Reviewerly/exHarmony huggingface dataset:
Sustainability of Cooperative Professional Development: Focused on Teachers’ Efficacy

In this study, we aim to gain critical insights into how cooperative professional development affects teachers’ efficacy. To this end, the purpose of this study is to identify cooperative professional development types (CPD-type) and to reveal the relationship between CPD-type and teachers’ efficacy in Korean middle schools, controlling for gender, age, years of experience and school climate. The data of this study are derived from the 2013 Teaching and Learning International Survey (TALIS) conducted by the OECD. The K-mean cluster analysis was used to identify distinct clusters of middle school teachers based on CPD. This process identified four specific groups: the disengaged group (36.8%), the collaborative group (11.3%), the activity-focused group (24.8%), and the coordinative group (27.1%). Hierarchical regression analysis revealed that all dummy variables of CPD-type were statistically significant, controlling for gender, age, years of experience, and school climate. Comparing the relative importance of each variable on teachers’ efficacy, the CPD1 (1 = the collaborative group) variable was most important. The results of this study provide a rationale for teachers to participate in collaborative professional development actively.
Decentralized Data Flows in Algebraic Service Compositions for the Scalability of IoT Systems

With the advent of the Internet of Things (IoT), scalability becomes a significant concern due to the huge amounts of data generated in IoT systems. A centralized data exchange is not desirable as it leads to a single performance bottleneck. Although a distributed data exchange removes the central bottleneck, it has network performance issues as data passes among multiple coordinators. A decentralized approach is the only solution that fully enables the realization of efficient IoT systems, since there is no single performance bottleneck and network overhead is minimized. In this paper, we present an approach that leverages the semantics of DX-MAN for realizing decentralized data flows in IoT systems. The algebraic semantics of such a model allows a well-defined structure of data flows which is easily analyzed by an algorithm that forms a direct relationship between data consumers and data producers. For the analysis, the algorithm takes advantage of the fact that DX-MAN separates control flow and data flow. Thus, our approach prevents passing data alongside control among multiple coordinators, so data is only read and written on a decentralized data space. We validate our approach using smart contracts on the Blockchain, and conducted experiments to quantitatively evaluate scalability. The results show that our approach scales well with the size of IoT systems.
Tabu Search in revealing the internal state of RC4+ cipher

Privacy and confidentiality can be secured by using cryptography. Thus, one needs to be sure that the ciphers being employed are secure and resistant to cryptanalysis. In the paper, a new state recovery attack on the RC4+ stream cipher is described. This attack is a plaintext attack where having both the plaintext and ciphertext the attacker can calculate the keystream what leads to revealing the internal state of the cipher. The presented approach is based on a Tabu Search with different types of neighbourhood and two types of aspiration proposed and considered. It requires checking 250 possible internal states on average in order to find the correct state. This is the best-known attack on RC4+. The other attacks that are known to date are a distinguishing attack and a differential fault analysis but only proposed approach can lead to revealing the internal state of RC4+ after checking so little permutations on average.
Using paradata to collect better survey data: Evidence from a household survey in Tanzania

Abstract Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system ( GPS ) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.
Multi-objective Policy Generation for Multi-robot Systems Using Riemannian Motion Policies

In many applications, multi-robot systems are required to achieve multiple objectives. For these multi-objective tasks, it is oftentimes hard to design a single control policy that fulfills all the objectives simultaneously. In this paper, we focus on multi-objective tasks that can be decomposed into a set of simple subtasks. Controllers for these subtasks are individually designed and then combined into a control policy for the entire team. One significant feature of our work is that the subtask controllers are designed along with their underlying manifolds. When a controller is combined with other controllers, their associated manifolds are also taken into account. This formulation yields a policy generation framework for multi-robot systems that can combine controllers for a variety of objectives while implicitly handling the interaction among robots and subtasks. To describe controllers on manifolds, we adopt Riemannian Motion Policies (RMPs), and propose a collection of RMPs for common multi-robot subtasks. Centralized and decentralized algorithms are designed to combine these RMPs into a final control policy. Theoretical analysis shows that the system under the control policy is stable. Moreover, we prove that many existing multi-robot controllers can be closely approximated by the framework. The proposed algorithms are validated through both simulated tasks and robotic implementations.
Image Super-Resolution via Simplified Dense Network With Non-Degenerate Layers

