Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation: Inexpensively separating cold data via garbage collection to improve the lifetime and performance of NAND flash SSDs

An effective way to mitigate the lifetime and performance problems of NAND flash SSDs induced by garbage collection is separating the cold data and hot data. However, all existing solutions do this separation from the viewpoint of the hot data and thus suffer from expensive cost on monitoring the update behaviors of workloads. Different from the conventional wisdom, we propose to do the separation from the viewpoint of the cold data, which is rooted on the following two dedicated observations. First, the data migrated by garbage collection demonstrate a cold property compared with the other data. Second, it is inexpensive to separate the former data from the latter data. In the proposed method, named Isolation, we inexpensively separate the cold data via garbage collection. More specifically, through writing the data migrated by garbage collection (cold data) to dedicated blocks and writing the other data (hot data) to other blocks, the separation is done naturally. To evaluate Isolation, we conduct extensive trace‐driven simulations under seven typical workloads. The simulation results demonstrate the effectiveness and efficiency of Isolation. In most cases, Isolation can reduce the number of erasures and average I/O response time up to 47.3% and 80.1%, respectively. Existing approaches integrated with Isolation can further reduce the number of erasures and average I/O response time up to 7.8%‐18.5% and 10.7%‐41.4%, respectively.
A Transverse Load Sensor With Ultra-Sensitivity Employing Vernier-Effect Improved Parallel-Structured Fiber-Optic Fabry-Perot Interferometer

The parallel structured fiber-optic Fabry-Perot interferometer (FPI) is theoretically proposed and experimentally demonstrated for transverse load measurement based on Vernier-effect. This kind of sensor consists of a hollow microsphere cavity fiber-optic FPI for sensing function and an air cavity for reference, both of which are parallel connected by a 3 dB optical fiber coupler with the functionality of Vernier-effect. Experimental results indicate that the proposed sensor can provide a high transverse load sensitivity of −3.75 nm/N, which is 3.4 times higher than single hollow microsphere cavity FPI. Moreover, with the excellent features of low cost, easy fabrication, and stable sensing, the parallel structured FPIs proposed will provide a new vision for high sensitivity transverse load sensing.
A Semantic Web Approach to Enable a Smart Route to Historical Archives

In this paper we show that an ontology-based approach can be beneﬁcial for enhancing the access to cultural resources, and in particular historical documents. The paper starts with an overview of our approach, aimed at providing online archival systems with a semantic layer based on Semantic Web standards (OWL 2 and RDF). Two projects are introduced, namely Harlock900 and PRiSMHA, carried out in collaboration with local cultural institutions owning rich historical archives. In particular, the paper describes the computational ontologies supporting the approach, and then focuses on two case studies showing that our framework provides better results if compared with standard access systems. The case studies show the enhancement provided by a semantically rich representation of time intervals and a detailed formal description of events and their participants.
Studies on visualisation in science classrooms: a systematic literature review

ABSTRACT Visualisation has been a critical means of scientific reasoning, knowledge development, and communication. In science classrooms, visualisation plays significant roles for teaching and learning. To better understand the landscape of the existing research on visualisation for supporting student science learning in K to 12 classroom contexts, we undertook a systematic review to investigate what research areas and contexts of visualisation have been examined by science educators and researchers. In particular, this review explored the domains of science subject areas, authors, types and purposes of visualisation in science education research published in peer-reviewed journals between 2011 and 2021 and indexed in the ERIC and Academic Search Complete databases. Our systematic search and subsequent screening process yielded 33 studies for analysis. The findings suggest (a) research on visualisation for student science learning spans across the main science domains (i.e. life science, physical science, chemistry, earth and space science), and grade levels (from Grades 1−12); (b) compared with students’ visualisation in social interactive contexts, more studies focused on individual students’ visualisation; and (c) the majority of the research focused on students’ concept learning through visualisation, rather than visualisation for scientific reasoning and communication. Based on these findings, suggestions for further research are discussed.
Development of an Instrument to Determine Satisfaction with the Use of Serious Video Games as Open Educational Resources from a Services Theory Approach

