Reviewerly/exHarmony huggingface dataset:
Developing a VR Simulator for Robotics Navigation and Human Robot Interactions employing Digital Twins

Providing care to seniors and adults with Developmental Disabilities (DD) has seen increased use and development of assistive technologies including service robots. Such robots ease the challenges associated with care, companionship, medication intake, and fall prevention, among others. Research and development in this field rely on in-person data collection to ensure proper robot navigation, interactions, and service. However, the current COVID-19 pandemic has caused the implementation of physical distancing and access restrictions to long-term care facilities, thus making data collection very difficult. This traditional method poses numerous challenges as videos may not be representative of the population in terms of how people move, interact with the environment, or fall. In this paper, we present the development of a VR simulator for robotics navigation and fall detection with digital twins as a solution to test the virtual robot without having access to the real physical location, or real people. The development process required the development of virtual sensors that are able to create LIDAR data for the virtual robot to navigate and detect obstacles. Preliminary testing has allowed us to obtain promising results for the virtual simulator to train a service robot to navigate and detect falls. Our results include virtual maps, robot navigation, and fall detection.
Spacing Loss for Discovering Novel Categories

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into singlestage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.
Mutual learning differential particle swarm optimization

This study proposes a mutual learning strategy to develop a high performance hybrid algorithm based on particle swarm optimization and differential evolution. In the mutual learning strategy, the position information in PSO subswarm is employed for DE mutation, and the DE individuals are used to construct learning exemplar for PSO subswarm together with particles’ historical best position. A novel elite DE mutation is proposed to speed up the convergence rate of DE subswarm. Based on mutual learning technique, the mutual learning differential evolution particle swarm optimization (MLDE-PSO) is proposed. To evaluate the performance of MLDE-PSO, three groups of test functions are employed, namely thirteen basic functions, thirteen rotated basic functions and thirty CEC2017 functions. The test results are compared with three state-of-the-art PSO algorithms, three recently PSO algorithms and DE/rand/1. The test results indicate that the proposed MLDE-PSO performs better than the other seven comparison algorithms, especially on rotated functions and CEC2017 functions. The rotation test shows that MLDE-PSO is not very sensitive to rotation transformation.
MCSE: Multimodal Contrastive Learning of Sentence Embeddings

Learning semantically meaningful sentence embeddings is an open problem in natural language processing.In this work, we propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective.Through experiments on a variety of semantic textual similarity tasks, we demonstrate that our approach consistently improves the performance across various datasets and pre-trained encoders.In particular, combining a small amount of multimodal data with a large text-only corpus, we improve the state-of-the-art average Spearman's correlation by 1.7%.By analyzing the properties of the textual embedding space, we show that our model excels in aligning semantically similar sentences, providing an explanation for its improved performance.
Polarized light sun position determination artificial neural network

Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.
TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.
Spreading Stupidity: Intellectual Disability and Anti-imperialist Resistance to Bioinformational Capitalism

We are aware that to resist in the coming age of bioinformational capitalism, we will require new knowledge ecologies. This knowledge must be socialist: able to resist the dominance of productivist and imperialist pedagogies that are saturated with capital, and now bioinformational capital’s aims. This knowledge must also be stupid: able to refuse bioinformational capital’s lust for visibility and access to the working-class' biology. Stupidity is able to resist primarily because it can’t be quantified, articulated, or rendered transparent. To express the importance of this refusal, we visit concepts of colonialism and disability, and feel the oppression experienced under bioinformational capitalism. Disabled and colonized struggles animate the importance of protecting anti-value and using anti-value in the struggle against oppression. In this chapter we propose a theory and practice of stupidity as anti-value: a socialist and anti-imperialist form of resistance.
Towards Meta-Learning Based Data Analytics to Better Assist the Domain Experts in Industry 4.0

Industrial Data Analytics (IDA) provide methods and tools to cope with the vast amounts of data. The big industrial data is generated continuously during the execution of manufacturing processes. Hence, the predictive maintenance is among the most critical activities of the manufacturing processes concerned by the IDA. We believe that the maintenance activity can be managed by using Machine Learning (ML) methodologies, especially the data analytics solutions based on meta-learning. The challenge is then to facilitate the application of ML by the industry 4.0 actors, who are supposedly not AI specialists. The automated machine learning (AutoML) seems to be the area dealing with this challenge. In this paper, we primarily discuss the challenges of assisting industry 4.0 actors to implement ML algorithms in the context of predictive maintenance. Later on, we present a novel AutoML based framework for the industry 4.0 actors and researchers, who presumably have limited expertise in ML domain. It aims to enable them to generate ML-based data analytics solutions and deploy these solutions in manufacturing workflows. Specifically, the framework implements the approaches based on meta-learning and uses the web semantic concepts in this regard. In the context of Industry 4.0 such approaches lead to the implementation of the smart factory concepts. It makes the factory processes more proactive on the basis of predictive knowledge extracted from the various manufacturing devices, sensors, and business processes in real-time.
Sustainable energy and digital currencies: challenges and future prospect

