Reviewerly/exHarmony huggingface dataset:
Ontologies in human–computer interaction: A systematic literature review

Human–Computer Interaction (HCI) is a multidisciplinary area that involves a diverse body of knowledge and a complex landscape of concepts, which can lead to semantic problems, hampering communication and knowledge transfer. Ontologies have been successfully used to solve semantics and knowledge-related problems in several domains. This paper presents a systematic literature review that investigated the use of ontologies in the HCI domain. The main goal was to find out how HCI ontologies have been used and developed. 35 ontologies were identified. As a result, we noticed that they cover different HCI aspects, such as user interface, interaction phenomenon, pervasive computing, user modeling / profile, HCI design, interaction experience and adaptive interactive system. Although there are overlaps, we did not identify reuse among the 35 analyzed ontologies. The ontologies have been used mainly to support knowledge representation and reasoning. Although ontologies have been used in HCI for more than 25 years, their use became more frequent in the last decade, when ontologies address a higher number of HCI aspects and are represented as both conceptual and computational models. Concerning how ontologies have been developed, we noticed that some good practices of ontology engineering have not been followed. Considering that the quality of an ontology directly influences the quality of the solution built based on it, we believe that there is an opportunity for HCI and ontology engineering professionals to get closer to build better and more effective ontologies, as well as ontology-based solutions.
Lasagna

Edge intelligence has already been widely regarded as a key enabling technology in a variety of domains. Along with the prosperity, increasing concern is raised on the security and privacy of intelligent applications. As these applications are usually deployed on shared and untrusted edge servers, malicious co-located attackers, or even untrustworthy infrastructure providers, may acquire highly security-sensitive data and code (i.e., the pre-trained model). Software Guard Extensions (SGX) provides an isolated Trust Execution Environment (TEE) for task security guarantee. However, we notice that DNN inference performance in SGX is severely affected by the limited enclave memory space due to the resultant frequent page swapping operations and the high enclave call overhead. To tackle this problem, we propose Lasagna, an SGX oriented DNN inference performance acceleration framework without compromising the task security. Lasagna consists of a local task scheduler and a global task balancer to optimize the system performance by exploring the layered-structure of DNN models. Our experiment results show that our layer-aware Lasagna effectively speeds up the well-known DNN inference in SGX by 1.31x-1.97x.
Time series prediction using machine learning: a case of Bitcoin returns

Purpose The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms. Design/methodology/approach The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&amp;P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking. Findings Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns. Originality/value This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.
A survey on advances of quantum repeater

Abstract The distribution of quantum entanglement is essential for quantum communication. Contrary to classical communication, the signal in quantum communication is impossible to copy due to the basic principles of quantum mechanics, i.e. , quantum state collapse and no-cloning theorem. However, the communication range is limited by inevitable photon loss. To counteract this issue, one can insert several repeater stations between two remote end nodes to divide the full distance into several shorter segments and successively operate entanglement swapping between adjacent repeater stations to enable long-distance quantum communication. Up to now, one can classify quantum repeaters (QRs) into three generations based on the methods of reducing errors resulted from photon loss and imperfect operations. In this paper, we will briefly review advances for each generation of QRs and discuss the advantages and challenges of each generation. Moreover, we also discuss some efficient proposals of building blocks to overcome the bottlenecks for QRs.
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion

Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs.Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs.Most existing methods have thus far encoded an entity pair and matched entity pairs by using the direct neighbors of concerned entities.In this paper, we propose the P-INT model for effective few-shot knowledge graph completion.First, P-INT infers and leverages the paths that can expressively encode the relation of two entities.Second, to capture the fine grained matches, P-INT calculates the interactions of paths instead of mixing them for each entity pair.Extensive experimental results demonstrate that P-INT outperforms the state-of-the-art baselines by 11.2-14.2% in terms of Hits@1.Our codes and datasets are online now 1 .
Public Perceptions on Climate Change: A Sentiment Analysis Approach

