Reviewerly/exHarmony huggingface dataset:
Blockchain-based Reputation for Intelligent Transportation Systems

A disruptive technology often used in finance, Internet of Things (IoT) and healthcare, blockchain can reach consensus within a decentralised network—potentially composed of large amounts of unreliable nodes—and to permanently and irreversibly store data in a tamper-proof manner. In this paper, we present a reputation system for Intelligent Transportation Systems (ITS). It considers the users interested in traffic information as the main actors of the architecture. They securely share their data which are collectively validated by other users. Users can choose to employ either such crowd-sourced validated data or data generated by the system to travel between two locations. The data saved is reliable, based on the providers’ reputation and cannot be modified. We present results with a simulation for three cities: San Francisco, Rome and Beijing. We have demonstrated the impact of malicious attacks as the average speed decreased if erroneous information was stored in the blockchain as an implemented routing algorithm guides the honest cars on other free routes, and thus crowds other intersections.
An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends

As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.
The privacy protection mechanism of Hyperledger Fabric and its application in supply chain finance

Blockchain technology ensures that data is tamper-proof, traceable, and trustworthy. This article introduces a well-known blockchain technology implementation—Hyperledger Fabric. The basic framework and privacy protection mechanisms of Hyperledger Fabric such as certificate authority, channel, Private Data Collection, etc. are described. As an example, a specific business scenario of supply chain finance is figured out. And accordingly, some design details about how to apply these privacy protection mechanisms are described.
Resolving Photon Numbers Using a Superconducting Nanowire with Impedance-Matching Taper

Time- and number-resolved photon detection is crucial for quantum information processing. Existing photon-number-resolving (PNR) detectors usually suffer from limited timing and dark-count performance or require complex fabrication and operation. Here, we demonstrate a PNR detector at telecommunication wavelengths based on a single superconducting nanowire with an integrated impedance-matching taper. The taper provides a kΩ load impedance to the nanowire, making the detector's output amplitude sensitive to the number of photon-induced hotspots. The prototyping device was able to resolve up to four absorbed photons with 16.1 ps timing jitter and <2 c.p.s. device dark count rate. Its exceptional distinction between single- and two-photon responses is ideal for high-fidelity coincidence counting and allowed us to directly observe bunching of photon pairs from a single output port of a Hong-Ou-Mandel interferometer. This detector architecture may provide a practical solution to applications that require high timing resolution and few-photon discrimination.
Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant's hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.
Impacto de la productividad científica sobre competencia digital de los futuros docentes: aproximación bibliométrica en Scopus y Web of Science

Digital competence can be understood as a set of essential skills that everyone must have developed by the end of the basic studies, as a requirement for lifelong learning and to get better future employment. The scientific interest projected in this subject has created different investigations, which are done with diverse populations. In this case, the present study aims to analyze the scientific production with the greatest impact on digital competence in teacher training in the field of higher education. To do this, eight combined searches were conducted using different keywords in two databases of international relevance: Scopus and Web of Science. The data were analyzed according to ten variables: year of publication, document type, journal or book where the paper is indexed, most productive authors, institutions, countries, subject area, keywords, language and most cited papers. The results show an overview of research on digital competence in future teachers. The highest scientific production is in recent years (2014-2017). There is a strong line of research that is published mainly through proceedings and articles in journals, written in English and that places Spain as the country that has conducted more research on this topic.
A multi-objective optimization method of initial virtual machine fault-tolerant placement for star topological data centers of cloud systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems. Static placement and dynamic management are two types of Virtual Machine (VM) management methods. VM dynamic management is based on the structure of the initial VM placement, and this initial structure will affect the efficiency of VM dynamic management. When a VM fails, cloud applications deployed on the faulty VM will crash if fault tolerance is not considered. In this study, a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors, including the service-level agreement violation rate, resource remaining rate, power consumption rate, failure rate, and fault tolerance cost. Then, a heuristic ant colony algorithm is proposed to solve the model. The service-providing VMs are placed by the ant colony algorithms, and the redundant VMs are placed by the conventional heuristic algorithms. The experimental results obtained from the simulation, real cluster, and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting

Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning. But such a sequential transfer learning paradigm often confronts the catastrophic forgetting problem and leads to sub-optimal performance. To fine-tune with less forgetting, we propose a recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks. Specifically, we introduce a Pretraining Simulation mechanism to recall the knowledge from pretraining tasks without data, and an Objective Shifting mechanism to focus the learning on downstream tasks gradually. Experiments show that our method achieves state-of-the-art performance on the GLUE benchmark. Our method also enables BERT-base to achieve better average performance than directly fine-tuning of BERT-large. Further, we provide the open-source RecAdam optimizer, which integrates the proposed mechanisms into Adam optimizer, to facility the NLP community.
An efficient quantum algorithm for the time evolution of parameterized circuits

