Reviewerly/exHarmony huggingface dataset:
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion

Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features from the pair of point clouds in a grid-wise attentive way, which is more fine- grained and more precise. In addition, we propose a SynAugment (Synchronized Augmentation) to enable our multi-modal framework to utilize all data augmentation approaches tailored to LiDAR-only methods. Lastly, we customize an effective and efficient feature extractor CPConv (Color Point Convolution) for pseudo point clouds. It can explore 2D image features and 3D geometric features of pseudo point clouds simultaneously. Our method holds the highest entry on the KITTI car 3D object detection leaderboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> †On the date of CVPR deadline, i.e., Nov.16, 2021, demonstrating the effectiveness of our SFD. Code will be made publicly available.
An Optical Image Encryption Algorithm Based on a Fractional-Order Laser Hyperchaotic System

In recent years, image encryption schemes based on optical methods have been extensively studied. However, possible optical encryption methods in combination with fractional-order laser hyperchaotic systems have not been reported. Therefore, an optical image encryption scheme based on fractional Fourier transform and five-dimensional host-induced nonlinearity fractional-order laser hyperchaotic system is studied in this paper. Firstly, the dynamical characteristics of the proposed fractional-order laser hyperchaotic system are analyzed, and the DSP platform is used to realize the system. Then, a novel image encryption scheme is proposed combining BP neural network, GF(17) domain diffusion and hyperchaotic, random point scrambling algorithm. Finally, the performance of the encryption scheme is analyzed in detail. This work provides an experimental basis and theoretical guidance for image secure communication combining fractional-order laser hyperchaotic systems and optical methods and offers a new research perspective for optical image encryption.
The mobile phone as an argument for good governance in sub-Saharan Africa

Purpose The purpose of this paper is to present theoretical and empirical arguments for the role of mobile telephony in promoting good governance in 47 sub-Saharan African countries for the period 2000–2012. Design/methodology/approach The empirical inquiry uses an endogeneity-robust GMM approach with forward orthogonal deviations to analyze the linkage between mobile phone usage and the variation in three broad governance categories – political, economic and institutional. Findings Three key findings are established: first, in terms of individual governance indicators, mobile phones consistently stimulated good governance by the same magnitude, with the exception of the effect on the regulation component of economic governance. Second, when indicators are combined, the effect of mobile phones on general governance is three times higher than that on the institutional governance category. Third, countries with lower levels of governance indicators are catching-up with their counterparts with more advanced dynamics. Originality/value The study makes both theoretical and empirical contributions by highlighting the importance of various combinations of governance indicators and their responsiveness to mobile phone usage.
A data citation roadmap for scholarly data repositories

Abstract This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH-funded BioCADDIE ( https://biocaddie.org ) project. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories. We describe the early adoption of these recommendations 18 months after they have first been published, looking specifically at implementations of machine-readable metadata on dataset landing pages.
KUKA Sunrise Toolbox: Interfacing Collaborative Robots With MATLAB

Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa, equipped with the KUKA Sunrise.OS controller, is one example of a collaborative/sensitive robot. This tutorial presents the KUKA Sunrise Toolbox (KST), a MATLAB toolbox that interfaces with KUKA Sunrise.OS. KST contains functionalities for networking, soft control in real time, point-to-point motion, parameter setters/getters, general purpose, and physical interaction. It includes approximately 100 functions and runs on a remote computer connected with the KUKA Sunrise controller via Transmission Control Protocol/Internet Protocol (TCP/IP). The potentialities of the KST are demonstrated in nine application examples.
Adoption of AI-Chatbots to Enhance Student Learning Experience in Higher Education in India

Today, every organisation depends on Information and Communication Technology (ICT) for the efficient service delivery and cost-effective application of technological resources. With growing preference towards faster services and acceptance of Artificial Intelligence (AI) based tools in business operations globally as well as in India, the global Chatbot market is going to accelerate in the next decade. In the era of AI, the Chatbot market is witnessing extraordinary growth with the increased demand for smartphones and increased use of messaging applications. In the past few years, the food delivery business, finance and the E-commerce industry have embraced Chatbot technology. One of the industries which can really benefit from using this technology is the educational sector. Education can benefit from Chatbot development. It can improve productivity, communication, learning, efficient teaching assistance, and minimize ambiguity from interaction. A new education platform can solve next-level problems in education using this technology as the engagement tool. The aim of this research paper is to find out the factors which affect the adoption of Chatbot technology in order to enhance the student learning experience in the Indian higher education sector. In this research, a Quantitative method is used through data collection from surveys of some of the prominent higher education institutes using Chatbot technology in India. It is expected that the research outcome will help Chatbot developers and higher education providers to better understand the requirements of students while providing an interactive learning and communication platform for them.
VAHAK: A Blockchain-based Outdoor Delivery Scheme using UAV for Healthcare 4.0 Services

