Reviewerly/exHarmony huggingface dataset:
On the power of one pure steered state for EPR-steering with a pair of qubits

As originally introduced, the EPR phenomenon was the ability of one party (Alice) to steer, by her choice between two measurement settings, the quantum system of another party (Bob) into two distinct ensembles of pure states. As later formalized as a quantum information task, EPR-steering can be shown even when the distinct ensembles comprise mixed states. Consider the scenario where Alice and Bob each have a qubit and Alice performs dichotomic projective measurements. In this case, the states in the ensembles to which she can steer form the surface of an ellipsoid ${\cal E}$ in Bob's Bloch ball. Further, let the steering ellipsoid ${\cal E}$ have nonzero volume. It has previously been shown that if Alice's first measurement setting yields an ensemble comprising two pure states, then this, plus any one other measurement setting, will demonstrate EPR-steering. Here we consider what one can say if the ensemble from Alice's first setting contains only one pure state $\mathsf{p}\in{\cal E}$, occurring with probability $p_\mathsf{p}$. Using projective geometry, we derive the necessary and sufficient condition analytically for Alice to be able to demonstrate EPR-steering of Bob's state using this and some second setting, when the two ensembles from these lie in a given plane. Based on this, we show that, for a given ${\cal E}$, if $p_\mathsf{p}$ is high enough [$p_{\sf p} > p_{\rm max}^{{\cal E}} \in [0,1)$] then any distinct second setting by Alice is sufficient to demonstrate EPR-steering. Similarly we derive a $p_{\rm min}^{{\cal E}}$ such that $p_\mathsf{p}>p_{\rm min}^{{\cal E}}$ is necessary for Alice to demonstrate EPR-steering using only the first setting and some other setting. Moreover, the expressions we derive are tight; for spherical steering ellipsoids, the bounds coincide: $p_{\rm max}^{{\cal E}} = p_{\rm min}^{{\cal E}}$.
Deep Reinforcement Learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain

Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we develop a decentralized architecture for reinforcement learning to allow multiple agents to learn optimal control policies on their own devices of the same kind but in varied environments. For such multiple agents, the traditional centralized learning algorithm usually requires a costly or time-consuming effort to develop the best-regulating policy and is incapable of scaling to a large-scale system. To address this issue, we propose a decentralized reinforcement learning algorithm (DecRL) and information exchange scheme for each individual device, in which each agent shares the individual learning experience and information with other agents based on local model training. We incorporate the algorithm into each agent in the proposed collaborative architecture and validate it in the telecommunication domain under emergency conditions, in which a macro base station (BS) is broken due to a natural disaster, and three unmanned aerial vehicles carrying BSs (UAV-BSs) are deployed to provide temporary coverage for mission-critical (MC) users in the disaster area. Based on the findings, we show that the proposed decentralized reinforcement learning algorithm can successfully support multi-agent learning, while the learning speed and service quality can be further enhanced.
Towards Better Trust in Human-Machine Teaming through Explainable Dependability

The human-machine teaming paradigm is increasingly widespread in critical domains, such as healthcare and domestic assistance. The paradigm goes beyond human-on-the-loop and human-in-the-loop systems by promoting tight teamwork between humans and autonomous machines that collaborate in the same physical space. These systems are expected to build a certain level of trust by enforcing dependability and exhibiting interpretable behavior. We present emerging results in this direction, with a novel framework aiming at achieving better trust in human-machine teaming leveraging formal analysis, as well as eXplainable AI. We illustrate our approach and the emerging results with an example from the healthcare domain.
A secure and effective image encryption scheme by combining parallel compressed sensing with secret sharing scheme

In the complex network environment, it is becoming more and more significant to protect the security of information during transmission. A secure and effective image encryption scheme by combining parallel compressed sensing with secret sharing scheme is presented in this paper. Firstly, an image is measured by parallel compressed sensing, which not only realizes compression and encryption in the meantime, but also reduces computational complexity and storage space, and enhances transmission efficiency. Secondly, a block Arnold transformation is applied to process the measurement value image, and the obtained image is called a secret image, so as to achieve the effects of scrambling and encryption. Then, the secret image is partitioned into n shadow images by Shamir’s (t,n)-threshold secret sharing scheme, at least any t of n shadows images are able to recover the secret image, so the size of shadow images can be adjusted according to different application scenarios, which is flexible. This method realizes the encryption effect, and enhances the security of the secret image. Finally, to ensure the security of shadow images, the Zigzag confusion is introduced to process n shadow images and realizes the encryption effect. Experimental results manifest that our scheme realizes security and availability of the network system under the condition of limited resources.
S-SNHF: sentiment based social neural hybrid filtering

Deep learning has yielded success in many research fields. In the last few years, deep learning techniques have been applied in recommender systems to solve cold start and data sparsity problems. However, only a few attempts have been made in social-based recommender systems. In this study, we address this issue and propose a novel recommendation model called Sentiment based Social Neural Hybrid Filtering (S-SNHF). This model combines collaborative and content-based filtering with social information using a deep neural architecture based on Generalized Matrix Factorization (GMF) and Hybrid Multilayer Perceptron (HybMLP). Furthermore, for achieving higher recommendation reliability, the hybrid sentiment analysis model is integrated to analyse users' opinions and infer their preferences. The results of the empirical study performed with three popular datasets show the contribution of both, social information and sentiment analysis on the recommendation performance and that our approach achieves significantly better recommendation accuracy, compared with state-of-the-art recommendation methods.
(Re)Connecting History to the Theory and Praxis of HCI

