Reviewerly/exHarmony huggingface dataset:
Topic Transferable Table Question Answering

Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question.However, in practical settings TableQA systems are deployed over table corpora having topic and word distributions quite distinct from BERT's pretraining corpus.In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTQ-TS 1 , consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTableQuestions datasets.We empirically show that, despite pre-training on large open-domain text, performance of models degrades significantly when they are evaluated on unseen topics.In response, we propose T3QA (Topic Transferable Table Question Answering) a pragmatic adaptation framework for TableQA comprising of: (1) topic-specific vocabulary injection into BERT, (2) a novel text-to-text transformer generator (such as T5, GPT2) based natural language question generation pipeline focused on generating topic specific training data, and (3) a logical form reranker.We show that T3QA provides a reasonably good baseline for our topic shift benchmarks.We believe our topic split benchmarks will lead to robust TableQA solutions that are better suited for practical deployment.
Neural Collaborative Autoencoder for Recommendation With Co-Occurrence Embedding

Collaborative filtering is the one of the most successful methods used by recommendation system to solve the information overload problem. Nevertheless, most collaborative filtering only uses explicit rating information to model the user, ignoring the impact of implicit information. In addition, they still utilize inner product to fit user-item interaction behavior, which leads to poor recommendation results. Thus, in this paper, we propose an autoencoder model based on implicit trust relationship between users, where we combine the intrinsic relationship of both the implicit trust information and user-item interaction behavior for collaborative recommendation. Two key challenges emerged in this process: first, how to extract implicit trust information of users, and second, how to model the complex user-item interaction behavior for recommendation. To solve these two challenges, we design a model named the Neural Collaborative Autoencoder for Recommendation with Co-occurrence Embedding (NCAR), which is divided into two parts: (1) the user co-occurrence matrix embedding part; (2) the collaborative neural recommendation part. First, NCAR extracts the user co-occurrence matrix from the rating information. Then, the auto-encoder is used to learn the co-occurrence embedding of each user with the correlation regularization method. Finally, this paper employs an interaction prediction module based on a deep neural network to learn the complex interaction behaviors between users and items. Experiments conducted on four public datasets show that the performance of NCAR is significantly better than that of the baseline method.
Deep Recommendation Model Combining Long - and Short-Term Interest Preferences

The existing sequential recommendation algorithms cannot effectively capture and solve the problems such as the dynamic preferences of users over time. This paper proposes a deep Recommendation model CLSR (Combines Long-term and Short-term interest Recommendation) that Combines long-term and short-term interest preferences. Firstly, the model models the potential feature representation of users and items, and uses the self-attention mechanism to capture the relationship between items in the interaction of users’ historical behavior, so as to better learn the short-term interest representation of users. At the same time, the BiGRU network is used to extract the features of users’ long-term interests on a deep level. Finally, the features of long-term and short-term interest are fused. On four publicly available datasets, experimental results show that the proposed method has better improvement on HR@N, NDCG@N and MRR@N, which validates the effectiveness of the model.
How to detect whether Shor’s algorithm succeeds against large integers without a quantum computer

Shor’s algorithm is a well-known probabilistic method for factoring large composite integers in polynomial-time on a quantum computer. The method computes the order r of a random element x in the group Z∗N and uses that information for splitting N with an application of the greatest common divisor algorithm. However, being probabilistic, the success of Shor’s algorithm relies on some special properties of N. If r is even and xr/2 £ -1 mod N, then gcd(xr/2 - 1, N) reveals a nontrivial factor of N and the method succeeds. But even assuming that r is even and being given the complete prime factorization of N it is not obvious whether xr/2 £ -1 mod N and, therefore, it is not easy to assert whether Shor’s algorithm would split N without running it and looking at its answer. We present a strategy for detecting whether the splitting occurs without any need for running the quantum order-finding algorithm, but we must be given the prime factorization of N. This has allowed us to produce the first direct evidence of the probability of success of Shor’s method. The composites chosen were the product of two randomly-generated probable primes of similar sizes that pass the Miller-Rabin test.
Blended Oral English Teaching Based on Core Competence Training Model