The determination of quality in Open Educational Resources (REAs) presents a special complication, this because they must be considered the characteristics of a software application, an instructional element and a resource of free distribution; this is even more complicated when referring to a serious video game; additionally, in general the instruments to determine the quality consider only the perspective of the expert in the area, without considering the opinion of the user, reason why, it is proposed to integrate in the determination of the quality aspects of the theory of services, this with the purpose of generating REAs that also provide a greater satisfaction of use to the student.
Revealing behaviours of concurrent functional programs by systematic testing

We aim to make it easier for programmers to write correct concurrent programs and to demonstrate that concurrency testing techniques, typically described in the context of simple core languages, can be successfully applied to languages with more complex concurrency. In pursuit of these goals, we develop three lines of work: Testing concurrent Haskell We develop a library for testing concurrent Haskell programs using a typeclass abstraction of concurrency, which we give a formal semantics. Our tool implements systematic concurrency testing, a family of techniques for deterministically testing concurrent programs. Along the way we also tackle how to soundly handle daemon threads, and how to usefully present complex execution traces to a user. We not only obtain a useful tool for Haskell programs, but we also show that these techniques work well in languages with rich concurrency abstractions. Randomised concurrency testing We propose a new algorithm for randomly testing concurrent programs. This approach is fundamentally incomplete, but can be suitable in cases where systematic concurrency testing is not. We show that our algorithm performs as well as a pre-existing popular algorithm for a standard set of benchmarks. This pre-existing algorithm requires the use of program-specific parameters, but our algorithm does not. We argue that this makes use and implementation of our algorithm simpler. Finding properties of programs We develop a tool for finding properties of sets of concurrency functions operating on some shared state, such as the API for a concurrent data type. Our tool enumerates Haskell expressions and discovers properties by comparing execution results for a variety of inputs. Unlike other property discovery tools, we support side effects. We do so by building on our tool for testing concurrent Haskell programs. We argue that this approach can lead to greater understanding of concurrency functions.
A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey’s 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.
Evaluation of the LDACS Cybersecurity Implementation

Communication, Navigation and Surveillance (CNS) infrastructure in civil aviation must evolve as fast as possible to cope with all challenges posed by the growth of the worldwide population, globalization and the worldwide demand for more and more mobility. Analogue systems are replaced by digital means and automation is becoming much more important to be able to handle the large amount of new entrants in the air traffic system. Spectrum saturation must be solved by introducing digital systems and safety and security of the safety critical infrastructure surrounding civil aviation must be constantly updated to support the ever-growing system complexity. As one of the Future Communication Infrastructure (FCI) candidates we introduce LDACS as the very first true integrated CNS system worldwide. In previous works we have already analyzed its cybersecurity, developed a cybersecurity architecture with corresponding algorithms and demonstrated the improvement in cybersecurity due to our security additions. Here we implement the LDACS cybersecurity architecture and evaluate the impact of introduced security overhead on the LDACS system. We conclude that the proposed protection mechanisms successfully mitigate previously identified risks and only add minor time, data and computation overhead on top of the LDACS protocol stack. With this result, the security solutions are a good candidate to be included in the SESAR wave 2 updated LDACS specification.
Vehicle Plate Monitoring and Information System Using Optical Character Recognition (OC) Technique

The existing license plate recognition is designed usually for automatic toll collection, maintaining traffic activities and law enforcement. The researches put an innovation to make and used this kind of system in which it may resolve the problem of inaccurate data of vehicles entering school premises of Mindanao University of Science and Technology (MUST). This problem then leads to the motivation of the researchers to conduct a research project about monitoring vehicles automatically. The system followed the steps of image processing with OCR. Using OCR the VPMIS is capable to convert captured images into a string and can be viewed as a text. Converted text will try to match text data from the database; if there is a match, the logs will be stored, if it doesn't the system will capture and save images into one folder. System Performance testing shows that 99.9 % the Vehicle Plate Monitoring and Information System (VPMIS) can detect license plate and 67% the system is capable to record with a specified parameter at two (2) meters away from the camera. The capacity to which the maximum limits of the system parameters meets the requirements
Analysis of the environmental efficiency in China based on the DEA cross‐efficiency approach under different policy objectives

