Reviewerly/exHarmony huggingface dataset:
Development and Validation of the Teachers’ Digital Competence Scale (TDiCoS)

The competences expected from teachers are described in several international reports. An investigation of teacher competences has a potential to analyze and delineate the current situation. Assessment of any construct necessitates utilization of appropriate scales with established validity and reliability. Currently available validated digital competence scales have examined digital competences of citizens, teachers, students, but most are based on only one digital competence framework. In this study a valid, reliable, and comprehensive scale entitled “Teachers’ Digital Competence Scale” (TDiCoS) for teachers of different subjects has been obtained by relying on standards/policy reports from several countries. TDiCoS items measure teachers' self-assessment and report of their use of digital technologies in their classrooms. TDiCoS was validated on a sample of 288 in-service teachers. In the final form TDiCoS is unidimensional and composed of 19 items. The model fit criteria, factor loadings, internal validity, and reliability of TDiCoS were examined and found to be good. Hence, it can inform design of new policies and teacher professional development programs targeting digital competences.
Precedential Constraint Derived from Inconsistent Case Bases

I explore a factor-based model of precedential constraint that, unlike existing models, does not rely on the assumption that the background set of precedent cases is consistent. The model I consider is a generalization of the reason model of precedential constraint that was suggested by Horty. I show that, within this framework, inconsistent case bases behave in a sensible and interesting way, both from a logical and a more practical perspective.
Estimation CNN-Based Person Re-Identification Accuracy in Video Using Different Datasets

The paper analyses the problem of person re-identification accuracy in distributed video surveillance systems by using various datasets for training convolutional neural networks (CNN). After analysis we constructed large joint image dataset of people consisting of CUHK02, CUHK03, Market-1501, DukeMTMC-ReID, MSMT17 and our collected PolReID. PolReID includes 52035 images for 657 people Experimental results on assessing re-identification accuracy based on the main metrics Rank and mAP are presented. The research was carried out for the most widely used CNNs in re-identification, such as ResNet-50, DenseNet121 and PCB. We show that the constructed large dataset allowed us to improve Rank1, mAP for all test sets.
Eagle perching optimizer for the online solution of constrained optimization

The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms.
Graph-based confidence verification for BPSK signal analysis under low SNRs

Confidence verification for intercepted signal analysis has important military applications where a priori knowledge of the signal or transmission channel is insufficient or absent. However, existing methods do not perform well under low signal-to-noise ratios (SNRs). In this study, we propose a graph-based verification algorithm for binary phase shift keying (BPSK) signal analysis. The correlation spectrum between the original observed signal and reference signal can be divided into blocks of equal size and the summation of each block (called the block summation (BS) spectrum) obtained. Then, the BS spectrum samples are transformed into a specific simple undirected graph using the signal-to-graph convertor. Accordingly, the verification issue can be simplified to determining whether the graph is fully-connected. The majorization relationships between the probability density functions of the BS and the original correlation spectra are evaluated. This is used to compare the connectivity of the graphs generated from those two types of samples, as well as their probabilities of being complete graphs. Simulation results confirm the superior performance of the proposed algorithm over previous confidence verification algorithms for low SNRs and in the presence of transmission impairments, as well as a lower computational complexity when compared with the conventional graph-based algorithm.
Nuclei and glands instance segmentation in histology images: a narrative review

Examination of tissue biopsy and quantification of the various characteristics of cellular processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance segmentation greatly assists the high-throughput quantification of cellular process and accurate appraisal of tissue biopsy. It subsequently makes a significant improvement to the computational pathology process for cancer diagnosis, treatment planning, and survival analysis. Recent advancements in the field of computer vision have automated the manual, laborious, and time-consuming histopathological analysis process. Automated image analysis of histopathological images for cells and tissues to trace the entirety of the ultrastructures, has been an active area of research in medical informatics for decades. The developments in computers, microscopy hardware, and the availability of large-scale public datasets have further fastened the development in this field. And the realization that scientific and diagnostic pathology calls for fresh ways to undertake, automated image analysis of histopathological images has captivated contemporary attention. In this survey, 126 papers illustrating the AI-based methods for nuclei and glands instance segmentation published in the last five years (2017–2022) are deeply analyzed, and the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented, and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and detailed insights on the grand challenges illustrating the top-performing methods specific to each challenge is also provided. Besides, we intended to give the reader the current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing on nuclei and glands instance segmentation.
AI Psychometrics: Assessing the psychological profiles of large language models through psychometric inventories

