Reviewerly/exHarmony huggingface dataset:
Evaluating subscore uses across multiple levels: A case of reading and listening subscores for young EFL learners

Stakeholders of language tests are often interested in subscores. However, reporting a subscore is not always justified; a subscore should provide reliable and distinct information to be worth reporting. When a subscore is used for decisions across multiple levels (e.g., individual test takers and schools), it needs to be justified for its reliability and distinctiveness at every relevant level. In this study, we examined whether reporting seven Reading and Listening subscores of the TOEFL Primary® test, a standardized English proficiency test for young English as a foreign language learners, could be justified for reporting at individual and school levels. We analyzed data collected in pilot administrations, in which 4776 students from 51 schools participated. We employed the classical test theory (CTT) based approaches of Haberman (2008) and Haberman, Sinharay, and Puhan (2009) for the individual and school-level investigations, respectively. We also supplemented the CTT-based approaches with a factor analytic approach for the individual level analysis and a multilevel modeling approach for the school-level analysis. The results differed across the two levels: we found little support for reporting the subscores at the individual level, but strong evidence supporting the added-value of the school-level subscores when the sample size for each school exceeds 50.
Scientists' data discovery and reuse behavior: (Meta)data fitness for use and the FAIR data principles

ABSTRACT Environmental science researchers frequently must work at regional and global scales that require them to search for, evaluate, and reuse data collected by others. This study assesses science data reusers' information‐seeking behavior to determine fitness for use , a concept from consumer behavior research that explains how consumers define and assess product quality and compatibility to satisfy their specific needs. Using a critical incident technique, 22 researchers in earth science described their most recent data discovery resulting in reuse. An interview schedule derived from the FAIR Data Principles framed participants' information‐seeking behavior along a sequence of actions, which included finding, accessing, making interoperable, and reusing data. Researchers' perspectives on how they discovered and evaluated data for reuse gives new insights into fitness for use of data by human consumers, and contributes to considerations for furthering work into machine‐actionable data and learning.
Coarse-to-fine salient object detection with low-rank matrix recovery

Low-rank matrix recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods seldom consider the inter-relationship among elements within these two components, thus are prone to generating scattered or incomplete saliency maps. In this paper, we introduce a novel and efficient LRMR-based saliency detection model under a coarse-to-fine framework to circumvent this limitation. First, we roughly measure the saliency of image regions with a baseline LRMR model that integrates a ℓ1-norm sparsity constraint and a Laplacian regularization smooth term. Given samples from the coarse saliency map, we then learn a projection that maps image features to refined saliency values, to significantly sharpen the object boundaries and to preserve the object entirety. We evaluate our framework against existing LRMR-based methods on three benchmark datasets. Experimental results validate the superiority of our method as well as the effectiveness of our suggested coarse-to-fine framework, especially for images containing multiple objects. MATLAB code for implementing our method is available at https://github.com/qizhust/HLRSaliency.
On Iterative Unscented Kalman Filter using Optimization

The unscented Kalman filter (UKF) is a very popular solution for estimation of the state in nonlinear systems. Similar to the extended Kalman filter (EKF) and contrary to the Kalman filter (KF) for linear systems, the UKF provides no guarantees that the filter updates will improve the filtered state estimate. In the past, the iterated EKF (IEKF) has been suggested as a way to online monitor the filter performance and try to improve it using optimization techniques. In this paper we do the same for the UKF, deriving six iterated UKF (IUKF) variations based on two cost functions and three optimization algorithms. The methods are evaluated and compared to IEKF versions and to two versions of the iterative posterior linearization filter (IPLF) in three benchmark simulation studies. The results show that IUKF algorithms can be used as a derivative free alternative to IEKF, and provide insights about the different design choices available in IUKF algorithms.
Self-adaptive Cobots in Cyber-Physical Production Systems

Absolute automation in certain industries, such as the automotive industry, has proven to be disadvantageous. Robots are fairly capable when performing tasks that are repetitive and demand precision. However, a hybrid solution comprised of the adaptability and resourcefulness of humans cooperating, in the same task, with the precision and efficiency of machines is the next step for automation. Manipulators, however, lack self-adaptability and true collaborative behaviour. And so, through the integration of vision systems, manipulators can perceive their environment and also understand complex interactions. In this paper, a vision-based collaborative proof-of-concept framework is proposed using the Kinect v2, a UR5 robotic manipulator and MATLAB. This framework implements 3 behavioural modes, 1) a Self-Adaptive mode for obstacle detection and avoidance, 2) a Collaborative mode for physical human-robot interaction and 3) a standby Safe mode. These modes are activated with recourse to gestures, by virtue of the body tracking and gesture recognition algorithm of the Kinect v2. Additionally, to allow self-recognition of the robot, the Region Growing segmentation is combined with the UR5's Forward Kinematics for precise, near real-time segmentation. Furthermore, self-adaptive reactive behaviour is implemented by using artificial repulsive action for the manipulator's end-effector. Reaction times were tested for all three modes, being that Collaborative and Safe mode would take up to 5 seconds to accomplish the movement, while Self-Adaptive mode could take up to 10 seconds between reactions.
Do Research and Practice of Code Smell Identification Walk Together? A Social Representations Analysis