The existing studies on environmental efficiency evaluation generally have the problem of efficiency overestimation. To solve this problem, a new data envelopment analysis (DEA) cross‐efficiency approach with undesirable outputs is developed to evaluate environmental efficiency from the perspectives of both self‐evaluation and peer evaluation. Then, three new evaluation strategies, namely, economic development strategy, environmental protection strategy, and win–win strategy, are proposed to reflect the needs of decision makers under different policy objectives. The proposed cross‐efficiency approach with different evaluation strategies not only realizes the cross evaluation of environmental efficiency, but also guarantees the relative uniqueness of the optimal solution on the basis of the preferences of decision makers. Combining the metafrontier DEA approach and DEA window analysis, a new cross‐efficiency analytical framework is constructed to gradually analyse the influences of policy objectives, technology heterogeneity, and dynamic correlation on the environmental efficiency. Subsequently, the environmental efficiency of China's economic development during 2006–2015 is in‐depth analysed on the basis of the proposed analytical framework, and some interesting conclusions, and some useful suggestions are obtained.
Tracking Community Consistency in Dynamic Networks: An Influence-Based Approach

The dynamic network data have become ubiquitous with the rapid development of Internet and smart devices. To effectively manage the involved vertices in networks, it is crucial to track the special community patterns and analyze the relationships among vertices. In this paper, we propose a new method to measure the coherence strength, also referred to as community consistency, of a community over a specific observation period. The measurement of community consistency is especially challenging given the dynamic community structure over time, i.e., vertices can leave their original communities and join new communities. In order to interpret the causes of evolving community structure and model the influence of evolving community structure on community consistency, we introduce an influence propagation process having a causal relation with the community consistency. Specifically, a generative model is proposed to combine the influence propagation and the network topological structure at each time step. The proposed influence-based approach for modeling evolution can be instantiated in a variety of real-world network data. The comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency. Besides, we conduct a case study to show the effectiveness of the proposed method in real-world applications.
Cognitive Flexibility Theory and the Accelerated Development of Adaptive Readiness and Adaptive Response to Novelty

This chapter examines expertise in complex and ill-structured domains from the perspective of cognitive flexibility theory (CFT). The emphasis is on adaptation under conditions of “ordinary novelty. ” An approach to situational novelty via meta-features of an adaptive mindset that generalizes across cases in ways that content does not, and that fosters the skill of novel rearrangement of previously encountered case features in ways that are adaptive to new situations, is presented. The chapter describes CFT’s theoretical and empirical approach to combating rigidity and oversimplification, and to accelerating expertise in assembling “schemas of the moment” with learning based on the principled development and application of computer-supported case-based environments. Receiving special emphasis are many new modes of deliberate practice of adaptive flexibility. The chapter concludes with societal implications for today’s rapidly changing and increasingly complex world.
Contemporary Machine Learning for Audio and Music Generation on the Web: Current Challenges and Potential Solutions

We evaluate specific Web-based technologies that can be used to implement complex contemporary Machine Learning systems for Computer Music research, in particular for the problem of audio signal generation. As a result of greater investment from large corporations including Google and Facebook in areas such as the development of Web-based, accelerated, cross-platform Machine Learning libraries, alongside greater interest and engagement from the academic community in exploring such approaches, Machine Learning is becoming much more prevalent on the Web. This could have great potential impact for Computer Music research, acting to democratise access to complex, accelerated Machine Learning technologies through increased usability and flexibility, in tandem with clear documentation and examples. However, some problems remain in relation to the creation of more complete Machine Learning pipe-lines for Music and Sound generation. We discuss some key potential challenges in this area, and attempt to evaluate some relevant solutions for developing more accessible Computer Music Machine Learning systems.
An Iterative Mixed Pixel Classification for Brain Tissues and White Matter Hyperintensity in Magnetic Resonance Imaging

