Reviewerly/exHarmony huggingface dataset:
Efficient Acceleration of Decision Tree Algorithms for Encrypted Network Traffic Analysis

Network traffic analysis and deep packet inspection are time-consuming tasks, which current processors can not handle at 100 Gbps speed. Therefore security systems need fast packet processing with hardware acceleration. With the growing of encrypted network traffic, it is necessary to extend Intrusion Detection Systems (IDSes) and other security tools by new detection methods. Security tools started to use classifiers trained by machine learning techniques based on decision trees. Random Forest, Compact Random Forest and AdaBoost provide excellent result in network traffic analysis. Unfortunately, hardware architectures for these machine learning techniques need high utilisation of on-chip memory and logic resources. Therefore we propose several optimisations of highly pipelined architecture for acceleration of machine learning techniques based on decision trees. The optimisations use the various encoding of a feature vector to reduce hardware resources. Due to the proposed optimisations, it was possible to reduce LUTs by 70.5 % for HTTP brute force attack detection and BRAMs by 50 % for application protocol identification. Both with only negligible impact on classifiers' accuracy. Moreover, proposed optimisations reduce wires and multiplexors in the processing pipeline, positively affecting the proposed architecture's maximal achievable frequency.
The effect of team value diversity on team performance: The mediating role of relationship conflict and the moderating effects of organization citizenship behavior and leader-member exchange quality

This paper aims to investigate the mediating role of relationship conflict and moderating role of organization citizenship behavior and leader-member-exchange quality on the relationship between team value diversity and team performance. Data was collected from 263 employees of the telecom sector addressing the variables of value diversity, team performance, relationship conflict, OCB and LMX. Regression analysis found that team value diversity negatively affects team performance, and relationship conflict significantly mediates this relationship. It was also found that OCB and LMX significantly moderate the relationship, such that in the presence of these two, value diversity doesn’t affect team performance rather team performance is positive when these two are present. The findings of this research confirm that for effective management of team diversity in organizations, team leaders must also wisely manage relationship conflict.
Clustering by Constructing Hyper-Planes

As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters' number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods' best results which are selected manually.
Extensive Analysis on Images Encryption using Hybrid Elliptic Curve Cryptosystem and Hill Cipher

The advancement of communication technology helps individual to share images through an Internet.However, the sharing through insecure channels may expose the images to certain attacks that will compromise their confidentiality.Image encryption is one of the methods used to protect against the confidentiality threat.A Hill Cipher has been applied in image encryption because of its simple operation and fast computation, but it also possesses a weak security level which requires the sender and receiver to use and share the same private key within an unsecure channel.Thus, there are many solutions been proposed in utilizing hybrid approach of Hill Cipher where one of them is Elliptic Curve Cryptosystem together with Hill Cipher (ECCHC) to utilize the beauty of Hill Cipher while managing its weaknesses.However, the ECCHC only been experimented over four images which leads to inaccuracy of the results.Thus, this study extended the experiments on 209 images from USC-SIPI database in order to investigate the efficiency of ECCHC.The result shows the ECCHC produces poor performances on security analysis on grayscale and RGB images, which then concludes it is not suitable to encrypt grayscale and RGB images.
Didactic opportunities and terms of use of web-quest technology in professional training of students

Educational web quest is an example of the introduction of the Internet in the learning process. Using the web quest as a pedagogical technology allows students to form and develop competencies in the use of information and communication technologies in the performance of educational tasks, research skills, skills of analysis and systematization of information received, teamwork skills and responsibility for the quality of their training. The article describes the didactic possibilities of an educational web quest in the educational process of College students.
Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning

HTTP Adaptive Streaming (HAS) is the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08% and parallel encoding time by 41.26%, with a 0.89% bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27% for the overall encoding and 27.71% for the parallel encoding on average with a 2.05% bitrate increase.
Mixed-precision weights network for field-programmable gate array

In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {−1,1}, ternary {−1,0,1}, and 32-bit floating-point. We further developed the MPWN from both software and hardware aspects. From the software aspect, we evaluated the MPWN on the Fashion-MNIST and CIFAR10 datasets. We systematized the accuracy sparsity bit score, which is a linear combination of accuracy, sparsity, and number of bits. This score allows Bayesian optimization to be used efficiently to search for MPWN weight space combinations. From the hardware aspect, we proposed XOR signed-bits to explore floating-point and binary weight spaces in the MPWN. XOR signed-bits is an efficient implementation equivalent to multiplication of floating-point and binary weight spaces. Using the concept from XOR signed bits, we also provide a ternary bitwise operation that is an efficient implementation equivalent to the multiplication of floating-point and ternary weight space. To demonstrate the compatibility of the MPWN with hardware implementation, we synthesized and implemented the MPWN in a field-programmable gate array using high-level synthesis. Our proposed MPWN implementation utilized up to 1.68-4.89 times less hardware resources depending on the type of resources than a conventional 32-bit floating-point model. In addition, our implementation reduced the latency up to 31.55 times compared to 32-bit floating-point model without optimizations.
New developments on regulation of cryptocurrency in China

