Reviewerly/exHarmony huggingface dataset:
Update (1.1) to ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments

This is an update to PII: S2352711018300608In this paper, we discuss ANDURYL, which is a Python-based open source successor of the MATLAB toolbox ANDURIL. The output of ANDURYL is in good agreement with the results obtained from ANDURIL and EXCALIBUR. Additional features available in ANDURYL, and not available in its predecessors, are discussed.
Minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts

Palaeography aims to study ancient documents and the identification of the people who participated in the handwriting process of a given document is one of the most important problems. To this aim, expert paleographers typically analyze handwriting features such as letter heights and widths, distances between characters and angles of inclination. With the aim of achieving more precise measures and also thanks to the availability of high-quality digital images, paleographers are starting to use digital tools. In this context, in previous studies, we proposed a pattern recognition system for distinguishing the writers of mediaeval books and also investigated which is the minimum amount of training data needed to achieve satisfactory results in terms of accuracy. In this paper, we present a reject option that allows us to implement a highly-reliable system for writer identification, trained on a reduced set of data. The experimental results, performed on two sets of digital images from medieval Bibles, show that rejecting only a few samples it is possible to strongly reduce the error rate.
A Novel BoVW Mimicking End-To-End Trainable CNN Classification Framework Using Optimal Transport Theory

An end-to-end trainable convolutional neural network (CNN) framework which mimics bag of visual words (BoVW) is proposed for image classification. To this end, a new paradigm for histogram-like image representation is introduced and optimal transport (OT) distance is utilized for the similarity assessment. Any patch of an image is considered as a unique visual word and the image is represented as the uniform histogram of the visual words with the histogram bins associated to embedding vectors according to the semantic meanings of the corresponding visual words. Thus, in the CNN framework, the output of the last convolutional block is considered as the global representation of the image and the embeddings are inherently learned within the classification framework. With the proposed formulation, undesired quantization for the BoVW representation is no more required; moreover, the learned CNN features are naturally interpretable. The experiments on CIFAR-10, CIFAR-100 and SVHN datasets show that the replacement of the global pooling and fully connected layers with the proposed representation together with OT distance improves the baseline CNN framework.
Analysis of Expert’s Opinion on Requirements Patterns for Software Product Families Framework Using GQM Method

Software product line engineering (SPLE), provides an opportunity to improve reuse of software artifacts through domain engineering and application engineering processes. During the domain engineering process, reuse activities of the product line are well-planned and subsequently executed in the application engineering process. This paper presents an analysis of interview result with experts in requirements engineering (RE) and software development for validating requirements pattern for software product families (RP-SPF) framework. The interview was conducted using goal questions metrics (GQM) method to define a goal and formulate research questions for conducting the interview. During the interview, 6 experts compared RP-SPF approach (systematic) with ad hoc (conventional) approach of reuse and documentation of requirements in terms of suitability, efficiency, and effectiveness in SPLE. The experts also gave their feedback on the perception of the use of RP-SPF tool. The analysis of the interview result shows that RP-SPF approach is suitable in SPLE and more efficient and effective than ad hoc approach of reuse and documentation of requirements.
Sequentially Refined Spatial and Channel-Wise Feature Aggregation in Encoder-Decoder Network for Single Image Dehazing

Single image haze removal is a challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed to solve this problem and they have achieved superior results. However, the performance of prior-based methods is limited by hand-designed features. Meanwhile, the learning-based methods have flaws in spatial correlation. Because they just employ the convolutional neural network (CNN) as an end-to-end mapping module, generating dehazed image directly or learning parameters in atmospheric scattering model, without associating the relationship between feature activation and distribution of haze. Instead of using prior or CNN to simply estimate parameters of atmospheric scattering model, we propose a sequentially refined Spatial and Channel-wise Feature Aggregation (SCFA) dehazing network, called SCFADN. Specifically, our network learns residues between clean images and hazy images through an improved encoder-decoder network which incorporated the proposed sequentially refined SCFA module. This module efficiently learns long-range feature correlation for dehazing residue modeling, which enables the shallow network removing haze effectively. Extensive experiments on synthetic and real datasets results demonstrate that the proposed method achieves superior performance over the state-of-the-art methods.
Single-Fusion Detector: Towards Faster Multi-Scale Object Detection

