Reviewerly/exHarmony huggingface dataset:
Sequences of Recommendations for Dynamic Groups: What Is the Role of Context?

Recommendation algorithms have been investigated and employed by many important companies in the past years: some scenarios, such as the one where a system suggests the points of interest to tourists, well adapt to sequence of recommendations to (groups of) users. We envision that sequence recommendations can be useful whenever the group of users has a limited time interval to spend together, since they reduce the time wasted in selecting the best next activity. In this paper, we investigate the role played by the context, i.e. the situation the group is currently experiencing, in the design of a system that recommends sequences of activities. We model the problem as a multi-objective optimization, where the satisfaction of the group and the available time interval are two of the functions to be optimized. In particular, the dynamic evolution of the group can be considered as the key contextual feature to produce better suggestions.
Real-Time Non-Motor Vehicle Violation Detection in Traffic Scenes

The blockchain technology in the traffic scene is developing rapidly recently. In this area, distributed system based technology like blockchain has been standing out with its decentralization, data-innovation and data transparency. We propose a real-time non-motor vehicle violation detecting approach, which can detect and collect violation information of non-motor vehicle and has been integrated into the distributed smart traffic management system. We propose a luminance balancing in the pre-processing steps to address the problem of brightness change in the background. We use the refined codebook algorithm to detect the motion part, and then propose a violation judging criteria based on feature matching to obtain the violation information. Experiments and application have shown the effectiveness and efficiency of our approach.
Part-Preserving Pose Manipulation for Person Image Synthesis

Manipulating person images under diverse poses, which transfers a person from one pose to another desired pose, is an interesting yet challenging task due to large non-rigid spatial deformation. Most existing works fail to preserve the fine-grained appearance consistency along with the pose changes due to the lack of explicit constraints and spatial modeling, leading to unrealistic results with severe artifacts. In this paper, we propose a novel Part-Preserving Generative Adversarial Network (PP-GAN) to achieve good manipulation quality by explicitly enforcing rich structure constraints over generative modeling. PP-GAN is proposed to decompose the challenging spatial transformation of the whole body into fine-grained part-level transformations, which are then integrated via human joint structure constraint. Given arbitrary poses, PP-GAN integrates human joint structure and region-level part cues as inputs to perform explicit generative modeling. Besides, we introduce a parsing-consistent loss to enforce semantic consistency among images with diverse poses, which guides the image synthesis from a semantic perspective. Extensive qualitative and quantitative evaluations on two benchmarks show that our PP-GAN significantly outperforms the state-of-the-art baselines in generating more realistic and plausible image synthesis results. PP-GAN successfully preserves part-level characteristics even for most challenging pose changes while prior works are easy to fail.
PydMobileNet: Pyramid Depthwise Separable Convolution Networks for Image Classification

Convolutional neural networks (CNNs) have shown remarkable performance in various computer vision tasks in recent years. However, the increasing model size has raised challenges in adopting them in real-time applications as well as mobile and embedded vision applications. Many works try to build networks as small as possible while still have acceptable performance. The state-of-the-art architecture is MobileNets. They use Depthwise Separable Convolution (DWConvolution) in place of standard Convolution to reduce the size of networks. This paper describes an improved version of MobileNet, called Pyramid Mobile Network. Instead of using just a 3 × 3 kernel size for DWConvolution like in MobileNet, the proposed network uses a pyramid kernel size to capture more spatial information. The proposed architecture is evaluated on two highly competitive object recognition benchmark datasets (CIFAR-10, CIFAR-100). The experiments demonstrate that the proposed network achieves better performance compared with MobileNet as well as other state-of-the-art networks. Additionally, it is more flexible in fine-tuning the trade-off between accuracy, latency and model size than MobileNets.
Learning deep features for online person tracking using non-overlapping cameras: A survey

Target-agnostic person tracking and re-identification across multiple non-overlapping cameras is an open vision problem. It is the task of maintaining the correct identity of people at different time instances and possibly different cameras. This study focuses on existing algorithms that facilitate online person tracking by using discriminative spatio-temporal features from video data, and presents the open issues and future research directions. The initial take on the problem introduces person tracking as a pure association problem, where the influence of human appearance, biometric and location information on re-identification are addressed explicitly. These constraints are modeled and used to understand and associate detections in real world environments. Next, a spatio-temporal model using LSTM networks for propagating associations and recovering from errors by taking advantage of the spatial and temporal information in videos is described. The spatio-temporal context indicates a way for discriminative appearance learning. The novelty of the mentioned approaches is that they do not require to learn target-specific appearance models and collect samples to distinguish different people from each other. The methods are evaluated on large-scale tracking datasets. State-of-the-art performance is achieved using motion metadata such as person bounding box and camera number, and shows better associations for the challenging exit-entry cases.
Understanding Similarities and Differences in Software Development Practices Across Domains

