Reviewerly/exHarmony huggingface dataset:
Research and Practice of Big Data Analysis Process Based on Hadoop Framework

With the rapid development of Internet, Internet of Things (IOT) and cloud computing technology, human society has entered the era of big data. How to mine effective information from massive data, assist decision-making, and realize the value of large data? Big data analysis plays a key role. Based on the analysis of Hadoop's ecological architecture, the basic process of large data analysis based on Hadoop was presented in this study, and the technical recommendation for off-line and real-time typical application scenarios was proposed. Finally, Hadoop was used to build the application environment, and WordCount case was combined to analyze Map and Reduce calculation process, in order to provide some reference value for the construction of big data platform and big data analysis and processing.
Keynote - Eye Itself as a Camera

Ayinography is the use of the eye itself as a camera. By recording brainwaves, we read the "mind's eye''. Wearable ayinographic technology represents a new opportunity to solve an old problem: the lack of integrity inherent in the world around us. We live in a world of half-truths where incomplete evidence is collected about almost every facet of our lives. Wearable computing can and should provide the answer that allows us to capture and tell our own story, in a way that builds trust and integrity in service of truth, and ultimately, service of humanity through extended-reality intelligence (Human-in-the-loop AI). More generally we advocate whole truth through a full panoramic 360° wearable vision system (Teth Vision™), and a complete understanding of fundamental physics through Integral Kinematics.
New Forms of Defining the Hidden Discrete Logarithm Problem

There are introduced novel variants of defining the discrete logarithm problem in a hidden group, which represents interest for constructing post-quantum cryptographic protocols and algorithms. This problem is formulated over finite associative algebras with non-commutative multiplication operation. In the known variant this problem, called congruent logarithm, is formulated as superposition of exponentiation operation and automorphic mapping of the algebra that is a finite non-commutative ring. Earlier it has been shown that congruent logarithm problem defined in the finite quaternion algebra can be reduced to discrete logarithm in the finite field that is an extension of the field over which the quaternion algebra is defined. Therefore further investigations of the congruent logarithm problem as primitive of the post-quantum cryptoschemes should be carried out in direction of finding new its carriers. The present paper introduces novel associative algebras possessing significantly different properties than quaternion algebra, in particular they contain no global unit. This difference had demanded a new definition of the discrete logarithm problem in a hidden group, which is different from the congruent logarithm. There are proposed several variants of such definition, in which it is used the notion of the local unite. There are considered right, left, and bi-side local unites. Two general methods for constructing the finite associative algebras with non-commutative multiplication operation are proposed. The first method relates to defining the algebras having dimension value equal to a natural number m &gt; 1, and the second one relates to defining the algebras having arbitrary even dimensions. For the first time the digital signature algorithms based on computational difficulty of the discrete logarithm problem in a hidden group have been proposed.
The Impact of Social Media Marketing on Consumer Purchase Intention: Consumer Survey in Saudi Arabia

The main purpose of the current research was to find out the impact of social media marketing on the purchase intention of the customers.The study was conducted in Saudi Arabia.It is a quantitative research based primary data collected through structured close ended questionnaire, distributed online.Results were drawn based on 121 responses using factor analysis, ANOVA and regression analysis.The results showed that 92.4% of the customer purchase intention has been explained by Social Media Marketing.The results are statistically significant.
The Use of Think Bingo Game to Improve Students’ Vocabulary Mastery

Vocabulary is one of the keys to learn a language especially English because knowing sufficient vocabulary will affect the four skills that must be mastered by the students. A good teacher must know strategies to improve the students' vocabulary and one of the them is Bingo game.  The aim of this research was to find out whether the use of Bingo game can improve students’ vocabulary at SMPN 16 Banda Aceh or not.  In this research, the researcher took 20 students from VIII-3 class as the sample. The research design of this research was one group pretest and posttest design.  To collect the data, the researcher used a vocabulary test which containing 20 questions on pretest and posttest. The calculation result of this research shows that the t score = 6,18 > t table = 1.72 with the level significance 0.05 and the degree of freedom (df) 19. Therefore, (Ho) is rejected and (Ha) is accepted. In line with this, it can be concluded that Bingo game can improve students’ vocabulary mastery and Bingo game can be recommended as one of the great strategies in teaching vocabulary to students.
A Critical Security Analysis of the Password-Based Authentication Honeywords System Under Code-Corruption Attack