Purpose To describe recent regulatory developments in China on digital renminbi (RMB), initial coin offerings (ICOs), cryptocurrency holdings, cryptocurrency exchanges and blockchain technology. Design/methodology/approach Describes a digital RMB pilot program, seven government agencies’ bans on ICOs and cryptocurrency exchanges, the legality of holding and trading cryptocurrencies in China and the government’s endorsement of blockchain technology. Findings No PRC law or regulation prohibits Chinese investors from holding or trading cryptocurrencies but Bitcoin is defined as a virtual commodity, not a currency. Despite a ban on ICOs and cryptocurrency exchanges, the People’s Bank of China (PBOC) and other government agencies endorse blockchain technology as long as its goal is to service the real economy. Originality/value Expert guidance from lawyer with experience in foreign investment, cross-border mergers and acquisitions, capital markets, fund formation and venture capital and private equity investments in China.
Review of the Literature on the Steganography Concept

Information hiding is a subject of study from past to present.Information hiding processes, called steganography, have been the focus of interest since the beginning of history because they are strategically interested in valuable information.In our study, the applications of Steganography with different fields from basic and primitive applications to today's technological applications have been studied.Image
Data-driven education quality management

A review of foreign and Russian research on the problems of assessing the quality of education at the institutional level is undertaken. Various aspects of the problem including the cross-country and sub-national contexts, the impact of international comparative research about in-school assessment, and teachers’ training in working with assessment data are compared. The effect of regional policies on the development of internal education quality evaluation systems in Russian schools is studied through analyzing official departmental websites. Expert evaluation method and the concordance coefficient are used to determine the key factors of the internal quality assessment system in general education institutions. The authors go beyond the control and inspection paradigm of education quality management and consider said systems from the point of the information-analytical paradigm. Data-driven management involves using data analysis and interpretation to predict students’ progress and motivational choice of educational profiles to design the content of electronic educational environments and individual educational trajectories.
RNN-LSTM Based Deep Learning Model for Tor Traffic Classification

Tor is an anonymous browser software running on an overlay network. Due to the nature of the end-to-end encryption channel, it is hard to analyse the network traffic. Thus, intruders prefer the Tor browser to hide their identity and access the offensive content. Tor relays are secure from network monitoring, tracking and surveillance. There are so many research contributions for tracking the network traffic and classifying it based on various features and attributes. In this paper, we explained RNN-LSTM-based deep learning model to classify the network traffic based on their nature Tor/non-Tor. We have tested the model with open data sets ISCXTor2016 data sets and samples retrieved in our environment using CIC-flowmeter-4.0. The binary classification model using RNN-LSTM classifies the network traffic with better accuracy and precision. The same experiment conducted in the traditional deep neural network model provides large false positives and false negatives. Here we also present a detailed study and analysis of the model compare with ANN classifiers and genetic-based feature selection method.
A regular expression generator based on CSS selectors for efficient extractionfrom HTML pages

Cascading style sheets (CSS) selectors are patterns used to select HTML elements.They are often preferred in web data extraction because they are easy to prepare and have short expressions.In order to be able to extract data from web pages by using these patterns, a document object model (DOM) tree is constructed by an HTML parser for a web page.The construction process of this tree and the extraction process using this tree increase time and memory costs depending on the number of HTML elements and their hierarchies.For reducing these costs, regular expressions can be considered as a solution.However, preparing regular expression patterns is a laborious task.In this study, a heuristic approach, namely Regex Generator (REGEXN), that automatically generates these patterns through CSS selectors is introduced and the performance gains are analyzed on a web crawler.The analysis shows that regular expression patterns generated by this approach can significantly reduce the average extraction time results from 743.31 ms to 1.03 ms when compared with the extraction process from a DOM tree.Similarly, the average memory usage drops from 1054.01 B to 1.59 B.Moreover, REGEXN can be easily adapted to the existing frameworks and tools in this task.
UX at the Right Level

Abstract Usability and user experience maturity models are used to evaluate the capabilities of an organization in order to provide an assessment of their ability to develop usable products. But, as the main focus of such models is on an all-encompassing organizational level, they are difficult to implement in more complex organizations with a wide range of diverse and interlinked projects. This paper presents a project related UX maturity model, which was developed at DB Systel to address this issue: the PUXMM. It takes into account the nested internal customer relationships between departments and subcontractors and applies a human-centered design approach. There are two practical application scenarios for the PUXMM. It can be used to determine the UX maturity level of an ongoing project and as checklist to align a project to a desired maturity level from the outset.
Large thickness measurement of glass plates with a spectrally resolved interferometer using variable signal positions

In order to measure exactly a large thickness of glass plate with a spectrally resolved interferometer using a spectral analyzer with a low resolution of 0.5 nm and a supercontinuum light source with a large bandwidth of about 300 nm, a new measurement method with a spectrally resolved interferometer is proposed where a variable signal position is generated by moving a reference surface with a piezoelectric stage. It is made clear how to decide the signal position by analyzing amplitude distribution of Fourier transform of the interference signal. Through four-step measurement by using four different optical configurations with four different signal positions the thickness of glass plate can be obtained from a slope of a spectral phase distribution which does not contain the refractive index of glass plate. A small measurement error of 50 nm is achieved in measuring 1 mm thickness of a glass plate.
HIStream-Import: A Generic ETL Framework for Processing Arbitrary Patient Data Collections or Hospital Information Systems into HL7 FHIR Bundles

