Reviewerly/exHarmony huggingface dataset:
Comparison among the natural radioactivity levels, the radiogenic heat production, and the land surface temperature in arid environments: A case study of the El Gilf El Kiber area, Egypt

The El Gilf El Kiber area is one of the most promising areas for urban growth and reclamation projects in the southwest of Egypt. In the study area, the lithological units vary in age from Precambrian (mainly granite) to Quaternary (mainly sand sediments). The analysis of airborne magnetic and gamma-ray spectrometry data was incorporated into that of the remote sensing data (Landsat-8). The integrated analysis was used to detect the lithological distributions and to delineate the subsurface basement rocks that control the radioactivity in the study area. It was also used to determine the relationship between radiogenic heat production (RHP) and land surface temperature (LST) that was retrieved from the Landsat-8 thermal infrared sensor (TIRS) data. The output of the principal component analysis (PCA) of Landsat-8 Operational Land Imager (OLI) data is a false-color map (PC1, PC2, and PC3 as red, green, and blue, respectively) displaying the various lithological units in the study area. The LST map, calculated from Landsat-8 TIRS data, shows that the surface temperatures range from approximately 41 to 53 °C. The highest concentrations of eU, eTh, and K in the area were 25 ppm, 121 ppm, and 7.8%, respectively. The RHP values ranged from 1.1 μW/m3 to 15.1 μW/m3. These relatively high anomalies are associated with basement outcrops and the sedimentary rocks overlying shallow basement rocks. The total horizontal derivative (THD) of the magnetic data delineates the horizontal extension of the basement rocks. The Curie point depth (CPD), estimated from magnetic data, ranges from 35.5 km to 41.0 km. The results also exhibited geothermal gradients ranging from 16.10 °C/km to 14.14 °C/km, and heat flow ranges from 35.4 mW/m2 to 40.8 mW/m2. It was evident after comparing that the RHP and LST maps exhibited the same features as the gamma-ray spectrometric maps. Moreover, the areas around the basement rocks exhibited higher radioactivity, RHP, and temperature than the basement rocks themselves.
Ontology-Based Model Identification of Industrial Energy Systems

The paper presents a novel approach for ontologybased model identification of industrial energy systems. Therefore, an ontology is designed which is extended by formal rules to automatically identify causal relations between the components' input and output quantities. Also, available sensor data are mapped with rules to these quantities to enable direct data access for model identification. The proposed approach is evaluated for a use case of an industrial heating process. The results show, that the required information is available in the extended ontology to automatically generate dynamic models of the plant equipment.
A test problem with difficulty in decomposing into sub-problems for model-based genetic algorithms

Fully or nearly decomposable problems have been often used as test problems in genetic algorithm (GA) researches. The two types of problems have the common feature that all bits in each sub-problem are used for calculating its fitness value. The present model-based GAs appropriately decompose the whole of such a problem into parts and efficiently solve it. However, so far there has not been a test problem in which sub-problems are overlapped and bits of each sub-problem in the overlapped parts used for calculating a fitness value of the sub-problem vary depending on a bit pattern of a given solution candidate. This type of problems might cause difficulty in the decomposition to model-based GAs. In the study we propose the overlapped chains problem (OCP) as this type of problem. The results of applying several GAs to instances of the OCP show that some model-based GA cannot yield better search performance than a simple operator-based GA.
Reducing race-based bias and increasing recidivism prediction accuracy by using past criminal history details

Recidivism, the propensity of convicts to reoffend after release from prison on parole, is a domain that has both benefited from machine learning based decision support systems and experienced race-based bias in the predictions. In this paper we propose an approach to select a model to increase recidivism prediction accuracy while reducing race-based bias. We built several models that each use offenders' current arrest's criminal information and accrue past criminal history information based on different numbers of prior arrests cycles. We then monitor accuracy and inherent bias in the results for different sub-populations. Finally, from the various criminal history-based models developed, we select the one that offers minimal bias for different subpopulations while increasing the accuracy by using False Positive Rate Parity. The approach allows adaptation to the diversity in training data, different crime types, and varied length of prior arrest cycle history. We assessed model prediction performance using ten independent iterations of Monte Carlo cross-validation.
Tunable Access Control for Data Sharing in Cloud

