Reviewerly/exHarmony huggingface dataset:
GDP: Group-Based Differential Privacy Framework for Secure Process Mining in the Internet of Medical Things

With the widespread adoption of Internet of Things (IoTs) technology in the healthcare domain, the Internet of Medical Things (IoMTs) has emerged as a promising paradigm for improving patient care and healthcare outcomes. IoMT generates vast amounts of data, including process logs. Therefore, Process Mining (PM) is a potential approach that can provide valuable insights into healthcare processes for several optimizations. The analysis of such sensitive data through PM raises privacy and security concerns. However, using PM for specific data allows IoMTs to secure the sensitive data. Therefore, this paper aims to evaluate existing PM algorithms, for behavior after adding noise in data without affecting PM results. Differential Privacy (DP) is a widely recognized privacy-preserving technique that provides strong privacy guarantees by injecting random noise into data. Therefore, this research presents a framework, called Group-based DP (GDP) for PM to protect the privacy of healthcare data in specific columns which are neither activity nor class ID. This study includes evaluation of prominent PM algorithms, including, Alpha algorithm, Heuristics algorithm, inductive algorithm, correlation mining algorithm and footprints algorithm. Furthermore, we suggested pertinent advanced research directions concerning real-world requirements.
The Development of Competences Specific to Primary Education Objects as a Prerequisite for the Development of Students' Digital Competence

The development of the digital competence is a lifelong learning goal, which will remain stable, taking into account the digitization of societies and global economies. In this context and unlike the other European states, in the Romanian curriculum, the digital competence can not be found among the acquisitions of learning at the primary education level. In the absence of some clearly formulated learning objectives, which could be integrated into the development of digital competence, the teaching staff has the option to adapt the learning activities or to propose some new ones, so that the projection of the lessons ensures a teaching approach adequate to the acquisition of digital knowledge and skills. This paper highlights the possibilities of developing the specific competences of the objects in the current education framework plan for primary education, in a prototype approach to support the premise of their integration in the development of digital competence.
Dynamic-Distance-Based Thresholding for UAV-Based Face Verification Algorithms

Face verification, crucial for identity authentication and access control in our digital society, faces significant challenges when comparing images taken in diverse environments, which vary in terms of distance, angle, and lighting conditions. These disparities often lead to decreased accuracy due to significant resolution changes. This paper introduces an adaptive face verification solution tailored for diverse conditions, particularly focusing on Unmanned Aerial Vehicle (UAV)-based public safety applications. Our approach features an innovative adaptive verification threshold algorithm and an optimised operation pipeline, specifically designed to accommodate varying distances between the UAV and the human subject. The proposed solution is implemented based on a UAV platform and empirically compared with several state-of-the-art solutions. Empirical results have shown that an improvement of 15% in accuracy can be achieved.
Developing Digital Skills Trough Blended Learning Activities

The article represents the developing digital skills through blended learning activities.Bibliography was studied for the comparative analysis between blended learning and digital skills. I left the research questions: What is Digital ability? What is mixed learning? What digital application are useful for developing digital skills in preschoolers? How do we develop digital skills through blended learning? We have found that digital skills can be developed through blended learning activities. Blended learning is an educational paradigm, a new way of teaching through the use of digital applications. Digital ability is a necessary to develop through blended learning system, as it brings an additional benefit to the professions of the future.
Large Scale Optical Projection Tomography without the Use of Refractive-Index-Matching Liquid

