Reviewerly/exHarmony huggingface dataset:
Efficient Transfer Learning Combined Skip-Connected Structure for Masked Face Poses Classification

Aiming at the new requirements of masked face poses classification during the epidemic outbreak, this paper proposes an efficient transfer learning approach combined skip-connected structure to improve the accuracy of masked face poses classification in the absence of masked face poses data.We have worked on the following two aspects: 1) According to the features transition of the convolutional neural networks, we propose an efficient transfer learning approach and opt for a more appropriate source domain to solve the problem that the specificity of features in the pre-trained deep networks will damage the performance when transferring to the target domain.First, a semisynthetic masked face poses dataset is constructed to replace ImageNet as the source domain, which can reduce the span of transfer and improve the pertinence of transfer learning.Second, the shallow networks which contain the general features are frozen while the deep networks which contain the specific features are retrained and the entire networks are finetuned afterwards.It optimizes the specific features in the source domain when transferring, and promoted transfer learning more effectively; 2) To further improve the overall accuracy by improving the accuracy of masked face pose classes with subtle differences, a skip-connected structure is proposed to fuse general features containing rich detailed information in the shallow networks into the classifier.Experiments on AlexNet and VGG16 show that the proposed method has certain advantages, and the overall accuracy can reach 96.43% and 99.29% at the final respectively.
Linear Representation-Based Methods for Image Classification: A Survey

In recent years, linear representation-based methods have been widely researched and applied in the image classification field. Generally speaking, there are three steps within linear representation-based classification (LRC) algorithms. The first step is coding, which uses all training samples to represent the test sample in a linear combination. The second step is subspace approximation, where residuals between the test sample and the linear combination of each class are calculated. The third step is classification, which assigns the class label to the minimum class-specific residual. We classify the LRC methods into six categories: 1) linear representation-based classification methods with norm minimizations, 2) linear representation-based classification methods with constraints, 3) linear representation-based classification methods with feature spaces, 4) linear representation-based classification methods with structural information, 5) linear representation with subspace learning, and 6) linear representation in semi-supervised learning and unsupervised learning. The purpose of this paper is to: 1) make an accurate and clear definition of the linear representation-based method, 2) provide a categorization and a comprehensive survey of the existing linear representation-based classification methods for image classification, 3) Summarize the main applications of linear representation-based methods, 4) provide extensive classification results and a discussion of the linear representation-based methods. Furthermore, this paper summarizes specific applications of the linear representation-based methods. Particularly, we performed extensive experiments to compare thirteen linear representation-based classification methods on seven image classification datasets.
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings.
Experiences Using Three App Prototyping Tools with Different Levels of Fidelity from a Product Design Student’s Perspective

Prototyping has become a widely embraced technique in different design fields to facilitate early user involvement to ensure that the end-product meets the users’ needs. Each design field has its tools and traditions for working with prototypes. This paper documents experiences with smartphone app prototyping from a product design student’s perspective. Three prototyping tools with different fidelity levels were explored. Based on these experiences we reflect upon the prototyping tool characteristics and their suitability for non-computer scientist. We envisage that our experiences may be useful for other product designers who want to develop smartphone apps.
An Information Systems Design Theory for Service Network Effects

Service platforms make software applications available as a service to end users. Platforms enable noticeable economic benefits for scaling and transforming a business. Their long-term competitiveness is ensured in controlled cooperation with channel intermediaries and network partners. Hence, service platforms must be designed to harness self-enforcing effects of value generation, so-called network effects. In an exaptation of existing knowledge, we present an information systems design theory to inform the design of methods that analyze, describe, and guide the design of service platforms through the means of causal loops and control methods. We describe the theory’s purpose and scope as well as the underlying justificatory knowledge behind the constructs and principles of form and function. The design theory covers the design of all service platform participants and activities as well as their transactions and influences in areas of staged platform authority, using enforcing and incentivizing control methods. We demonstrate the principles of implementation with an expository instantiation and apply it to the M-Engineering service platform, which offers surveillance, control, and data acquisition solutions. Furthermore, we present and discuss testable propositions and a study design to evaluate our design principles.
An Improved K-Means Algorithm Based on Fuzzy Metrics

The traditional K-means algorithm has been widely used in cluster analysis.However, the algorithm only involves the distance factor as the only constraint, so there is a problem of sensitivity to special data points.To address this problem, in the process of K-means clustering, ambiguity is introduced as a new constraint condition.Hence, a new membership Equation is proposed on this basis, and a method for solving the initial cluster center points is given, so as to reduce risks caused by random selection of initial points.Besides, an optimized clustering algorithm with Gaussian distribution is derived with the utilization of fuzzy entropy as the cost function constraint.Compared with the traditional clustering method, the new Equationâ Ȃ Źs membership degree can reflect the relationship between a certain point and the set in a clearer way, and solve the problem of the traditional K-means algorithm that it is prone to be trapped in local convergence and easily influenced by noise.Experimental verification proves that the new method has fewer iterations and the clustering accuracy is better than other methods, thus having a better clustering effect.
BEHRT-HF: an interpretable transformer-based, deep learning model for prediction of incident heart failure

