Reviewerly/exHarmony huggingface dataset:
SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss

Purpose: We propose a super-resolution (SR) method, named SR-CycleGAN, for SR of clinical computed tomography (CT) images to the micro-focus x-ray CT CT ( μCT ) level. Due to the resolution limitations of clinical CT (about 500×500×500 μm3/voxel ), it is challenging to obtain enough pathological information. On the other hand, μCT scanning allows the imaging of lung specimens with significantly higher resolution (about 50×50×50 μm3/voxel or higher), which allows us to obtain and analyze detailed anatomical information. As a way to obtain detailed information such as cancer invasion and bronchioles from preoperative clinical CT images of lung cancer patients, the SR of clinical CT images to the μCT level is desired. Approach: Typical SR methods require aligned pairs of low-resolution (LR) and high-resolution images for training, but it is infeasible to obtain precisely aligned paired clinical CT and μCT images. To solve this problem, we propose an unpaired SR approach that can perform SR on clinical CT to the μCT level. We modify a conventional image-to-image translation network named CycleGAN to an inter-modality translation network named SR-CycleGAN. The modifications consist of three parts: (1) an innovative loss function named multi-modality super-resolution loss, (2) optimized SR network structures for enlarging the input LR image to 2k -times by width and height to obtain the SR output, and (3) sub-pixel shuffling layers for reducing computing time. Results: Experimental results demonstrated that our method successfully performed SR of lung clinical CT images. SSIM and PSNR scores of our method were 0.54 and 17.71, higher than the conventional CycleGAN's scores of 0.05 and 13.64, respectively. Conclusions: The proposed SR-CycleGAN is usable for the SR of a lung clinical CT into μCT scale, while conventional CycleGAN output images with low qualitative and quantitative values. More lung micro-anatomy information could be observed to aid diagnosis, such as the shape of bronchioles walls.
Autoencoder: Issues, Challenges and Future Prospect

As of more recently, deep learning-based models have demonstrated considerable potential, as they have outperformed all traditional practices. When data becomes high dimensional, extraction of features and compression of data become progressively significant. In this paper, we describe the autoencoder deep learning algorithm. Autoencoder is primarily a neural network-based feature extraction methodology that accomplishes outstanding victory in producing highlights of high-dimensional data. Autoencoder assumes a principal job in unsupervised learning which targets to rework inputs into outputs with minimal reconstruction error.
Blockchain of things for healthcare asset management

Chapter Contents: Abstract 10.1 Introduction 10.2 Healthcare asset management 10.3 Challenges and opportunities in healthcare 10.3.1 Health and safety 10.3.2 Regulatory requirements 10.3.3 Data privacy 10.3.4 Data security 10.3.5 Device security 10.3.6 Equipment interoperability 10.3.7 Resource constraints 10.3.8 Sustainability 10.4 Blockchain: concepts and frameworks 10.4.1 Block structure 10.4.2 Smart contracts 10.4.3 Cryptography and distributed ledger technology 10.4.4 Consensus protocols 10.4.5 Blockchain classification 10.4.6 Blockchain frameworks 10.5 Blockchain of things architecture for healthcare asset management 10.6 Major healthcare application areas 10.6.1 Healthcare records 10.6.2 Device location management 10.6.3 Preventive and predictive analysis 10.6.4 Data visualisation 10.6.5 Forecasting 10.6.6 Assisted living and patient monitoring 10.6.7 Healthcare supply chain management 10.6.8 Acquiring/processing patient's clinical data 10.7 Conclusion and future work References
Handwritten Cursive English Character Recognition Using DAG-CNN

Handwritten character recognition (HCR) is considered as an important part of optical character recognition (OCR). Handwritten character recognition can be used in bank cheques, prescriptions of medicine translations, etc. Handwritten characters, when compared to the printed characters, are very difficult to recognize due to the varying writing styles of individuals. The size, shape, and angles of every letter vary from person to person and even a single person’s writing style will be different in two different documents. For character recognition approaches using geometrical features, structural features and statistical features were used traditionally. Nowadays, handwritten character recognition widely uses deep learning approaches. Deep learning techniques can learn the visual features automatically. This helps to avoid limiting the features like in traditional methods. Convolutional neural networks are a deep learning approach and it always gives better accuracy than the conventional techniques. In this paper, a novel deep learning technique, namely directed acyclic graph–convolutional neural network (DAG-CNN), is used for handwritten character recognition.
Improved polar-code-based efficient post-processing algorithm for quantum key distribution

