Reviewerly/exHarmony huggingface dataset:
ITERATIVE REFINEMENT FOR UNDERWATER 3D RECONSTRUCTION: APPLICATION TO DISPOSED UNDERWATER MUNITIONS IN THE BALTIC SEA

Abstract. With the rapid development and availability of underwater imaging technologies, underwater visual recording is widely used for a variety of tasks. However, quantitative imaging and photogrammetry in the underwater case has a lot of challenges (strong geometry distortion and radiometry issues) that limit the traditional photogrammetric workflow in underwater applications. This paper presents an iterative refinement approach to cope with refraction induced distortion while building on top of a standard photogrammetry pipeline. The approach uses approximate geometry to compensate for water refraction effects in images and then brings the new images into the next iteration of 3D reconstruction until the update of resulting depth maps becomes neglectable. Afterwards, the corrected depth map can also be used to compensate the attenuation effect in order to get a more realistic color for the 3D model. To verify the geometry improvement of the proposed approach, a set of images with air-water refraction effect were rendered from a ground truth model and the iterative refinement approach was applied to improve the 3D reconstruction. At the end, this paper also shows its application results for 3D reconstruction of a dump site for underwater munition in the Baltic Sea for which a visual monitoring approach is desired.
Integrating Mobile Devices in the Mathematics Curriculum

The case study took place in a primary school in Cyprus (student ages 6-12), which implemented a two-year multifaceted professional development program aimed at the effective integration of tablet technologies within the mathematics curriculum. The program adopted a systemic, school-based and collaborative model of professional development that focused on the broad preparation and ongoing engagement of all key stakeholders. This article focuses on research conducted during the first year of the program implementation. It portrays the initial state of mobile device use in the case study school ecosystem, describes the process of tablet integration within the school setting, and interrogates the self-reported reflections of a core team of six (n=6) teachers in the school regarding their professional learning experiences as a result of participating in the program. Findings suggest that the model of professional development adopted by the program effectively contributed to the integration of mobile devices into the school setting and to teacher professional learning.
STL: Online Detection of Taxi Trajectory Anomaly Based on Spatial-Temporal Laws

Aiming to promote the standardization of taxi services and protect the interests of passengers, many methods have been proposed to detect taxi trajectory anomaly based on collected large-scale taxi traces. However, most existing methods usually employ a counting-based policy to differentiate normal trajectories from anomalous ones, which may give rise to high false positives. In this paper, we propose an online detection method, named Spatial-Temporal Laws (STL). The basic idea of STL is that, given the displacement from the source point to the current point of a testing trajectory, if the current point is normal, either its driving distance or driving time should lie in a normal range. STL learns the two ranges from historical trajectories by defining two spatial-temporal models: one characterizing the relationship between displacement and driving distance, and another depicting the relationship between displacement and driving time. Consequently, STL is more precise compared with the counting-based methods, greatly reducing the number of false positives. Based on large-scale real-world taxi traces, STL is evaluated through a series of experiments which demonstrate its effectiveness and performance.
Assessment of wavelet-based spatial verification by means of a stochastic precipitation model (wv_verif v0.1.0)

Abstract. The quality of precipitation forecasts is difficult to evaluate objectively because images with disjointed features surrounded by zero intensities cannot easily be compared pixel by pixel: any displacement between observed and predicted fields is punished twice, generally leading to better marks for coarser models. To answer the question of whether a highly resolved model truly delivers an improved representation of precipitation processes, alternative tools are thus needed. Wavelet transformations can be used to summarize high-dimensional data in a few numbers which characterize the field's texture. A comparison of the transformed fields judges models solely based on their ability to predict spatial structures. The fidelity of the forecast's overall pattern is thus investigated separately from potential errors in feature location. This study introduces several new wavelet-based structure scores for the verification of deterministic as well as ensemble predictions. Their properties are rigorously tested in an idealized setting: a recently developed stochastic model for precipitation extremes generates realistic pairs of synthetic observations and forecasts with prespecified spatial correlations. The wavelet scores are found to react sensitively to differences in structural properties, meaning that the objectively best forecast can be determined even in cases where this task is difficult to accomplish by naked eye. Random rain fields prove to be a useful test bed for any verification tool that aims for an assessment of structure.
MILP formulations for spatio-temporal thermal-aware scheduling in Cloud and HPC datacenters

