Reviewerly/exHarmony huggingface dataset:
HEAD-QA: A Healthcare Dataset for Complex Reasoning

We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning.The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans.We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques.We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.
elPrep 4: A multithreaded framework for sequence analysis

We present elPrep 4, a reimplementation from scratch of the elPrep framework for processing sequence alignment map files in the Go programming language. elPrep 4 includes multiple new features allowing us to process all of the preparation steps defined by the GATK Best Practice pipelines for variant calling. This includes new and improved functionality for sorting, (optical) duplicate marking, base quality score recalibration, BED and VCF parsing, and various filtering options. The implementations of these options in elPrep 4 faithfully reproduce the outcomes of their counterparts in GATK 4, SAMtools, and Picard, even though the underlying algorithms are redesigned to take advantage of elPrep's parallel execution framework to vastly improve the runtime and resource use compared to these tools. Our benchmarks show that elPrep executes the preparation steps of the GATK Best Practices up to 13x faster on WES data, and up to 7.4x faster for WGS data compared to running the same pipeline with GATK 4, while utilizing fewer compute resources.
Inpainting of Dunhuang Murals by Sparsely Modeling the Texture Similarity and Structure Continuity

Ancient mural paintings often suffer from damage such as color degradation, pigment peeling, and even large-area shedding. Image inpainting techniques are widely used to virtually repair these damages. Generally, the inpainting task can be very challenging when structures are totally missing within a large area. In this article, we study mural image inpainting by incorporating structure information collected from line drawings, and propose a line-drawings-guided inpainting algorithm for repairing the damaged murals of Mogao Grottoes, Dunhuang. Unlike traditional methods that use one single patch to inpaint the target area, the proposed method constructs the target patch with a linear combination of multiple candidate patches. These candidate patches are selected by a sparse model, where two special constraints have been introduced to guarantee the texture similarity and structure continuity. Experimental results demonstrate the effectiveness of the proposed method.
Optimal Constant Acceleration Motion Primitives

This paper proposes new motion primitives that are time optimal and feasible for a vehicle (wheeled-mobile robot). They are parameterized by a constant acceleration and a constant deceleration in an obstacle-free environment between an initial and a final configuration with given poses and velocities. The derived compact parametric solution has an implicit optimal velocity profile, is continuous in C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , considers driving constraints, and is computationally efficient. The path is derived analytically considering constraints on maximal allowable driving velocity and accelerations. The obtained motion primitives have continuous transition of curvature and are, thus, easily drivable by the vehicle. The proposed motion primitives are evaluated on several path-planning examples, and the obtained solutions are compared to a numerically expensive planner using Bernstein-Bézier curve with path shape and velocity profile optimization. Numerous experiments have been conducted not only in the simulation environment, but also during testing and comparisons on the actual mobile robot platforms. It is shown that the proposed solution is computationally efficient, time optimal under given constraints and trapezoidal velocity profile assumptions, and is close to globally optimal solution with an arbitrary velocity profile.
Searching for Optimal Models: Comparing Two Encoding Approaches.

Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems.In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular.While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience.In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded.Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications.While both encodings have been applied to different use cases, no study has yet compared them systematically.To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance.Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.
Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

Face recognition systems have several potential applications, such as security and biometric access control.Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system.Face pose, non-uniform illuminations, and lowresolution are main factors that influence the performance of face recognition algorithms.This paper proposes a novel method to handle the aforementioned aspects.Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image.Meanwhile, the AdaBoost and the LDA are used to extract face features.In final stage, classic nearest centre classifier is used for face classification.Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal (0 0 ) face sample is available in gallery and seven poses (0 0 , ±30 0 , ±35 0 , and ±45 0 ) as a probe on the LFW and the CMU Multi-PIE databases.
The role of SMEs development on poverty alleviation in Nigeria

The role of SMEs development on poverty alleviation has been a core element of the current report.This study examined the role of small and medium enterprises on poverty eradication in Nigeria.Two research questions were posed for the study and two hypotheses formulated in line with the objectives.One hundred and forty two (142) questionnaires were administered randomly to the entire employee population of the SME companies that are registered with SMEDAN in Lagos Nigeria.The sample size was determined using Yamane formula.The data was analysed using manual and electronic based methods with the aid of data preparation grid and statistical package for the social sciences, (SPSS) statistical package version 21.0.The study made use of statistical tools which include: analysis of variance (ANOVA), correlation efficient and regression analysis in testing hypotheses where applicable.Therefore, the study found out that SME development affects poverty alleviation and also Training organized by SMEDAN affects SMEs employment creation.It was discovered that the small and medium enterprises provided employment opportunities, training ground, harnesses utilization of local resources and also acts as a pillar hold of a nation, Nigeria inclusive.The study concluded that a good development strategy if employed by these SMEs will grow to large-scale capital intensive.The study recommends that SMEs should source their loans from the financial institutions where interest rates are low.
Learning Features and Abstract Actions for Computing Generalized Plans

