Reviewerly/exHarmony huggingface dataset:
$$ \mathsf {FPL} $$: White-Box Secure Block Cipher Using Parallel Table Look-Ups

In this work, we propose a new table-based block cipher structure, dubbed $$ \mathsf {FPL} $$, that can be used to build white-box secure block ciphers. Our construction is a balanced Feistel cipher, where the input to each round function determines multiple indices for the underlying table via a probe function, and the sum of the values from the table becomes the output of the round function. We identify the properties of the probe function that make the resulting block cipher white-box secure in terms of weak and strong space hardness against known-space and non-adaptive chosen-space attacks. Our construction, enjoying rigorous provable security without relying on any ideal primitive, provides flexibility to the block size and the table size, and permits parallel table look-ups. We also propose a concrete instantiation of $$ \mathsf {FPL} $$, dubbed $$ \mathsf {FPL}_{\mathsf {AES}} $$, using (round-reduced) $$\mathsf {AES}$$ for the underlying table and probe functions. Our implementation shows that $$ \mathsf {FPL}_{\mathsf {AES}} $$ provides stronger security without significant loss of efficiency, compared to existing schemes including $$\mathsf {SPACE}$$, $$\mathsf {WhiteBlock}$$ and $$\mathsf {WEM}$$.
Robust Unscented Unbiased Minimum-Variance Estimator for Nonlinear System Dynamic State Estimation With Unknown Inputs

In this letter, a two-stage robust unscented unbiased minimum-variance (RU-UMV) estimator is proposed for nonlinear system dynamic state estimation with unknown inputs. In the first stage, by leveraging the statistical linerization and the relationship between unknown input vector and states, we derive a batch-mode regression form. It is shown that the application of weighted least squares for this form yields the same results as the UMV unscented Kalman filter. However, it lacks robustness to outliers. To deal with, robust generalized maximum-likelihood (GM)-estimator together with the projection statistics (PS) is developed, yielding robust state estimates. The latter are further used in the second stage for robust unknown input vector estimation. As a result, both innovation and observation/measurement outliers can be effectively suppressed. Illustrative examples are provided to demonstrate the robustness of the proposed method.
The development of information literacy in early childhood education teachers: A study from the perspective of the education center's character

The development of the digital competence is one of the current challenges of the educational community. Specifically, the area of information literacy is presented as a main pillar for the subsequent optimal development of a digital teaching competence. Based on this, the present article aims to evaluate the area of information literacy in practicing teachers from Early Childhood Education (N=57). A descriptive and inferential quantitative analysis was made for this piece of work in order to analyze the perceptions of the teachers towards this area, the accomplishment of the t-student test for the comparison of means between teachers of public and private with state-funded financial support centers and the application of a linear regression model that allows to predict the information literacy through the variable "education center". The results obtained reflected an optimal level of perceptions as for the information area, regardless of the type of school, as well as the possible prediction, regarding the school typology, of some information literacy variables.
Anomaly Detection in the HVAC System Operation by a RadViz Based Visualization-Driven Approach

The appearance of the smart houses, buildings, and cities has defined new attack scenarios targeting industrial information systems. The paper suggests a visualization-driven approach to the analysis of the data from heating, ventilating and conditioning system (HVAC). The key element of the approach is the RadViz visualization that is used to form daily operation patterns and can detect suspicious deviations that could be the signs of fraudulent activity in the system. It is supplemented by a matrix-based representation of the HVAC parameters that is constructed in the way that allows highlighting changes in values of parameters being analyzed. The distinctive feature of the proposed visualization models is the ability to display data from different data sources. To demonstrate and evaluate the efficiency of the proposed approach we used the VAST MiniChallenge-2 2016 data set that contains logs from the HVAC system and the access control system.
Balanced training/test set sampling for proper evaluation of classification models

