Reviewerly/exHarmony huggingface dataset:
Applications and Developments of the Lattice Attack in Side Channel Attacks

Partial key exposure attacks have become a growing threat to the cryptographic system, as the side channel attacks can usually obtain the partial information easily, which makes the partial key exposure attacks much more practical. The lattice attack is the most common method to process the partial key information to recover the secret key. In this paper, we systematically analyse the developments of the lattice attacks and its use in side channel attacks. First, we divide the lattice attack into three categories based on the lattice construction. Then we investigate the lattice attacks on each algorithms and the way different side channel data is processed by. Finally, we summary the development trend of the lattice attack in side channel attacks.
From Textline to Paragraph: A Promising Practice for Chinese Text Recognition

Although handwritten Chinese text recognition (HCTR) has achieved tremendous progress in the past decades, the traditional document analysis system suffers from two main problems: (1) The annotation of position and transcript at line level is costly to obtain; (2) The framework consists of several separately trained modules, and it’s difficult for the complex system to get satisfying results. Therefore, handwritten paragraph recognition attempts to incorporate the textline segmentation and recognition into a complete network. However, large character set and great insufficient training samples make it troublesome for handwritten Chinese paragraph recognition (HCPR). In this paper, a novel framework is proposed for HCPR. To make the training process faster and more stable, we put forward the Multi-Dimensional LSTM Convolutional Attention (MLCA) recognition framework. A new writing-style-aware image synthesis method is utilized as well to overcome the problem of data insufficiency. We conduct several experiments on the ICDAR-2013 competition dataset and the corresponding corrupted dataset. From the compelling results, we can draw an encouraging conclusion that it would be a promising trend to move from HCTR to HCPR for Chinese document analysis system.
Paying Deep Attention to Both Neighbors and Multiple Tasks

In this paper, we propose a novel deep attention neural network. This network is designed for multi-task learning. The network is composed of multiple dual-attention layers of attention over neighbors and tasks. The neighbor attention layer represents each data point by paying attention over its neighboring data points, i.e., the output of this layer of a data point is the weighted average of its neighbors’ inputs, and the weighting scores are calculated according to the similarity between the data point and its neighbors. The task attention layer takes the output of the neighbor attention layer as input, and transfer it to multiple task-specific representations for an input data point, and uses attention mechanism to calculate the outputs for different tasks. The output of the input data points for a task is calculated by a weighted average over all the task-specific representations, and the weighting scores are based on the similarity between the target task and the other tasks. The outputs of the neighbor attention layer and task attention layer are concatenated as the output of one dual-attention. To train the parameters of the network, we minimize the classification losses and encourage the correlation among different tasks. The experiments over the benchmark of multi-task learning show the advantage of the proposed method.
Attribute-Based Symmetric Searchable Encryption

Symmetric Searchable Encryption (SSE) is an encryption technique that allows users to search directly on their outsourced encrypted data while preserving the privacy of both the files and the queries. Unfortunately, majority of the SSE schemes allows users to either decrypt the whole ciphertext or nothing at all. In this paper, we propose a novel scheme based on traditional symmetric primitives, that allows data owners to bind parts of their ciphertexts with specific policies. Inspired by the concept of Attribute-Based Encryption (ABE) in the public setting, we design a scheme through which users can recover only certain parts of an encrypted document if and only if they retain a set of attributes that satisfy a policy. Our construction satisfies the important notion of forward privacy while at the same time supports the multi-client model by leveraging SGX functionality for the synchronization of users. To prove the correctness of our approach, we provide a detailed simulation-based security analysis coupled with an extensive experimental evaluation that shows the effectiveness of our scheme.
Generative Modelling of 3D In-Silico Spongiosa with Controllable Micro-structural Parameters

