Reviewerly/exHarmony huggingface dataset:
Installation, orientation, and layout

NodeXL is an Excel template that supports social network analysis and visualization in a spreadsheet environment. This chapter introduced the basics of NodeXL using a simple corporate connection network called the ABCD network. The spreadsheet includes an Edges worksheet that contains a row for each edge and a Vertices worksheet that contains a row for each vertex. The NodeXL ribbon and graph pane tools provide access to NodeXL features, such as the ability to change from an undirected to a directed network. Vertices can be arranged on the graph using a number of automatic layouts (e.g., Fruchterman-Reingold) or can be manually positioned and fixed into place if desired. Best practices for network visualization layouts are provided. Files from various network tools can be imported into and exported from NodeXL, which includes among the most customizable network visualization engines available. Files can be shared on the NodeXL Graph Gallery.
Tracking Ability: Defining Trackers for Measuring Educational Progress

In measurements that extend over longer periods of time, we might expect all sorts of changes in model parameters, such as ability and item difficulty. We define trackers as instruments with specific properties to deal with such changing parameters. First, trackers should allow for estimating dynamically changing parameters, adapting to possible changes in ability or item difficulty. Second, if no change occurs for some time, trackers should provide unbiased estimates with a known error variance. Such trackers retain the strengths of both state space models and rating systems, while resolve some of their weaknesses. These properties are especially suitable for educational measurement applications such as tracking individual progress or any aggregate thereof, as in reporting survey research.
Explaining Axiom Pinpointing

Axiom pinpointing refers to the task of highlighting (or pinpointing) the axioms in an ontology that are responsible for a given consequence to follow. This is a fundamental task for understanding and debugging very large ontologies. Although the name axiom pinpointing was only coined in 2003, the problem itself has a much older history, even if considering only description logic ontologies. In this work, we try to explain axiom pinpointing: what it is; how it works; how it is solved; and what it is useful for. To answer this questions, we take a historic look at the field, focusing mainly on description logics, and the specific contributions stemming from one researcher, who started it all in more than one sense.
RSA Using Montgomery Powering Ladder on Dual Core

RSA is widely used public key cryptographic algorithm. Security of RSA depends upon the factorization of the large integers. Key sizes must be large for highly secured RSA. But the computation time of RSA increases for larger key sizes. Sequential RSA encryption for 1024 bits takes large execution time. Montgomery powering ladder prevents simple power attack and can be parallelized. In this paper, parallel Montgomery powering adder is implemented on dual-core machine using the multiprocessing module of the python. The performance of the parallel Montgomery powering ladder is analyzed for various key sizes. The parallel Montgomery powering ladder shows 1.63 speedup over its sequential version.
Hardware Implementation of Census Stereo Matching Algorithm

A census stereo matching algorithm with adaptive transform window is presented in this paper, the hardware architecture of the algorithm is designed, the RTL codes of the architecture are simulated and synthesized to Altera's FPGA, the system is tested on FPGA development board. The experimental results show that for image pairs with a resolution of 640×480, the final processing speed of the algorithm on the FPGA can reach 32fps, which can meet the general real-time requirements.
Statistical Analysis of Micro-error Occurrence Probability for the Fitts’ Law-Based Pointing Task

Identifying mild cognitive impairment (MCI) at an early stage and preventing its progression to dementia has become an important task. In order to solve this problem, we focused on micro-errors (MEs), including stagnation of behavior, as a criterion for discriminating between healthy subjects and MCI patients. According to the naturalistic action test (NAT), when the difficulty of the task (index of difficulty: ID) is increased, the occurrence frequency increases drastically. In this research, we aimed to develop a model that simplifies the virtual kitchen challenge (VKC), which reproduces the NAT task on a tablet terminal, and estimates the ME occurrence probability based on a learning difficulty model that considers shape similarity. In this study, 20 university students were asked to perform a shape task. Using the generalized linear model showing the relationship between the occurrence probability of the ME and the result of the shape task, we confirmed that the ME occurrence probability increases with the difficulty level. Moreover, as future work, it is necessary to investigate the influence of handedness and gaze and the relation between color similarity and planning with regard to the ME occurrence probability.
Proof Complexity of Fragments of Long-Distance Q-Resolution