The practical, rapid, and accurate optical 3D reconstruction of transparent objects with contemporary non-contact optical techniques, has been an open challenge in the field of optical metrology. The combination of refraction, reflection, and transmission in transparent objects makes it very hard to use common off-the-shelf 3D reconstruction solutions to accurately reconstruct transparent objects in three dimensions without completely coating the object with an opaque material. We demonstrate in this work that a specific class of transparent objects can indeed be reconstructed without the use of opaque spray coatings, via Optical Projection Tomography (OPT). Particularly, the 3D reconstruction of large thin-walled hollow transparent objects can be achieved via OPT, without the use of refractive-index-matching liquid, accurately enough for use in both cultural heritage and beverage packaging industry applications. We compare 3D reconstructions of our proposed OPT method to those achieved by an industrial-grade 3D scanner and report average shape differences of ±0.34 mm for 'shelled' hollow objects and ±0.92 mm for 'non-shelled' hollow objects. A disadvantage of using OPT, which was noticed on the thicker 'non-shelled' hollow objects, as opposed to the 'shelled' hollow objects, was that it induced partial filling of hollow areas and the deformation of embossed features.
Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher-student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models.
Minor and trace elements in pyrite and marcasite from the Sarnak low-sulfidation gold deposit, SE Bulgaria. Part 1

Here we present statistical and geochemical data on 12 minor and trace elements from pyrite- and marcasite-bearing samples from drillhole cores in the Sarnak (also known as Surnak) gold deposit. The data were obtained by SEM-EDX and single-spot LA-ICP-MS analyses in marcasite and different pyrite zones – core, mantle, and rim.
Multi-Modal Image Registration Based on Phase Exponent Differences of the Gaussian Pyramid

In multi-modal images (MMI), the differences in their imaging mechanisms lead to large signal-to-noise ratio differences, which means that the matching of geometric invariance and the matching accuracy of the matching algorithms often cannot be balanced. Therefore, how to weaken the signal-to-noise interference of MMI, maintain good scale and rotation invariance, and obtain high-precision matching correspondences becomes a challenge for multimodal remote sensing image matching. Based on this, a lightweight MMI alignment of the phase exponent of the differences in the Gaussian pyramid (PEDoG) is proposed, which takes into account the phase exponent differences of the Gaussian pyramid with normalized filtration, i.e., it achieves the high-precision identification of matching correspondences points while maintaining the geometric invariance of multi-modal matching. The proposed PEDoG method consists of three main parts, introducing the phase consistency model into the differential Gaussian pyramid to construct a new phase index. Then, three types of MMI (multi-temporal image, infrared–optical image, and map–optical image) are selected as the experimental datasets and compared with the advanced matching methods, and the results show that the NCM (number of correct matches) of the PEDoG method displays a minimum improvement of 3.3 times compared with the other methods, and the average RMSE (root mean square error) is 1.69 pixels, which is the lowest value among all the matching methods. Finally, the alignment results of the image are shown in the tessellated mosaic mode, which shows that the feature edges of the image are connected consistently without interlacing and artifacts. It can be seen that the proposed PEDoG method can realize high-precision alignment while taking geometric invariance into account.
Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial

Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III). This tutorial explored methods such as K-fold cross-validation and nested cross-validation, highlighting their advantages and disadvantages across 2 common predictive modeling use cases: classification (mortality) and regression (length of stay). We aimed to provide readers with reproducible notebooks and best practices for modeling with electronic health care data. We also described sets of useful recommendations as we demonstrated that nested cross-validation reduces optimistic bias but comes with additional computational challenges. This tutorial might improve the community’s understanding of these important methods while catalyzing the modeling community to apply these guides directly in their work using the published code.
Efficient Execution of Cloud Resource Management in Cloud and Internet of Things Applications

The Internet of Things is essential for business. It makes it possible to gather and analyze huge amounts of data in real time. IoT devices also encourage computerization. They enable individuals to gain greater control over their circumstances, well-being, and safety. As a rule, there are two principal sorts of asset the executives move toward that concern framework and applications. All improvement groups that work with cloud situations will be influenced by the modern approaches to cloud administration. Utilization checking, asset assignment to applications and administrations based on their prerequisites, and capacity administration— all components of asset administration—guarantee that assets are utilized successfully. It might, for instance, utilize robotized apparatuses to screen how its servers are being utilized, donate more assets to administrations that are in great demand, and cut back on administrations that are not in great demand. The Internet of Things makes it conceivable to computerize regular undertakings that commonly consume a ton of assets and labor supply; thus, trading settings considering brief environment or use is one model. This opens a great deal of assets, permitting the organization to focus on development and a bigger vision of the business. It provides information to encourage better choices and tracks down holes in tasks, cycles, and business arrangements. It likewise makes an extraordinary association between the production line floor and the business. This implies expanded efficiency, even while reducing expenses and energy use.
Recent Trends of Federated Learning for Smart Healthcare Systems

