Reviewerly/exHarmony huggingface dataset:
Photonic and optoelectronic neuromorphic computing

Recent advances in neuromorphic computing have established a computational framework that removes the processor-memory bottleneck evident in traditional von Neumann computing. Moreover, contemporary photonic circuits have addressed the limitations of electrical computational platforms to offer energy-efficient and parallel interconnects independently of the distance. When employed as synaptic interconnects with reconfigurable photonic elements, they can offer an analog platform capable of arbitrary linear matrix operations, including multiply–accumulate operation and convolution at extremely high speed and energy efficiency. Both all-optical and optoelectronic nonlinear transfer functions have been investigated for realizing neurons with photonic signals. A number of research efforts have reported orders of magnitude improvements estimated for computational throughput and energy efficiency. Compared to biological neural systems, achieving high scalability and density is challenging for such photonic neuromorphic systems. Recently developed tensor-train-decomposition methods and three-dimensional photonic integration technologies can potentially address both algorithmic and architectural scalability. This tutorial covers architectures, technologies, learning algorithms, and benchmarking for photonic and optoelectronic neuromorphic computers.
Analytic Review of Using Augmented Reality for Situational Awareness

Situational awareness is the perception and understanding of the surrounding environment. Maintaining situational awareness is vital for performance and error prevention in safety critical domains. Prior work has examined applying augmented reality (AR) to the context of improving situational awareness, but has mainly focused on the applicability of using AR rather than on information design. Hence, there is a need to investigate how to design the presentation of information, especially in AR headsets, to increase users’ situational awareness. We conducted a Systematic Literature Review to research how information is currently presented in AR, especially in systems that are being utilized for situational awareness. Comparing current presentations of information to existing design recommendations aided in identifying future areas of design. In addition, this survey further discusses opportunities and challenges in applying AR to increasing users’ situational awareness.
An overview of food supply chain virtualisation and granular traceability using blockchain technology

The integration of sensors and internet to the supply chain has created a new window in the supply chain management called as virtual supply chain which bears the features such as real-time tracking and monitoring of goods flow in the physical supply chain. The food supply chain is one of the delicate supply chains due to perishability, which can be due to various physical and biological factors, also the stringent safety and sustainability requirements of the product. The use of sensors and IT technology enables real-time data collection of the changes that take place in the food supply chain. The paper presents the integration of blockchain to the current virtual supply chain thus eliminating or changing the roles of intermediaries in the virtual supply chain and facilitating transparency, integrity, and authenticity of the data in the food supply chain.
Spatially Regularized Structural Support Vector Machine for Robust Visual Tracking

Structural support vector machine (SSVM) is popular in the visual tracking field as it provides a consistent target representation for both learning and detection. However, the spatial distribution of feature is not considered in standard SSVM-based trackers, therefore leading to limited performance. To obtain a robust discriminative classifier, this paper proposes a novel tracking framework that spatially regularizes SSVM, which yields a new spatially regularized SSVM (SRSSVM). We utilize the spatial regularization prior to penalize the learning classifier with the same size as the target region. The location of classifier spatially located far from the center of region is assigned large weight and vice versa. Then, it is introduced into the SSVM model as a regularization factor to learn the robust discriminative model. Furthermore, an optimizing algorithm with dual coordination descent is presented to efficiently solve the SRSSVM tracking model. Our proposed SRSSVM tracking method has low computational cost like the traditional linear SSVM tracker while can significantly improve the robustness of the discriminative classifier. The experimental results on three popular tracking benchmark data sets show that the proposed SRSSVM tracking method performs favorably against the state-of-the-art trackers.
On Finding Quantum Multi-collisions

A k-collision for a compressing hash function H is a set of k distinct inputs that all map to the same output. In this work, we show that for any constant k, $$\varTheta \left( N^{\frac{1}{2}(1-\frac{1}{2^k-1})}\right) $$ quantum queries are both necessary and sufficient to achieve a k-collision with constant probability. This improves on both the best prior upper bound (Hosoyamada et al., ASIACRYPT 2017) and provides the first non-trivial lower bound, completely resolving the problem.
Heavy metals and geo-accumulation index development for groundwater of Mathura City, Uttar Pradesh