Context: It is frequently claimed the need for bridging the gap between software engineering research and practice. In this sense, the theory of social representations may be useful to characterize the actual concerns of software developers. It comprises the system of values, behaviors, and practices of communities regarding a particular social object, such as the task of smell identification. Aim: To characterize the social representations of smell identification by software developers. Method: Based on the answers given to a question-naire, we analyzed the associations made by the developers about smell identification, i.e., what immediately comes to their minds when they think about this task. Results: We found that developers strongly associate smell identification with the practice of smell removal and with the incidence of bugs. They also frequently associate the task with the practice of inspection and with the need of having individual skills. Besides, we verified that the current state of the art on smell identification partially address the social representations of the software developers. Conclusion: There is a considerable gap between the research of smell identification and its practice. We propose directions to mitigating this gap.
Strictness and connexivity

This paper discusses Aristotle's thesis and Boethius' thesis, the most distinctive theorems of connexive logic. Its aim is to show that, although there is something plausible in Aristotle's thesis and Boethius' thesis, the intuitions that may be invoked to motivate them are consistent with any account of indicative conditionals that validates a suitably restricted version of them. In particular, these intuitions are consistent with the view that indicative conditionals are adequately formalized as strict conditionals.
Estimation of Solar Power Output Using ANN Model: A Case Study of a 20-MW Solar PV Plan at Adrar, Algeria

In this paper, the Artificial Neural Networks (ANN) model have been exploited for estimation of the energy production of the 20-MW solar photovoltaic (PV) plant installed in Adrar in terms of energy yield, using a minimal knowledge of meteorological parameter include global solar radiation, relative humidity, wind speed, and ambient or air temperature. A comparative analysis has been carried out between the proposed artificial neural networks model and multiple linear regression (MLR) models in PV output estimation and performance is measured based on evaluation indexes, namely mean absolute percentage error, normalized mean absolute error, and normalized root mean square error respectively. Further, the comparison of the ANN model with MLR based models reveals the advantages and supremacy of the ANN model.
High Energy Efficiency FPGA-Based Accelerator for Convolutional Neural Networks Using Weight Combination

Convolutional neural network (CNN) has achieved excellent achievements in the field of image recognition. Because of the better energy efficiency and fewer development cycle, FPGA has been widely used as the implementation platform of CNN. The quantized CNN reduces the demand for storage and transmission bandwidth, and is easier to be implemented on FPGA platforms. And the weights of the quantized network contain a lot of repetitions and zeros. The repeatability and sparsity of these weight parameters can be well utilized in the hardware implementation of CNN. In this paper, a CNN accelerator is designed and implemented on the Virtex-7 FPGA, and the test network is AlexNet with 17 unique weights after quantization. By using weight combination method, the accelerator redesigned the calculation pattern of the processing elements (PEs), reducing the multiplication by 92.25%. The experiment result shows that our accelerator achieves 179 GOPS under 200 MHz clock frequency. The energy efficiency of 32.72 GOPS/Watt is obtained by combining the new PE with the systolic array architecture.
BTC-2019: The 2019 Billion Triple Challenge Dataset

Six datasets have been published under the title of Billion Triple Challenge (BTC) since 2008. Each such dataset contains billions of triples extracted from millions of documents crawed from hundreds of domains. While these datasets were originally motivated by the annual ISWC competition from which they take their name, they would become widely used in other contexts, forming a key resource for a variety of research works concerned with managing and/or analysing diverse, real-world RDF data as found natively on the Web. Given that the last BTC dataset was published in 2014, we prepare and publish a new version – BTC-2019 – containing 2.2 billion quads parsed from 2.6 million documents on 394 pay-level-domains. This paper first motivates the BTC datasets with a survey of research works using these datasets. Next we provide details of how the BTC-2019 crawl was configured. We then present and discuss a variety of statistics that aim to gain insights into the content of BTC-2019. We discuss the hosting of the dataset and the ways in which it can be accessed, remixed and used. Resource DOI: https://doi.org/10.5281/zenodo.2634588 Resource type: Dataset
Linked data through the lens of machine learning: an Enterprise view

