Reviewerly/exHarmony huggingface dataset:
swFLOW: A Dataflow Deep Learning Framework on Sunway TaihuLight Supercomputer

Deep learning technology is widely used in many modern fields and a number of deep learning models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems with specialized architectures such as Sunway TaihuLight. In this paper, we propose swFLOW: a TensorFlow-based dataflow deep learning framework on Sunway TaihuLight. Based on the performance analysis results on convolutional neural network (CNN), we optimize the convolution layer, reduce the data layout transpose operation and get 10.42x speedup compared to single management processing element (MPE) version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication and use data prefetch to avoid data fetch being a performance bottleneck. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8. Limited by the maximal executable batch size, the current performance of swFLOW is far from optimal. It is very necessary to further optimize using technology like remote direct memory access (RDMA) and model parallelism.
Effective and efficient indexing in cross-modal hashing-based datasets

To overcome the barrier of storage and computation, the hashing technique has been widely used for nearest neighbor search in multimedia retrieval applications recently. Particularly, cross-modal retrieval that searches across different modalities becomes an active but challenging problem. Although numerous of cross-modal hashing algorithms are proposed to yield compact binary codes, exhaustive search is impractical for large-scale datasets, and Hamming distance computation suffers inaccurate results. In this paper, we propose a novel search method that utilizes a probability-based index scheme over binary hash codes in cross-modal retrieval. The proposed indexing scheme employs a few binary bits from the hash code as the index code. We construct an inverted index table based on the index codes, and train a neural network for ranking and indexing to improve the retrieval accuracy. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated and compared with several state-of-the-art cross-modal hashing methods. Results show the proposed method effectively boosts the performance on search accuracy, computation cost, and memory consumption in these datasets and hashing methods. The source code is available on https://github.com/msarawut/HCI.
A Hybrid Incremental Regression Neural Network for Uncertain Data Streams

The design of classical regression algorithms was based on the assumption that all the required data is obtained at one time. With the emergence of big data, however, data is increasingly displayed in sequence form, such as in data streams, and can be read only once in a specific order. Many incremental regression algorithms which process data in a sequential manner have been proposed, but the accuracy of these algorithms deteriorates when the value of the data is uncertain. This paper proposes a hybrid incremental regression neural network based on self-organizing incremental neural network and incremental fuzzy support vector regression. In our proposed network, the neurons of the regression neural network are obtained by an improved self-organized incremental neural network (SOINN). This enables the regression neural network structure to self-organize as the number of neurons increases. An incremental fuzzy support vector regression (IFSVR) algorithm is then used to modify the parameters of the regression neural network. By combining the improved SOINN and IFSVR algorithms, our proposed hybrid incremental regression neural network is able to learn an accurate regression model from large uncertain data. Experiments on both artificial and real-world datasets indicate that our proposed hybrid incremental regression neural network achieves superior performance compared to other incremental regression algorithms.
EdgeBox: Confidential Ad-Hoc Personalization of Nearby IoT Applications

Steady progress in ubiquitous technologies and machine learning (ML) facilitates ever-new and better personalized IoT applications (e.g., digital assistants). However, current architectural concepts for personalization are either impractical or cannot support both sides without leaking sensitive data, namely the user's personal data or the provider's protected ML models. In this paper, we propose a novel approach termed EdgeBox for ad hoc personalizing nearby IoT applications while allowing both, (1) keeping the providers' ML models confidential and (2) privacy-preserving processing of personal user data, even on untrusted IoT devices. In short, confidential parts of the EdgeBox (e.g., the personalization task) are performed in an isolated trusted execution environment (TEE) within the IoT device's processor; the remote attested code in the TEE allows to confidentially load latest provider models and stream the required user data over wireless direct link ad-hoc protocol on demand. We evaluate our implemented proof-of-concept prototype (ad-hoc-initialized vs. pre-initialized) w.r.t. setup time and performance overhead. The results show real-time capabilities with an acceptable overhead, enabling a novel and confidential way to ad hoc personalize a user's dynamic environment in everyday life, supporting several assistant use cases.
A Response to Núñez et al.'s (2019) “What Happened to Cognitive Science?”

