Reviewerly/exHarmony huggingface dataset:
Radar + RGB Fusion For Robust Object Detection In Autonomous Vehicle

This paper presents two variations of architecture referred to as RANet and BIRANet. The proposed architecture aims to use radar signal data along with RGB camera images to form a robust detection network that works efficiently, even in variable lighting and weather conditions such as rain, dust, fog, and others. First, radar information is fused in the feature extractor network. Second, radar points are used to generate guided anchors. Third, a method is proposed to improve region proposal network [1] targets. BIRANet yields 72.3/75.3% average AP/AR on the NuScenes [2] dataset, which is better than the performance of our base network Faster-RCNN with Feature pyramid network(FFPN) [3]. RANet gives 69/71.9% average AP/AR on the same dataset, which is reasonably acceptable performance. Also, both BIRANet and RANet are evaluated to be robust towards the noise.
A study on spoken language identification using deep neural networks

In this paper, we investigate a previously proposed algorithm for spoken language identification based on convolutional neural networks and convolutional recurrent neural networks. We improve the algorithm by modifying the training strategy to ensure equal class distribution and efficient memory usage. We successfully replicate previous experimental findings using a modified set of languages. Our findings confirm that both a convolutional neural network as well as convolutional recurrent neural networks are capable to learn language-specific patterns in mel spectrogram representations of speech recordings.
A Novel Methodology-Based Joint Hypergeometric Distribution to Analyze the Security of Sharded Blockchains

Cryptocurrencies (e.g., Bitcoin and Ethereum), which promise to become the future of money transactions, are mainly implemented with blockchain technology. However, blockchain suffers from scalability issues. Sharding is the leading solution for blockchain scalability. Sharding splits the blockchain network into sub-chains called shards/committees. Each shard processes a sub-set of transactions, rather than the entire network processing all transactions. This raises security issues for sharding-based blockchain protocols. In this paper, we propose a novel methodology to analyze the security of these protocols (e.g., OmniLedger and RapidChain). In particular, this methodology estimates the failure probability of one sharding round taking into consideration the failure probabilities of all shards. To illustrate the effectiveness of the estimated failure probability, we conduct a numerical analysis of our methodology based on a huge number of trials. Finally, we compute confidence intervals to accurately estimate the failure probability and compare our methodology with existing approaches.
Parameters Identification Method of Interval Discrete Dynamic Models of Air Pollution Based on Artificial Bee Colony Algorithm

A new method for parametric identification of interval discrete dynamic models based on artificial bee colony algorithm has been proposed and justified. Proposed method is based on a new mathematical description of a searching process of a global minimum of discrete non-linear objective function under given constraints for the model parameters values. An example of the method application for identification of interval model of Carbon monoxide distribution process on a straight section of a street due to uniform vehicle traffic with a constant emission intensity is given. Higher efficiency of the proposed method compared to the existing methods of random search is shown.
Exceptional scatteredness in prime degree

Let q be an odd prime power and n an integer. Let ℓ∈Fqn[x] be a q-linearized t-scattered polynomial of linearized degree r. Let d=max⁡{t,r} be an odd prime number. In this paper we show that under these assumptions it follows that ℓ=x. Our technique involves a Galois theoretical characterization of t-scattered polynomials combined with the classification of transitive subgroups of the general linear group over the finite field Fq.
MC<sup>2</sup>RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference

This work discusses the implementation of Marko Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a novel architecture of SRAM by embedding it with random number generators (RNGs), digital-to-analog converters (DACs), and analog-to-digital converters (ADCs) so that SRAM arrays can be used for high performance Metropolis-Hastings (MH) algorithm-based MCMC sampling. Most of the expensive computations are performed within the SRAM and can be parallelized for high speed sampling. Our iterative compute flow minimizes data movement during sampling. We characterize power-performance trade-off of our design by simulating on 45 nm CMOS technology. For a two-dimensional, two mixture GMM, the implementation consumes ~ 91μW power per sampling iteration and produces 500 samples in 2000 clock cycles on an average at 1 GHz clock frequency. Our study highlights interesting insights on how low-level hardware non-idealities can affect high-level sampling characteristics, and recommends ways to optimally operate SRAM within area/power constraints for high performance sampling.
Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention

Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, there still does not exist such robust system for the recognition of words and sentences from singing voice. This paper proposes a complete pipeline for this task which may commonly be referred as automatic lyrics transcription (ALT). We have trained convolutional time-delay neural networks with self-attention on monophonic karaoke recordings using a sequence classification objective for building the acoustic model. The dataset used in this study, DAMP - Sing! 300x30x2 [1] is filtered to have songs with only English lyrics. Different language models are tested including MaxEnt and Recurrent Neural Networks based methods which are trained on the lyrics of pop songs in English. An in-depth analysis of the self-attention mechanism is held while tuning its context width and the number of attention heads. Using the best settings, our system achieves notable improvement to the state-of-the-art in ALT and provides a new baseline for the task.
Outliers in rules - the comparision of LOF, COF and KMEANS algorithms.

The aim of the article is the analysis of using LOF, COF and Kmeans algorithms for outlier detection in rule based knowledge bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently.
Distributed Multi-User Secret Sharing

We consider a distributed secret sharing system that consists of a dealer, n storage nodes, and m users. Each user is given access to a certain subset of storage nodes, where it can download the stored data. The dealer wants to securely convey a specific secret s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> to user j via storage nodes, for j=1,2, ..., m. More specifically, two secrecy conditions are considered in this multi-user context. The weak secrecy condition is that each user does not get any information about the individual secrets of other users, while the perfect secrecy condition implies that a user does not get any information about the collection of all other users' secrets. In this system, the dealer encodes secrets into several secret shares and loads them into the storage nodes. Given a certain number of storage nodes we find the maximum number of users that can be served in such a system and construct schemes that achieve this with perfect secrecy. We further define two major properties for such distributed secret sharing systems; communication complexity is defined as the total amount of data that users need to download in order to reconstruct their secrets; and storage overhead is defined as the total size of data loaded by the dealer into the storage nodes normalized by the total size of secrets. Lower bounds on the minimum communication complexity and the storage overhead are characterized given any n and m. We construct distributed secret sharing protocols, under certain conditions on the system parameters, that attain the lower bound on the communication complexity while providing perfect secrecy. Furthermore, we construct protocols, again under certain conditions, that simultaneously attain the lower bounds on the communication complexity and the storage overhead while providing weak secrecy, thereby demonstrating schemes that are optimal in terms of both parameters. It is shown how to modify the proposed protocols in order to construct schemes with balanced storage load and communication complexity.
Wearable Physical Activity Tracking Systems for Older Adults—A Systematic Review

Physical activity (PA) positively impacts the quality of life of older adults, with technology as a promising factor in maintaining motivation. Within Computer Science and Engineering, research investigates how to track PA of older adults for various purposes. We present a systematic review of 204 papers and discuss wearable tracking systems according to their purpose, technological context, and target audience, as well as design and evaluation processes with particular attention to the meaningful involvement of older adults. Our results show that most systems focus on supervising older adults in the context of disease and frailty management. Only few systems focus on supporting older adults by promoting rehabilitation and respecting agency of older adults via self-monitoring PA, or encouraging PA to maintain healthy levels of activity. Moreover, systems are often narrowly limited to walking, although older adults may enjoy a broader range of activities. Likewise, the involvement of older adults in design processes is scarce, and their experience with a given technology is rarely considered relevant for evaluation. In sum, we contribute an overview of wearable technology for tracking older adults’ PA, contextualize our findings within recommendations provided by Sports and Rehabilitation Science, and illustrate opportunities for future work.
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net

In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Decentralized multi-agent reinforcement learning methods have difficulty learning decentralized policies because of the environment appearing to be non-stationary due to other agents also learning at the same time. In this paper, we address this challenge by proposing a macro-action-based decentralized multi-agent double deep recurrent Q-net (MacDec-MADDRQN) which trains each decentralized Q-net using a centralized Q-net for action selection. A generalized version of MacDec-MADDRQN with two separate training environments, called Parallel-MacDec-MADDRQN, is also presented to leverage either centralized or decentralized exploration. The advantages and the practical nature of our methods are demonstrated by achieving near-centralized results in simulation and having real robots accomplish a warehouse tool delivery task in an efficient way.
Detection of Safety Helmet Wearing Based on Improved Faster R-CNN

