Reviewerly/exHarmony huggingface dataset:
Entity Alignment for Knowledge Graphs With Multi-Order Convolutional Networks

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes. To support multilingual applications, a significant number of language-specific KGs have been built by different parties using various data sources. As a result, these monolingual KGs are often disconnected, causing semantic heterogeneity and detracting from the original purpose of KGs. Entity alignment – the task of identifying corresponding entities across different KGs – has attracted a great deal of attention in both academia and industry. However, existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs that fuses different types of information in order to fully exploit the richness of KG data. The model captures the relation-based correlation between entities by using a multi-order graph convolutional neural (GCN) model that is designed to satisfy the consistency constraints, while incorporating the attribute-based correlation via a translation machine. We adopt a late-fusion mechanism to combine all the information together, which allows these approaches to complement each other and thus enhances the final alignment result, and makes the model more robust to consistency violations. Empirical results for various scenarios on real-world and synthetic KGs show that our model is up to 22.71 percent more accurate and orders of magnitude faster than existing baselines. We also demonstrate its sensitivity to hyper-parameters, effort saving in terms of labelling, and the robustness against adversarial conditions.
Can Nonlinear Parametric Oscillators Solve Random Ising Models?

We study large networks of parametric oscillators as heuristic solvers of random Ising models. In these networks, known as coherent Ising machines, the model to be solved is encoded in the coupling between the oscillators, and a solution is offered by the steady state of the network. This approach relies on the assumption that mode competition steers the network to the ground-state solution of the Ising model. By considering a broad family of frustrated Ising models, we show that the most-efficient mode does not correspond generically to the ground state of the Ising model. We infer that networks of parametric oscillators close to threshold are intrinsically not Ising solvers. Nevertheless, the network can find the correct solution if the oscillators are driven sufficiently above threshold, in a regime where nonlinearities play a predominant role. We find that for all probed instances of the model, the network converges to the ground state of the Ising model with a finite probability.
The Challenges of Privacy and Access Control as Key Perspectives for the Future Electric Smart Grid

The Electric Smart Grid (ESG) is referred to as the next generation electricity power network. The ESG is an intelligent critical infrastructure subject to various security vulnerabilities and especially data privacy breaches. This study presents a comprehensive overview of the latest privacy-preserving mechanisms and policies in the ESG, while promoting the employment of modern access control techniques towards preventing personal data disclosure, affecting both utility companies and consumers. Efficient categorization is provided regarding the proposed privacy preserving methods and characteristics, while focus is also given on the use of the Blockchain technology and the Multi Authority Access Control paradigm in the ESG infrastructure. The study concludes with a discussion upon privacy and access control challenges, as well as future work concerning the ESG.
An empirical study on decision making for quality requirements

[Context] Quality requirements are important for product success yet often handled poorly. The problems with scope decision lead to delayed handling and an unbalanced scope. [Objective] This study characterizes the scope decision process to understand influencing factors and properties affecting the scope decision of quality requirements. [Method] We studied one company's scope decision process over a period of five years. We analyzed the decisions artifacts and interviewed experienced engineers involved in the scope decision process. [Results] Features addressing quality aspects explicitly are a minor part (4.41%) of all features handled. The phase of the product line seems to influence the prevalence and acceptance rate of quality features. Lastly, relying on external stakeholders and upfront analysis seems to lead to long lead-times and an insufficient quality requirements scope. [Conclusions] There is a need to make quality mode explicit in the scope decision process. We propose a scope decision process at a strategic level and a tactical level. The former to address long-term planning and the latter to cater for a speedy process. Furthermore, we believe it is key to balance the stakeholder input with feedback from usage and market in a more direct way than through a long plan-driven process.
A Reflection on Virtual Reality Design for Psychological, Cognitive and Behavioral Interventions: Design Needs, Opportunities and Challenges

(2021). A Reflection on Virtual Reality Design for Psychological, Cognitive and Behavioral Interventions: Design Needs, Opportunities and Challenges. International Journal of Human–Computer Interaction: Vol. 37, No. 9, pp. 851-866.
Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation

Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.
A lightweight cyber security framework with context-awareness for pervasive computing environments

Internet of things (IoT) plays a key role in enabling smart sustainable cities. Pervasive computing over the IoT platform makes life more convenient by embedding sensors based on context-aware computing devices in the physical environment for the ubiquitous availability of computing resources. The sensors gather contextual information from the physical world and transmit it to receivers as per requirements or in case of environmental changes, such as temperature and humidity. However, the combination of dynamic operation and the need to handle sensitive and private data make the pervasive computing environment and IoT devices vulnerable to numerous attacks. Smart environments require a maximum level of safety assurance, such as trusted context producers and customers, which should protect sensitive information from exposure or monitoring. This paper discusses the major cyber threats in smart environments and proposes a novel lightweight security framework that authenticates and maintains the context providers and receivers. The cloud environment is adopted for user authentication at the user layer to implement access control and role assignment. Finally, the proposed security framework is implemented in the IBM cloud platform with six devices to evaluate its efficiency, sustainability, and secure communication.
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19

COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic and rapidly evolving situation with COVID-19 has made it difficult to access accurate, on-demand information regarding the disease. Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members. However, due to the nature of such sites, there are always a limited number of relevant questions and responses to search from, and posted questions are rarely answered immediately. With the advancements in the field of natural language processing, particularly in the domain of language models, it has become possible to design chatbots that can automatically answer consumer questions. However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data. In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses. We utilized the GPT-2 language model and applied transfer learning to retrain it on the COVID-19 Open Research Dataset (CORD-19) corpus. In order to improve the quality of the generated responses, we applied 4 different approaches, namely tf-idf, BERT, BioBERT, and USE to filter and retain relevant sentences in the responses. In the performance evaluation step, we asked two medical experts to rate the responses. We found that BERT and BioBERT, on average, outperform both tf-idf and USE in relevance-based sentence filtering tasks. Additionally, based on the chatbot, we created a user-friendly interactive web application to be hosted online.
Quantum robots can fly; play games: an IBM quantum experience

Quantum Robot is an excellent future application that can be achieved with the help of a quantum computer. As a practical example, quantum controlled Braitenberg vehicles proposed by Raghuvanshi et al. [Proceedings of the 37th International Symposium on Multiple-Valued Logic (2007)] is a mobile quantum system and hence acts as a quantum robot. Braitenberg vehicles are simple circuit robots which can experience natural behaviours like fear, aggression and love etc. These robots can be controlled by quantum circuits incorporating quantum principles such as entanglement and superposition. Complex behaviours can be mimicked by a quantum circuit that can be implemented in a quantum robot. Here we investigate the scheme of Raghuvanshi et al. and propose a new quantum circuit to make the quantum robot fly. We demonstrate one of its application in playing a game. The quantum robot we present here shows the behaviour of `fear' and its movement is deterministic in nature. This phenomenon can be successfully modelled in a game, where it can always avoid accident. The proposed quantum circuit is designed in IBM quantum experience describing the above protocol.
Flexible Subspace Clustering: A Joint Feature Selection and K-Means Clustering Framework

Regarding as an important computing paradigm, cloud computing is to address big and distributed databases and rather simple computation. In this paradigm, data mining is one of the most important and fundamental problems. A large amount of data is generated by sensors and other intelligent devices. Data mining for these big data is crucial in various applications. K-means clustering is a typical technique to group the similar data into the same clustering, and has been commonly used in data mining. However, it is still a challenge to the data containing a large amount of noise, outliers and redundant features. In this paper, we propose a robust K-means clustering algorithm, namely, flexible subspace clustering. The proposed method incorporates feature selection and K-means clustering into a unified framework, which can select the refined features and improve the clustering performance. Moreover, for the purpose of enhancing the robustness, the l2.p-norm is embedded into the objective function. We can flexibly choose appropriate p according to the different data and thus obtain more robust performance. Experimental results verify the presented method has more robust and better performance on benchmark databases compared to the existing approaches.
Universal Model for the Turn-On Dynamics of Superconducting Nanowire Single-Photon Detectors

