Reviewerly/exHarmony huggingface dataset:
A Novel Social Recommendation Method Fusing User’s Social Status and Homophily Based on Matrix Factorization Techniques

As one of the most successful recommendation techniques, collaborative filtering provides a useful recommendation by associating an active user with a crowd of users who share the same interests. Although some achievements have been achieved both in theory and practice, the efficiency of recommender systems has been negatively affected by the problems of cold start and data sparsity recently. To solve the above problems, the trust relationship among users is employed into recommender systems to build a learning model to further promote the prediction quality and users' satisfaction. However, most of the existing social networks-based recommendation algorithms fail to take into account the fact that users with different levels of trust and backgrounds, that is, user's social status and homophily have different degrees of influence on their friends. In this paper, a novel social matrix factorization-based recommendation method is proposed to improve the recommendation quality by fusing user's social status and homophily. User's social status and homophily play important roles in improving the performance of recommender systems. We first build a user's trust relationship network based on user social relationships and the rating information. Then, the degree of trust is calculated through the trust propagation method and the PageRank algorithm. Finally, the trust relationship is integrated into the matrix factorization model to accurately predict unknown ratings. The proposed method is evaluated using real-life datasets including the Epinions and Douban datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing social networks-based recommendation algorithms.
Digital Image Correlation Comparison of Damaged and Undamaged Aeronautical CFRPs During Compression Tests

The diffusion of composite materials in aeronautical and aerospace applications is attributable to the high specific mechanical properties they offer. In particular, the recent use of Carbon Fiber Reinforced Polymer (CFRP) materials is highly increased. The main disadvantage in using this kind of material is related to the possibility of including damages or defects not visible on the surface that compromise their behavior and make their use extremely unsafe if not properly supervised. The most conventional nondestructive techniques allow the detection of damages when they already compromise the life of these materials. The use of the same techniques makes it harder to monitor in-situ of the progress of damages, especially if they occur inside the materials. The implementation of the innovative strain analysis method, like those based on full-field measurements, could provide additional information about the damage mechanisms by supplying the complete strain distribution of the surface of the sample. The present paper examines the mechanical behavior of two different CFRP specimens, with and without damage, subjected to compressive load in an anti-buckling fixture by using the Digital Image Correlation (DIC). The purpose is to measure the out-of-plane displacements, characteristics of the compression tests, in all the points of the ROI (Region of Interest), using a full-field and noncontact technique. The innovative aspect of this work is therefore to solve this problem through an experimental approach with DIC 3D technique.
Deep Learning-Based Multi-scale Multi-object Detection and Classification for Autonomous Driving

Autonomous driving vehicles need to perceive their immediate environment in order to detect other traffic participants such as vehicles or pedestrians. Vision based functionality using camera images have been widely investigated because of the low sensor price and the detailed information they provide. Conventional computer vision techniques are based on hand-engineered features. Due to the very complex environmental conditions this limited feature representations fail to uniquely identify a specific object. Thanks to the rapid development of processing power (especially GPUs), advanced software frameworks and the availability of large image datasets, Convolutional Neural Networks (CNN) have distinguished themselves by scoring the best on populthis information, the boundingar object detection benchmarks in the research community. Using deep architectures of CNN with many layers, they are able to extract both low-level and high-level features from images by skipping the feature design procedures of conventional computer vision approaches. In this work, an end-to-end learning pipeline for multi-object detection based on one existing CNN architecture, namely Single Shot MultiBox Detector (SSD) [1], with real-time capability, is first reviewed. The SSD detector predicts the object’s position based on feature maps of different resolution together with a default set of bounding boxes. Using the SSD architecture as a starting point, this work focuses on training a single CNN to achieve high detection accuracy for vehicles and pedestrians computed in real time. Since vehicles and pedestrians have different sizes, shapes and poses, independent NNs are normally trained to perform the two detection tasks. It is thus very challenging to train one NN to learn the multi-scale detection ability. The contribution of this work can be summarized as follows:
Rician noise and intensity nonuniformity correction (NNC) model for MRI data

Rician noise and intensity nonuniformity are two common artifacts and usually coexist in magnetic resonance imaging (MRI) data. Many methods have been proposed in the literature dealing with either Rician noise or intensity nonuniformity individually. We numerically verify that the existence of intensity nonuniformity may lead to the underestimation of noise, which means intensity nonuniformity influences the performance of denoising and vice versa. Thus, we propose a novel restoration model via a maximum a posteriori (MAP) estimator by regarding MRI data as a combination of two multiplicative components, namely, the true intensity and the bias field, and a noise followed a Rician distribution. We also guarantee that the proposed model has at least one positive nontrivial solution theoretically. An efficient algorithm based on alternating minimization method is developed, all subproblems of which can be solved effectively by either Newton's method or closed-form solutions. Intensive numerical results on synthetic and real MRI data confirm the robustness of the method and its better performance for MRI data restoration.
Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral.Most sentiment analysis research focus on English lexicon vocabularies.However, Malay is still under-resourced.Research of sentiment analysis in Malaysia social media is challenging due to mixed language usage of English and Malay.The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach.Two lexicons of languages are combined in the system, then, the Twitter data were collected and the results were determined using graph.The results showed that the classifier was able to determine the sentiments.This study is significant for companies and governments to understand people's opinion on social network especially in Malay speaking regions.
Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-Commerce