Q-resolution is perhaps the most well-studied proof system for Quantified Boolean Formulas (QBFs). Its proof complexity is by now well understood, and several general proof size lower bound techniques have been developed. The situation is quite different for long-distance Q-resolution (LDQ-resolution). While lower bounds on LDQ-resolution proof size have been established for specific families of formulas, we lack semantically grounded lower bound techniques for LDQ-resolution. In this work, we study restrictions of LDQ-resolution. We show that a specific lower bound technique based on bounded-depth strategy extraction does not work even for reductionless Q-resolution by presenting short proofs of the QParity formulas. Reductionless Q-resolution is a variant of LDQ-resolution that admits merging but no universal reduction. We also prove a lower bound on the proof size of the completion principle formulas in reductionless Q-resolution. This shows that two natural fragments of LDQ-resolution are incomparable: Q-resolution, which allows universal reductions but no merging, and reductionless Q-resolution, which allows merging but no universal reductions. Finally, we develop semantically grounded lower bound techniques for fragments of LDQ-resolution, specifically tree-like LDQ-resolution and regular reductionless Q-resolution.
Password Guessing Using Machine Learning on Wearables

Wearables are now ubiquitous items equipped with a multitude of sensors such as GPS, accelerometer, or Bluetooth. The raw data from this sensors are typically used in a health context. However, we can also use it for security purposes. In this paper, we present a solution that aims at using data from the sensors of a wearable device to identify the password a user is typing on a keyboard by using machine learning algorithms. Hence, the purpose is to determine whether a malicious third party application could extract sensitive data through the raw data that it has access to.
Copy-Move Forgery Detection in Digital Images—Survey and Accuracy Estimation Metrics

Copy-move forgery is a common type of modification attack on images. In such type of manipulation, part of an image is copied and pasted on any other location of the same image. Hence, this form of manipulation is difficult to be identified by methods which look for statistical inconsistencies between different regions of an image. Existing methods are primarily motivated toward finding copy-moved area(s) in an image. In this chapter, we provide a detailed survey of existing techniques of copy-move forgery detection and demonstrate the application of three metrics for forgery detection, viz., detection accuracy, false positive rate, and false negative rate. On the basis of proposed parameters, we compare and analyze the success of different block-based copy-move forgery detection methods. These metrics help to choose the algorithm most suitable for the context.
Creating a User-Centric Data Flow Visualization: A Case Study

Vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. We present a case study in which we used human factors methods to develop a taxonomy for understanding data flow and the visual representations needed to support decision making for binary vulnerability analysis. Using an iterative process, we refined and evaluated the taxonomy by generating three different data flow visualizations for small binaries, trained an analyst to use these visualizations, and tested the utility of the visualizations for answering data flow questions. Throughout the process and with minimal training, analysts were able to use the visualizations to understand data flow related to security assessment. Our results indicate that the data flow taxonomy is promising as a mechanism for improving analyst understanding of data flow in binaries and for supporting efficient decision making during analysis.
Application of the Shuffled Frog Leaping Algorithm (SFLA) in Constructing Fuzzy Classification Systems

A Fuzzy Inference System (FIS) is a way of mapping an input space to an output space using the fuzzy logic. FISs are widely used to solve classification problems. The Shuffled Frog Leaping Algorithm (SFLA) is a metaheuristic inspired by the natural evolution of frogs in searching for the largest source of food. By using local and global searches simultaneously, SFLA is effective in solving various optimization problems. This paper first proposes a new method to create zero-order Sugeno Fuzzy Inference Systems using SFLA. Then, the paper introduces an approach to use resulting SFLA-based Fuzzy Inference Systems to build test oracles. Test oracle is a mechanism for determining whether a test on a software program has passed or failed. The experimental results show that SFLA creates fuzzy systems more efficiently than three other evolutionary algorithms, including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Moreover, with respect to the accuracy and convergence speed criteria, SFLA and PSO outperform other evolutionary algorithms, while their performances are comparable to each other. At last, the experimental results indicate that SFLA-based FISs can be used to create test oracles with acceptable accuracy.
Probabilistic Neural Networks for the Streaming Data Classification

Among the data stream mining algorithms proposed so far in the literature most of them are devoted mainly to the data classification task [1–3]. Although there exist a lot of methods for classification of static datasets, they can hardly be adapted to deal with data streams. This is due to the features of the data stream such as potentially infinite volume, fast rate of data arrival and the occurrence of concept drift.
Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.
Factors of the Readiness for Information Exchange in Pre-School Education Establishments

