Reviewerly/exHarmony huggingface dataset:
Probability Matrix Factorization Algorithm for Course Recommendation System Fusing the Influence of Nearest Neighbor Users Based on Cloud Model

With the explosion of data on the online course website, getting the required course information quickly and accurately becomes more and more difficult. In this paper, probability matrix factorization algorithm for course recommendation system fusing the influence of nearest neighbor users based on cloud model is proposed. The proposed algorithm uses the cloud model to compute user similarity and integrates social information into the course recommendation. The experimental results show that the algorithm can improve the accuracy of course recommendation effectively.
Quantum Programming Languages (Dagstuhl Seminar 18381)

This report documents the program and the outcomes of Dagstuhl Seminar18381 Quantum Programming Languages, which brought together researchers from quantum computing and classical programming languages.
Machine Learning and Other Emerging Decision Support Tools

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.
Word-level sentiment analysis with reinforcement learning

Reinforcement learning (RL) imitates how human perceive the word and acquire knowledge. We manage to propose a RL method to realize text sentiment analysis. We describe a new framework named Word-level Sentiment LSTM (WS-LSTM), which means we use such framework to get sentiment tendency for each word in a sentence. We suppose Positive, Neural and Negative as actions and establish three different LSTM tunnels for each action. When we choose an action for a word, relative LSTM tunnel will be chosen to handle the input. After traversing a whole sentence, we get both word-level sentiment sequence and a sentence-level representation. Such representation is used in classification and we success in getting a sentence-level sentiment analysis. Results show that our method can get sentiment for each word in a specific task. As to the word-level analysis, results from several datasets show that our method plays an acceptable job.
Segmentation of pathological liver tissue with Dilated Fully Convolutional Networks: A Preliminary Study

Early detection of liver cancer, whether from primary occurrence or from metastization is highly important to establish informed treatment decisions. Accurate delineation of the liver tissues of interest facilitates quantitative assessment of the regions of interest, treatment application, and prognosis. Segmentation of the liver in Computer Tomography (CT) images allows the extraction of the three-dimensional (3D) structure of the liver tissues in which the observation of their relative position to one another is particularly important in treatment scenarios of radiation therapy or interventional surgery planning. The adequate receptive field for the segmentation of such a big organ in CT images, from the remaining neighbouring organs was very successfully improved by the use of the state-of-the-art Convolutional Neural Networks (CNN) algorithms, however, certain issues still arise and are highly dependent of pre- or post- processing methods to refine the final segmentations. Here, the effects of Dilated Convolutional Networks is proposed, for the purpose of improving segmentation of liver tissues in CT. The introduction of a dilation module allowed the concatenation of feature maps with a richer contextual information. The hierarchical learning process given by different dilated convolutional layers is analysed quantitatively. Experiments on the MICCAI Lits challenge dataset are described achieving segmentations with a mean Dice coefficients of 95.57% and 59.36% for the liver and liver tumour, using a total number 30 CT test volumes.
Hierarchical multi-label news article classification with distributed semantic model based features

Automatic news categorization is essential to automatically handle the classification of multi-label news articles in online portal. This research employs some potential methods to improve performance of hierarchical multi-label classifier for Indonesian news article. First potential method is using Convolutional Neural Network (CNN) to build the top level classifier. The second method could improve the classification performance by calculating the average of the word vectors obtained from distributed semantic model. The third method combines lexical and semantic method to extract documents features, which multiplied word term frequency (lexical) with word vector average (semantic). Model build using Calibrated Label Ranking as multi-label classification method, and trained using Naïve Bayes algorithm has the best F1-measure of 0.7531. Multiplication of word term frequency and the average of word vectors were also used to build this classifiers. This configuration improved multi-label classification performance by 4.25%, compared to the baseline. The distributed semantic model that gave best performance in this experiment obtained from 300-dimension word2vec of Wikipedia’s articles. The multi-label classification model performance is also influenced by news’ released date. The difference period between training and testing data would also decrease models’ performance.
Detector-Agnostic Phase-Space Distributions