Abstract Núñez et al.'s (2019) negative assessment of the field of cognitive science derives from evaluation criteria that fail to reflect the true nature of the field. In reality, the field is thriving on both the research and educational fronts, and it shows great promise for the future.
Deeply-Learned Spatial Alignment for Person Re-Identification

A large class of Person Re-identification (ReID) approaches identify pedestrians with the TriHard loss. Though the TriHard loss is a robust ReID method, pose variance and viewpoint in pedestrians constrain the performance. To address this problem, we introduce a spatial transformer network (STN) to align pedestrians. Then, we illustrate the generality of the STN module in pose variance problem through the evaluations on feature representation network (FRN) like VGG, ResNet and DenseNet architectures respectively. Furthermore, based on the evaluation results, we propose a robust and high-performance ReID model which consists of the STN module, DenseNet backbone and TriHard loss. And finally, we prove that our ReID model is whole differentiable by formula derivation, therefore achieving an end-to-end high-performance ReID system. The experiments show that our ReID system outperforms the state-of-art methods on Market-1501, DukeMTMC-reID and CUHK03 datasets.
Highly Efficient Breadth-First Search on CPU-Based Single-Node System

With the rapid development of computer technology, the scale of graph increases explosively and large-scale graph computing has been the focus in recent years. Breadth-First Search (BFS) is one of the most important kernels in graph computing. It is the main kernel of the Graph500 benchmark that evaluates the performance of supercomputers and servers in terms of data-intensive applications. In this paper, we implement a highly efficient parallel BFS on CPU-based single-node systems. We propose two algorithm optimization techniques: round-robin vertex shuffle and jumping access. The former can improve the load balance for parallel implementation, while the latter can reduce unnecessary data accesses in the bottom-up algorithm. Our new implementation demonstrates strong scalability and achieves 65.2 GTEPS for Kronecker graph with 2^30 vertices and 2^34 edges on a 4-way Xeon server. The speedup is nearly 12.8 times faster than the original direction-optimizing algorithm. In terms of energy efficiency, the result on our self-designed ARM server is 254.6 MTEPS/W and is superior to conventional x86 server. To our best knowledge, this is the first work that evaluates BFS performance on ARM server and ARM server is suitable for data-intensive applications such as large-scale graph processing.
A comparative study of WhatsApp forensics tools

With the increasing number of mobile phones and mobile applications, there is a noticeable rise in cybercrimes. Hence, an urgent need for mobile forensics. Before starting investigation, the investigator should choose one of the acquisition types; physical acquisition, logical acquisition or manual acquisition. The current mobile acquisition tools use these methods to produce an image of the entire mobile content, files of specific datatypes, or data of a certain application. Unfortunately, the resultant output does not facilitate investigating cases related to specific mobile application, since the tool might acquire more than what is needed which requires investigators to filter data manually, or acquire all the application’s data without sufficient analysis. Both cases are effort and time consuming. This study analyzes and compares currently available forensics tools that are designed to extract WhatsApp data only. The comparative study is based on two aspects; National Institute of Standards and Technology (NIST) Mobile Device Tool Test Assertions and researchers’ requirements. The results of the comparative study showed a shortage in the current WhatsApp forensics tools as they do not satisfy all NIST Test Assertions. Additionally, several researchers’ requirements such as: creating projects, comprehensive analysis, applying filters and validating the extracted files, were not met in the studied tools.
FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition

Named entity recognition (NER) is a foundational technology for information extraction. This paper presents a flexible NER framework ( https://github.com/bke-casia/FLEXNER ) compatible with different languages and domains. Inspired by the idea of distant supervision (DS), this paper enhances the representation by increasing the entity-context diversity without relying on external resources. We choose different layer stacks and sub-network combinations to construct the bilateral networks. This strategy can generally improve model performance on different datasets. We conduct experiments on five languages, such as English, German, Spanish, Dutch and Chinese, and biomedical fields, such as identifying the chemicals and gene/protein terms from scientific works. Experimental results demonstrate the good performance of this framework.
Architecting IoT based Healthcare Systems Using Machine Learning Algorithms