Relevance is the core problem of a search engine, and one of the main challenges is the vocabulary gap between user queries and documents. This problem is more serious in e-commerce, because language in product titles is more professional. Query rewriting and semantic matching are two key techniques to bridge the semantic gap between them to improve relevance. Recently, deep neural networks have been successfully applied to the two tasks and enhanced the relevance performance. However, such approaches suffer from the sparseness of training data in e-commerce scenario. In this study, we investigate the instinctive connection between query rewriting and semantic matching tasks, and propose a co-training framework to address the data sparseness problem when training deep neural networks. We first build a huge unlabeled dataset from search logs, on which the two tasks can be considered as two different views of the relevance problem. Then we iteratively co-train them via labeled data generated from this unlabeled set to boost their performance simultaneously. We conduct a series of offline and online experiments on a real-world e-commerce search engine, and the results demonstrate that the proposed method improves relevance significantly.
Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctors and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.
An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care

Patient-centered care is an essential part of the implementation of integrated medicine, integrating humanistic care into nursing services, enhancing communication between caregivers and patients, and providing personalized service to patients. Based on the similarity of interval type-2 fuzzy numbers (IT2FNs), a novel similarity-based methodology is presented for the selection of the most suitable medical treatment under a patient-centered environment. First, we propose a new similarity based on the geometric properties of interval type-2 fuzzy numbers and present a new property based on the center of gravity. Meanwhile, in order to better highlight the advantages of the proposed similarity, we selected 30 samples for comparative experiments. Second, considering the straightforward logic of the multi-attributive border approximation area comparison (MABAC) method, we extended it based on similarity to make the decision more accurate. Finally, a realistic patient-centered type-2 diabetes treatment selection problem is presented to verify the practicality and effectiveness of the proposed algorithm. A comparative analysis with existing methods is also described.
From strong to weak temperature dependence of the two-photon entanglement resulting from the biexciton cascade inside a cavity

We investigate the degree of entanglement quantified by the concurrence of photon pairs that are simultaneously emitted in the biexciton-exciton cascade from a quantum dot in a cavity. Four dot-cavity configurations are compared that differ with respect to the detuning between the cavity modes and the quantum dot transitions, corresponding to different relative weights of direct two-photon and sequential single-photon processes. The dependence of the entanglement on the exciton fine-structure splitting $\ensuremath{\delta}$ is found to be significantly different for each of the four configurations. For a finite splitting and low temperatures, the highest entanglement is found when the cavity modes are in resonance with the two-photon transition between the biexciton and the ground state and, in addition, the biexciton has a finite binding energy of a few meV. However, this widely used configuration is rather strongly affected by phonons such that other dot-cavity configurations, that are commonly regarded as less suited for obtaining high degrees of entanglement, become more favorable already at temperatures on the order of $10\phantom{\rule{0.28em}{0ex}}\mathrm{K}$ and above. If the cavity modes are kept in resonance with one of the exciton-to-ground-state transitions and the biexciton binding energy is finite, the entanglement drastically drops for positive $\ensuremath{\delta}$ with rising temperatures when $T$ is below $\ensuremath{\simeq}4\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, but is virtually independent of the temperature for higher $T$.
How to evaluate the performance of gradual type systems

Abstract A sound gradual type system ensures that untyped components of a program can never break the guarantees of statically typed components. This assurance relies on runtime checks, which in turn impose performance overhead in proportion to the frequency and nature of interaction between typed and untyped components. The literature on gradual typing lacks rigorous descriptions of methods for measuring the performance of gradual type systems. This gap has consequences for the implementors of gradual type systems and developers who use such systems. Without systematic evaluation of mixed-typed programs, implementors cannot precisely determine how improvements to a gradual type system affect performance. Developers cannot predict whether adding types to part of a program will significantly degrade (or improve) its performance. This paper presents the first method for evaluating the performance of sound gradual type systems. The method quantifies both the absolute performance of a gradual type system and the relative performance of two implementations of the same gradual type system. To validate the method, the paper reports on its application to 20 programs and 3 implementations of Typed Racket.
Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.
A Real-Time and Hardware-Efficient Processor for Skeleton-Based Action Recognition With Lightweight Convolutional Neural Network

