Reviewerly/exHarmony huggingface dataset:
Preserving Users' Privacy for Location-Based Services

The increasing use of location-based services (LBS) poses a significant threat to location privacy of the user. The selection and transmission of dummy locations with the real location in a query has emerged as a common approach to preserve location privacy of a user in LBS. Most of the existing schemes employ entropy maximization as a metric for selecting dummy locations. However, the entropy maximization-based schemes do not preserve privacy in all cases. In this paper, we analyze the limitation of entropy maximization-based schemes in providing privacy. Further, we consider a scenario wherein a stationary user repeatedly uses LBS within a short interval of time and show the adverse impact of the repeated queries of the user on the performance of the existing scheme. We propose an enhanced scheme that preserves privacy of user even for repeated queries. Lastly, numerical results verify the performance of the proposed scheme.
Responsible Cooking with Learning Analytics

Misuse of students’ data may cause significant harm. Educational institutions have always analyzed their students’ data to some extent, but earlier that data was stored locally and in analog form. The digital revolution has led to more data being generated, stored, and linked, and more conclusions drawn from it. These new possibilities demand a new code of conduct for generating, processing, using, and archiving student data. It is crucial that ethical aspects are carefully considered before collecting and using such data in learning analytics scenarios. This chapter gives an overview of legal regulations, illustrates the critical aspects of working with digital student data, and provides practical frameworks for ethics and data protection. This chapter includes parts of legislations and guidelines that were presented in a published article: Steiner, C. M., Kickmeier-Rust, M. D., & Albert, D. (2016). LEA in private: a privacy and data protection framework for a learning analytics toolbox. Journal of Learning Analytics, 3(1), 66-90. This chapter updates and extends the original article.
Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm

Functional networks are a powerful extension of neural networks where the scalar weights are replaced by neural functions. This paper concerns the problem of parametric learning of the associative model, a functional network that represents the associativity operator. This problem can be formulated as a nonlinear continuous least-squares minimization problem, solved by applying a swarm intelligence approach based on a modified memetic self-adaptive version of the firefly algorithm. The performance of our approach is discussed through an illustrative example. It shows that our method can be successfully applied to solve the parametric learning of functional networks with unknown functions.
Scanning the Resilience of an Organization Employees to Social Engineering Attacks Using Machine Learning Technologies

The relevance of the article is due to an increase in the number of social engineering attacks on users of protected information systems of organizations. The increased danger of reducing the resistance of users to social engineering attacks is shown. A great interest in theoretical and practical issues of protecting organizations from social engineering attacks has been revealed. In modern practice, anthropogenic methods of protection against them are widely developed, which are implemented as part of raising awareness in the field of information security and the culture of cybersecurity, but their effectiveness is low. Therefore, there is a need to develop tools to protect organizations from socioengineering attacks. The market for pentesting services in this area is starting to develop. However, in Russia, these services are not cheap for organizations, and consulting companies, as a rule, are interested in the fact that the audit results motivate organizations to use their expensive information protection products. The article substantiates the possibility of solving the problem using machine learning technologies. The results of the development and implementation of an economical tool for internal testing of the organization's employees with the aim of increasing their resistance to social engineering attacks of various types and forms are described. The scientific novelty and practical significance of the author's software application lies in its multifunctionality: it allows not only to detect user vulnerabilities, but also to increase their involvement in the process of detecting social engineering attacks and to develop a culture of information security of the organization.
Wavelet Analysis based Handwritten Script Classification using Auto Encoders

