Reviewerly/exHarmony huggingface dataset:
Petrel: Community-aware Synchronous Parallel for Heterogeneous Parameter Server

As to address the impact of heterogeneity in distributed Deep Learning (DL) systems, most previous approaches focus on prioritizing the contribution of fast workers and reducing the involvement of slow workers, incurring the limitations of workload imbalance and computation inefficiency. We reveal that grouping workers into communities, an abstraction proposed by us, and handling parameter synchronization in community level can conquer these limitations and accelerate the training convergence progress. The inspiration of community comes from our exploration of prior knowledge about the similarity between workers, which is often neglected by previous work. These observations motivate us to propose a new synchronization mechanism named Community-aware Synchronous Parallel (CSP), which uses the Asynchronous Advantage Actor-Critic (A3C), a Reinforcement Learning (RL) based algorithm, to intelligently determine community configuration and fully improve the synchronization performance. The whole idea has been implemented in a system called Petrel that achieves a good balance between convergence efficiency and communication overhead. The evaluation under different benchmarks demonstrates our approach can effectively accelerate the training convergence speed and reduce synchro-nization traffic.
A case study of Singapore Management University Libraries: Adopting a mixed methods approach towards collection evaluation

With rising costs and limited resources, it has become increasingly important for academic libraries to make acquisition decisions prudently to build a collection that meets its users' learning, teaching, and research needs. As such, Singapore Management University's (“SMU”) Libraries use a patron-driven acquisitions process, whereby acquisition decisions are guided by users' needs and requests. This article seeks to investigate if the current patron-driven acquisitions approach adequately meets the research needs of SMU's users through a mixed methods approach. A collection analysis was first conducted on the SMU Libraries' publications' references from the years 2017 to 2018 using the Scopus database. To ensure that the results of the study were valid and reliable, this study also analysed the Library Service Quality Survey for 2018, in addition to interviews with selected undergraduate and postgraduate students. This resulted in the triangulation of these results enabling comparison and analysis across a range of dimensions. The findings show that the library collection was largely able to meet users' needs. Most of the cited publications could be found in the library collection, and users were satisfied with the collection. However, there was a lack of awareness of library resources and how to use them effectively.
Towards a Quantum Internet: Post-pandemic Cyber Security in a Post-digital World

A quantum internet could provide more effective security against private cyber crime, Russian cyber war and Chinese efforts to gain information dominance.
SideGuide:A Large-scale Sidewalk Dataset for Guiding Impaired People

In this paper, we introduce a new large-scale sidewalk dataset called SideGuide that could potentially help impaired people. Unlike most previous datasets, which are focused on road environments, we paid attention to sidewalks, where understanding the environment could provide the potential for improved walking of humans, especially impaired people. Concretely, we interviewed impaired people and carefully selected target objects from the interviewees' feedback (objects they encounter on sidewalks). We then acquired two different types of data: crowd-sourced data and stereo data. We labeled target objects at instance-level (i.e., bounding box and polygon mask) and generated a ground-truth disparity map for the stereo data. SideGuide consists of 350K images with bounding box annotation, 100K images with a polygon mask, and 180K stereo pairs with the ground-truth disparity. We analyzed our dataset by performing baseline analysis for object detection, instance segmentation, and stereo matching tasks. In addition, we developed a prototype that recognizes the target objects and measures distances, which could potentially assist people with disabilities. The prototype suggests the possibility of practical application of our dataset in real life.
Deep Learning Applied on Next Generation Sequencing Data Analysis

Deep learning is defined as the group of computational techniques allowing for the discovery of latent information within large amounts of data. Recently, many fields have seen the immense potential of deep learning to solve various tasks in ways which outperformed many other traditional methods. Genomic research could be the next frontier to take advantage of deep learning, as it has the perfect combination of vast amounts of data and diverse tasks. Here we present the platform we generated to combine deep learning and genomic sequencing data. We tested the platform on publicly available sequencing data from the gut microbiome of cancer patients. We showed that our platform is capable of classifying patients with higher accuracy than other methods, with some caveats. Overall, we believe genomic research is the next frontline for deep learning as there are exciting avenues waiting to be explored. We think that our platform, presented here, could serve as the basis for such future research.
Self-reinforcing Unsupervised Matching

