Reviewerly/exHarmony huggingface dataset:
Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion

Abstract Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application.
Improving the Accuracy of M-distance Based Nearest Neighbor Recommendation System by Using Ratings Variance

M-distance based recommendation system (MBR) is a nearest neighbor based recommendation method which uses the average of ratings given to an item as the attribute of that item.This attribute is used to determine similar items.Then, the average of the rating given to the similar items to an item of the active user determines the rating of that item.In this paper, to decrease the error of MBR, by combining the following ideas, eight MBR-based recommendation systems are proposed: (a) Using the variance of item ratings in addition to the average of item ratings, as two attributes of an item, for determining similar items in an item-based nearest neighbor method; (b) Using the variance of user ratings in addition to the average of user ratings, as two attributes of a user, for determining similar users in a user-based nearest neighbor method; (c) Using a weighted average method for combining the ratings of similar items or similar users; (d) Using ensemble learning.Experimental results on real datasets show that our proposed EVMBR and EWVMBR which use ensemble learning have the least error.The error of the suggested EWVMBR is at-least 20% lower than that of MBR, Slope-One, P-kNN, and C-kNN.
Correct-and-Memorize: Learning to Translate from Interactive Revisions

State-of-the-art machine translation models are still not on a par with human translators. Previous work takes human interactions into the neural machine translation process to obtain improved results in target languages. However, not all model--translation errors are equal -- some are critical while others are minor. In the meanwhile, same translation mistakes occur repeatedly in similar context. To solve both issues, we propose CAMIT, a novel method for translating in an interactive environment. Our proposed method works with critical revision instructions, therefore allows human to correct arbitrary words in model-translated sentences. In addition, CAMIT learns from and softly memorizes revision actions based on the context, alleviating the issue of repeating mistakes. Experiments in both ideal and real interactive translation settings demonstrate that our proposed CAMIT enhances machine translation results significantly while requires fewer revision instructions from human compared to previous methods.
Quiz Tool Within Moodle and Blackboard Mobile Applications

The development of a course is measured through Assessments. Tests and Quizzes measure performance and quality of course progress as well as student understanding of course contents. Most of Learning Management Systems (LMSs) provide a built in quiz tool to help instructor to design a quiz and obtain students records. Nowadays, LMSs companies provide versions (applications) that are compatible with mobile handheld devices. In this paper, we will investigate features available within quiz tool within the open source LMS Moodle and the proprietary LMS Blackboard.
Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition

Recent works use 3D convolutional neural networks to explore spatio-temporal information for human action recognition. However, they either ignore the correlation between spatial and temporal features or suffer from high computational cost by spatio-temporal features extraction. In this work, we propose a novel and efficient Mutually Reinforced Spatio-Temporal Convolutional Tube (MRST) for human action recognition. It decomposes 3D inputs into spatial and temporal representations, mutually enhances both of them by exploiting the interaction of spatial and temporal information and selectively emphasizes informative spatial appearance and temporal motion, meanwhile reducing the complexity of structure. Moreover, we design three types of MRSTs according to the different order of spatial and temporal information enhancement, each of which contains a spatio-temporal decomposition unit, a mutually reinforced unit and a spatio-temporal fusion unit. An end-to-end deep network, MRST-Net, is also proposed based on the MRSTs to better explore spatio-temporal information in human actions. Extensive experiments show MRST-Net yields the best performance, compared to state-of-the-art approaches.
ParHistVis: Visualization of Parallel Multilingual Historical Data

The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language.Often, those advantages cannot be fully exploited due to the sparse but high-dimensional nature of such historical data.To tackle this challenge, we introduce ParHistVis: a novel, free, easy-to-use, interactive visualization tool for parallel, multilingual, diachronic and synchronic linguistic data.We illustrate the suitability of the components of the tool based on a use case of word order change in Romance wh-interrogatives.
Allowed and forbidden bipartite correlations from thermal states

