Reviewerly/exHarmony huggingface dataset:
Hierarchical POMDP planning for object manipulation in clutter

Object manipulation planning in clutter suffers from perception uncertainties due to occlusion, as well as action constraints required by collision avoidance. Partially observable Markov decision process (POMDP) provides a general model for planning under uncertainties. But a manipulation task usually have a large action space, which not only makes task planning intractable but also brings significant motion planning effort to check action feasibility. In this work, a new kind of hierarchical POMDP is presented for object manipulation tasks, in which a brief abstract POMDP is extracted and utilized together with the original POMDP. And a hierarchical belief tree search algorithm is proposed for efficient online planning, which constructs fewer belief nodes by building part of the tree with the abstract POMDP and invokes motion planning fewer times by determining action feasibility with observation function of the abstract POMDP. A learning mechanism is also designed in case there are unknown probabilities in transition and observation functions. This planning framework is demonstrated with an object fetching task and the performance is empirically validated by simulations and experiments.
Security Evaluation of Y00 Protocol Based on Time-Translational Symmetry Under Quantum Collective Known-Plaintext Attacks

In this paper, we concretely formulate to derive the attacker's success probability of obtaining the shared secret keys for the Y00 protocol under a combination of a quantum collective attack with infinitely-long known-plaintext, naming it ''collective known-plaintext attack'' in this work.In contrast, our previous work showed only the necessary condition to design Y00 transmitters to be information-theoretic secure.The keystone of the security evaluations in this work is the time-translational symmetry of the Y00 signals modulated by pseudo-random number generators, such as linear-feedback shift registers or Mersenne twisters.With the assist of a true-random deliberate-signal-randomization, information-theoretic security would be realized.By numerical simulations, we can determine whether the designed Y00 transmitters are information-theoretic secure.However, this work's security evaluation may not apply to the transmitters with cryptographically-secure pseudo-random number generators because they might not have time-translational symmetry, even though such Y00 transmitters may be securer.We also describe future challenges for theorists to accelerate designing securer Y00 transmitters.
E-Commerce Storytelling Recommendation Using Attentional Domain-Transfer Network and Adversarial Pre-Training

In e-commerce platforms, there is an emerging type of content that tells a “story” about some merchandise in the form of multimedia (text, images, video), which is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">storytelling</i> . Well told stories, like advertisements, can inspire users to purchase the related products. Thus, e-commerce service provider is keen to disseminate storytelling items to potentially interested users. We address this requirement by a cross-domain personalized recommendation approach. Because storytelling is a new type of content, its related user actions are much less, more sparse than product-related user actions, thus we propose to use product-domain user actions to assist the identification of user preferences and to make storytelling recommendations. Our method has two technical contributions. First, since the user behavior patterns are different across the storytelling domain and the product domain, we propose an attentional domain-transfer network, which effectively selects the relevant items in the two domains to characterize user preferences. Second, although storytelling is about product, between the two domains there is a large gap: product description is objective and categorical, like “keywords,” but storytelling is close to human language. To bridge the domain gap, we propose a dual-domain contrastive adversarial learning method to pre-train the feature extractors for storytelling and product simultaneously. We conduct experiments on two industrial datasets, and the results demonstrate the advantage of our proposed method that consistently outperforms the state-of-the-art methods. Besides, our method can be used to recommend storytelling to products, which is a desired functionality for product providers. Our code and models are publicly available.
VNLawBERT: A Vietnamese Legal Answer Selection Approach Using BERT Language Model

Recently, with the development of NLP (Natural Language Processing) methods and Deep Learning, there are several solutions to the problems in question answering systems that achieve superior results. However, there are not many solutions to question-answering systems in the Vietnamese legal domain. In this research, we propose an answer selection approach by fine-tuning the BERT language model on our Vietnamese legal question-answer pair corpus and achieve an 87% F1-Score. We further pre-train the original BERT model on a Vietnamese legal domain-specific corpus and achieve a higher F1-Score than the original BERT at 90.6% on the same task, which could reveal the potential of a new pre-trained language model in the legal area.
Recent Advancements in Autonomous Robots and Their Technical Analysis