Remarkable gains in deep learning usually benefit from large-scale supervised data. Ensuring the intra-class modality diversity in training set is critical for generalization capability of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. Specifically, we propose a dynamic programming algorithm, dynamic position warping (DPW), to reveal the underlying element correspondence relationship between two matrix-form data in an order-preserving fashion, and devise a local feature adapter (LoFA) to allow for cross-modality similarity measurement. On these bases, we develop a two-tier self-reinforcing learning mechanism on both feature level and image level to optimize the LoFA. The proposed SUM framework requires no any supervision in emerging modality and only one template in seen modality, providing a promising route towards incremental learning and continual learning. Extensive experimental evaluation on two proposed challenging one-template visual matching tasks demonstrate its efficiency and superiority.
User Authentication Method using FIDO based Password Management for Smart Energy Environment

In a smart energy environment, user authentication is an essential process in smart energy. After the authentication process to verify whether the user is registered one, the user can access the smart service such as power consumption prediction, intelligent energy management. The user authentication technology has evolved to various authentication methods using ID/PW, security token and biometric information because of the diversification of the Internet of Things and social structure. Despite there are various authentication technologies, the ID/PW authentication still widely used because of low cost and convenience. However, the user using ID/PW methods should use different passwords for each service, as well as passwords that include special symbols. Moreover, it is difficult for users to remember complicated passwords and it is not easy to change passwords periodically. Therefore, in this paper, we propose the user authentication method using FIDO based password management. Through the password management, the user login the information system by password as well as biometric information using hardware security device. In addition, the method is compatible with legacy PW authentication. The proposed mechanism will enhance the security on ID/PW authentication method currently in use on most service.
Fine-grained image classification based on attention-guided image enhancement

Abstract Extracting distinguished fine-grained features is essential for fine-grained image recognition tasks. Many researchers use expensive manual annotations to learn to distinguish part models, which may not be possible in practical applications. Unlike previous strongly supervised fine grained classification networks that require additional image annotations, weakly supervised fine grained image classification only requires label annotations. Recently, image enhancement has been increasingly used in network structures, but random enhancement will lead to background noise and filter out irrelevant areas. In this article, we propose a weakly supervised fine-grained image classification network based on attention-guided image enhancement to study the effect of image enhancement on the classification network. In detail, we use the backbone network to generate the feature map of the image, then generate the corresponding attention map through a custom mask, and use the attention map to guide the image enhancement process (including image cropping and image dropping). We conducted experiments on three commonly used fine-grained image classification datasets, and achieved sota effects in CUB, FGVC-Aircraft, and Stanford Cars.
Secure Distribution of Factor Analysis of Mixed Data (FAMD) and Its Application to Personalized Medicine of Transplanted Patients

Factor analysis of mixed data (FAMD) is an important statistical technique that not only enables the visualization of large data but also helps to select subgroups of relevant information for a given patient. While such analyses are well-known in the medical domain, they have to satisfy new data governance constraints if reference data is distributed, notably in the context of large consortia developing the coming generation of personalised medicine analyses.
Extended Performance Guarantees for Receding Horizon Search with Terminal Cost

The computational difficulty of planning search paths that seek to maximize a general deterministic value function increases dramatically as desired path lengths increase. Mobile search agents with limited computational resources often utilize receding horizon methods to address the path planning problem. Unfortunately, receding horizon planners may perform poorly due to myopic planning horizons. We provide methods of incorporating terminal costs in the construction of receding horizon paths that provide a theoretical lower bound on the performance of the search paths produced. The results presented in this paper are of particular value in subsea search applications. We present results from simulated subsea search missions that use real-world data acquired by an autonomous underwater vehicle during a subsea survey of Boston Harbor.
Robust regression with compositional covariates including cellwise outliers

Abstract We propose a robust procedure to estimate a linear regression model with compositional and real-valued explanatory variables. The proposed procedure is designed to be robust against individual outlying cells in the data matrix (cellwise outliers), as well as entire outlying observations (rowwise outliers). Cellwise outliers are first filtered and then imputed by robust estimates. Afterwards, rowwise robust compositional regression is performed to obtain model coefficient estimates. Simulations show that the procedure generally outperforms a traditional rowwise-only robust regression method (MM-estimator). Moreover, our procedure yields better or comparable results to recently proposed cellwise robust regression methods (shooting S-estimator, 3-step regression) while it is preferable for interpretation through the use of appropriate coordinate systems for compositional data. An application to bio-environmental data reveals that the proposed procedure—compared to other regression methods—leads to conclusions that are best aligned with established scientific knowledge.
Semantic Private Information Retrieval: Effects of Heterogeneous Message Sizes and Popularities

