Reviewerly/exHarmony huggingface dataset:
Digitalization and Inclusive Education: Common Ground

The article addresses the issues related to digitalization in education and in this context – using of its achievements in the inclusive education. The authors analyze the implementation of digital technologies in the educational system, the benefits and risks of digitalization. The most important advantage of digitalization is its applicability in the system of inclusive education. The common ground between the inclusive approach and the use of digital technologies in the education of people with disabilities is studied. Digitalization in the modern world also influences on the civic engagement. In addition, digital citizenship makes it possible to develop professional competencies, ethical standards of culture among the young generation, in particular, among people with disabilities. In this article, the authors study the development of psychological, pedagogical, and communicative competencies of pedagogues required in inclusive education. The approaches to the organization of advanced training for teachers in professional educational institutions are shown. Digital educational technologies have undoubted benefits. If these technologies are used correctly in educational activities, they can help pedagogues to exempt from routine work, and to facilitate the fulfillment of educational tasks for children with disabilities. In order to do this, the entire process of digitalization and the use of artificial intelligence must be mastered. The main thing is to remember that “person” should be in the center of attention during the process of digitalization of socio-political processes.
Towards skills-based curriculum analytics

In an era that will increasingly depend upon lifelong learning, the LA community will need to facilitate the movement and sharing of data and information across institutional and geographic boundaries. This will help us to recognise prior learning (RPL) and to personalise the learner experience. Here, we explore the utility of skills-based curriculum analytics and how it might facilitate the process of awarding RPL between two institutions. We explore the potential utility of combining natural language processing and skills taxonomies to map between subject descriptions for these two different institutions, presenting two algorithms we have developed to facilitate RPL and evaluating their performance. We draw attention to some of the issues that arise, listing areas that we consider ripe for future work in a surprisingly underexplored area.
A Sentiment-Statistical Approach for Identifying Problematic Mobile App Updates Based on User Reviews

Mobile applications (apps) on IOS and Android devices are mostly maintained and updated via Apple Appstore and Google Play, respectively, where the users are allowed to provide reviews regarding their satisfaction towards particular apps. Despite the importance of user reviews towards mobile app maintenance and evolution, it is time-consuming and ineffective to dissect each individual negative review. In addition, due to the different app update strategies, it is uncertain that each update can be accepted well by the users. This study aims to provide an approach to detect the particular days during the mobile app maintenance phase when the negative reviews require developers’ attention. Furthermore, the method shall facilitate the mapping of the identified abnormal days towards the updates that result in such negativity in reviews. The method’s purpose is to enable app developers to respond swiftly to significant flaws reflected by user reviews in order to prevent user churns.
Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes✱

Recent developments in discovering dynamic treatment regimes (DTRs) have heightened the importance of deep reinforcement learning (DRL) which are used to recover the doctor’s treatment policies. However, existing DRL-based methods expose the following limitations: 1) supervised methods based on behavior cloning suffer from compounding errors; 2) the self-defined reward signals in reinforcement learning models are either too sparse or need clinical guidance; 3) only positive trajectories (e.g. survived patients) are considered in current imitation learning models, with negative trajectories (e.g. deceased patients) been largely ignored, which are examples of what not to do and could help the learned policy avoid repeating mistakes. To address these limitations, in this paper, we propose the adversarial cooperative imitation learning model, ACIL, to deduce the optimal dynamic treatment regimes that mimics the positive trajectories while differs from the negative trajectories. Specifically, two discriminators are used to help achieve this goal: an adversarial discriminator is designed to minimize the discrepancies between the trajectories generated from the policy and the positive trajectories, and a cooperative discriminator is used to distinguish the negative trajectories from the positive and generated trajectories. The reward signals from the discriminators are utilized to refine the policy for dynamic treatment regimes. Experiments on the publicly real-world medical data demonstrate that ACIL improves the likelihood of patient survival and provides better dynamic treatment regimes with the exploitation of information from both positive and negative trajectories.
Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study