Enterprises have adopted semantic Web technologies for data reuse and sharing purposes and machine learning techniques for data analysis. Semantic Web is a generative technology which aims to build a giant global dataspace on the Web by interlinking diverse data sources available on the Web. Machine learning is a field of computational science that includes several generative algorithms that adaptively learn and make data-driven predictions. However, their confluence presents significant challenges from an enterprise context - that has not been effectively addressed. To solve the challenges, linked data can be chosen as the best practice for data representation while machine learning as a paradigm for data analysis. The paper proposes a conceptual model that confluences machine learning and linked data approaches, specifically to cater to enterprise needs. The conceptual model is applicable across varied enterprises viz., education, banking, government and medical, to name a few.
Advances in ICP-MS technology and the application of multi-element geochemistry to exploration

There have been several advances in inductively coupled plasma-mass spectrometer (ICP-MS) analytical technologies in the last decade. Collision/reaction cell ICP-MS and triple quadrupole ICP-MS techniques can produce lower detection limits for select elements that experience interferences with a standard quadrupole (e.g. Se and As). Triple quadrupole ICP-MS, in particular, can eliminate virtually all polyatomic or isobaric interferences for highly accurate measurements of some element isotopes systematics that are of great interest in mineral exploration, namely Pb/Pb. Laser ablation ICP-MS has become more popular as an effective analytical tool to measure mineral grain trace elements, which could assist in vectoring to mineralization or exploration drill targets. The ablation of a spot on a Li-borate fused glass disk paired with XRF analysis has also gained popularity as an alternative to total whole rock characterization packages that employ several separate digestions and analytical methods. While there have been several advancements in ICP-MS technologies in exploration geochemistry, they have not been widely accepted or implemented. This slow adaptation could be due to the extended recession in the mining industry between 2012 and 2017. It is also possible that standard ICP-MS data (i.e. no collision/reaction cell) is still fit for purpose. This stands in stark contrast to implementation of ICP-MS in the previous decade (1997–2007), which was transformational for the industry. Consideration of all elements from large multi-element ICP-MS analytical suites for mineral exploration can be an extremely powerful tool in the exploration toolkit. The discovery of the White Gold District, Yukon, is a prime example of how the utilization of soil geochemical data, when plotted spatially, can vector to gold mineralization. The presence of Au + As + Sb soil anomalies were key to delineating mineralization, especially when accompanied by publicly available geological, geographical and geophysical data. Additionally, elements and element ratios not typically considered in Au exploration, including Ni and U, were utilized to determine the lithological and structural controls on mineralization. The availability of multi-element ICP-MS data was also useful in the discovery of the Cascadero Copper Taron Caesium deposit. Ore-grade Cs was discovered only because Cs was included in the multi-element ICP-MS exploration geochemistry suite. Before the availability of ICP-MS, it is unlikely that this deposit would have been discovered. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17
InfoLED: Augmenting LED Indicator Lights for Device Positioning and Communication

Augmented Reality (AR) has the potential to expand our capability for interacting with and comprehending our surrounding environment. However, current AR devices treat electronic appliances no different than common non-interactive objects, which substantially limits the functionality of AR. We present InfoLED, a positioning and communication system based on indicator lights that enables appliances to transmit their location, device IDs, and status information to the AR client without changing their visual design. By leveraging human insensitivity to high-frequency brightness flickering, InfoLED transmits all of that information without disturbing the original function as an indicator light. We envision InfoLED being used in three categories of application: malfunctioning device diagnosis, appliances control, and multi-appliance configuration. We conducted three user studies, measuring the performance of the InfoLED system, the human readability of the patterns and colors displayed on the InfoLED, and users' overall preference for InfoLED. The study results showed that InfoLED can work properly from a distance of up to 7 meters in indoor conditions and it did not interfere with our participants' ability to comprehend the high-level patterns and colors of the indicator light. Overall, study subjects prefer InfoLED to an ArUco 2D barcode-based baseline system and reported less cognitive load when using our system.
A Scalable Framework for Quality Assessment of RDF Datasets

