Reviewerly/exHarmony huggingface dataset:
Tinjauan Faktor-Faktor Penyebab Terjadinya Kerusakan Dokumen Rekam Medis Rawat Inap Di Rumah Sakit Putri Hijau Medan

Medical Record is a file containing records and documents regarding patient identity, examination, treatment, actions and other services for patients at health care facilities. The factors that affect archive damage can be divided into two, namely intrinsic factors which are the cause of damage originating from the archive object itself, for example the quality of paper and the influence of ink. extrinsic factors are the causes of damage that come from external factors of archive objects, such as physical, biological and chemical environmental factors. cross-sectional approach, that is, each research subject is only observed once and measurements are made on the status of the character or variable of the subject at the time of examination. Intrinsic factors which include paper, ink and adhesive with the amount of damage to medical record documents caused by ink with a total of 89 (29.5%). Extrinsic factors include physical, biological and chemical factors with 72 (23.9%) damage caused by fungi. The conclusion is that the biggest damage is caused by intrinsic factors, namely chemical damage as many as 89 (29.5%) medical record documents&#x0D;
Tainting in Smart Contracts: Combining Static and Runtime Verification

Smart contracts exist immutably on blockchains, making their pre-deployment correctness essential. Moreover, they exist openly on blockchains—open for interaction with any other smart contract and offchain entity. Interaction, for instance with off-chain oracles, can affect the state of the smart contract, and correctness of these smart contracts may depend on the trustworthiness of the data they manipulate or events they generate which, in turn, would depend on which parties or what information contributed to them. In this paper, we develop and present dynamic taint analysis techniques to enable data tainting in smart contracts. We propose an extension of Solidity that enables labelling inputs of interaction endpoints with dynamic data-carrying labels that capture actionable information about the sender. These labels can then be propagated dynamically across transactions to transitively dependent data. Specifications can then refer to such taints, for instance for ensuring that certain data could not have been influenced through interaction by a certain party. We further allow the use of taints as part of the language, affecting the control flow of the smart contract. To manage the overheads of such runtime tainting we develop sound static analysis-based techniques to prune away unnecessary instrumentation. We give a case study as a proof-of-concept, and measure the overheads associated with our additions before and after optimisation.
Cliques in realization graphs

The realization graph G(d) of a degree sequence d is the graph whose vertices are labeled realizations of d, where edges join realizations that differ by swapping a single pair of edges. Barrus (2016) [3] characterized d for which G(d) is triangle-free. Here, for any n≥4, we describe a structure in realizations of d that exactly determines whether G(d) has a clique of size n. As a consequence we determine the degree sequences d for which G(d) is a complete graph on n vertices.
Baseline correction using local smoothing optimization penalized least squares

Baseline shift, caused by interference factors, seriously contaminates the essential information of the analysis object. The current baseline correction methods maintain a constant smoothing parameter throughout the process, while the real analyzed signal is composed of multiple intervals with different smoothness. To address this problem, a local smoothing optimization penalized least squares (lsoPLS) method is presented in this work. The local smoothing optimization strategy using the derivative difference between the previously fitted baseline and the original signal is utilized to identify the interval location, and smoothing parameter is optimized by the interval characteristics. Two simulated cases and one real experiment confirm that the proposed method has better performance in the multi-interval baseline correction and can be served as an effective pre-processing method for the complex spectral signal.
Neural Machine Translation Transfer Model Based on Mutual Domain Guidance