The representation of quantum states via phase-space functions constitutes an intuitive technique to characterize light. However, the reconstruction of such distributions is challenging as it demands specific types of detectors and detailed models thereof to account for their particular properties and imperfections. To overcome these obstacles, we derive and implement a measurement scheme that enables a reconstruction of phase-space distributions for arbitrary states whose functionality does not depend on the knowledge of the detectors, thus defining the notion of detector-agnostic phase-space distributions. Our theory presents a generalization of well-known phase-space quasiprobability distributions, such as the Wigner function. We implement our measurement protocol, using state-of-the-art transition-edge sensors without performing a detector characterization. Based on our approach, we reveal the characteristic features of heralded single- and two-photon states in phase space and certify their nonclassicality with high statistical significance.
Quantum probabilistic associative memory architecture

We present a quantum probabilistic associative memory using the inverse of quantum Fourier transform and Grover's algorithm to recover existing or similar patterns in the memory. The content of the memory is created using a generator of a superposition state representing a given set of patterns. We discuss the architecture of the proposed memory including the storing, recovering and processing of similarity tolerance of the input query. The associative memory can extrapolate and recover similar stored patterns. The system is unitary and runs in O(n) steps, where n is the number of qubits of the patterns.
Determinants of Small and Medium Enterprises Performance in Nigeria: The Role of Government Support Policy

Small and medium enterprises (SMEs) play a significant social and economic role in both developed and developing nations.Despite the relevance and important of SMEs, the literature indicates there are very few studies that attempted to investigate the factors that influence the performance of SMEs in Nigeria, particularly the relationship between entrepreneurial orientation (EO), contemporary marketing (CM) and government support policyand the performance of small and medium enterprises (SMEs).The study used structured questionnaires; data was collected from 240 SMEs in northeast Nigeria.The findings of the study indicate a significant positive relationship between EO and CM on the Performance of SMEs.In addition, the results of the study validate that government support policy moderates the relationship between EO and CM on the performance of SMEs in Nigeria.The study implications for policy makers, government, regulators and SMEs owner-managers is to look at government support policy as it affects SMEs performance by providing conducive environment for business operation.
Detection of interacting groups based on geometric and social relations between individuals in an image

We propose a novel method to group individuals in daily life images based on their interactions. It is extremely useful to automatically understand the mutual relation between individuals appearing in images and to group them based on their interaction. However, this is an extremely challenging task. In this paper, we determine the interacting groups more precisely by considering not only the spatial information of individuals in an image but also the emotional correlation between individuals. This is in contrast to the existing methods of determining groups based on the distribution of individuals. In order to evaluate the performance of the proposed method, we created a new dataset with the images acquired in everyday life to address the issue of detecting interacting groups. The results of intensive comparison experiments with the existing grouping methods reveal that the proposed method exhibits superior performance in determining the interacting group.
Improved Maximum Margin Clustering via the Bundle Method

Maximum margin clustering (MMC) is an effective clustering algorithm, which first extends a large margin principle into unsupervised learning.This paper revisits the MMC problem and points out the potential problems encountered by a cutting plane approach.We propose an improved MMC algorithm via the bundle method (BMMC).Specifically, the constrained convex-concave procedure algorithm is first applied to decompose the MMC problem into a series of convex sub-problems, and then, the bundle method is adopted to efficiently solve each sub-problem.Moreover, a simpler formulation for the multi-class MMC is presented.In addition to clustering problems, the BMMC is also extended to the semi-supervised case by incorporating the pairwise constraints, which reveals its high scalability.Compared with the previous works, the proposed solution is much simpler and faster.The experiments on several data sets are conducted to demonstrate the effectiveness of our proposed algorithm.
Decomposed Local Models for Coordinate Structure Parsing

Hiroki Teranishi, Hiroyuki Shindo, Yuji Matsumoto. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Authoring tools for creating 360 multisensory videos—Evaluation of different interfaces