Due to the impressive growth in digital coins trading, most cryptocurrencies' market cap has increased drastically. Therefore, more people are engaged in the mining process, causing a significant increase in electrical power consumption. To make cryptocurrency technology sustainable, using renewables such as photovoltaic solar power, wind energy, tidal power, geothermal power, hydroelectric power, fuel cell, and biomass has been implemented. Moreover, to decrease electrical power consumption in the cooling process of mining systems and computers, using phase change material (PCM) has been recommended. Since the cryptocurrency mining process is very competitive, only those miners will survive who employ the most competitive mining systems and benefit from the lowest electrical power costs. While the profitability of renewable electricity-based mining is lower than grid-based mining, the latter method compensates for better sustainability in cryptocurrency and lower environmental costs. This paper reviews the possible ways to make the cryptocurrency mining process clean and environmentally friendly.
Security-driven distributed platforms for intellectual property resource provision - a case study of TSITE IP

Abstract In the information age, with the vigorous development of big data and artificial intelligence, intellectual property protection is an essential part of the current scientific and technological development. Intellectual property related data grows in a geometric progression, so the demand for IP data storage space is also increasing day by day. With the rise of cloud computing technology, intellectual property data distributed platforms based on cloud storage have also been produced one after another. Because the biggest feature of cloud storage is that storage is a service, it puts forward higher requirements for the intellectual property service industry. Firstly, it introduces the domestic intellectual property cloud platform services from the perspectives of government support, state-owned enterprises and private enterprises; Secondly, four typical distributed platforms provided by commercial resources are selected to introduce their operation modes, focusing on the problems faced by domestic intellectual property service modes; Secondly, it compares and discusses the current situation of domestic intellectual property distributed platforms; Then, aiming at the current domestic intellectual property service mode, taking tsite as an example, this paper puts forward the design and construction strategy of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age.
Blockchain and Cooperative Intelligent Transport Systems: Challenges and Opportunities

Cooperative Intelligent Transport Systems (C-ITS) will play a key role in the advent of the automated and connected vehicle. However, C-ITS will have to be secured to prevent attacks from malicious entities and to provide the critical services required by connected vehicles (road safety). To guarantee this security, a solution that is widely considered today is the Blockchain technology. Indeed, this flexible technology, based on a decentralized architecture, could enable the implementation of innovative security services in vehicular networks. That is why, in this paper, we try to evaluate the potential impact of the definition of Blockchain-based solutions in C-ITS. To this end, we first propose an analysis of the C-ITS requirements and the Blockchain features. Then, we identify the potential areas of use of the Blockchain technology in C-ITS and we study the benefits/drawbacks of Blockchain for these applications. Finally, we present some challenges that will have to be considered in future Blockchain-based services designed for C-ITS.
Development of soft skills competencies through the use of FLIGBY

Traditionally, serious games have been used in the development of hard skills, particularly in technical areas such as health, engineering, defence or the environment. However, they can also be applied in the assessment and development of soft skills, which are increasingly key competencies for an individual in the twenty-first century. In this sense, this study proposes the adoption of the FLIGBY serious game in a higher education institution to evaluate and develop students’ skills in this field. The findings indicate a large correspondence between the soft skills fundamental to the twenty-first century and the assessment dimensions recorded by the game. Furthermore, the findings reveal that FLIGBY can be used primarily to develop skills in dimensions such as leadership, conflict management, diplomacy and emotional intelligence. These findings are relevant for higher education institutions that intend to include and foster the development of soft skills competencies in their curricula.
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where largescale QA pairs are not readily available for model retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order to generate diverse types of questions that are essential for training QA models, we further introduce a seq2seq-based question phrase prediction (QPP) module that can be used together with most existing QG models to diversify the generation. Our comprehensive experiment results show that the QA corpus generated by our framework can improve QA models on the new contexts (up to 8% absolute gain in terms of Exact Match), and that the QPP module plays a crucial role in achieving the gain. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our dataset and code are available at: https://github.com/sunlabosu/CliniQG4QA/.
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach to investigate pseudo-labels and self-training in UDA for point clouds. We present two main innovations to make self-training effective on 3D data: i) refinement of noisy pseudo-labels by matching shape descriptors that are learned by the unsupervised task of shape reconstruction on both domains; ii) a novel self-training protocol that learns domain-specific decision boundaries and reduces the negative impact of mislabelled target samples and in-domain intra-class variability. RefRec sets the new state of the art in both standard benchmarks used to test UDA for point cloud classification, showcasing the effectiveness of self-training for this important problem.
OCR post-correction for detecting adversarial text images

