Reviewerly/exHarmony huggingface dataset:
Multi-step planning of eye movements in visual search

The capability of directing gaze to relevant parts in the environment is crucial for our survival. Computational models have proposed quantitative accounts of human gaze selection in a range of visual search tasks. Initially, models suggested that gaze is directed to the locations in a visual scene at which some criterion such as the probability of target location, the reduction of uncertainty or the maximization of reward appear to be maximal. But subsequent studies established, that in some tasks humans instead direct their gaze to locations, such that after the single next look the criterion is expected to become maximal. However, in tasks going beyond a single action, the entire action sequence may determine future rewards thereby necessitating planning beyond a single next gaze shift. While previous empirical studies have suggested that human gaze sequences are planned, quantitative evidence for whether the human visual system is capable of finding optimal eye movement sequences according to probabilistic planning is missing. Here we employ a series of computational models to investigate whether humans are capable of looking ahead more than the next single eye movement. We found clear evidence that subjects' behavior was better explained by the model of a planning observer compared to a myopic, greedy observer, which selects only a single saccade at a time. In particular, the location of our subjects' first fixation differed depending on the stimulus and the time available for the search, which was well predicted quantitatively by a probabilistic planning model. Overall, our results are the first evidence that the human visual system's gaze selection agrees with optimal planning under uncertainty.
A Systematic Review of the Convergence of Augmented Reality, Intelligent Virtual Agents, and the Internet of Things

In recent years we are beginning to see the convergence of three distinct research fields: augmented reality (AR), intelligent virtual agents (IVAs), and the Internet of things (IoT). Each of these has been classified as a disruptive technology for our society. Since their emergence, the advancement of knowledge and development of technologies and systems in these fields were traditionally performed with limited input from each other. However, over recent years, we have seen research prototypes and commercial products being developed that cross the boundaries between these distinct fields to leverage their collective strengths. In this paper, we review the body of literature published at the intersections between each two of these fields, and we discuss a vision for the nexus of all three technologies.
Automatic query reformulation for code search using crowdsourced knowledge

Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers of Stack Overflow (i.e., crowdsourced knowledge). We first motivate our idea using an exploratory study with 19 standard Java API packages and 344K Java related posts from Stack Overflow. Experiments using 175 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the Top-10 results for 83% of the queries, with 46% mean average precision and 54% recall, which are 66%, 79% and 87% higher respectively than that of the state-of-the-art. Reformulations using our suggested API classes improve 64% of the natural language queries and their overall accuracy improves by 19%. Comparisons with three state-of-the-art techniques demonstrate that RACK outperforms them in the query reformulation by a statistically significant margin. Investigation using three web/code search engines shows that our technique can significantly improve their results in the context of code search.
Low-light image enhancement using Gaussian Process for features retrieval

Low-light is a challenging environment for image processing and computer vision tasks, either in contrast enhancement for better visibility and quality, or application oriented tasks such as detection. We found that the current trend of low-light enhancement research is heavily on quality improvement. In this work, we aim to shift the focus towards a more functional direction, that is enhancement that prioritizes feature retrieval. For this reason, we first propose to model low-light enhancement as a set of localized functions using Gaussian Process (GP) that is trained at runtime using data from a simple Convolutional Neural Network (CNN) to provide the necessary feature information as reference. The CNN is in turn trained using large amount of synthetic data, based upon the luminance distribution of real world low-light images to learn the relationship between features and pixels. Secondly, we also proposed two new evaluation metrics to better assess enhancement algorithms to support high level computer vision tasks, namely, local features matching and intensity histogram similarity. In our experiments, our proposed low-light enhancement framework outperforms the state-of-the-arts with significant improvement in Recall, F1, and F2-score of SIFT features matching, and achieve comparable results for l1-norm distance of histograms as well as the PSNR. Moreover, our analysis of the performance showed that the PSNR quality metric is not only unable to assess the practicality of the results, but also inappropriately gives high assessment to low visual quality images.
Optimal design of photovoltaic shading systems for multi-story buildings