Abstract Authoring 360 multisensory videos is a true challenge as the authoring tools available are scarce and restrictive. In this paper, we propose an authoring tool with three different authoring interfaces (desktop, immersive, and tangible interface) for creating multisensory 360 videos with the advantage of having a live preview of the multisensory content that is being produced. An evaluation of the three authoring tools having into account gender, system usability, presence, satisfaction, and effectiveness (time to accomplish tasks, number of errors, and number of help requests) is presented. The sample consisted of 48 participants (24 males and 24 females) evenly distributed between the different interfaces (8 males and 8 females for each interface). The results revealed that gender does not have any impact in the studied interfaces regarding all the dependent variables; immersive and tangible interfaces have higher levels of satisfaction than desktop interface as it allows more interaction freedom, and desktop interface have the lowest time to accomplish the tasks because people are more familiar with keyboard and mouse.
Phishing Websites Classification using Association Classification (PWCAC)

Phishing is one of the cybercrime methods which seems to be increasing steadily targeting unsuspecting users. Nowadays, the impact of phishing extended to include organizations that provide services via the Internet. Such organizations become more susceptible to lose their reputation and their competitive edges because of phishing. Classifying a website into a phishing or legitimate depends mainly on the status of a set of significant attributes that exist in the website. Various solutions have been developed to mitigate phishing attacks. Yet, there is no solution that was able to solve the problem completely. One of the encouraging methods that can be utilized is the data mining. Specifically, the “induction of classification rules”. In this article, a novel association classification algorithm is suggested and applied to the well-known phishing websites dataset in the UCI repository. The experimental results were promising with respect to several evaluation criteria that are commonly utilised in evaluating classification data mining domains.
Extract Features from Periocular Region to Identify the Age Using Machine Learning Algorithms

Latest studies done on huge data collected from aging features proved that the performance of facial image based age estimation is low and need to be improved. One of the significant biometric traits for human recognition or search is Human age. Age assessment is very much exigent over other pattern recognition problems since the aging differs from person to person. This paper proposes a new framework that uses periocular region for age feature extraction and application of hybrid algorithm for age recognition. Firstly, preprocessing and periocular region normalization is done to acquire age invariant features. Secondly, the periocular region that underwent preprocessing is analyzed using hybrid approach, a novel machine algorithm that combines both SVM and kNN. The proposed technique generates the best recognition outputs.
A Lightweight Secure Auditing Scheme for Shared Data in Cloud Storage

A cloud platform provides users with shared data storage services. To ensure shared data integrity, it is necessary to validate the data effectively. An audit scheme that enables group members to modify data conducts the integrity verification of the shared data, but this approach results in complex calculations for the group members. The audit scheme of the designated agent implements a lightweight calculation for the group members, but it ignores the security risks between the group members and the agents. By introducing Hashgraph technology and designing a Third Party Medium (TPM) management strategy, a lightweight secure auditing scheme for shared data in cloud storage (LSSA) is proposed, which achieves security management of the groups and a lightweight calculation for the group members. Meanwhile, a virtual TPM pool is constructed by combining the TCP sliding window technology and interconnected functions to improve agent security. We evaluate our scheme in numerical analysis and in experiments, the results of which demonstrate that our scheme achieves lightweight computing for the group members and ensures the data verification process for security.
Multi-Atlas Based Method in Brain MR Image Segmentation

Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computer-aid brain disease analyses. However, the human brain has the complicated anatomical structure. Meanwhile, the brain MR images often suffer from the low intensity contrast around the boundary of ROIs, large inter-subject variance and large inner-subject variance. To address these issues, many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade. In this paper, multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods, conventional methods for label fusion, datasets that have been used for evaluating the multi-atlas methods, as well as the applications of multi-atlas based segmentation in clinical researches. We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.
U‐shaped development in error‐driven child phonology

Abstract Phonological regressions or U‐shaped development have frequently been observed in longitudinal studies of child speech production. However, the typology of which phonological patterns regress, and their implications for learning, have not been given much attention in the recent literature on constraint‐based phonological development. One basic question is simply the definition of a phonological regression, as created by the grammar or other mechanisms, which is in turn dependent on the type of grammar and learner assumed. This paper systematically addresses the question of whether or not attested phonological regressions are incompatible with an error‐driven approach to grammatical development, whereby each round of learning is predicted to move the learner closer to the target language. From this perspective, this survey discusses case studies of phonological regression in the literature, grouped according to their ease of explanation under error‐driven learning. Three types are identified and exemplified: regressions which are easily explained with existing error‐driven algorithms for constraint‐reranking; regressions which can also be derived through error‐driven learning by adopting an additional tool (for incorporating child‐specific phonetic experience); and regressions whose error‐driven motivation remains unclear. Another central theme of the survey is the degree of variation and lexical exceptionality among these regression patterns, and the extent to which such variability is captured in the learner's algorithms or grammar. Interim conclusions are provided, and necessary future directions for empirical and theoretical research are discussed. This article is categorized under: Linguistics &gt; Language Acquisition Linguistics &gt; Linguistic Theory
An Assessment of the Solar Photovoltaic Generation Yield in Malaysia using Satellite Derived Datasets