Public perception on climate change is a paramount component that affects the implementation of adaptation and mitigation measures. Taking into account the public perceptions on the issue may assist decision-makers in producing appropriate strategies to ameliorate the impacts of climate change. A corpus-driven sentiment analysis approach was done to classify the polarity of Malaysian public perceptions, identify the sentiment lexicon, and analyse the public sentiments. A part of a specialised corpus namely the Malaysian Diachronic Climate Change Corpus (MyDCCC) was developed from The Sun Daily and was used as the data for this study. The methodology involved the employment of Azure Machine Learning software to conduct sentiment analysis to explore the polarity of public sentiments, corpus analysis approach to identify the sentiment lexicon and discourse analysis to analyse public sentiments based on the identified sentiment lexicon. The results revealed that the majority of public sentiments appeared to be negative, depicting sentiment words such as long, critical, and serious. Positive sentiment words also prevailed such as better, best and hope . The discourse analysis revealed that the public is reasonably insightful of climate change although their sentiments appeared to be negative. However, the negative stance was largely influenced by the public's indignation with how decision-makers handle the climate change issue. Ironically, the negative sentiments may be an indication for the decision-makers to improve their approach in addressing climate change. This study has contributed significantly to research on public perceptions of climate change in the Malaysian context.
Efficient and Secure Cross-Domain Sharing of Blockchain Electronic Medical Records Based on Edge Computing

In this article, we analysed the problems of electronic medical records (EMRs) and found that the EMRs generated by different hospitals for the same patient are mutually independent and duplication and data sharing are difficult among hospitals. In order to solve this problem, this paper proposes an efficient and secure cross-domain sharing scheme of EMRs based on edge computing. The program allows the doctor to access the personal history EMRs through the patient’s authorization so that the doctor can understand the patient’s history of illness and, on this basis, generate a new medical record for the patient. Then, the doctor sends the EMRs to the edge server, and the server calculates the ciphertext and adds it to the patient’s personal medical record to complete the case update. Analysis shows that this solution can effectively prevent data tampering and forgery through blockchain and avoid privacy leakage problems in plaintext sharing by using searchable encryption and by relying on edge servers to solve nearby computing tasks and divert the computing capacity of cloud servers to improve efficiency. The security proof shows that the scheme satisfies the complex problem of the BDH assumption. Performance analysis shows that the scheme is feasible and efficient.
Robust Encryption to Enhance IoT Confidentiality for Healthcare Ecosystems

Within the foreseeable future, it is evident that IoT networks in healthcare demand a better quality of service in terms of confidentiality and accessibility. Although, a good fairness quotient is not meeting the required standards yet due to the limited privacy and inaccessibility of the network. We propose a robust encryption method to overcome the traditional terrestrial secure data sharing problems and approach the expected demands. Enabling cryptography in healthcare edge computing systems is a preliminary prerequisite, especially after integrating IoT framework in these ecosystems. Therefore, it is paramount to allow an indispensable tool for secure data exchange among digital devices embedded through the network. Therefore, we elaborate a privacy mechanism based on fundamental mathematics to transfer text information securely throughout our research work. Introducing an innovative cryptography technique that uses a novel category of matrix called circulant matrices aims to operate confidential transmission of text messages. In addition, we modified and integrated an archaic alphanumeric method called Gematria, which derives from the Greek word “geometria”, to assign numerical values to Latin letters.
Somebody Knows

Several different notions of group knowledge have been extensively studied in the epistemic and doxastic logic literature, including common knowledge, general knowledge (everybody-knows) and distributed knowledge. In this paper we study a natural notion of group knowledge between general and distributed knowledge: somebody-knows. While something is general knowledge if and only if it is known by everyone, this notion holds if and only if it is known by someone. This is stronger than distributed knowledge, which is the knowledge that follows from the total knowledge in the group. We introduce a modality for somebody-knows in the style of standard group knowledge modalities, and study its properties. Unlike the other mentioned group knowledge modalities, somebody-knows is not a normal modality; in particular it lacks the conjunctive closure property. We provide an equivalent neighbourhood semantics for the language with a single somebody-knows modality, together with a completeness result: the somebody-knows modalities are completely characterised by the modal logic EMN extended with a particular weak conjunctive closure axiom. We also show that the satisfiability problem for this logic is PSPACE-complete. The neighbourhood semantics and the completeness and complexity results also carry over to logics for so-called local reasoning (Fagin et al. 1995) with bounded ``frames of mind'', correcting an existing completeness result in the literature (Allen 2005).
Entropic entanglement criteria in phase space

We derive entropic inseparability criteria for the phase-space representation of quantum states. In contrast to criteria involving differential entropies of marginal phase-space distributions, our criteria are based on a joint distribution known as the Husimi $Q$ distribution. This distribution is experimentally accessible in cold atoms, circuit QED architectures, and photonic systems, and bears practical advantages compared to the detection of marginals. We exemplify the strengths of our entropic approach by considering several classes of non-Gaussian states where second-order criteria fail. We show that our criteria certify entanglement in previously undetectable regions, highlighting the strength of using the Husimi $Q$ distribution for entanglement detection.
Deep robust residual network for super-resolution of 2D fetal brain MRI