Despite recent improvements, the arbitrary sizes of objects still impede the predictive ability of object detectors. Recent solutions combine feature maps of different receptive fields to detect multi-scale objects. However, these methods have large computational costs resulting to slower inference time, which is not practical for real-time applications. Contrarily, fusion methods depending on large networks with many skip connections demand larger memory requirement, prohibiting usage in devices with limited memory. In this paper, we propose a more computationally efficient fusion method which integrates higher-order information to low-level feature maps using a single operation. Our method can flexibly adapt to any base network, allowing tailored performance for different computational requirements. Our approach achieves 81.7% mAP at 41 FPS on the PASCAL VOC dataset using ResNet-50 as the base network, which is superior in terms of both speed and mAP as compared to several state-of-the-art baselines, even those which use larger base networks.
SocialST: Social Liveness and Trust Enhancement Based Social Recommendation

With the potential value of social relations in recommender systems, social recommendations have attracted increasing attention in recent years. Most existing methods are based on the following assumptions: (1) all users can be affected by their friends easily, and (2) users have similar preferences with those that they trust. However, users who are inactive in social networks may be less influenced by friends, and most people share similar preferences with only a few trusted friends. In this paper, we propose a social recommendation method (SocialST) based on social liveness and trust enhancement. SocialST considers social liveness a key factor in the strength of friends' influence on user preference, and a LivenessRank algorithm is designed to compute social liveness. When modeling social relation, we used a power-growth relationship instead of linear function to simulate the relationship between trust strength and similarity of friend preference, thus strengthening the role of close friends. Experimental results from both the Epinions and Ciao datasets have shown that our algorithm outperforms other current social recommendation algorithms in terms of RMSE.
Study on Unknown Term Translation Mining from Google Snippets

Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.
Value Attribution through Provenance Tracking in Blockchain Networks

Blockchain networks are trustless, enabling different organizations to come together and collaborate towards a business goal. Enterprise blockchain networks are permissioned, where participation in the network is by invitation and each participant has an identity to transact on the network. Typically the incentive for joining such a permissioned network, is the value gained in terms of ease of doing business with untrusted entities, a reduction in disputes, or greater visibility into the business process and associated data, to name a few. This also means that participation in the network is asymmetric, i.e., different participants bring different levels of value to the network and gain different value from the network. The purpose of this paper is to present a mechanism to track the value brought and gained by participants, by using fine grained provenance tracking in a decentralized manner leveraging blockchain. In addition, the fine-grained provenance tracking permits us to share the value back with the contributing organizations in a fair and decentralized manner, paving the way for new modes of value sharing, incentivization and monetization mechanisms for organizations participating in the blockchain ecosystem.
Probabilistic Selection of Case-Based Explanations in an Underwater Mine Clearance Domain

Autonomous agents should formulate and achieve goals with minimum support from humans. Although this might be feasible in a perfectly static world, it is not as easy in the real world where uncertainty is bound to occur. One approach to solving such a problem is to formulate goals based on cases that explain discrepancies observed in the environment. However, in an uncertain world, multiple such cases often apply (i.e., as alternative explanations). Moreover, agents in the real world often have limited resources to achieve their missions. So, it is risky to generate and achieve goals for every applicable explanatory case. Our solution to these problems is to down-select the retrieved cases based on probabilities derived using Bayesian inference, then to monitor the selected cases' validity based on observed evidence. We evaluate the performance of an agent in an underwater mine clearance domain and compare it to another agent that selects a random case from the candidate set.
On Boosting Semantic Street Scene Segmentation with Weak Supervision

Training convolutional networks for semantic segmentation requires per-pixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work, we research and develop a hierarchical deep network architecture and the corresponding loss for semantic segmentation that can be trained from weak supervision, such as bounding boxes or image level labels, as well as from strong per-pixel supervision. We demonstrate that the hierarchical structure and the simultaneous training on strong (per-pixel) and weak (bounding boxes) labels, even from separate datasets, consistently increases the performance against per-pixel only training. Moreover, we explore the more challenging case of adding weak image-level labels. We collect street scene images and weak labels from the immense Open Images dataset to generate the OpenScapes dataset, and we use this novel dataset to increase segmentation performance on two established per-pixel labeled datasets, Cityscapes and Vistas. We report performance gains up to +13.2% mIoU on crucial street scene classes, and inference speed of 20 fps on a Titan V GPU for Cityscapes at 512 ×1024 resolution. Our network and OpenScapes dataset are shared with the research community.
Sampling to Maintain Approximate Probability Distribution Under Chi-Square Test

