Reviewerly/exHarmony huggingface dataset:
Datum–Wise Inference in Structured Environments

In various application domains (e.g., health, psychology), experts use Bayesian networks to represent relationships among variables. However, these variables are not in practice directly observable, but can be instead inferred via noisy but costly features. Herein, we study the problem of datum–wise feature selection and classification in the case where the label of each data instance is described by a known Bayesian network, and features are available at a cost. The goal is to accurately classify each data instance, while keeping the feature acquisition cost minimum. To this end, we first propose a forward pass algorithm that sequentially acquires features to infer the label of each variable in the Bayesian network. During this process, the proposed algorithm uses both the acquired features and the Bayesian network relationships. In an effort to improve classification accuracy, we also devise a backward pass algorithm, which exploits Bayesian network relationships along with evidence. We discuss the computational complexity of both algorithms and experimentally assess their performance on 11 datasets. We observe that the forward pass algorithm achieves higher accuracy using a small fraction of features compared to state–of–the–art, while the backward pass algorithm enhances accuracy without acquiring additional features.
AI as a violator of patent rights: determining liability

Due to the quick technological progress the AI which is capable of autonomous decision-making is becoming more prevalent. As AI is now capable of self-learning, the legal system needs to address the questions previously not considered. One of such questions is the potential of self-learning AI to develop in such a way that it infringes on IPR, including potential patent infringement. This infringement does not have to be foreseeable by any human involved in the making and using the AI, therefore sometimes, there will be no human to provide compensation. Without any laws or standards in place applicable to AI directly, current liability rules need to be changed to address this challenge.
Certification of cloud computing services in the Czech Republic and in EU

The author provides an analysis of the legal regulation of cloud computing effective in the Czech Republic and its relationship to the certification of cloud computing services in the European Union. The article answers the question of how both legal regulations will co-applicate when EUCS will be finished. Although the Czech cloud computing regulation could be considered as the cloud computing certification scheme, it is argued why both legal regulations will be effective at the same time and how it will be possible to achieve one certification through the other.
Optimization and Time Management of Weekly Class Schedule with Activity Constraints for College Students using Linear Programming

College students face a multitude of academic responsibilities over the course of their undergraduate studies. It is important for them to manage their time accordingly to accomplish their necessary academic tasks and preserve their personal growth. This paper aims to optimize the amount of time spent on curricular tasks within a week while accounting for the unit load for the term, lecture duration, personal time duration, and sleep duration of the student using Linear Programming. The methodology aims to minimize and maximize the designed objective function to acquire the maximum number of academic working hours of students within a 5-day academic week. The constraints of the model are based on survey results from students and the academic policies of the connected university. The model for both optimization cases is processed using MATLAB software equipped with an optimization toolkit. The results of the program presented an optimal solution of 76 and 119 hours for the minimization and maximization cases, respectively. Overall, the former acquired 44 hours while the latter only gained 1 hour for the maximum number of working hours in a week.
Real-Time Outlier Detection in Streaming Data: A Deep Learning-Based Approach

Outliers are observations that deviate significantly from the norm, and their detection has been a critical topic in various research areas and application domains, such as video surveillance, network intrusion detection, and disease outbreak detection. In recent years, deep learning-based techniques have shown to outperform machine learning and shallow approaches for outlier detection in streaming data, which are large and complex datasets. However, developing an effective and appropriate model for outlier detection is challenging due to the dynamic nature and variations of real-world applications and data.In this research, we propose a novel deep neural network (DNN) model for outlier detection in streaming data. Our model is developed with multiple hidden layers to improve feature abstraction and capabilities. We evaluate our proposed model on four real-world outlier benchmark datasets available at the UCI repository and compare its performance with state-of-the-art approaches. Our experiments demonstrate that our proposed model outperforms both machine learning algorithms and deep learning competitors, resulting in significant performance gains. In particular, our proposed approach achieves much higher accuracy, recall, and f1-score rates of 99.63%, 99.014%, and 99.437%, respectively, compared to other algorithms.Our proposed deep learning-based approach provides an effective and efficient solution for real-time outlier detection in streaming data. The experimental results show its superior performance compared to existing methods, which makes it a valuable contribution to the field of outlier detection.
Collaborative VR: Conveying a Complex Disease and Its Treatment

In medical research, there are constantly new findings that lead to new insights and novel treatments. Hence, physicians always need to stay up-to-date to provide high-quality patient care. Virtual reality is unique among the available learning methods as it offers the opportunity to collaboratively explore the inside of the human body to experience and understand the complex workings of medical interventions. We realized such a VR-Learning-System and assessed its potential with medical experts (n = 9). Initial results indicate that experts prefer the new system, and according to their subjective assessment, they achieved a higher learning outcome compared to conventional methods.
Unity as an Indicator of Theory Choice