The neural machine translation (NMT) model is a data hungry and domain-sensitive model but it is almost impossible to obtain a large number of labeled data for training it. This requires the use of domain transfer strategy. In order to solve the problem of domain data mismatch, this paper proposes a neural machine translation transfer model based on domain mutual guidance and establishes the continuous impact through the framework of mutual guidance. At the same time, self-ensemble and self-knowledge-distillation are used in these independent domains so that the model will not deviate from the domain too much. Furthermore, the model can better train the models from the batching way of domain data. It mainly uses the pretraining model out of domain, distillation of existing models in domain and data selection in the training process to guide the in-domain model. These are unified in the training framework, so that model training can be continuously and effectively guided in and out of domain. In this study, three typical experiment scenarios were comprehensive tested and our model was compared with many conventional classic methods. The experiment results showed that our proposed ”inter-domain transfer training” and “curriculum scheduling agent” was effective and robust. The most important results and findings are that this comprehensive guided training framework (intra-domain and inter-domain) is suitable for the domain transfer in different scenarios, and this framework doesn’t increase the decoding cost.
Spatial Encryption Revisited: From Delegatable Multiple Inner Product Encryption and More

Spatial encryption (SE), which involves encryption and decryption with affine/vector objects, was introduced by Boneh and Hamburg at Asiacrypt 2008. Since its introduction, SE has been shown as a versatile and elegant tool for implementing many other important primitives such as (Hierarchical) Identity-based Encryption ((H)IBE), Broadcast (H)IBE, Attribute-based Encryption, and Forward-secure cryptosystems. This paper revisits SE toward a more compact construction in the lattice setting. In doing that, we introduce a novel primitive called Delegatable Multiple Inner Product Encryption (DMIPE). It is a delegatable generalization of Inner Product Encryption (IPE) but different from the Hierarchical IPE (HIPE) (Okamoto and Takashima at Asiacrypt 2009). We point out that DMIPE and SE are equivalent in the sense that there are security-preserving conversions between them. As a proof of concept, we then successfully instantiate a concrete DMIPE construction relying on the hardness of the decisional learning with errors problem. In turn, the DMIPE design implies a more compact lattice-based SE in terms of sizes compared with SEs converted from HIPE (e.g., Xagawa’s HIPE at PKC 2013) using the framework by Chen et al. (Designs, Codes, and Cryptography, 2014). Furthermore, we demonstrate that one can also use SE to implement the Allow-/Deny-list encryption, which subsumes, e.g., puncturable encryption (Green and Miers at IEEE S &P 2015).
Statistical Analysis of Misfiling Causes in Higher Education: A Test Construction

Introduction: Misfiling remains a substantial challenge in data management in various institutions of higher learning. It has been asserted that one in every ten documents or files stored becomes immediately lost forever, due to inadequate filing techniques and practices. The results of this canker primarily are a high-profile failure in accountability and a retrogression in the development of the institution. There is paucity in scales developed for measuring misfiling. This study developed a scale called the data misfiling assessment scale (DMAS) to measure the various factors responsible for misfiling in institutions of higher learning and assessed them in terms of other relevant variables.&#x0D; Methodology: A questionnaire was designed and administered in a cross-sectional study conducted among all administrative assistants who were tasked with the utmost responsibility for filing in the offices of the University of Health and Allied Sciences (UHAS), Ho in the Volta Region of Ghana. The various components of the causes of the misfiling scale were found using factor analysis and were confirmed with confirmatory factor analysis.&#x0D; Results: The study revealed a highly reliable scale (Cronbach’s alpha of 0.85) of thirteen misfiling items sub-classified into four factors of misfiling namely; internal factors of misfiling, misfiling due to methods and tools, external factors of misfiling, and other factors of misfiling, with Cronbach’s alpha of 0.85, 0.77, 0.62, and 0.60, respectively. The four-factor structure of the DMAS was confirmed with CFA. A strong positive relationship was observed between internal and external factors of misfiling (p-value = 0.015) and between internal and other factors of misfiling (p-value = 0.032). External factors of misfiling also strongly correlated positively with other factors of misfiling (p-value = 0.025). Per the scale, participants who have file references in their offices were less likely to face other factors of misfiling (p-value = 0.008). Also, those working in offices with filing policies were less likely to experience internal and external factors of misfiling.&#x0D; Conclusion: With the help of this scale, factors responsible for misfiling and by extension, challenges with data management can be investigated and appropriate actions are taken for accurate data management in institutions of higher learning.
The research of motivation for learning a foreign language among cadets of higher military educational institutions (military educational units and higher education institutions)

The teaching of a foreign language in the system of higher education has a number of features due to the prospects of its use in the further professional activities of cadets, as well as professionally determined features of information perception and communicative aspects related to professional specialization. The formation of a high level of motivational attitudes to learning a foreign language has a clearly positive and even decisive impact on the success of learning a foreign language by cadets. The study of the process of teaching a foreign language (English) for cadets of non-linguistic specialties has a significant resonance in light of the prospects of Ukraine's integration with the Euro-Atlantic partnership countries, as stated in the relevant government regulations. The purpose of the article was to study the current state of motivation to learn a foreign language among cadets of HEI's non-linguistic specialties and develop a plan-prospectus for further control and formative impact on positive motivational attitudes of future servicemen in the process of learning a foreign (English) language to achieve a high level of competence (with a focus on the functional level of foreign language proficiency in accordance with NATO language standards STANAG 6001). 125 future servicemen were involved in the study. It was found that on average, a sufficient level of motivation to learn a foreign language prevails in the group of future military students. Thus, the majority of respondents (41.6 %) focused on a sufficient level of motivation, followed by a mediocre level of motivation (31.2 %), followed by one that needs significant improvement (10.4 %), a very good level (6.4 %), high (5.6 %) and critically low (4.8 %). Further work should be aimed at increasing very good and high levels of motivation. The next stage of the research should be the implementation of the generalized methods of stimulating motivation in learning English (through the active use of methods of personality-oriented learning) and checking the acquired level of motivation in a stable group of respondents of non-linguistic specialties.
The Saturation of Learning Online during a Pandemic

This research was motivated by many students who experienced boredom during online learning that occurred in Indonesia, including at SMP Negeri 39 Semarang. The purpose of this study was to determine the learning process during the pandemic, the obstacles and causes of students experiencing boredom in online learning in class VIII A and VIII I of SMP Negeri 39 Semarang. This study uses a qualitative method with a descriptive phenomenological research approach. Sources of data in this study are interviews, observation and documentation. The informants in this study were 4 students as primary and 2 teachers as objects. The data analysis technique uses an interactive analysis model. The results of this study indicate that class VIII A and VIII I SMP Negeri 39 Semarang experience online learning boredom during the pandemic due to various causal factors shown by students, one of which is the learning method that does not vary, learning is monotonous and many school assignments. Based on this, it can be suggested that the implementation of the online learning process should use varied learning methods so that students do not feel bored while studying and can arouse students' enthusiasm in participating in online learning.
Effects of Teachers' Demographic Characteristics and Self-Perceived Competencies on Their Self-Efficacy in Implementing Inclusive Education

The study examined the effects of teachers' demographic factors and competencies on their self-efficacy in implementing inclusive education.To accomplish this, the researcher had a sample of 187 teachers in schools SDO-City of Malolos during the academic year 2018-2019.Results of the regression analysis indicate that variables of regular teachers' demographic profile (except educational attainment) and competencies affect their efficacy in implementing inclusive education to a different extent as shown by the non-zero coefficients.This means that age, sex, length in service, disabilities handled, and competencies affect (but not to a significant extent) the teachers' efficacy in implementing inclusive education in which for every unit improvement in variables mentioned, teachers' efficacy can be expected to increase by 0.002, 0.004, 0.002, and 0.001, 0.428 respectively.It is highly recommended that the hiring and selection process for special education teacher applicants be tightened up, especially when it comes to personal demographic characteristics and professional competencies, including educational attainment, as this has an impact on the pursuit of a high-quality inclusive education.
Stochastic Future Prediction in Real World Driving Scenarios

Uncertainty plays a key role in future prediction. The future is uncertain. That means there might be many possible futures. A future prediction method should cover the whole possibilities to be robust. In autonomous driving, covering multiple modes in the prediction part is crucially important to make safety-critical decisions. Although computer vision systems have advanced tremendously in recent years, future prediction remains difficult today. Several examples are uncertainty of the future, the requirement of full scene understanding, and the noisy outputs space. In this thesis, we propose solutions to these challenges by modeling the motion explicitly in a stochastic way and learning the temporal dynamics in a latent space.
PREF: Predictability Regularized Neural Motion Fields

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping

Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signals by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks.
Embedding Kozen-Tiuryn Logic into Residuated One-Sorted Kleene Algebra with Tests

Kozen and Tiuryn have introduced the substructural logic $\mathsf{S}$ for reasoning about correctness of while programs (ACM TOCL, 2003). The logic $\mathsf{S}$ distinguishes between tests and partial correctness assertions, representing the latter by special implicational formulas. Kozen and Tiuryn's logic extends Kleene altebra with tests, where partial correctness assertions are represented by equations, not terms. Kleene algebra with codomain, $\mathsf{KAC}$, is a one-sorted alternative to Kleene algebra with tests that expands Kleene algebra with an operator that allows to construct a Boolean subalgebra of tests. In this paper we show that Kozen and Tiuryn's logic embeds into the equational theory of the expansion of $\mathsf{KAC}$ with residuals of Kleene algebra multiplication and the upper adjoint of the codomain operator.
First-order Policy Optimization for Robust Markov Decision Process

We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernels. The goal of planning is to find a robust policy that optimizes the worst-case values against the transition uncertainties, and thus encompasses the standard MDP planning as a special case. For $(\mathbf{s},\mathbf{a})$-rectangular uncertainty sets, we establish several structural observations on the robust objective, which facilitates the development of a policy-based first-order method, namely the robust policy mirror descent (RPMD). An $\mathcal{O}(\log(1/\epsilon))$ iteration complexity for finding an $\epsilon$-optimal policy is established with linearly increasing stepsizes. We further develop a stochastic variant of the robust policy mirror descent method, named SRPMD, when the first-order information is only available through online interactions with the nominal environment. We show that the optimality gap converges linearly up to the noise level, and consequently establish an $\tilde{\mathcal{O}}(1/\epsilon^2)$ sample complexity by developing a temporal difference learning method for policy evaluation. Both iteration and sample complexities are also discussed for RPMD with a constant stepsize. To the best of our knowledge, all the aforementioned results appear to be new for policy-based first-order methods applied to the robust MDP problem.
Assessing ASR Model Quality on Disordered Speech using BERTScore

Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speakers with speech impairments than typical English speakers. It is hard to determine if models can be be useful at such high error rates. This study investigates the use of BERTScore, an evaluation metric for text generation, to provide a more informative measure of ASR model quality and usefulness. Both BERTScore and WER were compared to prediction errors manually annotated by Speech Language Pathologists for error type and assessment. BERTScore was found to be more correlated with human assessment of error type and assessment. BERTScore was specifically more robust to orthographic changes (contraction and normalization errors) where meaning was preserved. Furthermore, BERTScore was a better fit of error assessment than WER, as measured using an ordinal logistic regression and the Akaike's Information Criterion (AIC). Overall, our findings suggest that BERTScore can complement WER when assessing ASR model performance from a practical perspective, especially for accessibility applications where models are useful even at lower accuracy than for typical speech.
The applications of EPR steering in quantum teleportation for two- or three-qubit system

EPR steering is an important quantum resource in quantum information and computation. In this paper, its applications in quantum teleportation are investigated. First of all, the upper bound of the average teleportation fidelity based on the EPR steering is derived. When the receiver can only perform the identity or the Pauli rotation operations, the X-type states which violate the three-setting linear steering inequality could be used for teleportation. In the end, the steering observables and the average teleportation fidelities of the two-qubit reduced states for three-qubit pure states maintain the same ordering. The complementary relations between the steerable observables and the average teleportation fidelities for three-qubit pure states are also established.
Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for $K$-class problems (Wu, Zhang and Liu, 2010; Wang, Zhang and Wu, 2019), where $K$ is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in $K$. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in $K$. Though not being most efficient in computation, the OVA offers the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate finite sample performance.