Since software engineering is globalized and not a homogeneous whole, we expect that development practices are differently adopted across domains. However, little is known about how practices are followed in different software domains (e.g., healthcare, banking, and Oil and gas). In this paper, we report the results of an exploratory and inductive research, in which we seek differences and similarities regarding the adoption of several widespread practices across 13 domains. We interviewed 19 worldwide developers with experience in multiple domains (i.e., cross-domain developers) from large multinational companies, such as Facebook, Google, and Macy's. We also run a Web survey to confirm (or not) the interview results. Our findings show that, in fact, different domains adopt practices in a different fashion. We identified that continuous integration practices are interrupted during important commerce periods (e.g., Black Friday) in the financial domains. We also noticed the company's culture and policies strongly influence the adopted practices, instead of the domain itself. Our study also has important implications for global software engineering practices. For instance, companies should provide targeted training for their development teams and new interdisciplinary courses in software engineering and other domains, such as healthcare, are highly recommended.
Bidirectional Long Short-Term Memory for Sentiment Analysis of Chinese Product Reviews

Sentiment analysis of online product reviews and other user generated contents is a meaningful research subject for its wide range of applications. Traditional feature-based methods suffer from the limited scope of insufficient features caused by too short length of text. Bidirectional Long Short-Term Memory(Bi-LSTM), a neural network that extracts features of text automated, broadly used in data processing and predictions. Our paper is to explore a way for Bi-LSTM to identify the emotional polarity of product reviews. Our experimental results demonstrate that our proposed method, compared with previously reported model, performs better in precision, recall and F -score evaluation on three reviews data sets respectively.
Learning a Deep Convolutional Network for Subband Image Denoising

Due to the fast inference and excellent learning capability, deep learning has become an effective means for image denoising and attracted considerable attention recently. However, for the images with rich textures and structures, the performance of deep learning approaches is still unsatisfactory. To address this issue, we develop a new convolutional neural network (CNN) for subband image denoising and name it SDCNN. In the proposed approach, we first decompose images into transform domain and denoise the coefficients of various subbands. By incorporating frequency information with spatial context, SDCNN is more effective in recovering image details. In particular, the introduced procedure of subband transform also plays the role of downsampling and enlarges the receptive field without increasing depth or sacrificing efficiency of network. Experimental results show that the SDCNN achieves promising results in terms of both objective and subjective performance.
Introducing the Open Science Chain

Data sharing is an integral component of research and academic publications, allowing for independent verification of results. Researchers have the ability to extend and build upon prior research when they are able to efficiently access, validate, and verify the data referenced in publications. Despite the well known benefits of making research data more open, data withholding rates have remained constant. Some disincentives to sharing research data include lack of credit, and fear of misrepresentation of data in the absence of context and provenance. While there are several research data sharing repositories that focus on making research data available, there are no cyberinfrastructure platforms that enable researchers to efficiently validate the authenticity of datasets, track the provenance, view the lineage of the data and verify ownership information. In this paper, we introduce and provide an overview of the NSF funded Open Science Chain, a cyberinfrastructure platform built using blockchain technologies that securely stores metadata and verification information about research data and tracks changes to that data in an auditable manner in order to address issues related to reproducibility and accountability in scientific research.
Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery

There is a great deal of debate over the importance of explanation in AI models inferred from health data. In particular, there is a balance that needs to be made between the accuracy of complex 'deep' models such as convolutional neural networks and the transparency of models that aim to model data in a more 'human' way such as expert systems. In this paper, we explore the use of temporal association rules to validate and uncover the meaning behind discrete hidden variables that have been inferred from clinical diabetes data. We use a recently published technique based upon the IC* (Induction Causation) algorithm that limits the number of hidden variables and places them within a network structure. Here, we take the hidden variables and compare their underlying discrete states to clusters that have been generated from temporal association rules. This allows us to characterise the hidden states based upon different sequences of complications. Results are very promising, with many hidden states aligning with the discovered clusters giving us a direct interpretation.
Learning Deep Image Priors for Blind Image Denoising

Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realistic domain. In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment. We tackle the domain alignment on two levels. 1) the feature-level prior is to learn domain-invariant features for corrupted images with different level noise; 2) the pixel-level prior is used to push the denoised images to the natural image manifold. The two image priors are based on H-divergence theory and implemented by learning classifiers in adversarial training manners. We evaluate our approach on multiple datasets. The results demonstrate the effectiveness of our approach for robust image denoising on both synthetic and real-world noisy images. Furthermore, we show that the feature-level prior is capable of alleviating the discrepancy between different level noise. It can be used to improve the blind denoising performance in terms of distortion measures (PSNR and SSIM), while pixel-level prior can effectively improve the perceptual quality to ensure the realistic outputs, which is further validated by subjective evaluation.
Fostering Natural Language Question Answering Over Knowledge Bases in Oncology EHR

This paper presents an approach for natural language question answering over a knowledge base generated by a medical texts information extraction process. The primary objective is to present a solution to help practitioners in oncology healthcare clinical environment with an intuitive method to access stored data. We identify health professional's needs in terms of information and interface with EHR systems. After that, we demonstrate a proposal to allow the integration of information extraction from clinical notes, knowledge base generation, and natural language question answering. The primary contributions are the identification of a solution to health professionals needs regarding usability in information access, and the demonstration of advantages obtained in representing health contents in a knowledge base.
MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a navïe extension form in the presence of multiple servers as well.
Quality Recovery for Image Recognition