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.
Assessing the Security Risks of Multicloud SaaS Applications: A Real-World Case Study

Cloud computing is widely believed to be the future of computing. It has grown from being a promising idea to one of the fastest research and development paradigms of the computing industry. However, security and privacy concerns represent a significant hindrance to the widespread adoption of cloud computing services. Likewise, the attributes of the cloud such as multi-tenancy, dynamic supply chain, limited visibility of security controls and system complexity, have exacerbated the challenge of assessing cloud risks. In this paper, we conduct a real-world case study to validate the use of a supply chaininclusive risk assessment model in assessing the risks of a multicloud SaaS application. Using the components of the Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, we show how the model enables cloud service providers (CSPs) to identify critical suppliers, map their supply chain, identify weak security spots within the chain, and analyse the risk of the SaaS application, while also presenting the value of the risk in monetary terms. A key novelty of the CSCCRA model is that it caters for the complexities involved in the delivery of SaaS applications and adapts to the dynamic nature of the cloud, enabling CSPs to conduct risk assessments at a higher frequency, in response to a change in the supply chain.
Graph Filtering Approach to PET Image Denoising

Positron emission tomography (PET) is an important biomedical imaging modality for disease, eg cancer, diagnosis. Its accuracy is often limited by high level of noise relative to signal due to constraints on injected dose and signal acquisition time. To denoise PET images, various denoising methods, such as nonlocal means (NLM) and block-matching and 3D (BM3D) filtering, have been used. Because PET images have high contrast and low spatial resolution, these denoising methods do not reduce the noise effectively while preserving the image edges. This paper proposes a novel approach to PET image denoising based on graph filtering. Since the graph spectrum distributions of image signals and noise in PET image are different, the proposed approach reduces the noise in PET image by graph spectral filtering. Simulated, preclinical and clinical datasets are used to evaluate the performance of the proposed approach. Compared with NLM and BM3D filtering, the proposed approach improves the peak signal-to-noise ratio and structural similarity index for the simulated dataset, and subjectively reduces the noise while preserving the image edges and fine details for the preclinical and clinical datasets.
Integrating Dual User Network Embedding with Matrix Factorization for Social Recommender Systems

To address the data sparsity problem faced by recommender systems, social network among users is often utilized to complement rating data for improving the recommendation performance. One of current trends is to combine the idea of matrix factorization (MF) for predicting ratings with the idea of graph embedding (GE) for analyzing social network towards recommendation tasks. Despite enjoying many advantages, the existing integrated models have two critical limitations. First, such models are designed to work with either explicit or implicit social network, but little is known in taking both into account. Second, the users’ embeddings learned by GE are fed to the downstream MF, but not reverse, which is sub-optimal because rating information is not considered for learning the users’ embeddings. In this paper, we propose a novel social recommendation algorithm which exploits both explicit and implicit social networks towards the task of rating prediction. In Particular, we seamlessly integrate MF model and GE model within a unified optimization framework, in which MF and GE tasks can be reinforced each other during the learning process. Our encouraging experimental results on three real-world benchmarks validate the superiority of the proposed approach to state-of-the-art social recommendation methods.
Explainable Classifier Supporting Decision-making for Breast Cancer Diagnosis from Histopathological Images