Password-based authentication is a widespread method to access into systems, thus password files are a valuable resource often target of attacks. To detect when a password file has been stolen, Juels and Rivest introduced the Honeywords System in 2013. The core idea is to store the password with a list of decoy words that are “indistinguishable” from the password, called honeywords. An adversary that obtains the password file and, by dictionary attack, retrieves the honeywords can only guess the password when attempting to log in: but any incorrect guess will set off an alarm, warning that file has been compromised. In a recent conference paper, we studied the security of the Honeywords System in a scenario where the intruder also manages to corrupt the server’s code (with certain limiting assumptions); we proposed an authentication protocol and proved it secure despite the corruption. In this extended journal version, we detail the analysis and we extend it, under the same attacker model, to the other two protocols of the original Honeywords System, the setup and change of password. We formally verify the security of both of them; further, we discuss that our design suggests a completely new approach that diverges from the original idea of the Honeywords System but indicates an alternative way to authenticate users which is robust to server’s code-corruption.
Exponential Separations in Local Differential Privacy Through Communication Complexity.

We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity. In particular, we 1) use a communication lower bound for the hidden layers problem to prove an exponential sample complexity separation between sequentially and fully interactive locally private protocols, and 2) use a communication lower bound for the pointer chasing problem to prove an exponential sample complexity separation between $k$ round and $k+1$ round sequentially interactive locally private protocols, for every $k$.
An Intuitionistic Fuzzy Cognitive Map Approach to Evaluate Success Factors of Lean Six Sigma Project Management Methodology

Lean project management is to integrate the principles of lean manufacturing into the project management processes. Besides, six sigma methodology aims to solve different business issues by constructing disciplined and structured procedures. Lean six sigma is considered as a business improvement tool that integrates two distinctive disciplines named as lean and six sigma complementing each other for improving business processes of organizations. Lean six sigma project management methodology refers to the combination of the waste reduction principles of lean and quality improvement objectives of six sigma. This work introduces an intuitionistic fuzzy cognitive map (IFCM) technique to assess the importance degrees of success factors of leans six sigma project management. The presence of causal links among evaluation criteria; uncertainty, vagueness, and hesitation in data lead to employ an intuitionistic decision aid approach to evaluate the success factors of lean six sigma project management. The application is illustrated via a case study which is conducted in a bank that performs in Turkish banking sector.
Making Sense of Complex Applications: Constructive Design, Features, and Questions

We highlight how concepts of constructive design help in the comprehension of complex systems, using the history and evolution of the Online Conference Service (OCS) and its product line, including the Online Journal Service for journal management, as examples for the needs and solutions of how to master design of systems with complex behaviour. They nicely summarize over 20 years of evolution of one of the most exciting and long lived joint research streams with Bernhard Steffen and our research group. The constructive design concepts we found most useful include the use of features to make large and complex systems more manageable, properties to formulate behavioural requirements on the models’ functionality as well as policies and access rights, and the role of questions as model checking problems as well as test-driven exploration.
Survey and Guidelines for the Design and Deployment of a Cyber Security Label for SMEs

Cyber Security risks and attacks are on the rise, especially at the light of the recent events in the geopolitical landscape. Cyber attacks are not longer targeting big organisations such as governments, institutions or global companies. Smaller businesses and even citizens are now also being hit by cyber attacks, either directly or as a result of side effects. At the same time, the regulation and legislative pressure to prevent cyber attacks is increasing, especially in Europe. In order to protect Small and Medium Enterprises (SMEs), different labels, specific standards or practical guidelines are being developed. This papers makes a comparative survey of such initiatives with the aim to initiate such an approach in Belgium in a consistent way with other existing approaches and also to enable longer term convergence with a possible European scheme. Our goal is to reach enough SMEs with a basic level of cyber security and engage them in continuous improvement to keep a sustainable but efficient level of security. At a more practical level, we report about how to set up the overall organisational structures, basic management processes and some supporting tools.
Adaptive patch feature matching and scale estimation for visual object tracking

