Reviewerly/exHarmony huggingface dataset:
Highly Efficient Privacy Preserving Location-Based Services with Enhanced One-Round Blind Filter

To take advantages of location-based services (LBS) while protecting user privacy against untrusted LBS providers, privacy preserving LBS have attracted increasing attention. Considering that users in an LBS system are often equipped with resource-constrained mobile devices, most existing privacy preserving LBS methods are based on anonymization techniques. However, these existing schemes still have some privacy and efficiency limitations. In this paper, we propose a novel privacy preserving LBS scheme, which simultaneously achieves user privacy protection and high query efficiency. Specifically, we utilize the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity technique and the pseudo random function to protect the location privacy and the query message privacy of users. We design an enhanced one-round blind filter protocol (ORBF <inline-formula><tex-math notation="LaTeX">$_e$</tex-math></inline-formula> ) based on the Paillier cryptosystem to securely filter out redundant records generated by the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity technique. Compared with existing solutions, our ORBF <inline-formula><tex-math notation="LaTeX">$_e$</tex-math></inline-formula> protocol not only ensures that users receive exactly satisfying results but also incurs a low computation and communication cost on the server side. Through the theoretical analysis and extensive experiments, we demonstrate the security and efficiency of our proposed scheme.
On initial population generation in feature subset selection

Performance of evolutionary algorithms depends on many factors such as population size, number of generations, crossover or mutation probability, etc. Generating the initial population is one of the important steps in evolutionary algorithms. A poor initial population may unnecessarily increase the number of searches or it may cause the algorithm to converge at local optima. In this study, we aim to find a promising method for generating the initial population, in the Feature Subset Selection (FSS) domain. FSS is not considered as an expert system by itself, yet it constitutes a significant step in many expert systems. It eliminates redundancy in data, which decreases training time and improves solution quality. To achieve our goal, we compare a total of five different initial population generation methods; Information Gain Ranking (IGR), greedy approach and three types of random approaches. We evaluate these methods using a specialized Teaching Learning Based Optimization searching algorithm (MTLBO-MD), and three supervised learning classifiers: Logistic Regression, Support Vector Machines, and Extreme Learning Machine. In our experiments, we employ 12 publicly available datasets, mostly obtained from the well-known UCI Machine Learning Repository. According to their feature sizes and instance counts, we manually classify these datasets as small, medium, or large-sized. Experimental results indicate that all tested methods achieve similar solutions on small-sized datasets. For medium-sized and large-sized datasets, however, the IGR method provides a better starting point in terms of execution time and learning performance. Finally, when compared with other studies in literature, the IGR method proves to be a viable option for initial population generation.
Software Quality Models: A Systematic Mapping Study

Quality Models play a critical role in assuring quality and have evolved over 40+ years. They provide support for defining quality attributes, building and measuring the quality of the resulting product. Each quality model adopts a critical view on quality in terms of a set of model elements and relationships between them. This study aims to provide an overview of the state-of-the-art research on quality models with a focus on encompassing model elements and their support to architecting quality. The study was conducted using systematic mapping as the research methodology. A total of 238 primary papers were classified based on the type of research, standards usage, and publication trends. We identified that 17% (40) of papers belong to quality models. These 40 models were analyzed for the underlying meta-model elements and their support for a quality architecture using Bayer's reference architecture framework. The architecture phase mapping analysis shows that quality planning phase is 100% supported, quality assessment is 75% supported, quality documentation is included in 40% models and quality realization aspect is barely considered in 13% models. Quality realization happens through software processes and patterns, and it is necessary to evolve quality models and software process architectures that correlate quality definitions and quality realization mechanisms. Future research is expected in this direction.
Flight Path Planning for a Solar Powered UAV in Wind Fields Using Direct Collocation

Achieving the long-endurance of a solar-powered unmanned aerial vehicle is difficult under a limited amount of energy and specific meteorological environments. Therefore, it is essential to manage the energy used by a solar-powered UAV during a mission. In this paper, a framework for energy-based path planning for a solar-powered UAV is presented. In order to generate the optimal path in terms of energy in a specific meteorological environment, the energy model of a solar-powered UAV is considered with regard to wind field. Additionally, the optimal energy path is examined through the optimal control technique. The optimal path is then applied to some case studies regarding solar-powered UAVs, and evaluated in order to verify the feasibility of the proposed approach. The optimization results show that the resulting path is indeed the optimal path, which increases the endurance of a flight and takes weather conditions into accounts. The proposed approach is also expected to find optimal paths in accordance with real weather environments and/or various weather situations.
VQL: Providing Query Efficiency and Data Authenticity in Blockchain Systems

