Reviewerly/exHarmony huggingface dataset:
TEE-Watchdog: Mitigating Unauthorized Activities within Trusted Execution Environments in ARM-Based Low-Power IoT Devices

Trusted execution environments (TEEs) are on the rise in devices all around us ranging from large-scale cloud-based solutions to resource-constrained embedded devices. With the introduction of ARM TrustZone-M, hardware-assisted trusted execution is now supported in IoT nodes. TrustZone-M provides isolated execution of security-critical operations and sensitive data-generating peripherals. However, TrustZone-M, like all other TEEs, does not provide a mechanism to monitor operations in the trusted areas of the device and software in the secure areas of an IoT device has access to the entire secure and nonsecure software stack. This is crucial due to the diversity of device manufacturers and component suppliers in the market, which manifests trust issues, especially when third-party peripherals are incorporated into a TEE. Compromised TEEs can be misused for industrial espionage, data exfiltration through system backdoors, and illegal data sharing. It is of utmost importance here that system peripheral behaviour in terms of resource access is in accordance with their intended usage that is specified during integration. We propose TEE-Watchdog, a lightweight framework that establishes MPU protections for secure system peripherals in TrustZone-enabled low-end IoT devices. TEE-Watchdog ensures blocking unauthorized peripheral accesses and logging of application misbehaviour running in the TEE based on a manifest file. We define lightweight specifications and structure for the application manifest file enlisting permissions for critical system peripherals using concise binary object representation (CBOR). We implement and evaluate TEE-Watchdog using a Musca-A2 test chipboard. Our microbenchmark evaluations on CPU time and RAM usage demonstrated the practicality of TEE-Watchdog. Securing the system peripherals using TEE-Watchdog protections induced a 1.4% overhead on the latency of peripheral accesses, which was 61 microseconds on our test board. Our optimized CBOR-encoded manifest file template also showed a decrease in manifest file size by 40% as compared to the standard file formats, e.g., JSON.
The Global Research Trends on the Growth of Remote Learning in Higher Education Institutions: A Bibliometric Analysis

Remote learning has rapidly emerged as one of the most fundamental approaches in facilitating self-directed learning in Higher Learning Institutions. The purpose of this paper was to conduct a bibliometric study on the global trends in research about remote learning during 1961 to 2021 period. A bibliometric analysis of 719 research articles from the Dimensions database was carried out. The VOSviewer1.6.16 software was used to carry out visual analysis of the publications to establish the network and overlay visualization maps. The results indicate that there has been a steady growth of research articles on remote learning during COVID-19 pandemic of which ‘Polytechnic Institute of Porto’ was the leading organization in terms of publications. Countries which are most productive include the United States, Russia and the United Kingdom while ‘Corter’ and ‘Kreijns’ were the most cited authors and ‘Lima’ and ‘Viegas’ were the authors with highest links in terms of collaboration. Regarding the co-occurrence of key words, it was found that keywords such as ‘experience’, ‘COVID’, ‘internet’ ‘poverty’ ‘interaction’, ‘inequality’, ‘social emotions’ and ‘online interactions’ had strong association with remote learning. The findings suggest the need for developing countries to allocate adequate funds on remote learning.
Decision-making method for multi-ship collision avoidance based on improved extensive game model

In the case of multi-ship encounter situations, most collision accidents are caused by not having timely and coordinated collision avoidance behaviors. Therefore, it is necessary to introduce an automatic collision avoidance method in the process of collision avoidance to quickly generate multi-ship collision avoidance decisions based on encounter situations. Multi-ship collision avoidance is a dynamic game used among officers under situations of multi-ship encounters. Considering the defects of the classic extensive game method in ship collision avoidance decision-making, the improved extensive game method is proposed, based on the velocity obstacle method. The feasible region of velocity is taken as the optional action range of the game to narrow down the action set. The simulation results show that the proposed method can effectively transform the multi-ship collision avoidance game problem into a 2-ship collision avoidance game problem, reduce the optional action range of a collision avoidance action set, and improve decision-making speed, while ensuring the safety of multi-ship navigation.
Trust of Information during the Dissemination of Popular Science Web Videos in the New Media Era

Web videos have gradually replaced text, voice, pictures, and other information carriers to become an important way of information dissemination in the new media era. As digital technology brings a new dissemination ecology, the original dissemination trust theory and its framework are facing the crisis of explanatory power failure. This paper considers the popular science web video as an object of study. It analyses and interprets the development of popular science web videos based on the evolution of dissemination form and the basic principle of social trust, from perspectives such as mediology, informatics, and sociology. To maintain or improve the trust relationship in web videos, it's necessary to find positive incentive and reverse punishment, and establish a trust certification and regulation mechanism. In this way, active dissemination and sharing of information can be promoted for a more vigorous society and culture. Moreover, this paper explores a new way of web video development from the perspective of trust.
Quantum limits for resolving Gaussian sources