This study provides new insights into the comprehensive energy and economic performances of Photovoltaic Shading Systems (PVSS) in multi-story buildings. A numerical shading model was developed to evaluate the shading effect from an upper PVSS row on its subjacent row. Simulation models based on EnergyPlus were developed to analyze the Net Electricity Consumption (NEC) of PVSS with different tilt angles and widths in different climates. Benefit per Capacity (BC) and the Cost of Benefit (CB) indicators were used to analyze the economic performances of PVSS. Finally, the optimum PVSS tilt angles and widths in different cities were obtained. Harbin, Beijing, Changsha, Kunming, and Guangzhou, were selected as representative cities for different geographical and climatic conditions. The results indicate that the optimum tilt angles for PVSS installed in Harbin, Beijing, Changsha, Kunming and Guangzhou are 55°, 50°, 40°, 40° and 30°, respectively. Optimum PVSS width for all five cities is 1.156 m (7 columns of standard solar cells). PVSS installed, using the optimal design scheme, in multi-story buildings have better energy-saving potentials than either rooftop photovoltaic systems or traditional power supply modes for commercial buildings in China.
EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics

Deep neural networks have been widely applied in various machine learning applications for mobile data analytics in cloud. However, this approach introduces significant data challenges, because the cloud operator can perform deep inferences on the available data. Recent advances in edge computing have paved the way to more efficient and private data processing at the edge of the network for simple tasks and lightweight models, but challenges still remain in building efficient complex models (e.g., deep learning) for edge computing. To tackle these issues, we propose EdgeSanitizer, a deep inference framework-based edge computing with local differential privacy for mobile data analytics. EdgeSanitizer leverages deep learning model to conduct data minimization and obfuscates the learned features by adaptively injecting noise, thereby forming a new protection layer against sensitive inference. We evaluate its performance in terms of data privacy and utility through theoretical analysis and experimental evaluation. The theoretical analysis proves that EdgeSanitizer can provide provable privacy guarantees with a large improvement in utility. And the experimental results demonstrate the robustness of our approach against sensitive inference, as well as its applicability on resource-constrained edge devices.
Theoretical study of a submarine to submarine quantum key distribution systems

Due to the absorption of water, communication between two parties submersed below the water is normally performed with acoustic waves. However, with the need for higher data rates, the use of RF or optical frequencies is needed. Currently, optical wavelengths have been demonstrated for classical communication over short distances. For these short distances, if a large amount of data needs to be transmitted securely, it is not feasible for both parties to return to the surface to communicate. Additionally, it can be assumed that a third party (Eve) is located in the channel trying to gather information. The solution is to use quantum key distribution (QKD) to generate the secure key, allowing the parties to continuously encrypt and transmit the data. It is assumed the BB84 protocol using pairs of polarization entangled photons generated from a spontaneous parametric down conversion (SPDC) source of Type-II. By using entangled photons, Eve is not able to gain information without being detected. In this work, horizontal oceanic channel is studied for various distances ranging from 10 m to 100 m, depth ranging from 100 m to 200 m, and surface chlorophyll-a concentrations at a wavelength of 532 nm. The secure key rates are calculated, assuming that a low-density parity check (LDPC) error correction code is used for information reconciliation. The maximum secure key rate and optimal number of average entangled photons transmitted are then studied for the various channels.
An Empirical Study on the Effectiveness of Feature Selection for Cross-Project Defect Prediction

Software defect prediction has attracted much attention of researchers in software engineering. At present, feature selection approaches have been introduced into software defect prediction, which can improve the performance of traditional defect prediction (known as within-project defect prediction, WPDP) effectively. However, the studies on feature selection are not sufficient for cross-project defect prediction (CPDP). In this paper, we use the feature subset selection and feature ranking approaches to explore the effectiveness of feature selection for CPDP. An empirical study is conducted on NASA and PROMISE datasets. The results show that both the feature subset selection and feature ranking approaches can improve the performance of CPDP. Therefore, we should select the representative feature subset or set a reasonable proportion of selected features to improve the performance of CPDP in future studies.
A deep learning approach for diagnosing schizophrenic patients

