Reviewerly/exHarmony huggingface dataset:
Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.
Masked Graph Convolutional Network

Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph which is usually constructed from the data features, while the graph convolutional neural networks smooth the node attributes, i.e., propagate the attributes, over the real graph topology. In this paper, they are interpreted from the perspective of propagation, and accordingly categorized into symmetric and asymmetric propagation based methods. From the perspective of propagation, both the traditional and network based methods are propagating certain objects over the graph. However, different from the label propagation, the intuition ``the connected data samples tend to be similar in terms of the attributes", in attribute propagation is only partially valid. Therefore, a masked graph convolution network (Masked GCN) is proposed by only propagating a certain portion of the attributes to the neighbours according to a masking indicator, which is learned for each node by jointly considering the attribute distributions in local neighbourhoods and the impact on the classification results. Extensive experiments on transductive and inductive node classification tasks have demonstrated the superiority of the proposed method.
Robust Human Activity Recognition based on Deep Metric Learning

In the domain of Activity Recognition, the proliferation of low-cost and sensor-enabled personal devices has led to significant heterogeneity in the data generated by users. Traditional approaches to this problem have previously relied on handcrafted features and template-matching methods, which have limited flexibility and performance with high variability. In this work we investigate the use of Deep Metric Learning in the domain of activity recognition. We use a deep Triplet Network to generate fixed-length descriptors from activity samples for purposes of classification. We carry out evaluation of our proposed method on five datasets from different sources with differing activities. We obtain classification accuracies of up to 96% in self-testing scenarios and up to 91% accuracy in cross-dataset testing without retraining. We also show that our method performs similarly to traditional Convolutional Neural Networks. The obtained results indicate the promise of this approach.
Pupillography as Indicator of Programmers' Mental Effort and Cognitive Overload

Our research explores a recent paradigm called Biofeedback Augmented Software Engineering (BASE) that introduces a strong new element in the software development process: the programmers' biofeedback. In this Practical Experience Report we present the results of an experiment to evaluate the possibility of using pupillography to gather biofeedback from the programmers. The idea is to use pupillography to get meta information about the programmers' cognitive and emotional states (stress, attention, mental effort level, cognitive overload,...) during code development to identify conditions that may precipitate programmers making bugs or bugs escaping human attention, and tag the corresponding code locations in the software under development to provide online warnings to the programmer or identify code snippets that will need more intensive testing. The experiments evaluate the use of pupillography as cognitive load predictor, compare the results with the mental effort perceived by programmers using NASATLX, and discuss different possibilities for the use of pupillography as biofeedback sensor in real software development scenarios.
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions

We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference. To address the curse of dimensionality, we exploit the intrinsic low-dimensional geometric structure of the posterior distribution in the high-dimensional parameter space via its Hessian (of the log posterior) operator and perform a parallel update of the parameter samples projected into a low-dimensional subspace by an SVN method. The subspace is adaptively constructed using the eigenvectors of the averaged Hessian at the current samples. We demonstrate fast convergence of the proposed method, complexity independent of the parameter and sample dimensions, and parallel scalability.
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information

We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering.Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation.Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics.Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate.With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering.The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.
Improving Answer Selection and Answer Triggering using Hard Negatives

In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering.We show that the choice of sampling strategy is key for achieving improved performance on these tasks.Evaluating on recent answer selection datasets -InsuranceQA, SelQA, and an internal QA dataset, we show that using hard negatives with relatively simple model architectures (bag of words and LSTM-CNN) drives significant performance gains.On In-suranceQA, this strategy alone improves over previously reported results by a minimum of 1.6 points in P@1.Using hard negatives with a Transformer encoder provides a further improvement of 2.3 points.Further, we propose to use quadruplet loss for answer triggering, with the aim of producing globally meaningful similarity scores.We show that quadruplet loss function coupled with the selection of hard negatives enables bag-of-words models to improve F1 score by 2.3 points over previous baselines, on SelQA answer triggering dataset.Our results provide key insights into answer selection and answer triggering tasks.
Absent words in a sliding window with applications

An absent word of a word y is a word that does not occur in y. It is then called minimal if all its proper factors occur in y. In fact, minimal absent words (MAWs) provide useful information about y and thus have several applications. In this paper, we propose an algorithm that maintains the set of MAWs of a fixed-length window sliding over y online. Our algorithm represents MAWs through nodes of the suffix tree. Specifically, the suffix tree of the sliding window is maintained using modified Senft's algorithm (Senft, 2005), itself generalizing Ukkonen's online algorithm (Ukkonen, 1995). We then apply this algorithm to the approximate pattern-matching problem under the Length Weighted Index distance (Chairungsee and Crochemore, 2012). This results in an online O(σ|y|)-time algorithm for finding approximate occurrences of a word x in y, |x|≤|y|, where σ is the alphabet size.
Does the Introduction of Lambda Expressions Improve the Comprehension of Java Programs?