This paper presents an application of semantically explainable classifier, Cumulative Fuzzy Class Membership Criterion (CFCMC), in medical domain, specifically for breast cancer detection from histopathological images. This classifier, in contrast with commonly used classifiers for image classification, is able to provide additional information about its classification results in human-friendly form. In this paper, we proposed a means for presenting the additional semantical informations that is potentionally useful for decision-making in medical domain. First, the classifier provides semantic explanation, regarding the possibility of misclassification of the test sample. Alongside with semantics, it provides visualization of similar and non-similar samples of different class. The classification performance of CFCMC is compared against three commonly used classifiers for image classification, Convolutional Neural Network (CNN), Stacked Auto-encoder (SAE) and Deep Multi-layered Perceptron. The experimental result shows that the CFCMC is not necessarily the best classifier. However, the ability to provide semantic and visual explanation of classification result allows the classifier to be applied as a supporting tool for pathologists in diagnostic of breast cancer.
Neural Networks Merging Semantic and Non-semantic Features for Opinion Spam Detection

In recent years, abundant online reviews on products and services have been generated by individuals. Since customers may refer to relevant online reviews when shopping, the existence of fake reviews can affect potential consumption. Opinion spam detection has attracted widespread attention from both the business and research communities. In this paper, a neural network model combining the semantic and non-semantic features based on the detailed feature exploration is established to detect opinion spams. First, the model learns discourse feature representation with hierarchical attention neural networks which can capture local and global semantic information. And then we synthesis the non-semantic features with multi-kernel convolution neural networks. Finally, the last state vectors of the two-feature learning networks are concatenated and taken as input to the softmax layer for classification. Experiments show that the proposed model is very effective and we get 0.853 AUC which outperforms the baseline methods. Besides, the experiment results on an additional dataset also indicate robustness of this identification model.
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results.
Neural Response Generation with Relevant Emotions for Short Text Conversation

Human conversations are often embedded with emotions. To simulate human conversations, the response generated by a chatbot not only has to be topically relevant to the post, but should also carry an appropriate emotion. In this paper, we conduct analysis based on social media data to investigate how emotions influence conversation generation. Based on observation, we propose methods to determine the appropriate emotions to be included in a response and to generate responses with the emotions. The encoder-decoder architecture is extended to incorporate emotions. We propose two implementations which train the two steps separately or jointly. An empirical study on a public dataset from STC at NTCIR-12 shows that our models outperform both a retrieval-based method and a generation model without emotion, indicating the importance of emotions in short text conversation generation and the effectiveness of our approach.
Contextual Relabelling of Detected Objects

Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes. It also can play an important role in improving object detection. In this work, we present two contextual models (rescoring and re-labeling models) that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the state-of-the-art RCNN-based object detection (Faster RCNN). We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO).
3D Room Reconstruction from A Single Fisheye Image

We propose a rapid and accurate approach to recover the layout of a room automatically from a single fisheye image. It decomposes the fisheye image to a set of perspective images and jointly extract line images from the fisheye image and perspective images for geometric information. The semantic information gained from semantic segmentation on a cylinder expansion of the fisheye image are then used for structure line determination. By considering distinct features contained in the perspective images, the invalid hypotheses are filtered effectively and the most accurate structure lines are selected to minimize computational cost. To evaluate the effectiveness of the proposed approach, we construct an annotated fisheye image dataset. Comprehensive experimental evaluation on the dataset illustrate that our proposed approach produces higher quality layout estimations than existing layout reconstruction approaches and being 6 times faster in the reconstruction time.
A random initialization deep neural network for discriminating malignant breast cancer lesions

Breast cancer is one of the most common cancer in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. Recent studies show that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long-term. While the consequences of a false positive diagnosis can be psychologically and socioeconomically burdensome, the result of a false negative diagnosis can be devastating, especially in terms of health detriment. In this context, the false positive and false negative rates commonly achieved by radiologists are extremely arduous to estimate and control, and some authors have estimated figures of up to 20% of total diagnoses or more. Novel ideas in computer-assisted diagnosis have been prompted by the introduction of deep learning techniques in general and of convolutional neural networks (CNN) in particular. In this paper, we design and validate an ad-hoc CNN architecture specialized in breast lesion classification and heuristically explore possible parameter combinations and architecture styles in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve good classification performance on the validation and test set, demonstrating how an ad-hoc, random initialization CNN architecture can provide practical aid in the classification and staging of breast cancer.