We illustrate how standard psychometric inventories originally designed for assessing non-cognitive human traits can be repurposed as diagnostic tools to evaluate analogous traits in large language models (LLMs). We start from the assumption that LLMs, inadvertently yet inevitably, acquire psychological traits (metaphorically speaking) from the vast text corpora on which they are trained. Such corpora contain sediments of the personalities, values, beliefs and biases of the countless human authors of these texts, which LLMs learn through a complex training process. The traits that LLMs acquire in such a way can potentially influence their behavior, i.e., their outputs in downstream tasks and applications in which they are employed, which in turn may have real-world consequences for individuals and social groups. By eliciting LLMs’ responses to language-based psychometric inventories we can bring their traits to light. Psychometric profiling enables researchers to study and compare LLMs in terms of non-cognitive characteristics thereby providing a window into the personalities, values, beliefs and biases these models exhibit (or mimic). We discuss the history of similar ideas and outline possible psychometric approaches for LLMs. We demonstrate one promising approach, zero-shot classification, for several LLMs and psychometric inventories. We conclude by highlighting open challenges and future avenues of research for AI Psychometrics.
Immersive and Challenging Experiences through A Virtual Reality Musical Instruments Game: An Approach to Gamelan Preservation

This study proposes the preservation of traditional Javanese musical instruments known as Gamelan using an immersive and challenging approach in a virtual reality game format. In an effort to achieve a challenging experience, the gamification is designed by observing musical instrument games selected based on their popularity in the Google Play and the App Store. Oculus Quest 2 which is a stand-alone Head Mounted Displays that provides a high-quality immersive display and more flexibility for users to move is chosen to present the game. Overall, the research target can be achieved based on the evaluation using Mean Opinion Scores. The degree of immersion, presence, likeable, challenging, and the value of cultural preservation contained in the game reaches a range of values between good and excellent. Meanwhile, cybersickness still seems to be a chore for developers to make Head Mounted Displays devices more comfortable.
Audio Capture Using Structural Sensors on Vibrating Panel Surfaces

The microphones and loudspeakers of modern compact electronic devices such as smartphones and tablets typically require case penetrations that leave the device vulnerable to environmental damage.To address this, the authors propose a surface-based audio interface that employs force actuators for reproduction and structural vibration sensors to record the vibrations of the display panel induced by incident acoustic waves.This paper reports experimental results showing that recorded speech signals are of sufficient quality to enable high-reliability automatic speech recognition despite degradation by the panel's resonant properties.The authors report the results of experiments in which acoustic waves containing speech were directed to several panels, and the subsequent vibrations of the panels' surfaces were recorded using structural sensors.The recording quality was characterized by measuring the speech transmission index, and the recordings were transcribed to text using an automatic speech recognition system from which the resulting word error rate was determined.Experiments showed that the word error rate (10%-13%) achieved for the audio signals recorded by the method described in this paper was comparable to that for audio captured by a high-quality studio microphone (10%).The authors also demonstrated a crosstalk cancellation method that enables the system to simultaneously record and play audio signals.
Imaging Modalities in Inflammatory Breast Cancer (IBC) Diagnosis: A Computer-Aided Diagnosis System Using Bilateral Mammography Images

