Reviewerly/exHarmony huggingface dataset:
Decision making support for privacy data upload in smart home

A smart home equipped with various smart devices allows a service provider to automatically identify daily living activities from sensor/appliance data, but it is risky for dwellers to upload all the data generated in the home. In this paper, we define a threat model in which an attacker(s) can access all or part of the smart home data uploaded to the untrusted cloud server and can physically observe activities. Hence, the attacker can identify the association between the data and the home by matching the uploaded data and the observed data. The proposed method employs k-anonymity for dwellers to make a decision on whether the data should be uploaded or not. We computed values of k from the existing datasets and asked 18 participants to answer upload/no-upload for each pair of activities and time zone. As a result, our k-anonymity based method can reflect the dweller's sensitivity of privacy in uploading the data.
Person Re-Identification Based on Multi-Parts of Local Feature Network

Person re-identification has become increasing popular because of its widely application in computer vision. In this paper, we propose a novel, simple and efficient person re-id network called MPLFN. The network combines two tasks: the classification task and the metric learning task. In the classification task, we uniformly partition N feature parts from an image, and compute the person classification loss in each part separately. Computing the part loss separately guides the network to focus on every body part and learn discriminative representations for each of them. And then in the metric learning task, we recalculate the distance of two images by the shortest path between two sets of feature parts. Then the distances are put into a triplet loss to perform a dynamic part alignment during the training. With the joint learning of these two tasks, the performance of the network is significantly enhanced. Compared with existing person re-id works, MPLFN achieves a better performance on three mainstream person re-identification datasets. Extensive experiments have been conducted to validate our proposed method.
Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters

\beginabstract Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. % Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. % Processing a large volume of the pathology reports in a timely manner is a continuing challenge for cancer registries. % The purpose of this study is to develop an information extraction pipeline to reliably and efficiently extract reportable information.\\ Method: % We have developed a novel inverse-regression (IR) based information extraction pipeline. % The inverse-regression based supervised filter has been successfully applied to many application domains. % However, its application to the information extraction from unstructured text is hindered primarily by the extreme high-dimensionality of n-gram representations of text. % In this study, we attempt to overcome the obstacles by a novel bootstrapping strategy. % First, we use an information-theoretic mutual information based filter to discard the excessive and redundant n-gram features. % This step reduces the size and improves the condition number of the sample covariance matrix, thus reducing the computational cost and improving the numerical stability of the subsequent inverse-regression step. % Then we use localized sliced inverse-regression (LSIR) to learn a low-dimensional discriminatory subspace for information inference. % In particular, we use the k-nearest neighbors of an unlabeled pathology report in the learned representation to infer the desired information from the labeled data in a supervised manner. % \\ % Results: The experiments were conducted on a set of de-identified pathology reports with human expert labels as the ground truth. % Our pipeline consistently performed better than or comparable to the best performing state-of-the-art methods while reducing the training and inference times substantially.\\ Conclusion: Our results demonstrate the potential of \emergencystretch 3em inverse-regression based information extraction pipeline for reliable and efficient information extraction from unstructured text. % The information extracted from the pathology reports can be used along with clinical information, medical imaging, and genomic information to instigate discoveries in cancer research. % \endabstract
Level-Set Formulation Based on an Infinite Series of Sample Moments for SAR Image Segmentation

SAR image segmentation plays a central role in geoscience and remote sensing of the environment. Recently, methodologies that apply traditional segmentation algorithms to maps of statistical information extracted from SAR image rather than to the raw data itself have shown promising results. Nonetheless, the application of more powerful segmentation methods to these maps is constrained by the lack of adequate statistical models for such data. In this letter, we present a level-set-based algorithm that embodies much of the data statistics without assuming any prior model for it. We also evaluated its performance on both real and synthetic SAR images.
Consistent Blind Image Deblurring Using Jump-Preserving Extrapolation

Blind image deblurring (BID) aims to estimate the true image from blurred observations when the blurring mechanism described by a point spread function (PSF) cannot be completely specified beforehand. This is a challenging ill-posed problem because there are infinitely many solutions even in cases when the PSF is location invariant. In reality, however, blurring often cannot be accurately described by a location-invariant PSF and the ill-posedness of the BID problem would be further exacerbated. In this article, we propose a novel BID methodology, which allows the PSF to change over locations. The true image is estimated by our jump-preserving extrapolation (JPEX) technique. The proposed method classifies the pixels in a blurred image into two categories: blurry pixels and sharp pixels. The key idea of JPEX is to remove blur involved in blurry pixels using nearby sharp pixels. Theoretical justifications show that the proposed method is statistically consistent. Numerical experiments with both simulated and real images demonstrate that it works well in applications. The comparison with state-of-the-art methods favors the proposed approach as well.
Autonomous, reconfigurable mobile vehicle with rapid control prototyping functionality

