Reviewerly/exHarmony huggingface dataset:
Photoacoustic Spectrometric Evaluation of Soil Heavy Metal Contaminants

Heavy metal pollution in soil has severe impact on the human health and global environment. There is an urgent need for cost-effective devices capable of recognizing and detecting heavy metallic matters in soils. A broadband photoacoustic spectrometric (PAS) system was established for the detection of heavy metal contaminants in soil. The heavy metal element lead (Pb) was selected as a preferred detection target. The near infrared photoacoustic spectra of contaminated soil samples with various concentrations of Pb were collected and the spectroscopic relationship between the soil absorption peaks and Pb concentrations was analyzed. Four advanced spectral preprocessing methods were explored to improve the robustness of the prediction model. Based on the maximal correlation coefficient and minimal root mean square error criteria of prediction, a two-layer feed-forward network with a continuum removing preprocessing method with a correlation coefficient as 0.96 was tested as the most appropriate method for Pb concentration prediction. This fact verifies that the broadband PAS evaluation methodology, as a nondestructive testing method, can be potentially used as an alternative quantification detection method of heavy metal contaminants in soil without the involvement of complicated sample pretreatment.
A Design of a Tax Prediction System based on Artificial Neural Network

It's is not easy for people to predict a tax income accurately. In most cases, an expert manually predict tax revenues for next month based on a heuristic. Although this method is simple and easy to use, it does not take into account the economic situation, the real estate market, GDP, etc., and therefore, the prediction error is very large, so it is difficult to actually use it. To solve this problem, this paper presents an auxiliary tax prediction system that is based on an artificial neural networks. The system can help experts to predict tax revenues efficiently.
Experimental verification of time-order-dependent correlations in three-qubit Greenberger-Horne-Zeilinger-class states

In this paper, we investigate the genuine three-way nonlocality which is recognized as the strongest form of tripartite correlations. We consider theoretically and experimentally a series of suitable Bell-type inequalities a violation of which is sufficient for the detection of three-way nonlocality. For the generalized GHZ (gGHZ) states, it is demonstrated that they do violate tripartite Bell-type inequalities for any degree of tripartite entanglement even if they do not violate Svetlichny inequality. It implies that three-way entangled gGHZ can always exhibit genuine three-way nonlocality under the requirement of time-order-dependent principle. Furthermore, we have determined the maximal amount of noise admissible for the gGHZ states to still remain genuine three-way nonlocal.
Automatic Document Annotation with Data Mining Algorithms

By combining both semantically annotated documents and semantically annotated services, it is possible for digital solutions to automatically retrieve and assign documents not only to their own services but also to those provided by others, thus improving and optimizing the experience of its users. Most of the information exchanged in and between services is still either in paper form or over email and is mostly unstructured and in lack of any form of annotation. Manual and semi-automatic approaches are not suitable to deal with the huge amounts of heterogeneous and constantly flowing data existent in this scenario, thus raising the issue of automatic annotation. In this paper, three data mining algorithms are used to annotate a set of documents and their results compared to manually provided annotations.
Contextualising the National Cyber Security Capacity in an Unstable Environment: A Spring Land Case Study

Threats to global cyber security, including physical, personnel, and information, continue to evolve and spread across a hyper-connected world, irrespective of international borders, in both their elaboration and the scale of their impact. This cyber domain represents a constant challenge to national security, as its socio-technical components are both real and cognisant. The exacerbation of cyber-attacks undermines countries’ stability, its escalation produces a landscape of genuine global threat, and the magnitude of its expanding attack mechanisms creates a ‘tsunami effect’ on national cyber defenses. This paper reviews the current politically unstable state of Spring Land’s cyber security capacity, utilising Interactive Management (IM) approach. It reports the findings of an IM session conducted during a workshop involving a total of 26 participants from the Spring Land National Cyber Security Authority (NCSA), other government agencies. The workshop utilised different IM techniques, such as Idea Writing (IW), Nominal Group Technique (NGT), and Interpretive Structural Modelling (ISM). Using trigger questions, based on the dimensions of the Cybersecurity Capacity Maturity Model for Nations (CCMM), a set of objectives was derived to contextualise and support identified the key initiatives for the development of national cyber security capacity in the country.
Average Restrain Divider of Evaluation Value (ARDEV) in data stream algorithm for big data prediction

