Reviewerly/exHarmony huggingface dataset:
Polynomial-based Lightweight Key Management in a Permissioned Blockchain

A permissioned blockchain platform comes with numerous assurances such as transaction confidentiality and system scalability to several organizations. Most permissioned blockchains rely on a Public-Key Infrastructure (PKI)as cryptographic tools to provide security services such as identity authentication and data confidentiality. Using PKI to validate transactions includes validating digital certificates of endorsement peers which creates an overhead in the system. Because public-key operations are computationally intensive, they limit the scalability of blockchain applications. Due to a large modulus size and expensive modular exponentiation operations, public-key operations such as RSA become slower than polynomial-based schemes, which involve a smaller modulus size and a less smaller number of modular multiplications. For instance, the 2048-bit RSA is approximately 15,728 times slower than a polynomial with a degree of 50 and 128-bit modulus size. In this paper, we propose a lightweight polynomial-based key management scheme in the context of a permissioned blockchain. Our scheme involves computationally less intensive polynomial evaluation operations such as additions and multiplications that result in a faster processing compared with public-key schemes. In addition, our proposed solution reduces the overhead of processing transactions and improves the system scalability. Security and performance analysis are provided in the paper.
A Qualitative Study on the Effects of Real-World Stimuli and Place Familiarity on Presence

We present a qualitative study investigating the effects of real-world stimuli and place familiarity on presence. The study was carried out using a prototype Virtual Reality system designed for participatory urban planning. The system uses consumer grade VR hardware for viewing 3D virtual models of future architectural plans at an urban site. We collected comprehensive qualitative data from 16 participants who used the VR system on-site to immersively view and vote on three different future plans at the real-world location while exposed to ambient non-visual stimuli emanating from the physical location. We provide qualitative findings regarding presence along the four recurrent themes revealed by the thematic analysis of the research data: visual content, impact of physical surroundings, navigation and interaction, and suitability for participatory urban planning. We also analyse the findings in terms of feasibility of such on-site VR experience and the applicability of large-scale city models for VR.
Determining inference semantics for disjunctive logic programs

In a seminal paper, Gelfond and Lifschitz [34] introduced simple disjunctive logic programs, where in rule heads the disjunction operator “|” is used to express incomplete information, and defined the answer set semantics (called GL-semantics for short) based on a program transformation (called GL-reduct) and the minimal model requirement. Our observations reveal that the requirement of the GL-semantics, i.e., an answer set should be a minimal model of rules of the GL-reduct, may sometimes be too strong a condition and exclude some answer sets that would be reasonably acceptable. To address this, we present an alternative, more permissive answer set semantics, called the determining inference (DI) semantics. Specifically, we introduce a head selection function to formalize the operator | and define answer sets as follows: (i) Given an interpretation I and a selection function sel, we transform a disjunctive program Π into a normal program ΠselI, called a disjunctive program reduct; (ii) given a base answer set semantics X for normal programs, we define I to be a candidate answer set of Π w.r.t. X if I is an answer set of ΠselI under X; and (iii) we define I to be an answer set of Π w.r.t. X if I is a minimal candidate answer set. The DI-semantics is general and applicable to extend any answer set semantics X for normal programs to disjunctive programs. By replacing X with the GLnlp-semantics defined by Gelfond and Lifschitz [33], we induce a DI-semantics for simple disjunctive programs, and by replacing X with the well-justified semantics defined by Shen et al. [65], we further induce a DI-semantics for general disjunctive programs. We also establish a novel characterization of the GL-semantics in terms of a disjunctive program reduct, which reveals the essential difference of the DI-semantics from the GL-semantics and leads us to giving a satisfactory solution to the open problem presented by Hitzler and Seda [36] about characterizing split normal derivatives of a simple disjunctive program Π such that answer sets of the normal derivatives are answer sets of Π under the GL-semantics. Finally we give computational complexity results; in particular we show that in the propositional case deciding whether a simple disjunctive program Π has some DI-answer set is NP-complete. This is in contrast to the GL-semantics and equivalent formulations such as the FLP-semantics [24], where deciding whether Π has some answer set is Σ2p-complete, while brave and cautious reasoning are Σ2p- and Π2p-complete, respectively, for both GL- and DI-answer sets. For general disjunctive programs with compound formulas as building blocks, the complexity of brave and cautious reasoning increases under DI-semantics by one level of the polynomial hierarchy, which thus offers higher problem solving capacity.
Pre-Service Teachers' Perceived Relevance of Educational Technology Course, Digital Performance