Sabitha, S. Rajasree, M. S.Attribute-Based Encryption (ABE) suffers communication and computation overhead due to the linearly varying size of the ciphertext and the secret key, depending on the number of attributes in the access policy. This paper proposes a multilevel attribute-based access control scheme for secure data sharing in the cloud to reduce the overhead. It produces a constant size ciphertext and a compact secret key to efficiently utilize the storage space and reduce the communication cost. This method flexibly shares ciphertext classes among the randomly selected users with a specific set of attributes. All other ciphertext classes outside the set remain confidential. It allows dynamic data updates and provides access control of varying granularity, at user-level, at file-level, and attribute-level. Granularity levels can be chosen based on applications and user demands. This scheme tackles user revocation and attribute revocation problems, and prevents forward and backward secrecy issues. It allows the data owner to revoke a specific user or a group of users. It is very useful for secure data storage and sharing.
Beyond the novelty effect: EFL learners' attitudes towards ICT use in the classroom

Abstract The present study explores high school students' (aged 16–17) attitudes towards the use of technology Information and Communication Technology (ICT) in the English as foreign language (EFL) classroom. While digital competence has become one of the core skills in our society, ICT is gaining more presence in the EFL teaching and learning. However, it is not widespread in the mainstream education, as it is the case of Spain, especially at high school level. There is also still some reticence on the part of the teachers to let the ICT be part and parcel of their daily practice. Since adolescents often experience a motivational drop as far as learning a foreign language (FL) in school setting is concerned, EFL teachers should promote the integration of ICT in classroom activities, as research has shown that technology-supported activities may contribute to engaging students in the process of learning a FL and act as a motivating factor. This paper examines 77 EFL learners' responses to a questionnaire administered before and after the implementation of an 18-month ICT-based intervention in their classroom. The main findings reveal that attitudes towards technology were positive and they were maintained beyond the possible “novelty effect”, which supports the claims in favor of ICT's integration in EFL teaching.
Frequent Itemset Mining with Hadamard Response Under Local Differential Privacy

Frequent itemset mining is a basic data mining task and has many applications in other data mining tasks. However, users’ personal privacy information will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. Data mining using local differential privacy involves two major problems. The first one is that the accuracy of the results after mining is low, and the other one is that the user transmits a large amount of data to the server, which results in higher communication costs. In this study, we demonstrate that the Hadamard response (HR) algorithm improves the accuracy of the results and reduces the communication cost from k to log k. Finally, we use the Frequent pattern tree (FP-tree) algorithm for frequent itemset mining to compare the existing algorithms.
SHX

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of crossover in real-coded genetic algorithm (RCGA), in this paper we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 4 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of accuracy.
Design of Virtual Reality Browser Platform for Programming of Quantum Computers via VR Headsets

Quantum computers are expected to offer an effect similar to the influence of the early integrated circuit computers. According to current systems, it is predicted that they will play an effective role in the emergence of a stronger technology with an increasing speed. Some high-tech companies have quantum computer designs that they actively put into use from the research and development phase to the problem-solving phase and these computers use different architectures. Unlike the others, IBM launched a cloud-based software infrastructure in 2016 and first introduced its 5-qubit quantum computer, which consists of sequential quantum ports architecture, to the use of researchers and interested parties via its web servers. Programming is done by using quantum gates via a web interface called quantum composer. Significant progress has also been made in virtual reality technologies and virtual reality based browser platforms have been developed. In this paper, studies on the using and training of the IBM quantum composer platform through a browser designed on the basis of virtual reality are presented.
An incremental GraphBLAS solution for the 2018 TTC Social Media case study

Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the “Social Media” case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts.
An annotated dataset of stack overflow post edits