We investigate the problem of semantic private information retrieval (semantic PIR). In semantic PIR, a user privately retrieves a message out of K independent messages stored in N replicated and non-colluding databases. The messages come with different semantics, i.e., the messages are allowed to have non-uniform a priori probabilities denoted by (p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> > 0, i ∈ [K]) and arbitrary message sizes (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , i ∈ [K]). We derive the semantic PIR capacity for general K, N. We present two achievable semantic PIR schemes: The first one is a deterministic scheme with non-uniform subpacketization. The second scheme is probabilistic and is based on choosing one query set out of multiple options at random to retrieve the required message without the need for exponential subpacketization. We derive conditions for the semantic PIR capacity to exceed the classical PIR capacity with equal priors and sizes. Our results show that the semantic PIR capacity can be larger than the classical PIR capacity when longer messages have higher popularities. However, when messages are of equal-length, the non-uniform priors cannot be exploited to improve the retrieval rate.
Monocular Depth Prediction through Continuous 3D Loss

This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR points, which enables us to leverage available open source datasets with camera-LIDAR sensor suites during training. Currently, accurate and affordable range sensor is not readily available. Stereo cameras and LIDARs measure depth either inaccurately or sparsely/costly. In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR's sparsity measurements. We applied the proposed loss in three state-of-the-art monocular depth prediction approaches DORN, BTS, and Monodepth2. Experimental evaluation shows that the proposed loss improves the depth prediction accuracy and produces point-clouds with more consistent 3D geometric structures compared with all tested baselines, implying the benefit of the proposed loss on general depth prediction networks. A video demo of this work is available at https://youtu.be/5HL8BjSAY4Y.
Blockchain as a Healthcare Insurance Fraud Detection Tool

Healthcare insurance is intended to help pay for the insured’s medical expenses by paying a policy premium. For this, the industry needs the collaboration of some entities, such as: doctors, health care centers, brokers, insurers, reinsurers. In this context, gathering the information necessary to assess and process claims is a major problem. As a consequence, these inconveniences are exploited by fraudsters and scammers. Faced with these challenges, blockchain can help solve them. This paper defines blockchain and investigates how its inherent characteristics can contribute to detecting healthcare insurance fraud. Then, a layered overview and model using smart contracts are defined. Finally, conclusions and recommendations are issued to address its implementation in the insurance market.
Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation

We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios. Our method is based on the observation that using the depth information to obtain 3D lifted points from 2D body landmark detections provides a rough estimate of the true 3D human pose, thus requiring only a refinement step. In that line our contributions are threefold. (i) we propose to perform 3D pose estimation from depth images by decoupling 2D pose estimation and 3D pose refinement; (ii) we propose a deep-learning approach that regresses the residual pose between the lifted 3D pose and the true 3D pose; (iii) we show that despite its simplicity, our approach achieves very competitive results both in accuracy and speed on two public datasets and is therefore appealing for multi-person HRI compared to recent state-of-the-art methods.
MPC-Graph: Feedback Motion Planning Using Sparse Sampling Based Neighborhood Graph

Robust and safe feedback motion planning and navigation is a critical task for autonomous mobile robotic systems considering the highly dynamic and uncertain nature scenarios of modern applications. For these reasons motion planning and navigation algorithms that have deep roots in feedback control theory has been at the center stage of this domain recently. However, the vast majority of such policies still rely on the idea that a motion planner first generates a set of open-loop possibly time-dependent trajectories, and then a set of feedback control policies track these trajectories in closed-loop while providing some error bounds and guarantees around these trajectories. In contrast to trajectory-based approaches, some researchers developed feedback motion planning strategies based on connected obstacle-free regions, where the task of the local control policies is to drive the robot(s) in between these particular connected regions. In this paper, we propose a feedback motion planning algorithm based on sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). The algorithm first generates a sparse neighborhood graph as a set of connected simple rectangular regions. After that, during navigation, an MPC based online feedback control policy funnels the robot with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs with guaranteed stability. In this framework, we can drive the robot to any goal location provided that the connected region network covers both the initial condition and the goal position. We demonstrate the effectiveness and validity of the algorithm on simulation studies.
Generator Based Methods for Off-line Handwritten Character Recognition