In computer vision, the object tracking technique is a complex task to predict the object target under challenging task occlusion, scaling, and illumination. A robust object tracking algorithm is proposed based on adaptive patch feature matching and scale estimation. Initially, tracking technique extracts geometric locations using defined directional distances and object location of the previous frame. The feature matching is carried out between the previous frame featured vector and overlapping patches feature vectors of the present frame. The matched patch is bounded by the identified location in the current frame. For object scaling, speeded up robust features point matching technique is applied to an enlarged patch of the present frame and the tracked patch of the previous frame. Finally, the proposed algorithm updates motion, feature, and geometric location vectors to keep tracking between successive frames. The feature vector update between successive frames solves the aforementioned challenging issues in object tracking. For experimentation, the proposed algorithm is tested with available tracking benchmarks. The performance evaluation results show that the proposed technique is better in terms of computational time and accuracy compared to conventional tracking techniques.
A Bayesian Information Criterion for Unsupervised Learning Based on an Objective Prior

Data processing techniques, such as mathematical formulas, statistical methods and machine learning algorithms, require a set of tools for evaluating knowledge extracted from data. In unsupervised learning it is impossible to use referential or predictive estimation. Therefore, the only reliable way to evaluate results of unsupervised learning is information estimation. Unfortunately, information estimation suffer from underfitting and overfitting. We propose a new method for evaluating unsupervised learning results, which is based on the Bayesian criterion for optimal decision and an objective prior probability distribution of partitions. We illustrate the proposed method application on Fisher’s iris data set by comparing original label distribution with results of clustering with different numbers of clusters. We show the method prevents underfitting and overfitting and verify it by comparing the recommended value with posterior distribution.
An Automatic Method for Extracting Innovative Ideas Based on the Scopus® Database

The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stop-words and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.
TUVD team at SemEval-2019 Task 6: Offense Target Identification

This article presents our approach for detecting a target of offensive messages in Twitter, including Individual, Group and Others classes.The model we have created is an ensemble of simpler models, including Logistic Regression, Naive Bayes, Support Vector Machine and the interpolation between Logistic Regression and Naive Bayes with 0.25 coefficient of interpolation.The model allows us to achieve 0.547 macro F1-score.
Virtual Adversarial Lipschitz Regularization

Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques. Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations. Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.
Sequential Indicator Simulation with Locally Varying Anisotropy – Simulating Mineralized Units in a Porphyry Copper Deposit

The definition of mineralized units is key to the predictive capacity of a resource model, since these units define homogeneous volumes where spatial estimation or simulation of the relevant grades can be performed. In this paper, we adapt sequential indicator simulation to model mineralization units in a large porphyry copper deposit, accounting for the weathering profile that defines the vertical zoning of these units. A locally varying anisotropy field is created from the geological interpretation of the contact between the mixed mineralized unit, where the rock mineralization transitions from supergene to hypogene sulphides. A sequential indicator simulation routine is modified to account for the local variations of the units, and all distances are computed through these folded surfaces. Sensitivities related to the main parameters of the simulation algorithm that accounts for the locally varying anisotropy are performed to select the optimum parameters. The final result is compared with conventional sequential indicator simulation, against the geological units logged in blast holes, at a much denser grid, showing an increase in the accuracy in predicting the mineralized unit from the drillhole logged data.
Usability and Security: A Case Study of Emergency Communication System Authentication

Usability and Security are two important concepts in any design. A system must be easy to use and at the same time secure, to prevent unauthorized users from gaining access to the system and the data stored in the system. The goal of this study is to review the efficacy of the user authentication methods implemented in systems used in emergency communication centers. Due to the nature of the tasks and the environment of emergency responding teams, their system needs to be available and easily accessible because time is an important factor for success. At the same time, the systems need to be highly secure due to the sensitive nature of the data being collected and communicated during the process. Surveys and interviews are used for this study to understand the user authentication methods currently adopted in public emergency communication systems, and the impact of the authentication method on the usability and security of the systems. The information collected through this study will inform developers and practitioners to design and choose more effective authentication mechanisms for emergency communication systems.
Programmable Quantum Processor with Quantum Dot Qubits