Today, big data processing has become a challenging task due to the amount of data collected using various sensors increasingly significantly. To build knowledge and predict the data, traditional data mining methods calculate all numerical attributes into the memory simultaneously. The data stream method is a solution for processing and calculating data. The method streams incrementally in batch form; therefore, infrastructure memory is sufficient to develop knowledge. The existing method for data stream prediction is FIMT-DD (Fast Incremental Model Tree-Drift Detection). Using this method, knowledge is developed in tree form for every instance. In this paper, enhanced FIMT-DD is proposed using ARDEV (Average Restrain Divider of Evaluation Value). ARDEV utilizes the Chernoff bound approach with error evaluation, improvement in learning rate, modification of perceptron rule calculation, and utilization of activation function. Standard FIMT-DD separates the tree formation process and perceptron prediction. The proposed method evaluates and connects the development of the tree for knowledge formation and the perceptron rule for prediction. The prediction accuracy of the proposed method is measured using MAE, RMSE and MAPE. From the experiment performed, the utilization of ARDEV enhancement shows significant improvement in terms of accuracy prediction. Statistically, the overall accuracy prediction improvement is approximately 6.99 % compared to standard FIMT-DD with a traffic dataset.
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models

We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the the target node following hypernym-hyponym relationships. In this way, and in contrast to other text-to-entity mapping systems, our model outputs hierarchically structured predictions that are fully interpretable in the context of the underlying ontology, in an end-to-end manner. We present a proof-of-concept experiment with encouraging results, comparable to those of state-of-the-art systems.
Software Development for Mobile Computing, the Internet of Things and Wearable Devices: Inspecting the Past to Understand the Future

Currently a convergence in software development for mobile computing (including mobile devices and special technology such as wearables) and the Internet of Things (IoT) can be observed.Devices from the fields are becoming part of a joint ecosystem and share the same infrastructure.Moreover, development processes have changed and user requirements have become very heterogeneous.We have been studying this development for a while, also by offering the fitting HICSS minitrack.In this article, we look into the past of software development for mobile devices, in the context of IoT, and for wearables.We analyse joint characteristics and show differences.Then we invite to a discussion that leads to a research outlook.While neither for industry nor for academia the journey is over, the convergence of fields should offer many new possibilities, prevent problems we faced in the past, but also introduce novel challenges.
Stratified Random Sampling from Streaming and Stored Data

Stratified random sampling (SRS) is a widely used sampling technique for approximate query processing. We consider SRS on continuously arriving data streams and statically stored data sets. We present a tight lower bound showing that any streaming algorithm for SRS over the entire stream must have, in the worst case, a variance that is $$\varOmega (r)$$ factor away from the optimal, where r is the number of strata. We present S-VOILA, a practical streaming algorithm for SRS over the entire stream that is locally variance-optimal. We prove that any sliding window-based streaming SRS needs a workspace of $$\varOmega (rM\log W)$$ in the worst case, to maintain a variance-optimal SRS of size M, where W is the number of elements in the sliding window. Due to the inherent high workspace needs for sliding window-based SRS, we present SW-VOILA, a multi-layer practical sampling algorithm that uses only O(M) workspace but can maintain an SRS of size close to M in practice over a sliding window. Experiments show that both S-VOILA and SW-VOILA result in a variance that is typically close to their optimal offline counterparts, which was given the entire input beforehand. We also present VOILA, a variance-optimal offline algorithm for stratified random sampling. VOILA is a strict generalization of the well-known Neyman allocation, which is optimal only under the assumption that each stratum is abundant. Experiments show that VOILA can have significantly smaller variance (1.4x to 50x) than Neyman allocation on real-world data.
Factors influencing use of information and communication technologies among farmers in rural Punjab, Pakistan

This study explored the factors influencing the application of Information and Communication Technologies (ICTs) among farmers in the Punjab province of Pakistan.The Punjab province is divided into three agro-ecological zones viz Northern, Central and Southern.In this study, one district from each zone was randomly selected.Total sample size comprises of 150 farmers.Farmers were interviewed using a self-administered interview schedule comprising of open and close-ended questions.Respondents' access to ICTs was low level.Chi-square test revealed a significant association between the age of respondents and adoption of ICTs.Young respondents were leading users of ICTs in the study area.Size of landholding and income level of the respondents had a significant association with the frequency of use of ICTs in agriculture.Among various ICTs tools, mobile/cell phone attracted 47.3% of respondents to access agricultural information.Factors militating use of ICTs include; electricity/power issues (x̄=2.95/3.00),language barriers (x̄=2.93/3.00),limited knowledge and skills about ICTs (x̄=2.89/3.00),high cost (x̄=2.87/3.00)and limited availability of ICTs (x̄=2.85/3.00).Extent of ICTs use to access agricultural information can surge with the unit income level rise, enhanced access to ICTs and enhanced skill level to use ICTs.
Parallax beam: a vision-based motion estimation method robust to nearly planar scenes