Blockchain, as the underlying technique of cryptocurrency, has triggered a wave of innovation in decentralized computing. Despite some research on blockchain data query, a primary concern for blockchain to be fully practical is to combat the data query inefficiency and query result authenticity. To provide both efficient and verifiable data query services for blockchain-based systems, we propose a Verifiable Query Layer (VQL). The middleware layer extracts transactions stored in the underlying blockchain system and efficiently reorganizes them in databases to provide various query services for public users. To prevent falsified data being stored in the middleware, a cryptographic hash value is calculated for each constructed database. The database fingerprint including the hash value and some database properties will be first verified by miners and then stored in the blockchain. We implement VQL and conduct extensive experiments based on a practical blockchain system Ethereum. The evaluation results demonstrate that VQL can effectively support various data query services and guarantee the authenticity of query results for the blockchain system.
A Usability and Acceptance Evaluation of the Use of Augmented Reality for Learning Atoms and Molecules Reaction by Primary School Female Students in Palestine

The chemical reaction between different molecules is an important learning subject in a chemistry course. Especially for elementary school students, this can be an abstract concept and therefore difficult to understand. One way to facilitate this learning process is to use Augmented Reality (AR) technology, which is considered as an added value compared with classical learning materials such as textbooks, two-dimensional images, video, and so forth. Among the different advantages of using AR technology in the context of the educational domain is the fact that three-dimensional technology is offering a safe environment especially if the students have to perform critical tasks such as simulating chemical reactions. This work investigates students' attitude toward the use of a mobile AR application for learning atoms' and molecules' reactions. In particular, we focused on female students because, in general, female students show less interest in science and technology than male students. We were keen to investigate whether the use of AR technology could change this attitude. The students are able to interact with different AR components to explore the possible reactions in a three-dimensional interface, to understand the structure and shape of atoms and molecules, and to view related explanations in their native language, that is, the Arabic language. The mobile AR application was evaluated by a class of 12- to 13-year-old (7th grade) students in a Palestinian primary school. The number of participants was 50, all female students. After analyzing the results, we can conclude that these female students had a positive attitude toward the use of this AR application in their learning process.
LADEC: The Large Database of English Compounds

The Large Database of English Compounds (LADEC) consists of over 8,000 English words that can be parsed into two constituents that are free morphemes, making it the largest existing database specifically for use in research on compound words. Both monomorphemic (e.g., wheel) and multimorphemic (e.g., teacher) constituents were used. The items were selected from a range of sources, including CELEX, the English Lexicon Project, the British Lexicon Project, the British National Corpus, and Wordnet, and were hand-coded as compounds (e.g., snowball). Participants rated each compound in terms of how predictable its meaning is from its parts, as well as the extent to which each constituent retains its meaning in the compound. In addition, we obtained linguistic characteristics that might influence compound processing (e.g., frequency, family size, and bigram frequency). To show the usefulness of the database in investigating compound processing, we conducted a number of analyses that showed that compound processing is consistently affected by semantic transparency, as well as by many of the other variables included in LADEC. We also showed that the effects of the variables associated with the two constituents are not symmetric. In short, LADEC provides the opportunity for researchers to investigate a number of questions about compounds that have not been possible to investigate in the past, due to the lack of sufficiently large and robust datasets. In addition to directly allowing researchers to test hypotheses using the information included in LADEC, the database will contribute to future compound research by allowing better stimulus selection and matching.
Managing Software Development Projects for Success: Aligning Plan- and Agility-Based Approaches to Project Complexity and Project Dynamism

In general, project complexity and project dynamism are recognized as potent characteristics that influence, usually in a negative way, the outcome of software development projects. However, with respect to the two dominant paradigms for managing software development projects (i.e., plan-based project approach and agility-based project approach), there is less agreement as to which one is better. Emerging research suggests that the preeminence of one approach over another is a matter of fit between the project management approach and the project context, including its complexity and dynamism. We contribute to this line of research by studying the moderating influence of the two project management approaches on the relationship between project complexity and project dynamism on project outcomes. Our analysis of data gathered from a survey of software development professionals shows that managing dynamism is critical for realizing project success, and an agility-based project approach is best suited for mitigating dynamism’s negative effects on project success.
Impacts of Artificial Intelligence on Public Administration: A Systematic Literature Review

