicitly assumes the beliefs to reside on the Euclidean ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> space only. The proposed extension generalizes to any mixture of Riemannian manifolds, provided the primary operations - addition and subtraction- are defined. Our motivation is primarily focused on state-estimation using non-Gaussian factor graphs for multimodal simultaneous localization and mapping in robotics. The paper presents the method as well as simulation and experimental results for validation. Our implementation is publicly available and allows for expansion with user-defined manifold mixtures.
Traditional Even Child Rearing in Nomadic Conditions

This article deals with the problem of indigenous peoples’ child-rearing practices based on their traditional way of life, language and authentic ethnic culture against the background of a strong national revival of the peoples of the Russian Federation. It shows that inclement natural and climatic conditions and the vulnerable traditional way of life of indigenous peoples of the North have necessitated creation of a special national policy towards their sustainable development, which would provide for an action framework to preserve their indigenous culture, traditional way of life, and primordial living environment. In particular, it resolves the essential contradiction between the existing unified system of school education and the relevance of variable organization of traditional education for children of the indigenous peoples of the North in the nomad camp, based on their ethnic, psychological, and physiological features of development. A model for traditional Even child rearing in the nomad camp has been developed and tested as a new form of organizing children’s life during summer holidays in the North. Special aspects of educational environment for Northern children in the nomad camp have been substantiated based on a curriculum with an ethnic focus aiming to form their identity as native speakers of the language and bearers of the culture of their people and as members of the northern ethnic group. The effectiveness of ethnocultural education of Even children in the nomad camp has been experimentally proved through an ethno-pedagogical theory and practice in specific natural conditions of the North.
FashionCapsNet: Kapsül Ağları ile Kıyafet Sınıflandırma

Convolutional Neural Networks (CNNs) are one of the most commonly used architectures for image-related deep learning studies. Despite its popularity, CNNs have some intrinsic limitations such as losing some of the spatial information and not being robust to affine transformations due to pooling operations. On the other hand, Capsule Networks are composed of groups of neurons, and with the help of its novel routing algorithms, they have the capability for learning high dimensional pose configuration of the objects as well. In this study, we investigate the performance of brand-new Capsule Networks using dynamic routing algorithm on the clothing classification task. To achieve this, we propose 4-layer stacked-convolutional Capsule Network architecture (FashionCapsNet), and train this model on DeepFashion dataset that contains 290k clothing images over 46 different categories. Thereafter, we compare the category classification results of our proposed design and the other state-of-the-art CNN-based methods trained on DeepFashion dataset. As a result of the experimental study, FashionCapsNet achieves 83.81% top-3 accuracy, and 89.83% top-5 accuracy on the clothing classification. Based upon these figures, FashionCapsNet clearly outperforms the earlier methods that neglect pose configuration, and has comparable performance to the baseline study that utilizes an additional landmark information to recover pose configuration. Finally, in the future, proposed FashionCapsNet may inherit extra performance boost on the clothing classification due to advances in the relatively new Capsule Network research.
Application of Business Intelligence for Customs Declaration: A Case Study in Indonesia

Abstract The customs declaration made by the self-assessment needs to be re-examined by the document inspector. Customs declaration may have potential errors to determine even intentionally or not. However, the re-examination by the document inspector has not been optimal. This study uses business intelligence to address this problem by providing analysis capabilities for document inspectors. This research focuses on developing a data warehouse with the Kimball methodology. The results of research in the form of data warehouse design. The data warehouse will be used later for dashboards, OLAP, and data mining in order to detect fraud. The data mining algorithms used are firmness tree, support vector machine, neural network, and several ensemble methods. The results of the data mining process show that SMOTE has a significantly increased sensitivity score than non-SMOTE technique.
Blockchain-based Electronic Voting System with Special Ballot and Block Structures that Complies with Indonesian Principle of Voting

Blockchain technology could be implemented not only in digital currency, but also in other fields.One such implementation is in democratic life, namely voting.This research focuses on designing a blockchain-based electronic voting system for medium to large-scale usage that complies with law, specifically voting principles in Indonesia.In this research, we proposed the following: a ballot design as block transaction employing UUID version 4, a modified block structure using SHA3-256 hash algorithm, and a voting protocol.The minimum length of a ballot is 43 bytes (excluding ECDSA signature) if one character is used as candidate's identifier and timestamp is stored as integer.We built a simulation program using Pythonbased Django web framework to cast 10,000 votes and mine them into blocks.Tampered transactions in each block could be detected and restored by synchronizing data with another node.We also evaluated the proposed system.By using this system, voters can exercise voting principles in Indonesia: direct, public, free, confidential, honest, and fair.
Enhancement of Missing Face Prediction Algorithm with Kalman Filter and DCF-CSR

Detection and tracking of moving objects in sequence videos has wide applications in security surveillance, and becomes concern to many researchers. In actual environmental conditions, with various lighting conditions, object tracking faces a number of challenges including partial or severe occlusion which causes some systems to lose information so that it is difficult to estimate object trajectory. In the domain of surveillance, human tracking should not only be based on face, but also based on other characteristics, so that wherever the person facing towards, the system is always able to do the tracking correctly. In this study the detection and alignment process employed Multi-task Cascaded Convolutional Networks and Kalman Filters to predict facial position. Then, at the times the face is not facing towards the camera, the system saves the color of the bounding box that was last seen and tracks by color using the Discriminative Correlation Filter with Channel and Spatial Reliability (DCF-CSR). The proposed method resulting in increasing a person's detection rate when facing away from the camera.
An investigation on support vector clustering for big data in quantum paradigm

The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible run-time improvements. We have developed and analyzed a quantum version of the support vector clustering algorithm. The proposed approach is based on the quantum support vector machine and quantum kernels (i.e., Gaussian and polynomial). The proposed quantum version of the SVM clustering method demonstrates a significant speed-up gain on the overall run-time complexity as compared to the classical counterpart.