The traditional mixed oral English teaching model has many obvious shortcomings, such as the inability to correct the students’ oral pronunciation errors and feed them back in time, which leads to the slow improvement of students’ English learning level. For this reason, this paper proposes a guided teaching model based on core literacy. According to the structure of the oral English mixed teaching model, determine the application plan of the oral English mixed teaching model, design the development environment, obtain the corpus, design the oral training model, extract the oral features, identify the wrong pronunciation and correct it in time, clarify the evaluation purpose, obtain preliminary evaluation indicators, reduce evaluation indicators and determine indicator weights, obtain indicator feature information, generate fuzzy rules, obtain fuzzy matrices, achieve quantitative evaluation, and synthesize all evaluation scores to construct a result vector matrix to realize the study of mixed spoken language teaching mode. Research shows that the mixed teaching method is effective and feasible and can effectively improve the accuracy of the evaluation results of the mixed oral English teaching model.
An Augmented Reality Piano Learning Tool

Music learning (here, thinking of playing piano) without professor can be a real challenge.This can be difficult to achieve, given some traditional approaches to manage this process are still insufficiently satisfactory.Beyond the wellknown computer-assisted music learning tools (1) screenbased and (2) hardware-based, a promising solution for piano learning is based on augmented reality (AR).This technology provides an ideal environment for the student, but of course, it is not enough to become a career musician.In this paper, we propose an application that helps all those who want to learn to play the piano in a friendly way, starting with understanding sheet music.By projecting information onto the instrument, our AR system can create more direct interactions for the user.
ADQ-GNN: Next POI Recommendation by Fusing GNN and Area Division with Quadtree

Point-of-interest (POI) recommendation, i.e., recommending attractive POIs to users, has attracted great attention in both academia and industry. In order to improve recommendation accuracy and rich users' experience, researchers put much effort into modeling users' check-in sequence information as well as the physical distance between POIs. However, the state-of-the-art methods do not take hierarchical relations into consideration when mining spatial information and neglect the impact of different time intervals when capturing temporal correlation. To this end, we propose a novel model by fusing Graph Neural Network (GNN) and area division with quadtree, which takes joint consideration of users' spatial, long-term, and short-term preferences in a unified way. Specifically, the proposed model exploits users' regional transition patterns in different granularity by means of quadtree map division, and adopts attention mechanism to dynamically update weight values of the above three types of preferences. Furthermore, GNN is introduced to capture the complex interaction relationship between different areas and POIs. Finally, we conduct extensive experiments on three real-world datasets, and the results demonstrate that our model significantly outperforms the state-of-the-art methods.
How Individuals With Low Back Pain Conceptualize Their Condition: A Collaborative Modeling Approach

Low back pain (LBP) is complex. This study aimed to use collaborative modeling to evaluate conceptual models that individuals with LBP have of their condition, and to compare these models with those of researchers/clinicians. Twenty-eight individuals with LBP were facilitated to generate mental models, using "fuzzy cognitive maps," that represented conceptualization of their own LBP and LBP "in general." "Components" (ie, causes, outcomes and treatments) related to pain, disability and quality of life were proposed, along with the weighted "Connections" between Components. Components were classified into thematic categories. Weighting of Connections were summed for each Component to judge relative importance. Individual models were aggregated into a metamodel. When considering their own condition, participants' models included 19(SD = 6) Components and 43(18) Connections with greatest weight on "Biomechanical" components. When considering LBP in general, models changed slightly. Patient models contrasted the more complex models of researchers/clinicians (25(7) Components; 77(42) Connections), with most weight on "Psychological" components. This study provides unique insight into how individuals with LBP consider their condition, which is largely biomedical and narrower than clinician/researcher perspectives. Findings highlight challenges for changing public perception of LBP, and provide a method with potential utility to understand how individuals conceptualize their condition. PERSPECTIVE: Collaborative modeling was used to understand how individuals with low back pain conceptualize their own condition, the condition in general, and compare this with models of expert researchers/clinicians. Data revealed issues in how individuals with back pain conceptualize their condition, and the method's potential utility for clinical evaluation of patients.
Multi-task learning for Chinese clinical named entity recognition with external knowledge