The realization of controllable couplings between any two qubits and among any multiple qubits is the critical problem in building a programmable quantum processor (PQP). We present a design to implement these types of couplings in a double-dot molecule system, where all the qubits are connected directly with capacitors and the couplings between them are controlled via the voltage on the double-dot molecules. A general interaction Hamiltonian of n qubits is presented, from which we can derive the Hamiltonians for performing operations needed in building a PQP, such as gate operations between arbitrary two qubits and parallel coupling operations for multigroup qubits. The scheme is realizable with current technology .
Alternative Estimators for the Effective Spread Derived from High-Frequency Data

According to the literature, various proxies for the effective bid/ask spread are utilized by researchers. They can be estimated from low- or high-frequency data. In this study, three alternative estimators for the effective spread derived from high-frequency data are investigated. We analyse the basic version of percentage effective spread and two modified versions of the Roll’s estimator for the effective spread. Data set contains intraday data rounded to the nearest second for 86 Warsaw Stock Exchange (WSE) traded companies, in the period from January 2005 to December 2016. We test distributional properties, linear and nonlinear dependences, as well as stationarity of the analysed daily time series. Moreover, the research hypothesis concerning the statistical significance of correlations between daily values of various effective spread estimates is tested. Furthermore, the study provides robustness analyses of the obtained results with respect to the whole sample and three consecutive subsamples, covering the pre-crisis, crisis and post-crisis periods. The empirical findings confirm significant relationships between alternative proxies for the effective spread and turn out to be robust to the choice of the period.
On Activation Function Coresets for Network Pruning

Model compression provides a means to efficiently deploy deep neural networks (DNNs) on devices that limited computation resources and tight power budgets, such as mobile and IoT (Internet of Things) devices. Consequently, model compression is one of the most critical topics in modern deep learning. Typically, the state-of-the-art model compression methods suffer from a big limitation: they are only based on heuristics rather than theoretical foundation and thus offer no worst-case guarantees. To bridge this gap, Baykal et. al. [2018a] suggested using a coreset, a small weighted subset of the data that provably approximates the original data set, to sparsify the parameters of a trained fully-connected neural network by sampling a number of neural network parameters based on the importance of the data. However, the sampling procedure is data-dependent and can only be only be performed after an expensive training phase. We propose the use of data-independent coresets to perform provable model compression without the need for training. We first prove that there exists a coreset whose size is independent of the input size of the data for any neuron whose activation function is from a family of functions that includes variants of ReLU, sigmoid and others. We then provide a compression-based algorithm that constructs these coresets and explicitly applies neuron pruning for the underlying model. We demonstrate the effectiveness of our methods with experimental evaluations for both synthetic and real-world benchmark network compression. In particular, our framework provides up to 90% compression on the LeNet-300-100 architecture on MNIST and actually improves the accuracy.
Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks

In this paper, we study the trainability of rectified linear unit (ReLU) networks. A ReLU neuron is said to be dead if it only outputs a constant for any input. Two death states of neurons are introduced; tentative and permanent death. A network is then said to be trainable if the number of permanently dead neurons is sufficiently small for a learning task. We refer to the probability of a network being trainable as trainability. We show that a network being trainable is a necessary condition for successful training and the trainability serves as an upper bound of successful training rates. In order to quantify the trainability, we study the probability distribution of the number of active neurons at the initialization. In many applications, over-specified or over-parameterized neural networks are successfully employed and shown to be trained effectively. With the notion of trainability, we show that over-parameterization is both a necessary and a sufficient condition for minimizing the training loss. Furthermore, we propose a data-dependent initialization method in the over-parameterized setting. Numerical examples are provided to demonstrate the effectiveness of the method and our theoretical findings.