Educational technology courses are designed to equip prospective teachers with knowledge and skills to use technology for effective instructional delivery. However, the way students perceive the appropriateness and relevance of the instructional content would go a long way in determining their performance and commitment to the course. Student performance in educational technology courses could be improved, if they perceive the instructional content to be relevant to their academic pursuits and future career development. Therefore, this study examined pre-service teachers' perceived relevance of educational technology content, digital tools and their performance in an educational technology course. The study was carried out on 261 pre-service teachers and the result showed that the perceived relevance of educational technology course by the students was high. The correlation coefficient (r) of 0.015 showed a positive relationship between pre-service teachers' perceived relevance of educational technology courses and the performance at the end of semester examination of the course.
Recognizing Email Spam from Meta Data Only

We propose a new spam detection approach based solely on meta data features gained from email headers. The approach achieves above 99 % classification accuracy on the CSDMC2010 dataset, which matches or surpasses state-of-the-art spam classifiers. We utilize a static set of engineered features, supplemented with automatically extracted features. The approach is just as effective for spam detection in end-to-end encryption, as our feature set remains unchanged for encrypted emails. In contrast to most established spam detectors, we disregard the email body completely and can therefore deliver very high classification speeds, as computationally expensive text preprocessing is not necessary.
Improved Chaotic Initialization of Particle Swarm applied to Feature Selection

This paper investigates initializations on both Chaotic Particle Swarm PSO and reduced PSO algorithm. The first initialization method is chaotic initialization with three variants of chaotic PSO. Several chaotic maps are tested. Moreover, to achieve improvement for PSO algorithm, the second initialization method called reduced size PSO (RedPSO) is characterized with limited size of features depending on the whole size of features. It has been found that the chaotic initialization method is better than random initialization for exiting methods (PSO), artificial bee colony (ABC) in term of minimal number of features and highest accuracy. Experimental results validate the chaotic initialization approach tested on UCI data.
Implementation of a Feature-Based Context-Oriented Programming Language

We implemented a feature-based context-oriented programming language, which clearly separates the notion of contexts from the notion of features. Contexts reify particular situations occurring in the surrounding environment, to which a software system can adapt. Features reify the system's behaviour; they are the language components that describe the system's functionality at a fine-grained level. Contexts are mapped to features, such that, when certain contexts become active at run-time, the corresponding features get selected and activated, thus adapting the system's functionality to those particular contexts. In this paper we show the object-oriented architecture, design and implementation issues of such a feature-based context-oriented programming language, which we implemented on top of the Ruby programming language as an application framework for context-oriented programmers. An important part of our language design is the explicit representation of contexts and features in terms of hierarchical tree structures that capture the structural constraints to be maintained at runtime. We illustrate our language design with a small example of a feature-based context-oriented program written in this language.
Authorship Attribution of Russian Forum Posts with Different Types of N-gram Features

Authorship attribution is an important field in online security. Recently there have been numerous successful works in authorship attribution in various European languages. Character n-grams are reported to be the best choice in authorship attribution, as they encode both style and content information. We evaluate different types of character n-gram features in an authorship attribution task in a real-world noisy dataset of Russian forum posts. We also supplement them with a number of new simple n-gram features capturing syntactic and discourse patterns. We perform authorship attribution in a single-topic and a cross-topic setting, as the research question is whether character n-grams capture both style and content information. Our results show that character n-grams are indeed very successful in Russian forum post authorship attribution. However, there is no clear distinction of style and content n-grams, as the same types of n-grams work well for both single-topic and cross-topic settings. In our experiments the generalized simple n-gram features which reveals syntactic and discourse patterns were proved to be also very important in authorship attribution of short informal Russian texts. They represent a different kind of authorship information and are a successful addition to the character n-grams in authorship attribution of forum texts in the Russian language.
Sparse Fractional Energy Distribution and its Application to Radar Detection of Marine Targets With Micro-Motion