Abstract Background/Introduction Predicting incident heart failure has been challenging. Deep learning models when applied to rich electronic health records (EHR) offer some theoretical advantages. However, empirical evidence for their superior performance is limited and they remain commonly uninterpretable, hampering their wider use in medical practice. Purpose We developed a deep learning framework for more accurate and yet interpretable prediction of incident heart failure. Methods We used longitudinally linked EHR from practices across England, involving 100,071 patients, 13% of whom had been diagnosed with incident heart failure during follow-up. We investigated the predictive performance of a novel transformer deep learning model, “Transformer for Heart Failure” (BEHRT-HF), and validated it using both an external held-out dataset and an internal five-fold cross-validation mechanism using area under receiver operating characteristic (AUROC) and area under the precision recall curve (AUPRC). Predictor groups included all outpatient and inpatient diagnoses within their temporal context, medications, age, and calendar year for each encounter. By treating diagnoses as anchors, we alternatively removed different modalities (ablation study) to understand the importance of individual modalities to the performance of incident heart failure prediction. Using perturbation-based techniques, we investigated the importance of associations between selected predictors and heart failure to improve model interpretability. Results BEHRT-HF achieved high accuracy with AUROC 0.932 and AUPRC 0.695 for external validation, and AUROC 0.933 (95% CI: 0.928, 0.938) and AUPRC 0.700 (95% CI: 0.682, 0.718) for internal validation. Compared to the state-of-the-art recurrent deep learning model, RETAIN-EX, BEHRT-HF outperformed it by 0.079 and 0.030 in terms of AUPRC and AUROC. Ablation study showed that medications were strong predictors, and calendar year was more important than age. Utilising perturbation, we identified and ranked the intensity of associations between diagnoses and heart failure. For instance, the method showed that established risk factors including myocardial infarction, atrial fibrillation and flutter, and hypertension all strongly associated with the heart failure prediction. Additionally, when population was stratified into different age groups, incident occurrence of a given disease had generally a higher contribution to heart failure prediction in younger ages than when diagnosed later in life. Conclusions Our state-of-the-art deep learning framework outperforms the predictive performance of existing models whilst enabling a data-driven way of exploring the relative contribution of a range of risk factors in the context of other temporal information. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): National Institute for Health Research, Oxford Martin School, Oxford Biomedical Research Centre
Cross-Lingual Passage Re-Ranking With Alignment Augmented Multilingual BERT

The task of Cross-lingual Passage Re-ranking (XPR) aims to rank a list of candidate passages in multiple languages given a query, which is generally challenged by two main issues: (1) the query and passages to be ranked are often in different languages, which requires strong cross-lingual alignment, and (2) the lack of annotated data for model training and evaluation.In this article, we propose a two-stage approach to address these issues.At the first stage, we introduce the task of Cross-lingual Paraphrase Identification (XPI) as an extra pre-training to augment the alignment by leveraging a large unsupervised parallel corpus.This task aims to identify whether two sentences, which may be from different languages, have the same meaning.At the second stage, we introduce and compare three effective strategies for cross-lingual training.To verify the effectiveness of our method, we construct an XPR dataset by assembling and modifying two monolingual datasets.Experimental results show that our augmented pre-training contributes significantly to the XPR task.Besides, we directly transfer the trained model to test on out-domain data which are constructed by modifying three multi-lingual Question Answering (QA) datasets.The results demonstrate the cross-domain robustness of the proposed approach.
Large-Scale Outlier Detection for Low-Cost PM₁₀ Sensors

