Reviewerly/exHarmony huggingface dataset:
Data mining techniques application for prediction in OLAP cube

Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.
The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

Abstract Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.
Fast Boolean Queries With Minimized Leakage for Encrypted Databases in Cloud Computing

This research revisits the fundamental problem of processing privacy-preserving Boolean queries over outsourced databases on untrusted public clouds. Much current searchable encryption (SE) schemes try to seek an appropriate trade-off between security and efficiency, yet most of them suffer from an unacceptable query leakage due to their conjunctive/disjunctive terms that are processed individually. We show, however, this trade-off still can be deeply optimized for more security. We consider a Boolean formula as a set of deterministic finite automatons (DFAs) and propose a novel approach to running an encrypted DFA, which can be effectively and efficiently processed by the cloud. We give three constructions for conjunctive, disjunctive, and Boolean queries, respectively. Their notable advantages are single-round, highly-efficient, adaptively-secure, and leakage-minimized. A lot of experiments are made to evaluate overall efficiency. Testing results show that the schemes achieve enhanced security almost without sacrificing anything of search efficiency.
Quantum phase transitions triggered by a four-level atomic system in dissipative environments

We investigate the quantum phase transitions (QPTs) of a four-level system in a noisy environment. The system is composed of a four-level atom which is driven by classical fields and simultaneously coupled with a quantum field mode. A second-order dissipative phase transition (DPT) is observed by combining unitary processes with dissipation dynamics. The analytical expression for the DPT in the anisotropic Rabi model is given. We demonstrate that the QPT in an open four-level system is characterized as several boundaries, which is completely different from that in a closed system. In addition, our model is robust against strong atomic spontaneous emission, providing a promising platform for observing DPT in an open system and studying the related dynamics associated with DPT.
Multiple Privacy Regimes Mechanism for Local Differential Privacy

Local differential privacy (LDP), as a state-of-the-art privacy notion, enables users to share protected data safely while the private real data never leaves user's device. The privacy regime is one of the critical parameters balancing between the correctness of the statistical result and the level of user's privacy. In the majority of current work, authors assume that the privacy regime is totally determined by the service provider and dispatched to all users. However, it is inelegant and unpromising for all users to accept the same privacy level in real world. In this paper, we propose a new LDP estimation method MLE which is applicable for the scenario of multiple privacy regimes. MLE uses the idea of parameter estimation to merge the results generated by users of different privacy levels. We also propose an extension of MLE to handle the situation when all users' regimes are in a continuous distribution. We also provide an Adapt estimator which assigns users to use different LDP schemes based on their regimes, and it performs better than the estimator with only one fixed LDP scheme. Experiments show that our methods provide a higher level of accuracy than previous proposals in this multiple regimes scenario.
DenseAttentionSeg: Segment hands from interacted objects using depth input

We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs. Our model is called DenseAttentionSeg, which contains a dense attention mechanism to fuse information in different scales and improves the results quality with skip-connections. Besides, we introduce a contour loss in model training, which helps to generate accurate hand and object boundaries. Finally, we propose and release our InterSegHands dataset, a fine-scale hand segmentation dataset containing about 52k depth maps of hand-object interactions. Our experiments evaluate the effectiveness of our techniques and datasets, and indicate that our method outperforms the current state-of-the-art deep segmentation methods on interaction segmentation.
Case Study – Exploring Children’s Password Knowledge and Practices

Children use technology from a very young age, and often have to authenticate themselves.Yet very little attention has been paid to designing authentication specifically for this particular target group.The usual practice is to deploy the ubiquitous password, and this might well be a suboptimal choice.Designing authentication for children requires acknowledgement of child-specific developmental challenges related to literacy, cognitive abilities and differing developmental stages.Understanding the current state of play is essential, to deliver insights that can inform the development of child-centred authentication mechanisms and processes.We carried out a systematic literature review of all research related to children and authentication since 2000.A distinct research gap emerged from the analysis.Thus, we designed and administered a survey to school children in the United States (US), so as to gain insights into their current password usage and behaviors.This paper reports preliminary results from a case study of 189 children (part of a much larger research effort).The findings highlight age-related differences in children's password understanding and practices.We also discovered that children confuse concepts of safety and security.We conclude by suggesting directions for future research.
The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models

Abstract Software vulnerabilities form an increasing security risk for software systems, that might be exploited to attack and harm the system. Some of the security vulnerabilities can be detected by static analysis tools and penetration testing, but usually, these suffer from relatively high false positive rates. Software vulnerability prediction (SVP) models can be used to categorize software components into vulnerable and neutral components before the software testing phase and likewise increase the efficiency and effectiveness of the overall verification process. The performance of a vulnerability prediction model is usually affected by the adopted classification algorithm, the adopted features, and data balancing approaches. In this study, we empirically investigate the effect of these factors on the performance of SVP models. Our experiments consist of four data balancing methods, seven classification algorithms, and three feature types. The experimental results show that data balancing methods are effective for highly unbalanced datasets, text‐based features are more useful, and ensemble‐based classifiers provide mostly better results. For smaller datasets, Random Forest algorithm provides the best performance and for the larger datasets, RusboostTree achieves better performance.
Monogamy relations of all quantum correlation measures for multipartite quantum systems

