Reviewerly/exHarmony huggingface dataset:
Many-objective robust trajectory optimisation under epistemic uncertainty and imprecision

This paper proposes a method to generate trajectories that are optimal with respect to multiple objectives and robust against epistemic uncertainty.Epistemic uncertainty is modelled with probability boxes and trajectories are optimised with respect to the lower expectations on cost functions and constraint satisfaction.The paper proposes an approach to the calculation of the lower expectation using Bernstein polynomials, and an efficient many-objective optimisation of the trajectories.A surrogate model of the lower expectation is combined with a dimensionality reduction technique to contain the computational cost and make the optimisation under epistemic uncertainty tractable.This approach is applied to the design of a rendezvous mission to Apophis with a spacecraft equipped with a low thrust engine.The paper presents both the case in which the thrust and specific impulse are affected by a time dependent uncertainty and the case in which the engine is affected by an outage that reduces the level of thrust at a random time along the trajectory.
AutoIAS

Automating architecture design for recommendation tasks becomes a trending topic because expert efforts are saved, and better performance is expected. Neural Architecture Search (NAS) is introduced to discover powerful CTR prediction model architectures in recent works. CTR prediction model usually consists of three components: embedding layer, interaction layer, and deep neural network. However, existing automation works focus on searching single component and leaving other components hand-crafted. The isolated searching will cause incompatibility among components and lead to weak generalization ability. Moreover, there is not a unified framework for integrated CTR prediction model architecture searching. This paper presents Automatic Integrated Architecture Searcher (AutoIAS), a framework that provides a practical and general method to find optimal CTR prediction model architecture in an automatic manner. In AutoIAS, we unify existing interaction-based CTR prediction model architectures and propose an integrated search space for a complete CTR prediction model. We utilize a supernet to predict the performance of sub-architectures, and the supernet is trained with Knowledge Distillation(KD) to enhance consistency among sub-architectures. To efficiently explore the search space, we design an architecture generator network that explicitly models the architecture dependencies among components and generates conditioned architectures distribution for each component. Experiments on public datasets show the outstanding performance and generalization ability of AutoIAS. Ablation study shows the effectiveness of the KD-based supernet training method and the Architecture Generator Network.
Fully Authentication Services Scheme for NFC Mobile Payment Systems

One commonly used wireless communication technology is Near-Field Communication (NFC). Smartphones that support this technology are used in contactless payment systems as identification devices to emulate credit cards. This technology has essentially focused on the quality of communication services and has somewhat disregarded security services. Communication messages between smartphones, the point of sale (POS), and service providers are susceptible to attack due to existing weaknesses, including that an adversary can access, block and modify the transmitted messages to achieve illegal goals. Therefore, there have been many research proposals in regards to authentication schemes for NFC communications in order to prevent various types of attacks. However, the proposed schemes remain inadequate to secure payment transactions in such systems. In this paper, we propose a fully authentication services scheme for NFC mobile payment systems in order to support a high security level. The proposed scheme has security services, such as a full authentication process, perfect forward secrecy, and simultaneous anonymity of the smartphone and POS. These security services have been validated using the BAN logic model and an automatic cryptographic protocol verifier (ProVerif) tool. A security analysis has clarified that the proposed scheme can prevent various types attacks. A comparison with recent authentication schemes demonstrates that the proposed scheme has an appropriate cost in different sides such as computation, communication and storage space. Therefore, the proposed scheme not only has appealing security features, but can also clearly be utilized in mobile payment systems.
Enhanced Security in Cloud Computing Using Neural Network and Encryption

In the last five years, demand for cloud computing among businesses and individual users is increasing immensely because of numerous reasons including, improved productivity, efficiency and speed, cost savings, performance, and most importantly security.Machine learning techniques are making progress in a variety of domains of cloud computing to resolve security concerns and manage data efficiently.In cloud security, a relatively novel approach is Artificial Neural Networks (ANN).We propose a new security design using neural network and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the information in an encrypted form without disclosing the data of the provider party to secure important information.We recommend a solution based on fully homomorphic encryption (FHE) to handle sensitive information without revealing the original data.The encryption technique we considered is matrix operation-based randomization and encipherment (MORE), which allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead.In this paper, we examined the speech and voice recognition problem and the performance of the proposed method has been validated in MATLAB simulation.Results showed that applying neural network training with MORE improved accuracy, runtime, and performance.These results highlight the potential of the proposed neural network and encryption technique to protect the privacy and providing high accuracy in a reasonable period when compared to other state of the art techniques.
Secure Blockchain Platform for Industrial IoT with Trusted Computing Hardware