This paper proposes a quality recovery network (QRNet) that recovers the image quality from distorted images and improves the classification accuracy for image classification using these recovered images as the classifier inputs, which are optimized for image quality loss and classification loss. In certain image classification tasks, classifiers based on deep neural networks achieve higher performance compared to those realized by humans. However, these tasks are based on images that are not distorted. To address distorted images, the classifier is fine-tuned with distorted images for practical applications. However, fine-tuning is insufficient for classifying images that include multiple distortion types with severe distortions and often requires the classifier to be retrained for adapting to distorted images, which is a time-consuming process. Therefore, we propose QRNet that generates recovered images for input to the classifier. To address multiple severe distortions, the proposed network is trained using multiple distortion-type images with our proposed loss, which comprises the image quality and classification losses. Moreover, by training the proposed network with multiple classifiers, the recovered images can be easily classified by a new classifier that is not used for training. The new classifier can classify the recovered images without retraining for adapting to distorted images. We evaluate our proposed network with classifiers on public datasets and demonstrate that it improves the classification accuracy for distorted images. Moreover, the experimental results demonstrate that our proposed network with the new classifier improves the classification accuracy.
Motif Discovery via Convolutional Networks with K-mer Embedding

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery.
Toward an Adaptive Architecture for Integrating Mobile Apps with LMS using Next Generation of SCORM

The evolution of mobile learning has changed the way of learning. With the growing popularity of using mobile devices, mobile learning has become important in teaching and learning in education. Most m-learning services are provided by Learning Management Systems (LMS). This article aims at designing a software architecture that focuses in particular on the integration of SCORM's next-generation specifications in order to adapt the features and mechanisms of the learning management system and its extension to the mobile scenario. the architecture has brought several interactivities that are not feasible with the specifications of the next generation SCORM. This architecture is implemented for the famous open source LMS Moodle. The document is intended for developers and stakeholders of Mobile Learning, as well as educators and technologists addressing standards and interoperability specifications learning in general.
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this chapter, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking. We show that this can be done either by sample-based or feature-based partitioning. However, we prove that when privacy-budgets are the same, feature-based partitioning requires fewer samples than sample-based one, and thus likely has better empirical performance. As transfer learning is difficult to be integrated with a differential privacy guarantee, we further combine the proposed method with hypothesis transfer learning to address the problem of learning across different organizations. Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i.e., MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of a significant concern.
Biologically Driven Artificial Intelligence

Artificial Intelligence (AI) based on the computational principles of the brain can overcome current shortcomings and lead to human-level AI.
Cross-project defect prediction using data sampling for class imbalance learning: an empirical study

The presence of defect data related to different projects leads to cross-project defect prediction an open issue in the field of research in software engineering. In cross-project defect prediction, the source and the target projects are different. The prediction model is trained by using the data sources of the different projects and then it is tested on the target data source. The data source from the varying projects leads to a highly imbalanced source dataset. The performance of the predictive model degrades due to this imbalance nature of the dataset. This is termed as the class imbalance problem in machine learning. This paper conducts an empirical analysis in a bi-fold manner. It evaluates whether data sampling techniques can handle the class imbalance problem and improve the performance of the predictive model for cross-project defect prediction (CPDP). Secondly, it also evaluates whether the results of CPDP after data sampling are comparable to within project defect prediction (WPDP). Ensemble learning classifiers are used as the predictive model over 12 publically available object-oriented project datasets. The experimental results infer that SMOTE oversampling can be applied to overcome the problem of class imbalance on CPDP. It also gives comparable results to WPDP with statistical significance.
A Model-Based Framework for Analyzing the Security of System Architectures

We introduce a compositional, model-based framework for modeling, visualizing and analyzing the security of system architectures. This work extends the framework we developed previously for analyzing safety [1]. With this extension, our framework can be used to analyze both safety and security, however this paper focuses on security. The major contribution of this paper is setting the terminology and methodology for building a tree for analyzing the security of a system. Defining precisely the qualitative and quantitative aspects of the tree is very important-just as fault trees are rooted in the theory of probability, we want our tree to be built on solid mathematical foundation. Based on [2] and [3], attack-defense tree is a better representation of a system over attack trees because the latter only captures attack scenarios and does not model the interaction between attacks and the defenses that could be put in place to guard against the attacks. More importantly, security of a system is constantly evolving-as better control measures are put in place, more sophisticated attacks are implemented. Therefore, modeling only attacks without considering the defenses in place is very limiting. Guided by some of the formalisms introduced in [2] [3], we extended their concepts to include guidelines and considerations from DO-326A and DO-356A so that the terminology used in the tree is relevant to the aviation industry. We reference measure theory and order theory to define functions for the quantitative aspects of the tree. We also made sure that the measures were consistent with the intuition of a security design engineer. Finally, we give an example of the modeling language and the attack-defense tree that is automatically generated.