In machine learning, classification involves identifying the categories or classes to which a new observation belongs based on a training set. The performance of a classification model is generally measured by the classification accuracy of a test set. The first step in developing a classification model is to divide an acquired dataset into training and test sets through random sampling. In general, random sampling does not guarantee that test accuracy reflects the performance of a developed classification model. If random sampling produces biased training/test sets, the classification model may result in bias. In this study, we show the problems of random sampling and propose balanced sampling as an alternative. We also propose a measure for evaluating sampling methods. We perform empirical experiments using benchmark datasets to verify that our sampling algorithm produces proper training and test sets. The results confirm that our method produces better training and test sets than random and several non-random sampling methods can.
Interpretable Learning Algorithm Based on XGBoost for Fault Prediction in Optical Network

We propose a fault prediction scheme using interpretable XGBoost based on actual datasets, which not only achieves high accuracy (99.72%) and low positive rate (0.18%), but also reveals the five most remarkable features that caused the fault.
Adaptive Sequential Constructive Crossover Operator in a Genetic Algorithm for Solving the Traveling Salesman Problem

Genetic algorithms are widely used metaheuristic algorithms to solve combinatorial optimization problems that are constructed on the survival of the fittest theory.They obtain near optimal solution in a reasonable computational time, but do not guarantee the optimality of the solution.They start with random initial population of chromosomes, and operate three different operators, namely, selection, crossover and mutation, to produce new and hopefully better populations in consecutive generations.Out of the three operators, crossover operator is the most important operator.There are many existing crossover operators in the literature.In this paper, we propose a new crossover operator, named adaptive sequential constructive crossover, to solve the benchmark travelling salesman problem.We then compare the efficiency of the proposed crossover operator with some existing crossover operators like greedy crossover, sequential constructive crossover, partially mapped crossover operators, etc., under same genetic settings, for solving the problem on some benchmark TSPLIB instances.The experimental study shows the effectiveness of our proposed crossover operator for the problem and it is found to be the best crossover operator.
Text Classification of Illegal Activities on Onion Sites

Onion sites work using the Hidden Service Protocol, which helps to keep a double anonymity. A such system allows sites to place malicious and illegal content. An identification and tracking of such resources is an important problem, that’s why the article sets a task of developing a system for accurate thematic classification of textual content blocks of hidden web pages using k nearest neighbors method. The article presents the method of content separation placed on Russian-language onion-sites. The research illustrates the analysis of text categorization results based on collected dataset for the implementation of machine learning.
An Efficient Vehicle Counting Method Using Mask R-CNN

In this paper, an accurate approach for vehicle counting in videos using Mask R-CNN and KLT tracker is proposed. Vehicle detection is performed for each N frames using Mask R-CNN instance segmentation model. This model outperforms other deep learning models that using bounding box detection as it provides a segmentation mask for each detected object, the outperformance comes up clearly in cases of occlusions. Once the objects are detected, their corner points are extracted and tracked. An efficient method is introduced to assign point trajectories to their corresponding detected vehicles. The proposed counting algorithm distinguishes precisely between the new vehicles and the counted ones. The experiments performed on diverse challenging videos show excellent results compared to state-of-the-art counting methods.
Improving binary classification using filtering based on k-NN proximity graphs

Abstract One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters selection was adopted as the solution of outlier minimization problem: k -NN proximity graph, power of distance and threshold parameters are selected in order to minimize outlier percentage in training data. Then performance of six commonly used classifiers (Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine and Decision Tree) and one heterogeneous classifiers combiner (DES-LA) are compared with and without filtering. Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of base classifiers, such as, its accuracy in local regions of the feature space around the query instance. In our case the combiner is based on the local accuracy of single classifiers and its output is a linear combination of single classifiers ranking. As results of filtering, accuracy of DES-LA combiner shows big increase for low-accuracy datasets. But filtering doesn’t have sufficient impact on DES-LA performance while working with high-accuracy datasets. The results are discussed, and classifiers, which performance was highly affected by pre-processing filtering step, are defined. The main contribution of the paper is introducing modifications to the DES-LA combiner, as well as comparative analysis of filtering impact on the classifiers of various type. Testing the filtering algorithm on real case dataset (Taiwan default credit card dataset) confirmed the efficiency of automatic filtering approach.
Assessing the Impact of OCR Errors in Information Retrieval