This paper focuses on scheduling problems related to the execution of computational jobs in datacenters with thermal constraints. Mixed integer linear programming (MILP) formulations are proposed that encompass both spatial and temporal aspects of the temperature evolution under a unified model. This model takes into account the dynamics of heat production and dissipation in order to schedule jobs at appropriate times on appropriate machines. The proposed MILP formulations are applicable to both high-performance computing (HPC) and Cloud settings, and can target several objectives including energy and makespan minimization, while incorporating the cooling costs and dynamic voltage and frequency scaling capabilities of servers. The applicability and usefulness of our formulations are demonstrated via several HPC and Cloud case-studies.
Cloud virtual machine lifecycle security framework based on trusted computing

As a foundation component of cloud computing platforms, Virtual Machines (VMs) are confronted with numerous security threats. However, existing solutions tend to focus on solving threats in a specific state of the VM. In this paper, we propose a novel VM lifecycle security protection framework based on trusted computing to solve the security threats to VMs throughout their entire lifecycle. Specifically, a concept of the VM lifecycle is presented divided up by the different active conditions of the VM. Then, a trusted computing based security protection framework is developed, which can extend the trusted relationship from trusted platform module to the VM and protect the security and reliability of the VM throughout its lifecycle. The theoretical analysis shows that our proposed framework can provide comprehensive safety to VM in all of its states. Furthermore, experiment results demonstrate that the proposed framework is feasible and achieves a higher level of security compared with some state-of-the-art schemes.
Recurrent age estimation

Age estimation is a challenging research topic in recent years. Existing approaches usually use only appearance features for age estimation. Personalized aging patterns, i.e., sequences of personal features, which have been shown as an important factor for improving age estimation accuracy, however, are not considered in their researches. We propose a novel model named recurrent age estimation (RAE), to make full use of appearance features as well as personalized aging patterns. RAE uses the CNN-LSTM architecture. Convolutional neural networks (CNNs) are trained to extract discriminative appearance features from face images, and long short-term memory networks (LSTMs) are employed to learn personalized aging patterns from sequences of personal features. Furthermore, we integrate the label distribution learning (LDL) scheme into LSTMs to exploit ambiguity from the real age and adjacent ages. The superiority of the RAE compared with existing approaches is shown by experimental results.
Learning regularized hoeffding trees from data streams

Learning from data streams is a hot topic in machine learning that targets the learning and update of predictive models as data becomes available for both training and query. Due to their simplicity and convincing results in a multitude of applications, Hoeffding Trees are, by far, the most widely used family of methods for learning decision trees from streaming data. Despite the aforementioned positive characteristics, Hoeffding Trees tend to continuously grow in terms of nodes as new data becomes available, i.e., they eventually split on all features available, and multiple times on the same feature; thus leading to unnecessary complexity. With this behavior, Hoeffding Trees lose the ability to be human-understandable and computationally efficient. To tackle these issues, we propose a regularization scheme for Hoeffding Trees that (i) uses a penalty factor to control the gain obtained by creating a new split node using a feature that has not been used thus far; and (ii) uses information from previous splits in the current branch to determine whether the gain observed indeed justifies a new split. The proposed scheme is combined with both standard and adaptive variants of Hoeffding Trees. Experiments using real-world, stationary and drifting synthetic data show that the proposed method prevents both original and adaptive Hoeffding Trees from unnecessarily growing while maintaining impressive accuracy rates. As a byproduct of the regularization process, significant improvements in processing time, model complexity, and memory consumption have also been observed, thus showing the effectiveness of the proposed regularization scheme.
MPSC: A Multiple-Perspective Semantics-Crossover Model for Matching Sentences

Sentence matching is crucial to many natural language processing (NLP) tasks. Generally, the degree of matching is measured from either of the two perspectives: topic-based match or semantic-based match. The former is to investigate if two sentences discuss the same topic, and the latter performs a deep level semantic matching of texts, which is currently the highlight in research. Deep semantic matching requires adequate modeling from the internal structure of the language objects as well as their interactions. To achieve this goal, this paper proposes a multiple-perspective semantics-crossover (MPSC) model for modeling the semantic-based match of two sentences. The model extracts the matching information of two sentences from the semantic interaction information generated from different angles, so as to calculate the matching degree of the two sentences. The MPSC model not only captures rich matching patterns at different levels but also acquires interactive features from different semantic angles. It can be used to address some important issues in NLP fields, such as information matching in text retrieval, question-answer matching in the Q&A system, and so on. The experimental results show that our proposed model of MPSC has better effectiveness than some popular semantic matching approaches.
The effect of the organizational communication climate and work enthusiasm on employee performance

