Reviewerly/exHarmony huggingface dataset:
Removal as a Method

This paper offers first-steps guidance towards the development of a methodology that embodies theoretical proposals for a fourth-wave, 'entanglement' approach to HCI. We propose the removal of technologies and the documenting of their absence as a method. Removal disrupts habitual relationships with our everyday technologies, revealing otherwise hidden knowledges. Removal as a method exemplifies that "you don't know what you've got till it's gone". We apply removal to the case of menstrual cycle tracking in two ways: literally through two autoethnographies, and hypothetically through semi-structured interviews. We show how this method especially facilitates emotional, embodied and cultural knowledge of the lived experience of self-tracking and we unpack some opportunities, implications and limitations in its use. Finally, we present how this method might be adopted by others and propose cases in which removal as a method might be applicable to study of a wider range of technologies beyond self-tracking.
The Implication of Blockchain as a Disruptive Technology for Construction Industry

Adoption of blockchain technology has been widely construed as disruptive. Blockchain is a distributed ledger technology. It is secure by design because of its cryptography and distributed consensus mechanism, which also offers anonymity, persistence, auditability and resilience. The construction industry adopts a project-based approach and globally has been considered as a fragmented sector, predominantly in unorganised space. Given these characteristics, the technology adoption has been slow. This study looks at the challenges and adopts interpretive structural modelling technique to understand the linkages between these barriers. This study can provide managerial insights into designing strategies for overcoming the barriers for adoption of this disruptive technology.
Facial Expression Recognition of Industrial Internet of Things by Parallel Neural Networks Combining Texture Features

Industrial Internet of Things (IIoT) has been widely applied in smart home, smart city, smart traffic, etc. It is a big challenge to recognize facial expression of IIoT systems more effectively. Current facial recognition methods only utilize singular facial images, so accurate features that are highly correlated with facial changes can hardly be extracted. In order to overcome this difficulty and improve facial expression recognition, in this article, we propose a parallel neural network combining texture features, which can be applied in facial expression recognition. This parallel neural network is constructed by convolution neural network, residual network, and capsule network. Additionally, texture analysis is conducted on facial expression images to extract abundant features. Eight texture features are extracted by gray-level co-occurrence matrix and integrated with features of original images. Finally, these integrated features extracted by three kinds of networks are used to classify facial images. Experimental results prove that the proposed approach has a high recognition rate and strong robustness compared to competitive algorithms. Remarkably, our accuracy reaches 98.14%, with an increase of 3.71% in comparison with ResNet, and the F1-score of 0.9801. It is thus verified from this result that the proposed algorithm has many outstanding advantages. The idea in combination with texture features also provides a new solution for image classification.
k-hop graph neural networks

Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler–Lehman test of graph isomorphism in terms of distinguishing non-isomorphic graphs. However, it was recently shown that this test cannot identify fundamental graph properties such as connectivity and triangle freeness. We show that GNNs also suffer from the same limitation. To address this limitation, we propose a more expressive architecture, k-hop GNNs, which updates a node’s representation by aggregating information not only from its direct neighbors, but from its k-hop neighborhood. We show that the proposed architecture can identify fundamental graph properties. We evaluate the proposed architecture on standard node classification and graph classification datasets. Our experimental evaluation confirms our theoretical findings since the proposed model achieves performance better or comparable to standard GNNs and to state-of-the-art algorithms.
An Empirical Assessment of the Intrusiveness and Reasonableness of Emerging Work Surveillance Technologies in the Public Sector

Abstract As public sector work environments continue to embrace the digital governance revolution, questions of work surveillance practices and its relationship to performance management continue to evolve, but even more dramatically in the contemporary period of many public servants being forced to shift to remote work from home in response to the COVID‐19 pandemic. This article presents the results of three surveys, two of them population‐based survey experiments, all conducted during the onset of the COVID‐19 pandemic in Canada that compare public servant (n = 346) and citizen (n = 1,008 phone; n = 2,001 web) attitudes to various cutting‐edge—though no doubt controversial among some—digital surveillance tools that can be used in the public sector to monitor employee work patterns, often targeted toward remote working conditions. The findings represent data that can help governments and public service associations navigate difficult questions of reasonable privacy intrusions in an increasing digitally connected workforce. Evidence for Practice New work surveillance technologies are available to use within the public sector and will present acceptability challenges to public managers as they contemplate the introduction of these technologies. Multimodal survey data from Canada reveals that public servants and citizens find these emerging work surveillance technologies to be quite intrusive and unreasonable but show relatively more tolerance for digital surveillance over physical surveillance practices. Understanding surveillance anxieties among targeted employees will be key to finding a balance between employee privacy rights and employer desires to manage employees in a remote or digital environment.
Direct and accurate phase unwrapping with deep neural network

