Reviewerly/exHarmony huggingface dataset:
Efficient and interactive spatial-semantic image retrieval

This paper proposes an efficient image retrieval system. When users wish to retrieve images with semantic and spatial constraints (e.g., a horse is located at the center of the image, and a person is riding on the horse), it is difficult for conventional text-based retrieval systems to retrieve such images exactly. In contrast, the proposed system can consider both semantic and spatial information, because it is based on semantic segmentation using fully convolutional networks (FCN). The proposed system can accept three types of images as queries: a segmentation map sketched by the user, a natural image, or a combination of the two. The distance between the query and each image in the database is calculated based on the output probability maps from the FCN. In order to make the system efficient in terms of both the computational time and memory usage, we employ the product quantization (PQ) technique. The experimental results show that the PQ is compatible with the FCN-based image retrieval system, and that the quantization process results in little information loss. It is also shown that our method outperforms a conventional text-based search system.
Fast phase-measuring profilometry through composite color-coding method

Phase-measuring profilometry (PMP) has become one of the most effective means of three-dimensional (3D) shape measurement. Owing to the low robustness of the phase-unwrapping algorithm and the large number of projection patterns, the off-the-shelf PMP methods have a limited speed and accuracy for the measurement of discontinuous objects. To improve the measurement speed and accuracy, a composite color-coding method is presented in this paper. The main idea of the coding is embedding sinusoidal stripes, phase-coding stripes, and Gray code into the red, blue, and green channels of a color pattern, respectively. The Gray code is used for identifying the order of the sub-stair phase, which is quantized into m levels with a stair height of 2π/m. Each stair corresponds to the 2π phase-changed period of the sinusoidal fringes. The complexity of the phase unwrapping is low, and the proposed method needs fewer patterns for measurement than conventional methods. The experimental results indicate that the proposed method can achieve more accurate and efficient 3D shape measurement than the traditional phase-coding methods and is suitable for measuring discontinuous objects with complex shapes.
Situating science in Africa: The dynamics of computing research in Nairobi and Kampala

Since the turn of the century, both Kampala and Nairobi have experienced a dramatic growth of computer science research, challenging accepted views of science in Africa. We deploy qualitative methods to follow active computer science researchers, graduate students, policy makers, administrators and entrepreneurs, in order to understand how computer science is enacted in these two cities. Our analysis focuses on four interrelated areas of labor, institutions, identities and scale. We illustrate the dynamics and frictions of computer science research across these areas, revealing the interlacing of moral economies of science and the political economy of higher education, the management of precarious professional lives and desire to get research done, and the pluralistic imaginations and multiple scales of computer science. Urban centers in East Africa are increasingly active in supporting granular and connective research communities that are socially transformative in ways that challenge conventional views of Africa as technologically dry. In this way, the computer science communities of Nairobi and Kampala are instructive for thinking about new geographies of science and technology studies.
Scale-Recurrent Multi-residual Dense Network for Image Super-Resolution

Recent advances in the design of convolutional neural network (CNN) have yielded significant improvements in the performance of image super-resolution (SR). The boost in performance can be attributed to the presence of residual or dense connections within the intermediate layers of these networks. The efficient combination of such connections can reduce the number of parameters drastically while maintaining the restoration quality. In this paper, we propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) that allow extraction of abundant local features from the image. Our scale recurrent design delivers competitive performance for higher scale factors while being parametrically more efficient as compared to current state-of-the-art approaches. To further improve the performance of our network, we employ multiple residual connections in intermediate layers (referred to as Multi-Residual Dense Blocks), which improves gradient propagation in existing layers. Recent works have discovered that conventional loss functions can guide a network to produce results which have high PSNRs but are perceptually inferior. We mitigate this issue by utilizing a Generative Adversarial Network (GAN) based framework and deep feature (VGG) losses to train our network. We experimentally demonstrate that different weighted combinations of the VGG loss and the adversarial loss enable our network outputs to traverse along the perception-distortion curve. The proposed networks perform favorably against existing methods, both perceptually and objectively (PSNR-based) with fewer parameters.
Ontology–based access to temporal data with Ontop: A framework proposal

Abstract Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL -based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.
Multiple changepoint detection in categorical data streams

The need for efficient tools is pressing in the era of big data, particularly in streaming data applications. As data streams are ubiquitous, the ability to accurately detect multiple changepoints, without affecting the continuous flow of data, is an important issue. Change detection for categorical data streams is understudied, and existing work commonly introduces fixed control parameters while providing little insight into how they may be chosen. This is ill-suited to the streaming paradigm, motivating the need for an approach that introduces few parameters which may be set without requiring any prior knowledge of the stream. This paper introduces such a method, which can accurately detect changepoints in categorical data streams with fixed storage and computational requirements. The detector relies on the ability to adaptively monitor the category probabilities of a multinomial distribution, where temporal adaptivity is introduced using forgetting factors. A novel adaptive threshold is also developed which can be computed given a desired false positive rate. This method is then compared to sequential and nonsequential change detectors in a large simulation study which verifies the usefulness of our approach. A real data set consisting of nearly 40 million events from a computer network is also investigated.
Robust scene text recognition: Using manifold regularized Twin-Support Vector Machine