Handwritten script recognition and classification is one of the unsolved challenges in an area of offline character recognition. In this paper, handwritten script classification is performed on different South Indian scripts Kannada, Telugu, Tamil, and Malayalam along with Devanagari and Roman Scripts. The proposed model for handwritten script classification initiates with a knowledge repository of word-level and character-level handwritten data samples that are collected from more than 500 writers and extracted using, projection histogram features of documents. Datasets are extracted at the character and word level separately and forwarded for wavelet feature analysis, using DB-3, DB-4, Haar, and Symlet, and the feature vectors are modeled for character level and word level datasets. These features are used for classification which is conducted separately for character and word level wavelet features. The features are employed separately for wavelets DB3, DB4, Haar, and Symlet during classification. A comparative analysis is conducted using a directional gradient feature which has shown dominant results towards printed script classification in literature. Classification is conducted using Autoencoders for both wavelet features and directional gradient features, along with that, the efficiency of conventional classifiers like support vector machines, KNN, and boosted trees is also tested with wavelet features. In the proposed method, Symlet features provide efficient performance with Autoencoders compared to other features with 70.61% accuracy.
Design of Photovoltaic Array Fault Online Evaluation System

Common failures of photovoltaic array include aging of the photovoltaic circuit, short circuit, open circuit, and shadow blocking, which will affect the generation efficiency and component safety of photovoltaic array. In this paper, an on-line fault diagnosis system of the photovoltaic array is proposed. Hall sensor is used to collect the output voltage and current data of the photovoltaic array. According to the difference of I-V characteristics under different fault types of photovoltaic array, the Extreme learning machine model is used to carry out on-line classification and evaluation of data, and the current status of the photovoltaic array is diagnosed and displayed. The system has the advantages of fast data analysis speed and high accuracy of working statejudgment.
Hybrid Approach For Real Time Crowd Activity Identification Using Segmentation

Crowd investigation turns into the most dynamic research in PC vision, in the shrewd video reconnaissance zone. In this research, an online solution is implemented to detect crowd abnormalities, which can be caused by panic. After calculating optical flow and calculating magnitudes using the Farneback method, an activity map is created using multiple frames to see the flow persistency overtime. The activity map is used to create two metrics, TOV (Temporal Occupancy Variation) and entropy, respectively. In this research, the measurements alongside an edge to the group are utilized. Subtleties will be examined in the accompanying segments. The movement directions are centered to watch the group and optical stream strategies are utilized to gain the streak lines and faculty directions. Also, the exhibition measurements are computed like accuracy FTR, TPR, and ROC.
Denoising Algorithm For Dual-Tree Complex Wavelet Disturbance Signal With Improved Threshold Function

At present, the collection and analysis of power quality signals are more and more interfered by noise, but the traditional denoising methods have many shortcomings. Based on the improved thresholding function (ITF), a dual-tree complex wavelet transform (DTCWT) denoising algorithm is proposed in this paper. Firstly, the original power quality disturbance signal is transformed by two discrete wavelet transform parallel to each other, and the wavelet coefficients of the real part and the imaginary part are obtained by sampling at two channels. The approximate translation invariance is guaranteed and the signal distortion is reduced. Then ITF is used to reduce the noise of the corresponding wavelet coefficients, which ensures the continuity at the threshold point and preserves the characteristic information at the mutation. The experimental results of various types of power quality disturbance signals show that compared with the traditional denoising algorithm, the signal-to-noise ratio (SNR) of the proposed denoising algorithm in this paper is further improved and the mean square error (MSE) is further reduced, and the denoising effect is obviously improved.
Removing a false sense of (open source) security

Software is at the heart of the ‘digital transformation’ as businesses rapidly transition from traditional legacy-based models to more robust modern digital business processes. The rapid pace of modern software development has allowed businesses to transform the way they run – yielding superior customer experiences, greater efficiencies, faster time to market and better cost optimisation. But there are many application security challenges that need to be understood and addressed. Security instrumentation technology gives developers current, real-time visibility into all open source components across their portfolio, so they know what needs to be secured, says Jeff Williams of Contrast Security.
Intelligent Response Retrieval for Semantically Similar Querying Using a Chatbot