In this paper a novel, to the best of our knowledge, deep neural network (DNN), VUR-Net, is proposed to realize direct and accurate phase unwrapping. The VUR-Net employs a relatively large number of filters in each layer and adopts alternately two types of residual blocks throughout the network, distinguishing it from the previously reported ones. The proposed method enables the wrapped phase map to be unwrapped precisely without any preprocessing or postprocessing operations, even though the map has been degraded by various adverse factors, such as noise, undersampling, deforming, and so on. We compared the VUR-Net with another two state-of-the-art phase unwrapping DNNs, and the corresponding results manifest that our proposal markedly outperforms its counterparts in both accuracy and robustness. In addition, we also developed two new indices to evaluate the phase unwrapping. These indices are proved to be effective and powerful as good candidates for estimating the quality of phase unwrapping.
Best arrangement of BIPV surfaces for future NZEB districts while considering urban heat island effects and the reduction of reflected radiation from solar façades

Building Integrated Photovoltaics (BIPV) constitute the way to reach Nearly Zero Energy Buildings and even zero energy districts (NZED). BIPV surfaces can operate on roofs and façades and their efficiency and productivity are related to orientation, shading, reflections from surrounding surfaces. The novelty of the present investigation relies on a chained model also able to account for the Urban Heat Island conditions. The building performance are analysed as EnergyPlus simulations, considering a multi thermal zone reference building located inside a district of similar characteristics. The PV power generation, from hourly to yearly values, is calculated accounting for PV module temperature, irradiance intensity and solar incident angles, by further developing the well known Sandia model. The whole model, applied to a particular city (41.9°N, 12.5°E), shows how the progressive increase of vertical PV surfaces on both the reference and surrounding buildings yields to a reduction of the energy production per PV unit area. The yearly NZED requirements is reached, in the present case, harvesting solar energy on 60% of rooftops and on 60% of the total area of the façades, with a 11% decrease in energy production per PV unit area due to "darkening" effects induced by PV surrounding buildings.
AulaVR: Virtual Reality, a telepresence technique applied to distance education

This paper discusses the use of a virtual reality application in distance education as an e-learning system. This application seeks that students of non-face-to-face education experience telepresence, that is, experience being inside a real classroom with a teacher and several students, this application in addition to improving the student experience when studying in a non-face-to-face way provides the facility that they can connect from anywhere through an Android mobile device with internet access. Through the use of virtual reality can be shared and synchronized spaces where the experience is interactive in a social environment where the results were favorable to the proposal.
Multiscale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic Power Generation Forecasting in Smart City Energy Management

Photovoltaic power generation forecasting (PVGF) is an attractive research topic for efficient energy management in smart city. In addition, the long short-term memory recurrent neural network (LSTM/RNN) has been actively utilized for predicting various time series tasks in recent years due to its outstanding ability to learn the feature of sequential time-series data. Although the existing forecasting models were obtained from learning the sequential PVGF data, it is observed that irregular factors made adverse effects on the forecasting results of very-short-term PVGF tasks, thus, the entire forecasting performance was deteriorated. In this regard, multiscale LSTM-based deep learning which is capable for forecasting very-short-term PVGF is proposed for efficient management. The model concatenates on two different scaled LSTM modules to overcome the deterioration that is originated from the irregular factors. Lastly, experimental results present the proposed framework can assist to forecast the tendency of PVGF amount steadily.
Xoodyak, a lightweight cryptographic scheme

In this paper, we present Xoodyak, a cryptographic primitive that can be used for hashing, encryption, MAC computation and authenticated encryption. Essentially, it is a duplex object extended with an interface that allows absorbing strings of arbitrary length, their encryption and squeezing output of arbitrary length. It inherently hashes the history of all operations in its state, allowing to derive its resistance against generic attacks from that of the full-state keyed duplex. Internally, it uses the Xoodoo[12] permutation that, with its width of 48 bytes, allows for very compact implementations. The choice of 12 rounds justifies a security claim in the hermetic philosophy: It implies that there are no shortcut attacks with higher success probability than generic attacks. The claimed security strength is 128 bits. We illustrate the versatility of Xoodyak by describing a number of use cases, including the ones requested by NIST in the lightweight competition. For those use cases, we translate the relatively detailed security claim that we make for Xoodyak into simple ones.
Comparative Analysis Using Supervised Learning Methods for Anti-Money Laundering in Bitcoin