Abstract Background Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. Methods To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. Results In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. Conclusions The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.
Attention Mechanism-Based Light-Field View Synthesis

The angular information of light lost in conventional images but preserved and stored in light-fields plays an instrumental role in many applications such as depth estimation, 3D reconstruction and post-capture refocusing. However, the limited angular resolution of light-field images due to the consumer hardware limitations is a major drawback in its widespread adoption. In this article, we present a novel deep learning-based light-field view synthesis method from a sparse set of input views. Our proposed method, an end-to-end trainable network, utilizes convolutional block attention modules to enhance the built-in depth image-based rendering. The proposed convolutional block attention module consists of two attention modules sequentially applied in the channel and spatial dimensions to focus the network on critical features. The proposed network architecture is a combination of three sub-networks, one for stereo feature extraction, another for disparity estimation, and the last for attention-based refinement. We present two schemes for the refinement network that perform equally well but differ in the number of parameters by 1.2% and execution time by 44%. Quantitative and qualitative results on four challenging real-world datasets show the superiority of the proposed method. Our proposed method shows considerable PSNR gains, around 1 dB compared to the state of the art and around 0.5 dB over our previous method LFVS-AM. In addition, ablation studies demonstrate the effectiveness of each module of the proposed method. Finally, the parallax edge precision-recall curve shows that our method better preserves parallax details.
How to get things done in social virtual reality – A study of team cohesion in social virtual reality–enabled teams

Social virtual reality (SVR) enables teams to operate in a virtual environment that simulates and enhances real-world interactions.However, there is an absence of empirical analysis of how SVR can affect the performance of virtual teams.This paper documents how SVR affects the formation of team cohesion (i.e., task cohesion and social cohesion), which is a critical success factor for team performance.To address this gap in the research, we conducted a qualitative study by interviewing 20 members from virtual teams assigned to perform a challenging collaborative task in SVR.As a contribution, our study identifies five primary affordances and 11 sub-affordances for team cohesion in SVR.We also found that team cohesion actualization was limited in the use of SVR environments of our study.However, we conclude that hindrances to team cohesion formation in SVR can be mitigated by focusing on the further development of material properties of SVR.
Research Support Services in Indonesian Academic Digital Libraries A Proposed Business Model and Prototype Design

Several studies have highlighted the critical role of academic digital libraries in providing research support services and improving research performance. Although several studies have discussed the value of realizing this role, a business model that can serve as a reference in carrying out this role is not yet available. This research aimed to propose a business model and a prototype design for Indonesia’s academic digital library for research support services. A focus group discussion was conducted involving nine experts to identify the key factors. They were then mapped onto a Business Model Canvas. The key activities in the business model guided the requirements gathering for developing a prototype. The requirements were obtained from the literature reviews with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The requirements were validated by interviewing library managers, research unit managers, and information technology managers. This research resulted in the academic digital library Business Model Canvas and the proposed prototype design, which consisted of a mobile application for member users and a back-office application to manage services by librarians. Further research on prototype implementation testing and the development of a successful implementation model is needed to strengthen this research.
A Smart Digital Attendance Monitoring System for Academic Institution Using Machine Learning Techniques

Face Recognition is the one of the foremost emerging and interesting technology. In this fast-evolving generation, attendance is usually a problem among school community and so as business community. One among the foremost efficient solution for this problem is that the Digital Attendance System. Now a days Computers plays a vital role in many fields. Likewise, in this field also, we will make an efficient use of it. Face detection and recognition-based attendance system may be a process of identifying face of the scholars using image of a face on the idea of high - definition monitor video and uploading the attendance of every student automatically, rather than manual attendance uploading process. In our face recognition Research work, a smart system is going to be ready to capture a frame from a video and process it accurately and predict which of the scholar is present therein class. There are number of algorithms and programme to detect the face of an individual, but the concept to be implemented here is Neural Networks. We used Neural Networks because, it will be used for the extraction of the frames from the video and convert it into images in so the face of the scholar will be often identified and updated automatically within the database.
Colonization by Algorithms in the Fourth Industrial Revolution