Research in vertebral bone micro-structure generally requires costly procedures to obtain physical scans of real bone with a specific pathology under study, since no methods are available yet to generate realistic bone structures in-silico. Here we propose to apply recent advances in generative adversarial networks (GANs) to develop such a method. We adapted style-transfer techniques, which have been largely used in other contexts, in order to transfer style between image pairs while preserving its informational content. In a first step, we trained a volumetric generative model in a progressive manner using a Wasserstein objective and gradient penalty (PWGAN-GP) to create patches of realistic bone structure in-silico. The training set contained 7660 purely spongeous bone samples from twelve human vertebrae (T12 or L1) with isotropic resolution of \(164~\upmu \)m and scanned with a high resolution peripheral quantitative CT (Scanco XCT). After training, we generated new samples with tailored micro-structure properties by optimizing a vector \(\textit{\textbf{z}}\) in the learned latent space. To solve this optimization problem, we formulated a differentiable goal function that leads to valid samples while compromising the appearance (content) with target 3D properties (style). Properties of the learned latent space effectively matched the data distribution. Furthermore, we were able to simulate the resulting bone structure after deterioration or treatment effects of osteoporosis therapies based only on expected changes of micro-structural parameters. Our method allows to generate a virtually infinite number of patches of realistic bone micro-structure, and thereby likely serves for the development of bone-biomarkers and to simulate bone therapies in advance.
Performance Analysis of Under-Sampling and Over-Sampling Techniques for Solving Class Imbalance Problem

Most of the traditional classification algorithms assume their training data to be well-balanced in terms of class distribution. Real-world datasets, however, are imbalanced in nature thus degrade the performance of the traditional classifiers. An imbalance data-set typically make prediction accuracy difficult. Data pre-processing approaches discuss this issue by using random under-sampling or oversampling techniques. To solve this problem, many strategies are adopted to balance the class distribution at the data level. The data level methods balance the imbalance distribution between majority and minority classes using either oversampling or under-sampling techniques. In this paper, we present the performance analysis of under-sampling method and oversampling methods. The methods are implemented with 5 conventional classifiers like C4.5 Decision Tree (DT), k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Naive Bayes (NB) on 15 real life data sets. The experimental results show comparative study of under-sampling and over sampling technique.
Text Generation in Discrete Space

Variational AutoEncoders (VAEs) are applied to many generation tasks while suffering from posterior collapse issue. Vector Quantization (VQ) is recently employed in VAE model on image generation, which could get rid of the posterior collapse problem and show its potentiality for more generation tasks. In this paper, the VQ method is applied to VAE on text generation. We elaborately design the model architecture to mitigate the index collapse issue brought in by VQ process. Experiments show that our text generation model can achieve better reconstruction and generation performance than other VAE based approaches.
Integration of Blockchain into IoT

This chapter introduces the blockchain concepts and their working principles. It covers in detail the notions of public ledgers, immutability, consensus algorithms existing in a blockchain. It also analyses the security flaws in IoT that need to be addressed to make them secure for crucial applications. The risks and challenges in integrating blockchain with IoT have been discussed. It also explains how to perform the feasibility analysis before performing the integration. The performance and security of the resulting integrated application are measured and traded so as to be practically feasible. This chapter focuses on various security limitations of IoT and usage of blockchain to overcome those issues and make them favourable for real-time applications.
Fine-Grained Semantics-Aware Heterogeneous Graph Neural Networks

Designing a graph neural network for heterogeneous graph which contains different types of nodes and links have attracted increasing attention in recent years. Most existing methods leverage meta-paths to capture the rich semantics in heterogeneous graph. However, in some applications, meta-path fails to capture more subtle semantic differences among different pairs of nodes connected by the same meta-path. In this paper, we propose Fine-grained Semantics-aware Graph Neural Networks (FS-GNN) to learn the node representations by preserving both meta-path level and fine-grained semantics in heterogeneous graph. Specifically, we first use multi-layer graph convolutional networks to capture meta-path level semantics via convolution on edge type-specific weighted adjacent matrices. Then we use the learned meta-path level semantics-aware node representations as guidance to capture the fine-grained semantics via the coarse-to-fine grained attention mechanism. Experimental results semi-supervised node classification show that FS-GNN achieves state-of-the-art performance.
An evaluation of the economics research in India during last three decades (1989-2019)

This paper presents a scientometric study of the research publications from the discipline of economics in India. The high-quality publications indexed in Web of Science over the last three decades are considered for the analysis. The main motive behind this study is to reveal the progress made in the discipline by the researchers affiliated with Indian institutions, their research outreach and their top research interests. The publications and citation structures are analyzed first giving the publication distribution over time followed by the general citation structure, top cited journals and the top twenty-five cited publications. Further, author and institutions are analyzed along with a detailed cocitation analysis. The results indicate that the Indian Statistical Institute (ISI) and University of Delhi have brought more significant publications with ISI being the most collaborative institution. The results also show that competition, economic growth, developing country and unemployment are the most significant research topics in the country. Though currently the contribution of India to the global economics research is very less but the trends show that it will substantially increase over the years.
A Comparative Study on Interactive Segmentation Algorithms for Segmentation of Animal Images