This study aims at analysing the effect of the organizational communication climate and working enthusiasm on employee performance both directly and through employee job satisfaction at Economics Faculty in Private Islamic Universities in Makassar.The sampling technique used in this study was purposive sampling.Thus, the samples in this study were all employees who work at Economics Faculty in Private Islamic Universities in Makassar in which the total samples were 100 people.The data were analysed using multiple linear regression and path analysis.The study found that the organizational communication climate and work enthusiasm affect work satisfaction at Economics Faculty in Private Islamic Universities in Makassar.It is also found that organizational communication climate and work enthusiasm affect employee performance at Economics Faculty in Private Islamic Universities in Makassar.Furthermore, it is shown that job satisfaction affects employee performance at Economics Faculty in Private Islamic Universities in Makassar.
White-Box Implementation of Shamir’s Identity-Based Signature Scheme

Digital signature schemes have been extensively studied in the literature, where a large number of such schemes with different properties have been designed for different applications. For example, identity-based signature (IBS) schemes can efficiently map a user's digital public key to his/her real-world identity (e.g., e-mail address). However, existing implementations of IBS schemes are not generally designed for white-box security (WBS), particularly concerning the protection of the private key when special attackers have full access to the execution environment. Therefore, in this paper, we propose the first white-box implementation for the classical Shamir's IBS scheme. The basic idea is to utilize a mathematical transformation for embedding private key into some special tables, such that the original private key could be “invisible” during the execution process. We then analyze the security requirements achieved in our implementation, including the conventional black-box security under the random oracle model and WBS (e.g., key recovery attack resilience). This is the first IBS scheme implementation that satisfies WBS. It is also shown from the simulation that the implementation incurs a constant computational cost, which is realistic in deployments where a high security level is required.
Students’ Perceptions of the Benefits of Mobile Polling Technology in Teaching and Learning in College: Implications of Students’ Participation and academic Performance

Students’ perceptions of the benefits of mobile polling technology in teaching and learning in college: Implications of student’ participation and academic performance. Objectives: This study aims to analyze students’ perceptions of interactive teaching using Poll-Everywhere application as a useful teaching methodology. Methods: Strategic Lecturers enter questions using the Poll-Everywhere application. Students’ perceptions of interactive teaching that effectively uses this technology are statistically evaluated using a questionnaire. Findings: The level of student academic ability increased significantly between the two sections for Reading III Test (M = 89.74, SD = 3.26), and Reading V ( M = 90.42, SD = 3.91). Conclusions: Students consider it a useful method for giving real feedback, which encourages their performance and participation. Lecturers regard Poll-Everywhere as an effective teaching innovation that encourages deeper information storage and as an effective teaching aid in monitoring student progress and identifying shortcomings. Keywords: Poll-Everywhere, student’ participation, academic performance, feedback, Mobile Polling. Abstrak: Persepsi siswa tentang manfaat teknologi polling seluler dalam pengajaran dan pembelajaran di perguruan tinggi: Implikasi dari partisipasi mahasiswa dan kinerja akademik. Tujuan: Studi ini bertujuan untuk menganalisis persepsi mahasiswa tentang pengajaran interaktif menggunakan aplikasi Poll-Everywhere sebagai metodologi pengajaran yang berguna. Metode: Strategis Dosen memasukkan pertanyaan menggunakan aplikasi Poll-Everywhere. Persepsi mahasiswa tentang pengajaran interaktif yang efektif menggunakan teknologi ini dievaluasi secara statistik menggunakan kuesioner. Temuan: Tingkat kemampuan akademik mahasiswa meningkat secara signifikan antara dua bagian untuk Tes Membaca III (M = 89,74, SD = 3.26), dan Membaca V (M = 90.42, SD = 3.91). Kesimpulan: Mahasiswa menganggapnya sebagai metode yang berguna untuk memberikan umpan balik nyata, yang mendorong kinerja dan partisipasi mereka. Dosen menganggap Poll-Everywhere sebagai inovasi pengajaran yang efektif yang mendorong penyimpanan informasi yang lebih dalam dan sebagai alat bantu pengajaran yang efektif dalam memantau kemajuan siswa dan mengidentifikasi kekurangan. Kata kunci: Poll-Everywhere, partisipasi siswa, kinerja akademik, umpan balik, Polling Seluler. DOI: http://dx.doi.org/10.23960/jpp.v9.i1.201912
Fuzzy Delphi Method for Evaluating HyTEE Model (Hybrid Software Change Management Tool with Test Effort Estimation)