The purpose of this paper is to discuss and present a technical analysis of the recent advancements in autonomous robots equipped with a manipulator. The autonomous robots include unmanned aerial vehicle (UAV), unmanned underwater vehicle (UUV), and unmanned ground vehicle (UGV). A manipulator can make an autonomous robot more adaptable and robust but it can also affect its performance as well. Several issues can arise because of the installation of a manipulator like the robot becoming unstable due to the extra weight, slow convergence, and errors in the path planning. Therefore, this study presents the numerous recent techniques that are in use to counter the aforementioned problems. The methodology and approach used in this paper are to first present the dynamic model of the autonomous robot. Then, the study offers a performance analysis of the specific robot in question. Finally, the paper formulates the limitations of the recently proposed techniques in the form of a table for each vehicle. The key findings of this study are a comprehensive review of the aforesaid techniques and their technical analysis. The unique contribution of this study is to present some of the limitations that these methods have so the researcher can better select the method according to the mission requirement.
Blind multicarrier waveform recognition based on spatial-temporal learning neural networks

Blind multicarrier waveform recognition has become a more daunting task and open problem for the current and future radio surveillance and signals interception, with the advent of new multicarrier technologies such as the state-of-the-art F-OFDM, UFMC, FBMC, OTFS, GFDM and CP-OFDM techniques. Therefore, the practical recognition scheme for multicarrier waveforms is necessary to keep up with the pace. To tackle this challenge, we propose a novel multicarrier waveform recognition framework based on Spatial Temporal-Convolutional Long Short-Term Deep Neural Network (ST-CLDNN) in the entirely blind context. The complementary information of the raw in-phase, quadrature and amplitude samples are first extracted to provide more distinguishable features. Then ST-CLDNN collects the advantages of one-dimensional convolutional and long short-term memory (LSTM) to extract high-level spatial and temporal features for the recognition task. Later, we introduce the transfer learning strategy to put the computational resource to good use and obviate the retraining from scratch for a time-varying channel. Experimental results indicate that the proposed ST-CLDNN can perform better than the traditional feature-based classifiers and existing deep learning (DL)-based neural networks, and deliver a substantial recognition performance in a time-varying multipath fading channel.
Target Recognition and Range-measuring Method based on Binocular Stereo Vision

Aiming at the problems of high cost and limited installation of traditional unmanned vehicle environment perception methods, this paper proposes a method of personnel identification and distance measurement based on the fusion of YOLOv4 and binocular stereo vision. Through the annotation of the data set, the Darknet deep learning framework is used to train and recognize the personnel, and the binocular camera disparity data is used for personnel distance detection. The experimental results show that the recognition accuracy of this method is 0.941 and the distance error is less than 5%, which can meet the task requirements of unmanned vehicle and provide technical support for solving the environment perception problems of autonomous driving vehicle.
Let’s Gamble: How a Poor Visualization Can Elicit Risky Behavior

Data visualizations are standard tools for assessing and communicating risks. However, it is not always clear which designs are optimal or how encoding choices might influence risk perception and decision-making. In this paper, we report the findings of a large-scale gambling game that immersed participants in an environment where their actions impacted their bonuses. Participants chose to either enter a lottery or receive guaranteed monetary gains based on five visualization designs. By measuring risk perception and observing decision-making, we present suggestive evidence that people were more likely to gamble when presented area proportioned triangle and circle designs. Using our results, we model risk perception and discuss how our findings can improve visualization selection.
Vehicle Object Detection Based on Improved RetinaNet

Abstract Aiming at the low efficiency of vehicle object detection in real scenes, this paper proposes an improved RetinaNet. An octave convolution structure and a weight pyramid structure are introduced respectively to improve the detection performance of RetinaNet for vehicles. Specifically, we use octave convolution instead of the traditional convolution layer to improve the feature map’s representation of detailed information. In addition, in order to improve the quality of feature fusion, a weighted feature pyramid network (WFPN) structure is proposed to limit the propagation of gradients between different levels. The experimental results on the DETRAC data set show that the method has good detection results for vehicle targets of different scales in different scenarios, and can better meet the needs of practical applications.
Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network

