Reviewerly/exHarmony huggingface dataset:
PERBEDAAN KADAR HEMOGLOBIN MENGGUNAKAN METODE HB METER DAN HEMATOLOGY ANALYZER

Examination of hemoglobin levels before blood donation is an important check to certify that potential donors can donate blood. Hemoglobin levels can be measured using the hematology analyzer method where this method checks more quickly and the results issued usually have gone through quality control carried out by an in-house laboratory, it's just that this method has limitations including treatment, room temperature, must be controlled periodically. Checking hemoglobin levels can also be measured using the Hb meter method. Checking hemoglobin levels can also be done using Hb meters which are widely used by health services, such as clinical laboratories, health centers and hospitals. The advantages of the Hb meter include the results obtained faster and cheaper. This study aims to determine differences in the results of examination of hemoglobin levels using the Hb meter method with a hematology analyzer. The research method used is True Experiment with the type of research The Posttest Only Design. The sample in this study were 16 prospective blood donors at PMI Yogyakarta City. Respondents who had been given an explanation and signed an informed consent were then taken a sample of their venous blood and examined the hemoglobin level using the Hb meter and hematology analyzer methods. results are recorded and then data processing is carried out using univariate analysis and bivariate analysis. Based on the results of the study it was concluded that there was no significant difference between the results of examining hemoglobin levels using the Hb meter method and the Hematology Analyzer (p = 0.321 : p &gt; 0.05). The average value of hemoglobin levels in the Hb meter method is 14.631 g/dl while the average value of hemoglobin levels in the Hematology Analyzer method is 14.294 g/dl.
Reflexive technology as a pedagogical tool to support high school students in the process of professional self-determination

The chapter discusses the theoretical and methodological foundations for supporting the professional self-determination of high school students, modeling the reflexive technology of the process of supporting professional self-determination and its implementation in the space of an educational organization. The idea of the study is that the result of supporting professional self-determination of high school students is an understanding of personal characteristics considered in line with reflective activity, and most importantly, the acquisition of self-determination skills. The purpose of the study is to create and describe the theoretical and conceptual foundations of the model of reflexive technology for supporting the professional self-determination of high school students in the framework of secondary education. Research results show that the development of students' reflective skills and the preservation of a sustainable professional interest occur as a result of the implementation of reflective technology. The author comes to the conclusion that self-promotion in professional and personal terms can be considered the meaning of the process of supporting the professional self-determination of high school students.
Emotion Recognition in Individuals with Down Syndrome: A Convolutional Neural Network-Based Algorithm Proposal

This research introduces an algorithm that automatically detects five primary emotions in individuals with Down syndrome: happiness, anger, sadness, surprise, and neutrality. The study was conducted in a specialized institution dedicated to caring for individuals with Down syndrome, which allowed for collecting samples in uncontrolled environments and capturing spontaneous emotions. Collecting samples through facial images strictly followed a protocol approved by certified Ethics Committees in Ecuador and Colombia. The proposed system consists of three convolutional neural networks (CNNs). The first network analyzes facial microexpressions by assessing the intensity of action units associated with each emotion. The second network utilizes transfer learning based on the mini-Xception architecture, using the Dataset-DS, comprising images collected from individuals with Down syndrome as the validation dataset. Finally, these two networks are combined in a CNN network to enhance accuracy. The final CNN processes the information, resulting in an accuracy of 85.30% in emotion recognition. In addition, the algorithm was optimized by tuning specific hyperparameters of the network, leading to a 91.48% accuracy in emotion recognition accuracy, specifically for people with Down syndrome.
4DL: a four-valued dynamic logic and its proof theory

Abstract Transition systems are often used to describe the behaviour of software systems. If viewed as a graph then, at their most basic level, vertices correspond to the states of a program and each edge represents a transition between states via the (atomic) action labelled. In this setting, systems are thought to be consistent and at each state formulas are evaluated as either true or false. On the other hand, when a structure of this sort—e.g. a map where states represent locations, some local properties are known and labelled transitions represent information available about different routes—is built resorting to multiple sources of information, it is common to find inconsistent or incomplete information regarding what holds at each state, both at the level of propositional variables and transitions. This paper aims at bringing together Belnap’s four values, Dynamic Logic and hybrid machinery such as nominals and the satisfaction operator, so that reasoning is still possible in face of contradicting evidence. Proof-theory for this new logic is explored by means of a terminating, sound and complete tableau system.
Bilateral Rules as Complex Rules