Clinical trials need efficient tools to assist in recruiting patients at risk of Alzheimer disease and related dementias (ADRD). Early detection can also assist patients with financial planning for long-term care. Clinical notes are an important, underutilized source of information in machine learning models because of the cost of collection and complexity of analysis.This study aimed to investigate the use of deidentified clinical notes from multiple hospital systems collected over 10 years to augment retrospective machine learning models of the risk of developing ADRD.We used 2 years of data to predict the future outcome of ADRD onset. Clinical notes are provided in a deidentified format with specific terms and sentiments. Terms in clinical notes are embedded into a 100-dimensional vector space to identify clusters of related terms and abbreviations that differ across hospital systems and individual clinicians.When using clinical notes, the area under the curve (AUC) improved from 0.85 to 0.94, and positive predictive value (PPV) increased from 45.07% (25,245/56,018) to 68.32% (14,153/20,717) in the model at disease onset. Models with clinical notes improved in both AUC and PPV in years 3-6 when notes' volume was largest; results are mixed in years 7 and 8 with the smallest cohorts.Although clinical notes helped in the short term, the presence of ADRD symptomatic terms years earlier than onset adds evidence to other studies that clinicians undercode diagnoses of ADRD. De-identified clinical notes increase the accuracy of risk models. Clinical notes collected across multiple hospital systems via natural language processing can be merged using postprocessing techniques to aid model accuracy.
Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.
Pilot Site Deployment of an IoT Solution for Older Adults’ Early Behavior Change Detection

The world demography is continuously changing. During the last decade, we noticed a regular variation in the world demography leading to a nearly balanced society share between the young and aging population. This increasing older adult population is facing many problems. In fact, the transition to the aging period is associated with physical, psychological, cognitive, and societal changes. Negative behavior changes are considered as indicators of older adults’ frailty. This is why it is important to detect such behavior changes early in order to prevent isolation, sedentary lifestyle, and even diseases, and therefore delay the frailty period. This paper exhibits a proof-of-concept pilot site deployment of an Internet of Thing (IoT) solution for the continuous monitoring and detection of older adults’ behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage.
Information Security Awareness in Romanian Public Administration: An Exploratory Case Study

This article analyzes the way public servants understand the necessity for information security in the digitalisation of public administration and sets out to describe a series of technical solutions with a view to protecting digital data.This paper focuses on a study which was carried out on the basis of a questionnaire submitted to three entities from the Romanian Public administration.The objective of this questionnaire was to identify the vulnerable points in certain areas, such as: managing accessibility in user interface design, password management, preventing cybersecurity incidents, response capacity to cybersecurity incidents, personal data protection, data backup and recovery and personal evaluation.The analysis of the results emerging from this questionnaire provides certain conceptual solutions, which can be adopted so that information security in the Romanian digital public administration can be ensured.The solutions presented in this paper are based on the analysis of international documents in this field.This article can also be a practical guide both for the Romanian decision makers and civil servants, enabling them to ensure data security and protection in the organisational structures of public administration.
Learned Resolution Scaling Powered Gaming-as-a-Service at Scale

Built on the explosive advancement of cloud and telecommunication technologies, Gaming-as-a-Service (GaaS) or cloud gaming system is expected to revolutionize the traditional multi-billion video game market in the near future. This wave is analogous to the rise of live-video-streaming-based-Netflix to replace conventional DVD rental business for movies and TVs. In practice, a successful GaaS platform need to operate in a transparent mode without requiring substantial efforts from both content providers and end users, and offer the pristine quality of experience (QoE) at an affordable cost. Our analysis suggests that GaaS provisioning cost can be reduced significantly by enforcing the game video rendering and streaming at a lower resolution (so as to increase the user concurrency in the cloud and reduce the streaming bandwidth over the network). However, streaming video at a lower resolution may deteriorate the QoE. To maintain the client QoE at the level using the default-native resolution for streaming or even enhance it, we introduce the learned resolution scaling (LRS), which leverages the computational capabilities at clients/edges to restore/improve the reconstructed image/video quality via stacked deep neural networks (DNN). We integrate this LRS into a commercialized GaaS platform - AnyGame, to study its efficiency and complexity quantitatively. Extensive real-life experiments have shown that LRS-powered AnyGame offers the state-of-the-art performance, and the lower operational cost, paving the road for a potential success of GaaS over the Internet. Additionally, we dive into proposed LRS via ablation studies to further demonstrate its consistent performance, including the discussions on trade-off between efficiency and complexity, alternative training sets, etc.
Meta-Learning for Robust Child-Adult Classification from Speech