The advent of machines power-driven by Artificial Intelligence (AI) have strongly influenced the world in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. The future of AI is promising and is offering a wide range of opportunities for scholars and academics. Although the theme has received a considerable attention over the last years, much has been speculated and little is known about its impacts on the Public Administration. Thus, the purpose of this article is to make the result of those impacts less ambiguous. To this end, we have conducted a systematic review to provide a comprehensive analysis on the latest impacts of AI on the Public Administration. Our intent is to narrow the field of study, while AI is being continuously strengthened with new empirical evidences.
Assessing Transferability From Simulation to Reality for Reinforcement Learning

Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviating the need for expensive real-world experiments. However, the direct transfer of learned behavior from simulation to reality is a major challenge. Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the `Simulation Optimization Bias` (SOB). In this case, the optimizer exploits modeling errors of the simulator such that the resulting behavior can potentially damage the robot. We tackle this challenge by applying domain randomization, i.e., randomizing the parameters of the physics simulations during learning. We propose an algorithm called Simulation-based Policy Optimization with Transferability Assessment (SPOTA) which uses an estimator of the SOB to formulate a stopping criterion for training. The introduced estimator quantifies the over-fitting to the set of domains experienced while training. Our experimental results on two different second order nonlinear systems show that the new simulation-based policy search algorithm is able to learn a control policy exclusively from a randomized simulator, which can be applied directly to real systems without any additional training.
Differentially Private Double Spectrum Auction With Approximate Social Welfare Maximization

Spectrum auction is an effective approach to improve the spectrum utilization, by leasing an idle spectrum from primary users to secondary users. Recently, a few differentially private spectrum auction mechanisms have been proposed, but, as far as we know, none of them addressed the differential privacy in the setting of double spectrum auctions. In this paper, we combine the concept of differential privacy with double spectrum auction design and present a differentially private double spectrum auction mechanism with approximate social welfare maximization (DDSM). Specifically, we design the mechanism by employing the exponential mechanism to select clearing prices for the double spectrum auction with probabilities exponentially proportional to the related social welfare values and then improve the mechanism in several aspects, such as the designs of the auction algorithm, the utility function, and the buyer grouping algorithm. Through theoretical analysis, we prove that DDSM achieves differential privacy, approximate truthfulness, and approximate social welfare maximization. Extensive experimental evaluations show that DDSM achieves a good performance in terms of social welfare.
Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media

Recent years have witnessed a surge of manipulation of public opinion and political events by malicious social media actors. These users are referred to as “Pathogenic Social Media (PSM)” accounts. PSMs are key users in spreading misinformation in social media to viral proportions. These accounts can be either controlled by real users or automated bots. Identification of PSMs is thus of utmost importance for social media authorities. The burden usually falls to automatic approaches that can identify these accounts and protect social media reputation. However, lack of sufficient labeled examples for devising and training sophisticated approaches to combat these accounts is still one of the foremost challenges facing social media firms. In contrast, unlabeled data is abundant and cheap to obtain thanks to massive user-generated data. In this paper, we propose a semi-supervised causal inference PSM detection framework, SemiPsm, to compensate for the lack of labeled data. In particular, the proposed method leverages unlabeled data in the form of manifold regularization and only relies on cascade information. This is in contrast to the existing approaches that use exhaustive feature engineering (e.g., profile information, network structure, etc.). Evidence from empirical experiments on a real-world ISIS-related dataset from Twitter suggests promising results of utilizing unlabeled instances for detecting PSMs.
Self-Regulation as a Basic Element of the Professional Culture of Engineers