In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators used a secondary dataset provided by The National Institute of Health. The experimentation involves analyzing this dataset for the existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by the application of deep learning techniques using three hidden layers in the model. The results obtained indicate that the new deep learning technique formulated by the investigators provide a higher accuracy in diagnosing schizophrenia. These results suggest that deep learning may provide a paradigm shift in diagnosing schizophrenia.
Semantic-Enhanced and Context-Aware Hybrid Collaborative Filtering for Event Recommendation in Event-Based Social Networks

The fast development of event-based social networks (EBSN) provides a convenient platform for recruiting offline participants via online event announcements. Given its ever-increasing new events, how to accurately recommend users their most preferred ones is a key to the success of an EBSN. In this paper, we propose a semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation, which combines semantic content analysis and contextual event influence for user neighborhood selection. In particular, we first exploit the latent topic model for analyzing event description text and establish each user a long-term interest model and short-term interest model from her event registration history. We next establish each event an influence weight to jointly represent its social impact among users and its semantic uniqueness among events. For one user, we select her neighbors according to their long-term interest similarities weighted by events' influences. For new event recommendation, we construct a user-event rating matrix based on users' short-term interest models and for each user, we compute event rating predictions from her neighbors' ratings. The experiments based on the real-world dataset demonstrate the superiority of our algorithm over the peer schemes.
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition

This paper proposes Convolve, Attend and Spell, an attention-based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR .
Densely connected attentional pyramid residual network for human pose estimation

Research on 3D human pose estimation based on deep neural networks has recently witnessed substantial progress in both accuracy and execution efficiency. Many methods combine the deep neural network-based 2D pose estimation and 3D pose matching. However, (1) multiscale analysis has the potential to improve inference accuracy, and (2) isometric constraint is not considered in the 3D matching stage. In this paper, a new module named the Densely Connected Attentional Pyramid Residual Module (DCAPRM) in the bottom-up mapping stage is presented to effectively increase the inference accuracy. A new isometric regularization term is also proposed to punish limb extension or shrinkage in the top-down fitting phase. The performance of our approach on 3D human pose datasets is evaluated. The experimental results show that our approach provides better results than other approaches in terms of the accuracy of 3D human pose estimation.
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Optimal Transport (OT) is a very popular framework for performing colour transfer in images and videos. We have proposed an alternative framework where the cost function used for inferring a parametric transfer function is defined as the robust L2 divergence between two probability density functions (Grogan and Dahyot, 2015). In this paper, we show that our approach combines many advantages of state of the art techniques and outperforms many recent algorithms as measured quantitatively with standard quality metrics, and qualitatively using perceptual studies (Grogan and Dahyot, 2017). Mathematically, our formulation is presented in contrast to the OT cost function that shares similarities with our cost function. Our formulation, however, is more flexible as it allows colour correspondences that may be available to be taken into account and performs well despite potential occurrences of correspondence outlier pairs. Our algorithm is shown to be fast, robust and it easily allows for user interaction providing freedom for artists to fine tune the recoloured images and videos (Grogan et al., 2017).
Exploratory study on Class Imbalance and solutions for Network Traffic Classification