Computational modeling of naturalistic conversations in clinical applications has seen growing interest in the past decade. An important use-case involves child-adult interactions within the autism diagnosis and intervention domain. In this paper, we address a specific sub-problem of speaker diarization, namely child-adult speaker classification in such dyadic conversations with specified roles. Training a speaker classification system robust to speaker and channel conditions is challenging due to inherent variability in the speech within children and the adult interlocutors. In this work, we propose the use of meta-learning, in particular prototypical networks which optimize a metric space across multiple tasks. By modeling every child-adult pair in the training set as a separate task during meta-training, we learn a representation with improved generalizability compared to conventional supervised learning. We demonstrate improvements over state-of-the-art speaker embeddings (x-vectors) under two evaluation settings: weakly supervised classification (upto 14.53% relative improvement in F1-scores) and clustering (upto relative 9.66% improvement in cluster purity). Our results show that protonets can potentially extract robust speaker embeddings for child-adult classification from speech.
Winter is here! A decade of cache-based side-channel attacks, detection &amp; mitigation for RSA

Timing-based side-channels play an important role in exposing the state of a process execution on underlying hardware by revealing information about timing and access patterns. Side-channel attacks (SCAs) are powerful cryptanalysis techniques that focus on the underlying implementation of cryptographic ciphers during execution rather than attacking the structure of cryptographic functions. This paper reviews cache-based software side-channel attacks, mitigation and detection techniques that target various cryptosystems, particularly RSA, proposed over the last decade (2007–2018). It provides a detailed taxonomy of attacks on RSA cryptosystems and discusses their strengths and weaknesses while attacking different algorithmic implementations of RSA. A threat model is presented based on the cache features that are being leveraged for such attacks across cache hierarchy in computing architectures. The paper also provides a classification of these attacks based on the source of information leakage. It then undertakes a qualitative analysis of secret key retrieval efficiency, complexity, and the features being exploited on target cryptosystems in these attacks. The paper also discusses the mitigation and detection techniques proposed against such attacks and classifies them based on their effectiveness at various levels in caching hardware and leveraged features. Finally, the paper discusses recent trends in attacks, the challenges involved in their mitigation, and future research directions needed to deal with side-channel information leakage.
Application of Blockchain Technology in e-LoA Technopreneurship Journal

&#x0D; LoA or Letter of Acceptance is a statement received. LoA is usually used as a statement of scholarship acceptance or even journal acceptance. In addition, the current issuance of LoA is still conventional, using paper as the access medium. This is certainly inefficient. In addition, the issuance of LoAs that can be accessed through the website also has certain problems. Many parties doubt the security of the LoA issued through the website, whether confidentiality is really maintained or not. To solve this problem, e-LoA (electronic LoA) was implemented using blockchain technology on the Technopreneurship Journal website. With the use of blockchain technology on this website, LoA data cannot be changed, duplicated or deleted. So that it can improve security and support transparency in the issuance and access of LoA. e-LoA issued through the website also makes it easier for parties to access and download a verified LoA.
Differential Privacy-Based Indoor Localization Privacy Protection in Edge Computing