The strong connection between correlations and quantum thermodynamics raises a natural question about the preparation of correlated quantum states from two copies of a thermal qubit. In this work we study the specific forms of allowed and forbidden bipartite correlations. As a consequence, we extend the result to Separable (SEP) but not Absolutely Separable (AbSEP) class of product states. Preparation of a general form of entanglement from arbitrary thermal qubits is studied and as an application we propose a strategy to establish sustained entanglement between two distant parties. The threshold temperature to produce entanglement from two copies of a thermal qubit has also been discussed from the resource theoretic perspective, which ensures that the bound on the temperature can be superseded with the help of a resource state. A dimension dependent upper-bound on the temperature is derived, below which two copies of any d-dimensional thermal state can be entangled in 2xd dimension.
A Semantics-based Model for Predicting Children's Vocabulary

Intelligent tutoring systems (ITS) provide educational benefits through one-on-one tutoring by assessing children's existing knowledge and providing tailored educational content. In the domain of language acquisition, several studies have shown that children often learn new words by forming semantic relationships with words they already know. In this paper, we present a model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary knowledge. We show that the proposed semantics-based model outperforms models that do not use word semantics (semantics-free models) on average. A subject-level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, we use two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Our results motivate the use of semantics-based models to assess children's vocabulary knowledge and build ITS that maximizes children's semantic understanding of words.
Learning Classical Planning Strategies with Policy Gradient

A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner’s state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner’s performance relative to the baselines of plain bestfirst search and a uniform policy.
Group LASSO with Asymmetric Structure Estimation for Multi-Task Learning

Group LASSO is a widely used regularization that imposes sparsity considering groups of covariates. When used in Multi-Task Learning (MTL) formulations, it makes an underlying assumption that if one group of covariates is not relevant for one or a few tasks, it is also not relevant for all tasks, thus implicitly assuming that all tasks are related. This implication can easily lead to negative transfer if this assumption does not hold for all tasks. Since for most practical applications we hardly know a priori how the tasks are related, several approaches have been conceived in the literature to (i) properly capture the transference structure, (ii) improve interpretability of the tasks interplay, and (iii) penalize potential negative transfer. Recently, the automatic estimation of asymmetric structures inside the learning process was capable of effectively avoiding negative transfer. Our proposal is the first attempt in the literature to conceive a Group LASSO with asymmetric transference formulation, looking for the best of both worlds in a framework that admits the overlap of groups. The resulting optimization problem is solved by an alternating procedure with fast methods. We performed experiments using synthetic and real datasets to compare our proposal with state-of-the-art approaches, evidencing the promising predictive performance and distinguished interpretability of our proposal. The real case study involves the prediction of cognitive scores for Alzheimer's disease progression assessment. The source codes are available at GitHub.
Generative Visual Dialogue System via Weighted Likelihood Estimation

The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow. Traditional Maximum Likelihood Estimation-based methods only learn from positive responses but ignore the negative responses, and consequently tend to yield safe or generic responses. To address this issue, we propose a novel training scheme in conjunction with weighted likelihood estimation method. Furthermore, an adaptive multi-modal reasoning module is designed, to accommodate various dialogue scenarios automatically and select relevant information accordingly. The experimental results on the VisDial benchmark demonstrate the superiority of our proposed algorithm over other state-of-the-art approaches, with an improvement of 5.81% on recall@10.
Show, Reward, and Tell

Despite the promising progress made in visual captioning and paragraphing, visual storytelling is still largely unexplored. This task is more challenging due to the difficulty in modeling an ordered photo sequence and in generating a relevant paragraph with expressive language style for storytelling. To deal with these challenges, we propose an Attribute-based Hierarchical Generative model with Reinforcement Learning and adversarial training (AHGRL). First, to model the ordered photo sequence and the complex story structure, we propose an attribute-based hierarchical generator. The generator incorporates semantic attributes to create more accurate and relevant descriptions. The hierarchical framework enables the generator to learn from the complex paragraph structure. Second, to generate story-style paragraphs, we design a language-style discriminator, which provides word-level rewards to optimize the generator by policy gradient. Third, we further consider the story generator and the reward critic as adversaries. The generator aims to create indistinguishable paragraphs to human-level stories, whereas the critic aims at distinguishing them and further improving the generator. Extensive experiments on the widely used dataset well demonstrate the advantages of the proposed method over state-of-the-art methods.
The Effect of Business Process Re-Engineering on Organizational Performance: The Mediating Role of Information and Communications Technology