In this paper, we present an efficient method for single image super-resolution based on the dense network.First, considering that the dense architectures reuse all the previous features in each layer and cause a huge memory overhead, we simplify the dense architecture and only reuse the hierarchical features in the reconstruct layer; therefore, the redundancy in dense architecture is reduced.Subsequently, since the information of the degraded low-resolution image is much less than the potential high-resolution image, we propose a non-degenerate layer to address the degeneracy problem caused by the loss of input information.In the non-degenerate layer, we introduce a skip connection to the linear transformation to eliminate the singularity and utilize an invertible nonlinear activation function to avoid the dead zone.In addition, the direct reconstruction scheme is adopted for efficient models, and the structural similarity is utilized for better human perception.The experiments show promising results in terms of quantitative and qualitative results, which indicates that our method is effective and superior.
Developing a ‘router examination at scene’ standard operating procedure for crime scene investigators in the United Kingdom

As the majority of dwellings now maintain some form of Internet connectivity, the examination of routers at crime scenes is an increasing requirement. Due to cost and resourcing constraints, police forces are looking to transfer responsibility for carrying out this task to front line crime scene investigators, despite such staff typically lacking specialist training for this type of examination. Such strategies are potentially high-risk, as the mishandling of home routers can result in the oversight or destruction of potentially evidential information denoting home network usage, configuration and connected devices. This information can support scene examiners with their searching practices, ensuring all connected devices within the vicinity of a scene are accounted for or seized, whilst also supporting further technical investigations. This work documents the forensic examination of Sky, BT, Virgin, EE and TalkTalk routers and analysis of relevant evidence content including records of attached and currently connected devices, and, network configuration settings. A standard operating procedures has been developed and offered, to support crime scene investigation staff carry out router investigations.
MoA-Net: Self-supervised Motion Segmentation

Most recent approaches to motion segmentation use optical flow to segment an image into the static environment and independently moving objects. Neural network based approaches usually require large amounts of labeled training data to achieve state-of-the-art performance. In this work we propose a new approach to train a motion segmentation network in a self-supervised manner. Inspired by visual ecology, the human visual system, and by prior approaches to motion modeling, we break down the problem of motion segmentation into two smaller subproblems: (1) modifying the flow field to remove the observer's rotation and (2) segmenting the rotation-compensated flow into static environment and independently moving objects. Compensating for rotation leads to essential simplifications that allow us to describe an independently moving object with just a few criteria which can be learned by our new motion segmentation network - the Motion Angle Network (MoA-Net). We compare our network with two other motion segmentation networks and show state-of-the-art performance on Sintel.
Dataset Diversity for Metamorphic Testing of Machine Learning Software

Machine learning software is non-testable in that training results are not available in advance. The metamorphic testing, using pseudo oracle, is promising for software testing of such machine learning programs. Machine learning software, indeed, works on a collection of a large number of data, and thus slight changes in the input training dataset have a large impact on training results. This paper proposes a new metamorphic testing method applicable to neural network learning models. Key ideas are dataset diversity as well as behavioral oracle. Dataset diversity takes into account the dataset dependency of training results, and provides a new way of generating follow-up test inputs. Behavioral oracle monitors changes of certain statistical indicators as training processes proceed and is a basis of metamorphic relations to be checked. The proposed method is illustrated with a case of software testing of neural network programs to classify handwritten numbers.
The Effect of Barcode Technology Use on Pathology Specimen Labeling Errors