Abstract Combined with one-time pad encryption scheme, quantum key distribution guarantees the unconditional security of communication in theory. However, error correction and privacy amplification in the post-processing phase of quantum key distribution result in high time delay, which limits the final secret key generation rate and the practicability of quantum key distribution systems. To alleviate this limitation, this paper proposes an efficient post-processing algorithm based on polar codes for quantum key distribution. In this algorithm, by analyzing the channel capacity of the main channel and the wiretap channel respectively under the Wyner’s wiretap channel model, we design a codeword structure of polar codes, so that the error correction and privacy amplification could be completed synchronously in a single step. Through combining error correction and privacy amplification into one single step, this efficient post-processing algorithm reduces complexity of the system and lower the post-processing delay. Besides, the reliable and secure communicaiton conditions for this algorithm has been given in this paper. Simulation results show that this post-processing algorithm satisfies the reliable and secure communication conditions well.
A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems

As big data mining technology penetrates into various fields, cross-domain topics driven by data predictive analysis have become important entry points for solving traditional problems. Due to the complex changes of the pressure sensor and the interaction of different grouped trains during the train braking process, the mechanism modeling is difficult, the data is highly temporalized, and the data distribution is not stable. Facing the development trend of long-grouped-heavy-duty train captains, if the braking analysis of the train by temporal data mining of small groups can be used for predictive analysis, it will make innovative progress in the entire train braking field. This paper focuses on combining latest technology such as machine learning, transfer learning and lifelong learning to construct the first predictive analysis research framework in the field of train braking systems. Based on the principle of train braking process and temporal data collected from intelligent experiment platform, a baseline has firstly been built to solve fixed-grouped and multi-grouped temporal prediction problems. Then a predictive algorithm for model verification and update for lifelong learning is established to automatically update model parameters over time. Finally, relying on the parameter transfer in transfer learning, a multi-grouped temporal data prediction analysis is performed. Through comparing the training results of the “pre-trained” model on the general domain, the “tuned” model on both general domain and the target domain, and the “target only” model on the target domain separately, multi-domain tuning results show their applicable scope and transfer conditions. In summary, this work can contribute to intelligently upgrading the semi-physical intelligent test platform for long-grouped-heavy-duty trains.
Environmental Adaptive Privacy Preserving Contact Tracing System: A Construction From Public Key Rerandomizable BLS Signatures

The COVID-19 pandemic has made the scientific community devise means to implement ''contact tracing'' mechanisms to mitigate the spread of the infection.The crucial idea is to scan and record close contacts between users using mobile device, in order to notify persons when their close contact(s) is diagnosed positive.First, the ability granted to service providers of the contact tracing systems to access user data violates user privacy, and attackers can fabricate identities and contact records in their devices, which harms the integrity of the system.Moreover, current contact tracing systems' false-positive rate is too high to be practical as they do not filter scan results outside the range of infections, since the range of transmission for droplets is far less than the scanning range for Bluetooth Low Energy used by these systems.Furthermore, current systems neglect airborne transmission, a far cry from a tool against viruses suspended in the air.In this paper, we propose a cryptographic framework for contact tracing and provide a construction based on public key rerandomizable BLS signature, being capable of providing users of contact tracing with comprehensive privacy protection.Besides, we also implement a commitment scheme to prevent fabrication of identities and contact records.To prove the concept of our framework and to solve other problems mentioned above, we proposed a new contact tracing system, using environmental factors (temperature, humidity and airflow) to filter out results outside estimated effective transmission distance, and also take airborne transmission into consideration.Finally, we evaluate the performance of our design by implementing our algorithm on mobile devices with satisfactory results.
COVID-19 y competencia digital: percepción del nivel en futuros profesionales de la educación