Throughout this article, we are researching two distinct algorithms to separate animals from animal pictures. Since animals appear in a very complex background and often surrounded by greenery, segmenting an animal from its background is a very challenging task. Animal segmentation further helps the problem of animal identification and classification. Here the animals are segmented using graph cut and similarity region merging techniques. To determine the efficiency of our process, an analysis is carried out on our own dataset of 50 animal types, comprising 5000 images. The various performance measures such as Information Variation, Global Consistency Error, Probabilistic Rand Index, and Boundary Displacement Error are used for the purpose of assessment.
Complementary dimension reduction

Abstract The goal of supervised dimension reduction (SDR) is to find a compact yet informative representation of the feature vector. Most SDR algorithms are formulated to solve sequential optimization problems with objective functions being linear functions of the L 2 norm of the data, for example, the well‐known Fisher's discriminant analysis (FDA). A drawback of such objective functions is that they favor directions that result in large between‐class distances; however, if the large between‐class distance is mainly from classes that have already been well separated by prior directions, the new direction leads to a negligible improvement over classification accuracy. To address this issue, we introduce an objective function that directly quantifies classification accuracy, and present an efficient algorithm that retrieves directions sequentially from this nonlinear objective function. A key feature of our algorithm is that each sequentially added direction works complementarily with the previous sequentially‐solved directions to boost the discriminative power of the reduced space as a whole. So we name our new algorithm “Complementary Dimension Analysis” (CDA). We have further generalized CDA to retrieve sparse directions that involve only a small fraction of the features. Finally we demonstrate the utility of our algorithms on several simulated and real datasets.
A test space for virtual rotation measurement in MaxWhere

This demo presents a virtual reality test space, which is part of a test battery that is now under development as a part of an aptitude test and skill development project to support human resource assessment. The test space focuses on subjects' ability to both internally visualize and to manipulate the spatial orientation of a 3D cube with specific, configurable parts missing.
Visualization of Evolution of Component-Based Software Architectures in Virtual Reality

The visualization of software architectures by the use of effective and feasible visual metaphors provides an intuitive approach to comprehend the implemented architecture of a software project. In this regard not only the visualization of the latest status of the implemented architecture is important, but also the visualization of the history of software architectures. Such visualizations show dependencies and contexts in which design decisions were made. Resulting information supports programmers to understand systems and to recognize disadvantageous design decisions. A software which is particularly suited for the visualization of component-based software is IslandViz. This software visualizes OSGi-based software architectures in Virtual Reality with an island metaphor, but at this point the history of an architecture is not taken into account. In this paper we present how IslandViz can be extended to visualize the history of software architectures of OSGi-based software projects. For this purpose we use algorithms for dynamic graphs to realize a dynamic positioning of the islands and an adaptable layout of the regions on the islands. The aim is to ensure that the user's orientation in the virtual environment is preserved even if elements of the visualization must adapt due to changes in the software history.
MEASURING THE WEBSITE QUALITY OF ADISUTJIPTO COLLEGE OF TECHNOLOGY BASED ON THREE DIMENSIONS (USABILITY, INFORMATION QUALITY AND SERVICE INTERACTION)

Perceptions about the quality of education are the ability to provide an assessment of the superiority of an educational service that can produce academic and non-academic excellence for students by reffering to input, the process of outcomes and the impact it causes.One of the non-academic service on institutions to users, especially students is a website.One of the vision of educational institutions is to provide service to students electronically through website and be expected that the college website can provide the best possible information to students.This study discusses how to measure the website quality of Adisutjipto College of Tehcnology (STTA) using 3 (three) dimensions; Usability, Information Quality and Service Interaction.This research is a descriptive study with a survey method.The research instrument used a questionnaire.The research subjects were 50 STTA students.The results of the measurement survey which are affected by those three dimensions shows that the quality of the STTA's website is in the "Medium" category.
Cluster Validity Index for Big Data Based on Density Discriminant Analysis

