Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Oracle Database Incremental Data Capture Based on Trigger and Identification Table

Abstract Data synchronization technology is one of the key technologies of data storage in distributed database. At present, it is an effective method to achieve data synchronization between source database and target database by capturing incremental data and transferring incremental files. Based on the research of Oracle database and the analysis of commonly using incremental data capture methods, this paper designed DML trigger and incremental data identification table to store incremental identification data in the identification table, and generated incremental XML files for network transmission according to the identification table. Finally, the feasibility of this method was tested. Tests showed that the method was reliable and stable, and could be applied to data synchronization in distributed databases.
Research on Missile Data Security Based on Blockchain

Abstract Existing aviation missile business data records and storage have the disadvantage of being tampered with easily, being traced difficultly, and other security risks. Aiming at these problems, this paper applies blockchain technology to the data recording and storage scene of aviation missile business, carries on the network structure design, uses the current mainstream encryption algorithm and the encryption technology, constructs the block chain based aviation missile service data recording storage system, and describes the system working principle and how the system protects the data. Security analysis shows that the system can effectively protect the security record and storage of data, and lays a certain foundation for further exploring the application of blockchain in military.
Multifractal vector optical fields

We introduce the concept of multifractal into vector optical fields (VOFs). We propose, design and generate new fractal VOFs-multifractal VOFs (MF-VOFs), in which multifractal structure and VOF act as the lattice and the base, respectively. We generate two kinds of MF-VOFs experimentally and explore their focusing behaviors. We also investigate the self-healing and information recovering abilities of MF-VOFs, comparing with those of single-fractal VOFs (SF-VOFs) when their lattices are composed of the same hierarchy of fractal geometries. The results show that MF-VOFs have better self-healing and information recovering abilities than that of traditional SF-VOFs, meaning that MF-VOFs have better ability to resist the information loss during the focusing and imaging processes. These properties may find potential applications in information transmission, optical communication, and so on.
A Blockchain-Enabled Society

In this talk, I will cover the basic applications of blockchain, in particular for fintech, supply chain and food safety area. I will discuss the blockchain technology behind these applications and will further discuss the use cases in Australia.
Learning Rate Adaptive Kernel Correlation Filter Tracking Algorithm Combined with Multi-feature Fusion

The tracking approaches based on correlation filter have shown their particular importance due to the performance superiority from the perspectives of accuracy and speed. However, some issues of these tracking approaches are still to be addressed, such as target scale variation and occlusion environment. In this paper, we propose a learning rate adaptive kernel correlation filter tracking algorithm combined with multi-feature fusion to address the issues presented above, whereas existing KCF only uses single feature. In the proposed approach, color attributes and histogram of oriented gradient are combined for the subsequent dimensionality reduction by principal components analysis for the precise feature presentation of targets. Besides, we employ average peak-to correlation energy (APCE) in the update of our model learning rate for solving the problem of serious occlusion of the target. Our proposed tracker keeps a good performance for the scale variation by using an accurate scale estimation method. The experimental results on OTB50[1] dataset show that a significant performance improvement is obtained by our approach, compared with other methods based on correlation filter trackers, where the tracking precision is 0.84, success rate is 0.82. At the same time, it has a real-time tracking speed of 40 frame/s.
A Case Study of Writing to Learn to Program: Codebook Implementation and Analysis

Abstract Introductory computer science courses continue to be a barrier to engineers who are pursuing a computing degree pathway. Learning to program requires students to self-monitor and self-assess their programming metacognition. Based on the idea that writing is a visual representation of thinking, Writing-to-learn (WTL) activities are usually short, low-stakes writing assignments that are designed to promote reflection, analysis, synthesis, and deeper understanding of course material. WTL activities can promote metacognition in any discipline. The research discussed in this paper focuses on answering the following two research questions: RQ1) What do source code comments tell us about novice programmers’ organizational and thinking processes while coding? RQ2) How do WTL activities impact novice programmers’ organizational and thinking processes? In this paper, we present a case study that demonstrates how we used WTL techniques and the literate programming paradigm in an introductory programming course. Next, we detail our analysis using three laboratory assignments to describe how we analyzed student submissions to create a qualitative codebook for students’ Visual Organization Strategy and Thinking Processes. Students’ Visual Organization Strategy is a top-level overview of the organizational structure of students’ code and the underlying characteristics such as white-space, commenting patterns, and blocks of code that students use to visually communicate the structure of their code. We identified five Visual Organization Strategies: Block-level, Unitization, Every-line, insufficient and None. Student’s Thinking Processes are a line-by-line determination of the level of programming metacognition and strategic knowledge. Our efforts resulted in six categories of Thinking Processes: Organizational, Reflective, Conceptual, Literal, Insufficient, and None. We conclude the paper with initial classification results for WTL sections, and a description of future applications of our research.
Lightweight Datapath Implementation of ANU Cipher for Resource-Constrained Environments