Data integration is a necessary and important step to perform translational research and improve the sample size beyond single data collections. For health information, the most recent established communication standards is HL7 FHIR. To bridge the concepts of “minimal invasive” data integration and open standards, we propose a generic ETL framework to process arbitrary patient related data collections into HL7 FHIR – which in turn can then be used for loading into target data warehouses. The proposed algorithm is able to read any relational delimited text exports and produce a standard HL7 FHIR bundle collection. We evaluated an implementation of the algorithm using different lung research registries and used the resulting FHIR resources to fill our i2b2 based data warehouse as well an OMOP common data model repository.
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multioutput Gaussian Process Model

A recent novel extension of multioutput Gaussian processes (GPs) handles heterogeneous outputs, assuming that each output has its own likelihood function. It uses a vector-valued GP prior to jointly model all likelihoods’ parameters as latent functions drawn from a GP with a linear model of coregionalization (LMC) covariance. By means of an inducing points’ framework, the model is able to obtain tractable variational bounds amenable to stochastic variational inference (SVI). Nonetheless, the strong conditioning between the variational parameters and the hyperparameters burdens the adaptive gradient optimization methods used in the original approach. To overcome this issue, we borrow ideas from variational optimization introducing an exploratory distribution over the hyperparameters, allowing inference together with the posterior’s variational parameters through a fully natural gradient (NG) optimization scheme. Furthermore, in this work, we introduce an extension of the heterogeneous multioutput model, where its latent functions are drawn from convolution processes. We show that our optimization scheme can achieve better local optima solutions with higher test performance rates than adaptive gradient methods for both the LMC and the convolution process model. We also show how to make the convolutional model scalable by means of SVI and how to optimize it through a fully NG scheme. We compare the performance of the different methods over the toy and real databases.
Understanding mathematics of Grover’s algorithm

Abstract We analyze the mathematical structure of the classical Grover’s algorithm and put it within the framework of linear algebra over the complex numbers. We also generalize it in the sense, that we are seeking not the one ‘chosen’ element (sometimes called a ‘solution’) of the dataset, but a set of m such ‘chosen’ elements (out of $$n&gt;m)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>&gt;</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> . Besides, we do not assume that the so-called initial superposition is uniform. We assume also that we have at our disposal an oracle that ‘marks,’ by a suitable phase change $$\varphi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>φ</mml:mi></mml:math> , all these ‘chosen’ elements. In the first part of the paper, we construct a unique unitary operator that selects all ‘chosen’ elements in one step. The constructed operator is uniquely defined by the numbers $$\varphi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>φ</mml:mi></mml:math> and $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> which is a certain function of the coefficients of the initial superposition. Moreover, it is in the form of a composition of two so-called reflections. The result is purely theoretical since the phase change required to reach this heavily depends on $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> . In the second part, we construct unitary operators having a form of composition of two or more reflections (generalizing the constructed operator) given the set of orthogonal versors. We find properties of these operations, in particular, their compositions. Further, by considering a fixed, ‘convenient’ phase change $$\varphi ,$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>φ</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math> and by sequentially applying the so-constructed operator, we find the number of steps to find these ‘chosen’ elements with great probability. We apply this knowledge to study the generalizations of Grover’s algorithm ( $$m=1,\phi =\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>ϕ</mml:mi><mml:mo>=</mml:mo><mml:mi>π</mml:mi></mml:mrow></mml:math> ), which are of the form, the found previously, unitary operators.
Ergonomic Design Development of the Visual Experience Database Headset

Head-mounted devices allow recording of eye movements, head movements, and scene video outside of the traditional laboratory setting. A key challenge for recording comprehensive first-person stimuli and behavior outside the lab is the form factor of the head-mounted assembly. It should be mounted stably on the head to minimize slippage and maximize accuracy of the data; it should be as unobtrusive and comfortable as possible to allow for natural behaviors and enable longer duration recordings; and it should be able to fit a diverse user population. Here, we survey preliminary design iterations of the Visual Experience Database headset, an assembly consisting of the Pupil Core eye tracker, the Intel RealSense T265 ™ (T265) tracking camera, and the FLIR Chameleon™3 (FLIR) world camera. Strengths and weaknesses of each iteration are explored and documented with the goal of informing future ergonomic design efforts for similar head-mounted systems.
Unbiased estimation of the gradient of the log-likelihood for a class of continuous-time state-space models

Abstract In this paper, we consider static parameter estimation for a class of continuous-time state-space models. Our goal is to obtain an unbiased estimate of the gradient of the log-likelihood (score function), which is an estimate that is unbiased even if the stochastic processes involved in the model must be discretized in time. To achieve this goal, we apply a doubly randomized scheme , that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization. Our novel estimate helps facilitate the application of gradient-based estimation algorithms, such as stochastic-gradient Langevin descent. We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee–Glynn estimator.