As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate industrial IoT (IIoT) applications. However, implementing a blockchain platform in IIoT scenarios confronts various security challenges due to the rigorous deployment condition. To this end, we present a novel design of secure blockchain based on trusted computing hardware for IIoT applications. Specifically, we employ the trusted execution environment (TEE) module and a customized security chip to safeguard the blockchain against different attacking vectors. Furthermore, we implement the proposed secure IIoT blockchain on the ARM-based embedded device and build a small-scale IIoT network to evaluate its performance. Our experimental results show that the secure blockchain platform achieves a high throughput (150TPS) with low transaction confirmation delay (below 66ms), demonstrating its feasibility in practical IIoT scenarios. Finally, we outline the open challenges and future research directions.
A BERT Based Approach to Measure Web Services Policies Compliance With GDPR

Data confidentiality is an issue of increasing importance.Several authorities and regulatory bodies are creating new laws that control how web services data is handled and shared.With the rapid increase of such regulations, web service providers face challenges in complying with these evolving regulations across jurisdictions.Providers must update their service policies regularly to address the new regulations.The challenge is that regulatory documents are large text documents and require substantial human effort to comprehend and enforce.On the other hand, web service provider privacy policies are relatively short compared to the regulatory texts, so it is hard to determine if an organization's policy document addresses the regulation's essential elements.We have developed a framework to automatically compare web service policies with regulatory policies to measure how closely the web service provider complies with a regulation.In this paper, we present our framework's details along with the results of analyzing a corpus of 3,000 privacy policies against GDPR.Our framework uses BiLSTM multi-class classification and a BERT extractive summarizer.We evaluate the framework's efficacy by checking the context similarity score between summarized GDPR and web service provider privacy policies.
Intelligent Learning Assistant using BERT and LSTM

Learning and preparing for the examinations have been a tough task according to the students' perspective, especially when the students are learning remotely. Subjects like languages or history require a lot of reading and remembrance skills associated with it. In this paper, we propose a question-answering model based on BERT (Bidirectional Encoder Representations from Transformers) that can assist learners of all ages with immediate feedback from the topic. We incorporate LSTM (Long Short- Term Memory) along with BERT into our application. This has helped in achieving a language translation from English to Hindi that helps non-native English users understand the context. Our model can be applied in assisting learners of any age.
Toward a typology of weak-signal early alert systems: functional early warning systems in the post-COVID age

Purpose The study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design. Design/methodology/approach The authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems. Findings Proposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses. Originality/value The authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513 .
The temporal pattern of VR sickness during 7.5-h virtual immersion

Abstract In this study, we assessed the relationship between exposure duration and VR sickness severity during 7.5-h virtual immersion. First, we showed that the VR sickness severity was positively correlated to the exposure duration: the longer participants were exposed to the VR environment, the more severe sickness symptoms they had. Second, we showed a dynamic sickness adaptation process during a long time of VR exposure: the sickness adaption effect that had already been established could be broken as the exposure duration continued to increase, and a new sickness adaption process would establish. Moreover, we showed a distinguishable symptom profile of HMD compared with LCD, which was insusceptible of exposure duration. This is the first report presenting the temporal pattern of VR sickness during such long-duration exposure. Our study could offer a predictive model of VR sickness severity level during long virtual immersion and provide suggestions for the use of VR technology for scientific study, clinical application, and business entertainment.
Multi party secure data access management in cloud using user centric block chain data encryption

Cloud is the most dominant platform being used by different organizations towards providing service access to their users and customers. Services provided are intended to store and maintain data belongs to the organizations. Access restriction and data access management are identified as key issue at the point of storage management in cloud. The data security in cloud decides the Quality of Service (QoS) performance of the entire environment. Towards this, different approaches available to handle the storage management and access restriction issues. Still, the approaches are not up to the expected range of performance to maximize the QoS performance. Also, the methods faces higher data leakage, security issues as well as higher impact of tampering, which increases the requirement of block chains towards secure data access. To improve the performance, an efficient multi party secure data access management (MSDAM) is presented in this article. The method is focused to provide data handle in different parties which perform authentication on the data update requests. Also, the method distributes the key set and scheme set for different user at different session which is framed according to user centric features. The method initializes key sets and scheme sets to be distributed to the user in a least fact randomization model. Also, the method generates block chains for the user data which is encrypted using User Centric Block chain Encryption (UCBE) scheme where the key and schemes are selected with randomization technique. Similarly, the hash code has been generated using Distancing method. The proposed method improves the performance in all the QoS parameters of the cloud.
Medical Term and Status Generation From Chinese Clinical Dialogue With Multi-Granularity Transformer