The fractional Fourier transform (FRFT), a time-frequency analysis tool, is an effective tool for non-stationary signal processing. Some non-stationary signals, especially chirp signals, appear sparse in fractional Fourier domain (FRFD). To leverage this inherent sparsity, by combining adaptive atomic decomposition in FRFD with sparse representation technique, a novel energy density function, called sparse fractional energy distribution, is proposed for signals that are sparsely represented in FRFD. The relationships between the proposed distribution and other time-frequency distributions are depicted. The performance of the proposed distribution in chirp signal processing is analyzed. Both the theoretical analysis and simulations demonstrate that the proposed distribution achieves multicomponent resolution and robustness against noise. Furthermore, the application to detection and extraction for marine target with micro-motion of the proposed distribution is presented. Experiments are conducted to validate that the proposed distribution can yield promising detection results.
Preserving Location Privacy in Cyber-Physical Systems

The trending technological research platform is Internet of Things (IoT)and most probably it will stay that way for a while. One of the main application areas of IoT is Cyber-Physical Systems (CPSs), in which IoT devices can be leveraged as actuators and sensors in accordance with the system needs. The public acceptance and adoption of CPS services and applications will create a huge amount of privacy issues related to the processing, storage and disclosure of the user location information. As a remedy, our paper proposes a methodology to provide location privacy for the users of CPSs. Our proposal takes advantage of concepts such as mix-zone, context-awareness, and location-obfuscation. According to our best knowledge, the proposed methodology is the first privacy-preserving location service for CPSs that offers adaptable privacy levels related to the current context of the user.
Possibility of distinction of violin timbre by spectral envelope

In this study, the sounds of violins, performed by a violinist, were recorded and the distribution of peak frequencies of the spectral envelope of the recorded sound data was analyzed. The distribution of peak frequencies is different for each violin, but some tendencies were found. Questionnaires were used to examine the distinction in violin timbre by the difference in the patterns of peak frequencies. When the patterns of the two violins were similar, the subjects responded that the timbre of both violins was similar than the other violin whose pattern of peak frequencies was different. The present study discussed the contribution of the similarity of patterns and the difference between patterns to the identification of the timbre.
A Proof-Theoretic Perspective on SMT-Solving for Intuitionistic Propositional Logic

Claessen and Rósen have recently presented an automated theorem prover, intuit, for intuitionistic propositional logic which utilises a SAT-solver. We present a sequent calculus perspective of the theory underpinning intuit by showing that it implements a generalisation of the implication-left rule from the sequent calculus LJT, also known as G4ip and popularised by Roy Dyckhoff.
Ontology-based data access – Beyond relational sources

The database (DB) landscape has been significantly diversified during the last decade, resulting in the emergence of a variety of non-relational (also called NoSQL) DBs, e.g., xml and json-document DBs, key-value stores, and graph DBs. To enable access to such data, we generalize the well-known ont ology-based data access (OBDA) framework so as to allow for querying arbitrary data sources using sparql. We propose an architecture for a generalized OBDA system implementing the virtual approach. Then, to investigate feasibility of OBDA over non-relational DBs, we compare an implementation of an OBDA system over MongoDB, a popular json-document DB, with a triple store. This article is an extended and revised version of an article that appeared in the proceedings of the 17th International Conference of the Italian Association for Artificial Intelligence (AI*IA) [4].
A Feature Detection and Matching Algorithm Based on Harris Algorithm

Harris corner detection algorithm can detect stable image feature points in the image. But there are some problems such as large amount of computation, low positioning accuracy, only detecting corner positions without feature descriptor, which limit the application of the algorithm. In order to solve these problems, a new image feature extraction algorithm is proposed in this paper, which firstly screens the image twice, then the Harris corner detection algorithm is used to detect the corner position, and the corner position is optimized to sub-pixel level by iterative algorithm. Finally, the rotation invariant fast extraction (RIFD) descriptor is used to represent the feature point information. The experimental results show that the proposed algorithm effectively overcomes the shortcomings of the Harris algorithm, it can quickly and accurately extract stable features in the image. It has great application prospects in many image matching systems.
Product recommendation based on genetic algorithm

In this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided.
Reversible Data Hiding for DICOM Image Using Lifting and Companding