With the advent of technologies like IoT, the need for lightweight designs and architectures are in focus. The lightweight cryptography is the frontrunner for resolving the issues related to the memory size, power consumption and lesser gate counts while implementing the designs in resource-constrained environments. This paper focuses on lightweight datapath implementation of cipher ANU for environments like IoT. The 8-bit datapath design for ANU cipher is proposed and implemented in this paper for small, tiny 8-bit microcontrollers. The previous datapath implementation of ANU cipher results in higher gate counts and thus consumes more footprint area. The design proposed in this paper is lightweight and suitable for the environments where designs metrics like Gate Counts and power dissipation plays an important role. The paper presents detailed analysis and implementation of 8-bit datapath design of ANU cipher and compares with other existing datapaths designs on various design metrics.
The use of E-olymp Internet Portal in Programming Competitions

E-olymp Internet portal was created to engage the students of higher education institutions and secondary schools to participate in programming contests, as well as to improve the quality of training future specialists in the information technology and programming areas.
A Survey on Network Traffic Identification

With the rapid development of the Internet, the scale of Internet users has been expanding. At the same time, various mobile phone applications have emerged in an endless stream. While providing users with a variety of services, it also leads to the difficulty of user identification. All those things bring new challenges to traffic identification. Based on the importance of Internet traffic identification for Internet management and security, this paper describes the classification methods and problems faced by Internet traffic identification. Moreover, current work about user-related and application-related traffic identification methods is summarized and analyzed. At the end of this article, we will discuss the future research prospects of traffic identification and summarize the content of the article.
Scalable detection of latent patterns across scales -- from nanomagnets to astrophysics

Detection of patterns hidden in data is a core challenge across many disciplines. In this manuscript, we highlight two low-cost tools for the extraction of latent patterns - namely, the latent entropy and the latent dimension measures. Here the aim is to disentangle the effects of predictability and the memory of a system in the presence of unobservable (latent) effects. Our results reveal a drastically-increased sensitivity and performance for a broad range of problems. Disparate and striking examples included are 1) revealing unexplored material inhomogeneities from magneto-optical Kerr effect measurements, 2) new insights into the predictability of the climate phenomenon El Nino, and 3) the analysis of an amateur telescope movie of the Andromeda galaxy exposing features otherwise unobservable without recourse to larger instruments. Last but not least, we show how these measures reveal otherwise invisible bias patterns imposed by popular data compression tools.
Modeling Inter-Category Typicality within a Symbolic Search Framework

This paper addresses category typicality in the context of a category naming task. In contrast to the predominant effort with gradient models, a symbolic search framework is taken. Within this framework, the SCA (Symbolic Concept Acquisition) model demonstrates varying response times as a function of an instance’s intra-category typicality. Here its coverage is expanded to inter-category typicality. A functionally motivated extension for SCA is advanced that pursues search backtracking under ambiguous cases. I explain how the backtracking extension accounts for inter-category typicality effects, and support it with some empirical evidence. I discuss how the effect generalizes to a larger class of symbolic search models.
An Improved Pig Latin Algorithm for Lightweight Cryptography

The security of data transmitted has paramount significance in the exchange and storage of information. Cryptography provides a solution to a lot of real-world data security problems over digital communication channels. While cryptographic algorithms are not the only requirement for providing security, but these are the first step toward providing the required security over data transmission and communication. Cryptography is the science of keeping messages secure from unauthorized recipients. In this paper, we present a new cryptographic algorithm which is an amalgamated version of Pig Latin, a popular argot or jargon and RSA, one of the most popular asymmetric key cryptosystems. The modified Pig Latin involves a layer of encryption using the cryptographic algorithm RSA to provide better security than RSA alone, thereby increasing the security. The practical performance of the algorithm is analyzed in terms of its empirical computational complexity. There are several cryptographic algorithms which are used to secure information in the form of encryption and decryption. The running time of the proposed algorithm is compared with a present cryptographic algorithm AES-128. Because of the simplicity of the algorithm, it can be implemented for messaging applications used in mobile phones and to secure data stored on other small storage devices.
Computational Linguistics: Analysis of The Functional Use of Microsoft Text Word Processor Text Corrector