Pathology specimen labeling errors occur for a variety of reasons. We investigated the use of barcode technology as a method to improve the accuracy of pathology specimen labeling and patient safety. We also assessed nurses' perceptions of system quality, information quality, service quality, user satisfaction, and net benefits. Sixty-eight perioperative nurses who work in a teaching hospital in Taiwan completed the survey. Nurses scored net benefits as highly contributing to their satisfaction, whereas system quality contributed most to dissatisfaction. Further, we analyzed pathology specimen records before and after implementing the barcode system and found that specimen management errors significantly decreased. The use of a reliable barcode system could improve specimen labeling accuracy and enhance nurses' satisfaction with this technology.
Bayesian hidden Markov models for dependent large-scale multiple testing

An optimal and flexible multiple hypotheses testing procedure is constructed for dependent data based on Bayesian techniques, aiming at handling two challenges, namely dependence structure and non-null distribution specification. Ignoring dependence among hypotheses tests may lead to loss of efficiency and bias in decision. Misspecification in the non-null distribution, on the other hand, can result in both false positive and false negative errors. Hidden Markov models are used to accommodate the dependence structure among the tests. Dirichlet mixture process prior is applied on the non-null distribution to overcome the potential pitfalls in distribution misspecification. The testing algorithm based on Bayesian techniques optimizes the false negative rate (FNR) while controlling the false discovery rate (FDR). The procedure is applied to pointwise and clusterwise analysis. Its performance is compared with existing approaches using both simulated and real data examples.
Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

The significant properties of the maximum likelihood (ML) estimate are consistency, normality, and efficiency. While it has been proven that these properties are valid when the sample size approaches infinity, the behavior of an ML estimator when working with small sample sizes is largely unknown. However, in real tasks, we usually do not have sufficient data to completely fulfill the conditions of an optimal ML estimate. The question arises as to what amount of data is required to be able to estimate a Gaussian model that provides sufficiently accurate likelihood estimates. This issue is addressed with respect to the number of dimensions of the pattern space.
A Partition Based Framework for Large Scale Ontology Matching

: Large amount of data coming from different sources and formats is available on the web which generates heterogeneity problem. Semantic web technologies play an important role for collecting, merging, matching and aggregating big data from heterogeneous resources by determining the semantic correspondence between the entities. However, achieving good efficiency is major challenge for large scale ontology matching task. Objective: We propose a PBOM framework for coping with the large scale ontology matching problem. Methods: Our proposal first selects the source ontology and calculates the similarity of concepts within source ontology by using Lin measure. We use clustering algorithm for partition of the source ontology, obtained clusters of source ontology then used to divide the target ontology. During matching process, we run matchers from the pool of the matchers over each clusters. We aggregate the result of element level matchers and structure level matchers according to weighted sum aggregation. Each cluster is executed in its processor in parallel with other clusters. Results: We have presented step wise execution of proposed approach over one cluster of source and target ontology. The evaluation of our framework is performed by OAEI datasets of bibliographic benchmark 2014, biomedical track 2015 and anatomy 2016. Conclusion: Results show that, the performance of our approach is better in term of F-measure. The combination of clustering algorithm and parallel processing reduces the memory space and time complexity of matching process.
A Real-Time Twitter Trend Analysis and Visualization Framework

Trend analysis over Twitter offers organizations a fast and effective way of predicting the future trends. In the recent years, a wide range of indicators and methods were used for predicting the trend on Twitter with varying results, unfortunately most of the research focused only on the emerging trends which has gained long-term attention on the Twitter platform. This article depicts trend variations, i.e. to predict whether the trend on Twitter will gain attention or not in the next few hours. Hence a novel method called: “Twitter Trend Momentum (TTM)” is introduced for trend prediction which is the enhancement of a well-known stock market indicator called moving average convergence divergence (MACD). Reason analysis for trend variation is also carried out as an extension to the authors' research work. An evaluation of the framework showed the best results which are applied to build a real-time web application called “TwitTrend.” The application acts as a real-time update and recommendation system of top trends to users.
New version of continuous–discrete cubature Kalman filtering for nonlinear continuous–discrete systems