In this work, a reversible watermarking technique is proposed for DICOM (Digital Imaging and Communications in Medicine) image that offers high embedding capacity (payload), security and fidelity of the watermarked image. The goal is achieved by embedding watermark based on companding in lifting based discrete wavelet transform (DWT) domain. In the embedding process, the companding technique is used to increase the data hiding capacity. On the other hand, a simple linear function is used in companding to make the scheme easy to implement, and content dependant watermark is used to make the scheme robust to collusion operation. Moreover, unlike previously proposed reversible watermarking techniques, this novel approach does not embed the location map in the host image that ultimately helps to achieve high fidelity of the watermarked image. The advantage of the proposed scheme is demonstrated by simulation results and also compared with selected other related schemes.
Differential Privacy with Variant-Noise for Gaussian Processes Classification

Incredible capacity of machine learning models to mine the underlying information has led to concerns of privacy disclosure. This makes privacy-preserving learning algorithms become a hot spot. In this paper, we focus on Gaussian processes classification (GPC) with a provable secure and feasible privacy model, differential privacy (DP). First we apply a functional mechanism to design a basic privacy-preserving GP classifier. This involves finding the sensitivity of the outputs, and adding a Gaussian process noise proportional to the sensitivity to the trained classifier. Then we propose a variant-noise mechanism to perturb the classifier with different scaled noise based on the density of dataset. We show that this method can significantly reduce the added noise, whilst sufficiently maintaining the accuracy of the classifier both in theory and experiments.
A Comparison of fastText Implementations Using Arabic Text Classification

The quality of word representation is crucial to obtain good results in many natural language processing tasks. Recently, many word representation models (word embeddings), such as fastText, have been developed. In this research, we compared the algorithms for the fastText implementation, Facebook’s official implementation, and Gensim’s implementation using the same pre-trained fastText model. Using multi-class classification, we evaluated these embeddings. According to the results, the Facebook implementation performed better than Gensim’s implementation, with an average accuracy of 78.22% and 56.73%, respectively, for sentence embeddings and an average accuracy of 79.43% and 57.95%, respectively, for word embeddings.
Novelty Detection via Network Saliency in Visual-Based Deep Learning

Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erroneous, perhaps life-threatening one. Prior work on novelty detection deal with highly structured data and do not translate well to dynamic, real-world situations. This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by leveraging information learned by the trained prediction model and a new image similarity metric. We demonstrate the efficacy of this method through experiments on a real-world driving dataset as well as on our in-house indoor racing environment.
Dynamic Recognition for User Age-Group Classification Using Hand-Writing Based Finger on Smartphones

The way people interact with handheld devices such as smartphone and tablet tends heavily dependent on age and experience. It can argued that the automatic identification of an age group or a level of user's experience based on the way they are using the devices could contribute greatly to providing adaptive usage environment for each user. This study aims to investigate the effectiveness of employing the dynamic features generated by users of smartphones and tablets to automatically recognise their age group. To achieve that we created a database of over 2520 trials from 42 participants of elderly (60+) and younger users (20-39) using finger based handwriting of 10 different English words. The user recognition consists of three stages: collecting touch hand writing data, extracting features, and classification. Handwriting on touchscreen data was collected on two sizes of smartphones devices based finger. The features used were force pressure (FP), movement time (MT), and signature precision (SP). In the training dataset, the feature's average for each trial of 6 across 10 words was calculated. A KNN classification is used to classify user age. The study considered number of users in the training dataset for 100%, 50%, and one user (i.e. 1%). The results revealed there were high classification accuracy on small smartphone compared to mini-tablet. The classification accuracy using the combined features for all users on the training dataset was (82%) on small smartphone and (77%) on mini-tablet. We found that the classification of younger users (95%) were more accurate than the elderly users (55%). The study provides an evidence of the possibility of classifying user age group based on hand writing words on touchscreen based finger.
Utilizing Free Augmented Reality App for Learning Geometry at Elementary School in Taiwan

From random interviews of mathematics teachers, the researchers are conscious that students have difficulties in solving problems regarding compound body volume measurement. The researchers found the main factor involved in the difficulties was incomplete spatial concepts. Augmented reality (AR), which is a kind of educational technology, has been widely applied in the educational field in recent years. AR provides two- or three-dimensional objects and/or information and interaction with them. These characteristics can compensate for the insufficient characterization of compound-body volume in traditional education environments. The paper studies evaluation in utilizing free augmented reality to learn volumetric measurement of compound bodies to complete spatial concepts as well as improve the students' learning performance. The finding suggests that the positive impact on visualization and interaction as well as attitude lead students to be more engaged in learning activities with less cognitive effort, resulting in better learning performance.