Superconducting nanowire single-photon detectors (SNSPDs) are widely used in quantum information science and quantum optics because of their excellent properties, yet much of their dynamical behavior still is not fully understood. This study identifies characteristic time and resistance scales for the SNSPD detection process, and uses them to develop a universal model of SNSPD behavior. The authors furthermore clarify how different detector parameters affect the output signal, specifically exploring the effects of photon number, detector length, and bias current. These results will have an impact on SNSPD fabrication and encourage innovative applications of these sensors.
Adversarial Multi-Label Variational Hashing

In this paper, we propose an adversarial multi-label variational hashing (AMVH) method to learn compact binary codes for efficient image retrieval. Unlike most existing deep hashing methods which only learn binary codes from specific real samples, our AMVH learns hash functions from both synthetic and real data which make our model effective for unseen data. Specifically, we design an end-to-end deep hashing framework which consists of a generator network and a discriminator-hashing network by enforcing simultaneous adversarial learning and discriminative binary codes learning to learn compact binary codes. The discriminator-hashing network learns binary codes by optimizing a multi-label discriminative criterion and minimizing the quantization loss between binary codes and real-value codes. The generator network is learned so that latent representations can be sampled in a probabilistic manner and used to generate new synthetic training sample for the discriminator-hashing network. Experimental results on several benchmark datasets show the efficacy of the proposed approach.
Deep neural network approach to estimation of power production for an organic Rankine cycle system

In this study, the possibility of using Stepwise multilinear regression and deep learning models to estimate the behaviour of the organic Rankine cycle (ORC) has been investigated. It was found that a number of parameters affects the performance of the turbine and hence the amount of power obtained by the ORC. Therefore, limited and simulative parameters might not be sufficient to obtain the best prediction expression. In the present study, the data obtained from a 10 kW ORC system was used as the basis for deep learning models. To this end, the variable selection, which represents the inputs to the neural network, is included in the first steps of a stepwise multilinear regression (SMLR). The aim of the deep learning (DL) models is to use the capabilities of dense layers, and then to strengthen SMLR contributions. The main aim here was to estimate the power generation of the expander, which has an important role in deciding the ORC’s performance. The present study is intended to act as a crucial resource for defining an active estimation procedure for the ORC system through the use of DL. Therefore, an interoperability framework is proposed to estimate ORC power production using SMLR and DL as a new approach in this study. The interoperability approach for the proposed models (SMLR and DL) was found to be successful.
Scene text removal via cascaded text stroke detection and erasing

Abstract Recent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.
Privacy-Preserving Reinforcement Learning Using Homomorphic Encryption in Cloud Computing Infrastructures

Reinforcement learning (RL) is a learning technique that enables state-dependent learning through feedback from an environment and makes an action decision for maximizing a reward without prior knowledge of the environment.If these RL techniques are used for data-centric services running on cloud computing, serious data privacy issues may occur because it is required to exchange privacy-related user data for RL-based services between the users and the cloud computing platform.We consider using homomorphic encryption (HE) scheme, which enables cloud computing platforms to perform arithmetic operations without decrypting ciphertexts.Using the HE scheme, users are allowed to deliver only ciphertexts to the cloud computing platform for using RL-based services.We propose a privacy-preserving reinforcement learning (PPRL) framework for the cloud computing platform.The proposed framework exploits a cryptosystem based on learning with errors (LWE) for fully homomorphic encryption (FHE).Performance analysis and evaluation for the proposed PPRL framework are conducted in a variety of cloud computing-based intelligent service scenarios.
Blockchain-Based Verifiable Tracking of Resellable Returned Drugs