The purpose of this study is to attempt to highlight the factors of business process reengineering (BPR) processes that effect on organizational performance in the Drivers and Vehicles License Department (DVLD) in Jordan through using ICT as a mediating role. From previous studies, five factors of BPR were adopted for this study, including &amp;quot;Ability to change, Selecting BPR Methodology, Organizational Culture, BPR strategic alignment, and Top management support &amp;quot;. ICT was used as an intermediary role for this structure to promote organizational performance. The study used the PLS software to analyze the basic relationships amongst the most relevant combinations between BPR, ICT, and Organizational Performance with a total of five constructs. As a result of the findings of operational, middle, and top levels (n=124) working in Drivers and Vehicles License Department (DVLD), it was awarded the King Abdullah II Award for Excellence in Jordan for 2018. It was found that there was a strong and statistically positive significant relationship between several variables of business process reengineering and organizational Performance by using the ICT as a mediating variable.
Progressive Gaussian approximation filter with adaptive measurement update

This paper presents a progressive Gaussian approximation filter with adaptive measurement update for nonlinear systems. First, a progressive Gaussian filtering (PGF) framework with variable step-size is presented by using the Bayesian rule, and second, a progressive Gaussian approximation filter is designed under the framework of the variable step-size PGF, where the means and covariances of corresponding Gaussian distributions are calculated by using the unscented transform. Moreover, termination conditions for stopping the progressive measurement update are given, which are not only good for the computational efficiency, but also improve the adaptive ability against measurement uncertainties. Finally, by simulations of a target tracking example, it demonstrates that the proposed method has higher computational efficiency and robustness than the existing PGAF.
Cas-GANs: An Approach of Dialogue Policy Learning based on GAN and RL Techniques

Dialogue management systems are commonly applied in daily life, such as online shopping, hotel booking, and driving booking. Efficient dialogue management policy helps systems to respond to the user in an effective way. Policy learning is a complex task to build a dialogue system. There are different approaches have been proposed in the last decade to build a goal-oriented dialogue agent to train the systems with an efficient policy. The Generative adversarial network (GAN) is used in the dialogue generation, in previous works to build dialogue agents by selecting the optimal policy learning. The efficient dialogue policy learning aims to improve the quality of fluency and diversity for generated dialogues. Reinforcement learning (RL) algorithms are used to optimize the policies because the sequence is discrete. In this study, we have proposed a new technique called Cascade Generative Adversarial Network (Cas-GAN) that is combination of the GAN and RL for dialog generation. The Cas-GAN can model the relations between the dialogues (sentences) by using Graph Convolutional Networks (GCN). The graph nodes are consisting of different high level and low-level nodes representing the vertices and edges of the graph. Then, we use the maximum log-likelihood (MLL) approach to train the parameters and choose the best nodes. The experimental results compared with the HRL, RL agents and we got state-of-the-art results.
Breast Density Classification using Local Septenary Patterns: A Multi-resolution and Multi-topology Approach

We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.
Differentially Private High-Dimensional Data Publication via Markov Network

Differentially private data publication has recently received considerable attention.However, it faces some challenges in differentially private high-dimensional data publication, such as the complex attribute relationships, the high computational complexity and data sparsity.Therefore, we propose PrivMN, a novel method to publish high-dimensional data with differential privacy guarantee.We first use the Markov model to represent the mutual relationships between attributes to solve the problem that the direction of relationship between variables cannot be determined in practical application.We then take advantage of approximate inference to calculate the joint distribution of high-dimensional data under differential privacy to figure out the computational and spatial complexity of accurate reasoning.Extensive experiments on real datasets demonstrate that our solution makes the published high-dimensional synthetic datasets more efficient under the guarantee of differential privacy.
Design and implementation of an automatically aligned solar tracking system