The development of an interactive environment that allows for a significant enhancement in interaction opportunities with information resources for a child is one of the primary tasks of the pre-school educational process organisation today. This study involved 50 establishments for children in Chelyabinsk (N = 1280: 230 children, 350 teachers, 700 parents). We used two techniques to collect data: a questionnaire for teachers on the use of Information Exchange in the educational process, their attitude towards ICTs, and the available resources (resources included technological aspects, such as infrastructure, equipment, and software, as well as educational aspects, such as further training and the availability of a qualified methodologist); and a conversation with children on their experience in using and operating digital devices for various tasks. The questions were divided into four groups that provided us with data on the following topics: the degree of inclusion of parents in the interaction (a triad of a child, a digital device, and a parent), the awareness of the child of the capacity and resources of ICTs, motivation and interests that can be realized through digital technologies, and self-reflection as the capacity of the child to predict and analyse their activities. The Hypothesis that the level of readiness for information exchange in subjects of the educational process can be increased through further training of pre-school teachers and the establishment of a single information space in a kindergarten group has been proven right. The findings the study proved such an influence and proposed directions for further studying.
Touch and Move: Incoming Call User Authentication

This paper presents two methods of implicit authentication during answering an incoming call based on user behavior biometrics. Such methods allow to increase usability of authentication against common PIN or graphical password. Also, a concept of authentication system based on presented methods is proposed. The paper shows that user’s touch dynamics and movement of the hand towards the ear when accepting the call provide all necessary information for authentication and there is no need for user to enter a PIN or graphical password.
BookTubing Across Regions: Examining Differences Based on Nonverbal and Verbal Cues

BookTubers are a rapidly growing community in YouTube who shares content related to books. Previous literature has addressed problems related to automatically analyzing opinions and mood of video logs (vlogs) as a generic category in YouTube. Unfortunately, the population studied is not diverse. In this work, we study and compare some aspects of the geographic/cultural context of BookTube videos, comparing non-western (Indian) and Western populations. The role played by nonverbal and verbal cues in each of these contexts are analyzed automatically using audio, visual, and text features. The analysis shows that cultural context and popularity can be inferred to some degree using multimodal fusion of these features. The best obtained results are an average precision-recall score of 0.98 with Random Forest in a binary India vs. Western video classification task, and 0.75 in inferring binary popularity levels of BookTube videos.
Composition Closure of Linear Weighted Extended Top-Down Tree Transducers

Linear weighted extended top-down tree transducers with regular look-ahead and with weights from a semiring are formal models that are used in syntax-based statistical machine translation. The composition hierarchies of some restricted versions of such weighted tree transducers (also without regular look-ahead) are considered. In particular, combinations of the restrictions of $$\varepsilon $$ -freeness (all rules consume input), nondeletion, and strictness (all rules produce output) are considered. The composition hierarchy is shown to be finite for all but one $$\varepsilon $$ -free variant of these weighted transducers over any commutative semiring.
Regularization in DQN for Parameter-Varying Control Learning Tasks

As an important technique of preventing overfitting, regularization is widely used in supervised learning. However, regularization has not been systematically studied in deep reinforcement learning (deep RL). In this paper, we study the generalization of deep Q-network (DQN), applying with mainstream regularization approaches, including l1, l2 and dropout. We pay attention on agent's performance not only in original environments, but also in parameter-varying environments which are variational but the same task type. Furthermore, the dropout is modified to make it more adaptive to DQN. Then, a new dropout is proposed to speed up the optimization of DQN. Experiments show that regularization helps deep RL achieve better performance in both original and parameter-varying environments when the number of samples is insufficient.
Virtual Reality Environments (VREs) for Training and Learning

VR technologies are gaining momentum in the field of education and particularly in the use of Virtual Reality (VR)-based learning. Within Virtual Reality Environments (VREs) realistic world situations are simulated, facilitating the transfer of the knowledge and skills gained within the virtual world to the real one. In this chapter, we provide a review of several advantages of using VR technology in education and training. In addition, we examine different challenges and potential problems that need to be considered in order to successfully integrate VR in training activities. We also exemplify the promising prospect of this technology in education by describing two novel VR applications. The first one aims to support educators in improving their teaching practice. Using VR technology, the teacher is given the opportunity to experience the student's point of view during a classic room and cultivate their empathy skills. The second one aims to support teachers in creating VR serious games by lowering the difficulty of developing this type of educational artefact through intuitive interaction and eliminating the need for learning new design language.
Optimization of Demodulation for Air–Gap Data Transmission Based on Backlight Modulation of Screen

