Reviewerly/exHarmony huggingface dataset:
Learning Local Quality-Aware Structures of Salient Regions for Stereoscopic Images via Deep Neural Networks

The perceptual quality of stereoscopic images plays an essential role in the human perception of visual information. However, most available stereoscopic image quality assessment (SIQA) methods evaluate 3D visual experience using hand-crafted features or shallow architectures, which cannot model the visual properties of stereo images well. In this paper, we use convolutional neural networks (CNNs) to learn deeper local quality-aware structures for stereo images. With different inputs, two CNN models are designed for no-reference SIQA tasks. The one-column CNN model directly accepts a cyclopean view as the input, and the three-column CNN model jointly considers the cyclopean, left and right views as CNN inputs. The two SIQA frameworks share the same implementation approach: First, to overcome the obstacle of limited SIQA datasets, we accept image patches that have been cropped from corresponding stereopairs as inputs for local quality-sensitive feature extraction. Next, a local feature selection algorithm is used to remove related features on non-salient patches, which could cause large prediction errors. Finally, the reserved local visual structures of salient regions are aggregated into a final quality score in an end-to-end manner. Experimental results on three public SIQA databases demonstrate that our method outperforms most state-of-the-art no-reference (NR) SIQA methods. The results of a cross-database experiment also show the robustness and generality of the proposed method.
CoNNa–Hardware accelerator for compressed convolutional neural networks

In this paper, we propose a novel Convolutional Neural Network hardware accelerator called CoNNA, capable of accelerating pruned, quantized CNNs. In contrast to most existing solutions, CoNNA offers a complete solution to the compressed CNN acceleration, being able to accelerate all layer types commonly found in contemporary CNNs. CoNNA is designed as a coarse-grained reconfigurable architecture, which uses rapid, dynamic reconfiguration during CNN layer processing. The CoNNA architecture enables the on-the-fly selection of the CNN network that should be accelerated and also supports the acceleration of CNN networks with dynamic topology. Furthermore, by being able to directly process compressed feature and kernel maps, and skip all ineffectual computations during CNN layer processing, the CoNNA CNN accelerator is able to achieve higher CNN processing rates than some of the previously proposed solutions. The CoNNA architecture has been implemented using Xilinx ZynqUtrascale+ FPGA family and compared with seven previously proposed CNN hardware accelerators. Results of the experiments seem to indicate that the CoNNA architecture is up to 14.10, 6.05, 4.91, 2.67, 11.30, 3.08 and 3.58 times faster than previously proposed MIT's Eyeriss, NullHop, NVIDIA's Deep Learning Accelerator (NVDLA), NEURAghe, CNN_A1, fpgaConvNet, and Deephi's Aristotle CNN accelerators respectively, while using identical number of computing units and operating at the same clock frequency.
CWPC_BiAtt: Character–Word–Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character–Word–Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.
Human Posture Recognition in Intelligent Healthcare

Abstract With the rapid growth of medical technology, intelligent healthcare has gradually integrated into our lives, and its core technology is the human body motion state detection. According to the basic tools being used, we divide this technology into two categories: vision-based detection and sensor-based detection, and take the fall monitoring as an example for elaboration. Finally, different methods are compared and analyzed, the difficulties that need to be solved in this field are summarized, the future trends are included too.
Noise tolerance of Dicke states

Starting from explicitly constructing an entanglement witness for the mixture of a Dicke state with white noise (noisy Dicke state), we provide an entanglement criterion for any multiqubit state. The criterion is necessary and sufficient for noisy $W$ states of three, four, and five qubits. We demonstrate an entanglement criterion for any four-qubit state. The criterion is necessary and sufficient for the generalized noisy four-qubit Dicke states. We present the sufficient criterion of full separability for a noisy $2k$ qubit Dicke state with $k$ excitations. Then we generalize the entanglement witness and criterion to the mixture of white noise and generic $n$-qubit Dicke state with $k$ excitations based on strong numeric evidence. Apart from noisy Dicke states, we also consider the entanglement properties of simultaneously amplitude damped and dephased Dicke states, and depolarized multiqubit $W$ states.
Classification from Triplet Comparison Data

Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal embedding, where we want to learn an embedding in a Euclidean space of the given instances that preserve the comparison order as much as possible. Unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only from triplet comparison data without all the labels has remained unanswered. In this letter, we give a positive answer to this important question by proposing an unbiased estimator for the classification risk under the empirical risk minimization framework. Since the proposed method is based on the empirical risk minimization framework, it inherently has the advantage that any surrogate loss function and any model, including neural networks, can be easily applied. Furthermore, we theoretically establish an estimation error bound for the proposed empirical risk minimizer. Finally, we provide experimental results to show that our method empirically works well and outperforms various baseline methods.
Context-aware MDNet for target tracking in UAV remote sensing videos

With the maturity and popularity of unmanned aerial vehicle (UAV) technology, remote sensing target tracking in the aerial videos from UAVs has drawn much attention in recent years. Given that UAV aerial video has low resolution, multiple similar disruptors and rapid perspective changes, and the main tracking methods in this research field generally have low tracking performance and timeliness, we propose a remote sensing target tracking method for UAV aerial video based on a context-aware multi-domain convolutional neural network (CAMD). First, in the design of the tracking network structure, we fuse multiple convolutional layers using residual connections to boost the effectiveness of regression learning. Then, in the offline pretraining stage, a Rotation Adversarial Autoencoders (RAAE) was adopted to generate typical easily confused negative samples for enhancing the capacity to distinguish between targets and the background interference. Finally, when the estimated target score was in the ‘fuzzy interval’, we introduced a response-adaptive context-aware correlation filter (RA-CACF) module into our network architecture to improve the tracking performance. Qualitative and quantitative evaluations using public and homemade hard datasets demonstrate that the proposed method can achieve high accuracy and efficiency results compared to state-of-the-art trackers.
FSSE: An Effective Fuzzy Semantic Searchable Encryption Scheme Over Encrypted Cloud Data

Currently, searchable encryption has attracted considerable attention in the field of cloud computing. The existing research mainly focuses on keyword-based search schemes, most of which support the exact matching of keywords. However, keyword-based search schemes ignore spelling errors and semantic expansions of keywords. The significant drawback makes the existing techniques unsuitable in cloud computing as it greatly affects system usability and can not completely satisfy the users' search intentions. In this paper, we propose an effective fuzzy semantic searchable encryption scheme (FSSE) that supports multi-keyword search over encrypted data in cloud computing. In our scheme, we exploit a keyword fingerprint generation algorithm to generate a fingerprint set of the keyword dictionary and a fingerprint of the query keywords, and employ Hamming distance to quantify keywords similarity. Based on the proposed fingerprint generation algorithm and Hamming distance, we realize fuzzy search. Furthermore, we utilize the semantic expansion technique to expand query keywords and calculate the semantic similarity between the query keywords and the expanded word of the query keywords to achieve the semantic search. To improve the search efficiency, we construct an inverted index structure and use the vector intersection matching as well as short-circuit matching operations to effectively filter irrelevant documents. The theoretical analysis and experimental results demonstrate that our proposed scheme satisfies the security guarantee of searchable encryption, enhances system usability, and is more efficient in comparison with the state of the art schemes.
Automatic pH and Humidity Control System for Hydroponics Using Fuzzy Logic

Hydroponics is a system of growing plants using water as the media without involving other planting media such as soil. Hydroponics becomes one of the options for people to grow crops with minimal land. Hydroponics requires sufficient care because the conditions needed by the plant cannot depend on the elements found in the soil like conventional farming in general but directly controlled by the system. The most important conditions for hydroponics are pH and humidity. To control the levels of these conditions, the proposed hydroponic system is built using water pumps, an exhaust fan, and sensors that can be monitored in real-time. In this research artificial intelligence, the fuzzy logic has been added into the system, hence it works automatically to provide pH and humidity needed by hydroponic plants.
Human accuracy in mobile data collection