The Internet of Things (IoT) has brought a revolutionary change in the healthcare system. Smart devices have helped people maintain their health by collecting and storing a wide range of data. Artificial intelligence (AI) has made its promising way in several areas. They help in the early diagnosis of various diseases along with storage and interpretation of health data. However, due to the lack of communication between devices and the risk of transmission of data, the efficiency of AI devices is questionable. To avoid the transmission of data, Federation learning (FL) was highlighted as an approach where issues related to the security of sensitive data can be reduced significantly. The combination of FL, AI, and Explainable Artificial Intelligence (XAI) techniques can minimize several limitations and challenges in the healthcare system. This chapter presents an overview of FL's application in healthcare. Different studies presented data about FL and its usage in healthcare. Currently, this paradigm approach is successfully used by specialists in diagnostic purposes.
A Generic Study of Linguistic Information Based on the Chinese Idiom Knowledge Base and Its Expansion

The Chinese language is rich in set phrases and idiomatic expressions of various types. By definition, a set phrase is an inclusive concept that consists of not only lexical units, such as idioms and proper nouns, but also non-lexical units, such as proverbs, maxims, adages, and poems, as long as they are “fixed” in form. These constructions are regarded as multiword expressions (MWEs) in linguistics. This chapter will describe the linguistic information obtained from a generic investigation of the Chinese Idiom Knowledge Base (CIKB), along with its expansion to other similar language resources. In Chinese information processing, idiomatic expressions play an important role in word segmentation, which is a prerequisite for other natural language processing (NLP) tasks. The current challenge is constructing knowledge bases for Chinese idiomatic expressions with relatively complete entries and annotations. In our study, we focused on how the constituents in a fossilized composition like an idiom affect semantic and grammatical properties. As an important Chinese language resource, our knowledge base of Chinese idiomatic expressions is expected to play a major role in practices such as linguistic research, teaching Chinese as a foreign language, and as a tool for preserving non-material Chinese cultural and historical heritage.
Using Ensemble Learning and Random Forest Techniques to Solve Complex Problems

The branch of computer science and artificial intelligence known as machine learning is used to program machines to learn. Algorithms for machine learning are software programs or methods used to find hidden patterns in data, predict outcomes, and improve performance based on past performance. A technique used in machine learning called ensemble learning combines several models, such as classifiers or experts that have been carefully constructed to solve a particular computational intelligence problem. Ensemble refers to a collaborative effort to create a single impact. An ensemble can predict events more accurately and perform better in general than a single contributor. A random forest is a technique for ensemble learning in which many decision trees are combined to create the forest. This chapter covers the fundamentals of ensemble learning using random forest, implementation with real-world examples, and developing a model.
Universally Composable Auditable Surveillance