Abstract Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.
Supervised machine learning in the mass spectrometry laboratory: A tutorial

As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is an opportunity to improve technological literacy of ML-based software within the clinical laboratory scientist community. To this end, we present a basic overview of ML and a tutorial of an ML-based experiment using a previously published MS dataset. The purpose of this paper is to describe the fundamental principles of supervised ML, outline the steps that are classically involved in an ML-based experiment, and discuss the purpose of good ML practice in the context of a binary MS classification problem.
The Digital Divide in Healthcare: A Socio-Cultural Perspective of Digital Literacy

As the use of IT for health management increases, threats to exacerbate existing health inequalities increases.Through semi-structured expert interviews, this qualitative study aims to examine the Socio-Cultural characteristics associated with access to and use of technologies for health managing purposes and its relationship with health literacy.Inspired by Bourdieu, the paper uses a socio-cultural perspective to understand the choices and lifestyle of individuals, based on the underlying human mechanisms that may function as determinants of the (in)equality which digitalization of health services may entail.The study focuses on the following question: How do social factors create and reproduce (in)equality in behavioral skills, regarding digital health technologies?The study contributes with a multidimensional perspective on social factors that influences the use and acceptance of digital health technologies from the expert's perspectives.
Obstacle Avoidance Path Planning for UAV Based on Improved RRT Algorithm

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used.
A multibranch, multitarget neural network for rapid point-source inversion in a microseismic environment: examples from the Hengill Geothermal Field, Iceland

SUMMARY Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M ≤ 1.6) earthquakes at the Hellisheiði geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity.
Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning

We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastConv</i> , a template-based code auto-generation open-source library that can automatically generate high-performance deep learning convolution kernels of arbitrary matrices/tensors shapes. FastConv is based on the Winograd algorithm, which is reportedly the highest performing algorithm for the time-consuming layers of convolutional neural networks. ARM CPUs cover a wide range of designs and specifications, from embedded devices to HPC-grade CPUs. The leads to the dilemma of how to consistently optimize Winograd-based convolution solvers for convolution layers of different shapes. FastConv addresses this problem by using templates to auto-generate multiple shapes of tuned kernels variants suitable for skinny tall matrices. As a performance portable library, FastConv transparently searches for the best combination of kernel shapes, cache tiles, scheduling of loop orders, packing strategies, access patterns, and online/offline computations. Auto-tuning is used to search the parameter configuration space for the best performance for a given target architecture and problem size. Results show 1.02x to 1.40x, 1.14x to 2.17x, and 1.22x and 2.48x speedup is achieved over NNPACK, ARM NN, and FeatherCNN on Kunpeng 920. Furthermore, performance portability experiments with various convolution shapes show that FastConv achieves 1.2x to 1.7x speedup and 2x to 22x speedup over NNPACK and ARM NN inference engine using Winograd on Kunpeng 920. CPU performance portability evaluation on VGG–16 show an average speedup over NNPACK of 1.42x, 1.21x, 1.26x, 1.37x, 2.26x, and 11.02x on Kunpeng 920, Snapdragon 835, 855, 888, Apple M1, and AWS Graviton2, respectively.
Autonomous agents and multiagent systems

The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.
Retrieval-Based Transformer Pseudocode Generation

The comprehension of source code is very difficult, especially if the programmer is not familiar with the programming language. Pseudocode explains and describes code contents that are based on the semantic analysis and understanding of the source code. In this paper, a novel retrieval-based transformer pseudocode generation model is proposed. The proposed model adopts different retrieval similarity methods and neural machine translation to generate pseudocode. The proposed model handles words of low frequency and words that do not exist in the training dataset. It consists of three steps. First, we retrieve the sentences that are similar to the input sentence using different similarity methods. Second, pass the source code retrieved (input retrieved) to the deep learning model based on the transformer to generate the pseudocode retrieved. Third, the replacement process is performed to obtain the target pseudo code. The proposed model is evaluated using Django and SPoC datasets. The experiments show promising performance results compared to other language models of machine translation. It reaches 61.96 and 50.28 in terms of BLEU performance measures for Django and SPoC, respectively.