For nonlinear continuous-discrete systems, this paper elaborates a new accurate implementation of continuous-discrete cubature Kalman filter (CD-CKF). As the main contribution of this work, the new Kalman prediction stage begins by integrating the nonlinear continuous model for all the cubature sample vectors; the prior estimate state and covariance prediction are based on the weighted statistics of these integrated cubature sample vectors and the Gauss-Legendre approximation scheme. The new square root form CD-CKF is also derived and accurately implemented by combining with the modified variable stepsize NIRK. As the advantages of proposed approach, the complicated and error-prone processes of solving covariance differential equation or calculating derivatives are avoided, while the positive semi-definiteness of prior error covariance are numerically guaranteed. Simulations of traffic control scenarios further confirm the new approach's superior filtering performance in both reliability and accuracy.
Unveiling Exception Handling Guidelines Adopted by Java Developers

Despite being an old language feature, Java exception handling code is one of the least understood parts of many systems. Several studies have analyzed the characteristics of exception handling code, trying to identify common practices or even link such practices to software bugs. Few works, however, have investigated exception handling issues from the point of view of developers. None of the works have focused on discovering exception handling guidelines adopted by current systems - which are likely to be a driver of common practices. In this work, we conducted a qualitative study based on semi-structured interviews and a survey whose goal was to investigate the guidelines that are (or should be) followed by developers in their projects. Initially, we conducted semi-structured interviews with seven experienced developers, which were used to inform the design of a survey targeting a broader group of Java developers (i.e., a group of active Java developers from top-starred projects on GitHub). We emailed 863 developers and received 98 valid answers. The study shows that exception handling guidelines usually exist (70%) and are usually implicit and undocumented (54%). Our study identifies 48 exception handling guidelines related to seven different categories. We also investigated how such guidelines are disseminated to the project team and how compliance between code and guidelines is verified; we could observe that according to more than half of respondents the guidelines are both disseminated and verified through code inspection or code review. Our findings provide software development teams with a means to improve exception handling guidelines based on insights from the state of practice of 87 software projects.
Linear and Efficient String Matching Algorithms Based on Weak Factor Recognition

We present a simple and very efficient algorithm for string matching based on the combination of weak factor recognition and hashing. Despite its quadratic worst-case running time, our algorithm exhibits a sublinear behaviour. We also propose some practical improvements of our algorithm and a variant with a linear worst-case time complexity. Experimental results show that, in most cases, some of the variants of our algorithm obtain the best running times when compared, under various conditions, against the most effective algorithms present in the literature. For instance, in the case of small alphabets and long patterns, the gain in running time is up to 18%. This makes our proposed algorithm one of the most flexible solutions in practical cases.
Deep Learning for Matching in Search and Recommendation

Matching is the key problem in search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as "learning to match". In recent years, deep learning has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep learning for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine learning techniques are utilized to address the matching problems in search and recommendation. Lastly, we elaborate how deep learning can be effectively used to solve the matching problems in both tasks.
Informing Website Navigation Design with Team-Based Card Sorting

In 2016, Utah State University (USU) Libraries redesigned the library website's main menu and underlying information architecture (IA) in response to a number of known usability problems and limitations. Card sorting studies were conducted with a group of USU undergraduate students and a mixed group of faculty and graduate students to help develop a better understanding of users' mental models of library-related research and service tasks. Participants worked in teams to sort, rank and label cards pertaining to the content and feature of the library's website. Afterwards, participants discussed and performed usability tasks on each other's categories. Results were used to inform the design of a new IA and menu structure, while best practices from usability studies and trends in academic library website design were used to help with menu and link labeling. The final design was validated through follow-up discussions with staff, usability tests, and category/reverse category tests.