Reliable channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) systems to achieve high data rate. In this paper, a deep learning-based channel estimation method that combined with image super-resolution (SR) and convolutional neural network (CNN) is proposed. Using the idea of model-driven approach, the network is initialized by the least square estimation and then trained offline to extract valid features of two-dimensional channel response matrices for high accuracy channel estimates. The results show that the proposed method significantly outperforms the linear minimum mean squared error (LMMSE) estimator in mean square error (MSE) and has potential in spectrum saving.
Fostering trustworthy data sharing: Establishing data foundations in practice

Abstract Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.
Capturing killers: the construction of CCTV evidence during homicide investigations

Drawing upon quantitative and qualitative data gathered during a four-year ethnographic study of 44 British homicide investigations, this paper advances the sparse literature on how closed-circuit television (CCTV) contributes to criminal investigations and the risks associated with its use. Based on insights gleaned from interviews with homicide detectives, analysis of case files and observations of live homicide investigations, we examine how CCTV is used during homicide investigations focusing principally on two key investigative moments – identifying and charging suspects. Our quantitative data indicate that CCTV is used more frequently than any other kind of forensic science or technology to both identify and charge suspects. Nevertheless, our qualitative data reveal numerous challenges associated with how CCTV footage is recovered, viewed, shared, interpreted and packaged for court. We reveal the individual and organisational processes and workarounds that have emerged in a socio-technical landscape that lacks clear standards and principles. We discuss the implications of these findings for practice and policy and their relevance to questions about the socially constructed nature of forensic scientific knowledge.
Combining Tensor Slice and Singular Value for Blind Light Field Image Quality Assessment

Light field image (LFI) collects radiance from rays in different directions, offers powerful capabilities for immersive media and computer vision. As the high-dimensional data, LFI suffers from spatial as well as angular information distortions in its processing, which brings new challenges to image quality assessment (IQA). Based on the strong ability of tensor about representing high-dimensional data and distortion characteristics of LFI, this paper proposes a method of combining tensor slice and singular value for blind light field image quality assessment (TSSV-LFIQA) to effectively evaluate the quality of LFI content. Specifically, five-order tensor representation of LFI is firstly defined which contains light ray intensity, angular information and color information of the LFI. Secondly, the first slice sharpness measurement and the other slice information distribution are used to describe the tensor slice spatial feature (TSSF) of the LFI. Moreover, singular value angular feature (SVAF) is also proposed to measure the angular consistency of LFI by further unfolding the five-order tensor of LFI and analyzing the percentage of singular values. The experimental results show that benefiting from the combination of TSSF and SVAF, the proposed TSSV-LFIQA method is statistically superior to the existing IQA methods, and matches well with human subjective opinions.
Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method

Long-distance detection of traffic signs provides drivers with more reaction time, which is an effective technique to reduce the probability of sudden accidents. It is recognized that the imaging size of far traffic signs is decreasing with distance. Such a fact imposes much challenge on long-distance detection. Aiming to enhance the recognition rate of long-distance small targets, we design a four-scale detection structure based on the three-scale detection structure of YOLOv3 network. In order to reduce the occlusion effects of similar objects, NMS is replaced by soft-NMS. In addition, the datasets are trained and the K-Means method is used to generate the appropriate anchor boxes, so as to speed up the network computing. By using these methods, better experimental results for the recognition of long-distance traffic signs have been obtained. The recognition rate is 43.8 frames per second (FPS), and the recognition accuracy is improved to 98.8%, which is much better than the original YOLOv3.
DLU-Net for Pancreatic Cancer Segmentation

