Reviewerly/exHarmony huggingface dataset:
Evaluating the anterior corneal surface using an improved null-screen system

In this work, a conical corneal null-screen topographer is presented. The device is designed to adapt to the human morphology of the face and measures the human cornea assuming that its shape is an aspherical surface. The evaluation of the performance of the conical null-screen corneal topographer includes calibration of the device and evaluation and compensation of the distortion introduced by the lens used to acquire the images. For the calibration of the device we perform the evaluation of a spherical reference surface of 7.8 mm radius of curvature and 11 mm in diameter. Here we obtain an rms difference in sagitta between the evaluated surface and the best fitting sphere of about 2.1 μm. We present examples of surface topography measurements on some human corneas. Elevation, sagittal curvature, and meridional curvature maps can be calculated. Other geometrical parameters such as the radius of curvature and the conic constant are obtained. Results of the corneal surface topography were compared with commercial Placido-based corneal topography device.
A Convolutional Neural Network for Language-Agnostic Source Code Summarization

Descriptive comments play a crucial role in the software engineering process. They decrease development time, enable better bug detection, and facilitate the reuse of previously written code. However, comments are commonly the last of a software developer’s priorities and are thus either insufficient or missing entirely. Automatic source code summarization may therefore have the ability to significantly improve the software development process. We introduce a novel encoder-decoder model that summarizes source code, effectively writing a comment to describe the code’s functionality. We make two primary innovations beyond current source code summarization models. First, our encoder is fully language-agnostic and requires no complex input preprocessing. Second, our decoder has an open vocabulary, enabling it to predict any word, even ones not seen in training. We demonstrate results comparable to state-of-the-art methods on a single-language data set and provide the first results on a data set consisting of multiple programming languages.
Digital Transformation at Carestream Health

Carestream Health sought to transform ordering supplies through an online system. The goal was to convert all customers to the new system, but adoption stalled at around 20%. It had failed to recognize the complexity of building and implementing a system to serve customers in 168 countries. Local differences and regulations meant it had more than 168 different ways of engaging with customers. Carestream revisited its plan to discover how to jointly modify systems of engagement and the ordering system in order to stimulate adoption. By early 2018, it had achieved 60% electronic ordering and was planning for 90%.
A cost-based storage format selector for materialized results in big data frameworks

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously, by deploying data-intensive workflows (DIWs). These DIWs of different users share many common tasks (i.e, 50–80%), which can be materialized and reused in future executions. Materializing the output of such common tasks improves the overall processing time of DIWs and also saves computational resources. Current solutions for materialization store data on Distributed File Systems by using a fixed storage format. However, a fixed choice is not the optimal one for every situation. Specifically, different layouts (i.e., horizontal, vertical or hybrid) have a huge impact on execution, according to the access patterns of the subsequent operations. In this paper, we present a cost-based approach that helps deciding the most appropriate storage format in every situation. A generic cost-based framework that selects the best format by considering the three main layouts is presented. Then, we use our framework to instantiate cost models for specific Hadoop storage formats (namely SequenceFile, Avro and Parquet), and test it with two standard benchmark suits. Our solution gives on average 1.33$$\times $$ speedup over fixed SequenceFile, 1.11$$\times $$ speedup over fixed Avro, 1.32$$\times $$ speedup over fixed Parquet, and overall, it provides 1.25$$\times $$ speedup.
Developers’ Diverging Perceptions of Productivity

To overcome the ever-growing demand for software, software development organizations strive to enhance the productivity of their developers. But what does productivity mean in the context of software development? A substantial amount of work on developer productivity has been undertaken over the past four decades. The majority of this work considered productivity from a top-down perspective (the manager view) in terms of the artifacts and code created per unit of time. Common examples of such productivity measures are the lines of source code modified per hour, the resolution time for modification requests, or function points created per month. These productivity measures focus on a single, output-oriented factor for quantifying productivity, and do not take into account developers' individual work roles, practices and other factors that might affect their productivity, such as work fragmentation, the tools used, or the work/office environment. In our research, we investigated how productivity could be quantified from the bottom-up, following a mixed-methods approach that involved more than 800 software developers. By investigating developers' individual productivity, it is possible to better understand the individual work habits and patterns, how they relate to the productivity perceptions and also which factors are most relevant for a developer's productivity.
Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound

Described here is a novel texture extraction method based on auto-mutual information (AMI) for classifying breast lesions. The objective is to extract discriminating information found in the non-linear relationship of textures in breast ultrasound (BUS) images. The AMI method performs three basic tasks: (i) it transforms the input image using the ranklet transform to handle intensity variations of BUS images acquired with distinct ultrasound scanners; (ii) it extracts the AMI-based texture features in the horizontal and vertical directions from each ranklet image; and (iii) it classifies the breast lesions into benign and malignant classes, in which a support-vector machine is used as the underlying classifier. The image data set is composed of 2050 BUS images consisting of 1347 benign and 703 malignant tumors. Additionally, nine commonly used texture extraction methods proposed in the literature for BUS analysis are compared with the AMI method. The bootstrap method, which considers 1000 bootstrap samples, is used to evaluate classification performance. The experimental results indicate that the proposed approach outperforms its counterparts in terms of area under the receiver operating characteristic curve, sensitivity, specificity and Matthews correlation coefficient, with values of 0.82, 0.80, 0.85 and 0.63, respectively. These results suggest that the AMI method is suitable for breast lesion classification systems.
Improved Decoy-State Measurement-Device-Independent Quantum Key Distribution With Imperfect Source Encoding

Measurement-device-independent quantum key distribution (MDI-QKD) is designed to be secure against any detector-side channel attacks.However, assumption of characterized sources in preparation is a demanding requirement and can not be fully satisfied in real-life implementations.Luckily, by utilizing the data of mismatched bases, the system can even generate secure keys with uncharacterized sources.Here, we give a framework on investigating the practical performance of an improved MDI-QKD with uncharacterized sources, where the three-intensity decoy-state method is employed and full parameter optimizations are applied.Simulation results show that the improved method can significantly enhance the performance compared with the original MDI-QKD with uncharacterized sources.
Performance Analysis of Deep Learning Libraries: <i>TensorFlow </i>and <i>PyTorch</i>

Through the increase in deep learning study and use, in the last years there was a development of specific libraries for Deep Neural Network (DNN).Each one of these libraries has different performance results and applies different techniques to optimize the implementation of algorithms.Therefore, even though implementing the same algorithm and using different libraries, the performance of their executions may have a considerable variation.For this reason, developers and scientists that work with deep learning need scientific experimental studies that examine the performance of those libraries.Therefore, this paper has the aim of evaluating and comparing these two libraries: TensorFlow and PyTorch.We have used three parameters: Hardware utilization, hardware temperature and execution time in a context of heterogeneous platforms with CPU and GPU.We used the MNIST database for training and testing the LeNet Convolutional Neural Network (CNN).We performed a scientific experiment following the Goal Question Metrics (GQM) methodology, data were validated through statistical tests.After data analysis, we show that PyTorch library presented a better performance, even though the TensorFlow library presented a greater GPU utilization rate.
Educational Effect of Participation in Robot Competition on Experience-Based Learning

In recent years, instructional robot materials have often been used in robotics and engineering education. We use LEGO Mindstorms which is an educational robot development kit in its curriculum. In this subject, students are taught basic subjects such as robot mechanisms, robotic control, and programming. To enhance the subject’s educational effects, the students are set the objective of entry into a robot competition. In the subject, the students are grouped into teams comprising two or three members to undertake the aforementioned task, with the objective of improving their communication skills and problem-solving capacities. The effects of participation in the robot competition were observed in the improved performances in the robot competition implemented in a class (hereinafter called “classroom competition”) held after the SMART competition. In the questionnaire survey conducted at the end of the subject, the upper-class students, in particular, conveyed favorable views on the use of LEGO Mindstorms and participation in the robot competition. On comparing the realized educational effects on the first- and third-year students, positive effects were confirmed in both groups.
Robust active attacks on social graphs

In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication. Naive anonymisation of social network graphs often consists in deleting all identifying information of the users, while maintaining the original graph structure. Various types of attacks on naively anonymised graphs have been developed. Active attacks form a special type of such privacy attacks, in which the adversary enrols a number of fake users, often called sybils, to the social network, allowing the adversary to create unique structural patterns later used to re-identify the sybil nodes and other users after anonymisation. Several studies have shown that adding a small amount of noise to the published graph already suffices to mitigate such active attacks. Consequently, active attacks have been dubbed a negligible threat to privacy-preserving social graph publication. In this paper, we argue that these studies unveil shortcomings of specific attacks, rather than inherent problems of active attacks as a general strategy. In order to support this claim, we develop the notion of a robust active attack, which is an active attack that is resilient to small perturbations of the social network graph. We formulate the design of robust active attacks as an optimisation problem and we give definitions of robustness for different stages of the active attack strategy. Moreover, we introduce various heuristics to achieve these notions of robustness and experimentally show that the new robust attacks are considerably more resilient than the original ones, while remaining at the same level of feasibility.
Block Partitioning and Merging for Processing Large-Scale Structure From Motion Problems in Distributed Manner