This paper presents the design and implementation of a closed-loop solar tracker system. The demand for clean energy sources has increased along with the rising demand for electrical energy and the increasing amount of environment pollution triggered by fuel consumption. Among these sources, solar energy is considered the most feasible given its wide availability and easy operation in different environments. The main purpose of this study is to maximize the generated photovoltaic power and reduce CO&lt;sub&gt;2&lt;/sub&gt; emissions by designing an efficient and low-cost solar tracking system. An aligned closed-loop solar tracker is designed and constructed to achieve the best accuracy. The proposed system shows more freedom in its movement to overcome the problems associated with the tilt of the frame-holder. A PIC microcontroller based on the Flowcode programming language is used, the position feedback is detected by using a photo-sensor, and the H-Bridge driver is used to control two DC motors. According to the experimental results, the proposed system shows significant improvements in efficiency compared with stationary solar tracking systems.
An active learning framework for set inversion

Set inversion is a classical problem in control theory that has many important applications in various fields of science and engineering. The state-of-the-art method for solving this problem, Set Inverter Via Interval Analysis (SIVIA), usually does not work well in high dimensions and often fails to recover sets with complicated structures. In this work, we propose a new approach to the problem of set inversion, which employs techniques from machine learning to resolve these issues. Our algorithm can handle problems in high dimensions and achieve the same level of accuracy with fewer data points compared to SIVIA. We illustrate the performance of our method in various simulation studies and apply it to investigate the dynamics of the 17th-century plague in Eyam village, England.
SinoPedia—A Linked Data Services platform for decentralized knowledge base

Knowledge bases are largely developed and utilized in academic and industrial fields, such as DBPedia, VIAF, LoC, Getty, and which are published in the Linked Data format. However, if you want to view different resources on these knowledge bases, you have to switch between different web pages from these resources. Therefore, we proposed a decentralized data hub named SinoPedia, which consists of several linked data services and can re-publish these RDF data in one platform. Firstly, these different Linked Data services include: Linked Data Transformation Service (LDTS), Linked Data Query Service (LDQS), Linked Data Publishing Service (LDPS) and Linked Data Knowledge Service (LDKS). The resource URI is the basis and core of the linked data, thus we will focus on the resource forwarding mechanism in LDPS service which can rewrite resource URI using a global and standard format among knowledge bases. Some knowledge bases were configured in SinoPedia platform in this paper. In addition to the above services, Linked Data Reasoning Service (LDRS) and Linked Data Intelligence Service (LDIS) will be added to the platform in the future. In short, all of these Linked Data Services will form the core framework in order to providing a good linked data application ecosystem.
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset

Typically clustering algorithms provide clustering solutions with prespecified number of clusters. The lack of a priori knowledge on the true number of underlying clusters in the dataset makes it important to have a metric to compare the clustering solutions with different number of clusters. This article quantifies a notion of persistence of clustering solutions that enables comparing solutions with different number of clusters. The persistence relates to the range of dataresolution scales over which a clustering solution persists; it is quantified in terms of the maximum over two-norms of all the associated cluster-covariance matrices. Thus we associate a persistence value for each element in a set of clustering solutions with different number of clusters. We show that the datasets where natural clusters are a priori known, the clustering solutions that identify the natural clusters are most persistent - in this way, this notion can be used to identify solutions with true number of clusters. Detailed experiments on a variety of standard and synthetic datasets demonstrate that the proposed persistence-based indicator outperforms the existing approaches, such as, gap-statistic method, X-means, Gmeans, PG-means, dip-means algorithms and informationtheoretic method, in accurately identifying the clustering solutions with true number of clusters. Interestingly, our method can be explained in terms of the phase-transition phenomenon in the deterministic annealing algorithm, where the number of distinct cluster centers changes (bifurcates) with respect to an annealing parameter.