An enormous amount of drug supply is regularly wasted due to several reasons related to incorrect prescription, purchase of unnecessary quantities, drug intolerance, allergy, or interactions.On the other hand, these medicines may be needed by patients who cannot afford to purchase them.To address this challenge, we propose a fully decentralized solution that governs the return and redistribution of unused drugs that are fit for usage.Our approach exploits the decentralized blockchain technology, smart contracts, and decentralized storage systems such as the Interplanetary File Systems (IPFS).We develop a system that provides the ability for customers as well as pharmacies to return re-usable, resellable drugs for donation or resale at a lower price.The proposed scheme tracks the production of drugs from manufacturers to traders that subsequently sell them to customers.Unused drugs can be returned after approval by specialized entities and then redistributed.In particular, we present the decentralized system architecture and implement a prototype on a test Ethereum blockchain platform to present the suggested workflow.Further, we provide system evaluation focused on assessing system functionality, performance, execution cost, and security of smart contracts and their robustness.We have also made our smart contracts code publicly available on Github. 1 .
Computing exact solutions of consensus halving and the Borsuk-Ulam theorem

We study the problem of finding an exact solution to the Consensus Halving problem. While recent work has shown that the approximate version of this problem is PPA -complete [29], [30], we show that the exact version is much harder. Specifically, finding a solution with n agents and n cuts is FIXP -hard, and deciding whether there exists a solution with fewer than n cuts is ETR -complete. Along the way, we define a new complexity class, called BU, which captures all problems that can be reduced to solving an instance of the Borsuk-Ulam problem exactly. We show that FIXP⊆BU⊆TFETR and that LinearBU=PPA, where LinearBU is the subclass of BU in which the Borsuk-Ulam instance is specified by a linear arithmetic circuit.
Learning with Noisy Class Labels for Instance Segmentation

Instance segmentation has achieved siginificant progress in the presence of correctly annotated datasets. Yet, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. In addition, limited experience and knowledge of annotators can also lead to mislabeled object classes. To solve this issue, a novel method is proposed in this paper, which uses different losses describing different roles of noisy class labels to enhance the learning. Specifically, in instance segmentation, noisy class labels play different roles in the foreground-background sub-task and the foreground-instance sub-task. Hence, on the one hand, the noise-robust loss (e.g., symmetric loss) is used to prevent incorrect gradient guidance for the foreground-instance sub-task. On the other hand, standard cross entropy loss is used to fully exploit correct gradient guidance for the foreground-background sub-task. Extensive experiments conducted with three popular datasets (i.e., Pascal VOC, Cityscapes and COCO) have demonstrated the effectiveness of our method in a wide range of noisy class labels scenarios. Code will be available at: github.com/longrongyang/LNCIS .
Learning meta-knowledge for few-shot image emotion recognition

Previous studies have demonstrated that images are of great importance in attracting people’s attention and motivating them to take action. Various attributes (e.g., colors, aesthetics, and embedded objects) related to images are considered driving factors. Among which emotions in images, in particular, play a critical role in stimulating individuals, based on the Stimulus–Organism–Response theory. Consequently, many researchers put great efforts to understand image emotions, ranging from developing theoretical models to a broad spectrum of applications. Due to the complex and unstructured characteristics of images, identifying image emotions is challenging. Although some significant progress in image emotion classification has been achieved, inherent constraints still remain unaddressed. For example, acquiring a sufficiently large amount of labeled data to train a good model is costly and inevitably requires lots of human efforts. Besides, building a generalized model applicable to different datasets still needs a deep exploration. Image emotions are very subjective, which also makes such a classification task difficult. This paper proposes a general meta-learning framework for the few-shot image emotion classification, called Meta-IEC. Meta-IEC provides the capability of: (i) adapting to a similar dataset but new classes that have not been encountered before, and (ii) generalizing to a completely different dataset where emotion classes are unseen in the training dataset and only very few labeled images are available. Meta-IEC is also able to capture the uncertainty and ambiguity during the meta-testing, where we implement a hierarchical Bayesian graphical model to understand latent relationships among various parameters between meta-training and meta-testing. Extensive experiments on three commonly used datasets empirically demonstrate the superiority of our method over several state-of-the-art baselines. For example, our meta-learning based model can achieve performance improvement up to 5+%. We also provide some managerial implications on parameter sensitivity and label selection of meta-training and meta-testing.