The intention of this work is to show a prototyping path while developing control algorithms with special emphasis on a hardware platform and a software framework. In principle, both components are born separately so the integration may be complex. What’s more, the project concerns a real-time controlled system. Communication between the control object and the external computer takes place wirelessly. The control and communication tasks must ensure autonomous operation of the controlled object. An additional difficulty is the critical control time due to a choice of a controlled system. It is a two-wheeled unstable mobile vehicle used as a transporter. The work is a review of the software and hardware tools used successfully by the authors. Although the tools themselves are not enough, engineering skills are also welcome.
A Hybrid Solution for Smart Supermarkets Based on Actuator Networks

Smart supermarkets are a recent innovation that allows customers to have a seamless shopping experience. The Internet of Things (IoT) is a key component of smart supermarkets. Take AmazonGo as an example, it successfully implements cashier-less store checkout. However, many staff members are still required to support the running of the supermarket. Labor is always a big cost. In this paper, we propose a hybrid solution for smart supermarkets based on actuator networks as well as IoT, in order to save labor costs and to serve customers effectively and efficiently.
Adding Constraint Tables to the DMN Standard: Preliminary Results

The DMN standard allows users to build declarative models of their decision knowledge. The standard aims at being simple enough to allow business users to construct these models themselves, without help from IT staff. To this end, it combines simple decision tables with a clear visual notation. However, for real-life applications, DMN sometimes proves too restrictive. In this paper, we develop an extension to DMN’s decision table notation, which allows more knowledge to be expressed, while retaining the simplicity of DMN. We demonstrate our new notation on a real-life case study on product design.
Social Environment Simulation in VR Elicits a Distinct Reaction in Subjects with Different Levels of Anxiety and Somatoform Dissociation

Virtual reality has taken many great strides in the recent years. It is increasingly used and is an accepted means of delivering behavioral therapy for phobias and anxiety disorders. In this paper, we examine methods of virtual reality stress induction for use in treatment of somatoform and anxiety disorders, as well as the adequate measures of the evoked stress response. In total, 42 healthy subjects took part in testing as part of this study. The results show that electrodermal activity is more sensitive in capturing a subject reaction to non-interactive social environment simulation while cardiovascular parameters better reflect task-related stress. Furthermore, our results suggest a distinct relationship between electrodermal activity and anxiety and cardiovascular parameters and somatoform dissociation. These results can point to a possibility of virtual reality utilization in the research and treatment of disorders in which anxiety and somatization are important features.
Internet Web Trust System Based on Smart Contract

The current Internet web trust system is based on the traditional PKI system, to achieve the purpose of secure communication through the trusted third party. However, with the increase of network nodes, various problems appear in the centralization system of public key infrastructure (PKI). In recent years, in addition to cryptographic problems, attacks against PKI have focused on the single point of failure of certificate authority (CA). Although there are many reasons for a single point of failure, the purpose of the attack is to invalidate the CA. Thus a distributed authentication system is explored to provide a feasible solution to develop distributed PKI with the rise of the blockchain. Due to the automation and economic penalties of smart contracts, a PKI system is proposed based on smart contracts. The certificate chain was constructed in the blockchain, and a mechanism was adopted for auditing access to CA nodes in the blockchain. Experimental results show that security requirements of CA are met in this system.
Evaluating Performance and Accuracy Improvements for Attention-OCR

In this paper we evaluated a set of potential improvements to the successful Attention-OCR architecture, designed to predict multiline text from unconstrained scenes in real-world images. We investigated the impact of several optimizations on model's accuracy, including employing dynamic RNNs (Recurrent Neural Networks), scheduled sampling, BiLSTM (Bidirectional Long Short-Term Memory) and a modified attention model. BiLSTM was found to slightly increase the accuracy, while dynamic RNNs and a simpler attention model provided a significant training time reduction with only a slight decline in accuracy.
A Hierarchical Label Network for Multi-label EuroVoc Classification of Legislative Contents