Precise and reliable estimation of energy yield for solar photovoltaic (PV) system is imperative for the accurate design of PV system components and power system planning studies with high penetration of renewables. A number of studies have conducted related to PV yield assessments in Europe and some parts of Asia. However, PV yield assessment for various locations in Malaysia is yet to be conducted. This work assesses the average annual PV yield (kWh/kWp/y) in Malaysia for the installed capacity in 2015. Meteorological data from Photovoltaic Geographic Information System (PVGIS) is utilized for yield evaluation. The comparative yield estimation based on PVGIS and yearlong-recorded data for installed PV system at the University of Malaya is conducted first. A little error is observed between the field measurements and the yield studies based on PVGIS data. Then, the expected average yield for the individual region in Malaysia is estimated from PVGIS. Finally, based on the obtained results the individual region in Malaysia is ranked for PV availability. This study could provide very useful information to the power utilities, PV industries, and policymakers regarding the planning of large-scale PV plant, network operation, and future PV system development in Malaysia.
Social Account Matching in Online Social Media using Cross-linked Posts

An online user creates his/her social account on multiple social media networks to interact with friends and relatives. Therefore, an individual produces diverse contents on each social network. As different social networks provide dissimilar but complementary services, integration of user-generated content collected from different social media profile of the same user may help us in understanding the actual behavior of the user. For this, we need to search the profile of a specific user on different social media services. In this paper, we propose a framework that exploits the user-generated posts to match the user identity across social networks. This framework uses a set of queries and automatically extracts unique usernames to build a dataset. Experimentally, we find that recall varies from 0.48 to 0.78 across different pairs of social networks.
Moderate deviation and restricted equivalence functions for measuring similarity between data

In this work we study the relation between moderate deviation functions, restricted dissimilarity functions and restricted equivalence functions. We use moderate deviation functions in order to measure the similarity or dissimilarity between a given set of data. We show an application of moderate deviate functions used in the same way as penalty functions to make a final decision from a score matrix in a classification problem.
Single Image Restoration for Participating Media Based on Prior Fusion

This paper describes a method to restore degraded images captured in a participating media-fog, turbid water, sand storm, etc. Differently from the related work that deals with each medium separately, we obtain generality by using an image formation model and a fusion of new image priors.
A framework for space-efficient variable-order Markov models

Abstract Motivation Markov models with contexts of variable length are widely used in bioinformatics for representing sets of sequences with similar biological properties. When models contain many long contexts, existing implementations are either unable to handle genome-scale training datasets within typical memory budgets, or they are optimized for specific model variants and are thus inflexible. Results We provide practical, versatile representations of variable-order Markov models and of interpolated Markov models, that support a large number of context-selection criteria, scoring functions, probability smoothing methods, and interpolations, and that take up to four times less space than previous implementations based on the suffix array, regardless of the number and length of contexts, and up to ten times less space than previous trie-based representations, or more, while matching the size of related, state-of-the-art data structures from Natural Language Processing. We describe how to further compress our indexes to a quantity related to the redundancy of the training data, saving up to 90% of their space on very repetitive datasets, and making them become up to 60 times smaller than previous implementations based on the suffix array. Finally, we show how to exploit constraints on the length and frequency of contexts to further shrink our compressed indexes to half of their size or more, achieving data structures that are a hundred times smaller than previous implementations based on the suffix array, or more. This allows variable-order Markov models to be used with bigger datasets and with longer contexts on the same hardware, thus possibly enabling new applications. Availability and implementation https://github.com/jnalanko/VOMM Supplementary information Supplementary data are available at Bioinformatics online.