This special issue builds on and expands HCI’s engagement with historical approaches, questioning our field’s ontological orientations and offering new methods for examining the past. The set of articles featured reinvigorates questions on whose technological labor matters, how we might challenge the racist and misogynistic consequences of HCI’s inter-disciplinary inheritances, and offer new modes of liberatory world-making. Taken together, this collection serves as an act of reclamation of the lineage of our field, providing resources and guidance for a more just present and future.
Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods

Abstract Automated labelling of radiology reports using natural language processing allows for the labelling of ground truth for large datasets of radiological studies that are required for training of computer vision models. This paper explains the necessary data preprocessing steps, reviews the main methods for automated labelling and compares their performance. There are four main methods of automated labelling, namely: (1) rules‐based text‐matching algorithms, (2) conventional machine learning models, (3) neural network models and (4) Bidirectional Encoder Representations from Transformers (BERT) models. Rules‐based labellers perform a brute force search against manually curated keywords and are able to achieve high F1 scores. However, they require proper handling of negative words. Machine learning models require preprocessing that involves tokenization and vectorization of text into numerical vectors. Multilabel classification approaches are required in labelling radiology reports and conventional models can achieve good performance if they have large enough training sets. Deep learning models make use of connected neural networks, often a long short‐term memory network, and are similarly able to achieve good performance if trained on a large data set. BERT is a transformer‐based model that utilizes attention. Pretrained BERT models only require fine‐tuning with small data sets. In particular, domain‐specific BERT models can achieve superior performance compared with the other methods for automated labelling.
Digital competence and digital technology: a curriculum analysis of Norwegian early childhood teacher education

This paper explores how digital competence and digital technology are addressed in Norwegian early childhood teacher education (ECTE). The aim is to provide a national overview of the ECTE institutions' programme and relevant course plans to identify how the preservice teachers are being prepared for their future professional life as digitally competent early childhood teachers. We have conducted a summative content analysis of national regulations, national guidelines, programme and course plans by drawing on Goodlad et al.'s curriculum theory. The findings demonstrate that digital competence and digital technology are clearly emphasised and addressed at the ideological curriculum level but less so at the formal and perceived levels. Data shows that there is a need to define and operationalise professional digital competence from an ECTE perspective. Furthermore, there is a need to more explicitly describe the learning outcomes in the programme and course plans, focusing on both theoretical and practical aspects.
The Effect of Augmented Reality Technology on Primary School Students’ Achievement, Attitudes Towards the Course, Attitudes Towards Technology, and Participation in Classroom Activities

AbstractThe research aims to investigate the impact of augmented reality (AR) technologies developed within the framework of primary school students' on student achievement, attitudes towards the course, attitudes towards technology, and participation in classroom activities. The contents of AR-based learning materials have been developed by 4th-grade teachers and writers within the context of the existing curriculum. The study process was carried out based on the instructional design model. The research was performed in conjunction with the quasi-experimental design among the experimental designs of quantitative research methods. Using the cluster random sampling method, students in the experimental and control groups were selected among 11 primary schools. The findings indicate that contrary to the students in the control group, the students in the experimental group had higher academic achievement and participation in classroom activities, and also their attitudes to the course and applied technology were more positive. Moreover, concerning the experimental group students, a positive and significant correlation between their academic achievements, attitudes towards the course, attitudes towards technology, and their participation and participation in classroom activities have been observed.Keyword: Augmented realityapplications in subject areasprimary school studentinteractive learning environments Ethical approvalThis study was approved by Atatürk University ethics unit board with the decision dated 16.09.2019 and numbered 12/01.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Research Fund of the Ataturk University [Project Number: 7511].Notes on contributorsYavuz SökmenYavuz Sökmen is an instructor in the Department of Primary Education at Ataturk University in Erzurum, Turkey. His research interests are motivation, self-regulation, self-efficacy, primary education, augmented reality, and digital storytelling.İsmail Sarikayaİsmail Sarikaya is an instructor in the Department of Primary Education at Bayburt University in Bayburt Turkey. His research interests are self-regulation, educational techonology, self-efficacy, classroom activities, effective properties, and primary education.Ahmet NalçacıAhmet Nalçacı is an instructor in the Department of Turkish and Social Sciences Education at Kahramanmaraş Sütçü İmam University in Kahramanmaraş, Turkey. His research interests are values education, primary education, social studies education, motivation, curriculum, augmented reality, and digital storytelling.
Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convection-Permitting Mode with Ground Measurements

Abstract The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m −2 (19%); MAE = 172 W m −2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.
Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.
UAV Network Path Planning and Optimization Using a Vehicle Routing Model

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications
Adoption of Blockchain Technology in the Financial Sector

Purpose: The main aim of this study is to explore the emergence and significance of blockchain technology in the financial system. This analysis examines the early stages of the adoption of blockchain technology.Need of the study: To ascertain the viability of blockchain systems as a viable, fair, and traceable way of storing transaction records in Indian banking and financial services organisations.Methodology: By virtue, this study is exploratory, following access to related studies on the implementation and applications of blockchain technology in the financial sector.Findings: This study explores blockchain technology, its adoption, types, usefulness, benefits, challenges, and security concern in the banking sector.Implications: This study will contribute to future research on applications of blockchain technology in the financial sector. It will help the researcher understand this technology’s importance and complications in the financial system.
Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces

The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.
The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.