While choosing scientific theories scientists often show natural biases, such as regarding unity, identity, simplicity, and novelty as indicators of theory choice. Unity is determined by the heterogeneous conceptual and empirical scope directly involved in theory through a logical intermediary. In physics, the pursuit of unity is one of the unremitting motives for scientific innovation and progress. Maxwell’s Electromagnetic theory unifies electricity, magnetism, and light. Einstein unified not only time and space but also matter and energy. Einstein failed to complete the unified field theory in his lifetime, but his pursuit of unification was not wrong. Quantum mechanics is a theory of describing nuclear forces in mathematical language, which successfully unifies three forces except for gravity. In today’s view, string theory is not generally acceptable to most people, but it has some strong local advantages, so it can be pursued
A Generic Transform from Multi-round Interactive Proof to NIZK

We present a new generic transform that takes a multi-round interactive proof for the membership of a language $$\mathcal {L}$$ and outputs a non-interactive zero-knowledge proof (not of knowledge) in the common reference string model. Similar to the Fiat-Shamir transform, it requires a hash function $$\textsf{H}$$ . However, in our transform the zero-knowledge property is in the standard model, and the adaptive soundness is in the non-programmable random oracle model ( $$\textsf{NPROM}$$ ). Behind this new generic transform, we build a new generic OR-composition of two multi-round interactive proofs. Note that the two common techniques for building OR-proofs (parallel OR-proof and sequential OR-proof) cannot be naturally extended to the multi-round setting. We also give a proof of security for our OR-proof in the quantum oracle model ( $$\textsf{QROM}$$ ), surprisingly the security loss in $$\textsf{QROM}$$ is independent from the number of rounds.
Keynote Speaker: Reconstructing Reality: From Physical World to Virtual Environments

With increasing availability of data in various forms from images, audio, video, 3D models, motion capture, simulation results, to satellite imagery, representative samples of the various phenomena constituting the world around us bring new opportunities and research challenges. Such availability of data has led to recent advances in data-driven modeling. However, most of the existing example-based synthesis methods offer empirical models and data reconstruction that may not provide an insightful understanding of the underlying process or may be limited to a subset of observations. In this talk, I present recent advances that integrate classical model-based methods and statistical learning techniques to tackle challenging problems that have not been previously addressed. These include flow reconstruction for traffic visualization, learning heterogeneous crowd behaviors from video, simultaneous estimation of deformation and elasticity parameters from images and video, and example-based multimodal display for VR systems. These approaches offer new insights for understanding complex collective behaviors, developing better models for complex dynamical systems from captured data, delivering more effective medical diagnosis and treatment, as well as cyber-manufacturing of customized apparel. I conclude by discussing some possible future directions and challenges
Evaluation of the Success of the Academic Information System (SIAMIK) with the DeLone and McLean Model

Evaluation of information systems is needed to determine the quality of system use. Therefore, it is necessary to measure the success of an information system, one of which is the DeLone and McLean model, which consists of six factors measuring the success of an information system. SIAMIK is an academic information system designed to meet the minimum requirements needed in processing academic information. This research is divided into several stages to test the 10 initial hypotheses. The data collected were 1560 correspondents from questionnaires distributed to students as SIAMIK users. Furthermore, the results of the validity and reliability tests were continued with SEM analysis for hypothesis testing. The evaluation results were obtained for the 10 hypotheses that have been formed. there are 3 accepted hypotheses and 7 rejected hypotheses. Therefore, it can be concluded that the use of SIAMIK within the scope of Atma Jaya Makassar University has not been successful.
Study of Cybersickness Prediction in Real Time Using Eye Tracking Data

Cybersickness seriously degrades users' experiences of virtual real-ity (VR). The level of cybersickness is commonly gauged through a simulator sickness questionnaire (SSQ) administered after the expe-rience. However, for observing the user's health and evaluating the VR content/device, measuring the level of cybersickness in real time is essential. In this study, we examined the relationship between eye tracking data and sickness level, then predicted the sickness level using machine learning methods. Some characteristics of eye related indices significantly differed between the sickness and non-sickness groups. The machine learning methods predicted cybersickness in real time with an accuracy of approximately 70%.
Utilizing a Neutrosophic Fuzzy Logic System with ANN for Short-Term Estimation of Solar Energy