The important factor for clustering unsupervised data is the Cluster Validity Index indicating appropriate number of clusters. The paper proposes the application of the unsupervised density discriminant analysis algorithm for cluster validation in the context of Big Data. In particular, the experiment was conducted to perform clustering tasks on big dataset by using centroid based clustering algorithm and apply unsupervised density discriminant analysis algorithm to find the most appropriate number of clusters. The performance evaluation was performed by means of processing time. The result shows that the time used to perform the clustering task depends on number of features and clusters.
On the cyclic order distribution and partitioning of linear cyclic codes

In this paper, we propose a way to partition any linear cyclic code in its cyclic equivalence classes according to the algebraic structure of the code. Such partition is useful to generate cyclically permutable codes directly from linear cyclic codes. We consider the case where the size of the finite field and the block length of the code are coprimes. Moreover, we present a criterion to construct maximal cyclic order codes.
Privacy-Preserving and Outsourced Density Peaks Clustering Algorithm

With the powerful platform of cloud, it is attractive for an organization with massive data to outsource to a cloud provider. However, the data owner may have concerns about the privacy of its data. In this paper, we propose a privacy-preserving and outsourced density peaks clustering algorithm over encrypted data (PPODP for short) in the cloud, which can achieve efficient density peaks clustering without revealing privacy. However, since the ciphertext distances under homomorphic encryption cannot be compared, we introduce an order-preserving encryption scheme that enables the cloud provider to compare encrypted distances. To guarantee the efficiency of the algorithm, we develop three optimization modules to ensure that PPODP runs efficiently. In addition, these optimization modules can be widely applied to other similar privacy-preserving machine learning algorithms. Finally, formal security analysis and extensive evaluation demonstrate that our PPODP achieves similar performance and efficiency while preserving data privacy compared with density peaks clustering over plaintexts.
Research on Financial Abnormal Transaction Model of Securities Market Based on Big Data Technology

Abstract Under the background of the current big data era, all walks of life in China are carrying out reform and innovation on their operating mechanism, so as to better cope with the challenges brought by the new era. The research of financial abnormal transaction model in China’s securities market also needs to follow the development trend of the times. Compared with the past, great changes have taken place in the research of financial abnormal transaction model in the securities market. In order to meet the challenges brought by the new era to the research of financial abnormal transaction model of securities market, this paper puts forward the application of big data technology to the financial abnormal transaction model of securities market This paper analyzes the research status of financial abnormal transaction model in China’s securities market, and formulates a new research scheme for financial abnormal transaction model of securities market in the era of big data, so as to meet the new needs of the research on financial abnormal transaction model of China’s securities market in the new era Please. Through long-term observation, it can be found that some of the analysis methods proposed in this paper have important practical significance for the research of abnormal trading model of securities market based on big data technology.
Evaluating Performance of Boneh-Shaw Finger Printing Codes under Minority Value Collusion Attacks

Fingerprinting codes are used to protect copyright issues of digital media. The digital media before distribution is embedded with fingerprinting codes. Each intended recipient has a unique fingerprinting code inserted in digital media. If any user makes illegal copies of distribution, then these fingerprinting codes can be considered to identify the culprit user. The threat to fingerprinting codes comes from collusion attacks. Collusion attacks are attacks where, group of users having different finger printing codes of the same content collude together collectively to launch attack against fingerprinting code. Thus collusion attacks are real challenge to fingerprinting codes and thus they pose a challenge to protect the copy right of the digital media. This paper evaluates performance of Boneh-Shaw fingerprinting codes by mounting Minority Value Collusion attacks.
A Novel Hybrid Algorithm Based on Bacterial Foraging Optimization and Grey Wolf Optimizer

A novel hybrid algorithm named GMBFO, with the combination between Grey Wolf Optimizer and the modified Bacterial Foraging Optimization, is presented in the paper. To improve the fixed chemotaxis step size in the standard BFO algorithm, the paper incorporates a nonlinear-decreasing adaptive mechanism into BFO. Besides that, an effective swarm learning strategy with the other three current global best individuals is proposed. In the dispersal and elimination step, we adopt the roulette wheel selection and local mutation mechanism to improve the diversity of the whole bacterial population. To testify the optimization performance of the proposed GMBFO, six benchmark functions with 45 dimensions are selected. Compared with BFO and the other three BFO variants, the GMBFO algorithm has an excellent capability in function optimization.