Over the last years, Linked Data has grown continuously. Today, we count more than 10,000 datasets being available online following Linked Data standards. These standards allow data to be machine readable and inter-operable. Nevertheless, many applications, such as data integration, search, and interlinking, cannot take full advantage of Linked Data if it is of low quality. There exist a few approaches for the quality assessment of Linked Data, but their performance degrades with the increase in data size and quickly grows beyond the capabilities of a single machine. In this paper, we present DistQualityAssessment – an open source implementation of quality assessment of large RDF datasets that can scale out to a cluster of machines. This is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. The work presented here is integrated with the SANSA framework and has been applied to at least three use cases beyond the SANSA community. The results show that our approach is more generic, efficient, and scalable as compared to previously proposed approaches. Resource type Software Framework Website http://sansa-stack.net/distqualityassessment/ Permanent URL https://doi.org/10.6084/m9.figshare.7930139
Extracting Distribution Network Fault Semantic Labels From Free Text Incident Tickets

Increased monitoring of distribution networks and power system assets present utilities with new opportunities to predict and forestall system failures.Although automated pattern recognition methodologies have given other industries significant advantage, power system operators face additional challenges before these can be realized.The effort of apportioning ground truth to fault data creates a knowledge bottleneck that can make utilizing automatic classification techniques impossible.Surrogate approaches using operational process outputs such as maintenance tickets as labels can be challenging owing to the causal ambiguity of these written records.To approach a solution, this paper demonstrates utilizing natural language processing techniques to disambiguate the free text in maintenance tickets for onward use in supervised learning of fault prediction and classification techniques.A demonstration of this approach on an established power quality fault data set is provided for illustration.
Sample selection-based hierarchical extreme learning machine

Large amounts of training data in machine learning can keep the accuracy high to a certain extent, but the time costs are high because of the exorbitant amount of data and their dimensionality. Therefore, how to simultaneously select the most useful training data set and extract the main features of the samples, especially for image data, are essential problems that urgently need to be solved in the field of large-scale machine learning. Herein, a training sample selection method that is based on the fuzzy c-means clustering algorithm (FCM) is proposed for the problems. It first utilises condensed nearest neighbour (CNN) to make a preliminary selection of training samples. Then, it utilises the FCM to get the centres of the selected data, and, finally, it effectively condenses the sample using a compression parameter. Meanwhile, considering the critical influence of the sample features on the classification model, this paper selects the hierarchical extreme learning machine (H-ELM) model to better solve the classification task. Based on this, the paper presents the FCM-CNN-H-ELM framework for data classification, which combines FCM-Based CNN and H-ELM. The results of the experiments show that the proposed training sample selection method and classification framework can guarantee consistent, even higher, prediction results with a small number of training samples, and significantly reduce the training time.
Anytime algorithms for the longest common palindromic subsequence problem

The longest common palindromic subsequence (LCPS) problem aims at finding a longest string that appears as a subsequence in each of a set of input strings and is a palindrome at the same time. The problem is a special variant of the well known longest common subsequence problem and has applications in particular in genomics and biology, where strings correspond to DNA or protein sequences and similarities among them shall be detected or quantified. We first present a more traditional A* search that makes use of an advanced upper bound calculation for partial solutions. This exact approach works well for instances with two input strings and, as shown in experiments, outperforms several other exact methods from the literature. However, the A* search also has natural limitations when a larger number of strings shall be considered due to the problem’s complexity. To effectively deal with this case in practice, anytime A* search variants are investigated, which are able to return a reasonable heuristic solution at almost any time and are expected to find better and better solutions until reaching a proven optimum when enough time given. In particular a novel approach is proposed in which Anytime Column Search (ACS) is interleaved with traditional A* node expansions. The ACS iterations are guided by a new heuristic function that approximates the expected length of an LCPS in subproblems usually much better than the available upper bound calculation. This A*+ACS hybrid is able to solve small to medium-sized LCPS instances to proven optimality while returning good heuristic solutions together with upper bounds for large instances. In rigorous experimental evaluations we compare A*+ACS to several other anytime A* search variants and observe its superiority.
NeuroEvolution: Using Genetic Algorithm for optimal design of Deep Learning models

Convolutional Neural Networks (CNN) have proved to be influential in image classification techniques. However, CNN has not proved much useful in time series prediction. This is because of the causal nature of time series data. With proper modifications in the architecture of CNN the model can perform well on time series prediction as well. However, the modification and design of CNN architecture require expertise and knowledge of data. In order to tackle this problem, a genetic algorithm solution is proposed, that designs its own optimal CNN architecture and was proved useful in predicting time series data. The robustness and accuracy of the model were tested on our dataset and compared with an RNN architecture. It was found that the CNN model performed well irrespective of the duration of prediction and these results are comparable to state of the art optimal RNN models.