EuroVoc is a thesaurus maintained by the European Union Publication Office, used to describe and index legislative documents. The Eurovoc concepts are organized following a hierarchical structure, with 21 domains, 127 micro-thesauri terms, and more than 6,700 detailed descriptors. The large number of concepts in the EuroVoc thesaurus makes the manual classification of legal documents highly costly. In order to facilitate this classification work, we present two main contributions. The first one is the development of a hierarchical deep learning model to address the classification of legal documents according to the EuroVoc thesaurus. Instead of training a classifier for each level, our model allows the simultaneous prediction of the three levels of the EuroVoc thesaurus. Our second contribution concerns the proposal of a new legal corpus for evaluating the classification of documents written in Portuguese. Our proposed corpus, named EUR-Lex PT, contains more than 220k documents, labeled under the three EuroVoc hierarchical levels. Comparative experiments with other state-of-the-art models indicate that our approach has competitive results, at the same time offering the ability to interpret predictions through attention weights.
PEARL

Deep neural networks have demonstrated promising prediction performance on many health analytics tasks. However, the interpretability of the deep models is often lacking. In comparison, classical interpretable models such as decision rule learning do not lead to the same level of accuracy as deep neural networks (DNN) and can also be too complex to interpret (e.g., due to large tree depths). In this work, we propose Prototype LeArNing via Rule Learning (PEARL), which iteratively constructs a decision rule list to guide a neural network to learn representative prototypes that can be explained by the associated rules. The resulting prototype neural network inherits both the prediction power of DNNs and interpretability associated with rules, thus can provide accurate and interpretable predictions. Evaluated on real world health datasets, PEARL demonstrates state-of-the-art accuracy to various DNN baselines and interpretable results that are simpler than standard decision trees can provide.
A Study on Part Affinity Fields Implementation for Human Pose Estimation with Deep Neural Network

This paper investigates the implementation of part affinity fields in deep neural network to estimate human body pose from images and videos. The deep neural network is capable to perform human pose estimation under various body position and activities, based on human localization and human pose detection. Human localization is inferred from the probability and affinity map calculated from the input data, while human pose detection is achieved through automated key point annotation in the affinity map cluster and skeleton generation from the detected key points. Our image-based pose estimation is conducted on several images containing single and multiple human subjects performing different activities. Our video-based pose estimation is carried on videos with different contrast conditions and different moving activities. We analyze the accuracy of automated key point annotation and its influence to the accuracy of human pose estimation.
Quality-Elasticity: Improved Resource Utilization, Throughput, and Response Times Via Adjusting Output Quality to Current Operating Conditions

This work addresses two related problems for on-line services, namely poor resource utilization during regular operating conditions, and low throughput, long response times, or poor performance under periods of high system load. To address these problems, we introduce our notion of quality-elasticity as a manner of dynamically adapting response qualities from software services along a fine-grained spectrum. When resources are abundant, response quality can be increased, and when resources are scarce, responses are delivered at a lower quality to prioritize throughput and response times. We present an example of how a complex online shopping site can be made quality-elastic. Experiments show that, compared to state of the art, improvements in throughput (57% more served queries), lowered response times (8 time reduction for 95th percentile responses), and an estimated 40% profitability increase can be made using our quality-elastic approach. When resources are abundant, our approach may achieve upwards of twice as high resource utilization as prior work in this field.
Speaker-Informed time-and-Content-Aware attention for spoken language understanding

To mitigate the ambiguity of spoken language understanding (SLU) of an utterance, we propose contextual models that can consider the relevant context by using temporal and content-related information effectively. We first propose two axes: ‘Awareness’ and ‘Attention Level’. Awareness includes three methods that consider the timing or content-similarity of context. The Attention Level includes three methods that consider speaker roles to calculate the importance of each historic utterance. By combining one method from each axis, we build various contextual models. The proposed models are designed to use a dataset to automatically learn the importance of previous utterances in terms of time and content. We also propose various speaker information that would be helpful to improve SLU accuracy. The proposed models achieved state-of-the-art F1 scores in experiments on the Dialog State Tracking Challenge (DSTC) 4 and Loqui benchmark datasets. We applied in-depth analysis to verify that the proposed methods are effective to improve SLU accuracy. The analysis also demonstrated the effectiveness of the proposed methods.
Model-Agnostic Interpretation of Cancer Classification with Multi-Platform Genomic Data