The collection of participant data 'in the wild' is widely employed by Human-Computer Interaction researchers. A variety of methods, including experience sampling, mobile crowdsourcing, and citizen science, rely on repeated participant contributions for data collection. Given this strong reliance on participant data, ensuring that the data is complete, reliable, timely, and accurate is key. Although previous work has made significant progress on ensuring that a sufficient amount of data is collected, the accuracy of human contributions has remained underexposed. In this article we argue for an emerging need for an increased focus on this aspect of human-labelled data. The articles published in this special issue demonstrate how a focus on the accuracy of the collected data has implications on all aspects of a study – ranging from study design to the analysis and reporting of results. We put forward a five-point research agenda in which we outline future opportunities in assessing and improving human accuracy in mobile data collection.
A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

In recent years, computing devices have become widely distributed, and the accumulated data from these devices are growing rapidly, especially as they are increasingly equipped with various sensors and RF communication capabilities.Data science, including machine learning technology, has contributed to the better handling the large amounts of data and feature selection techniques have been a useful strategy.As data amounts continue to grow, scaling features will became crucial in data science.In this paper, we propose a novel filter-based feature-selection method in the context of keystroke dynamics authentication.In particular, we propose a new feature-scoring method and apply it to keystroke-dynamics-based authentications.We implement keystroke-dynamics-based authentications multi-factored with PIN-based authentications and collect data from actual users' testing experiments.Then, we apply our feature-selection method and compare the performance with that when using all of the features and existing feature-selection methods.Our experimental results show that the classification performance by the proposed method is superior to those of the other methods by up to 21.8%.Moreover, our method provides security to other users' data sets, as the method utilizes only mean values from imposter data.Our feature-selection method contributes to improving the quality of keystroke dynamics authentications without user privacy issues.More generally, our method can also be applied to other data-mining data sets, such as IoT sensor data sets.
GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis

Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years, spectral sparsification techniques have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this work proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is also introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to very large-scale integration computer-aided design, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications. For instance, a sparse SDD matrix with 40 million unknowns and 180 million nonzeros can be solved (1E-3 accuracy level) within 2 min using a single CPU core and about 6-GB memory.
CR-U-Net: Cascaded U-Net with Residual Mapping for Liver Segmentation in CT Images

Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver segmentation in CT scan is important for diagnosis and analysis of liver lesions. However, the accuracy of existing liver segmentation methods is slightly insufficient. In this paper, we propose a liver segmentation architecture named CR-U-Net, which is composed of cascade U-Net combined with residual mapping. We make use of the MDice loss function for training in CR-U-Net, and the second-level of cascade network is deeper than the first-level to extract more detailed image features. Morphological algorithms are utilized as an intermediate-processing step to improve the segmentation accuracy. In addition, we evaluate our proposed CR-U-Net on liver segmentation task under the dataset provided by the 2017 ISBI LiTS Challenge. The experimental result demonstrates that our proposed CR-U-Net can outperform the state-of-the-art methods in term of the performance measures, such as Dice score, VOE, and so on.
Self-reciprocal and self-conjugate-reciprocal irreducible factors of x − λ and their applications

In this paper, we present some necessary and sufficient conditions under which an irreducible polynomial is self-reciprocal (SR) or self-conjugate-reciprocal (SCR). By these characterizations, we obtain some enumeration formulas of SR and SCR irreducible factors of xn−λ, λ∈Fq⁎, over Fq, which are just open questions posed by Boripan et al. (2019). We also count the numbers of Euclidean and Hermitian LCD constacyclic codes and show some well-known results on Euclidean and Hermitian self-dual constacyclic codes in a simple and direct way.
SgxPectre: Stealing Intel Secrets From SGX Enclaves via Speculative Execution

The speculative execution of side-channel vulnerabilities in microarchitecture processors has raised concerns about the security of Intel's Software Guard eXtensions (SGX). We present SgxPectre attacks, the SGX variants of Spectre attacks, which exploit speculative execution vulnerabilities to subvert the confidentiality of SGX enclaves; evaluate Intel's existing countermeasures against SgxPectre attacks; and discuss security implications.
Deep Fashion Recommendation System with Style Feature Decomposition