Computational linguistics is a field of study that lies at the interface between Linguistics and Computer Science.Though, it is an area that lacks the cooperation of both areas of knowledge as well as other areas of the Cognitive Sciences.The field of Computational Linguistics has the posturing of attending to Computation with regard to the treatment of linguistic data, analyzing the approach and the application of the computational components that try to reproduce the natural language phenomenon.The present study aims to show the advance of computational linguistics, its motivations, applications, as well as the relation with the natural language, comparing the form of application of the computation with the linguistic functioning of the language of Portuguese Language.The study proposes a linguistic analysis of the text correctors with the approach of its limitations and inaccuracies compared to the natural language.Some phrases in the mother tongue were selected and inserted into Word as tests to fix possible grammatical errors and to check the current text corrector limitations, for later analysis and collection of results.The results revealed that Microsoft Word language reviser can not correct all Portuguese language errors.This indicates that it is necessary to review the conditions and operations of the Microsfot Word reviser engine.
Looking for Opportunities

Procurement legislation stipulates that information about the goods, services, or works, that tax-funded authorities wish to purchase are made publicly available in a procurement contract notice. However, for businesses wishing to tender for such competitive opportunities, finding relevant procurement contract notices presents a challenging professional search task. In this talk, we will provide an overview of procurement search and then describe the challenges in addressing the related search and recommendation tasks.
FRAME Revisited: An Interpretation View Based on Particle Evolution

FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals. The maximum likelihood estimation (MLE) is applied by default, yet conventionally causes the unstable training energy that wrecks the generated structures, which remains unexplained. In this paper, we provide a new theoretical insight to analyze FRAME, from a perspective of particle physics ascribing the weird phenomenon to KL-vanishing issue. In order to stabilize the energy dissipation, we propose an alternative Wasserstein distance in discrete time based on the conclusion that the Jordan-Kinderlehrer-Otto (JKO) discrete flow approximates KL discrete flow when the time step size tends to 0. Besides, this metric can still maintain the model’s statistical consistency. Quantitative and qualitative experiments have been respectively conducted on several widely used datasets. The empirical studies have evidenced the effectiveness and superiority of our method.
Cloud Migration- Risks and Solutions

A real IT revolution, cloud computing offers businesses the opportunity to save money and simplify their IT infrastructure, while increasing productivity. The migration to the cloud remains complex; we must choose an efficient and secure solution adapted to the business strategy. This paper gives and overview on Cloud Computing and its various compositions. The paper also describes the advantages and disadvantages of migration to cloud and discusses the security challenges and risks of Cloud. Finally, the paper presents solutions to reduce risks and How to reduce risks and to develop security in Cloud.
Quantum Inspired High Dimensional Conceptual Space as KID Model for Elderly Assistance

In this paper, we propose a cognitive system that acquires knowledge on elderly daily activities to ensure their wellness in a smart home using a Knowledge-Information-Data (KID) model. The novel cognitive framework called high dimensional conceptual space is proposed and used as KID model. This KID model is built using geometrical framework of conceptual spaces and formal concept analysis (FCA) to overcome imprecise concept notation of conceptual space with the help of topology based FCA. By doing so, conceptual space can be represented using Hilbert space. This high dimensional conceptual space is quantum inspired in terms of its concept representation. The knowledge learnt by the KID model recognizes the daily activities of the elderly. Consequently, the model identifies the scenario on which the wellness of the elderly has to be ensured.
Lüders Channels and SIC Existence

The Luders rule provides a way to define a quantum channel given a quantum measurement. Using this construction, we establish an if-and-only-if condition for the existence of a $d$-dimensional Symmetric Informationally Complete quantum measurement (a SIC) in terms of a particular depolarizing channel. Moreover, the channel in question satisfies two entropic optimality criteria.
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as $m$ increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i.e., MMI-ALI for $m$-domain joint distribution matching. As an $m$-domain ensemble model of ALIs \cite{dumoulin2016adversarially}, MMI-ALI is adversarially trained with maximizing Multivariate Mutual Information (MMI) w.r.t. joint variables of each pair of domains and their shared feature. The negative MMIs are upper bounded by a series of feasible losses that provably lead to matching $m$-domain joint distributions. MMI-ALI linearly scales as $m$ increases and thus, strikes a right balance between efficacy and scalability. We evaluate MMI-ALI in diverse challenging $m$-domain scenarios and verify its superiority.
Key Establishment à la Merkle in a Quantum World