Background: The Java programming language version eighth introduced a number of features that encourage the functional style of programming, including the support for lambda expressions and the Stream API. Currently, there is a common wisdom that refactoring a legacy code to introduce lambda expressions, besides other potential benefits, simplifies the code and improves program comprehension. Aims: The purpose of this paper is to investigate this belief, conducting an in depth study to evaluate the effect of introducing lambda expressions on program comprehension. Method: We conduct this research using a mixed-method study. First, we quantitatively analyze 66 pairs of real code snippets, where each pair corresponds to the body of a method before and after the introduction of lambda expressions. We computed two metrics related to source code complexity (number of lines of code and cyclomatic complexity) and two metrics that estimate the readability of the source code. Second, we conduct a survey with practitioners to collect their perceptions about the benefits on program comprehension, with the introduction of lambda expressions. The practitioners evaluate a number between three and six pairs of code snippets, to answer questions about possible improvements. Results: We found contradictory results in our research. Based on the quantitative assessment, we could not find evidences that the introduction of lambda expressions improves software readability--one of the components of program comprehension. Differently, our findings of the qualitative assessment suggest that the introduction of lambda expression improves program comprehension. Implications: We argue in this paper that one can improve program comprehension when she applies particular transformations to introduce lambda expressions (e.g., replacing anonymous inner classes by lambda expressions). In addition, the opinion of the participants shine the opportunities in which a transformation for introducing lambda might be advantageous. This might support the implementation of effective tools for automatic program transformations. Finally, our results suggest that state-of-the-art models for estimating program readability are not helpful to capture the benefits of a program transformation to introduce lambda expressions.
GEMAS: Geochemical background and mineral potential of emerging tech-critical elements in Europe revealed from low-sampling density geochemical mapping

The demand for ‘high-tech’ element resources (e.g., rare earth elements, lithium, platinum group elements) has increased with their continued consumption in developed countries and the emergence of developing economies. To provide a sound knowledge base for future generations, it is necessary to identify the spatial distribution of critical elements at a broad-scale, and to delineate areas for follow-up surveys. Subsequently, this knowledge can be used to study possible environmental consequences of the increased use of these resources. In this paper, three critical industrial elements (Sb, W, Li) from low-sampling density geochemical mapping at the continental-scale are presented. The geochemical distribution and spatial patterns have been obtained from agricultural soil samples (Ap-horizon, 0–20 cm; N = 2108 samples) collected at a density of 1 site per 2500 km2 and analysed by ICP-MS after a hot aqua regia digestion as part of the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) soil-mapping project in 33 European countries. Most of the geochemical maps show exclusively natural background element concentrations with minor, or without, anthropogenic influence. The maximum extent of the last glaciation is marked as a discrete element concentration break, and a distinct difference occurs in element concentration levels between the soil of northern and southern Europe, most likely an effect of soil genesis, age and weathering. The Sb, W and Li concentrations in soil provide a general overview of element spatial distribution in relation to complexity of the underlying bedrock and element mobility in the surface environment at the continental-scale. The chemical composition of agricultural soil represents largely the primary mineralogy of the source bedrock, the effects of pre- and post-depositional chemical weathering, formation of secondary products, such as clays, and element mobility, either by leaching or mineral sorting. Observed geochemical patterns of Li, W and Sb can be often linked with known mineralisation as recorded in the ProMine Mineral Database, where elements in question occur either as main or secondary resources. Anthropogenic impact has only been identified locally, predominantly in the vicinity of large urban agglomerations. Unexplained high element concentrations may potentially indicate new sources for high-tech elements and should be investigated at a more detailed scale.
A Cluster-Based Hybrid Feature Selection Method for Defect Prediction

Machine learning is an effective method for software defect prediction. The performance of learning models can be affected by irrelative and redundant features. Feature selection techniques select a subset of most impactful relevant features that will result in higher accuracy and efficiency of models. This paper proposed a Cluster-based Hybrid Feature Selection method (CHIFS) for software defect prediction. A spectral cluster-based Feature Quality coefficient (FQ) was defined as a comprehensive measurement of feature relevance and redundancy. The final feature subset was iteratively selected from feature sequence ranked by FQ. The proposed CHIFS method was validated in the experiments using 3 classifiers with 15 open datasets from Promise Repository. Experimental results showed that the CHIFS method performed better than traditional methods in terms of accuracy and efficiency on a wide range of datasets.
A New Searching Approach Using Improved Multi-Ant Colony Scheme for Multi-UAVs in Unknown Environments