Inflammatory breast cancer (IBC) is an aggressive type of breast cancer. It leads to a significantly shorter survival than other types of breast cancer in the U.S. The American Joint Committee on Cancer (AJCC) defines the diagnosis based on specific criteria. However, the clinical presentation of IBC in North Africa (Egypt, Morocco, and Tunisia) does not agree, in many cases, with the AJCC criteria. Healthcare providers with expertise in IBC diagnosis are limited because of the rare nature of the disease. This paper reviewed current imaging modalities for IBC diagnosis and proposed a computer-aided diagnosis system using bilateral mammograms for early and improved diagnosis. The National Institute of Cancer in Egypt provided the image dataset consisting of IBC and non-IBC cancer cases. Type 1 and Type 2 fuzzy logic classifiers use the IBC markers that the expert team identified and extracted carefully. As this research is a pioneering work in its field, we focused on breast skin thickening, its percentage, the level of nipple retraction, bilateral breast density asymmetry, and the ratio of the breast density of both breasts in bilateral digital mammogram images. Granulomatous mastitis cases are not included in the dataset. The system’s performance is evaluated according to the accuracy, recall, precision, F1 score, and area under the curve. The system achieved accuracy in the range of 92.3–100%.
Automatic Modulation Classification Based on CNN and Multiple Kernel Maximum Mean Discrepancy

Automatic modulation classification plays a significant role in numerous military and civilian applications. Deep learning methods have attracted increasing attention and achieved remarkable success in recent years. However, few methods can generalize well across changes in varying channel conditions and signal parameters. In this paper, based on an analysis of the challenging domain shift problem, we proposed a method that can simultaneously achieve good classification accuracy on well-annotated source data and unlabeled signals with varying symbol rates and sampling frequencies. Firstly, a convolutional neural network is utilized for feature extraction. Then, a multiple kernel maximum mean discrepancy layer is utilized to bridge the labeled source domain and unlabeled target domain. In addition, a real-world signal dataset consisting of eight digital modulation schemes is constructed to verify the effectiveness of the proposed method. Experimental results demonstrate that it outperforms state-of-the-art methods, achieving higher accuracy on both source and target datasets.
Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals

Wireless sensor networks are among the most promising technologies of the current era because of their small size, lower cost, and ease of deployment.With the increasing number of wireless sensors, the probability of generating missing data also rises.This incomplete data could lead to disastrous consequences if used for decision-making.There is rich literature dealing with this problem.However, most approaches show performance degradation when a sizable amount of data is lost.Inspired by the emerging field of graph signal processing, this paper performs a new study of a Sobolev reconstruction algorithm in wireless sensor networks.Experimental comparisons on several publicly available datasets demonstrate that the algorithm surpasses multiple state-of-the-art techniques by a maximum margin of 54%.We further show that this algorithm consistently retrieves the missing data even during massive data loss situations.
Lightweight Wavelet-Based Transformer for Image Super-Resolution

Suffering from the inefficiency of deeper and wider networks, most remarkable super-resolution algorithms cannot be easily applied to real-world scenarios, especially resource-constrained devices. In this paper, to concentrate on fewer parameters and faster inference, an end-to-end Wavelet-based Transformer for Image Super-resolution (WTSR) is proposed. Different from the existing approaches that directly map low-resolution (LR) images to high-resolution (HR) images, WTSR also implicitly mines the self-similarity of image patches by a lightweight Transformer on the wavelet domain, so as to balance the model performance and computational cost. More specifically, a two-dimensional stationary wavelet transform is designed for the mutual transformation between feature maps and wavelet coefficients, which reduces the difficulty of mining self-similarity. For the wavelet coefficients, a Lightweight Transformer Backbone (LTB) and a Wavelet Coefficient Enhancement Backbone (WECB) are proposed to capture and model the long-term dependency between image patches. Furthermore, a Similarity Matching Block (SMB) is investigated to combine global self-similarity and local self-similarity in LTB. Experimental results show that our proposed approach can achieve better super-resolution performance on the multiple public benchmarks with less computational complexity.
Efektivitas Pengembangan Media Pembelajaran Komik Berbasis Kodular untuk Kemampuan Pemahaman Literasi Matematika Siswa SDS Edustar