Educational technology has gained prominence due to the hybrid and virtual models that have been developed due to the restrictions and confinements derived from the global pandemic by COVID-19. This situation of greater use of ICT affects the digital competence of students, one of the areas that requires the most development and training. The objective of this study is to know the perception about the level of digital competence of 84 future education professionals who begin their studies at the University of Malaga in the academic year 2021-2022. The data, collected through a validated instrument (Digital Competence Questionnaire for Future Teachers), reflect a remarkable level of digital competence, with differences based on sex, highlighting the better perception of men compared to women in terms of creativity in innovation with ICT. The need to reinforce initial and continuing training programs regarding the development of digital competence is concluded.
Enhanced Security for Smart Door Using Biometrics and OTP

Several home surveillance systems have been developed throughout the years to prevent unwanted entry, but they have all failed to provide enough security and authenticity. In places like our homes, colleges, banks and companies, the control systems are utilized to keep our people and assets secure. This paper demonstrates how fingerprints and one-time pass code (OTP) encryption keys may be added to a smart entrance to increase overall privacy and allow us to safely and conveniently enter our houses. This has the ability to address the difficulties that earlier systems have and reducing by the identifying procedure using a biometric fingerprint sensor and providing an encrypted connection by employing a central server to provide OTP verification. Compared to previous approaches, our methodology has given improved performance and efficiency.
Adoption of the OMNET++ Simulator for the Computer Networks Learning: A Case Study in CSMA Schemes

Implementation and adoption of modern tools and technologies in the educational process is an important task given their demand at the time of the total transition of society to the digital representation of various processes. Accordingly, existing methods and tools need further improvement. Modern e-learning systems are mainly focused on providing teachers with the opportunity to manage distance classes, create content materials, prepare tasks, participate in discussions, conduct evaluations, and distribute information. When studying computer networks, given the fact that modern network simulators have built-in tools for visualization, it is possible and advisable to include these simulators in the process of education. This article presents our experience of using the OMNeT++ Discrete Event Simulator with the INET framework in lecturing the methods of common physical environment access. The main focus is the study of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and Carrier Sense Multiple Access with Collision Detection (CSMA/CD) schemes. Wireless networks, including Wi-Fi, use CSMA/CA scheme while CSMA/CD is used in wired networks that include hubs. The proposed approach, demonstrating the process and results of modeling, successfully combines the study of media access control (MAC) basic principles with their deep understanding. The game-based KAHOOT application was used to evaluate learning results.
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks

Pikoulis, Erion Vasilis Mavrokefalidis, Christos Nousias, Stavros Lalos, Aris S.Deep learning and especially the use of Deep Neural Networks (DNNs) provide impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. This becomes problematic when, for instance, real-time mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while, at the same time, having an acceleration gain compared to conventional, k-means-based approaches. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification and object detection validate the effectiveness of the proposed method in MCA tasks.
Voice Controlled Devices: Awareness, Usage, and Reservations of Young Adults

Voice Controlled Devices (VCDs) are becoming increasingly popular, both as integrated voice control functionality in mobile and stationary devices and in the form of devices that can be controlled primarily by voice, such as smart speakers.Young adults have already grown up with digital technologies that can be operated via peripherals or touchscreens. It is particularly interesting whether this younger generation, familiar with conventional human-computer interfaces, will accept the voice control function. In this context, however, there is a lack of studies that address awareness, usage, and reservations of voice-controlled interfaces among young adults.This paper presents a quantitative study (n=246) that shows that while 96% of young adults are aware of VCDs, only 18% regularly use the voice control feature. However, 77% expressed concerns that their data would not be safe by using VCDs.
A tutorial on cognitive modeling for cognitive aging research.