Generalized planning is concerned with the computation of plans that solve not one but multiple instances of a planning domain. Recently, it has been shown that generalized plans can be expressed as mappings of feature values into actions, and that they can often be computed with fully observable non-deterministic (FOND) planners. The actions in such plans, however, are not the actions in the instances themselves, which are not necessarily common to other instances, but abstract actions that are defined on a set of common features. The formulation assumes that the features and the abstract actions are given. In this work, we address this limitation by showing how to learn them automatically. The resulting account of generalized planning combines learning and planning in a novel way: a learner, based on a Max SAT formulation, yields the features and abstract actions from sampled state transitions, and a FOND planner uses this information, suitably transformed, to produce the general plans. Correctness guarantees are given and experimental results on several domains are reported.
An Innovative Preschool Education Method Based on Computer Multimedia Technology

Hybrid learning is the fruit of deep integration between computer multimedia technology and the Internet technology. This learning method has attracted much attention in the academia, as it enriches the teaching resources, improves the flexibility of learning time and enhances the teacher-student interaction. This paper attempts to develop on innovative preschool education method based on computer multimedia technology. For this purpose, the popular intelligent teaching platform was introduced to construct a hybrid teaching mode based on the Rain Classroom (RC). Then, it applied this mode to the teaching of the course Modern Educational Technology, and evaluated the application effect through questionnaire survey. The results show that this hybrid teaching method is suitable for the students, and capable of improving their learning interest and overall ability. The research provides new theoretical and practical results of preschool education teaching, and promotes the reform of preschool education.
Aggregated Wasserstein Distance and State Registration for Hidden Markov Models

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time position follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as HMM. The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions. The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples. It is invariant to relabeling or permutation of states. The distance is defined meaningfully even for two HMMs that are estimated from data of different dimensionality, a situation that can arise due to missing variables. This distance quantifies the dissimilarity of HMMs by measuring both the difference between the two marginal GMMs and that between the two transition matrices. Our new distance is tested on tasks of retrieval, classification, and t-SNE visualization of time series. Experiments on both synthetic and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.
Garamon: A Geometric Algebra Library Generator

This paper presents both a recursive scheme to perform Geometric Algebra operations over a prefix tree, and Garamon, a C++ library generator implementing these recursive operations. While for low dimension vector spaces, precomputing all the Geometric Algebra products is an efficient strategy, it fails for higher dimensions where the operation should be computed at run time. This paper describes how a prefix tree can be a support for a recursive formulation of Geometric Algebra operations. This recursive approach presents a much better complexity than the usual run time methods. This paper also details how a prefix tree can represent efficiently the dual of a multivector. These results constitute the foundations for Garamon, a C++ library generator synthesizing efficient C++/Python libraries implementing Geometric Algebra in both low and higher dimensions, with any arbitrary metric. Garamon takes advantage of the prefix tree formulation to implement Geometric Algebra operations on high dimensions hardly accessible with state-of-the-art software implementations. Garamon is designed to produce easy to install, easy to use, effective and numerically stable libraries. The design of the libraries is based on a data structure using precomputed functions for low dimensions and a smooth transition to the new recursive products for higher dimensions.
Sparse online collaborative filtering with dynamic regularization

Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches.
The Usability Testing of VR Interface for Tourism Apps

Virtual reality (VR) is considered to be an emerging technology. This study compared the usability differences of VR travel software, such as Google Street View, VeeR VR, and Sites in VR, for mobile phones. In the pilot study, three post-graduate students and one interface expert were invited to participate in the designed experimental tasks to provide opinions on the first draft of the questionnaire. Next, thirty college students were recruited to join the formal experiment. After operating the VR interface, they were asked to fill out the questionnaire, and a semi-structured interview was conducted. The results are described as follows: (1) Intuitive operation is required to allow people to select objects smoothly; (2) the chosen object requires a feedback mode to inform the user that the object has been selected; (3) the speed of the feedback mode should be adjustable to fulfil the needs of most people; (4) the contrast of icon color needs to be improved to ensure the most efficient verification of the operations; and (5) a search button or reminder function can be added to aid first-time users.
Semi-supervised distributed representations of documents for sentiment analysis

Learning document representation is important in applying machine learning algorithms for sentiment analysis. Distributed representation learning models of words and documents, one of neural language models, have overcome some limits of vector space models such as bag-of-words model and have been utilized successively in many natural language processing tasks including sentiment analysis. However, because such models learn the embeddings only with a context-based objective, it is hard for embeddings to reflect the sentiment of texts. In this research, we address this problem by introducing a semi-supervised sentiment-discriminative objective using partial sentiment information of documents. Our method not only reflects the partial sentiment information, but also preserves local structures induced from original distributed representation learning objectives by considering only sentiment relationships between neighboring documents. Using real-world datasets, the proposed method has been validated by sentiment visualization and classification tasks. The visualization results of Amazon review datasets demonstrate the enhancement of the sentiment class separation when document representations of our proposed method are compared to other methods. Sentiment prediction from our representations also appears to be consistently superior to other representations in both Amazon and Yelp datasets. This work can be extended to develop effective document embeddings applied to other discriminative tasks.
Artificial Intelligence Treatment Decision Support For Complex Breast Cancer Among Oncologists With Varying Expertise

