Reviewerly/exHarmony huggingface dataset:
Prediksi Pasien Covid-19 yang Meninggal di Jawa Timur Menggunakan Metode Regresi Spline Truncated

Corona Virus Disease 2019 (COVID-19) was first discovered at the end of 2019 in Wuhan, China. The virus attacks the respiratory system with pneumonia-like symptoms. This virus is classified as a new virus so it does not have an antidote and has spread throughout the world until it is out of control. There are more than 200 countries reporting covid-19 cases including Indonesia. Because the biggest effect of COVID-19 is death, so researchers need to predict whether the mortality rate of COVID-19 patients. One method for predicting covid-19 patients who died in East Java is using the spline truncated regression method. Spline regression is a regression analysis that is able to estimate data that does not have a specific pattern and has a tendency to seek data estimates fro, the formed pattern on its own, while truncated is a function that can be interpreted as a function of slice. And produces a GCV value of 51.0167, a MAPE value 47.0096, MSE 32.9825, and R-Sq 0.9998. Based on the MAPE value, the prediction model is said to be still reasonable.
Moral/Value Education: History and its Present State

This research paper aims to provide a comprehensive background of moral and value education starting form ancient age through medieval period till the modern period. The history of value education must be studied in order to understand how value education has arrived at its current state. First, education in Indian traditions was more comprehensive, with the ultimate goal of education under the Vedic education system being the cultivation of character. However, there are some distinctions in the western educational system regarding value education. This paper will look at how value education was in the ancient world, how it persisted in the Middle Ages, and how education was completely restructured at the dawn of the modern era. Finally, this paper will give details about the current situation of value and character education, as well as how nation states are adopting value education in their own countries.
Detecting Phishing Website using Machine Learning

Phishing attacks continue to pose a major threat for computer system defenders, often forming the first step in a multi-stage attack. There have been great strides made in phishing detection; however, some phishing emails appear to pass through filters by making simple structural and semantic changes to the messages. We tackle this problem through the use of a machine learning classifier operating on a large corpus of phishing and legitimate emails. We design SAFEPC (Semi-Automated Feature generation for Phish Classification), a system to extract features, elevating some to higher level features, that are meant to defeat common phishing email detection strategies. To evaluate SAFE-PC, we collect a large corpus of phishing emails from the central IT organization at a tier-1 university. The execution of SAFE-PC on the dataset exposes hitherto unknown insights on phishing campaigns directed at university users. SAFEPC detects more than 70a state-of-the-art email filtering tool. It also outperforms Spam Assassin, a commonly used email filtering tool. We also developed an online version of SAFE-PC, that can be incrementally retrained with new samples. Its detection performance improves with time as new samples are collected, while the time to retrain the classifier stays constant.
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?

Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities. In this work, we investigate how aligning representations affects the visual features obtained from cross-view and cross-modal contrastive learning on images and point clouds. On five real-world datasets and on five tasks, we train and evaluate 108 models based on four pretraining variations. We find that cross-modal representation alignment discards complementary visual information, such as color and texture, and instead emphasizes redundant depth cues. The depth cues obtained from pretraining improve downstream depth prediction performance. Also overall, cross-modal alignment leads to more robust encoders than pre-training by cross-view alignment, especially on depth prediction, instance segmentation, and object detection.
Detection-aware multi-object tracking evaluation

How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.
Error mitigated quantum circuit cutting

We investigate an error mitigated tomographic approach to the quantum circuit cutting problem in the presence of gate and measurement noise. We explore two tomography specific error mitigation techniques; readout error mitigated conditional fragment tomography, which uses knowledge of readout errors on all cut and conditional qubit measurements in the tomography reconstruction procedure; and dominant eigenvalue truncation (DEVT), which aims to improve the performance of circuit cutting by performing truncation of the individual conditional tomography fragments used in the reconstruction. We find that the performance of both readout error mitigated tomography and DEVT tomography are comparable for circuit cutting in the presence of symmetric measurement errors. For gate errors our numerical results show that probability estimates for the original circuit obtained using DEVT outperforms general circuit cutting for measurement, depolarization and weakly biased Pauli noise models, but does not improve performance for amplitude damping and coherent errors, and can greatly decrease performance for highly biased Pauli noise. In cases where DEVT was effective, it as also found to improve performance of partial tomographic reconstruction using at least 50% of the full tomographic data with a conditional least-squares tomographic fitter, while linear inversion tomography with or without DEVT mitigation was found to perform poorly with with partial data.
Improving dermatology classifiers across populations using images generated by large diffusion models

Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this paper, we show that DALL$\cdot$E 2, a large-scale text-to-image diffusion model, can produce photorealistic images of skin disease across skin types. Using the Fitzpatrick 17k dataset as a benchmark, we demonstrate that augmenting training data with DALL$\cdot$E 2-generated synthetic images improves classification of skin disease overall and especially for underrepresented groups.
Limitations of Quantum Measurements and Operations of Scattering Type under the Energy Conservation Law

It is important to improve the accuracy of quantum measurements and operations both in engineering and fundamental physics. It is known, however, that the achievable accuracy of measurements and unitary operations are generally limited by conservation laws according to the Wigner-Araki-Yanase theorem (WAY theorem) and its generalizations. Although many researches have extended the WAY theorem quantitatively, most of them, as well as the original WAY theorem, concern only additive conservation laws like the angular momentum conservation law. In this paper, we explore the limitation incurred by the energy conservation law, which is universal but is one of the non-additive conservation laws. We present a lower bound for the error of a quantum measurement using a scattering process satisfying the energy conservation law. We obtain conditions that a control system Hamiltonian must fulfill in order to implement a controlled unitary gate with zero error when a scattering process is considered. We also show the quantitative relationship between the upper bound of the gate fidelity of a controlled unitary gate and the energy fluctuation of systems when a target system and a control system are both one qubit.
Data Origin Inference in Machine Learning

It is a growing direction to utilize unintended memorization in ML models to benefit real-world applications, with recent efforts like user auditing, dataset ownership inference and forgotten data measurement. Standing on the point of ML model development, we introduce a process named data origin inference, to assist ML developers in locating missed or faulty data origin in training set without maintaining strenuous metadata. We formally define the data origin and the data origin inference task in the development of the ML model (mainly neural networks). Then we propose a novel inference strategy combining embedded-space multiple instance classification and shadow training. Diverse use cases cover language, visual and structured data, with various kinds of data origin (e.g. business, county, movie, mobile user, text author). A comprehensive performance analysis of our proposed strategy contains referenced target model layers, available testing data for each origin, and in shadow training, the implementations of feature extraction as well as shadow models. Our best inference accuracy achieves 98.96% in the language use case when the target model is a transformer-based deep neural network. Furthermore, we give a statistical analysis of different kinds of data origin to investigate what kind of origin is probably to be inferred correctly.
The Mediating Role of Corporate Governance on the Relationship between Accounting Information System and Risk Management: The Case of the Jordanian Industrial Shareholding Companies

the primary aim of this study is to examine the mediating effect of corporate governance on the relationship between accounting information system (AIS) and risk management. The population of the study comprised of the entire Jordanian Industrial Shareholding Companies numbering 54. The study objectives were achieved by developing a questionnaire and distributing copies to the employees of the companies accounting departments. Data gathered was analyzed using SPSS 25.0 tool to test the relationships between AIS, corporate governance and risk management. Based on the findings, corporate governance fully mediated the relationship between AIS and risk management in the context of the Jordanian Industrial Shareholding Companies.
Supervisory Styles, Stress, and Decision Making: An Application of Prospect Theory