White matter hyperintensities (WMH) generally can be detected and diagnosed by magnetic resonance imaging (MRI). It has been pointed out that WMH is closely associated with stroke, cognitive impairment, dementia, and even is very relevant to the increased risk of death. This paper proposes a new iterative linearly constrained minimum variance (ILCMV) classification-based method which expands an iterative constrained energy minimization (ICEM) detection-based method developed for hyperspectral image classification. It explores the potential of ILCMV combined with different spatial filters in classification of brain normal tissues and WMH and also develops an alternative version of ILCMV, called Multi-class ICEM (MCICEM) for a comparative study. The synthetic images in BrainWeb are used for quantitative evaluation of ILCMV and the real brain MR images are used for visual assessment. The experimental results suggest that the Gaussian filter is most suitable for ILCMV and MCICEM if the computational time is factored into consideration. Otherwise, ILCMV/MCICEM combined with a Gabor filter yields the best classification. In addition, the average Dice similarity indexes (DSI) of CSF/GM/WM volume measurement produced by ILCMV method combined with Gaussian filter were 0.936/0.948/0.975 in synthetic MR images with all noise levels and were better than the results reported in the literature. ILCMV can simultaneously classifies brain normal tissues and WMH lesions in MR brain images and does better than detection of WMH alone. In addition, its computational time is also less than MCICEM. It is our belief that the proposed methodology demonstrates its promising in classification of brain tissue and WMH in MRI applications.
Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.
THE USE OF EDUCATIONAL TECHNOLOGIES IN HEALTH: AN INTEGRATING LITERATURE REVIEW

It is understood as Educational Technology a set of mediators devices of the teaching-learning process that must contain a logical organization, so that they can be systematically planned, observed, understood and transmitted. In this scenario, the objective of this study was to analyze the literature on Educational Technologies applied to Health. As an methodology, an integrative review was carried out, consisting of a broad analysis of theoretical and empirical literature that aims to understand a certain phenomenon from studies above. The search was carried out in the CAPES database, the main Brazilian database, between 2013-2018, in Portuguese, English, French and Spanish through the descriptors "Educational technologies" and "Health", which were duly translated into the other languages correspondents. As a result, 30 articles were analyzed in two moments: individually from a literature review protocol developed and validated by the author of the study and together from the Content Analysis technique. In general, sixteen dependent and four independent technologies were found and it was realized that they were validated mainly by specialists in the field and by the target audience for whom they were intended. The theoretical anchorage found in the literature for the basis of the development of technologies was the Arch of Mangueares and six categories of analysis were found that discuss their use. Thus, it is concluded that technologies help in the process of training health professionals and empowerment for the process of treatment of users of services. Resources such as the applications end up facilitating access to health services, and the insertion of new technologies, especially those mediated by the Internet, has modified the educational paradigms
Scoring and classifying regions via multimodal transportation networks

In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions by their transportation features and to classify them by their synergistic properties and dominant mode of transportation. We use twelve measures collected at five different radii for five distinct combinations of transportation networks to rank locations by their transportation characteristics. We introduce an additional 114 scores derived from the 300 measures to assess, among other things, access to public transportation, the effectiveness of each mode of transportation, and synergies among the modes of transportation. Additionally, we leverage those scores to classify our locations as being train-centric, bus-centric, or car-centric and to uncover geographic patterns in these characteristics. We find that business hubs, despite having low populations, are so conveniently reachable via train and road systems that they consistently achieve the highest sociability and convenience scores. Suburban regions have more serviceable bus systems, but lower connectivity overall resulting in lower reachable populations despite greater local populations. Even though Tokyo has the largest and densest public transportation system in the world we find that the road network consistently dominates the train and bus networks for all accessibility measures.