Nowadays abundant information on a particular topic is available, but it takes time to search for the right content from the copious content available. An interesting way is querying an intelligent bot for the response. Additionally, an interactive bot increases learner's interest and enlightens their desire to learn more. In this paper, an interactive intelligent bot is developed in the domain-specific area and trained it in such a way that it can respond to the user's queries by integrating it with a corpus of a specific domain. It can also recite the response at a required rate to make the conversation more interactive. Queries of the user will be a quest with a similar question in the repository and evaluate the value of sentence similarity also by considering synonyms from the corpus to make the retrieval more efficient and analyses the threshold to respond or not. It can assess the wrong and right, assist in the best way possible.
A Low Cost High Performance Polynomial Multiplier Design For FPGA Implementation

In the last few years, with the fleeting progress of quantum computer the traditional encryption technology is being threatened. Lattice based cryptography is a new post quantum cryptography which has been proved the security under the quantum computer attack. However, due to the limitation of communication frequency and power consumption, it is difficult to implement the lattice based cryptographic algorithm on the existing general processor platform. The polynomial multiplication is the most basic and critical operation in these lattice based cryptographic systems. It is also the computationally intensive operation which is more suitable for hardware implement. In this paper, we proposed a small area, low-power polynomial multiplier design in a specific field. Our scheme not only ensures the performance requirements, but also reduces the overhead of area and power consumption. The proposed scheme in this paper need not use the DSP resources on FPGA so that we can maximize the parallel NTT butterfly operators to improve the computing efficiency. It is a low-cost, high-performance implementation scheme.
Image Smoothing Algorithm Based on Gradient Analysis

In this paper image smoothing algorithm based on gradient analysis is proposed. Our algorithm uses filtering and to achieve edge-preserving smoothing it uses two components of gradient vectors: their magnitudes (or lengths) and directions. Our method discriminates between two types of boundaries in given neighborhood: regular and irregular ones. Regular boundaries have small deviations of gradient angles and the opposite for irregular ones. To measure closeness of angles cosine of doubled difference is used. As additional measure that helps to discriminate the types of boundaries inverted gradient values were used. When gradient magnitudes are inverted bigger values refer to textures (insignificant changes in gradient) and smaller refer to strong boundaries. So textures would have bigger weights and hence they would appear smoother. We also propose to filter image of gradient magnitudes with median filter to enhance visual quality of results. The method proposed in this paper is easy to implement and compute and it gives good results in comparison with other techniques like bilateral filter.
M2P2: Multimodal Persuasion Prediction Using Adaptive Fusion

Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage persuasive advocates for their position in the presence of adversaries who are critical of them. Debates represent a common platform for these forms of adversarial persuasion. This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate while the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Though DOP has been previously studied, we are the first to study IPP. Past studies on DOP fail to leverage two important aspects of multimodal data: 1) multiple modalities are often semantically aligned, and 2) different modalities may provide diverse information for prediction. Our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf{M2P2}$</tex-math></inline-formula> (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem. To leverage the alignment of different modalities while maintaining the diversity of the cues they provide, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf{M2P2}$</tex-math></inline-formula> devises a novel adaptive fusion learning framework which fuses embeddings obtained from two modules – an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alignment</i> module that extracts shared information between modalities and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneity</i> module that learns the weights of different modalities with guidance from three separately trained unimodal reference models. We test <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf{M2P2}$</tex-math></inline-formula> on the popular IQ2US dataset designed for DOP. We also introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV show) for IPP. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf{M2P2}$</tex-math></inline-formula> significantly outperforms 4 recent baselines on both datasets.
Diffusion-Driven X-Ray Two-Dimensional Patterns Denoising

The use of a mathematical model is proposed in order to denoise X-ray two-dimensional patterns. The method relies on a generalized diffusion equation whose diffusion constant depends on the image gradients. The numerical solution of the diffusion equation provides an efficient reduction of pattern noise as witnessed by the computed peak of signal-to-noise ratio. The use of experimental data with different inherent levels of noise allows us to show the success of the method even in the case, experimentally relevant, when patterns are blurred by Poissonian noise. The corresponding MatLab code for the numerical method is made available.
Ambitions and Ambivalences in Participatory Design: Lessons from a Smart Workplace Project