The monogamy relations of quantum correlation restrict the sharability of quantum correlations in multipartite quantum states. We show that all measures of quantum correlations satisfy some kind of monogamy relations for arbitrary multipartite quantum states. Moreover, by introducing residual quantum correlations, we present tighter monogamy inequalities that are better than all the existing ones. In particular, for multi-qubit pure states, we also establish new monogamous relations based on the concurrence and concurrence of assistance under the partition of the first two qubits and the remaining ones.
3D КАРТУВАННЯ ЦИФРОВОЇ КОМПЕТЕНТНОСТІ В СИСТЕМІ ОСВІТИ УКРАЇНИ

The article introduces the developed 3D mapping of digital competency. The necessity of radical changes aimed at improving the quality and competitiveness of education, changing the role of scientific and pedagogical staff in Ukraine in accordance with world requirements is substantiated. The analysis of the state policy and regulatory framework in the field of digital skills and competencies in Ukraine, research on information and communication competency in the European educational space, in particular UNESCO recommendations, the digital competency framework for citizens DigComp 2.0 and the digital competency framework for educators DigCompEdu, requirements for digital literacy in the United States, in particular the standards of the International Society of Technology in Education (ISTE), as well as development of digital competency in Ukraine. As a result of the analysis of these documents, questionnaires were developed for three groups of respondents – pedagogical staff and students of institutions of higher education and teachers of general secondary education institutions - and questionnaires were conducted in six Ukrainian universities within the framework of the international project Erasmus + MoPED. The developed 3D mapping reflects the trends of understanding by pedagogical staff and students of institutions of higher education and teachers of secondary education institutions of the main modern educational trends, the use of innovative pedagogical technologies and digital instruments in the educational process. The attitude of the three groups of respondents to the actual educational trends, innovative pedagogical technologies and the level of interest in mastering digital tools and their ability to use them effectively in the educational process according to the results of the questionnaire has been highlighted, which made it possible to compare the attitude of pedagogical staff of institutions of higher education, teachers and students to the above categories at the present stage.
Trajectory planning method for improving alignment accuracy of probes for dual-robot air-coupled ultrasonic testing system

Composite workpieces, especially the complex-curved surfaces composite workpieces, have been increasingly used in different industries. Non-destructive testing of these parts has become an urgent problem to be addressed. To solve the problem, this article presents a dual-robot air-coupled ultrasonic non-destructive testing scheme and introduces the structure of the system and a general calibration method for the workpiece frame of a dual-robot system in detail. Importantly, this article proposes a tangential constraint method, which makes the probes completely aligned during the inspection process. Verification experiments and ultrasonic testing experiments for a glued multilayered composite workpiece were performed using the dual-robot air-coupled ultrasonic non-destructive testing system. A comparative experiment was also performed using a dual-robot water jet-coupling ultrasonic testing system. Experimental results show that the dual-robot non-destructive testing scheme and the tangential constraint method function well, and all the artificial defects on the sample can be detected by both kinds of testing methods. Vivid 3-D C-scan image based on the test result is provided for convenience of observation. In other words, a kind of flexible versatile testing platform with multiple degrees of freedom is established.
An Analysis on Barriers to Biomass and Bioenergy Development in Rural China Using Intuitionistic Fuzzy Cognitive Map

Biomass is viewed as one of the critical renewable energies and it widely exists in nature. Developing bioenergy has been promoted as a viable mean of dealing with environment issues that are related to the utilization of fossil fuel. However, due to many obstacles, the biomass and bioenergy technology has not won widespread support in developing countries, like China, with vast land area, particularly in rural area. Furthermore, most existing researches just focused on the description of the influence factors, along with the solution to the technical problems, while many social factors are overlooked. In fact, the process of developing biomass is indeed complicated due to the need for consensus and active participation of the various stakeholders, such as the government, the industry, and the local residents. Therefore, while integrating the intuitionistic fuzzy logic and fuzzy cognitive map, this study constructs an intuitionistic fuzzy cognitive map (IFCM) that is in line with experts’ suggestions and the current literature to investigate how to promote the development of biomass through enhancing public acceptance. We conduct several simulations from the perspective of different stakeholders, according to the IFCM. The analysis results reveal the influence mechanism in the system and illustrate the effect of various factors that are stressed by every stakeholder. The research design also provides a reference for future studies.
Short-range-order minerals as powerful factors explaining deep soil organic carbon stock distribution: the case of a coffee agroforestry plantation on Andosols in Costa Rica