Abstract&#x0D; The purpose of our paper was to investigate the influence of supervisory styles on the manager's perceived stress and decision making. Supervisory styles are a combination of the leadership dimensions, consideration and initiation structure behaviors, and performance measurements, objective and subjective. We used a within-subjects experimental structure to determine the relative effects of perceived stress while making a decision under each supervisory style as well as relative risk-taking under each supervisory style. We found that managers whose supervisors use considerate leadership styles make riskier decisions than managers whose supervisors use initiates structure leadership styles. Furthermore, managers whose organizations use objective performance measures will report more stress than managers whose organizations use subjective measures. The initiates structure with objective performance measures supervisory style induced more perceived stress and resulted in lower risk-taking by the subjects than the other supervisory style combinations. Our research provides evidence that combinations of team-level leadership and organization level performance measures combine to produce unintended outcomes from the firm's managers.
Systemic Risk Contribution and Bank’s Competitiveness

There are two competing views about bank’s competitiveness and its systemic risk contribution: competition-stability and competition-fragility. Previous research shows mixed results. To test empirically the relationship, this research proposes a quadratic functional form that may reconciliate these two opposite views. Using Marginal Expected Shortfall as individual bank’s systemic risk contribution measurement and Lerner Index as individual bank’s competitiveness, this research find that the relationship resembles U-shape. In the first phase, competition creates prudent banking operation and low systemic risk contribution. But when competition become excessive, competition drive dominant bank to be a systematically important financial institution which may cause a serious systemic defaults and threat financial system stability.
Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts

Statements issued by public officials will be pros and cons in the community, there are those who respond positively, negatively or respond neutrally. Likewise on Instagram social media, every statement written on Instagram will get various responses written by netizens in the comment’s column posted. Netizen is an acronym for internet citizens, namely people who are actively using the internet. Due to the large number of comments, it is difficult to see whether the public response is a positive, negative or neutral comment when responding to statements from public officials. Whether the statements issued by public officials through Instagram have a positive, negative or neutral impact, so that if they can be grouped into labels, it can be seen how much public opinion is against these public figures. On social media accounts, not all comments written by netizens have the same writing structure, so we need a mechanism that is able to help analyze comments from netizens by classifying them into positive, negative or neutral response classes. By applying POS Tagging to determine opinion sentences or not and also the Naïve Bayes Classifier method and the tf-idf feature to be able to classify comments into several classes of positive, negative or neutral comments. The classification testing stage uses the cross validation method to test the accuracy of the naive bayes classification algorithm and the tf-idf feature.
Immersive Neural Graphics Primitives

Neural radiance field (NeRF), in particular its extension by instant neural graphics primitives, is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its potential, research on the combination of NeRF and virtual reality (VR) remains sparse. Currently, there is no integration into typical VR systems available, and the performance and suitability of NeRF implementations for VR have not been evaluated, for instance, for different scene complexities or screen resolutions. In this paper, we present and evaluate a NeRF-based framework that is capable of rendering scenes in immersive VR allowing users to freely move their heads to explore complex real-world scenes. We evaluate our framework by benchmarking three different NeRF scenes concerning their rendering performance at different scene complexities and resolutions. Utilizing super-resolution, our approach can yield a frame rate of 30 frames per second with a resolution of 1280x720 pixels per eye. We discuss potential applications of our framework and provide an open source implementation online.
Specognitor: Identifying Spectre Vulnerabilities via Prediction-Aware Symbolic Execution

Spectre attacks exploit speculative execution to leak sensitive information. In the last few years, a number of static side-channel detectors have been proposed to detect cache leakage in the presence of speculative execution. However, these techniques either ignore branch prediction mechanism, detect static pre-defined patterns which is not suitable for detecting new patterns, or lead to false negatives. In this paper, we illustrate the weakness of prediction-agnostic state-of-the-art approaches. We propose Specognitor, a novel prediction-aware symbolic execution engine to soundly explore program paths and detect subtle spectre variant 1 and variant 2 vulnerabilities. We propose a dynamic pattern detection mechanism to account for both existing and future vulnerabilities. Our experimental results show the effectiveness and efficiency of Specognitor in analyzing real-world cryptographic programs w.r.t. different processor families.
A Privacy-Preserving Outsourced Data Model in Cloud Environment

Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes.
Beyond Mahalanobis-Based Scores for Textual OOD Detection