Proof-theoretic semantics is an inferentialist theory of meaning originally developed in a unilateral framework. Its extension to bilateral systems opens both opportunities and problems. The problems are caused especially by Coordination Principles (a kind of rule that is not present in unilateral systems) and mismatches between rules for assertion and rules for rejection. In this paper, a solution is proposed for two major issues: the availability of a reduction procedure for tonk and the existence of harmonious rules for the paradoxical zero-ary connective \(\bullet\). The solution is based on a reinterpretation of bilateral rules as complex rules, that is, rules that introduce or eliminate connectives in a subordinate position. Looking at bilateral rules from this perspective, the problems faced by bilateralism can be seen as special cases of general problems of complex systems, which have been already analyzed in the literature. In the end, a comparison with other proposed solutions underlines the need for further investigation in order to complete the picture of bilateral proof-theoretic semantics.
On Program Completion, with an Application to the Sum and Product Puzzle

Abstract This paper describes a generalization of Clark’s completion that is applicable to logic programs containing arithmetic operations and produces syntactically simple, natural looking formulas. If a set of first-order axioms is equivalent to the completion of a program, then we may be able to find standard models of these axioms by running an answer set solver. As an example, we apply this “reverse completion” procedure to the Sum and Product Puzzle.
Design and simulation of large-diameter off-axis collimator

In this work, a large-aperture and long-focal length off-axis two-trans collimator was designed. Structural design is based on optical specifications. Firstly, the structural design of the main mirror holder was carried out, and the influence of the support method of the main mirror holder on the surface shape of the main mirror was analyzed using ANSYS and MATLAB. The analysis results show that the reflective primary mirror of large-aperture off-axis collimator is supported in such a way that the Root Mean Square (RMS) of the primary mirror is 0.025 λ(λ=632.8nm). The secondary mirror adjustment mechanism is a free moving device and is capable of six degrees of freedom of adjustment. The RMS of the system is 0.054 λ (λ=632.8nm), which meets the design requirements and can provide some guidance for the design of similar structures.
E-YOLOv4-tiny: a traffic sign detection algorithm for urban road scenarios

In urban road scenes, due to the small size of traffic signs and the large amount of surrounding interference information, current methods are difficult to achieve good detection results in the field of unmanned driving.To address the aforementioned challenges, this paper proposes an improved E-YOLOv4-tiny based on the YOLOv4-tiny. Firstly, this article constructs an efficient layer aggregation lightweight block with deep separable convolutions to enhance the feature extraction ability of the backbone. Secondly, this paper presents a feature fusion refinement module aimed at fully integrating multi-scale features. Moreover, this module incorporates our proposed efficient coordinate attention for refining interference information during feature transfer. Finally, this article proposes an improved S-RFB to add contextual feature information to the network, further enhancing the accuracy of traffic sign detection.The method in this paper is tested on the CCTSDB dataset and the Tsinghua-Tencent 100K dataset. The experimental results show that the proposed method outperforms the original YOLOv4-tiny in traffic sign detection with 3.76% and 7.37% improvement in mAP, respectively, and 21% reduction in the number of parameters. Compared with other advanced methods, the method proposed in this paper achieves a better balance between accuracy, real-time performance, and the number of model parameters, which has better application value.
Understanding the effect of a virtual moderator on people’s perception in remote discussion using social VR

Social VR enables people to join a remote discussion by keeping a high social presence and physical proximity using embodied avatars. However, the missing nonverbal cues, such as mutual eye contact and nuanced facial expression, make it challenging for distributed members to manage turn-taking, which could lead to unequal participation and affect trust building. Therefore, we propose a virtual moderator to make distributed members feel included by seeing their nonverbal behavior. The virtual moderator was designed with a “prompt Q&amp;amp;A″ feature to enable users to share feedback and an “attention guidance” feature to encourage participation. The preliminary result of a controlled experiment in social VR with 30 participants showed that seeing the virtual moderator’s body orientation enhanced participants’ psychological safety. In contrast, the prompt Q&amp;amp;A feature enhanced the perceived co-presence of their remote counterparts. We discussed how nonverbal behavior could be designed using a virtual moderator to shape human perception of the group discussion in social VR. We also pointed out challenges when providing multiple supports simultaneously in social VR.
Trace Element Characteristics of Pyrite and Arsenopyrite from the Golden Ridge Gold Deposit, New Brunswick, Canada: Implications for Ore Genesis