In computer vision, the epipolar geometry embeds the geometrical relationship between two views of a scene. This geometry is degenerated for planar scenes as they do not provide enough constraints to estimate it without ambiguity. Nearly planar scenes can provide the necessary constraints to resolve the ambiguity. But classic estimators such as the 5-point or 8-point algorithm combined with a random sampling strategy are likely to fail in this case because a large part of the scene is planar and it requires lots of trials to get a nondegenerated sample. However, the planar part can be associated with a homographic model and several links exist between the epipolar geometry and homographies. The epipolar geometry can indeed be recovered from at least two homographies or one homgraphy and two noncoplanar points. The latter fits a wider variety of scenes, as it is unsure to be able to find a second homography in the noncoplanar points. This method is called plane-and-parallax. The equivalence between the parallax and the epipolar lines allows to recover the epipole as their common intersection and the epipolar geometry. Robust implementations of the method are rarely given, and we encounter several limitations in our implementation. Noisy image features and outliers make the lines not to be concurrent in a common point. Also off-plane features are unequally influenced by the noise level. We noticed that the bigger the parallax is, the lesser the noise influence is. We, therefore, propose a model for the parallax that takes into account the noise on the features location to cope with the previous limitations. We call our method the “parallax beam.” The method is validated on the KITTI vision benchmark and on synthetic scenes with strong planar degeneracy. The results show that the parallax beam improves the estimation of the camera motion in the scene with planar degeneracy and remains usable when there is not any particular planar structure in the scene.
Inside the front door: Tailoring a leisure reading collection to attract an academic population

While some academics still maintain that an academic library is no place for leisure reading, many are in favor of maintaining at least a small distinct collection of such books. Strategically located and attractively displayed, current popular titles have the potential to generate traffic to the library and improve circulation statistics. Having taken the leap at Florida Gulf Coast University Library, a close statistical analysis of this new collection’s circulation over time has revealed not only that we all use it, but also that there are diverse but consistent patterns and preferences at play. Could the collection’s circulation rate be increased by tailoring it to an academic population? This study was designed to find out.
Survey on Optimization Algorithms Used for Feature Selection Techniques in Web Page Classification

Web page classification refers to one of the significant research are in the web mining domain. Enormous quantity of data existing in the web demands the essential development of various effective and robust techniques to undergo web mining task that involves the process to categorizing the web page based on the data labels. It also includes various other tasks such as web crawling, analysis of web links and contextual advertising process. Existing machine learning and data mining techniques are being efficiently used for various web mining processes which include classification of web pages. Using of multiple classifier techniques are most promising research area while considering machine learning that works on the base of merging various classifiers with difference in base classifier and/or dataset distribution. With this several classification models are constructed that is highly robust in nature. This review paper, comparison has been done between FA, PSO, ACO, GA and IWT, to evaluate best fit algorithm for classifying web pages.
Exploiting the Nonlinear Dynamics of Optically Injected Semiconductor Lasers for Optical Sensing

Optically injected semiconductor lasers are known to display a rich variety of dynamic behaviours, including the emission of excitable pulses, and of rare giant pulses (often referred to as optical rogue waves). Here, we use a well-known rate equation model to explore the combined effect of excitability and extreme pulse emission, for the detection of variations in the strength of the injected field. We find parameter regions where the laser always responds to a perturbation by emitting an optical pulse whose amplitude is above a pre-defined detection threshold. We characterize the sensing capability of the laser in terms of the amplitude and the duration of the perturbation.
White Ignorance, Epistemic Injustice and the Challenges of Teaching for Critical Social Consciousness

This chapter discusses systemic ignorance and its management as a way to frame Trump’s policies and discourse. It focuses on the scholarship around epistemic injustice. The study of epistemologies of ignorance exposes the ways that power works through knowing and unknowing to maintain systems of social injustice. Contributory injustice expands the notion of willful ignorance in that the injustice involves not only the willful dismissal of epistemic resources developed by the marginalized but also the continued insistence on employing dominant epistemic resources that distort the experience of the marginalized. Systemic ignorance has built-in epistemic resources to protect the system that normalizes bias and, even when bias is recognized, it does not necessarily lead to acknowledging the value of marginalized knowledge. Critical consciousness, a concept attributed to the educator Paulo Freire, involves the ability to know the social world and to take action against the systemic oppression illuminated by that understanding.
Hierarchical prosody modeling for Mandarin spontaneous speech