We present a novel method using Convolutional Autoencoder and Generative Adversarial Network for off-line classification of handwritten alphabets and numerals of different scripts namely English, Devanagari and Kannada. Here we have used the concept of generation of a predefined view from any novel view as used in facial recognition problem, for handwritten character recognition (HCR) challenge. Our investigations in Autoencoder based method show that the reduced dimension representation of the dataset is not quite clusterable thus reducing the classification accuracy. So we used Convolutional Autoencoder for dimension reduction of the data. We then further added Adversarial Learning to the Convolutional Autoencoder model using GAN architecture. On average we achieved better results with the GAN based methods. A detailed discussion justifying the reasons behind this is presented with the help of observations from our experiments in the main article. Quantitative comparison of our results with state of the art is also presented in the article.
Prediction of Solar Photovoltaic Power Generation Based on MLP and LSTM neural networks

In recent years, with the increasing of photovoltaic (PV) penetration, it is very important to improve the fore-casting accuracy of the PV generation for the real-time dispatching operation of distribution network. In this paper, the multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks are used to predict and analyze the electricity consumption data in Shanghai, Jiangsu, and Zhejiang. In the mean while the accuracy of the prediction result is improved by the enhanced algorithm during control experiments. It has been proved that the algorithm proposed in this paper is an effective and reliable method for PV power generation prediction. The comparison of prediction result shows that the root mean square error (RMSE) of LSTM model for PV power generation is slightly more accurate than that of MLP model.
Micro Gesture Recognition with Terahertz Radar Based on Diagonal Profile of Range-Doppler Map

Gestures can be gradually used to achieve natural and direct communication between people and machines, not limited to people. However, micro gesture motion sensing using traditional sensors and techniques is challenging because of the difference and diversity of finger motion. This paper proposes a novel method for micro gesture recognition based on the high resolution of terahertz radar, which can capture fine changes during gesture movement. From these radar echoes collected in gesture motion, the low dimensional projection features of diagonal profile based on range-Doppler map (RDM) are extracted to characterize the motion difference between gestures. Then, a convolutional neural network (CNN) is employed to realize the gesture recognition system. The experimental results show that the proposed method can achieve a high recognition rate of 98.06% with six dynamic micro gestures across eight participants.
SCUR: Smart Contracts with a Static Upper-Bound on Resource Usage

Transactions (TX) sent and executed by a public Blockchain (BC) need to pay a TX fee. The fee is relative to the resources consumed when including the TX into a block. The sender of the TX pays for the fee in the native currency of the BC used. Such a fee protects the network from Denial-of-Service (DoS) attacks and provides incentives for TXs inclusion. BCs that support Smart Contracts (SC), thus, TXs can execute user-provided code, require a mechanism for fee calculation depending on the resources consumed by the code executed. Ethereum uses an elaborated mechanism compared to Bitcoin, leading to an increase in the attack surface of their SCs. The mechanism that makes the Bitcoin script - a limited version of SCs - more secure is the ability to compute, independent of the BC's state, an upperbound on resources needed to execute this TX. Thus, this work proposes a new approach enabling security benefits by defining an upper-bound on resource usage for general SCs. Furthermore, the approach allows for the payment of fees in any currency and not just the native currency of the BC.
Study on Learner's Facial Expression Recognition

Learners' facial expressions are part of their language and are often used to express emotions. With the development of online learning platforms, people are paying more and more attention to the emotions of learners. If a machine wants to intelligently recognize student emotions, it must find feature vectors that the machine can calculate. It was found through research that the features used in basic facial expression recognition failed to extract the learner's expression features and there was redundant information. In order to extract the learner's facial expression features, this paper improves the basic facial expression feature vector. The convolutional neural network classifier is designed to classify learners' emotions. The two methods of manual collection and crawler collection are used to construct the learner's expression data set. Experimental results show that the proposed algorithm has a high accuracy rate to recognize learners' facial expressions.