Evaluating the air quality of classrooms is important as children spend a large amount of time at school. Massey University (NZ) led the development of a low-cost and affordable Indoor Air Quality (IAQ) platform called SKOMOBO that was deployed on a large scale across the classrooms of primary schools in New Zealand. When the data from SKOMBO units were collected, it was important to detect any unexpected high air pollution events. To address this concern, we propose a study of outlier detection for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> dataset from SKOMOBO units using MSD-Kmeans. MSD-Kmeans combines the statistical method of Mean and Standard Deviation (MSD) with the machine learning clustering algorithm K-means where the former eliminates as many noisy data to minimize the inference on clustering while the latter is able to achieve better local optimal clustering. We compare the performance of MSD-Kmeans with other similar outlier detection algorithms. Our experimental results illustrate that MSD-Kmeans outperforms the majority of performance indicators ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> TPR, FPR, Accuracy, F-measures) compared to other similar methods. We conclude that it is feasible to use MSD-Kmeans as an effective outlier detection tool on large scale datasets.
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by a greedy strategy, which may lose the local patch details outside the chosen candidate. In this paper, we propose a novel spatiotemporal graph neural network (STG-Net) to reconstruct more accurate masks for video object segmentation, which captures the local contexts by utilizing all proposals. In the spatial graph, we treat object proposals of a frame as nodes and represent their correlations with an edge weight strategy for mask context aggregation. To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph. The joint use of both local patch details and temporal relationships allow us to better address the challenges such as object occlusions and missing. Without online learning and fine-tuning, our STG-Net achieves state-of-the-art performance on four large benchmarks, demonstrating the effectiveness of the proposed approach.
Block-based image matching for image retrieval

Due to the lighting, translation, scaling and rotation, image matching is a challenge task in computer vision area. In the past decades, local descriptors (e.g. SIFT, SURF and HOG, etc.) and global features (e.g. HSV, CNN, etc.) play a vital role for this task. However, most image matching methods are based on the whole image, i.e., matching the entire image directly base on some image representation methods (e.g. BoW, VLAD and deep learning, etc.). In most situations, this idea is simple and effective, but we recognize that a robust image matching can be realized based on sub-images. Thus, a block-based image matching algorithm is proposed in this paper. First, a new local composite descriptor is proposed, which combines the advantages of local gradient and color features with spatial information. Then, VLAD method is used to encode the proposed composite descriptors in one block, and block-CNN feature is extracted at the same time. Second, a block-based similarity metric is proposed for similarity calculation of two images. Finally, the proposed methods are verified on several benchmark datasets. Compared with other methods, experimental results show that our method achieves better performance.
Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems

Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.
Combining Statistical Features and Local Pattern Features for Texture Image Retrieval

The complementary fusion of global and local features can effectively improve the performance of image retrieval. This article proposes a new local texture descriptor, combined with statistical modeling in transform domain for texture image retrieval. The proposed local descriptor calculates the eight directions of the central pixel by using the relationship between the central pixel and the neighboring pixels in six directions, which is called the local eight direction pattern (LEDP). In the texture image retrieval system of this article, the feature extraction part combines global statistical features and local pattern features. Among them, both the relative magnitude (RM) sub-band coefficients and relative phase (RP) sub-band coefficients are modeled as wrapped Cauchy (WC) distribution in the dual-tree complex wavelet transform (DTCWT) domain, and the global statistical features employ the parameters of this model; while the local pattern features respectively choose the local binary pattern (LBP) histogram features in the spatial domain and the LEDP histogram features of each direction sub-band in the DTCWT domain. On the other hand, the similarity measurement selects matching distances for different features and combines them in the form of convex linear optimization. Texture image retrieval experiments are conducted in the Corel-1k database (DB1), Brodatz texture database (DB2) and MIT VisTex texture database (DB3), respectively. Experimental results show that, compared with the best existing methods, the approach proposed in this article has achieved better retrieval performance.
Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.
Development of a speed protector to optimize user experience in 3D virtual environments

Virtual walking in virtual environments (VEs) requires locomotion interfaces, especially when the available physical environment is smaller than the virtual space due to virtual reality facilities limitations; many navigation approaches have been proposed according to different input conditions, target selection and speed selection. With current technologies, the virtual locomotion speed for most VR systems relies primarily on rate-control devices (e.g., joystick). The user has to manage manual adaptation of the speed, based on the size of the VE and personal preferences. However, this method cannot provide optimal speeds for locomotion as the user tends to change the speed involuntarily due to non-desired issues including collisions or simulator sickness; in this case, the user may have to adjust the speed frequently and unsmoothly, worsening the situation. Therefore, we designed a motion protector that can be embedded into the locomotion system to provide optimal speed profiles. The optimization process aims at minimizing the total jerk when the user translates from an initial position to a target, which is a common rule of the human motion model. In addition to minimization, we put constraints on speed, acceleration and jerk so that they do not exceed specific thresholds. The speed protector is formulated mathematically and solved analytically in order to provide a smooth navigation experience with a minimum jerk of trajectory. The assessment of the speed protector was conducted in a user study measuring user experience with a simulator sickness questionnaire, event-related skin conductance responses (ER-SCR), and a NASA-TLX questionnaire, showing that the designed speed protector can provide more natural and comfortable user experience with appropriate acceleration and jerk as it avoids abrupt speed profiles.