Text recognition in natural scene images endures as a challenging problem, attributable to a highly variable appearance in an unconstrained environment. The proposed work is novel of its kind, which improved the generalization of the Twin Support Vector Machine (T-SVM) by manifold regularization, that was further extended to validate and recognize the text in the natural scene images. The multi-class T-SVM is augmented with ambient regularizer term and intrinsic regularizer term, assisting to form a smooth function for the model. Text comprehension from natural scene includes the localization, recognition, and reconstruction of the text. The work includes an additional module, revalidation that discards the false positives from the text objects detected during the localization phase. Then in recognition phase, each letter from the pool of text objects is identified for the appropriate class and provided as output to the text construction phase, which builds the text using the pool of coordinates associated with the object. The model is evaluated against traditional methods like Support Vector Machine (SVM), T-SVM, LST-SVM(Least Square Twin Support Vector Machine) and also with other research of the same kind and shows the accuracy of 84.91% with ICDAR 2015, 84.21% with MSRA 500 and 86.21% with SVT. The analyses of the results show that the model was capable of recognizing most of the characters from the image along with consummate accuracy level.
Everybody Needs Somebody Sometimes: Validation of Adaptive Recovery in Robotic Space Operations

This letter assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learning approach using data we collected from humans executing simulated mission scenarios. We present a method of structuring human-factors experiments that permits collection of relevant indicator of opportunity and assigned assistance task performance data, as well as evaluation of our adaptive approach, without requiring large numbers of test subjects. Application of our reinforcement learning algorithm to our experimental data shows that our adaptive assistant selection approach can achieve lower cumulative regret compared to existing nonadaptive baseline approaches when using real human data. Our work has applications beyond space robotics to any application where autonomy failures may occur that require external intervention.
Assessing the best edit in perturbation-based iterative refinement algorithms to compute the median string

Different pattern recognition techniques such as clustering, k-nearest neighbors classification or instance reduction algorithms require prototypes to represent pattern classes. In many applications, strings are used to encode instances, for example, in contour representations or in biological data such as DNA, RNA and protein sequences. Median strings have been used as representatives of a set of strings in different domains. Finding the median string is an NP-Complete problem for several formulations. Alternatively, heuristic approaches that iteratively refine an initial coarse solution by applying edit operations have been proposed. We propose here a novel algorithm that outperforms state of the art heuristic approximations to the median string in terms of convergence speed by estimating the effect of a perturbation in the minimization of the expressions that define the median strings. We present comparative experiments to validate these results.
Robust fuzzy clustering for multiple instance regression

Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods.
Mobile Device- and Video-Aided Flipped English Classrooms

The last two decades have witnessed a large number of studies on the flipped classroom. Learning attitudes, interest and intrinsic motivation in mobile device-aided flipped English class (MFC) has, however, seldom been studied in comparison with the traditional video-aided flipped English class (VFC). Randomly selecting 375 participants and using reliable scales, this study identified the differences in and correlations of learning attitudes, interest and intrinsic motivation between MFC and VFC. After an experiment for one academic year and semi-structured interviews, it was concluded that: (1) The learning attitude in MFC was significantly more positive than that in VFC; (2) The learning interest in MFC was significantly more positive than that in VFC; (3) The intrinsic learning motivation in MFC was significantly more positive than that in VFC; (4) The learning attitude, interest and intrinsic motivation were significantly and mutually correlated in both MFC and VFC. Reasons for the findings are also discussed, coupled with future research directions.
Practical fault resilient hardware implementations of AES

Security is a challenging issue in resource-constrained applications, e.g. in an embedded system. This study focused on practical lightweight fault-tolerant strategies for hardware implementation of Advanced Encryption Standard (AES) to mitigate the-reliability issue of secure architectures. In this work, a-fault-tolerant architecture called configurable fault-tolerant AES (CFTA), and its variants, called robust CFTA (R-CFTA), R-CFTA+, high throughput CFTA (HT-CFTA), HT-CFTA4R, HT-CFTA8R, and HT-CFTA+, are introduced. Proposed approaches exploit the-inherent parallel architecture of AES for employing redundancy at low cost. CFTA and HT-CFTA can tolerate all single permanent and transient faults in the-AES blocks and also all multiple permanent and transient faults in the-same block. R-CFTA upgrades the-fault-tolerant aspect of CFTA and HT-CFTA and it is also able to tolerate all single- and multiple-transient faults in two AES functional blocks during a round. The proof is provided to show the fault masking ability of provided architectures. Furthermore, R-CFTA+ and HT-CFTA+, which are suitable for high-security sensitive applications are suggested. In addition, the proposed fault-tolerant designs are implemented on both field programmable gate array and application-specific integrated circuit platforms, and their implementation area, frequency, and throughput are discussed and compared with other related works. Moreover, system-efficiency, as an important design metric, is reported for proposed structures.
Statistical mechanical analysis of learning dynamics of two-layer perceptron with multiple output units