In data management center, sometimes it is necessary to provide a subset to show data characteristics, among which probability distribution is an important one. Sampling is a fundamental method to generate data subsets. But how to sample a minimum subset with fixed approximation ratio of probability distributions is still a problem. In this paper, we define the approximation ratio as the significant difference level in chi-square test and use this test to formulate the sampling problem. We decompose the probability distribution as conditional probabilities based on Bayesian networks and propose a heuristic search algorithm to generate the subset by designing two scoring functions, which are based on chi-square test and likelihood functions, respectively. Experiments on four types of datasets with size 60000 show that when setting significant difference level $$\alpha $$ to 0.05, the algorithm could exclude $$99.5\%$$ , $$97.5\%$$ , $$84.8\%$$ and $$90.8\%$$ samples based on their Bayesian networks, respectively.
An Iterative Sure-Let Deconvolution Algorithm Based on BM3D Denoiser

Recently, the plug-and-play priors (PPP) have been a popular technique for image reconstruction. Based on the basic iterative thresholding scheme, we in this paper propose a new iterative SURE-LET deconvolution algorithm with a plug-in BM3D denoiser. To optimize the deconvolution process, we linearly parametrize the thresholding function by using multiple BM3D denoisers as elementary functions. The key contributions of our approach are: (1) the linear combination of several BM3D denoisers with different (but fixed) parameters, which avoids the manual adjustment of a single non-linear parameter; (2) linear parametrization makes the minimization of Stein's unbiased risk estimate (SURE) finally boil down to solving a linear system of equations, leading to a very fast and exact optimization during each iteration. In particular, the SURE of BM3D denoiser is approximately evaluated by finite-difference Monte-Carlo technique. Experiments show that the proposed algorithm, in average, achieves better deconvolution performance than other state-of-the-art methods, both numerically and visually.
Multiple kernel learning by empirical target kernel

Multiple kernel learning (MKL) aims at learning an optimal combination of base kernels with which an appropriate hypothesis is determined on the training data. MKL has its flexibility featured by automated kernel learning, and also reflects the fact that typical learning problems often involve multiple and heterogeneous data sources. Target kernel is one of the most important parts of many MKL methods. These methods find the kernel weights by maximizing the similarity or alignment between weighted kernel and target kernel. The existing target kernels implement a global manner, which (1) defines the same target value for closer and farther sample pairs, and inappropriately neglects the variation of samples; (2) is independent of training data, and is hardly approximated by base kernels. As a result, maximizing the similarity to the global target kernel could make these pre-specified kernels less effectively utilized, further reducing the classification performance. In this paper, instead of defining a global target kernel, a localized target kernel is calculated for each sample pair from the training data, which is flexible and able to well handle the sample variations. A new target kernel named empirical target kernel is proposed in this research to implement this idea, and three corresponding algorithms are designed to efficiently utilize the proposed empirical target kernel. Experiments are conducted on four challenging MKL problems. The results show that our algorithms outperform other methods, verifying the effectiveness and superiority of the proposed methods.
Modeling and Simulation of the Future

The process of long-term reflection involves a wide and deep inference activity aimed at recognizing the most convenient future for the studied system, in contrast to what deterministic models based on trends and the intensive processing of historical information offer, which focus on the identification of certainties. To reduce the uncertainty that occurs it is necessary to find safety points to reach the future that was chosen as the most convenient, within a time horizon and implement its strategies. This chapter discusses the fundamental elements to develop a proposal to model and simulate the future, which respond to the ideas that the prospective defines about the future, combining Soft Computing and Prospective methods.
Support Vectors Classification Method Based on Matrix Exponent Boundary Fisher Projection

Boundary Fisher analysis is a supervised dimension reduction method, the original data characteristics are preserved after dimension reduction, but singularity of matrix is involved in data optimization. By boundary Fisher thought, support vectors classification method based on matrix exponent boundary Fisher projection is proposed. In the process of Fisher optimization, the inverse matrix is transformed into the matrix exponent inverse matrix, and solve singularity problem of Fisher optimization, then obtain the optical projection matrix, the original training samples are projected to the low dimension space, which are used to train support vector machine(SVM). Experiments on two artificial data sets and UCI standard data set show that the proposed method can solve singularity problem, especially in the small samples.
Assessing the Quality Impact of Features in Component-Based Software Architectures