Network Traffic Classification is a fundamental component in network management, and the fast-paced advances in Machine Learning have motivated the application of learning techniques to identify network traffic. The intrinsic features of Internet networks lead to imbalanced class distributions when datasets are conformed, phenomena called Class Imbalance and that is attaching an increasing attention in many research fields. In spite of performance losses due to Class Imbalance, this issue has not been thoroughly studied in Network Traffic Classification and some previous works are limited to few solutions and/or assumed misleading methodological approaches. In this article, we deal with Class Imbalance in Network Traffic Classification, studying the presence of this phenomenon and analyzing a wide number of solutions in two different Internet environments: a lab network and a high-speed backbone. Namely, we experimented with 21 data-level algorithms, six ensemble methods and one cost-level approach. Throughout the experiments performed, we have applied the most recent methodological aspects for imbalanced problems, such as: DOB-SCV validation approach or the performance metrics assumed. And last but not least, the strategies to tune parameters and our algorithm implementations to adapt binary methods to multiclass problems are presented and shared with the research community, including two ensemble techniques used for the first time in Machine Learning to the best of our knowledge. Our experimental results reveal that some techniques mitigated Class Imbalance with interesting benefit for traffic classification models. More specifically, some algorithms reached increases greater than 8% in overall accuracy and greater than 4% in AUC-ROC for the most challenging network scenario.
Color image quantization using the shuffled-frog leaping algorithm

Swarm-based algorithms define a family of methods that consider a population of very simple individuals that cooperate to solve a difficult problem. The Shuffled frog-leaping algorithm is a method of this type that has been applied to solve different types of problems. This article describes the application of this algorithm to the color quantization problem. Although the selected method was developed to solve optimization problems, this work shows how it can be adapted to solve the problem proposed. The proposed method uses the mean squared error as the objective function of the optimization problem to be solved. To reduce the execution time of the algorithm, it is applied to a subset of pixels of the original image. As a result, a quantized palette is obtained that is used to define the quantized image. Computational results indicate that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by several well-known color quantization methods.
EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Outsourcing an image classification task raises privacy concerns, both from the image provider’s perspective, who wishes to keep their images confidential, and from the classification algorithm provider’s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, secure against malicious adversaries. EPIC builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX’18) –the state-of-the-art in private image classification– and it improves the communication cost by 50 times, with a 7% higher accuracy on CIFAR-10 dataset. For the same accuracy as Gazelle achieves on CIFAR-10, EPIC is 700 times faster and the communication cost is reduced by 500 times.
ActiveLink: Deep Active Learning for Link Prediction in Knowledge Graphs

Neural networks have recently been shown to be highly effective at predicting links for constructing knowledge graphs. Existing research has mainly focused on designing 1) deep neural network models that are expressive in capturing fine-grained semantics, e.g., NTN and ConvE, but that are however less scalable; or 2) shallow models that are scalable, e.g., TransE and DistMult, yet limited in capturing expressive semantic features. In this work, we demonstrate that we can get the best of both worlds while drastically reducing the amount of data needed to train a deep network by leveraging active learning.
ShinyItemAnalysis for Teaching Psychometrics and to Enforce Routine Analysis of Educational Tests

This work introduces ShinyItemAnalysis, an R package and an online shiny application for psychometric analysis of educational tests and items.ShinyItemAnalysis covers a broad range of psychometric methods and offers data examples, model equations, parameter estimates, interpretation of results, together with a selected R code, and is therefore suitable for teaching psychometric concepts with R. Furthermore, the application aspires to be an easy-to-use tool for analysis of educational tests by allowing the users to upload and analyze their own data and to automatically generate analysis reports in PDF or HTML.We argue that psychometric analysis should be a routine part of test development in order to gather proofs of reliability and validity of the measurement, and we demonstrate how ShinyItemAnalysis may help enforce this goal.
How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

Triplestores are data management systems for storing and querying RDF data. Over recent years, various benchmarks have been proposed to assess the performance of triplestores across different performance measures. However, choosing the most suitable benchmark for evaluating triplestores in practical settings is not a trivial task. This is because triplestores experience varying workloads when deployed in real applications. We address the problem of determining an appropriate benchmark for a given real-life workload by providing a fine-grained comparative analysis of existing triplestore benchmarks. In particular, we analyze the data and queries provided with the existing triplestore benchmarks in addition to several real-world datasets. Furthermore, we measure the correlation between the query execution time and various SPARQL query features and rank those features based on their significance levels. Our experiments reveal several interesting insights about the design of such benchmarks. With this fine-grained evaluation, we aim to support the design and implementation of more diverse benchmarks. Application developers can use our result to analyze their data and queries and choose a data management system.