One of the primary sources of renewable energy in the coming years is thought to be solar energy. Solar energy and other renewable energy sources do, moreover, have a disadvantage in that it is hard to forecast when they will be available. The best use of solar energy is impacted by this issue, particularly when it is combined with other sources. As a result, the organization and economy of solar energy depend on accurate solar energy forecasting techniques. Predicting solar energy shortly is the study’s major goal. This paper describes the study of Neutrosophic fuzzy logic with artificial neural networks (NFL-ANN) to anticipate solar photovoltaic (PV) plant output power with the use of specified input factors known as meteorological information, such as sunshine length, humidity levels, temperature, air pressure, and others, artificial neural networks are used to forecast the outcome. NFL represents a generalised logic, which can manage stochasticity learning mistakes and unpredictability that fuzzy logic lacks. It offers the results of the calculation section. Excellent performance computer processors and NFL provide reasonable accuracy estimates of solar plant outputs as well as system reliability to consider environmental factors. The investigation was carried out with the use of MATLAB programming. With the assistance of statistical markers like mean absolute percentage error (MAPE), mean absolute error (MAE), root means square error (RMSE), and determinant coefficient, the suggested NFL-ANN approach is evaluated and compared to other approaches that are already in use. In comparison to existing techniques, the suggested NFL-ANN provides superior accuracy and lesser prediction error, according to the study’s findings. This research will be enhanced to forecast power without any loss.
Investigation of Optimizations for FPGA-based Controls Hardware for Experiments using Superconducting Quantum Computing Systems

identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.
A data-driven approach to optimizing clinical study eligibility criteria

Feasible, safe, and inclusive eligibility criteria are crucial to successful clinical research recruitment. Existing expert-centered methods for eligibility criteria selection may not be representative of real-world populations. This paper presents a novel model called OPTEC (OPTimal Eligibility Criteria) based on the Multiple Attribute Decision Making method boosted by an efficient greedy algorithm. It systematically identifies the optimal criteria combination for a given medical condition with the optimal tradeoff among feasibility, patient safety, and cohort diversity. The model offers flexibility in attribute configurations and generalizability to various clinical domains. The model was evaluated on two clinical domains (i.e., Alzheimer's disease and Neoplasm of pancreas) using two datasets (i.e., MIMIC-III dataset and NewYork-Presbyterian/Columbia University Irving Medical Center (NYP/CUIMC) database). We simulated the process of automatically optimizing eligibility criteria according to user-specified prioritization preferences and generated recommendations based on the top-ranked criteria combination accordingly (top 0.41–2.75%) with OPTEC. Harnessing the power of the model, we designed an interactive criteria recommendation system and conducted a case study with an experienced clinical researcher using the think-aloud protocol. The results demonstrated that OPTEC could be used to recommend feasible eligibility criteria combinations, and to provide actionable recommendations for clinical study designers to construct a feasible, safe, and diverse cohort definition during early study design.
Continuous cross-modal hashing

Generally, multimodal data with new classes arrive continuously in the real world. While advanced cross-modal hashing (CMH) focuses primarily on batch-based data with previously observed classes (ASCs), it disregards the effect of newly arriving classes (ANCs) on hash-code conflicts. In addition, class-level continuous hashing scenarios do not suit themselves well with the generic CMH configuration. To solve the aforementioned issues, we propose a novel framework, called CT-CMH, for the new task of continuous cross-modal hashing. For dealing with ANCs, CMH models require the ability of continuous learning, i.e. they can preserve the knowledge of previously observed data and, more crucially, they can be adapted to unseen data with ANCs. Specifically, we introduce the adaptive weight importance updating (AWIU) mechanism to alleviate the catastrophic forgetting problem of CMH and a new hash-code divergence (HCD) method to eliminate hash-code conflicts between ASCs and ANCs. When CT-CMH is equipped with both AWIU and HCD, it can consistently achieve high retrieval performance. The experiment results and visualization analyses validate the effectiveness of our approach. To the best of our knowledge, we are the first to introduce and implement the task of CCMH for ANCs.
On the incompatibility of accuracy and equal opportunity

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. (Adv Neural Inf Process Syst 29, 2016) proposed the notion of equality of opportunity (EO), which is compatible with maximal accuracy when the target label is deterministic with respect to the input features. In the probabilistic case, however, the issue is more complicated: It has been shown that under differential privacy constraints, there are data sources for which EO can only be achieved at the total detriment of accuracy, in the sense that a classifier that satisfies EO cannot be more accurate than a trivial (i.e., constant) classifier. In this paper, we strengthen this result by removing the privacy constraint. Namely, we show that for certain data sources, the most accurate classifier that satisfies EO is a trivial classifier. Furthermore, we study the admissible trade-offs between accuracy and EO loss (opportunity difference) and characterize the conditions on the data source under which EO and non-trivial accuracy are compatible.
Improving classical capacity of qubit dynamical maps through stationary state manipulation