When changes are made to a software system during development and maintenance, they need to be tested again i.e. regression test to ensure that changes behave as intended and have not impacted the software quality.This research will produce an automated tool that can help the software manager or a maintainer to search for the coverage artifact before and after a change request.Software quality engineer can determine the test coverage from new changes which can support cost estimation, effort, and schedule estimation.Therefore, this study is intended to look at the views and consensus of the experts on the elements in the proposed model by benefitting the Fuzzy Delphi Method.Through purposive sampling, a total of 12 experts from academic and industrial have participated in the verification of items through 5-point linguistic scales of the questionnaire instrument.Outcome studies show 90% of elements in the proposed model consists of change management, traceability support, test effort estimation support, regression testing support, report and GUI meet, the value threshold (d construct) is less than 0.2 and the percentage of the expert group is above 75%.It is shown that elements of all the items contained in the venue are needed in the HyTEE Model (Hybrid Software Change Management Tool with Test Effort Estimation) based on the consensus of experts.
Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks

The paper proposes the dynamic modification of the activation function in a learning technique, more exactly backpropagation algorithm.The modification consists in changing slope of sigmoid function for activation function according to increase or decrease the error in an epoch of learning.The study was done using the Waikato Environment for Knowledge Analysis (WEKA) platform to complete adding this feature in Multilayer Perceptron class.This study aims the dynamic modification of activation function has changed to relative gradient error, also neural networks with hidden layers have not used for it.
A new JPEG image steganalysis technique combining rich model features and convolutional neural networks

The best traditional steganalysis methods aiming at adaptive steganography are the combination of rich models and ensemble classifier. In this study, a new steganalysis method for JPEG images based on convolutional neural networks is proposed to solve the high dimension problem in steganalysis from another aspect. On the basis of the original rich model, the algorithm adds different sizes of discrete cosine transform (DCT) basis functions to extract different detection features. Different features are combined at the fully connected layer through inputting 2-D feature values to the neural network convolutional layer for predictive classification. Experimental results show that convolutional neural networks as classifiers do not require a large number of training samples, and the final classification performance is better than that of the original ensemble classifier.
Email Phishing: An Enhanced Classification Model to Detect Malicious URLs

Phishing is the process of enticing people into visiting fraudulent websites and persuading them to enter their personal information.Number in phishing email are spread with the aim of making web users believe that they are communicating with a trusted entity or organization.Phishing is deployed by the use of advanced and harmful tactics like malicious or phishing URLs.So, it becomes necessary to detect malicious or phishing URLs in the present scenario.Numerous antiphishing techniques are in vogue to discriminate fake and the authentic website but are not effective.This research, focuses on the relevant URLs features that discriminate between legitimate and malicious/phishing URLs.The impact of email phishing can be largely reduced by adopting an appropriate combination of all these features with classification techniques.Therefore, an Enhanced Malicious URLs Detection (EMUD) model is developed with machine learning techniques for better classification and accurate results.
Does Diversity Affect User Satisfaction in Image Search

Diversity has been taken into consideration by existing Web image search engines in ranking search results. However, there is no thorough investigation of how diversity affects user satisfaction in image search. In this article, we address the following questions: (1) How do different factors, such as content and visual presentations, affect users’ perception of diversity? (2) How does search result diversity affect user satisfaction with different search intents? To answer those questions, we conduct a set of laboratory user studies to collect users’ perceived diversity annotations and search satisfaction. We find that the existence of nearly duplicated image results has the largest impact on users’ perceived diversity, followed by the similarity in content and visual presentations. Besides these findings, we also investigate the relationship between diversity and satisfaction in image search. Specifically, we find that users’ preference for diversity varies across different search intents. When users want to collect information or save images for further usage (the Locate search tasks), more diversified result lists lead to higher satisfaction levels. The insights may help commercial image search engines to design better result ranking strategies and evaluation metrics.
Online Multivariate Anomaly Detection and Localization for High-Dimensional Settings

This paper considers the real-time detection of abrupt and persistent anomalies in high-dimensional data streams. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that scales well to high-dimensional datasets. The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types. Thanks to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure. Its asymptotic optimality and computational complexity are comprehensively analyzed. In conjunction with the detection method, an effective technique for localizing the anomalous data dimensions is also proposed. The practical use of proposed algorithms are demonstrated using synthetic and real data, and in variety of applications including seizure detection, DDoS attack detection, and video surveillance.