Machine learning models are often criticised for being black-boxes. Recent work in this field has aimed to address this criticism by developing methods to explain the underlying behaviour of machine learning models. These explanations are designed to help the end-user interpret how the models input features are used to make a prediction. Here, we present an extension to one such method, referred to as local interpretable model-agnostic explanations, to interpret multimodal tumor type classification from multi-platform genomic data. We propose a framework for transparent biomedical machine learning by leveraging interpretable dimensionality reduction to facilitate gene-wise explanations for the model behaviour. Using RNA-seq expression and single nucleotide variation (SNV) data from eight cancer types, our experimental results uncovered the models use of clinically relevant genes for cancer cell stratification. We demonstrate that model-agnostic explanations can provide valuable information to a clinician or scientist when predictive ability and interpretability are of absolute importance.
Application of Blockchain Technology in Energy Internet Market and Transaction

Abstract Based on the blockchain technology, important datas such as transactions, scheduling, finance, and energy contracts are distributedly recorded, various resources such as distributed energy generation, energy transmission, energy consumption, and energy storage are aggregated for collaborative optimization control and market trading. In this paper, we build blockchain-based new generation energy Internet with extensive interconnection, intelligent decision making, real-time interaction, and open sharing. The application of blockchain technology in energy Internet includes photovoltaic energy microgrid, blockchain-based energy Internet entities, blockchain-based power trading, and energy asset securitization. Technical frameworks for energy Internet market trading system based on blockchain technology are proposed. Distributed energy ledger and energy trading smart contract are utilized. Distributed trading rules, algorithms and processes of energy system and microgrid are established. Energy Internet transactions and payment settlement system are constructed. Energy producers and consumers trade peer-to-peer on the platform to improve system efficiency and security.
Evaluating CNNs on the Gestalt Principle of Closure

Deep convolutional neural networks (CNNs) are widely known for their outstanding performance in classification and regression tasks over high-dimensional data. This made them a popular and powerful tool for a large variety of applications in industry and academia. Recent publications show that seemingly easy classification tasks (for humans) can be very challenging for state of the art CNNs. An attempt to describe how humans perceive visual elements is given by the Gestalt principles. In this paper we evaluate AlexNet and GoogLeNet regarding their performance on classifying the correctness of the well known Kanizsa triangles and triangles where sections of the edges were removed. Both types heavily rely on the Gestalt principle of closure. Therefore we created various datasets containing valid as well as invalid variants of the described triangles. Our findings suggest that perceiving objects by utilizing the principle of closure is very challenging for the applied network architectures but they appear to adapt to the effect of closure.
Vision-based Navigation of UAV with Continuous Action Space Using Deep Reinforcement Learning

Autonomous navigation of unmanned aerial vehicles (UAV) in an unknown environment is a challenging task that attracts many researchers. There exist many solutions to the problem, one promising method is deep reinforcement learning. In this paper, we investigate a deterministic policy based actor-critic learning framework on vision-based navigation of an autonomous UAV within a simulated environment. In particular, navigation with high-level input and continuous output is considered. In simulation, the observation of UAV is a depth image from front camera while the target information is a vector, to combine them together, we calculate the track angle between UAV and target, and encode it into a depth image together observation, making up to state representation. In the framework of our algorithm, actor network adopts convolutional layer to deal with high-level input, while critic network employs merge layer to balance state information and action information. The result of the experiment supports the idea of full control of an autonomous UAV through deep reinforcement learning as we solve the task successfully. Besides, comparison with other method was conducted to further explore the advantage of the method.
Introducing Knowledge Graphs to Decision Support Systems Design

Recent progress in cognitive technologies has driven decision support system (DSS) development in more complex directions. One of the main challenges in efficient DSS design is knowledge acquisition, especially in complicated and uncertain decision contexts. The more knowledge available to the system, the better decisions can be generated by a DSS. Representation of knowledge plays an important role in finding solutions to problems. With advances in the Semantic Web, knowledge can be represented in structured formats such as ontologies, which ease search and reasoning tasks. However, new data cannot be easily integrated nor updated in ontologies in real-time. Consequently, knowledge graphs (KGs) have emerged as a dynamic, scalable and domain independent form of knowledge representation. This paper explores how KGs can enhance the decision-making process in DSSs. Moreover, the paper presents a framework that may facilitate the integration of KGs into the DSS design.