The pancreas is located in the deep abdominal cavity of the human body. It is small in size and variable in shape, which makes the location and diagnosis of pancreatic cancer in abdominal computed tomography (CT) scan images especially difficult. The existing segmentation models of pancreatic cancer have been able to locate the pancreas correctly, but they can't yet segment the edge of the pancreas accurately enough. This paper proposes an extension of the convolutional network U-Net, which is called DLU-Net, for accurately cutting out the irregular shape of pancreatic cancer and improving the segmentation accuracy for pancreatic cancer. In DLU-Net, we use deformable convolution modules to strengthen the ability of the network to model the target edge. To facilitate the transmission of features and reuse the features to reduce the complexity of the network, we add densely connected convolutions. Moreover, Bi-Directional Convolutional Long-Short Term Memory (BConvLSTM) structures are applied to combine the features of different scales by using temporal and spatial correlations. The model was evaluated on the following two datasets: abdominal CT images of pancreatic cancer patients of Medical Segmentation Decathlon (MSD) and abdominal CT images of pancreatic cancer patients of Changhai Hospital. The experimental results show that DLU-Net can more accurately segment the edge of cancer, and has an excellent performance in other segmentation indicators.
Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

One of the principal uses of physical-space sensors in public safety applications is the detection of unsafe conditions (e.g., release of poisonous gases, weapons in airports, tainted food). However, current detection methods in these applications are often costly, slow to use, and can be inaccurate in complex, changing, or new environments. In this paper, we explore how machine learning methods used successfully in cyber domains, such as malware detection, can be leveraged to substantially enhance physical space detection. We focus on one important exemplar application-the detection and localization of radioactive materials. We show that the ML-based approaches can significantly exceed traditional table-based approaches in predicting angular direction. Moreover, the developed models can be expanded to include approximations of the distance to radioactive material (a critical dimension that reference tables used in practice do not capture). With four and eight detector arrays, we collect counts of gamma-rays as features for a suite of machine learning models to localize radioactive material. We explore seven unique scenarios via simulation frameworks frequently used for radiation detection and with physical experiments using radioactive material in laboratory environments. We observe that our approach can outperform the standard table-based method, reducing the angular error by 37% and reliably predicting distance within 2.4%. In this way, we show that advances in cyber-detection provide substantial opportunities for enhancing detection in public safety applications and beyond.
Can Big Data guide prognosis and clinical decisions in epilepsy?

Abstract Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of “‐omics” domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence‐based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision‐making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.
Enhancing the robustness of watermarked medical images using heuristic search algorithm

Nowadays, multimedia elements such as image, video, animation, audio, and software can be distributed in a short time anywhere in the world using internet technology. Content owners are concerned about copyright protection and authentication. Watermarking is one of the method to protect patient information in medical images such as magnetic resonance and ultrasound imaging. In the literature, there are several methods proposed using both frequency-domain (etc. digital cosine transforms (DCT), digital wavelet transforms (DWT), digital Fourier transforms (DFT), digital radon transform (DRT)) and spatial domain (least significant bits (LSB)) methods. Mostly digital wavelet transform based watermarking methods give very promising results after common attacks. In the DWT algorithm, scaling factor is used in embedding and extraction. A scaling factor is a number between 0 and 1 which has been determined by the user which is not efficient. We can use the brute force method which solves a problem by checking all the possible cases, but it is slow. In this work, we use simulated annealing heuristic search algorithm to find out the best scaling factor to reach more robust, transparent, high data capacity and resistant watermarking method in medical images. Experimental results show that simulated annealing-based scaling factor determination in frequency domain watermarking gives more robust, resistant, and transparent watermarked images.
Supervised Fractional-Order Embedding Multiview Canonical Correlation Analysis via Ordinal Label Dequantization for Image Interest Estimation

Supervised fractional-order embedding multiview canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) for image interest estimation is presented in this paper. SFEMCCA-OLD is a CCA method that realizes accurate integration of features including low-dimensional ordinal label features. In general, since information is lost due to a limitation of the number of classes, i.e., the dimension of ordinal label information is smaller than those of other features, derivation of highly accurate integration of features is difficult. In SFEMCCA-OLD, the dimension of the ordinal label information can be increased by estimation of the canonical correlation between multiview features. We call this approach ordinal label dequantization. In addition, by introducing a fractional-order technique, our method can calculate optimal projections for noisy data such as real data. Experimental results show that the accuracy of SFEMCCA-OLD for image interest estimation is better than that of recent CCA-based methods.