In this research, an important topic of cooperative search for multi-dynamic targets in unknown sea area by unmanned aerial vehicle (UAV) is studied based on improved multi-ant colony theory (IMAC). A specialized multi-UAV sea area search map is established, in which a novel target probability map (TPM) was designed to reduce the impact of uncertainties caused by dynamics targets. Then, the transfer rules of the cell were determined for multi-UAV by improving the behavior criterion of the ant colony algorithm and the updating principle of the pheromone map. Finally, the performance of the proposed method is tested in several search scenarios with different features, and the statistical comparison with the traditional algorithm shows the superiority of the new method.
Self-referenced multiple-beam interferometric method for robust phase calibration of spatial light modulator

Phase-only liquid crystal spatial light modulator has wide ranging applications that require accurate phase retardance. The phase calibration of the spatial light modulator is therefore of vital importance. Available self-referenced calibration methods face the challenges of high time consumption, low efficiency, and low stability against the conditions. A self-referenced multiple-beam interferometric method is proposed to derive the global grayscale-phase response. As is presented theoretically and experimentally, the proposed method reduces the measuring time and improves the calibration efficiency by encoding multiple fringes in a single hologram. Results also show that the method is equally accurate when compared with traditional two-beam interferometric method, whereas providing a greater robustness against measuring errors since the standard deviation is only 56% of that of the traditional method.
Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics

Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP) programming and execution model. BSP permits bulk-communication and uses large messages which are supported efficiently by current message transport layers, but bulk-synchronization can exacerbate the performance impact of load imbalance because a round cannot be completed until every host has completed that round. Asynchronous distributed graph analytics systems circumvent this problem by permitting hosts to make progress at their own pace, but existing systems either use global locks and send small messages or send large messages but do not support general partitioning policies such as vertex-cuts. Consequently, they perform substantially worse than bulk-synchronous systems. Moreover, none of their programming or execution models can be easily adapted for heterogeneous devices like GPUs. In this paper, we design and implement a lock-free, non-blocking, bulk-asynchronous runtime called Gluon-Async for distributed and heterogeneous graph analytics. The runtime supports any partitioning policy and uses bulk-communication. We present the bulk-asynchronous parallel (BASP) model which allows the programmer to utilize the runtime by specifying only the abstract communication required. Applications written in this model are compared with the BSP programs written using (1) D-Galois and D-IrGL, the state-of-the-art distributed graph analytics systems (which are bulk-synchronous) for CPUs and GPUs, respectively, and (2) Lux, another (bulk-synchronous) distributed GPU graph analytical system. Our evaluation shows that programs written using BASP-style execution are on average ~1.5x faster than those in D-Galois and D-IrGL on real-world large-diameter graphs at scale. They are also on average ~12x faster than Lux. To the best of our knowledge, Gluon-Async is the first asynchronous distributed GPU graph analytics system.
Optimizing Service Placement for Microservice Architecture in Clouds

As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process. We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. LBANN combines multiple levels of parallelism and exploits some of the worlds largest supercomputers.We demonstrate our framework by creating a complex predictive model based on multi-variate data from high-energy-density physics containing hundreds of millions of images and hundreds of millions of scalar values derived from tens of millions of simulations of inertial confinement fusion. Our approach combines an HPC workflow and extends LBANN with optimized data ingestion and the new tournament-style training algorithm to produce a scalable neural network architecture using a CORAL-class supercomputer. Experimental results show that 64 trainers (1024 GPUs) achieve a speedup of 70.2× over a single trainer (16 GPUs) baseline, and an effective 109% parallel efficiency.
Extending Event Detection to New Types with Learning from Keywords

Traditional event detection classifies a word or a phrase in a given sentence for a set of prede- fined event types. The limitation of such pre- defined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detec- tion that describes types via several keywords to match the contexts in documents. This fa- cilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new for- mulation. Our extensive experiments demon- strate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem
Development of the multifunctional rescue robot FUHGA2 and evaluation at the world robot summit 2018

Robots are needed in rescue activities and the inspection of infrastructure. We developed the rescue robot FUHGA2 so that it has various functions. FUHGA2 has high mobility, with main crawlers covering the body and four sub-crawlers, high dexterity using a long six-axis arm, and high searching ability using various sensors. Furthermore, a novel parallel gripper unit with wire-jamming mechanism is mounted at the tip of the arm, which can change its hardness and fit its shape to an object. Additionally, high Maneuvering is achieved by displaying sensor information to the operator clearly and intuitive operation command design. FUHGA2 won the World Robot Summit 2018 Standard Disaster Robotics Challenge, which is a competition for rescue robots. We achieved high scores in all categories of tasks. In this paper, we describe the development of FUHGA2 and the performance evaluation results from the World Robot Summit 2018.
Multimodal databases

research-article Multimodal databases Share on Author: Michel Valstar View Profile Authors Info & Claims The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging DirectionsJuly 2019 Pages 393–421https://doi.org/10.1145/3233795.3233807Published:01 July 2019 0citation104DownloadsMetricsTotal Citations0Total Downloads104Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access