In modern software development processes, existing software components are increasingly used to implement functionality instead of developing it from scratch. Reuse of individual components or even more complex subsystems leads to more cost-efficient development and higher quality of software. Subsystems often offer a variety of features whose use is associated with unclear effects on the quality attributes of the software architecture, such as performance. It is unclear, whether the quality requirements for the system can be met by using a certain feature of a particular subsystem. After initial selection, features must be incorporated in the target architecture. Due to a multitude of possibilities of placing the subsystem in the target system to be used, many architectural candidates may result which have to be evaluated in existing decision support solutions. The approach presented here enables software architects to automatically evaluate with the help of software architecture models the effects on quality of using individual features in an existing software architecture. The result helps to automatically evaluate design decisions regarding features and to decide whether their use is compatible with the quality requirements. We show the benefits of our approach using different decision scenarios driven by features and their placement alternatives. All scenarios are automatically evaluated, demonstrating how decisions can be made to best meet the requirements.
Humanities in the Structure of Library Bibliographic Classification

The constant increase in the number of publications containing the term “humanitarian” in the title, the subject and methodological diversity of these works testify to the formation of the sphere of humanitarian knowledge, including not only traditional Humanities (cultural studies, literary studies, philology, linguistics, etc.), but also philosophical, transdisciplinary, methodological, popular science and even pseudo-scientific texts. All this literature needs to be systematized. Meanwhile, the literature on various aspects of humanitarian sphere in the system of Library Bibliographic Classification (LBC) is scattered in different sections. In part, it can be found in the sections of philosophy, epistemology and philosophical anthropology, in part it can be placed in cultural studies, in the philosophy of science. In addition, there are texts that are difficult to classify basing on the existing dividing. The term “humanitarian” did not immediately begin to be used in compilation of LBC Schedules that is largely due to the lack of clear differentiation of social and humanitarian knowledge. Until now, there are different traditional approaches in literature in considering their correlation. In our opinion, the trends towards their increasingly clear differentiation are quite clear. Therefore, the determination of the place for literature in the humanitarian sphere in the LBC Schedules is becoming more and more important. According to the authors’ opinion, it would be logical in front of the row of the Humanities to single out the section “Humanities as a whole”, which would head the LBC sector “Culture. Science. Education”. The authors suggest putting here the literature on General characteristic features of humanitarian knowledge, its role in spiritual culture, philosophical and cultural reflections on the humanitarian sphere. Publications in which researchers are trying to implement a new synthesis of the Humanities would also find their place here. The section may include literature on humanitarian knowledge, its structure, features and functions in society. An important feature of the development of the humanitarian sphere is the development of problems of practical application of humanitarian knowledge. Literature on digital Humanities, humanitarian informatics, humanitarian technologies and humanitarian expertise will also be reflected in this section.
Visual Analytic System for Subject Matter Expert Document Tagging using Information Retrieval and Semi-Supervised Machine Learning

We present a system that combines ambient visualization, information retrieval and machine learning to facilitate the ease and quality of document classification by subject matter experts for the purpose of organizing documents by “tags” inferred by the resultant classifiers. This system includes data collection, a language model, query exploration, feature selection, semi-supervised machine learning and a visual analytic workflow enabling non-data scientists to rapidly define, verify, and refine high-quality document classifiers.
Karamel: A System for Timely Provisioning Large-Scale Software across IaaS Clouds

Cloud-native systems and application software platforms are becoming increasingly complex, and, ideally, they are expected to be quick to launch, elastic, portable across different cloud environments and easily managed. However, as cloud applications increase in complexity, so do the resultant challenges in configuring such applications, and orchestrating the deployment of their constituent services on potentially different cloud operating systems and environments. This paper presents a new orchestration system called Karamel that addresses these challenges by providing a cloud-independent orchestration service for deploying and configuring cloud applications and platforms across different environments. In Karamel, we model configuration routines with their dependencies in composable modules, and we achieve a high level of configuration/deployment parallelism by using techniques such as DAG traversal control logic, dataflow variable binding, and parallel actuation. In Karamel, complex distributed deployments are specified declaratively in a compact YAML syntax, and cluster definitions can be validated using an external artifact repository (GitHub).