To improve software engineering, software repositories have been mined for code snippets and bug fixes. Typically, this mining takes place at the level of files or commits. To be able to dig deeper and to extract insights at a higher resolution, we hereby present an annotated dataset that contains over 7 million edits of code and text on Stack Overflow. Our preliminary study indicates that these edits might be a treasure trove for mining information about fine-grained patches, e.g., for the optimisation of non-functional properties.
Imperialist Competitive Algorithm with Independence and Constrained Assimilation

This work proposes an improved Imperialist Competitive Algorithm (ICA) based algorithm for solving constrained combinatorial problems, called ICA with Independence and Constrained Assimilation (ICAwICA). The proposed algorithm introduces the concept of colony independence - a free will to choose between classic ICA assimilation to the empire's imperialist or any other imperialist in the population. Furthermore, a constrained assimilation process has been implemented that combines classical ICA assimilation and revolution operators, while maintaining population diversity. In order to evaluate the performance and generalisation aspects of the proposed approach, two different kinds of combinatorial benchmark problems were selected - subset selection and routing, Multiple Knapsack Problem (MKP) and Multiple Depot Vehicle Routing Problem (MDVRP), respectively. The algorithm showed definite improvement over classic ICA and outperformed most of the competition on both types of problems across multiple instances, indicating the generic, universal nature of the ICAwICA. Moreover, it ranked 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> among the recently published algorithms that are customised to the specific problem with the use of problem-specific operators, while the proposed algorithm had no such operators.
Using 2D digital image correlation to investigate the flexural behavior of continuous composite beams

Abstract This paper aims at studying the flexural behavior of continuous composite beams casting from normal concrete and Slurry Infiltrated Fiber Concrete (SIFCON) by using DIC (Digital Image Correlation) technique. The DIC results are compared with the standard methods measurement. DIC is a new non-contacting and non-destructive optical technique based on digital photographs for the tested area to compare them at different stages of loading. Tests were performed on three full-scale T-section continuous beam specimens subjected to two-point loads. In this paper, the effect of using SIFCON at compression or tension parts of plastic hinge locations on the flexural capacity, moment redistribution and crack width are investigated experimentally. The results indicate that the DIC technique gives very good correspondence comparing with the results recorded by standard methods measurement. In addition, it was found that the enhancement in moment redistribution ratio for the beam with SIFCON at compression parts of plastic hinge zones was significantly higher by about 38% comparing with the beam with SIFCON at tension parts of plastic hinge zones.
Digital Identity Management System Using Blockchain Technology

Digital identity plays the main role in the digital world. Thus, many researchers emphasize the determination of the accurate identity of users. This paper proposes the blockchain-based digital identity management system in Myanmar, to decrease the use of national identity document. The occurrence of duplicated identity numbers in Myanmar is the main issue, and another problem is how to recover the lost national identity card. Trusted humans and organizations that have the responsibility for the entire activity need secure data management. Blockchain technology built on the internet could use current identity data in a peer-to-peer, interoperable way to eliminate third parties. Using the benefit of blockchain, the unique and persistent identity number of the citizen will be stored in the blockchain network. The user could store encrypted private, personal, and sensitive data, as well as share verifications about the information on the blockchain.
A Point Light Source Interference Removal Method for Image Dehazing

Single image haze removal has been a challenging problem and the performance of the most existing dehazing methods is degraded when point light sources exist in the hazy image. In this paper, we propose a point light source interference removal method (PLiSIR) to reduce the interferences when estimating the atmospheric light. According to our observation, the pixel intensity around the point light sources can be modeled approximately by Gaussian distribution. The locations of the interfered pixels are obtained reasonably regardless of the specific number of light sources. A binary masking map is then created for distinguishing whether the pixel is affected by light sources and thus PLiSIR can be adopted to dehazing algorithms by removing the interfered pixels, during the estimation of the atmospheric light. To demonstrate how to apply PLiSIR to different algorithms, we select the dark channel prior dehazing method (DCP) and the color attenuation prior dehazing method (CAP) as two carrier methods and introduce the adaptations accordingly. Experimental results indicate that the PLiSIR can assist DCP and CAP to better estimate the atmospheric light, and thus generate better dehazing results compared to the original DCP and CAP methods. Moreover, PLiSIR also helps DCP and CAP to simplify the parameter adjustment process of the guided filter. At last, we compare our modified DCP approach (which we refer to PLiSIR-DCP) with the state-of-the-art nighttime dehazing algorithm to present an approach which is suitable for both daytime and nighttime haze removal.
What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters

According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people’s amount of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-related content. The problem we address in this paper is the identification of users’ implicit topic preferences by analyzing the content categories they tend to post on Twitter. Our proposal is significant given that modeling their multi-topic profile may be useful to find patterns or association between preferences for categories, discover trending topics and cluster similar users to generate better group recommendations of content. In the present work, we propose a method based on the Mixed Gaussian Model to extract the multidimensional preference representation for 399 Ecuadorian tweeters concerning twenty-two different topics (or dimensions) which became known by manually categorizing 68.186 tweets. Our experiment findings indicate that the proposed approach is effective at detecting the topic interests of users.
On The Generation of Unrestricted Adversarial Examples

Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged. Even in this limited setting and despite extensive studies in recent years, there is no defence against adversarial examples for complex tasks (e.g., ImageNet). However, for simpler tasks like handwritten digit classification, a robust model seems to be within reach. Unlike perturbation-based adversarial examples, the adversary is not limited to small norm-based perturbations in unrestricted adversarial examples. Hence, defending against unrestricted adversarial examples is a more challenging task. In this paper, we show that previous methods for generating unrestricted adversarial examples ignored a large part of the adversarial subspace. In particular, we demonstrate the bias of previous methods towards generating samples that are far inside the decision boundaries of an auxiliary classifier. We also show the similarity of the decision boundaries of an auxiliary classifier and baseline CNNs. By putting these two evidence together, we explain why adversarial examples generated by the previous approaches lack the desired transferability. Additionally, we present an efficient technique to create adversarial examples using generative adversarial networks to address this issue. We demonstrate that even the state-of-the-art MNIST classifiers are vulnerable to the adversarial examples generated with this technique. Additionally, we show that examples generated with our method are transferable. Accordingly, we hope that new proposed defences use this attack to evaluate the robustness of their models against unrestricted attacks.
Finite blockchain games

This paper studies the dynamic construction of a blockchain by competitive miners. In contrast to the literature, we assume a finite time horizon. Moreover, miners are rewarded for blocks that eventually become part of the longest chain. It is shown that popular mining strategies such as adherence to conservative mining or to the longest-chain rule constitute pure-strategy Nash equilibria. However, these equilibria are not subgame perfect.
Person Name Recognition with Fine-grained Annotation

Person names are essential and important entities in the Named Entity Recognition (NER) task. Traditional NER models have shown success in recognising well-formed person names from text with consistent and complete syntax, such as news articles. However, user-generated text such as academic homepages, academic resumes, articles in online forums and social media may contain lots of free-form text with incomplete syntax including person names with various forms. This brings significant challenges for the NER task. In this paper, we address person name recognition in this context by proposing a fine-grained annotation scheme based on anthroponymy together with a new machine learning model to perform the task of person name recognition. Specifically, our proposed name annotation scheme labels fine-grained name forms including first, middle, or last names, and whether the name is a full name or initial. Such fine-grained annotations offer richer training signals for models to learn person name patterns in free-form text. We then propose a Co-guided Neural Network (CogNN) model to take full advantage of the fine-grained annotations. CogNN uses co-attention and gated fusion to co-guide two jointly trained neural networks, each focusing on different dimensions of the name forms. Experiments on academic homepages and news articles demonstrate that our annotation scheme together with the CogNN model outperforms state-of-the-art significantly.
The Research the Informatization of Rural Basic Education in Guangxi from the Perspective of Educational Modernization

Educational modernization is an inevitable trend of educational development. The educational informatization is not only an important symbol, but also the basic support and necessary means to realize educational modernization. This paper analyzes the data of Guangxi in the construction of rural basic education informatization in the aspects of fund investment, school running conditions, teacher training, platform construction and so on in the last five years, so as to analyzes the existing problems theoretically, and puts forward some improvement strategies on how to solve the problem.