Heavy metal pollution in urban areas of India is very severe and complex.This study has been conducted to assess the health risk of inhabitants by heavy metals (Cd, Mn, Fe, Cu, Pb, Zn, Ni, and Cr) through the groundwater intake of Mathura city of Uttar Pradesh, India.A total of 65 groundwater samples were collected, and these samples were analyzed for the presence of heavy metals (Cd, Mn, Fe, Cu, Pb, Zn, Ni, and Cr).The total concentration of heavy metals present in groundwater was in the order of Ni > Fe > Pb > Cr > Cd > Zn > Mn > Cu Sediment quality indicator such as heavy metal pollution index (HPI) and geo-accumulation index (I geo ) were also evaluated in addition to multivariate statistical techniques, such as Pearson correlation matrix.The HPI mean value has been found to be 66.61 indicating the groundwater quality is in poor condition.The I geo results confirm the certain extent of heavy metals contamination in the groundwater of Mathura city with respect to Pb, Cd, Ni, Fe, and Cr.The correlation matrix of the heavy metals in the groundwater has also been evaluated.No significant correlations have been observed among most of these heavy metals, indicating different anthropogenic and natural sources of contamination.
Pollution mapping of Yamuna River segment passing through Delhi using high-resolution GeoEye-2 imagery

River Yamuna is the largest tributary of river Ganges and has been acclaimed as a heavenly waterway in Indian mythology. However, 22-km segment of river Yamuna passing through Delhi from downstream of Wazirabad barrage up to Okhla barrage is considered as the filthiest stretch having been rendered into a sewer drain. The present study employs high-resolution GeoEye-2 imagery for mapping and monitoring pollution levels within the river segment by testing correlation between water quality parameters (WQPs) and the corresponding spectral reflectance values of the image. A total of 100 water samples collected from random sampling locations along the river segment were analyzed for 12 WQPs in the laboratory and grouped into two classes, namely (WQP)organic and (WQP)inorganic. Several spectral band combinations as well as single bands were tested for any significant correlation with the two formulated WQP classes by performing multiple linear regression analysis. Results reveal that spectral band combination, i.e., $$\left\{ {\overline{{\left( {RGB} \right)}} \times \sqrt {B/R} } \right\},$$ and the two formulated WQP classes exhibit strong positive correlation with R = 0.92 and 0.91 (R2 ~ 0.85 and 0.82; RMSE ~ 1.03 and 1.12) for calibration data and 0.85 and 0.84 (i.e., R2 ~ 0.74 and 0.72; RMSE ~ 1.45 and 1.64) for validation data, respectively. The spatial distribution maps depicting pollution levels of two WQP classes were generated in GIS framework, substantiating to the actual in situ pollution concentration levels in the river segment. The methodology adopted in the present study and results obtained validate the potential of high-resolution GeoEye-2 imagery for monitoring and mapping pollution levels in the water bodies.
Towards understanding the security of modern image captchas and underground captcha-solving services

Image captchas have recently become very popular and are widely deployed across the Internet to defend against abusive programs. However, the ever-advancing capabilities of computer vision have gradually diminished the security of image captchas and made them vulnerable to attack. In this paper, we first classify the currently popular image captchas into three categories: selection-based captchas, slide-based captchas, and click-based captchas. Second, we propose simple yet powerful attack frameworks against each of these categories of image captchas. Third, we systematically evaluate our attack frameworks against 10 popular real-world image captchas, including captchas from tencent.com, google.com, and 12306.cn. Fourth, we compare our attacks against nine online image recognition services and against human labors from eight underground captcha-solving services. Our evaluation results show that (1) each of the popular image captchas that we study is vulnerable to our attacks; (2) our attacks yield the highest captcha-breaking success rate compared with state-of-the-art methods in almost all scenarios; and (3) our attacks achieve almost as high a success rate as human labor while being much faster. Based on our evaluation, we identify some design flaws in these popular schemes, along with some best practices and design principles for more secure captchas. We also examine the underground market for captcha-solving services, identifying 152 such services. We then seek to measure this underground market with data from these services. Our findings shed light on understanding the scale, impact, and commercial landscape of the underground market for captcha solving.
Direct Visualization of Cross-Sectional Strain Distribution in Flexible Devices