Unmanned aerial vehicle (UAV) is used in various smart applications, such as defense, civilian, and healthcare services. As data in these applications flow through an open channel, i.e., the Internet, so security and privacy always a challenging issue. Though many solutions exist for this problem in literature, but these solutions are not adequate to handle security, privacy, latency, and efficient real-time delivery of healthcare services remotely over the wireless communication channel. Moreover, the existing UAV systems have security, reliability, latency, and storage cost issues, which restricts their applicability shortly. Motivated from these facts, this paper proposes VAHAK, an Ethereum Blockchain (BC) based secure outdoor healthcare medical supplies using UAVs. VAHAK provides reliable communication between the UAVs and the entities in a decentralized manner, which ensures the early delivery of required medical supplies to the critical patients. In VAHAK, security, privacy, and reliability issues have been resolved using Ethereum smart contract (ESC), while storage cost issues are handled with IPFS protocol. The security vulnerabilities of the VAHAK are tested on MyThril open-source tool. VAHAK is efficient in terms of data storage cost as it uses the InterPlanetary File System (IPFS) for healthcare record storage and 5G-enabled Tactile Internet (TI) for communication, respectively. Finally, VAHAK performance evaluation demonstrates its effectiveness as compared to the traditional systems where it outperforms the existing schemes with respect to various performance evaluation metrics, such as scalability, latency, and network bandwidth.
Learning with Privileged Information for Efficient Image Super-Resolution

Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on achieving performance gains in terms of quality metrics, such as PSNR and SSIM, over classical approaches. They typically require a large amount of memory and computational units. FSRCNN, consisting of few numbers of convolutional layers, has shown promising results, while using an extremely small number of network parameters. We introduce in this paper a novel distillation framework, consisting of teacher and student networks, that allows to boost the performance of FSRCNN drastically. To this end, we propose to use ground-truth high-resolution (HR) images as privileged information. The encoder in the teacher learns the degradation process, subsampling of HR images, using an imitation loss. The student and the decoder in the teacher, having the same network architecture as FSRCNN, try to reconstruct HR images. Intermediate features in the decoder, affordable for the student to learn, are transferred to the student through feature distillation. Experimental results on standard benchmarks demonstrate the effectiveness and the generalization ability of our framework, which significantly boosts the performance of FSRCNN as well as other SR methods. Our code and model are available online: https://cvlab.yonsei.ac.kr/projects/PISR .
Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering

Kun Xu, Yuxuan Lai, Yansong Feng, Zhiguo Wang. 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.
A Generic Workflow for the Data FAIRification Process

The FAIR guiding principles aim to enhance the Findability, Accessibility, Interoperability and Reusability of digital resources such as data, for both humans and machines. The process of making data FAIR (“FAIRification”) can be described in multiple steps. In this paper, we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards. The FAIRification workflow should be applicable to any type of data and has been developed and used for “Bring Your Own Data” (BYOD) workshops, as well as for the FAIRification of e.g., rare diseases resources. The steps are: 1) identify the FAIRification objective, 2) analyze data, 3) analyze metadata, 4) define semantic model for data (4a) and metadata (4b), 5) make data (5a) and metadata (5b) linkable, 6) host FAIR data, and 7) assess FAIR data. For each step we describe how the data are processed, what expertise is required, which procedures and tools can be used, and which FAIR principles they relate to.
Contrastive Learning of Structured World Models

A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

The extraction of labels from radiology text reports enables large-scale training of medical imaging models.Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual annotations by experts.In this work, we introduce a BERTbased approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations.We demonstrate superior performance of a biomedically pretrained BERT model first trained on annotations of a rulebased labeler and then fine-tuned on a small set of expert annotations augmented with automated backtranslation.We find that our final model, CheXbert, is able to outperform the previous best rule-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.
A survey of challenges for runtime verification from advanced application domains (beyond software)

Abstract Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.
Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects - [Hands on]

When opportunity knocks, open the door: No one has taken heed of the adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the CUDA parallel computing software framework in 2006.
Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers

Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification.A neuropathological diagnostic platform is designed comprising a slide scanner and deep convolutional neural networks (CNNs) to classify 5 major histological subtypes of glioma to assist pathologists. The CNNs were trained and verified on over 79 990 histological patch images from 267 patients. A logical algorithm is used when molecular profiles are available.A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which learns the recognizable features of glioma histology CNN-based independent diagnostic testing on data from 56 patients with 17 262 histological patch images demonstrated patch level accuracy of 86.5% and patient level accuracy of 87.5%. Histopathological classifications could be further amplified to integrated neuropathological diagnosis by 2 molecular markers (isocitrate dehydrogenase and 1p/19q).The model is capable of solving multiple classification tasks and can satisfactorily classify glioma subtypes. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma.
Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.
Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter

The problem of automatic extraction of sentiment expressions from informal text, as in microblogs such as tweets is a recent area of investigation. Compared to formal text, such as in product reviews or news articles, one of the key challenges lies in the wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns. In this work, we present an optimization-based approach to automatically extract sentiment expressions for a given target (e.g., movie, or person) from a corpus of unlabeled tweets. Specifically, we make three contributions: (i) we recognize a diverse and richer set of sentiment-bearing expressions in tweets, including formal and slang words/phrases, not limited to pre-specified syntactic patterns; (ii) instead of associating sentiment with an entire tweet, we assess the target-dependent polarity of each sentiment expression. The polarity of sentiment expression is determined by the nature of its target; (iii) we provide a novel formulation of assigning polarity to a sentiment expression as a constrained optimization problem over the tweet corpus. Experiments conducted on two domains, tweets mentioning movie and person entities, show that our approach improves accuracy in comparison with several baseline methods, and that the improvement becomes more prominent with increasing corpus sizes.
Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.