The processing time of incremental structure from motion increases exponentially with the number of images.As a result, a huge amount of time is needed for large datasets.In this paper, to improve time efficiency, a block partitioning and a merging strategy are proposed.We automatically split the image set into several overlapping subsets, and then each subset can be processed in parallel.Finally, the reconstruction results of each subset can be merged together according to the shared images and tie points.The image adjacency matrix obtained from the feature matching result is the input of our block partitioning algorithm.And by repeatedly using the matrix bandwidth reduction algorithm to reorder the images, the block can be partitioned into subsets.The partitioning result is satisfactory, namely, images assigned into a subset have a very strong connection, and the shape of each subset is compact.Most importantly, the algorithm is very simple and fast.We have successfully processed many large-scale aerial image datasets in a computer cluster system with 10 processing nodes.And, the time efficiency and the precision of the reconstruction are satisfactory. INDEX TERMS Block partitioning, block
Weakly-Supervised Learning of a Deep Convolutional Neural Networks for Semantic Segmentation

Deep convolutional neural networks (DCNNs) trained on the pixel-wise annotated images have dramatically improved the state-of-the-art in semantic segmentation.However, due to the high cost of labeling training data, its application has great limitation.In this paper, we propose a DCNNs model for generating the pixel-level labels using the image-level annotation.The model consists of an encoder-decoder, a feature decomposer, and a multi-label classifier.The encoder extracts the deep convolutional feature maps of the input image.The feature decomposer can decompose the convolutional feature extracted by the encoder into feature components of different semantics.The decomposer is based on the orthogonal non-negative matrix factorization (NMF) technology.The function of the decoder is to map the feature components of different semantics to the input resolution images.The decoder uses the position index of maximum pooling provided by the corresponding encoder to perform non-linear up-sampling, which eliminates the learning requirement of up-sampling.Since the image reconstruction is conducted according to the semantic categories, image regions of different semantics are restored to different images.Then, the regions of different semantics can be segmented through the posted-processing algorithms.The experimental results on open data set show that the proposed model outperforms some recently developed methods.
Accurate and Fast Blur Detection Using a Pyramid M-Shaped Deep Neural Network

Blur detection is aimed at estimating the probability of each pixel being blurred or non-blurred in an image affected by motion or defocus blur. This task has gained considerable attention due to its promising application fields in computer vision. Accurate differentiation of anomalous regions (including the sharp but homogeneous regions and pseudo-sharp backgrounds) and motion-blurred regions are main challenges in blur detection, in which both conventional and recently developed blur detection methods have limited performance and low time efficiency. To address these issues, this paper develops an accurate and fast blur detection method for both motion and defocus blur using a new end-to-end deep neural network. First, a novel multi-input multi-loss encoder-decoder network (M-shaped) is proposed to learn rich hierarchical representations related to blur. Then, to resolve the problem shows that blur degree is susceptible to scales, we construct a pyramid ensemble model (PM-Net) consisting of different scales of M-shaped subnets and a unified fusion layer. The experiments demonstrate that the proposed PM-Net can accurately handle those challenging scenarios with anomalous regions for both defocus and motion blur. Our method performs better than previous state-of-the-art methods. It achieves the $F_{1}$ -score of 0.893 for only defocus blur and 0.884 for joint motion and defocus blur, both of which significantly surpass previous methods on the benchmark BDD dataset. We also test our PM-Net on another public CDD dataset composed of challenging defocused images. The proposed method also outperforms other published methods with an $F_{1}$ -score of 0.885. In addition, our proposed method is hundreds of times faster (millisecond) than other state-of-the-art methods (second). Moreover, our experiments also demonstrate that the PM-Net is robust to noise and has a good generalization property.
The Cost of a WARC: Analyzing Web Archives in the Cloud

The value of web archives to support scholarship in the humanities and social sciences is slowly being realized by the increasing availability of scalable tools and platforms. The cost of providing scholarly access is a critical component of developing a long-term sustainability strategy. This paper attempts to answer a straight-forward question: How much does it cost to analyze web archives in the cloud? To make this question more concrete, we examine the creation of three derivatives (extraction of collection statistics, full text, and the webgraph) that serve as the starting points of many scholarly inquiries. Our analysis shows that these typical derivatives costs around US$7 per TB using our Archives Unleashed Toolkit. We describe in detail the methodology and assumptions made to arrive at this figure. To our knowledge, we are the first to quantify the economics of scholarly access to web archives, and we believe that this information is valuable for service planning by archives, libraries, and other institutions.
Quantum-inspired algorithm for general minimum conical hull problems