For flexible devices that inevitably undergo repetitive deformations, it is important to evaluate and control the mechanical strain imposed on the flexible systems for enhancing the reliability. In this paper, a novel experimental method to directly visualize cross-sectional strain distribution in the thin flexible devices is proposed. Digital image correlation (DIC) is effectively adapted by using microscopic images of the cross section for accurate analysis of the microscale deformations. To conduct the DIC strain analysis, speckle patterning is accomplished by using microparticles from diamond-abrasive suspensions with optimized fabrication conditions. First, the cross-sectional micro-DIC analysis is performed successfully for 100 μm-thick substrates. Full-field strain quantification and easy inspection of a neutral plane are demonstrated and compared with results of finite element analysis simulation. Using the presented method, generation of multiple neutral planes is clearly visualized for a trilayer structure with a very soft adhesive midlayer, where strain decoupling occurs by severe shear deformation of the soft adhesive layer. Furthermore, bending strain distribution in a flexible fabric-reinforced polymer (FRP) substrate is also investigated to analyze and predict fatigue fracture in the complex inner structure under repetitive bending loading.
Local Smoothness of Graph Signals

Analysis of vertex-varying spectral content of signals on graphs challenges the assumption of vertex invariance and requires the introduction of vertex-frequency representations as a new tool for graph signal analysis. Local smoothness, an important parameter of vertex-varying graph signals, is introduced and defined in this paper. Basic properties of this parameter are given. By using the local smoothness, an ideal vertex-frequency distribution is introduced. The local smoothness estimation is performed based on several forms of the vertex-frequency distributions, including the graph spectrogram, the graph Rihaczek distribution, and a vertex-frequency distribution with reduced interferences. The presented theory is illustrated through numerical examples.
Evaluating information security core human error causes (IS-CHEC) technique in public sector and comparison with the private sector

The number of reported public sector information security incidents has significantly increased recently including 22% related to the UK health sector. Over two thirds of these incidents pertain to human error, but despite this, there are limited published related works researching human error as it affects information security. This research conducts an empirical case study into the feasibility and implementation of the Information Security Core Human Error Causes (IS-CHEC) technique which is an information security adaptation of Human Error Assessment and Reduction Technique (HEART). We analysed 12 months of reported information security incidents for a participating public sector organisation providing healthcare services and mapped them to the IS-CHEC technique. The results show that the IS-CHEC technique is applicable to the field of information security but identified that the underpinning HEART human error probability calculations did not align to the recorded incidents. The paper then proposes adaptation of the IS-CHEC technique based on the feedback from users during the implementation. We then compared the results against those of a private sector organisation established using the same approach. The research concluded that the proportion of human error is far higher than reported in current literature. The most common causes of human error within the participating public sector organisation were lack of time for error detection and correction, no obvious means of reversing an unintended action and people performing repetitious tasks.
Approximated oracle filter pruning for destructive CNN width optimization

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference
Exploiting Images for Video Recognition: Heterogeneous Feature Augmentation via Symmetric Adversarial Learning

Training deep models of video recognition usually requires sufficient labeled videos in order to achieve good performance without over-fitting. However, it is quite labor-intensive and time-consuming to collect and annotate a large amount of videos. Moreover, training deep neural networks on large-scale video datasets always demands huge computational resources which further hold back many researchers and practitioners. To resolve that, collecting and training on annotated images are much easier. However, thoughtlessly applying images to help recognize videos may result in noticeable performance degeneration due to the well-known domain shift and feature heterogeneity. This proposes a novel symmetric adversarial learning approach for heterogeneous image-to-video adaptation, which augments deep image and video features by learning domain-invariant representations of source images and target videos. Primarily focusing on an unsupervised scenario where the labeled source images are accompanied by unlabeled target videos in the training phrase, we present a data-driven approach to respectively learn the augmented features of images and videos with superior transformability and distinguishability. Starting with learning a common feature space (called image-frame feature space) between images and video frames, we then build new symmetric generative adversarial networks (Sym-GANs) where one GAN maps image-frame features to video features and the other maps video features to image-frame features. Using the Sym-GANs, the source image feature is augmented with the generated video-specific representation to capture the motion dynamics while the target video feature is augmented with the image-specific representation to take the static appearance information. Finally, the augmented features from the source domain are fed into a network with fully connected layers for classification. Thanks to an end-to-end training procedure of the Sym-GANs and the classification network, our approach achieves better results than other state-of-the-arts, which is clearly validated by experiments on two video datasets, i.e., the UCF101 and HMDB51 datasets.
Description-Based Zero-shot Fine-Grained Entity Typing