The Golden Ridge gold deposit is located in southwestern New Brunswick, in the Canadian Appalachians. Gold mineralization is consistently associated with acicular arsenopyrite, and to a lesser degree with pyrite, disseminated in host rocks, sulphide veinlets, quartz-carbonate veins, and the breccia matrix. According to petrographic-based textural differences, four types of pyrite and two types of arsenopyrite are recognized with associated assemblages. Based on SEM-BSE imaging and LA-ICP-MS spot analyses of the different types of pyrites and arsenopyrites, “invisible gold” (solid solution in the crystal lattice of pyrite and arsenopyrite or &lt;100 nm nanoparticles) and micrometer-size inclusions were identified as the main forms of Au. Four syn-gold mineralization pulses of fluid are suggested. The initial hydrothermal fluid, which generated low-grade pyrite (Py-I) enriched in Sb, Pb, Cu, Co, Ni, and Bi, was followed by a second pulse of fluid enriched in arsenic and gold, generating coprecipitated Py-II and Asp-I. The third and fourth pulses were enriched in both arsenic and gold and precipitated Py-III, then coprecipitated Py-IV and Asp-II, which constitute the most important Au depositional episodes. The repeated occurrence of growth zones with Au enrichment in the arsenian pyrites (Py-II, Py-III, and Py-IV) indicate surface growth during metal deposition and disequilibrium crystallization processes.
Lightweight Security for IoT

With the ever-increasing demand for IoT Devices which enable all objects to connect and exchange information in applications such as healthcare applications, Industry 4.0, smart cities and smart homes, etc. IoT devices play a crucial role in our day-to-day life like homes, offices, healthcare, wearable, and agriculture. With the development of IoT devices, securing device-to-device communication has attracted more and more attention and we need to ensure the privacy and security of data amongst these IoT devices. User authentication has emerged as a major security concern while connecting IoT devices and the cloud. Many authentication schemes like mutual authentication, group authentication have been proposed to ensure only authenticated users and with very high confidence we can rely on the decision-making process. Symmetric key based as well as Asymmetric key-based solutions have been proposed but due to the resource constraint nature of the IoT devices designing lightweight, robust, provably secure authentication schemes is a big challenge. This paper discusses the various authentication techniques designed for low-powered IoT devices and proposes a lightweight authentication scheme for IoT.
Implementation of the technology of tutor support in the process of formation of conflict competence of teachers at professional development courses

The chapter is devoted to the problem of the organization of tutor support of teachers in the system of additional professional education, ensuring the effective formation of their conflictological competence. The author presents the stages and conditions for the implementation of an individual educational program for the formation of conflictological competence of teachers in the process of tutor support at advanced training courses. The chapter concludes that the developed methodological equipment of the course and the accompanying activities of the tutor provide opportunities for teachers to better navigate the advanced training courses. It also allows students – teachers to independently choose the practical material they are interested in for the assimilation and subsequent creative application of the acquired knowledge and skills. The created favorable conditions will contribute to the subsequent systematic reflection of professional activity on the basis of innovative approaches in the education system.
Embodiment of virtual feet correlates with motor performance in a target-stepping task: a pilot study

Immersive Virtual Reality (IVR) has gained popularity in neurorehabilitation for its potential to increase patients’ motivation and engagement. A crucial yet relatively unexplored aspect of IVR interfaces is the patients’ representation in the virtual world, such as with an avatar. A higher level of embodiment over avatars has been shown to enhance motor performance during upper limb training and has the potential to be employed to enhance neurorehabilitation. However, the relationship between avatar embodiment and gait performance remains unexplored. In this work, we present the results of a pilot study with 12 healthy young participants that evaluates the effect of different virtual lower limb representations on foot placement accuracy while stepping over a trail of 16 virtual targets. We compared three levels of virtual representation: i) a full-body avatar, ii) only feet, and iii) no representation. Full-body tracking is computed using standard VR trackers to synchronize the avatar with the participants’ motions. Foot placement accuracy is measured as the distance between the foot’s center of mass and the center of the selected virtual target. Additionally, we evaluated the level of embodiment over each virtual representation through a questionnaire. Our findings indicate that foot placement accuracy increases with some form of virtual representation, either full-body or foot, compared to having no virtual representation. However, the foot and full-body representations do not show significant differences in accuracy. Importantly, we found a negative correlation between the level of embodiment of the foot representation and the distance between the placed foot and the target. However, no such correlation was found for the full-body representation. Our results highlight the importance of embodying a virtual representation of the foot when performing a task that requires accurate foot placement. However, showing a full-body avatar does not appear to further enhance accuracy. Moreover, our results suggest that the level of embodiment of the virtual feet might modulate motor performance in this stepping task. This work motivates future research on the effect of embodiment over virtual representations on motor control to be exploited for IVR gait rehabilitation.
KDM Security IBE Based on LWE beyond Affine Functions