In 1974, Ralph Merkle proposed the first unclassified protocol for secure communications over insecure channels. When legitimate communicating parties are willing to spend an amount of computational effort proportional to some parameter N, an eavesdropper cannot break into their communication without spending a time proportional to $$N^2$$ , which is quadratically more than the legitimate effort. In a quantum world, however, Merkle’s protocol is immediately broken by Grover’s algorithm, but it is easily repaired if we are satisfied with a quantum protocol against which a quantum adversary needs to spend a time proportional to $$N^{3/2}$$ in order to break it. Can we do better? We give two new key establishment protocols in the spirit of Merkle’s. The first one, which requires the legitimate parties to have access to a quantum computer, resists any quantum adversary who is not willing to make an effort at least proportional to $$N^{5/3}$$ , except with vanishing probability. Our second protocol is purely classical, yet it requires any quantum adversary to work asymptotically harder than the legitimate parties, again except with vanishing probability. In either case, security is proved for a typical run of the protocols: the probabilities are taken over the random (or quantum) choices made by the legitimate participants in order to establish their key as well as over the random (or quantum) choices made by the adversary who is trying to be privy to it.
A fuzzy decision aid for evaluating agile project management performance indicators

This work introduces a multi-criteria decision approach, named as fuzzy cognitive map (FCM), which represents experts’ knowledge and human experience. In this study, FCM is apt to assess performance indicators of agile project management in banking sector by introducing concepts to define criteria and revealing causality links among the concepts for modeling a system’s behavior. The resulting values of performance indicators are determined by applying the iterative formulation of FCM.
Sample Compression for Real-Valued Learners

We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016). In extending this technique to real-valued hypotheses, we also obtain an efficient regression-to-bounded sample compression converter. To our knowledge, this is the first general compressed regression result (regardless of efficiency or boundedness) guaranteeing uniform approximate reconstruction. Along the way, we develop a generic procedure for constructing weak real-valued learners out of abstract regressors; this may be of independent interest. In particular, this result sheds new light on an open question of H. Simon (1997). We show applications to two regression problems: learning Lipschitz and bounded-variation functions.
Machine Learning Algorithms for Automatic Anomalies Detection in Data Storage Systems Operation

Data storage reliability and availability play important role for a wide range of services and business processes. Manufacturers provide data storage systems that resistant to hardware and software failures but not for all cases. Well-timed detection of these failures help to recover the system faster and prevent the failures before they occur. In this work a range of machine learning and time series analysis algorithms for failures detection is considered. The algorithms are applied and compared on the real data storage system. Preliminary results show that binary classification methods demonstrate high failure detection and low false alarm rates. Time series prediction based approach shows similar results and outperforms one-class classification methods.
Minimum Volume Topic Modeling

We propose a new topic modeling procedure that takes advantage of the fact that the Latent Dirichlet Allocation (LDA) log-likelihood function is asymptotically equivalent to the logarithm of the volume of the topic simplex. This allows topic modeling to be reformulated as finding the probability simplex that minimizes its volume and encloses the documents that are represented as distributions over words. A convex relaxation of the minimum volume topic model optimization is proposed, and it is shown that the relaxed problem has the same global minimum as the original problem under the separability assumption and the sufficiently scattered assumption introduced by Arora et al. (2013) and Huang et al. (2016). A locally convergent alternating direction method of multipliers (ADMM) approach is introduced for solving the relaxed minimum volume problem. Numerical experiments illustrate the benefits of our approach in terms of computation time and topic recovery performance.
Toward postquantum security for embedded cores

Firmware authenticity is a key factor in the proper function and security assurance of a platform built with embedded microcontroller cores. With the need for PQC-secure cryptographic algorithms to continue to assure this authenticity, Intel is investigating possibilities currently under consideration in various standardization processes. Our priority in this selection is and will always be security. Regarding performance, we are specifically focusing on the capabilities of platforms' embedded cores to execute PQC algorithms and looking for algorithms that have parameters allowing flexibility in fitting their execution to the capabilities of both current and planned embedded cores.
Taxation of digital assets

in this article, the authors identified the problem of the gap in the legal regulation of taxation in the turnover of digital assets, justified the importance and the need to optimize the fiscal public-legal support of these relations and suggested effective measures to eliminate this lack of the legal regulation mechanism.At present, such new categories as digital assets and digital resources are widespread; the theory develops the issues of designing the mechanism of legal regulation of relations in the sphere of their creation, distribution, consumption, alienation (turnover).One of the obligatory segments