With the advance of Bitcoin technology, money laundering has been incentivised as a den of Bitcoin blockchain, in which the user's identity is hidden behind a pseudonym known as address. Although this trait permits concealing in the plain sight, the public ledger of Bitcoin blockchain provides more power for investigators and allows collective intelligence for anti-money laundering and forensic analysis. This fascinating paradox arises in the strength of Bitcoin technology. Machine learning techniques have attained promising results in forensic analysis, in order to spot suspicious behaviour in Bitcoin blockchain. This paper presents a comparative analysis of the performance of classical supervised learning methods using a recently published data set derived from Bitcoin blockchain, to predict licit and illicit transactions in the network. Besides, an ensemble learning method is utilised using a combination of the given supervised learning models, which outperforms the given classical methods. This experiment is performed using a newly published data set derived from Bitcoin blockchain. Our main contribution points out that using ensemble learning approach outperforms the performance of the classical learning models used in the original paper, using Elliptic data set, a time series of Bitcoin transaction graph with node transactions and directed payments flow edges. Using the same data set, we show that we are able to predict licit/illicit transactions with an accuracy of 98.13% and F1 score equals to 83.36% using the proposed method. We discuss the variety of supervised learning methods, and their capabilities of assisting forensic analysis, and propose future work directions.
Human Activity Recognition Based on Dynamic Active Learning

Activity of daily living is an important indicator of the health status and functional capabilities of an individual. Activity recognition, which aims at understanding the behavioral patterns of people, has increasingly received attention in recent years. However, there are still a number of challenges confronting the task. First, labelling training data is expensive and time-consuming, leading to limited availability of annotations. Secondly, activities performed by individuals have considerable variability, which renders the generally used supervised learning with a fixed label set unsuitable. To address these issues, we propose a dynamic active learning-based activity recognition method in this work. Different from traditional active learning methods which select samples based on a fixed label set, the proposed method not only selects informative samples from known classes, but also dynamically identifies new activities which are not included in the predefined label set. Starting with a classifier that has access to a limited number of labelled samples, we iteratively extend the training set with informative labels by fully considering the uncertainty, diversity and representativeness of samples, based on which better-informed classifiers can be trained, further reducing the annotation cost. We evaluate the proposed method on two synthetic datasets and two existing benchmark datasets. Experimental results demonstrate that our method not only boosts the activity recognition performance with considerably reduced annotation cost, but also enables adaptive daily activity analysis allowing the presence and detection of novel activities and patterns.
Modern Virtual Reality Headsets

The virtual reality (VR) industry has greatly evolved over the recent years. VR, being an extremely promising technology, has already found its use in various different areas, and most likely will continue to expand further. Thanks to the accumulated scientific knowledge base in the field of VR, technological progress and human enthusiasm, new ways of providing a better, more effective immersive experience in VR are constantly emerging. In this paper, we investigate the hardware aspect of the current VR technologies. We examine the classification of modern VR head-mounted displays (HMDs) and conduct a comparative analysis on an example of several relevant for today headsets, such as: Oculus Rift S, HTC Vive Pro, HTC Vive Cosmos, Valve Index and Samsung HMD Odyssey+. The results of the analysis show that overall HTC Vive Pro has the best qualities among the other devices. In addition, based on this study, we choose this VR headset to be used in the future development of an industrial training VR simulator, to the process of which a separate article will be devoted.
Event-Centric Hierarchical Representation for Dense Video Captioning