The amount of images with embedded text shared on Online Social Networks (OSNs), such as Twitter or Facebook has been growing in recent years. It is becoming important to analyse the images uploaded into these platforms, as adversaries may spread images with toxic content or misinformation (i.e. spam). Optical character recognition (OCR) systems have been used to detect images with malicious content, where the embedded text gets extracted and classified using machine learning algorithms. However, most existing OCR-based systems are adversary-agnostic models, in which the extracted text from an image is not checked by humans before the classification. Consequently, these fully automated models become vulnerable to minor modifications of images’ pixels or textual content (e.g., character-level perturbations), which do not affect human understanding, but could cause the OCR systems to misrecognise the embedded text. In this paper, we propose an OCR post-correction algorithm to improve the robustness of OCR-based systems against images with perturbed embedded texts. Experimental results showed that our proposed algorithm improves the robustness of three state-of-the-art OCR models with at least 10% against adversarial text images, and it outperforms five spellcheckers in correcting adversarial text. Also, we evaluated the perceptibility of our adversarial images, and this study showed that 91% of the participants were able to correctly recognise the adversarial text images. Additionally, we developed an adversary-aware OCR-based system for detecting adversarial text images using the proposed algorithm, and our evaluation results showed considerable improvement in the performance of an OCR-based system.
Accurate and prompt answering framework based on customer reviews and question-answer pairs

As e-commerce markets have gradually expanded, online shopping malls have provided various services aiming to secure competitiveness. A service for providing an accurate and prompt response when a customer writes an inquiry regarding a product represents a space directly connected to the customer and plays an important role, as it is directly related to product sales. However, the current online shopping mall answering service has disadvantages, e.g., it takes time for an administrator to write an answer directly, or to provide an answer within a set of answers. In this paper, we propose an answer framework for solving this problem, based on customer reviews. When a user writes a query, the framework provides an appropriate answer in real time through the system’s question-and-answer pairs and customer reviews. The framework’s performance is verified through a qualitative evaluation. In addition, it is confirmed that a customized model for reflecting the characteristics of each shopping mall can be created by using additional information from the collected data. The proposed framework is expected to support customers’ online shopping through more reliable and efficient information retrieval, and to reduce shopping mall operation and maintenance costs.
High-fidelity and large-scale reconfigurable photonic processor for NISQ applications

Reconfigurable linear optical networks are a key component for the development of optical quantum information processing platforms in the NISQ era and beyond. We report the implementation of such a device based on an innovative design that uses the mode mixing of a multimode fiber in combination with the programmable wavefront shaping of a SLM. The capabilities of the platform are explored in the classical regime. For up to 8 inputs and a record number of 38 outputs, we achieve fidelities in excess of 93%, and losses below 6.5dB. The device was built inside a standard server rack to allow for real world use and shows consistent performance for 2x8 circuits over a period of 10 days without re-calibration.
A blockchain-based framework for drug traceability

The traceability of drugs remains a significant issue, especially with the number of products proposed on the Web. Detecting counterfeit products is a challenge, given what these products could represent as a danger to health and the economy. Several studies have been made in the literature and tried to propose solutions. However, several limitations remain. In this context, this paper presents our framework, ChainDrugTrac, for the traceability and detection of counterfeit pharmaceutical products. After studying existing work and identifying the limits, our contributions could be resumed around 1) proposing architecture for drug traceability based on the blockchain and 2) developing a blockchain-based prototype supporting the proposed architecture. The prototype has been implemented to evaluate our proposal and show its effectiveness.
Learning Kernel-Smoothed Machine Translation with Retrieved Examples

How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?Despite the great success of neural machine translation, updating the deployed models online remains a challenge.Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples.However, non-parametric methods are prone to overfit the retrieved examples.In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online.Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods.The code and trained models are released at https://github.com/jiangqn/KSTER.
Open Knowledge Graphs Canonicalization using Variational Autoencoders

Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples.Existing approaches to solve this problem take a two-step approach.First, they generate embedding representations for both noun and relation phrases, then a clustering algorithm is used to group them using the embeddings as features.In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA) 1 , a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.Our evaluation over multiple benchmarks shows that CUVA outperforms the existing state-of-the-art approaches.Moreover, we introduce CANONICNELL, a novel dataset to evaluate entity canonicalization systems.