In order to ensure the safety of workers and the stable operation of the power grid, the power grid companies in China have developed a very strict safety control system which contains many regulations, such as safety regulations and the two-ticket regulations. However, some workers are still lack of safety awareness in that they even do not wear safety helmets when carrying out construction or maintenance projects in substations. Safety helmet is an indispensable safety tool in electric power work, which can maintain the head safety of workers at all times and avoid fatal injuries such as electric shock and strike. Working without safety helmet is not only a violation of the safety control system, but also a manifestation of not being responsible for personal life and property. Nevertheless, the existing control means can not identify and prevent such behavior timely, efficiently and accurately. In order to better avoid this unsafe behavior, this paper proposes the Improved Faster R-CNN algorithm to inspect the wearing of safety helmet. Considering the real situation, the Retinex image enhancement is introduced to improve image quality for the outdoor complex scenes in substations. K-means++ algorithm is also adopted for better adaptation to the small size helmet. The experimental results show that compared with the Faster R-CNN algorithm, the mean average precision of the Improved Faster R-CNN is improved and the real-time automatic detection of the wearing of safety helmets is realized.
ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks

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Stochastic Quasi-Newton Methods

Large-scale data science trains models for data sets containing massive numbers of samples. Training is often formulated as the solution of empirical risk minimization problems that are optimization programs whose complexity scales with the number of elements in the data set. Stochastic optimization methods overcome this challenge, but they come with their own set of limitations. This article discusses recent developments to accelerate the convergence of stochastic optimization through the exploitation of second-order information. This is achieved with stochastic variants of quasi-Newton methods that approximate the curvature of the objective function using stochastic gradient information. The reasons for why this leads to faster convergence are discussed along with the introduction of an incremental method that exploits memory to achieve a superlinear convergence rate. This is the best-known convergence rate for a stochastic optimization method. Stochastic quasi-Newton methods are applied to several problems, including prediction of the click-through rate of an advertisement displayed in response to a specific search engine query by a specific visitor. Experimental evaluations showcase reductions in overall computation time relative to stochastic gradient descent algorithms.
Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

A novel Fusion Convolutional Network (FCN) is proposed in this research for potential real-time monitoring of insulators using unmanned aerial vehicle (UAV) edge devices. Precise airborne imaging of outdoor objects, such as high voltage insulators, suffers from varied object resolution, cluttered backgrounds, unclear or contaminated surfaces, and illumination conditions. Accurate information about the insulator surface condition is essential and is of a high priority since insulator breakdown is a leading cause of electrical failure. A multi-modal information fusion (MMIF) system is developed during this research to analyze and classify possible contaminations present on the electrical insulators. A novel system, referred to as FCN, consists of a Convolutional Neural Network (CNN) and a binary Multilayer Neural Network (MNN) sub-classifier. While constructing the MMIF dataset for training and testing the novel FCN, the image classification output of the CNN is combined with the leakage current values (LCV) obtained as the classification output of MNN. Each sample of the MMIF dataset is, therefore, represented as a series of fusions. Later, sub-classifiers, of the FCN, are trained to identify the contamination types in the fusion series by implementing a voting system of sub-classifiers which is trained to identify a given class. As a result of the implementation of the proposed FCN, the classification accuracy increased by 8.4%, i.e., from 92% to 99.76%. To compare and benchmark the performance of proposed FCN, conventional classification algorithms are also implemented on the fusion of features that are extracted employing the wavelet transform and PCA methods. State-of-the-art CNN architectures are also discussed on account of their time consumption and memory usage. The conceptualization of a potential hardware implementation of the proposed FCN, on emerging edge devices, is also provided for completeness of the discussion. Pertinent outcomes of this research can be further extended to other potential applications of airborne imaging.
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving

The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component. In this case, leveraging the benefits of deep reinforcement learning for high-level decision making requires special architectures to deal with multiple variable-length sequences of different object types, such as vehicles, lanes or traffic signs. At the same time, the architecture has to be able to cover interactions between traffic participants in order to find the optimal action to be taken. In this work, we propose the novel Deep Scenes architecture, that can learn complex interaction-aware scene representations based on extensions of either 1) Deep Sets or 2) Graph Convolutional Networks. We present the Graph-Q and DeepScene-Q off-policy reinforcement learning algorithms, both outperforming state-ofthe-art methods in evaluations with the publicly available traffic simulator SUMO.
"But where would I even start?"