We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems using parameterized quantum circuits. The method, named "projected - Variational Quantum Dynamics" (p-VQD) realizes an iterative, global projection of the exact time evolution onto the parameterized manifold. In the small time-step limit, this is equivalent to the McLachlan's variational principle. Our approach is efficient in the sense that it exhibits an optimal linear scaling with the total number of variational parameters. Furthermore, it is global in the sense that it uses the variational principle to optimize all parameters at once. The global nature of our approach then significantly extends the scope of existing efficient variational methods, that instead typically rely on the iterative optimization of a restricted subset of variational parameters. Through numerical experiments, we also show that our approach is particularly advantageous over existing global optimization algorithms based on the time-dependent variational principle that, due to a demanding quadratic scaling with parameter numbers, are unsuitable for large parameterized quantum circuits.
Geometry-Aware Face Completion and Editing

Face completion is a challenging generation task because it requires generating visually pleasing new pixels that are semantically consistent with the unmasked face region. This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region. Firstly, a facial geometry estimator is learned to estimate facial landmark heatmaps and parsing maps from the unmasked face image. Then, an encoder-decoder structure generator serves to complete a face image and disentangle its mask areas conditioned on both the masked face image and the estimated facial geometry images. Besides, since low-rank property exists in manually labeled masks, a low-rank regularization term is imposed on the disentangled masks, enforcing our completion network to manage occlusion area with various shape and size. Furthermore, our network can generate diverse results from the same masked input by modifying estimated facial geometry, which provides a flexible mean to edit the completed face appearance. Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well.
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions such as illumination and precipitation can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analysing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process, eliminating the need for labour-intensive hand labelling of validation data. The system integrity can be monitored as the performance of the vision sensors are validated against a different sensor modality. This is necessary for detecting failures that are inherent to vision technology. Experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, existence of shadows, etc.. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.
Cashless Society – The Future of Money or a Utopia?

Abstract In recent years we have witnessed a growing trend in cashless transactions as well as products and services sold exclusively in this way. Also, after the onset of the global financial crisis, private crypto currencies appeared that have raised some concerns. All of these changes beg the question of whether modern societies are moving towards a cashless society. This also raises a number of other dilemmas such as whether cashless societies have negative implications, whether they have what should be the response of economic policymakers, who would be potential winners and losers, and the like. The paper analyses the arguments both in favour and against cashless society, the future of crypto currencies, as well as potential responses of economic policymakers to the emergence of a cashless society. The paper concludes with the observation that it is not reasonable to expect the transition to a cashless society in the near future but the next step in the evolution of money might be the appearance of central bank digital currencies, at least in some countries.
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive to the application of pre-trained language models such as XLNet. To address this issue, we propose an all-in-one XLNet model, namely DialogXL, with enhanced memory to store longer historical context and dialog-aware self-attention to deal with the multi-party structures. Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data. Second, we introduce dialog-aware self-attention in replacement of the vanilla self-attention in XLNet to capture useful intra- and inter-speaker dependencies. Extensive experiments are conducted on four ERC benchmarks with mainstream models presented for comparison. The experimental results show that the proposed model outperforms the baselines on all the datasets. Several other experiments such as ablation study and error analysis are also conducted and the results confirm the role of the critical modules of DialogXL.
Generating Fluent Adversarial Examples for Natural Languages

Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge.Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients.Secondly, the fluency of the generated examples cannot be guaranteed.In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients.Experiments on IMDB and SNLI show that our proposed MHA outperforms the baseline model on attacking capability.Adversarial training with MHA also leads to better robustness and performance.
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog

Rashmi Gangadharaiah, Balakrishnan Narayanaswamy. 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.
Adaptive parallel execution of deep neural networks on heterogeneous edge devices

New applications such as smart homes, smart cities, and autonomous vehicles are driving an increased interest in deploying machine learning on edge devices. Unfortunately, deploying deep neural networks (DNNs) on resource-constrained devices presents significant challenges. These workloads are computationally intensive and often require cloud-like resources. Prior solutions attempted to address these challenges by either introducing more design efforts or by relying on cloud resources for assistance.
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation

Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.Existing approaches to generating text from AMR have focused on training sequenceto-sequence or graph-to-sequence models on AMR annotated data only.In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring.Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures.In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.
Semantics for Robotic Mapping, Perception and Interaction: A Survey

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey therefore provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity, in which semantics are extracted, used, or both. Within these broad categories we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics, including mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where...
Alexa Teacher Model

We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.
Self-Sovereign Identity Specifications: Govern Your Identity Through Your Digital Wallet using Blockchain Technology

Digital identity is one of the biggest challenges in cyberspace. This field has been evolving for many decades with a number of Identity Management (IDM) models being proposed and employed; however, few were able to solve the issue of sovereignty of an identity and storage-control of its associated personal and confidential data. Self-Sovereign Identity (SSI) was introduced to solve this crucial issue offering a user full sovereignty of their identity and storage-control of their associated personal and confidential data. Alongside ownership of an identity, it maintains all private information in a Digital Wallet which is owned and controlled by the user. However, SSI is an emerging IDM, therefore it requires careful evaluation of various aspects of SSI for it to become an operative IDM. This paper proposes several specifications to evaluate any SSI solution. Subsequently, it analyses two emerging SSI solutions uPort and Sovrin. Finally, an evaluation of uPort and Sovrin SSI is performed utilising the proposed specifications, highlighting their strengths and limitations.