A significant amount of the textual content available on the Web is stored in PDF files. These files are typically converted into plain text before they can be processed by information retrieval or text mining systems. Automatic conversion typically introduces various errors, especially if OCR is needed. In this empirical study, we simulate OCR errors and investigate the impact that misspelled words have on retrieval accuracy. In order to quantify such impact, errors were systematically inserted at varying rates in an initially clean IR collection. Our results showed that significant impacts are noticed starting at a 5% error rate. Furthermore, stemming has proven to make systems more robust to errors.
Quantum channel capacities

Abstract A brief general review is presented of the theory of information transmission capacities of quantum communication channels, which is a development of the classical Shannon theory. Unlike a classical communication channel, a quantum channel is characterised by a whole set of different capacities, which depend on the type of transmitted information (classical or quantum) and on additional resources used during transmission. The main characteristics of a quantum channel are considered: classical capacity, capacity assisted by entanglement between the channel input and output, quantum capacity and secret classical capacity. The unique role of the quantum entanglement property, which manifests itself, in particular, in a nonclassical phenomenon of capacity superadditivity, is emphasised.
Investigating Toxicity Across Multiple Reddit Communities, Users, and Moderators

Online platforms like Reddit enable users to build communities and converse about diverse topics and interests. However, with the increasing number of users that post disturbing comments containing profanity, harassment, and hate speech, otherwise known as toxic comments. Moderators often struggle with managing the safety of discussions in online communities. To address these issues, we need to detect toxic comments and the root causes of toxicity in discussion threads, i.e., toxicity triggers. Additionally, we need to investigate the toxic posting behavior of users to understand how it differs across online communities and consolidate our findings with moderators from Reddit. In this work, we present our approach, which builds on state-of-the-art methods of toxic comment and toxicity trigger detection. Lastly, we present our research findings of investigating toxicity across users and moderators on Reddit.
Secure Two-Party Computation in a Quantum World

Secure multi-party computation has been extensively studied in the past years and has reached a level that is considered practical for several applications. The techniques developed thus far have been steadily optimized for performance and were shown to be secure in the classical setting, but are not known to be secure against quantum adversaries. In this work, we start to pave the way for secure two-party computation in a quantum world where the adversary has access to a quantum computer. We show that post-quantum secure two-party computation has comparable efficiency to their classical counterparts. For this, we develop a lattice-based OT protocol which we use to implement a post-quantum secure variant of Yao’s famous garbled circuits (GC) protocol (FOCS’82). Along with the OT protocol, we show that the oblivious transfer extension protocol of Ishai et al. (CRYPTO’03), which allows running many OTs using mainly symmetric cryptography, is post-quantum secure. To support these results, we prove that Yao’s GC protocol achieves post-quantum security if the underlying building blocks do.
Characterization of Mineralogy in the Highland Valley Porphyry Cu District Using Hyperspectral Imaging, and Potential Applications

The Highland Valley Copper (HVC) district in British Columbia, Canada, is host to at least four major porphyry Cu systems: Bethlehem (~209 Ma), and Valley, Lornex, and Highmont (~208 to 207 Ma). High spatial resolution (0.2–1.0 mm/pixel) hyperspectral imagery in the shortwave infrared (SWIR) were acquired on 755 rock samples and 400 m of continuous drill core. Spectral metrics are used to measure the relative abundance of 12 minerals and an additional metric is derived to estimate white mica grain size. In the Valley and Lornex deposits, coarse-grained white mica is associated with mineralization and is detectable up to 4 km away from the deposits. Kaolinite is present within 2 km of the mineralized centers but does not necessarily occur within strongly mineralized intervals. Prehnite is ubiquitous from 4 to 8 km from the deposits. In the Bethlehem deposit, tourmaline and epidote are associated with mineralization. We propose a spectral alteration score based on these proximal hyperspectral SWIR mineralogical patterns to assist explorers in targeting porphyry Cu systems when using drill core, surface rock samples and potentially remote sensing imagery. In a production environment, this metric could serve to facilitate ore-sorting.
Parsing human image by fusing semantic and spatial features: A deep learning approach