Data gathering and information processing have evolved to where it is almost unfathomable how much exists in digital form today.The generation thereof also no longer involves an explicit instruction from human to machine but can happen in real-time without human intervention.Artificial intelligence, machine learning, and cognitive computing are being utilized to mine data from a variety of sources.One such (profitable) source is human beings.Digital algorithms are designed to harness the power of technology to gather information.There has always been a sense of secrecy regarding some information (classified, top secret, confidential, etc.) but the Fourth Industrial Revolution has created the means to gather extremely large amounts of data, unknown to its sources.Anthropological value systems should become a fundamental foundation of digital algorithms.Such an approach could prevent software from exploiting its sources, especially minorities.Value systems together with ethics are guided by people's culture.In ethically aligned algorithm design, value systems and digital technologies intersect and govern how algorithms are developed, the way data is engaged, and further the discipline of digital humanities.
Second‐order asymmetric convolution network for breast cancer histopathology image classification

Recently, convolutional neural networks (CNNs) have been widely utilized for breast cancer histopathology image classification. Besides, research works have also convinced that deep high-order statistic models obviously outperform corresponding first-order counterparts in vision tasks. Inspired by this, we attempt to explore global deep high-order statistics to distinguish breast cancer histopathology images. To further boost the classification performance, we also integrate asymmetric convolution into the second-order network and propose a novel second-order asymmetric convolution network (SoACNet). SoACNet adopts a series of asymmetric convolution blocks to replace each stand square-kernel convolutional layer of the backbone architecture, followed by a global covariance pooling to compute second-order statistics of deep features, leading to a more robust representation of histopathology images. Extensive experiments on the public BreakHis dataset demonstrate the effectiveness of SoACNet for breast cancer histopathology image classification, which achieves competitive performance with the state-of-the-arts.
Heterogeneous Fault Prediction Using Feature Selection and Supervised Learning Algorithms

Software Fault Prediction (SFP) is the most persuasive research area of software engineering. Software Fault Prediction which is carried out within the same software project is known as With-In Fault Prediction. However, local data repositories are not enough to build the model of With-in software Fault prediction. The idea of cross-project fault prediction (CPFP) has been suggested in recent years, which aims to construct a prediction model on one project, and use that model to predict the other project. However, CPFP requires that both the training and testing datasets use the same set of metrics. As a consequence, traditional CPFP approaches are challenging to implement through projects with diverse metric sets. The specific case of CPFP is Heterogeneous Fault Prediction (HFP), which allows the program to predict faults among projects with diverse metrics. The proposed framework aims to achieve an HFP model by implementing Feature Selection on both the source and target datasets to build an efficient prediction model using supervised machine learning techniques. Our approach is applied on two open-source projects, Linux and MySQL, and prediction is evaluated based on Area Under Curve (AUC) performance measure. The key results of the proposed approach are as follows: It significantly gives better results of prediction performance for heterogeneous projects as compared with cross projects. Also, it demonstrates that feature selection with feature mapping has a significant effect on HFP models. Non-parametric statistical analyses, such as the Friedman and Nemenyi Post-hoc Tests, are applied, demonstrating that Logistic Regression performed significantly better than other supervised learning algorithms in HFP models.
Towards Interpretable Dispatching Rules: Application of Expression Simplification Methods

Genetic programming (GP) is a powerful hyper-heuristic method used for evolving dispatching rules (DRs). DRs are commonly used to solve scheduling problems in which scheduling decisions have to be performed in a small amount of time, and are often based on incomplete information. Although GP is the most commonly used method for evolving DRs, it suffers from a serious problem called bloat, which represents the uncontrolled growth of expression trees during evolution. Bloat usually has two important repercussions on the evolved DRs. First, DRs become hard to understand and it is unclear by which strategy they perform scheduling decisions. Secondly, some trees can also include parts that are a result of overfitting on the training set and which reduce their generalization ability. To deal with the problem of bloating DRs, we propose a simplification method consisted of two parts: algebraic reduction and pruning. The simplification method is applied after the normal evolution process with GP is done to reduce the complexity of the evolved DRs. The results demonstrate that it is possible to reduce the number of nodes in an expression tree without significantly deteriorating its performance. This shows that the DRs evolved by GP are bloated and that substantial parts of them are redundant.