We determine analytically the quantum Cram\'er-Rao bound for the estimation of the separation between two point sources in arbitrary Gaussian states. Our analytical expression is valid for arbitrary sources brightness, and it allows to determine how different resources, such as mutual coherence (induced by thermal correlations or displacement) or squeezing affect the scaling of the ultimate resolution limit with the mean number of emitted photons. In practical scenarios, we find coherent states of the sources to achieve quantum optimal resolution.
Attributing AUC-ROC to Analyze Binary Classifier Performance

Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an additive/linear function over the data samples, therefore such segmenting the overall AUC-ROC is different from tabulating the AUC-ROC of data segments. To segment the overall AUC-ROC, we must first solve an \emph{attribution} problem to identify credit for individual examples. We observe that AUC-ROC, though non-linear over examples, is linear over \emph{pairs} of examples. This observation leads to a simple, efficient attribution technique for examples (example attributions), and for pairs of examples (pair attributions). We automatically slice these attributions using decision trees by making the tree predict the attributions; we use the notion of honest estimates along with a t-test to mitigate false discovery. Our experiments with the method show that an inferior model can outperform a superior model (trained to optimize a different training objective) on the inferior model's own training objective, a manifestation of Goodhart's Law. In contrast, AUC attributions enable a reasonable comparison. Example attributions can be used to slice this comparison. Pair attributions are used to categorize pairs of items -- one positively labeled and one negatively -- that the model has trouble separating. These categories identify the decision boundary of the classifier and the headroom to improve AUC.
Blockchain and IoT Technology in Healthcare: A Review

The tremendous shift in technology has led to many unconnected things getting interconnected via IoT. IoT is one of the major modes of collecting data from various networked resources and other connected devices. The broad range of IoT, with its huge heterogeneity in handling data, addresses many challenges in the realm of healthcare. Blockchain technology has elevated the use of distributed storage in a positive way. The recent emergence of this technology has paved way for potentially enormous utilization in various fields. Blockchain technology in the fields of IT, finance, industries, government, healthcare, media, and law enforcement has altered the service quality levels to an ethical ideal. Blockchain, in conjunction with IoT, facilitates decentralized collection and storage of data. Integrating blockchain with IoT has emerged as a cutting-edge tool for the decentralized sharing of medical records, monitoring of patients, ensuring the privacy of patient records, predicting the quantum of insurance, and managing supply chains.
Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanations for the predictions of such a mobile application called DemPredict using methods from the field of Explainable Artificial Intelligence (XAI). In order to evaluate which method is best suited, different XAI approaches are used and compared. However, the comparability of the results is a key challenge. By evaluating the trustworthiness, stability, and computation time of the methods, it is possible to identify the optimal XAI approaches for the respective algorithms.
The Study of Teaching Staff Motivation to Use Mobile Technologies in Teaching Mathematics

The use of distance learning platforms, digital educational resources and mobile devices expand pedagogical arsenal. The subject of the study is teachers’ readiness to use mobile technologies in education, for the issue of motivation remains important. The purpose is to identify motivational factors and define mathematics students and teachers’ motivation level to use mobile technologies in teaching. The survey involved 74 people, of which 38 were bachelor students of Elabuga Institute of Kazan Federal University in the pedagogical direction (profile “Mathematics and Physics”) and 36 mathematics teachers of the Republic of Tatarstan. The author identified motivational factors characterizing mathematics teachers and students’ attitude to mobile learning and defined the degree of their relationship with the teaching staff readiness to use mobile technologies in teaching mathematics. The results of the survey confirmed the teachers and students’ high motivation to use mobile technologies in teaching mathematics. The calculation of the Spearman correlation coefficient allowed concluding that the level of motivation decreases with age, but the relationship is weak, insignificant. The results can be used to define the level of motivation and assess the motivational factor significance in the teaching staff training, retraining and advanced training in the field of mathematics mobile learning.
Evaluation of Open-source Tools for Differential Privacy

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals that consent to share their privacy-sensitive information and the ones that do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data and thus there is an inherent trade-off between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools were proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP's inherent trade-off as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above trade-off, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.
Use of digital technology by a teacher to improve the effectiveness of the process of students’ training and professional self-identification

The quality of the formation of fundamental competencies depends on the professional actions of the faculty in a qualified and professionally organized work in the relevant subject area of teaching, which is associated with planning, organization, the conduct of practical classes, and evaluation of the activities involved in students’ training and upbringing. A teacher is qualified to analyze the characteristics and problems of professional work, both theoretically and practically; to derive solutions considering social and ethical responsibilities; to systematically interpret and present to students technical, economic, and social relations and events, as well as forming the educational information and communication system in their professional educational and scientific contexts. The article is devoted to the place and role of the teacher in the educational process of higher education associated with the professional content and the combined capabilities of traditional and modern methods that ensure a successful learning process.
Using TBLT Framework in Technology-mediated Environments to Enhance Students’ Vocabulary Retention and Interpreting Skills