This paper addresses the problem of the formation of the self-regulation of educational activities of students studying in a technical university. The purpose of this paper is to discuss the problem of the self-regulation of educational activities of students, presenting data from an experimental study of students’ self-regulation. Special attention is paid to the distinction between the terms self-control and self-regulation. This paper presents data on the state of self-regulation among students studying technical specialties. The research methodology is based on the theoretical analysis of the results of scientific works, which present the main provisions on the formation of students’ self-regulation. The research methods include questioning and a methodical experiment. The stages of the formation of students’ self-regulation are highlighted, and the main directions of teachers’ work on the formation of students’ self-regulation are determined. The structure of self-regulation is described and the condition for its formation is justified using the technology of self-regulation formation. Based on the study, students’ self-regulation is linked to the awareness of their professional and personal goals, as well as to the level of regulatory and reflective qualities. Personal and professional self-improvement in the process of vocational education depends on the level of the formation of regulatory and reflexive qualities.
Dynamic Paths of Complexity and Accuracy in Second Language Speech: A Longitudinal Case Study of Chinese Learners

Abstract The present study investigated the developmental patterns of Chinese EFL(AQ) learners’ oral language in terms of complexity and accuracy and looked into the dynamic interactions between them within the framework of Complex Dynamic Systems Theory (CDST). The data were analysed using dynamic analyses (moving min–max graphs, moving correlations and Monte Carlo Simulations). It was found that, firstly, at the group level, the general developmental trends of both complexity and accuracy showed improvements. Secondly, at the individual level, the developmental patterns were non-linear and dynamic with high degrees of variability, and individual language development was influenced by the initial states. Thirdly, the analyses revealed a complex interplay between complexity and accuracy, which gradually shifted from a clearly competitive relationship during the early stages to a supportive relationship in later stages. This shift in interaction shows that complexity goes hand in hand with accuracy, which corroborates the interconnectedness of subsystems as one of the major characteristics of CDST. The findings confirm the applicability of CDST approaches to L2 oral development and carry valuable implications for CDST theory development and oral language teaching.
Two types of permutation polynomials with special forms

Let q be a power of a prime and Fq be a finite field with q elements. In this paper, we propose four families of infinite classes of permutation trinomials having the form cx−xs+xqs, and investigate the relationship between two types of widely discussed permutation polynomials. Based on this relation, many classes of permutation polynomials having the form (xqk−x+δ)s+cx without restriction on δ are derived.
Scalable Logo Recognition Using Proxies

Logo recognition is the task of identifying and classifying logos. Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations of logos, brands and re-training to cover every variation is impractical. In this paper, we formulate logo recognition as a few-shot object detection problem. The two main components in our pipeline are universal logo detector and few-shot logo recognizer. The universal logo detector is a class-agnostic deep object detector network which tries to learn the characteristics of what makes a logo. It predicts bounding boxes on likely logo regions. These logo regions are then classified by logo recognizer using nearest neighbor search, trained by triplet loss using proxies. We also annotated a first of its kind product logo dataset containing 2000 logos from 295K images collected from Amazon called PL2K. Our pipeline achieves 97% recall with 0.6 mAP on PL2K test dataset and state-of-the-art 0.565 mAP on the publicly available FlickrLogos-32 test set without fine-tuning.
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition

In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker verification (SV) system. We start our approach by carefully designing a data augmentation process to cover a wide range of acoustic conditions and to obtain rich training data for various components of our SV system. We augment several well-known databases used in SV with artificially noised and reverberated data and we use them to train a denoising autoencoder (mapping noisy and reverberated speech to its clean version) as well as an x-vector extractor which is currently considered as state-of-the-art in SV. Later, we use the autoencoder as a preprocessing step for a text-independent SV system. We compare results achieved with autoencoder enhancement, multi-condition PLDA training and their simultaneous use. We present a detailed analysis with various conditions of NIST SRE 2010, 2016, PRISM and with re-transmitted data. We conclude that the proposed preprocessing can significantly improve both i-vector and x-vector baselines and that this technique can be used to build a robust SV system for various target domains.
“Bad smells” in software analytics papers

There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and poorly understood underlying phenomena are causing some concern as to the reliability of studies. Our goal is to provide guidance for producers and consumers of software analytics studies (computational experiments and correlation studies). We propose using “bad smells”, i.e., surface indications of deeper problems and popular in the agile software community and consider how they may be manifest in software analytics studies. We list 12 “bad smells” in software analytics papers (and show their impact by examples). We believe the metaphor of bad smell is a useful device. Therefore we encourage more debate on what contributes to the validity of software analytics studies (so we expect our list will mature over time).