How to parse the human image to obtain the text label corresponding to the human body is a critical task for human-computer interaction. Although previous methods have significantly improved the parsing performance, the problem of parsing confusion and tiny target missing remains unresolved, which leads to errors and incomplete inference accordingly. Targeting at these drawbacks, we fuse semantic and spatial features to mine the human body information based on the Dual Pyramid Unit convolutional neural network, named as DPUNet. DPUNet is composed of Context Pyramid Unit (CPU) and Spatial Pyramid Unit (SPU). Firstly, we design the CPU to aggregate the local to global semantic information, which exports the semantic feature for eliminating the semantic confusion. To capture the tiny targets for preventing the details from missing, the SPU is proposed to incorporate the multi-scale spatial information and output the spatial feature. Finally, the features of two complementary units are fused for accurate and complete human parsing results. Our approach achieves more excellent performance than the state-of-the-art methods on single human and multiple human parsing datasets. Meanwhile, the proposed framework is efficient with a fast speed of 41.2fps.
Effectiveness and Stakeholders’ Perceptions of the Integration of Automated E-Learning Courses into Vocational Education Programmes in Universities in Ukraine

The purpose of this research was to identify whether the integration of the automated vocational e-courses into vocational education could bring the students to the same academic achievements as the tutor-moderated ones, and whether the stakeholders of education perceive the automation of e-learning positively or negatively, and what impact factors triggered their perceptions. The baseline study used the e-course evaluation checklist to assess the e-course structure and content from eight randomly selected universities. Four hundred and four students and thirty-one instructors participated in the baseline study, first pilot, and core experiment. The instruments utilised to monitor the variables in the pilots were as follows: the sampled studentsâ€™ academic records, a Criteria Cognitive Aptitude Test, a Rasch Measurement Model, and the Kolbâ€™s Learning Style Questionnaire. The IBM SPSS Statistics 5.0.0.1. Software package was used to process the data drawn for the above measurements. The above measurements were followed by the focus group and nine education stakeholdersâ€™ perceptions analyses using the Triangle Assessment Method. The study provided new evidence that automated e-course delivery can lead to approximately the same statistically significant improvements in the studentsâ€™ vocational competence, academic motivation, and learning styles proving that it might be considered to be a feasible instructional tool. Additionally, it suggested that the use of automated educational e-course assisted by a virtual agent had been a more cost-efficient option. https://doi.org/10.26803/ijlter.19.5. 3
The effect of artificial intelligence on the sales graph in Indian market

Abstract. Artificial Intelligence (AI) has been the biggest revolution of the 21st century impacting every aspect of the business, sales being no different. The paper experiments the effect of marketing on 4500 customers using AI and humans. The outcomes of the research reveal the effectiveness of AI is the same as experienced salesmen and 2.7 times better than inexperienced salesmen is closing the sales calls. The sales graph experienced a dip by over 86.23% when it was revealed to the customer that the interface is with the machine, not humans and reduced the duration of the call substantially. The paper shows that Indians do not believe Artificial Intelligence and still prefer human interface as they do not trust machines over human emotions. The effectiveness of AI drastically reduces despite its superiority over humans in various aspects. The paper identifies the strategies to overcome the trust deficit that exists among Indian customers. The outcomes show how AI can be used, and how marketing could be done using AI in conservative markets such as India.
An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration

Purpose Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach. Design/methodology/approach An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing. Findings The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools. Originality/value The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.
Aarogya Setu- Carrying Your Privacy in Your Hands?

There are numerous benefits of having a data protection legislation in place to guide a nation in how to protect the personal data of individuals. These guiding factors maybe transparency, accountability, ensuring rights of the individuals, listing out organizational measures and the compliance to be undertaken, to name a few. In the current context of having contact tracing apps, it becomes all the more important to have a data protection legal framework. The article seeks to understand the privacy concerns spelled out by the Aarogya Setu App, briefly compare the same with practices across different countries and understanding the need for legislative action concerning the privacy issues.In absence of a legislation, compliance with the privacy principles is not enough. The absence of a national or applicable law that posits data protection principles, provisions and organizational measures, should not permit denial of the basic tenets and continued provision of basic minimum rights of personal data protection and privacy, which must be ensured to all individuals at all times. Finally, it must be borne in mind that the adoption of contact tracing apps will put to test the balance between state surveillance and user privacy. The need of a data protection legislation is very telling in the current times and speaks for itself.