Abstract. Soil organic carbon (SOC) constitutes the largest terrestrial C stock, particularly in the Andosols of volcanic areas. Quantitative information on distribution of SOC stocks is needed to construct a baseline for studying temporal changes in SOC. The spatial variation of soil short-range-order minerals such as allophane usually explains the variability of topsoil SOC contents, but SOC data for deeper soil layers are needed. We found that within a 1 km2 Costa Rican basin covered by coffee agroforestry, SOC stocks in the upper 200 cm of soil were highly variable (24 to 72 kg C m−2). Topsoil SOC stocks were not correlated with SOC stocks present in deeper layers. Diffuse-reflectance mid-infrared (MIR) spectroscopy made possible the analysis of a large number of samples (69 soil profiles, i.e. 598 soil samples) for ammonium-oxalate and sodium-pyrophosphate-extractable forms of Al, Fe, and Si, as well as SOC content and bulk density. Using the MIR spectra, we identified two different soil materials, which were identified as allophanic and halloysitic soil material. Allophanic soil occurred on top of the halloysitic soil. The thickness of the allophanic soil material, rich in SRO minerals and related to a young andic A horizon, explained the variability of SOC. This study illustrates that knowledge of topography and pedogenesis is needed to understand and extrapolate the distribution of SOC stocks at landscape scales.
Optimisation of transmission map for improved image defogging

Outdoor images taken in foggy weather are not suitable for automation due to low contrast. It is a challenging task to remove fog from images specially when the image contains large sky region. The authors propose dark channel-based single image defogging technique to estimate atmospheric light which represents the amount of luminance in a scene in the absence of fog. This atmospheric light is used to reconstruct fog-free image with a transmission map. Transmission map represents the effect of fog with respect to depth in image. In this study, they propose four transmission maps to reconstruct the images with different colour contrast. Proposed method adaptively selects a transmission map depending upon the fog density to reconstruct image with optimal colour contrast. The transmission map is refined by applying Laplacian filter followed by the guided filter. Previously, dark channel prior based methods were considered to be less effective for images with large sky region, but the proposed method reconstructs better result consistently for such images, independent of the density of the fog. Experimental results show that images reconstructed by proposed method are qualitatively better than the previously proposed methods.
An Adaptive and Secure Holographic Image Watermarking Scheme

A novel adaptive secure holographic image watermarking method in the sharp frequency localized contourlet transform (SFLCT) domain is presented. Based upon the sine logistic modulation map and the logistic map, we develop an encrypted binary computer-generated hologram technique to fabricate a hologram of a watermark first. Owing to the enormous key space of the encrypted hologram, the security of the image watermarking system is increased. Then the hologram watermark is embedded into the SFLCT coefficients with Schur decomposition. To obtain better imperceptibility and robustness, the entropy and the edge entropy are utilized to select the suitable watermark embedding positions adaptively. Compared with other watermarking schemes, the suggested method provides a better performance with respect to both imperceptibility and robustness. Experiments show that our watermarking scheme for images is not only is secure and invisible, but also has a stronger robustness against different kinds of attack.
FJLT-FLSH: More Efficient Fly Locality-Sensitive Hashing Algorithm via FJLT for WMSN IoT Search

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson-Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.
A Socio-technical Systems Approach to Design and Support Systems Thinking in Cybersecurity and Risk Management Education

Cybersecurity decisions are made across a range of social, technical, economic, regulatory and political domains. There is a gap between what companies and institutions plan to do while developing their internal IS-related policies and what should be done according to a multi-stakeholder system perspective in this area. Our task as researchers is to bridge this gap by offering potential solutions. The aim of our work is to promote the usage of the socio-technical systems (STS) approach to support the emerging role of systems thinking in cybersecurity education, using simulation as a supporting tool for learning. Meanwhile, new trends in cybersecurity curricula suggest an important shift toward new thinking approaches such as adversarial and systems thinking. We explored individuals’ adversarial and systems thinking skills in an open agent-based simulated environment and subsequently assessed the impact based on a participant survey. We discuss these results and point to directions for further investigation. The second contribution of the article is the provision of a tool for developing target users’ skills in making quantitative risk decisions and giving them a deeper understanding of the importance and use of key indices in the cyber risk management process.
On Assisted Living of Paralyzed Persons through Real-Time Eye Features Tracking and Classification using Support Vector Machines

Background: The eye features like eye-blink and eyeball movements can be used as a module in assisted living systems that allow a class of physically challenged people speaks – using their eyes. The objective of this work is to design a real-time customized keyboard to be used by a physically challenged person to speak to the outside world, for example, to enable a computer to read a story or a document, do gaming and exercise of nerves, etc., through eye features tracking Method: In a paralyzed person environment, the right-left, up-down eyeball movements act like a scroll and eye blink as a nod. The eye features are tracked using Support Vector Machines (SVMs). Results: A prototype keyboard is custom-designed to work with eye-blink detection and eyeball-movement tracking using Support Vector Machines (SVMs) and tested in a typical paralyzed person-environment under varied lighting conditions. Tests performed on male and female subjects of different ages showed results with a success rate of 92%. Conclusions: Since the system needs about 2 seconds to process one command, real-time use is not required. The efficiency can be improved through the use of a depth sensor camera, faster processor environment, or motion estimation.