This paper describes a generative model for extracting medical terms and their status from Chinese medical dialogues. Notably, the extracted semantic information plays an essential role in downstream tasks such as automatic medical scribe and automatic diagnosis system. However, how to effectively leverage dialogue context to generate medical terms and their corresponding status accurately remains less explored. Existing generative methods treat dialogue text as concentrated long text without considering the characteristics of conversation, such as colloquialism, redundancy, interactions, etc. Various colloquial medical information is frequently discussed between doctor and patient. Each of the speakers (doctor and patient) plays a specific role in the goals of interaction. Thus the role information and interactions between utterances are vital. Besides, current generative methods only utilize character-level tokens ignoring the word-level tokens, which is the smallest meaningful utterance in Chinese. In this paper, we propose a Multi-granularity Transformer (MGT) model to enhance the dialogue context understanding from multi-granularity features. We introduce word-level information by adapting a Lattice-based encoder with our proposed relative position encoding method. We further introduce utterance-level interaction information by proposing a Role Access Controlled Attention (RaCa) mechanism. Experimental results on two benchmark datasets illustrate our model's validity and effectiveness, achieving state-of-the-art performance on both datasets.
Edge-to-cloud Virtualized Cyberinfrastructure for Near Real-time Water Quality Forecasting in Lakes and Reservoirs

The management of drinking water quality is critical to public health and can benefit from techniques and technologies that support near real-time forecasting of lake and reservoir conditions. The cyberinfrastructure (CI) needed to support forecasting has to overcome multiple challenges, which include: 1) deploying sensors at the reservoir requires the CI to extend to the network’s edge and accommodate devices with constrained network and power; 2) different lakes need different sensor modalities, deployments, and calibrations; hence, the CI needs to be flexible and customizable to accommodate various deployments; and 3) the CI requires to be accessible and usable to various stakeholders (water managers, reservoir operators, and researchers) without barriers to entry. This paper describes the CI underlying FLARE (Forecasting Lake And Reservoir Ecosystems), a novel system co-designed in an interdisciplinary manner between CI and domain scientists to address the above challenges. FLARE integrates R packages that implement the core numerical forecasting (including lake process modeling and data assimilation) with containers, overlay virtual networks, object storage, versioned storage, and event-driven Function-as-a-Service (FaaS) serverless execution. It is a flexible forecasting system that can be deployed in different modalities, including the Manual Mode suitable for end-users’ personal computers and the Workflow Mode ideal for cloud deployment. The paper reports on experimental data and lessons learned from the operational deployment of FLARE in a drinking water supply (Falling Creek Reservoir in Vinton, Virginia, USA). Experiments with a FLARE deployment quantify its edge-to-cloud virtual network performance and serverless execution in OpenWhisk deployments on both XSEDE-Jetstream and the IBM Cloud Functions FaaS system.
In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models

The use of vehicle in-cabin monitoring has been increasing to fulfil the specifications of European safety regulations. These regulations present several requirements for detecting driver distraction, and more complex requirements are soon to be expected in higher automation levels. Today's restraint systems provide optimal protection in standard frontal seat positions and deviations to this might cause severe airbag-induced injuries. This makes in-cabin monitoring critical to improve safety and mitigate dangerous situations in case of a crash, and especially in high levels of autonomous driving. Defining the best sensor positioning inside the vehicle's cabin is a challenge due to its constraints and limitations. The main aim of this work was to verify if simulated 3D human models integrated into a 3D modelled vehicle interior environment can be used to run Deep Learning based human pose estimation models. To perform such task, we utilized the software MakeHuman combined with Blender, to build the virtual environment and create photorealistic scenes containing selected front occupants' postures use cases and then feed into Openpose and Mask R-CNN models. The results showed that using a 2D HPE (Human Pose Estimation) network pre-trained on real data, can detect successfully photorealistic synthetic data of humans under complex scenarios. It is also shown that complex and rare postures can cause failure on 2D HPE detections, as shown in the literature review. This work helps to define the most suitable camera positions which, in combination with specific camera lenses, can deliver quality images for a robust pose detection.
Text-Guided Legal Knowledge Graph Reasoning

Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in https://github.com/zjunlp/LegalPP for reproducibility.
VR Collaborative Object Manipulation Based on Viewpoint Quality

We introduce a collaborative manipulation method to improve the efficiency and accuracy of object manipulation in virtual reality applications with multiple users. When multiple users manipulate an object in collaboration, a certain user may have a better perspective than other users at a certain moment, and can clearly observe the object to be manipulated and the target position, and it is more efficient and accurate for him to manipulate the object. We construct a viewpoint quality function and evaluate the viewpoints of multiple users by calculating its three components: the visibility of the object need to be manipulated, the visibility of target, the depth and distance combined of the target. By comparing the viewpoint quality of multiple users, the user with the highest viewpoint quality is determined as the dominant manipulator, who can manipulate the object at the moment. A temporal filter is proposed to filter the dominant sequence generated by the previous frames and the current frame, which reduces the dominant manipulator jumping back and forth between multiple users in a short time slice, making the determination of the dominant manipulator more stable. We have designed a user study and tested our method with three multi-user collaborative manipulation tasks. Compared to the previous methods, our method showed significant improvement in task completion time, rotation accuracy, user participation and task load.
Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.