User privacy is becoming increasingly important in our digital society. Yet, many applications face legal requirements or regulations that prohibit unconditional anonymity guarantees, e.g., in electronic payments where surveillance is mandated to investigate suspected crimes. As a result, many systems have no effective privacy protections at all, or have backdoors, e.g., stored at the operator side of the system, that can be used by authorities to disclose a user’s private information (e.g., lawful interception). The problem with such backdoors is that they also enable silent mass surveillance within the system. To prevent such misuse, various approaches have been suggested which limit possible abuse or ensure it can be detected. Many works consider auditability of surveillance actions but do not enforce that traces are left when backdoors are retrieved. A notable exception which offers retrospective and silent surveillance is the recent work on misuse-resistant surveillance by Green et al. (EUROCRYPT’21). However, their approach relies on extractable witness encryption, which is a very strong primitive with no known efficient and secure implementations. In this work, we develop a building block for auditable surveillance. In our protocol, backdoors or escrow secrets of users are protected in multiple ways: (1) Backdoors are short-term and user-specific; (2) they are shared between trustworthy parties to avoid a single point of failure; and (3) backdoor access is given conditionally. Moreover (4) there are audit trails and public statistics for every (granted) backdoor request; and (5) surveillance remains silent, i.e., users do not know they are surveilled. Concretely, we present an abstract UC-functionality which can be used to augment applications with auditable surveillance capabilities. Our realization makes use of threshold encryption to protect user secrets, and is concretely built in a blockchain context with committee-based YOSO MPC. As a consequence, the committee can verify that the conditions for backdoor access are given, e.g., that law enforcement is in possession of a valid surveillance warrant (via a zero-knowledge proof). Moreover, access leaves an audit trail on the ledger, which allows an auditor to retrospectively examine surveillance decisions. As a toy example, we present an Auditably Sender-Traceable Encryption scheme, a PKE scheme where the sender can be deanonymized by law enforcement. We observe and solve problems posed by retrospective surveillance via a special non-interactive non-committing encryption scheme which allows zero-knowledge proofs over message, sender identity and (escrow) secrets.
The Chinese Generalized Function Word Usage Knowledge Base and Its Applications

The study of modern Chinese function word usage is of great significance in Chinese syntax analysis and semantic understanding. In this chapter, we will first describe the triune Chinese generalized function word usage knowledge base, which includes a usage dictionary, a usage rule base, a usage corpus, and defined function words such as adverbs, prepositions, conjunctions, auxiliaries, modal particles, and location words. With this constructed knowledge base, we were able to examine the automatic recognition of Chinese function word usages using the rule-based method, the statistics-based method, and the combined rule-based/statistics-based method, respectively. In this chapter, we will also discuss the applications of the Chinese function word usage knowledge base (CFKB) in syntactic analysis, grammar error analysis, information extraction, and Chinese deep semantic understanding.
Disability is no laughing matter. A critical discourse analysis of disability mockery on social media in Brazil

There is a growing body of literature addressing the surge of different types of online harassment (e.g. hate speech, misogyny, racism, religious intolerance, cyberbullying, revenge porn, homophobia, etc.). Yet, despite the undeniable contribution brought by this literature, there is still another aspect that remains underexplored. That is, the interplay between politically motivated hate speech and disability. Furthermore, the potential harm and adverse societal impacts of this type of manifestation can be amplified when triggered by extremist far-right political discourses. Thus, to examine this phenomenon and unpack the embedded meaning of this type of social media discourse, the present study develops a critical discourse analysis on a sample of 160 comments publicly available on YouTube belittling the current Brazil president’s disability (who lost a finger during his youth). The investigation reveals that many social media users find amusement in somebody else’s disability. However, mocking this condition represents a way to remove a person’s value and reduce them solely to their physical or mental impairment, independently of the level of severity. Ultimately, this sort of discursive strategy objectifies the individual, disregards the disability condition and turns them into a laughable object.
Two examples of implementation of the if/then/else conditional statement with quantum computers

While quantum algorithms are known to be very powerful, there are also people who think that “quantum computers will never be able to run the if/then/else type of logic”. On the other hand, though many experts know that such a claim is a misconception, in literature there is still lacking of a general description of the procedure for implementing the if/then/else conditional statements in the quantum way. In the current work, we present such a general method in details, explaining how the unitary transformation required for any if/then/else operation can be constructed from simpler unitary transformations and projective operators. Moreover, by using the well-known controlled-NOT gate, Fredkin gate and Toffoli gate as examples, it is elaborated that the structure of the existing conditional quantum operations agrees with our general method. An even more complicated example is also provided, showing that our method is convenient for solving real-world if/then/else problems with quantum computers. Thus the result not only refutes the above claim, but also helps to develop the skill on quantum programming.
Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously.