Reviewerly/exHarmony huggingface dataset:
The Rate of Convergence of Variation-Constrained Deep Neural Networks

Multi-layer feedforward networks have been used to approximate a wide range of nonlinear functions. An important and fundamental problem is to understand the learnability of a network model through its statistical risk, or the expected prediction error on future data. To the best of our knowledge, the rate of convergence of neural networks shown by existing works is bounded by at most the order of $n^{-1/4}$ for a sample size of $n$. In this paper, we show that a class of variation-constrained neural networks, with arbitrary width, can achieve near-parametric rate $n^{-1/2+\delta}$ for an arbitrarily small positive constant $\delta$. It is equivalent to $n^{-1 +2\delta}$ under the mean squared error. This rate is also observed by numerical experiments. The result indicates that the neural function space needed for approximating smooth functions may not be as large as what is often perceived. Our result also provides insight to the phenomena that deep neural networks do not easily suffer from overfitting when the number of neurons and learning parameters rapidly grow with $n$ or even surpass $n$. We also discuss the rate of convergence regarding other network parameters, including the input dimension, network layer, and coefficient norm.
Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM

Drowsiness while driving is one of the main causes of traffic accidents it affects the level of focus of the driver. Therefore, we need an automatic drowsiness detection mechanism for the driver to provide a warning or alarm so that an accident can be avoided. In this study, we design and simulate a system to detect drowsiness through the driver’s yawn expression. The acquisition is made by recording the face from two shooting points including the dashboard and front mirrors in the car. From the video recording, then it is taken into several images with a size of 128x82 pixels which are used as training and testing data. This image is then processed using Principal Component Analysis (PCA) for feature extraction and classified using a Support Vector Machine (SVM). From the tests carried out, the system generates the highest accuracy of 98%. This best performance is obtained by SVM with polynomial kernel in the camera position on the dashboard. Meanwhile, based on compression testing, the image that can still meet system requirements is 25% of the original size. It is hoped that the proposed drowsiness detection method in this study can be applied for real-time drowsiness detection in vehicles.
Analysis of Factors that Affect the Success of E-Learning Implementation of STMIK BI Balikpapan

E-learning in higher education is a technique to improve learning and teaching experience, and as a tool to educate students through digital media, with or without the guidance of their instructors. STMIK BI Balikpapan has been using it since 2015, but its implementation has not been as optimal as expected. The research aims to identify the factors that influence the success of the application of e-learning in STMIK BI Balikpapan by referring to the model adopted from TAM (Technology Acceptance Model) and TOE (technological, organizational and environment). The research respondents were 94 people. Data were collected through questionnaires and analyzed using the Structural Equation Model (SEM) through the Smart PLS program. The results showed that of the four hypotheses tested, one hypotheses had significant influence (habits) and the other three hypotheses were not significant (connections, motivation and facility).
Library Research Guides: English: Oral Presentations

Resources to support English language students at Bond University Resources to help students research their assignment
ANALISIS KUMULATIF COVID-19 PROVINSI PAPUA TAHUN 2020 MENGGUNAKAN MODEL DISTRIBUSI JOHNSON SB

Coronavirus belongs to the coronaviridae family. The coronavirus family groups are alpha (α), beta (β), gamma (γ) and delta (δ) coronavirus. Although research related to covid-19 in several provinces in Indonesia has been conducted by several researchers so far there has been no research related to the Covid-19 model in Papua province. One of the obstacles faced by some researchers is related to the Covid-19 data parameters which are difficult to estimate, so that the model formulated could not describe the outbreak well. Therefore the aim of this study is to conduct a cumulative analysis of the 2020 Papua province Covid-19 using the Johnson SB distribution model. The methods used to perform the analysis are Kolmogorov Smirnov for testing the suitability of the Covid-19 data to the model, Johnson SB to show the data distribution model, Maximum Likelihood to estimate the parameters and the Johnson SB cumulative distribution function to describe the probability of Covid-19 data. 19 Papua Province in 2020. The secondary data on the number of Covid-19 cases in Papua, obtained from the Papua Provincial Health Office is used in this research. The results showed that, the highest increase in the number of patients every day, starting from September 1 2020 to October 31, 2020 for infected cases was on 16-17 September, by 274 patients. Meanwhile, most recovery (308 patients) happened to be on 30-31 October and the highest death (5 people) was on 27-28 September. The highest cumulative probability for cases of infection, recovery and death were (Confirmed &lt;4965) = 0.3, Prob(Cured &lt;6408) = 0.9 and Prob(died &lt;91) = 0.4 respectively.
Automation approach for Unity based multi-player card game

Approach to bring efficient, generic, and reusable solutions for challenges while automating the logic validation of a card games, specifically built with unity engine.&lt;br&gt;
Interpretable Machine Learning for Meteorological Data

Weather forecasting is the task to predict the state of the atmosphere in a given location. In the past, the weather forecast has been done through physical models of the atmosphere as a fluid. It becomes the problem of solving sophisticated equations of fluid dynamics. In recent years, machine learning algorithms have been used to speed up weather data modeling, a computationally intensive task. Machine learning algorithms learn from data and produce relevant predictions. In addition to prediction, there is a need of providing knowledge about domain relationships inside the data. This paper provides a new approach using interpretable machine learning for explaining the characteristic variables of meteorological data. Interpretable machine learning is the use of machine learning models for the extraction of knowledge in the data. An illustration is shown on characteristic variables of meteorological data.
A new method of linear support vector regression with interval data

‎In this paper‎, ‎the linear support vector regression approach is proposed for solving the regression problem with interval data‎, ‎which is called interval support vector regression(ISVR)‎. ‎The ISVR approach is equivalent to solving a linear constrained quadratic programming problem (QPP) with an interval cost coefficient in which the value of the objective function is in an interval‎. ‎Instead of solving an interval QPP‎, ‎we solve two QPPs and prove that the cost values of these two problems are the lower bound and the upper bound of the target value of the interval QPP‎. ‎We show these two mentioned QPPs are equivalent to two support vector regression problems which the first problem applies the lower bound of data and the second problem considers the upper bound of the data‎. ‎to obtain the regression function‎. ‎Some experiments are made to demonstrate the performance of our method compared with the known algorithms on several artificial‎, ‎benchmark and real practical datasets‎.‎‎‎
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs.

We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, C_V, that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communication from the prover by R-F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2^{R-F}. Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero-knowledge holds against a semi-malicious verifier that maliciously selects its random tape and then plays honestly. Consequently, we show that some classical HVSZK proof systems, like the one for the complete Statistical-Distance problem (Sahai and Vadhan, JACM 2003) admit randomness sparsification with no penalty. Along the way we introduce new notions of pseudorandomness against interactive proof systems, and study their relations to existing notions of pseudorandomness.
Learning capabilities of belief propagation based algorithms for sparse binary neural networks

With the growth in size and complexity of modern data sets, the computational and energetic costs of training large, fully connected neural networks, became an important issue. Therefore exploring the learning capabilities of sparse (possibly binarized) architectures, that possess many less degrees of freedom but empirically appear to generalize well, is an important research direction. In this work, the learning performance of belief propagation (BP) based algorithms, applied to simple two layer sparse neural networks with discrete synapses, is analysed. Initially, the framework in which the work is carried on is the so called teacher-student scenario, in which the learning problem corresponds to the inference of the weight values of a teacher network whose architecture is given. In this first part BP provides encouraging results, allowing to perfectly reconstruct the weights of the ‘teacher’ network that generated the training data, using only a small number of data points. Subsequently, the focus shifts to the mismatched setting with a discrepancy between the used architecture and the one of the ‘teacher’ network. The results are analysed in order to assess whether BP-based learning is relevant in this case as well, and if yes, to which extent of mismatch this is possible.
4차 산업혁명 시대 예술ㆍ과학 융합 교육프로그램 설계 : 콘텐츠를 활용한 STEAM을 중심으로

The year 2020 was a time when the coronavirus infections-19 (COVID-19) caused various changes in society. In particular, the fields that have been conducted face-to-face have been greatly confused by the transition to an online non-face-to-face method, and this is the case with the field of education. There are two main advantages of offline education. The first is that we can improve our understanding through communication with teachers, and the second is that we can develop social skills through interaction with friends. But as online classes progressed due to corona 19, interaction could not be achieved. As a result, the motivation for learning has been reduced due to difficulties in real-time feedback, and the participation rate has been significantly lowered, especially in lower grades, raising concerns about the learning gap that will occur after corona 19. However, there are some cases in which online classes were conducted as effectively as offline classes by utilizing various contents. What they have in common is the use of content. Teachers generally improved the quality of education by linking interesting sights and videos that enhance learning comprehension. The provided video conveys learning-related content into stories, enabling intuitive observation. Many students were already enjoying these videos through VOD (Video on Demand) such as TV and YouTube, they were able to connect their easy access to content and interest in learning. Appropriate use of video content has rather increased the learning effect and should continue after corona 19. Therefore, it is necessary to study methodologies that apply video content efficiently to education. This study looked at the steps that needed content application through the development of education programs, and observed its meaning. Students were curious about the content, motivated to learn and participated in learning on their own. Intuitive learning, conducted through appreciation, play and content production, provided an opportunity to learn on their own in everyday life.
On Positivity Bias in Negative Reviews

Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality. But what about the language used in negative reviews? Consistent with prior work, we show that English negative reviews tend to contain more positive words than negative words, using a variety of datasets. We reconcile this observation with prior findings on the pragmatics of negation, and show that negations are commonly associated with positive words in negative reviews. Furthermore, in negative reviews, the majority of sentences with positive words express negative opinions based on sentiment classifiers, indicating some form of negation.
Multitask Learning for Citation Purpose Classification.

We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with-attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.
Expressive Attribute-Based Encryption with Constant-Size Ciphertexts from the Decisional Linear Assumption

We propose a key-policy attribute-based encryption (KP-ABE) scheme with constant-size ciphertexts, whose almost tightly semi-adaptive security is proven under the decisional linear (DLIN) assumption in the standard model. The access structure is expressive, that is given by non-monotone span programs. It also has fast decryption, i.e., a decryption includes only a constant number of pairing operations. As an application of our KP-ABE construction, we also propose an efficient, fully secure attribute-based signatures with constant-size secret (signing) keys from the DLIN. For achieving the above results, we extend the sparse matrix technique on dual pairing vector spaces. In particular, several algebraic properties of an elaborately chosen sparse matrix group are applied to the dual system security proofs.
Object Material Confirmation for Source Code Comparison on Embedded System

In case of evaluating the similarity of the source code analysis material in the embedded system, the provided source code must be confirmed to be executable.However, it is currently being in which compilation and interface matching with hardware are provided in an unconfirmed materials.The complainant assumes that many parts of the source code are similar because the characteristics of the operation are similar and the expression of the function is similar.As for the analysis result, the analysis result may appear different than expected due to these unidentified objects.In this study, the improvement direction is sugested through the case study by the analysis process of the source code and the similarity of the unverified source code.
Feeding PIDza to VIVO: data ingest with SPARQL-Generate

The first hurdle after installing VIVO is to fill it with an initial set of data about an institution, its researchers and their publications. Done manually it is a cumbersome and time-consuming process. One approach to overcome this is to use open-data containing a persistent identifier(PID) like ROR, ORCID or DOI. The advantage lies in the reduced processing of input data: since data does not need to be disambiguated, the data ingestion process can be reduced to mapping the data to the VIVO ontology. While several tools exist that are able to import one PID-identified object into VIVO, the release of Datacite Commons takes this approach to the next level. Datacite Commons offers an interface to a so-called PID-Graph: a structure of multiple connected data objects each identified by a PID. It makes queries possible that take advantage of the connections between several PIDs like e.g. querying an organization (identified by a ROR iD) and its affiliated persons (identified by their ORCID iD) and subsequently their publications (identified by a DOI), and thus providing a quick data basis for an empty Research Information System.In this talk, we will present a microservice importing data from the Datacite Commons PID-Graph and the ROR API into VIVO ( https://github.com/vivo-community/generate2vivo ). This microservice is based on lifting rules defined using the SPARQL-Generate RDF transformation language, which we will overview beforehand. SPARQL-Generate is an expressive template-based language to generate RDF streams or text streams from RDF datasets and document streams in arbitrary formats (for more information see website https://w3id.org/sparql-generate/ )
The Problem of Teaching Children of Immigrants in the Russian Federation

The publication proposes to discuss the problem of teaching children of immigrants who have arrived in our country and need to adapt to the new social, linguistic and cultural environment. The principles underlying the organization of education for children of immigrants in the Russian Federation, and the conditions for their education in educational institutions of our country, which were formulated at a meeting of the Council under the President of the Russian Federation for Interethnic Relations on March 30, 2021, are given. The content of the state order of the Ministry of Education of the Russian Federation to the Federal State the budgetary scientific institution “Institute of Education Development&#x0D; Strategy of the Russian Academy of Education” to study the difficulties faced by children of immigrants in the process of adaptation. The result of the study should be the creation of a set of guidelines for teachers organizing the work of classes in which children of immigrants study.
Correction to: Effective Multimodal Opinion Mining Framework Using Ensemble Learning Technique for Disease Risk Prediction

In the original version of chapter 67, the following belated correction has been incorporated: The author name “G. S. Priyanka” has been changed to “G. Priyanka”. The chapter and book have been updated with this change.
Quantum Computers - An Emerging Cybersecurity Threat

Quantum computational supremacy may potentially endanger the current cryptographic protection methods. Although quantum computers are still far from a practical implementation in information processing and storage, they should not be overlooked in the context of cybersecurity. Quantum computers operate with qubits - units of information that are governed by the fundamental principles of quantum physics, such as quantum superposition of states and quantum coherence. In order to address the new challenge that quantum computers pose to cybersecurity, the very principles of their operation have to be understood and are overviewed in this contribution.
An efficient and secure scheme of verifiable computation for Intel SGX

Cloud computing offers resource-constrained users big-volume data storage and energy-consuming complicated computation. However, owing to the lack of full trust in the cloud, the cloud users prefer privacy-preserving outsourced data computation with correctness verification. However, cryptography-based schemes introduce high computational costs to both the cloud and its users for verifiable computation with privacy preservation, which makes it difficult to support complicated computations in practice. Intel Software Guard Extensions (SGX) as a trusted execution environment is widely researched in various fields (such as secure data analytics and computation), and is regarded as a promising way to achieve efficient outsourced data computation with privacy preservation over the cloud. But we find two types of threats towards the computation with SGX: Disarranging Data-Related Code threat and Output Tampering and Misrouting threat. In this paper, we depict these threats using formal methods and successfully conduct the two threats on the enclave program constructed by Rust SGX SDK to demonstrate their impacts on the correctness of computations over SGX enclaves. In order to provide countermeasures, we propose an efficient and secure scheme to resist the threats and realize verifiable computation for Intel SGX. We prove the security and show the efficiency and correctness of our proposed scheme through theoretic analysis and extensive experiments. Furthermore, we compare the performance of our scheme with that of some cryptography-based schemes to show its high efficiency.
MULTIPERIODIC SOLUTIONS OF SECOND-ORDER SYSTEMS WITH DIFFERENTIATION OPERATOR ON THE LYAPUNOV'S VECTOR FIELD

The problem of the existence and integral representation of a unique multiperiodic solution of a second-order linear inhomogeneous system with constant coefficients and a differentiation operator on the direction of the main diagonal of the space of time variables and of the vector fields in the form of Lyapunov systems with respect to space variables were considered. The multiperiodicity of zeros of this operator and the condition for the absence of a nonzero multiperiodic and real-analytic solution of the homogeneous system corresponding to the given system are established. An integral representation of solutions of an inhomogeneous linear autonomous system that multiperiodic in time variables and realanalytic in space variables is obtained. The existence theorem of a unique multiperiodic in time variables and real-analytic in space variables solutions of the original linear system in terms of the Green's function under sufficiently general conditions is substantiated.