Interpretation is a demanding skill, especially for specific fields in business such as marketing, accounting, trading, etc. Students majoring in Business English Language at Van Lang University reported that they are faced with the dilemma of choosing the right definition for a keyword. This has prevented them from understanding and analyzing the message before rendering it into the target language. To remedy the problem, vocabulary quizzes were administered on the university's Learning Management System (LMS) platform. The focus is on enhancing vocabulary retention. The course followed the Tase-Based Language Teaching (TBLT) framework to maximize the comprehension of the context of the vocabulary and the students. Code-mixing was used to design vocabulary quizzes. The study is a quantitative experiment with the aim of finding a correlation between the use of TBLT and the improvement of students in their vocabulary retention. The results indicate that there is a direct and positive connection between them.
Improvement and Application of Fractional Particle Swarm Optimization Algorithm

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes.
Causal Associations Among Diseases and Imaging Findings in Radiology Reports

This study explored the ability to identify causal relationships between diseases and imaging findings from their co-occurrences in radiology reports. A natural language processing (NLP) system with negative-expression filtering detected positive mentions of 16,912 disorders, interventions, and imaging findings in 1,702,462 consecutive radiology reports; the 55,564 causal relations defined by the Radiology Gamuts Ontology (RGO) served as reference standard. Conditions were considered to co-occur if they were present in reports from the same patient. The ϕ and κ statistics both achieved AUC0.70, P<0.001 in identifying causal relationships from pairwise co-occurrence data. Analysis of radiology reports can identify a large proportion of known causal associations among diseases and imaging findings. Automated approaches hold promise to identify causal relationships among diseases and imaging findings from their co-occurrence in text-based radiology reports.
Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study

Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance. Keywords: Emotion Detection, Feature Engineering, Term-Weighting, Text Mining
Prototyping in Smart Product Design: Investigating Prototyping Tools to Support Communication of Interactive and Environmental Qualities

Abstract The increasing availability of smart products creates a more pronounced need for designers to prototype and communicate interactive and environmental qualities of product during their design process. This paper explores which elements of User journey, Storyboards and Wireframes contribute to communicating these qualities, and how they might integrate with sketching. Results show depictions of user and temporal elements alongside low fidelity sketches are deemed most important. Our findings form the basis to propose and subsequently test combined prototyping approaches in future research.
Design by Prototyping: Increasing Agility in Mechatronic Product Design through Prototyping Sprints

Abstract This paper adapts the agile scrum sprint, typically used in software development, to a prototyping sprint for mechatronic product design. The Design by Prototyping framework describes how the prototyping sprint can be used to manage the prototyping process in design projects through an agile-stage-gate hybrid model. A comparison of 18 student projects using either prototyping sprints or a traditional iterative prototyping approach shows that prototyping sprints helped students make more deliberate, strategic decisions about their use of prototypes.
Anaphora Resolution in Thai EDU Segmentation

Human knowledge is mostly in the form of unstructured text.Text can be transcribed into various languages such as the Thai language.To extract knowledge from Thai text, natural language tasks such as word segmentation, Elementary Discourse Unit (EDU) segmentation, and anaphora resolution is the needed tasks.Some interesting phenomena such as non-referential anaphora and the ellipsis of the owner are the significant problems that are necessary to resolve before constructing the complete semantic in the Natural Language Processing (NLP) application.The non-referential anaphora must be detected before identifying the referential anaphora to improve the precision of the anaphora resolution.The ellipsis of the owner is also a crucial problem that needs to be resolved to find the complete semantics.This study presents the methodology to resolve the anaphora from Thai EDU segmentation.The methodology is divided into 2 parts: Thai morphological analysis and the anaphora resolution.The ranking model is applied to resolve the reference of anaphora with the features from the surface word, surround word, syntactic information, and ontology.The results show that precision is 0.77, recall is 0.84 and the F1 score is 0.81.
Special Issue: Theory and Practice in Remote Teaching, Online Learning, and Distance Education for K-12

There are many situations where digital technology has served as the lifeline or salvation for society. Unexpected and unpredictable situations like catastrophic floods, blizzards, earthquakes, and more recently the Covid-19 pandemic have forced families to stay home, meaning that digital technologies have become important tools for people to work and learn. Other examples are societal and regional challenges such as lack of qualified teachers, diminishing birth rates, and difficulties in filling classrooms that force digital and educational development. The symposium held in May 2021, which resulted in this special issue, was intended to explore possibilities for ensuring equal access to education in rural schools using remote teaching. At the symposium, different strategies to increase access to education in the context of K-12 using digital technologies was presented and discussed. It brought together researchers from Europe and North America, all represented in this issue.