Air–gap is an efficient technique for the improving of computer security. Proposed technique uses backlight modulation of monitor screen for data transmission from infected computer. The optimization algorithm for the segmentation of video stream is proposed for the improving of data transmission robustness. This algorithm is tested using Monte Carlo approach with full frame analysis for different values of standard deviations of additive Gaussian noise. Achieved results show improvements for proposed selective image processing for low values of standard deviation about ten times.
Soft Biometrics Estimation Using Shearlet and Waveatom Transforms With Three Different Classifiers

The goal is to find the best feature extraction, which performs the smallest feature vector length and gives the highest performance. In this paper, we proposed a methodology to extract effective features from facial images using two multiresolution transforms; waveatom and shearlet, for estimating gender, ethnicity, facial expression and age. Three classifiers used to perform the final estimation, which are: Artificial Neural Network (ANN), Support vector machine (SVM) and Self-Organization Map (SOM). A comparative study is made to determine the best extractor and classifier. Experiments carried out on a large database collected from three different databases: US Adult Faces, Extended Cohn-Kanade and FG-NET database. The experimental results of the proposed methodology using waveatom transform proved to be effective in the three classifiers, In contrast of shearlet transform.
On the Non-tightness of Measurement-Based Reductions for Key Encapsulation Mechanism in the Quantum Random Oracle Model

Key encapsulation mechanism (KEM) variants of the Fujisaki-Okamoto (FO) transformation (TCC 2017) that turn a weakly-secure public-key encryption (PKE) into an IND-CCA-secure KEM, were widely used among the KEM submissions to the NIST Post-Quantum Cryptography Standardization Project. Under the standard CPA security assumptions, i.e., OW-CPA and IND-CPA, the security of these variants in the quantum random oracle model (QROM) has been proved by black-box reductions, e.g., Jiang et al. (CRYPTO 2018), and by non-black-box reductions (EUROCRYPT 2020). The non-black-box reductions (EUROCRYPT 2020) have a liner security loss, but can only apply to specific reversible adversaries with strict reversible implementation. On the contrary, the existing black-box reductions in the literature can apply to an arbitrary adversary with an arbitrary implementation, but suffer a quadratic security loss.
Incremental precision-preserving symbolic inference for probabilistic programs

We present ISymb an incremental symbolic inference framework for probabilistic programs in situations when some loop-manipulated array data, upon which their probabilistic models are conditioned, undergoes small changes. To tackle the path explosion challenge, ISymb is intra-procedurally path-sensitive except that it conducts a “meet-over-all-paths” analysis within an iteration of a loop (conditioned on some observed array data). By recomputing only the probability distributions for the paths affected, ISymb avoids expensive symbolic inference from scratch while also being precision-preserving. Our evaluation with a set of existing benchmarks shows that ISymb can lead to orders of magnitude performance improvements compared to its non-incremental counterpart (under small changes in observed array data).
CoWLight

Memory deduplication in virtualized systems is shown to be a very useful memory optimization as it is simple to use and provides memory efficient cloud hosting. However, memory deduplication based side channel attacks---information disclosure attacks and covert channel construction across virtual machines---can be mounted using the timing information available because of Copy-on-Write (CoW) fault handling semantics. The CoW semantic has been a necessary-evil with regard to deduplication as it plays a vital role in supporting guest OS transparent deduplication but enables a timing channel for exploitation. Thus to decimate the huge access time difference between a normal write and a write to a shared page, we propose CoWLight, a combination of hardware and software techniques for handling the CoW page faults in an efficient manner. In this work, we propose to address the security issues at its genesis as opposed to mitigate the side-effects by offloading the CoW fault handling to the hardware itself. Further, we show that CoWLight can reduce the access latency differences significantly (by up to 30x) which is within the noise thresholds in a moderately busy system.
A schema matching approach for integrated mobility service

Abstract We propose a schema matching method of transport service data in different schema from multiple data sources. Nowadays, open data about transport services are increasing. However, there are some challenges to use them, because they have complicated data structure such as deeply nested objects or linked small objects. And their disparate data structures make data matching more difficult. So, we present a method to make match transport services data by exploiting the data structure, terminology, and domain knowledge. The proposed method gradually adds linked objects to matching target in matching process to find corresponding attributes. Generally, wider matching target space raises false positive rate. So, our proposed method calculates similarity score of attributes taking into account reference relationships between linked objects. We experimentally evaluate the method in real‐world transport service data and confirm that the method can improve the matching accuracy.