The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, is troubling. Various studies suggest that the plateau phenomenon is frequently caused by the network being trapped in the singular region on the loss surface, a region that stems from the symmetrical structure of neural networks. However, these studies all deal with networks that have a one-dimensional output, and networks with a multidimensional output are overlooked. This paper uses a statistical mechanical formalization to analyze the dynamics of learning in a two-layer perceptron with multidimensional output. We derive order parameters that capture macroscopic characteristics of connection weights and the differential equations that they follow. We show that singular-region-driven plateaus diminish or vanish with multidimensional output, in a simple setting. We found that the more non-degenerative (i.e. far from one-dimensional output) the model is, the more plateaus are alleviated. Furthermore, we showed theoretically that singular-region-driven plateaus seldom occur in the learning process in the case of orthogonalized initializations.
Feedback motion planning of unmanned surface vehicles via random sequential composition

In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In order to illustrate the main approach and show the feasibility of the method, we performed simulations and tested the overall performance and applicability for future experimental applications. We also tested the robustness of the method under relatively extreme environmental uncertainty. Simulation results indicate that our method can produce robust and computationally feasible solutions for a broad class of USVs.
Human motion tracking: A comparative study

In this paper, we aim to carry out a brief study of a new tracking system and the results of existing tracking systems including Compressive Tracking (CT), Fragment Tracker (FRAG), Multiple Instance Learning Tracker (MIL), and Tracking Learning Detection algorithm (TLD). For this purpose, we present the principles of those tracking systems. Then, we test them on several datasets that contain challenging attributions. Finally, we evaluate them qualitatively and quantitatively by two widely used criterions: the Average Center Error (ACE) and the Average Overlap Error (AOE). We conclude our paper by discussing the obtained results and the future works in this field.
A Module-based Approach to Teaching Big data and Cloud Computing Topics at CS Undergraduate Level

Big data and cloud computing collectively offer a paradigm shift in the way businesses are now acquiring, using and managing information technology. This creates the need for every CS student to be equipped with foundational knowledge in this collective paradigm and to possess some hands-on experience in deploying and managing big data applications in the cloud. We argue that, for substantial coverage of big data and cloud computing concepts and skills, the relevant topics need to be integrated into multiple core courses across the undergraduate CS curriculum rather than creating additional standalone core or elective courses and performing a major overhaul of the curriculum. Our approach to including these topics is to develop autonomous learning modules for specific core courses in which their coverage might find an appropriate context. In this paper, three such modules are discussed and our classroom experiences during these interventions are documented. So far, we have achieved reasonable success in attaining student learning outcomes, enhanced engagement, and interests. Our objective is to share our experience with the academics who aim at incorporating similar pedagogy and to receive feedback about our approach.
Interval Granular Fuzzy Models: Concepts and Development

In this paper, we present a concept of interval granular fuzzy models and elaborate on their detailed development scheme and performance evaluation. The crux of the underlying design is that the information granules and information granularity which play a pivotal role in human cognitive and decision-making activities are incorporated into existing fuzzy modeling methods to realize system modeling at the level of information granules. The overall development process of interval granular fuzzy model includes two stages. At the first stage, we form input-error data by computing residual error resulting from the well-established numeric model and then establish granular structures positioned in the input space by clustering input-error data and invoking the principle of justifiable information granularity for weighted data. These granular structures discovered in the input space become condition parts of the rules of the developed interval granular fuzzy model. At the second stage, the interval information granules positioned in the error space are directly induced with the aid of granular structures discovered in the input space. These interval information granules describe the range of residual error produced by the numeric model and are exploited to form the conclusion part of the corresponding rules of the developed interval granular fuzzy model. So far, the interval granular fuzzy model is completely constructed, whose rules help compensate the discrepancies of the numeric model. The output of the developed model is an interval information granule showing a range of possible residual error produced by the numeric model. The several performance indices are presented to evaluate the developed interval granular fuzzy model. A series of numeric experiments completed for synthetic data and real-world data coming from the machine learning repository provide a useful insight into the effectiveness of the presented development scheme, reveal the impact of some parameters on the performance of the developed model and demonstrate its advantages.
Empower Your Agile Organization: Community-Based Decision Making in Large-Scale Agile Development at Ericsson

When adopting agile development in a global organization with up to 40 teams working on a single product, Ericsson had to decide how to handle decisions affecting several teams. The solution, called community-based decision making, grew out of this experience.