In this paper, a hierarchical prosody model (HPM)-based method for Mandarin spontaneous speech is proposed. First, an HPM is designed for describing relations among acoustic features of utterances, linguistic features of texts, and prosodic tags representing the underlying hierarchical prosodic structures of utterances. Subsequently, a sequential optimization algorithm is employed to train the HPM based on a large conversational speech corpus, the Mandarin Conversational Dialogue Corpus (MCDC), which features orthographic transcriptions and prosodic event annotations. In this unsupervised training method, all utterances of the MCDC are labeled with two types of prosodic tags, namely, break and prosodic states, automatically and simultaneously. After training, the HPM parameters are examined to identify critical prosodic properties of Mandarin spontaneous speech, which are then compared with their counterparts in the read-speech HPM. The prosodic tags on the studied utterances enable mapping of various prosodic events onto the hierarchical prosodic structures of the utterances. Prosodic analyses of some disfluent events are conducted using the prosodic tags affixed to the MCDC. Finally, an application of the HPM to assist in Mandarin spontaneous-speech recognition is discussed. Significant relative error rate reductions of 9.0%, 9.2%, 15.6%, and 7.3% are obtained for base-syllable, character, tone, and word recognition, respectively.
Content Based Image Retrieval by Convolutional Neural Networks

In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content Based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low-level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.
Peningkatan Hasil Belajar Matematika Melalui Model Group Investigation Dengan Media Games Puzzle Pada Kelas V Sekolah Dasar

Abstrak Penelitian ini bertujuan untuk mendeskripsikan model pembelajaran kooperatif tipe Group Investigation dengan media games puzzle sebagai alternatif meningkatkan hasil belajar siswa kelas V. Jenis penelitian ini adalah penelitian tindakan kelas (PTK). Subjek penelitian ini adalah siswa kelas V SD dengan jumlah siswa 22. Metode penelitian ini menggunakan metode penelitian kuantitatif dan kualitatif dengan instrumen pengumpulan data tes hasil belajar, observasi, dan dokumentasi. Hasil penelitian menunjukan model pembelajaran Group Investigation dengan media games puzzle dapat meningkatkan hasil belajar Matematika, siswa tuntas pada kondisi awal 59% dan siswa tidak tuntas 41% meningkat menjadi 68% siswa tuntas dan 32% siswa tidak tuntas di siklus I, meningkat kembali di siklus II siswa tuntas 95% dan siswa tidak tuntas 5% dan rata-rata kelas yang semula 62 meningkat menjadi 64,5 pada siklus I serta meningkat lagi pada siklus II menjadi 80.AbstractThis study aims to describe the cooperative learning model of the Group Investigation type with media games puzzle as an alternative to improving class V student learning outcomes. This type of research is classroom action research (PTK). The subject of this study is the fifth grade elementary school students with a number of students 22. The method of this research uses quantitative and qualitative research methods with the instruments of data collection test results of learning, observation, and documentation. The results show that the Group Investigation learning model with media puzzle games can improve Mathematics learning outcomes, students complete the initial conditions 59% and students do not complete 41% increase to 68% students complete and 32% students do not complete in the first cycle, increasing again in the cycle II students complete 95% and students do not complete 5% and the class average which was originally 62 increased to 64.5 in the first cycle and increased again in the second cycle to 80.
Exploring Automotive Stakeholder Requirements for Architecture Optimization Support

In automotive system design, system architects increasingly use model-based approaches for defining product line architectures (PLAs). This trend is strongly supported by the emergence of domain standards like EAST-ADL and AUTOSAR that facilitate a system design approach based on systematic reuse. In our work, we focus on the automated exploration of PLA design spaces by means of multi-objective optimization and ask what are challenges that stakeholders face as part of this design task. Optimizing PLA design spaces shares certain challenges with conventional architecture optimization, like conflicting quality goals and potentially vast design spaces. However, PLA optimization exacerbates these challenges by an additional layer of complexity in the system architecture, due to product line variability in the system. Our main focus in this paper is on achieving a better understanding of the stakeholder requirements on a support infrastructure for automated PLA design space exploration based on PLA system models. We also address the challenges that stakeholders must overcome when integrating such an approach into an existing process landscape. For that purpose, we conducted a study with automotive industry experts and analyzed the findings using the qualitative data analysis methods of Grounded Theory. Based on the study results, we derive a set of challenges for the realization and integration of automated and semi-automated PLA design space exploration in industrial environments.