Deep learning methods have boosted the adoption of NLP systems in real-life applications. However, they turn out to be vulnerable to distribution shifts over time which may cause severe dysfunctions in production systems, urging practitioners to develop tools to detect out-of-distribution (OOD) samples through the lens of the neural network. In this paper, we introduce TRUSTED, a new OOD detector for classifiers based on Transformer architectures that meets operational requirements: it is unsupervised and fast to compute. The efficiency of TRUSTED relies on the fruitful idea that all hidden layers carry relevant information to detect OOD examples. Based on this, for a given input, TRUSTED consists in (i) aggregating this information and (ii) computing a similarity score by exploiting the training distribution, leveraging the powerful concept of data depth. Our extensive numerical experiments involve 51k model configurations, including various checkpoints, seeds, and datasets, and demonstrate that TRUSTED achieves state-of-the-art performances. In particular, it improves previous AUROC over 3 points.
Linguistics in Social Media: An Analysis on the Writing Conventions of Students’ Blog Posts

This study analyzed the writing conventions of students’ blog posts as to mechanics, usage, and sentence formation. The blog entries of college students taking a language class served as the sources of data in this mixed-methods study. Content analysis was used in order to determine the errors in the text. Each sentence was analyzed to identify the errors committed by the students. These were then categorized according to the indicators found in the framework. The quantitative data were treated using the frequency and percentage of the errors committed by the students. The mechanics category had the highest number of errors, according to the findings. The errors made by the students as to mechanics were in punctuation, capitalization, and spelling; on usage, they were in verb tense, word order, and S-V agreement; and on sentence formation, they were in simple, compound, and complex sentences. The act of writing is incredibly difficult and requires adhering to the correct grammar rules, selecting the right words, utilizing the correct punctuation, and organizing ideas into the appropriate sentences. This study suggested that the language teacher may offer power point games, worksheets, and online games on verb tenses; students may trade assignments with their peers so that they can proofread one another's entries; and students may be given an infographic on "Writing Online: Improving Writing Skills through Blogging" to help them identify the typical mistakes made while writing online and advice on how to avoid them.
Contribution of Rabindranath as a follower of Sanskrit education

The aim of education, as Rabindranath Tagore sees it, is to give a sense of one's identity as a total man and to bring education in harmony with life. He believed that self-realization was the goal of education. A total man is the one who thinks of himself first and foremost as human being. Rabindranath was conscious of the educational environment of Asrama, learning from the dramas of Kalidasa and verses of Upanishadas, Vedas, Gita and its impact on the overall mental and spiritual development of students though restricted to privileged sections of society only. Rabindranath was highly influenced by the basic principles and structure of thought rooted to Upanishadas and Gita specifically as true essence bearer of three fundamental principles. The first principle states the importance of wisdom in controlling senses by overcoming egoistic ignorance to achieve self-purification of inner soul. The second principle is to achieve a sense of identity with the universe. The third principle is to see God in one’s own self. The personal experience of universal consciousness and of God is made possible by the combination of three processes namely Salutation and Submission, Integration and Service and Execution. According to Rabindranath, Education is a process for growth of mind and soul, establishing a community of spirit with man and nature. This educational process must aspire for the entry into the vast universal life and seek their fulfilment and rewards in its experiences. The Education is a process of self-purification for the pupil through the application of three principles namely freedom, inclusiveness and universalization. Rabindranath, born into family of a rich and intellectuals, though not having any formal education, but had undergone through Vedic literature like Upanishada, Puranas, Great epics etc., classic Sanskrit literature under the watchful eyes of Debendranath Tagore. His deep appreciation of Sanskrit literary works is reflected in his work of criticism, entitled “Prachin Sahitya”. His building of educational institutions like Santiniketan and Viswabhararti reflects this ideology only. According to Rabindranath, the Gayatri mantra appears to signify the identity of the person in sync with nature and universe. The Upanishadas also have left a binding impression over the poet reflected in some of his works. The great epics Ramayana and the Mahabharata, Puranic literature, Dharmashastra all have seemed to enter his soul. The poet is indebted to classical Sanskrit literature specially Kalidasa in terms of concepts of dualism.