Funding bodies increasingly require researchers to address gender in their proposals ---often framed around binary notions. With power structures emboldening inequality highly prevalent and persuasive in Western societies, these seep into current practices of Computer Science and Human-Computer Interaction (HCI) research. Hence, developing an increased awareness of our societal responsibility towards equity can be challenging without an appropriate starting point. We present a close reading of literature discussing gender sensitivity in HCI research to provide practical guidance in the form of recommendations for the design, proposal, conduct and presentation of research. Our analysis provides a starting point for HCI students and interested researchers to explore questions and issues around gender and to identify how gender relates to their research. This sensitisation may aid them in further reflecting on how they might better serve marginalised populations through their work.
Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification

In this paper, we present a novel and effective data augmentation method for convolutional neural network(CNN) on image classification tasks. CNN-based models such as VGG, Resnet and Densenet have achieved great success on image classification tasks. The common data augmentation methods such as rotation, crop and flip are always used for CNN, especially under the lack of data. However, in some cases such as small images and dispersed feature of objects, these methods have limitations and even can decrease the classification performance. In this case, an operation that has lower risk is important for the performance improvement. Addressing this problem, we design a data augmentation method named circular shift, which provides variations for the CNN-based models but does not lose too much information. Three commonly used image datasets are chosen for the evaluation of our proposed operation, and the experiment results show consistent improvement on different CNN-based models. What is more, our operation can be added to the current set of augmentation operation and achieves further performance improvement.
A Survey of Text Alignment Visualization

Text alignment is one of the fundamental techniques text-related domains like natural language processing, computational linguistics, and digital humanities. It compares two or more texts with each other aiming to find similar textual patterns, or to estimate in general how different or similar the texts are. Visualizing alignment results is an essential task, because it helps researchers getting a comprehensive overview of individual findings and the overall pattern structure. Different approaches have been developed to visualize and help making sense of these patterns depending on text size, alignment methods, and, most importantly, the underlying research tasks demanding for alignment. On the basis of those tasks, we reviewed existing text alignment visualization approaches, and discuss their advantages and drawbacks. We finally derive design implications and shed light on related future challenges.
Avoid Touching Your Face: A Hand-to-face 3D Motion Dataset (COVID-away) and Trained Models for Smartwatches

World Health Organisation (WHO) advises that humans must try to avoid touching their eye, nose and mouth, which is an effective way to stop the spread of viral diseases. This has become even more prominent with the widespread coronavirus (COVID-19), resulting in a global pandemic. However, we humans on average touch our face (eye, nose and mouth) 10-20 times an hour [22] [12], which is often the primary source [15] of getting infected by a variety of viral infections including seasonal Influenza, Coronavirus, Swine flu, Ebola virus, etc. Touching our face all day long is a quirk of human nature [13] and it is extremely difficult to train people to avoid touching their face. However, wearable devices and technology can help to continuously monitor our movements and trigger a timely event reminding people to avoid touching their face. In this work, we have collected a hand-to-face multi-sensor 3D motion dataset and named it COVID-away dataset. Using our dataset, we trained models that can continuously monitor human arm/hand movement using a wearable device and trigger a timely notification (e.g. vibration) to warn the device users when their hands are moved (unintentionally) towards their face. Our trained COVID-away models can be easily integrated into an app for smartwatches or fitness bands. Our evaluation shows that the Minimum Covariance Determinant (MCD) model produces the highest F1-score (0.93) using just the smartwatch's accelerometer data (39 features). Both the dataset and trained models are openly available on the Web at https://github.com/bharathsudharsan/COVID-away.