Abstract We analyze the evolution of Holevo and entanglement-assisted classical capacities for two classes of phase-covariant channels. In particular, we show that these capacities can be improved by changing the stationary state of the channel, which is closely related to its non-unitality degree. The more non-unital the channel, the greater its capacity. The channel parameters are engineered through mixtures on the level of dynamical maps, time-local generators, and memory kernels, for which we propose construction methods. For highly non-unital maps, we achieve a temporary increase in the classical capacity that exceeds the entanglement-assisted classical capacity of the unital map. This shows that non-unitality can become a better quantum resource for information transition purposes than quantum entanglement.
The weeding handbook: a shelf-by-shelf guide

"The weeding handbook: a shelf-by-shelf guide." Journal of the Australian Library and Information Association, 72(2), pp. 216–217
Collection management in the cloud: a guide for using cloud computing technologies in libraries (Library and Information Technology Association (LITA Guides))

"Collection management in the cloud: a guide for using cloud computing technologies in libraries (Library and Information Technology Association (LITA Guides))." Journal of the Australian Library and Information Association, 72(2), pp. 207–208
Assessing the comprehension of the students in Philippine fable short stories: Basis for an intervention program

Reading is particularly challenging because there are so many factors to consider. However, some studies show that there are ways to support readers in improving their reading abilities. This study tested students' comprehension of short stories based on Philippine fables. Background knowledge, active reading skills, critical thinking, intense reading, reading background, story comprehension, the impact of short stories, and the use of short stories in language learning are all indications of reading comprehension. A quantitative, non-experimental Intervention Program was used in this investigation. For first- through fourth-year English education majors, researchers identify 402 respondents. There are 80 items total in the questionnaire, 10 for each indication. According to the results, Active reading and Reading background had the highest mean of 2 for reading comprehension. On the other hand, story comprehension scored the lowest mean of 6 for reading comprehension. According to the data, pupils struggle, especially with reading. Researchers developed an intervention program to gauge students' reading comprehension (Action Plan on Reading Intervention for Struggling Students). This can assess their reading abilities and help them to be good readers.
Geodesics

General relativity is a subject that most undergraduates in physics are particularly curious about, but it has a reputation for being very difficult. This book provides as gentle an introduction to general relativity as possible, leading you through the necessary mathematics in order to arrive at important results. Of course, you cannot avoid the mathematics of general relativity altogether, but, using this book, you can gain an appreciation of tensors and differential geometry at a pace you can keep up with. Early chapters build up to a complete derivation of Einstein's Equations, while the final chapters cover the key applications on black holes, cosmology and gravitational waves. It is designed as a coursebook with just enough material to cover in a one-semester undergraduate class, but it is also accessible to any numerate readers who wish to appreciate the power and beauty of Einstein's creation for themselves.
PELATIHAN PEMBUATAN KEMASAN YANG KEKINIAN UNTUK PRODUK BATIK TIGA NEGERI KHAS SURAKARTA

Batik Tiga Negeri is one of the historic batiks on the north coast of the island of Java which was made to be traded from the start. Packaging for Batik Tiga Negeri is needed in addition to protecting the product, as well as for communication media in spreading about one of the nation's wealth that can be proud of. Packaging can have a useful multi-function. There are many packaging options, ranging from plastik to paper and cloth, all of which can display product labels.&#x0D; Through this service activity, it is hoped that the millennial generation, namely Regina Pacis Solo High School students will understand: 1) How far can the main motifs of Batik Tiga Negeri developed according to the style of young people? 2) How student’s creativity in making packaging for Batik Tiga Negeri products is not only attractive, useful, but also can be an effective communication medium. In other words, from the work produced, it will further clarify the role of packaging for Batik Tiga Negeri handicraft products other than as a wrapper.&#x0D; The method used is community service in the form of training. Beginning with providing material to young people as the nation's next generation, about the archipelago's wastra, especially batik. Training on making designs on packaging to wrap Tiga Negeri batik products, in order to produce a unique packaging with the main motif idea of Batik Tiga Negeri. The purpose of this activity is to make students aware of the importance of maintaining the ancestral heritage which is the wealth of this nation.