Dense video captioning aims to localize and describe multiple events in untrimmed videos, which is a challenging task that draws attention recently in computer vision. Although existing methods have achieved impressive performance, most of them only focus on local information of event segments or very simple event-level context, overlooking the complexity of event-event relationship and the holistic scene. As a result, the coherence of captions within the same video could be damaged. In this article, we propose a novel event-centric hierarchical representation to alleviate this problem. We enhance the event-level representation by capturing rich relationship between events in terms of both temporal structure and semantic meaning. Then, a caption generator with late fusion is developed to generate surrounding-event-aware and topic-aware sentences, conditioned on the hierarchical representation of visual cues from the scene level, the event level, and the frame level. Furthermore, we propose a duplicate removal method, namely temporal-linguistic non-maximum suppression (TL-NMS) to distinguish redundancy in both localization and captioning stages. Quantitative and qualitative evaluations on the ActivityNet Captions and YouCook2 datasets demonstrate that our method improves the quality of generated captions and achieves state-of-the-art performance on most metrics.
A New Framework for Smartphone Sensor-Based Human Activity Recognition Using Graph Neural Network

Automatic human activity recognition (HAR) through computing devices is a challenging research topic in the domain of computer vision. It has widespread applications in various fields such as sports, healthcare, criminal investigation and so on. With the advent of smart devices like smartphones, availability of inertial sensors like accelerometer and gyroscope can easily be used to track our daily physical movements. State-of-the-art deep neural network models like Convolutional Neural Network (CNN) do not need any additional feature extraction for such applications. However, it requires huge amount of data for training which is time consuming, and requires ample resource. Another limiting factor of CNN is that it considers only the features of an individual sample for learning without considering any structural information among the samples. To address the aforesaid issues, we propose an end-to-end fast Graph Neural Network (GNN) which not only captures the individual sample information efficiently but also the relationship with other samples in the form of an undirected graph structure. To the best of our knowledge, this is the first work where the time series data are transformed into a structural representation of graph for the purpose of HAR using sensor data. Proposed model has been evaluated on 6 publicly available datasets, and it achieves nearly 100% recognition accuracy for all the 6 datasets. Source code of this work is available at https://github.com/riktimmondal/HAR-Sensor.
IFRS, FDI, economic growth and human development: The experience of Anglophone and Francophone African countries

This study examines the relationships of IFRS, FDI, human development and economic growth in Anglophone and Francophone African countries between 1997 and 2017. We find that FDI, openness, schooling, and control of corruption have a positive effect of countries' decisions to adopt IFRS. The results also indicate that FDI has a positive impact on economic growth, and that this positive impact is enhanced by IFRS. Furthermore, we find that Anglophone and common law African countries adopt IFRS more than Francophone countries. Our analysis shows that FDI is positively related to decisions to adopt IFRS in both groups.
Towards Systematic Design of General Type-2 Fuzzy Logic Controllers: Analysis, Interpretation, and Tuning

This article aims to provide a new perspective on how the deployment of general type-2 (GT2) fuzzy sets affects the mapping of a class of fuzzy logic controllers (FLCs). It is shown that an α-plane represented a GT2-FLC is easily designed via baseline type-1 and interval type-2 FLCs and two design parameters (DPs). The DPs are the total number of α planes and the tuning parameter of the secondary membership function that are interpreted as sensitivity and shape DPs, respectively. We provide a clear understanding and interpretation of the sensitivity and shape DPs on controller performance through various comparative analyses. We present design approaches on how to tune the shape DP by providing a tradeoff between robustness and performance. We also propose two online scheduling mechanisms to tune the shape DP. We explore the effect of the sensitivity DP on the GT2-FLC and provide practical insights on how to tune the sensitivity DP. We present an algorithm for tuning the sensitivity DP that provides a compromise between computational time and sensitivity. We validate our analyses, interpretations, and design methods with experimental results conducted on a drone. We believe that this article provides clear explanations on the role of DPs on the performance, robustness, sensitivity, and computational time of GT2-FLCs.
Experimental Characterization of Unsharp Qubit Observables and Sequential Measurement Incompatibility via Quantum Random Access Codes

Unsharp measurements are increasingly important for foundational insights in quantum theory and quantum information applications. Here, we report an experimental implementation of unsharp qubit measurements in a sequential communication protocol, based on a quantum random access code. The protocol involves three parties; the first party prepares a qubit system, the second party performs operations that return both a classical and quantum outcome, and the latter is measured by the third party. We demonstrate a nearly optimal sequential quantum random access code that outperforms both the best possible classical protocol and any quantum protocol that utilizes only projective measurements. Furthermore, while only assuming that the involved devices operate on qubits and that detected events constitute a fair sample, we demonstrate the noise-robust characterization of unsharp measurements based on the sequential quantum random access code. We apply this characterization towards quantifying the degree of incompatibility of two sequential pairs of quantum measurements.