A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem. The best-known solution to tackle general minimum conical hull problems is the divide-and-conquer anchoring learning scheme (DCA), whose runtime complexity is polynomial in size. However, big data is pushing these polynomial algorithms to their performance limits. In this paper, we propose a sublinear classical algorithm to tackle general minimum conical hull problems when the input has stored in a sample-based low-overhead data structure. The algorithm's runtime complexity is polynomial in the rank and polylogarithmic in size. The proposed algorithm achieves the exponential speedup over DCA and, therefore, provides advantages for high dimensional problems.
Towards a diversified knowledge organization system

Purpose The need for inclusive and logically consistent representation of diverse and even confronting viewpoints on the domain knowledge has been widely discussed in the literature in the past decade. The purpose of this paper is to propose a generic model for building an open coherent diversified knowledge organization system (KOS). Design/methodology/approach The proposed model incorporates a generic epistemological component, the validity scope type, assigned to each statement in the constructed KOS. Statements are clustered by their association with various validity scope types into internally coherent subsystems. These subsystems form a knowledge organization network connected through the universal (consensual) subsystems with more than one validity scope type. The model extends the Galili’s Cultural Content Representation paradigm, which divides the knowledge content of a scientific theory into two confronting parts: body and periphery. Findings The knowledge organization network model makes it possible to comparatively examine similarities and differences among various viewpoints and theories on the domain knowledge. The presented approach conforms with the principle of Open Knowledge Network initiative for creation of open accessible knowledge. Practical implications The proposed model can be used for ontological reasoning by a variety of information services, such as ontology-based decision-support and learning systems, diversified search and customer management applications. Social implications The model enables explicit representation of social and cultural minority voices and historical knowledge in the KOS. Originality/value The main contribution of the proposed model is that it generalizes and enhances various previously proposed representations of epistemological aspects of KOS and allows for multiple inter-linked subsystems to coherently co-exist as part of the extensible network.
An Assmus--Mattson Theorem for Rank Metric Codes

A $t$-$(n,d,\lambda)$ design over $\mathbb{F}_{q}$, or a subspace design, is a collection of $d$-dimensional subspaces of $\mathbb{F}_{q}^n$, called blocks, with the property that every $t$-dimensional subspace of $\mathbb{F}_{q}^n$ is contained in the same number $\lambda$ of blocks. A collection of $n \times m$ matrices over $\mathbb{F}_{q}$ is said to hold a $t$-design over $\mathbb{F}_{q}$ if the set of column spaces of its elements forms the blocks of a subspace design. We use notions of puncturing and shortening of rank metric codes and the rank metric MacWilliams identities to establish conditions under which the words of a given rank in a linear rank metric code hold a $t$-design over $\mathbb{F}_{q}$. We show that for $\mathbb{F}_{q^m}$-linear vector rank metric codes, the property of a code being maximum rank distance (MRD) is equivalent to its minimal weight codewords holding trivial subspace designs, and show that this characterization does not hold for $\mathbb{F}_{q}$-linear matrix MRD codes that are not linear over $\mathbb{F}_{q^m}$. Finally, using arguments based on covering radius and external distance, we establish various existence results that apply to both the rank and the Hamming metric.
Building a telescope to look into high-dimensional image spaces

In Grenanderâs work, an image pattern is represented by a probability distribution whose density is concentrated on different low-dimensional subspaces in the high-dimensional image space. Such probability densities have an astronomical number of local modes corresponding to typical pattern appearances. Related groups of modes can join to form macroscopic image basins (known as Hopfield memories in the neural network community) that represent pattern concepts. Grenander pioneered the practice of approximating an unknown image density with a Gibbs density. Recent works continue this paradigm and use neural networks that capture high-order image statistics to learn Gibbs models capable of synthesizing realistic images of many patterns. However, characterizing a learned probability density to uncover the Hopfield memories of the model, encoded by the structure of the local modes, remains an open challenge. In this work, we present novel computational experiments that map and visualize the local mode structure of Gibbs densities. Efficient mapping requires identifying the global basins without enumerating the countless modes. Inspired by Grenanderâs jump-diffusion method, we propose a new MCMC tool called Attraction-Diffusion (AD) that can capture the macroscopic structure of highly non-convex densities by measuring