Skeleton-based human action recognition (HAR) has been extensively studied these years because body skeleton has the simple but informative representation of human action, which greatly reduces the computation complexity compared with the image-based HAR. As a result, it is suitable for low power implementation in embedded platforms. In this brief, we present a systematic approach to developing a hardware-efficient and low-power processor for real-time skeleton-based HAR. First, a lightweight HAR algorithm only using the one-dimensional convolutional neural network (1D-CNN) is proposed. Second, the singular value decomposition is employed to compress the weights in the fully connected (FC) layers of the proposed convolutional neural network. Third, a hardware processor implementing the proposed algorithm is presented. Aimed at optimizing area and energy, this processor utilizes a flexible structure supporting different kernel sizes of the 1D-CNN and reuses hardware in both convolution layers and FC layers. The proposed processor is implemented under SMIC 65-nm CMOS technology and consumes a total area of 1.016 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Experimental results show that the proposed processor can achieve state-of-the-art classification accuracy in NTU RGB+D dataset and SBU dataset while outperforming previous solutions in area and energy efficiency.
Employing community question answering for online discussions in university courses: Students’ perspective

In university courses as well as in MOOCs, Community Question Answering (CQA) systems have been recently recognized as a promising alternative to standard discussion forums for mediating online discussions. Despite emerging research on educational CQA systems, a study investigating when and how to use these systems to support university education is still missing. We stress that such a study should effectively take into account students' perceptions of CQA systems rather than relying solely on an analysis of their effective usage based on data available in system logs. This paper therefore systematically analyzes perspective of 182 computer science students from three universities located in three different countries on concept of educational CQA systems. Students’ opinions were collected using a questionnaire that was designed to assess conceptual issues of using CQA systems in university education, their core features as well as particular implementation of our CQA system Askalot. The analysis of collected responses based on non-parametric statistical tests revealed that there are various significant differences in the perception of CQA systems between different types of students (e.g. passive and active) and in different deployment settings (e.g. when using CQA in a small number of 1–3 courses during one academic year or in 50 courses for 5 years). The obtained findings supported by content and usage analysis finally resulted into the summary of 12 main characteristics of CQA systems which describe their suitability for mediating online discussions in different deployment settings and for different types of students.
Efficiently Mining Frequent Itemsets on Massive Data

Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions. The existing algorithms cannot compute frequent itemsets on massive data efficiently, since they either require multiple-pass scans on the table or construct complex data structures which normally exceed the available memory on massive data. This paper proposes a novel precomputation-based frequent itemset mining (PFIM) algorithm to compute the frequent itemsets quickly on massive data. PFIM treats the transaction table as two parts: the large old table storing historical data and the relatively small new table storing newly generated data. PFIM first pre-constructs the quasi-frequent itemsets on the old table whose supports are above the lower-bound of the practical support level. Given the specified support threshold, PFIM can quickly return the required frequent itemsets on the table by utilizing the quasi-frequent itemsets. Three pruning rules are presented to reduce the size of the involved candidates. An incremental update strategy is devised to efficiently re-construct the quasi-frequent itemsets when the tables are merged. The extensive experimental results, conducted on synthetic and real-life data sets, show that PFIM has a significant advantage over the existing algorithms and runs two orders of magnitude faster than the latest algorithm.
Rethinking access: how humanitarian technology governance blurs control and care

Surprisingly little attention is paid to the role of digital technology and related forms of data production, storage, processing, and sharing in humanitarian governance. This paper uses Michael Barnett's ( ) conceptualisation of humanitarian governance when arguing for a better accounting of technology in literature on humanitarian governance. Specifically, it proposes a two‐fold alertness to governance of (a) the uses of new technology and (b) that which is produced by digital technologies. This elucidates important issues, including that of access to digitalised data collected from humanitarian subjects, with implications for their (in)security. The paper concludes by suggesting that access is no longer ‘only’ about challenges of gaining access to vulnerable populations, but also about challenges of preventing access to vulnerable digital bodies and their use for aggressive purposes. In short, access and protection acquire a new dimension and analyses of humanitarian governance must be more attentive to the role of digital technology.
Measuring Fiber Orientation of Elliptical Fibers from Optical Microscopy

The orientation of fibers with elliptical cross-sections cannot be estimated using standard optical microscopy analysis methods in which the ratio of the minor-axis to the major-axis and orientation of the major-axis are directly used to determine the fiber spherical coordinates, θ and ϕ . A new method for estimating the orientation of fibers with elliptical cross-sections is presented and validated using both simulations and experiments. Fibers with elliptical cross-sections rather than circular possess a roll degree of freedom, which significantly affects the dimensions of projected cross-sections in viewing planes. The equations of the projected ellipse of an elliptic cylinder onto a viewing plane are determined in terms of typical spherical coordinate system angles, θ and ϕ , the roll angle, α , and the fiber semi-major and semi-minor diameters. Fiber angles are determined by numerical fitting of the developed equations to measured ellipses. An ambiguity in the determined angles is identified, and, in the special case of fiber bundles, a scheme is presented by which the ambiguity can be resolved. Validation experiments showed that the method is quite effective at estimating fiber orientation from micrographs when fiber cross-section dimensions are measured beforehand, and the additional ambiguity is resolved easily in the case of fiber bundles.