With the popularity of smart devices and the widespread use of the Wi-Fi-based indoor localization, edge computing is becoming the mainstream paradigm of processing massive sensing data to acquire indoor localization service. However, these data which were conveyed to train the localization model unintentionally contain some sensitive information of users/devices, and were released without any protection may cause serious privacy leakage. To solve this issue, we propose a lightweight differential privacy-preserving mechanism for the edge computing environment. We extend ε-differential privacy theory to a mature machine learning localization technology to achieve privacy protection while training the localization model. Experimental results on multiple real-world datasets show that, compared with the original localization technology without privacy-preserving, our proposed scheme can achieve high accuracy of indoor localization while providing differential privacy guarantee. Through regulating the value of ε, the data quality loss of our method can be controlled up to 8.9% and the time consumption can be almost negligible. Therefore, our scheme can be efficiently applied in the edge networks and provides some guidance on indoor localization privacy protection in the edge computing.
On the dynamics of two photons interacting with a two-qubit coherent feedback network

The purpose of this paper is to study the dynamics of a quantum coherent feedback network composed of two two-level systems (qubits) driven by two counter-propagating photons, one in each input channel. The coherent feedback network enhances the nonlinear photon–photon interaction inside the feedback loop. By means of quantum stochastic calculus and the input–output framework, the analytic form of the steady-state output two-photon state is derived. Based on the analytic form, the applications on the Hong–Ou–Mandel (HOM) interferometer and marginally stable single-photon devices using this coherent feedback structure have been demonstrated.
Image Processing for Laser Imaging Using Adaptive Homomorphic Filtering and Total Variation

Laser active imaging technology has important practical value and broad application prospects in military fields such as target detection, radar reconnaissance, and precise guidance. However, factors such as uneven laser illuminance, atmospheric backscatter, and the imaging system itself will introduce noise, which will affect the quality of the laser active imaging image, resulting in image contrast decline and blurring image edges and details. Therefore, an image denoising algorithm based on homomorphic filtering and total variation cascade is proposed in this paper, which strives to reduce the noise while retaining the edge features of the image to the maximum extent. Firstly, the image type is determined according to the characteristics of the laser image, and then the speckle noise in the low-frequency region is suppressed by adaptive homomorphic filtering. Finally, the image denoising method of minimizing the total variation is adopted for the impulse noise and Gaussian noise. Experimental results show that compared with separate homomorphic filtering, total variation filtering, and median filtering, the proposed algorithm significantly improves the contrast, retains edge details, achieves the expected effect. It can better adjust the image brightness and is beneficial for subsequent processing.
The <i>R</i> Package <b>stagedtrees</b> for Structural Learning of Stratified Staged Trees

stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorithms are implemented, as well as various functionalities to plot the models and perform inference. The capabilities of stagedtrees are illustrated using mainly two datasets both included in the package or bundled in R.
Analysis of Spatial and Temporal Variability of the PAR/GHI Ratio and PAR Modeling Based on Two Satellite Estimates

The main objectives of this work are to address the analysis of the spatial and temporal variability of the ratio between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI), as well as to develop PAR models. The analysis was carried out using data from three stations located in mainland Spain covering three climates: oceanic, standard Mediterranean, and continental Mediterranean. The results of this analysis showed a clear dependence between the PAR/GHI ratio and the location; the oceanic climate showed higher values of PAR/GHI compared with Mediterranean climates. Further, the temporal variability of PAR/GHI was conditioned by the variability of clearness index, so it was also higher in oceanic than in Mediterranean climates. On the other hand, Climate Monitoring Satellite Facility (CM-SAF) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data were used to estimate PAR as a function of GHI over the whole territory. The validation with ground measurements showed better performance of the MODIS-estimates-derived model for the oceanic climate (root-mean-square error (RMSE) around 5%), while the model obtained from CM-SAF fitted better for Mediterranean climates (RMSEs around 2%).
Cloud Resource Optimization System Based on Time and Cost

Resource management in cloud could be a time and cost-effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud. In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource-management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.