Recently, convolutional neural network (CNN) -based fashion recommendation techniques, which automatically recommend the matching clothes to the consumer, have been widely researched. In general, the feature vector of a fashion item, i.e. clothes vector, obtained by CNN conveys two types of information: style and category, where the style indicates the distinctive characteristic of the clothes and the category represents the common properties of the clothes in the same class. Due to the mixed information of style and category, however, the clothes vector often recommends the unmatching clothes. To solve this problem, we propose a style feature extraction (SFE) layer, which effectively decomposes the clothes vector into style and category. Based on the characteristics that the category information has small variations in the same class while being distinguished from other classes, we extract and remove the category information from the clothes vector to obtain more accurate style information. Experimental results show that the proposed method achieves state-of-the-art results in terms of link prediction, which is a performance measure of a stylish match. In addition, as a simple CNN layer, it is expected that the proposed SFE layer is compatible with all popular CNN architectures.
Self-Supervised Learning of Class Embeddings from Video

This work explores how to use self-supervised learning on videos to learn a class-specific image embedding that encodes pose and shape information in the form of landmarks. At train time, two frames of the same video of an object class (e.g. human upper body) are extracted and each encoded to an embedding. Conditioned on these embeddings, the decoder network is tasked to transform one frame into another. To successfully perform long range transformations (e.g. a wrist lowered in one image should be mapped to the same wrist raised in another), we introduce a new hierarchical probabilistic network decoder model. Once trained, the embedding can be used for a variety of downstream tasks and domains. We demonstrate our approach quantitatively on three distinct deformable object classes - human full bodies, upper bodies, faces - and show experimentally that the learned embeddings do indeed generalise. They achieve state-of-the-art performance in comparison to other self-supervised methods trained on the same datasets, and approach the performance of fully supervised methods.
Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval

We present Capreolus, a toolkit designed to facilitate end-to-end it ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models within a common framework. Our toolkit adopts a standard reranking architecture via tight integration with the Anserini toolkit for candidate document generation using standard bag-of-words approaches. Using Capreolus, we are able to reproduce Yang et al.'s recent SIGIR 2019 finding that, in a reranking scenario on the test collection from the TREC 2004 Robust Track, many neural retrieval models do not significantly outperform a strong query expansion baseline. Furthermore, we find that this holds true for five additional models implemented in Capreolus. We describe the architecture and design of our toolkit, which includes a Web interface to facilitate comparisons between rankings returned by different models.
Denoising of Radar Pulse Streams With Autoencoders

There are many cases in which the noise corrupts the signals in a significant manner. To better analyze these signals, the noise must be removed from the signals for further data analysis, and the process of noise removal is referred to as denoising. In this letter, we propose a novel approach to the pulse denoising problem by extracting features from time of arrival (TOA) sequences using the autoencoders. The noise-contaminated TOA sequence is first coded into a binary vector and then fed into an autoencoder for training. Then, the trained autoencoder is capable of generating the original TOA sequence without lost and spurious pulses. Moreover, the proposed method does not require a noise-free TOA sequence as a priori as with conventional autoencoders. Simulation results show that the new technique can deal with TOA sequences with complex pulse repetition interval (PRI) modes that have not been tackled before. In addition, the proposed method has a better performance in noisy environments than conventional methods and general deep neural network structures.
Reflexive VR Storytelling Design Beyond Immersion: Facilitating Self-Reflection on Death and Loneliness

This research examines the reflexive dimensions of cinematic virtual reality (CVR) storytelling. We created Anonymous, an interactive CVR piece that employs a reflexive storytelling method. This method is based on distancing effects and is used to elicit audience awareness and self-reflection about loneliness and death. To understand the audience's experiences, we conducted in-depth interviews to study which design factors and elements prompted reflexive thoughts and feelings. Our findings highlight how the audience experience was impacted by four reflexive dimensions: abstract and minimal aesthetics, everyday materials and textures, the restriction of control, and multiple, disembodied points of view. We use our findings to discuss how these dimensions can inform the design of VR storytelling experiences that provoke self and social reflection.