Cognitive aging researchers are interested in understanding how cognitive processes change in old age, but the relationship between hypothetical latent cognitive processes and observed behavior is often complex and not fully accounted for in standard analyses (e.g., Analysis of variance [ANOVA]). Cognitive models formalize the relationship between underlying processes and observed behavior and are more suitable for identifying what processes are associated with aging. This article provides a tutorial on how to fit and interpret cognitive models to measure age differences in cognitive processes. We work with an example of a two choice discrimination task and describe how to fit models in the highly flexible modeling software Stan. We describe how to use hierarchical modeling to estimate both group and individual effects simultaneously, and we detail model fitting in a Bayesian statistical framework, which, among other benefits, enables aging researchers to quantify evidence for null effects. We contend that more widespread use of cognitive modeling among cognitive aging researchers may be useful for addressing potential issues of nonreplicability in the field, as cognitive modeling is more suitable to addressing questions about what cognitive processes are (or are not) affected by aging. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Governance of Autonomous Agents on the Web: Challenges and Opportunities

The study of autonomous agents has a long history in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies, and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies, and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this article motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.
Formación permanente del profesorado universitario en tiempos de COVID-19: entre la necesidad y la obligación

The aim of this study is to analyse the training activities carried out by university teaching staff to deal with the period of crisis caused by COVID-19, in terms of the training topics in which they were interested, their perception of the learning acquired and their intentions to transfer this learning. Using a quantitative methodology, an ad hoc questionnaire was designed in the form of a Likert scale. The results show that, during this exceptional period, teachers focused mainly on learning about institutional technological tools and the application of different methodological strategies. In addition, their perception was that they improved their digital competences as well as those related to online teaching. Finally, they showed interest in transferring their learning to apply methodologies related to e-learning in their classes. We believe that universities should continue to take advantage of the momentum and continue to invest in training their teaching staff in the implementation of teaching methods that decisively incorporate technologies that generate higher levels of resilience to deal with emergency situations such as those experienced.
Aspect-Based Sentiment Analysis Through EDU-Level Attentions

A sentence may express sentiments on multiple aspects. When these aspects are associated with different sentiment polarities, a model’s accuracy is often adversely affected. We observe that multiple aspects in such hard sentences are mostly expressed through multiple clauses, or formally known as elementary discourse units (EDUs), and one EDU tends to express a single aspect with unitary sentiment towards that aspect. In this paper, we propose to consider EDU boundaries in sentence modeling, with attentions at both word and EDU levels. Specifically, we highlight sentiment-bearing words in EDU through word-level sparse attention. Then at EDU level, we force the model to attend to the right EDU for the right aspect, by using EDU-level sparse attention and orthogonal regularization. Experiments on three benchmark datasets show that our simple EDU-Attention model outperforms state-of-the-art baselines. Because EDU can be automatically segmented with high accuracy, our model can be applied to sentences directly without the need of manual EDU boundary annotation.
Digital Text Authentication Using Deep Learning: Proposition for the Digital Quranic Text

Nowadays, the detection of digital texts manipulation is a hot topic in the field of natural language processing and artificial intelligence. This type of text spreads quickly and inexpensively, which can cause great concern due to its negative impact on social life. The text authentication process has gained a great deal of interest. However, the authentication of Arabic texts is still under development. The Quranic text is one of the Arabic texts sensitive to change and the most vulnerable to falsification at all. In order to prevent misuse of this type of texts, in this research a deep learning approach based on LSTM network and the pretrained Word Embeddings has been developed for authentication one of the manipulations types of the Arabic Quranic texts. By building a model that enables Internet users to automatically validate the arrangement of the Quran content, the experimental results showed that the proposed approach can effectively improve the accuracy of text classification and achieve a significant time difference compared to previous works.
Perceptions of Students in Indonesian Higher Education Institutions Regarding Internet Access for Online (Remote) Learning during the COVID-19 Pandemic

While the COVID-19 crisis has sparked renewed interest in online learning research, there are only a few well-documented studies on students' perceptions of Internet access in online (remote) learning. As a result, we adopted a quantitative approach to investigate students' perceptions of Internet access for online (remote) learning in Indonesian higher education institutions during the COVID-19 pandemic. The data were analysed using descriptive statistics and content analysis. The findings indicate that students in Indonesian higher education institutions negatively perceive their experience with online (remote) learning during the COVID-19 pandemic. They are dissatisfied with the poor performance of the Internet as well as the high cost of Internet access. They also have concerns and impediments regarding online (remote) learning. Finally, the current study's implications are discussed, along with some recommendations for future research.