Students do not understand the basic concepts of mathematics. Even understanding mathematics lessons in class is still difficult, let alone solving mathematical literacy problems. This is due to the lack of learning media facilities that support mathematics literacy learning. So that due to a lack of understanding of the concepts of mathematics, and considers mathematics lessons are boring lessons. Therefore, it is necessary to develop flexible teaching materials that can be used in any situation to support students' understanding of mathematical literacy. Thus, the researcher developed a codular-based comic learning media for students' mathematical literacy comprehension skills. This study aimed to see the effectiveness of the codular-based comics learning media for the ability to understand mathematical literacy of elementary school students Edustar. This research was conducted at SDS Edustar Batam with research subjects in grades 4, 5, and 6. The data collection of this study used the results of the post-test of students on students' mathematical literacy of 62.01%. So it can be concluded that the codular-based comics learning media is effectively used as a medium for learning students' mathematical literacy.
Scalable Marginalization of Correlated Latent Variables with Applications to Learning Particle Interaction Kernels

Marginalization of latent variables or nuisance parameters is a fundamental aspect of Bayesian inference and uncertainty quantification. In this work, we focus on scalable marginalization of latent variables in modeling correlated data, such as spatio-temporal or functional observations. We first introduce Gaussian processes (GPs) for modeling correlated data and highlight the computational challenge, where the computational complexity increases cubically fast along with the number of observations. We then review the connection between the state space model and GPs with Matérn covariance for temporal inputs. The Kalman filter and Rauch-Tung-Striebel smoother were introduced as a scalable marginalization technique for computing the likelihood and making predictions of GPs without approximation. We introduce recent efforts on extending the scalable marginalization idea to the linear model of coregionalization for multivariate correlated output and spatio-temporal observations. In the final part of this work, we introduce a novel marginalization technique to estimate interaction kernels and forecast particle trajectories. The computational progress lies in the sparse representation of the inverse covariance matrix of the latent variables, then applying conjugate gradient for improving predictive accuracy with large data sets. The computational advances achieved in this work outline a wide range of applications in molecular dynamic simulation, cellular migration, and agent-based models.
On the TREC Blog Track

The rise of blogging as a new grassroots publishing medium and the many interesting peculiarities that characterise blogs compared to other genres of documents opened up several new interesting research areas in the information retrieval field. The Blog track was introduced in 2006 as part of the renowned Text REtrieval Conference (TREC) evaluation forum, to drive research on the blogosphere and to facilitate experimentation and evaluation of blog search techniques. This paper reports on two years of the Blog track at TREC. We describe the blog search tasks investigated at TREC, and discuss the main lessons we learnt from the track. We conclude the paper with discussions of the broader implications of the Blog track lessons and possible directions for the future, with the aim to uncover and explore the richness of information available in the blogosphere.
Determinants of Consumer Confidence Index to Predict the Economy in Indonesia

Psychological factors play an important role in the economy and in predicting the state of the economy.One of the measurement tools of these factors is the consumer confidence index (CCI), which has recently received much attention from both researchers and policymakers.For years, Bank Indonesia has conducted surveys through face-to-face and phone call interviews with several respondents who were used as research samples to control information on economic fundamentals.The purpose of this study is to explore a new variable that is thought to affect the consumer confidence index in Indonesia and explain the influence between variables from the results of secondary data processing.This study used the multivariate regression model and t-test equations with a significance level of 5%.The variables used in this study are the foreign exchange rate, unemployment rate, corruption control index, inflation rate, and consumer confidence index from January 2015 to December 2019 in Indonesia.The results conclude that Indonesia's consumer confidence index is influenced by the inflation rate, the unemployment rate, the exchange rate, and the conditions for controlling corruption.The multivariate regression model generated from this study is a novelty in research on the consumer confidence index.This study also provides an alternative way for Bank Indonesia to evaluate the consumer confidence index, which previously used the face-to-face interview method, and the phone survey turned into using secondary data source.The use of secondary data for the multivariate regression model will accelerate policy making and is more efficient in terms of costs.
An Overview of Environmental Scalability and Security in Hybrid Cloud Infrastructure Designs

The practice of using IT resources via the internet with pay-as-you-go pricing is known as cloud computing. The cloud computing market has grown dramatically in the last few years. Most companies are transmitting from on-premises data centers to cloud computing. You can use the cloud as an alternative to purchasing and maintaining computer hardware and software. You can spend time, energy, or money doing everything yourself. This paper presents an overview of the architecture, scalability, economics, consistency-availability-partition, transactions, and security of cloud-based system designs. This study assesses the current cloud development ecosystem using various criteria.