Rasha Obeidat, Xiaoli Fern, Hamed Shahbazi, Prasad Tadepalli. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Distributed Collaborative Feature Selection Based on Intermediate Representation

Feature selection is an efficient dimensionality reduction technique for artificial intelligence and machine learning. Many feature selection methods learn the data structure to select the most discriminative features for distinguishing different classes. However, the data is sometimes distributed in multiple parties and sharing the original data is difficult due to the privacy requirement. As a result, the data in one party may be lack of useful information to learn the most discriminative features. In this paper, we propose a novel distributed method which allows collaborative feature selection for multiple parties without revealing their original data. In the proposed method, each party finds the intermediate representations from the original data, and shares the intermediate representations for collaborative feature selection. Based on the shared intermediate representations, the original data from multiple parties are transformed to the same low dimensional space. The feature ranking of the original data is learned by imposing row sparsity on the transformation matrix simultaneously. Experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
Unsupervised Context Rewriting for Open Domain Conversation

Context modeling has a pivotal role in open domain conversation.Existing works either use heuristic methods or jointly learn context modeling and response generation with an encoder-decoder framework.This paper proposes an explicit context rewriting method, which rewrites the last utterance by considering context history.We leverage pseudoparallel data and elaborate a context rewriting network, which is built upon the Copy-Net with the reinforcement learning method.The rewritten utterance is beneficial to candidate retrieval, explainable context modeling, as well as enabling to employ a single-turn framework to the multi-turn scenario.The empirical results show that our model outperforms baselines in terms of the rewriting quality, the multi-turn response generation, and the end-to-end retrieval-based chatbots.
The Biological and Biographical Basis of Precision Medicine

Construction of a patient narrative (case history) is a core strategy in the care of patients. Recent advances in biomarker identification and digital sensors to monitor physiological and behavioral features have made constructing a case history more complex. Notably, however, although a biological profile is increasingly a part of the patient’s profile, an analogous patient-based biographical (life experience) profile is typically overlooked. Evolving concepts such as allostasis and allostatic load refer to processes promoting stability of physiological systems in the presence of diverse life experiences. Integrating details of both biology and biography is a goal of “precision medicine.” In this review, we describe how complex interactions between biology and biography affect disease risk and treatment response and highlight a strategy to develop narratives that establish the integration of biology and biography as the scientific basis for precision medicine.
Nuclear Instance Segmentation Using a Proposal-Free Spatially Aware Deep Learning Framework

Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep learning based framework to address these challenges. To this end, we propose a spatially-aware network (SpaNet) to capture spatial information in a multi-scale manner. A dual-head variation of the SpaNet is first utilized to predict the pixel-wise segmentation and centroid detection maps of nuclei. Based on these outputs, a single-head SpaNet predicts the positional information related to each nucleus instance. Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively. The output of the clustering method is the final nuclear instance segmentation mask. We applied our method on a publicly available multi-organ data set ( https://nucleisegmentationbenchmark.weebly.com/ ) and achieved state-of-the-art performance for nuclear segmentation.
Video Retrieval with Similarity-Preserving Deep Temporal Hashing

Despite the fact that remarkable progress has been made in recent years, Content-based Video Retrieval (CBVR) is still an appealing research topic due to increasing search demands in the Internet era of big data. This article aims to explore an efficient CBVR system by discriminately hashing videos into short binary codes. Existing video hashing methods usually encounter two weaknesses originating from the following sources: (1) Most works adopt the separated stages method or the frame-pooling based end-to-end architecture. However, the spatial-temporal properties of videos cannot be fully explored or kept well in the follow-up hashing step. (2) Discriminative learning based on pairwise or triplet constraints often suffers from slow convergence and poor local optimization, mainly because of the limited samples for each update. To alleviate these problems, we propose an end-to-end video retrieval framework called the Similarity-Preserving Deep Temporal Hashing (SPDTH) network. Specifically, we equip the model with the ability to capture spatial-temporal properties of videos and to generate binary codes by stacked Gated Recurrent Units (GRUs). It unifies video temporal modeling and learning to hash into one step to allow for maximum retention of information. We also introduce a deep metric learning objective called ℓ 2 All _ loss for network training by preserving intra-class similarity and inter-class separability, and a quantization loss between the real-valued outputs and the binary codes is minimized. Extensive experiments on several challenging datasets demonstrate that SPDTH can consistently outperform state-of-the-art methods.