PURPOSE The aim of the current study was to assess treatment concordance and adherence to National Comprehensive Cancer Network breast cancer treatment guidelines between oncologists and an artificial intelligence advisory tool. PATIENTS AND METHODS Study cases of patients (N = 1,977) who were at high risk for recurrence or who had metastatic disease and cell types for which the advisory tool was trained were obtained from the Chinese Society for Clinical Oncology cancer database (2012 to 2017). A cross-sectional observational study was performed to examine treatment concordance and guideline adherence among an artificial intelligence advisory tool and 10 oncologists with varying expertise—three fellows, four attending physicians, and three chief physicians. In a blinded fashion, each oncologist provided treatment advice on an average of 198 cases and the advisory tool on all cases (N = 1,977). Results are reported as rates and logistic regression odds ratios. RESULTS Concordance for the recommended treatment was 0.56 for all physicians and higher for fellows compared with chief and attending physicians (0.68 v 0.54; 0.49; P = .001). Concordance differed by hormone receptor subtype–TNM stage, with the lowest for hormone receptor–positive human epidermal growth factor receptor 2/neu-positive cancers (0.48) and highest for triple-negative breast cancers (0.71) across most TNM stages. Adherence to National Comprehensive Cancer Network guidelines was higher for oncologists compared with the advisory tool (0.96 v 0.82; P &lt; .003) and lower for fellows compared with attending physicians (0.93 v 0.98; 0.96; P = .04). CONCLUSION Study findings reflect a complex breast cancer case mix, the limits of medical knowledge regarding optimum treatment, clinician practice patterns, and use of a tool that reflects expertise from one cancer center. Additional research in different practice settings is needed to understand the tool’s scalability and its impact on treatment decisions and clinical and health services outcomes.
Towards Interpersonal Assistants: Next-Generation Conversational Agents

We propose novel interpersonal assistants, a next-generation conversational agent which is always-on, unobtrusively serving natural human-to-human conversations. We deepen the motivation and design insights with real practices in language delays and parent–child conflicts. We then present a common platform initiative to effectively support rapid development of interpersonal assistant applications, with a highlight on the key functional element of turn isolations and technical insights on microstructural dynamics.
A Full-Spectrum Blockchain-as-a-Service for Business Collaboration

Private and consortium blockchains have recently raised interest in the blockchain community around facilitating business collaboration. In a business network, multiple participants either (1) work with a centralized authority to maintain a complete and verifiable ledger, or (2) transact in a decentralized manner to maintain an independent shared ledger. A centralized, private blockchain system, such as Amazon QLDB, addresses scenario one with good price-to-performance ratio. The second scenario is commonly backed by a consortium blockchain network, such as Hyperledger Fabric, that requires greater and more complex hardware and software to handle complicated consensus and peer-to-peer communications. The disparate design objectives and resultant principles complicate efforts to handle both scenarios within a single blockchain system. Moreover, the two situations are often mixed together for a user in different business activities, and interchangeable with the evolution of the business collaboration. From the outset, users face a dilemma in choosing which blockchain system type will handle their long-term needs. In this paper, we present a novel Full-Spectrum Blockchain as a Service (FSBaaS) built on top of a high-performance centralized private blockchain runtime called Blockchain Lite and a Hyperledger Fabric blockchain runtime. It handles different scenarios flexibly by providing a unified interface for the two blockchain runtimes from programing model and RESTful APIs to the tenant model. A BaaS tenant can subscribe to the two runtimes simultaneously and migrate data from one to the other as needed. Experimental results contrasted the performance of two blockchain runtimes and the practicality of the proposed BaaS.
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP

Information about individuals can help to better understand what they say, particularly in social media where texts are short.Current approaches to modelling social media users pay attention to their social connections, but exploit this information in a static way, treating all connections uniformly.This ignores the fact, well known in sociolinguistics, that an individual may be part of several communities which are not equally relevant in all communicative situations.We present a model based on Graph Attention Networks that captures this observation.It dynamically explores the social graph of a user, computes a user representation given the most relevant connections for a target task, and combines it with linguistic information to make a prediction.We apply our model to three different tasks, evaluate it against alternative models, and analyse the results extensively, showing that it significantly outperforms other current methods.
Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes.In problem domains like feature selection, the solutions are often constrained to the binary values viz.0 and 1.With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy.The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions.For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms.It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.