We reflect on how practices of care configure workplace participatory design (PD) efforts by locating ways in which care manifests in the ambitions of actors as they confront their ambivalent relations to a smart workplace technology. We explore these issues through a participatory design effort centered around the development and implementation of a machine learning (ML) system to support the work of IT architects employed by a large global company. Following an Agile approach, these IT architects actively shaped the project's trajectories, offering feedback on various system prototypes as well as the alignment between the project's various aims and their everyday work practices. Our findings center around six standpoints (strategizing, managing, building, researching, integrating, and transforming) that organize “matters of care” emerging from our twenty-three-month-long engagement with this project. Workers inhabited different standpoints at different times (and some, at times, simultaneously), revealing a dynamic and shifting orientation toward caring. We discuss the implications of these findings for understanding contemporary workplace PD efforts.
Likelihood-free inference with deep Gaussian processes

Surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. The current state-of-the-art performance for this task has been achieved by Bayesian Optimization with Gaussian Processes (GPs). While this combination works well for unimodal target distributions, it is restricting the flexibility and applicability of Bayesian Optimization for accelerating likelihood-free inference more generally. This problem is addressed by proposing a Deep Gaussian Process (DGP) surrogate model that can handle more irregularly behaved target distributions. The experiments show how DGPs can outperform GPs on objective functions with multimodal distributions and maintain a comparable performance in unimodal cases. At the same time, DGPs generally require much fewer data to achieve the same level of performance as neural density and kernel mean embedding alternatives. This confirms that DGPs as surrogate models can extend the applicability of Bayesian Optimization for likelihood-free inference (BOLFI), while only adding computational overhead that remains negligible for computationally intensive simulators.
Isomorphism Theorems for Basic Constructive Algebraic Structures with Special Emphasize On Constructive Semigroups with Apartness—An Overview

Mitrović, Melanija Silvestrov, SergeiThis overview is an introduction to the basic constructive algebraic structures with apartness with special emphasises on a set and semigroup with apartness. The main purpose of this paper, inspired by Bauer [2], is to make some sort of understanding of constructive algebra in Bishop’s style position for those (classical) algebraists as well as for the ones who apply algebraic knowledge who might wonder what is constructive algebra all about. Every effort has been made to produce a reasonably prepared text with such definite need. In the context of basic constructive algebraic structures constructive analogous of isomorphism theorems will be given. Following their development, two points of view on a given subject: classical and constructive will be considered. This overview is not, of course, a comprehensive one.
A Watermarking Detection Algorithm Based on Statistical Properties for Vector Geographic Data

Abstract The watermarking detection algorithm for vector geographic data was designed based on the detected watermark bits characteristic. Firstly, the statistical characteristic of extracted watermark bits was analyzed; the probability-distribution function of detected watermark bits corresponding to the certain index was established on the condition that there was no watermark in detected data. And then a watermarking self-detection algorithm is present for vector geographic data. Finally, the experiments were made to verify the robustness and detection efficiency of the proposed algorithm. The experimental results show that we can judge whether there is watermark in detected data or not without the participation of the original data and the original watermark, and the proposed watermarking detection is more efficient than the common watermarking detection. The proposed algorithm has good performance in the aspects of self-detection, robustness and detection efficiency.
An Empirical Study of Observation-weighting Method for Mining Actionable Behavioral Rules

Abstract Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among a variety types of actionable knowledge, the actionable behavioral rule plays an important and unique role because it can directly and explicitly suggest actions to users to positively influence their behaviors of concerned entities. The problem of mining such rules is a search problem within a framework of support and expected utility. The previous mining approach assumes that each instance which supports a rule has the uniform contribution to the support. However, this assumption is usually violated in practice, and thus will hinder the performance of algorithms for mining such rules. To handle this problem, in this paper, we propose several observation-weighting models for support based on different functions. We further empirically investigate these models. Based on the results of our experimental study, we gain a thorough insight into the selection of various observation-weighting models for this domain.