Key-dependent message (KDM) security identity-based encryption (IBE) schemes aim to solve the security risks caused by the dependency between plaintext and secret keys in traditional IBE schemes. However, current KDM-IBE schemes are only secure with respect to affine functions, which limits their security level when a message is derived from the evaluation of a polynomial function using the secret key. To address this issue, in this study, we propose a novel approach to construct a KDM-IBE scheme with respect to polynomial or even arbitrary functions that achieves maximum security based on the learning with errors (LWE) assumption. Our approach overcomes two major technical barriers to constructing KDM-IBE schemes with respect to polynomial functions. Compared to existing KDM-IBE schemes, our proposed scheme ensures the secrecy of the key-related plaintext, even when it is obtained using arbitrary functions, not just affine functions. Thus, our approach provides a more robust solution to the security risks inherent in traditional IBE schemes.
Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold

A denoising algorithm was proposed for point cloud with high-density noise. The algorithm utilized geometric metrics on the statistical manifold and applied the idea of clustering K-means based on local statistical characteristics between noise and valid data. First, by calculating the expectation and covariance matrix of the data points, the point cloud with high-density noise was projected onto the Gaussian distribution family manifold, aiming to form the parameter point cloud. Afterwards, the geometry metrics were assigned to the manifold, and the K-means algorithm was applied to cluster the parameter point cloud, so as to classify the valid data and noise. Furthermore, in order to analyze the robustness of the means with different metrics, the approximate values of their influence functions were calculated, respectively. Finally, simulation analysis was conducted using the algorithm based on geometric metrics to verify the effectiveness in point cloud denoising.
Learning argumentation frameworks from labelings

We consider the problem of learning argumentation frameworks from a given set of labelings such that every input is a σ-labeling of these argumentation frameworks. Our new algorithm takes labelings and computes attack constraints for each argument that represent the restrictions on argumentation frameworks that are consistent with the input labelings. Having constraints on the level of arguments allows for a very effective parallelization of all computations. An important element of this approach is maintaining a representation of all argumentation frameworks that satisfy the input labelings instead of simply finding any suitable argumentation framework. This is especially important, for example, if we receive additional labelings at a later time and want to refine our result without having to start all over again. The developed algorithm is compared to previous works and an evaluation of its performance has been conducted.
Formation of students' research skills in the process of planning and conducting a physical experiment with the use of homemade equipment

The chapter describes ways to form research skills and create students' cognitive interest in the study of physics. The possibilities of using home-made equipment in physics for the organization of research activities are revealed. Examples of home-made devices used to study mechanical, light and electrical phenomena are given. Devices mounted from the simplest available materials are described, which allow organizing research on the study of physical phenomena, establishing scientific facts and discovering physical laws.
Text-Independent Speaker Recognition System Using Feature-Level Fusion for Audio Databases of Various Sizes

Abstract To improve the speaker recognition rate, we propose a speaker recognition model based on the fusion of different kinds of speech features. A new type of feature aggregation methodology with a total of 18 features is proposed and includes mel frequency cepstral coefficient (MFCC), linear predictive coding (LPC), perceptual linear prediction (PLP), root mean square (RMS), centroid, and entropy features along with their delta ( Δ ) and delta–delta ( ΔΔ ) feature vectors. The proposed approach is tested on five different sizes of speech datasets, namely the NIST-2008, voxforge, ELSDSR, VCTK, and voxceleb1 speech corpora. The results are evaluated using the MATLAB classification learner application with the linear discriminant (LD), K nearest neighbor (KNN), and ensemble classifiers. For the NIST-2008 and voxforge datasets, the best SI accuracy of 96.9% and 100% and the lowest speaker verification (SV) equal error rate (EER) values of 0.2% and 0% are achieved with the LD and KNN classifiers, respectively. For the VCTK and ELSDSR datasets, the best SI accuracy of 100% and the lowest SV EER of 0% are achieved with all three classifiers using different feature-level fusion approaches, while the highest SI accuracy and lowest EER achieved on the voxceleb1 database are 90% and 4.07%, respectively, using the KNN classifier. From the experimental results, it is observed that the fusion of different features with their delta and delta–delta values shows an increase in speaker identification accuracy of 10–50%, and the EER value for SV is reduced compared to the value obtained with a single feature.
Content-Aware Image Resizing Technology Based on Composition Detection and Composition Rules

A novel content-aware image resizing mechanism based on composition detection and composition rules is proposed to address the lack of esthetic perception in current content-aware resizing algorithms. A composition detection module is introduced for the detection of the composition of the input image types in the proposed algorithm. According to the classification results, the corresponding composition rules in computational esthetics are selected. Finally, the algorithm performs the operations of seam carving using the corresponding esthetic rules. The resized image not only protects the important content of the image, but also meets the composition rules to optimize the overall visual effect of the image. The simulation results show that the proposed algorithm achieves a better visual effect. Compared with the existing algorithms, the proposed algorithm not only effectively protects important image content, but also protects important structures and improves the overall beauty of the image.