Reviewerly/exHarmony huggingface dataset:
Cohesive Devices Realization in The Jakarta Post News Editorials: A Discourse Analysis

&lt;p&gt;Organizing ideas and opinions into sentences and paragraphs through editorials within news articles must be paid careful attention. To achieve that, each and every paragraph needs to be cohesive and meaningfully linked to each other. Hence, this study attempts to analyze and investigate the English cohesive devices and their realization within three The Jakarta Post news editorials entitled Respecting Boundaries, Freedom of Religion at Risk, and Honesty the Best Policy in constructing cohesive sentences so as to convey argumentative and persuasive opinions to readers. Krippendorff’s (2018) content analysis was applied as an analytical method to elucidate the cohesive devices and describe their discursive realization within the news editorials. Moreover, Halliday &amp;amp; Hasan’s (1976) framework of cohesion was also applied as an analytical tool to elucidate the cohesive devices in the selected editorials. Based on the findings, it was figured out that there are only three types of cohesive devices found from the three news editorials; reference (71%), conjunction (15%), and lexical cohesion (14%). The discursive realization of the cohesive devices has revealed that the writers of the three selected news editorials adequately utilized almost all of the cohesive devices in order to constitute cohesive textures that lead to persuasive and comprehensive opinions and points of view.&lt;/p&gt;
The Analysis of the Use of Prepositional Phrase in National Column of The Jakarta Post

&lt;p class="8Abstractcontent"&gt;The aim of this research is to know the use of prepositional Phrases especially of the use of Adjectival Prepositional Phrases and also Adverbial Prepositional Phrases. This research was taken from “The Jakarta Post” newspaper” which is a daily published English newspaper in Jakarta. The approach of this research using the descriptive qualitative which drive at the facts that are found sistematically and factually. The writers involve themselves as an instrument of the research because in doing the research, the writers can make other people involve in collecting the data since the technic of the research itself is literary works that will be conducted by the writers themselves.The research revealed that: from the 1&lt;sup&gt;st&lt;/sup&gt;, to the 2&lt;sup&gt;nd&lt;/sup&gt; , to the 3&lt;sup&gt;rd&lt;/sup&gt;, to the 4&lt;sup&gt;th&lt;/sup&gt; edition of the news available in National Column of “The Jakarta Post” newspaper that the amount of the existence of the Adverbial Prepositional Phrases are higher than the amount of the existence of the Adjectival Prepositional Phrases.&lt;/p&gt;
Implementasi Teknologi QR Code Pada Pengarsipan Bukti Pembayaran di kampus IIB Darmajaya

The QR code is a two-dimensional barcode matrix code created by a Japanese company, DensoWave in 1994. "QR" which stands for "Quick Response" can contain many kinds of information in it such as text, URL addresses, telephone numbers, and other files. QR codes are usually applied to various products or goods to show additional information about the product or item. To find out what is in the QR code, you need a smartphone with a camera that can scan the QR code. The rapid development of the times and the increasingly creative mindset of humans have resulted in many thoughts. Many people are starting to compete to be able to lighten the job and make work more effective. This creates various ways or methods that make life easier. Not only work hard but also work smart. Implementation of various methods and combining one method with other methods is starting to be widely applied. For example in this study where this research discusses implementing the use of QR codes for archiving proof of payment.
Implementasi Teknologi Barcode Untuk Pintu Ruangan Dengan PI Camera

Providing a door security system makes it essential that the latest technology is being applied to carry out this purpose. University classrooms usually implement automatic door locks equipped with sensors to allow access to the classroom. Door access requires more autonomous, integrated and intelligent functions to enhance the usability of automatic smart locks. This research demonstrates the development of a secure door lock system using Quick Response (QR) technology and PI Camera to access classrooms at universities where authorized persons will also be able to monitor who has accessed the classroom. The system can log data and track entry and exit access when accessing the security door from the website server. In this case, the authorized individual will only get permission to access the door. This is initial work to test the function of the system for property and facility use such as in offices, universities, and laboratories. Based on the results and data collection, the testbed development has worked well as a preliminary study to provide smart security doors using QR Code.
Implementasi QR Code Pada Perpustakan Dengan Metode RAD

The library is a source of knowledge because that is where books are useful for those who need them. But today's libraries still use the old method of borrowing and returning books by library visitors, so visitors still find it difficult to borrow and return library books. Therefore we want to carry out a new innovation, namely by using QR Code technology to borrow and return books at the library which can make it easier for library visitors.
Problems in learning to read for the lower grades: A case study of class II elementary school students

Many students experience learning difficulties, one of which is learning to read. The purpose of this study was to analyse the problems of learning to read in class II students of SD Negeri 2 Pengadegan. This type of research is qualitative research. Data collection techniques used observation, interview, and documentation methods. They are checking data validity using triangulation. The results showed that several children still had difficulty in learning to read, which was influenced by two factors, namely internal factor and external factor. The internal factor is the student's lack of interest and lazy behaviour in learning to read. External factors influencing students' reading difficulties can be grouped into the family, school and community environment. From the results of this study, it can be concluded that the learning difficulties experienced by many class II elementary school students are reading learning difficulties. The implications of this research are expected to help teachers provide actions to overcome various learning difficulties experienced by students.
Using VOA news material through virtual small group discussion on the students’ reading comprehension for university level

This study aims to determine the effect of using VoA News material through Virtual Small Group discussions on students' reading comprehension at the university level. It was carried out by using a quasi-experimental design. The population of this research was the second semester of PGMI-20 STIT AL-Kifayah Riau, Consisting of 222 students. The researcher took 47 students as the research sample, with 23 students as an experimental class in PGMI B-20 and 24 as control class in PGMI A-20. The sample was selected by using purposive sampling techniques. This research instrument was a written test used in the pre-test and post-test. The data were analyzed using a T-test of Independent sample t-test and a paired sample t-test. The study's findings indicated substantial changes in reading comprehension abilities between the experimental and control groups. The experimental group benefited from VOA News Material via Virtual Small Group Discussion.
Learning non-Gaussian graphical models via Hessian scores and triangular transport

Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables. Knowing the sparsity of such a graphical model is valuable for modeling multivariate distributions and for efficiently performing inference. While the problem of learning graph structure from data has been studied extensively for certain parametric families of distributions, most existing methods fail to consistently recover the graph structure for non-Gaussian data. Here we propose an algorithm for learning the Markov structure of continuous and non-Gaussian distributions. To characterize conditional independence, we introduce a score based on integrated Hessian information from the joint log-density, and we prove that this score upper bounds the conditional mutual information for a general class of distributions. To compute the score, our algorithm SING estimates the density using a deterministic coupling, induced by a triangular transport map, and iteratively exploits sparse structure in the map to reveal sparsity in the graph. For certain non-Gaussian datasets, we show that our algorithm recovers the graph structure even with a biased approximation to the density. Among other examples, we apply SING to learn the dependencies between the states of a chaotic dynamical system with local interactions.
Multichromatic Quantum Superpositions in Entangled Two-Photon Absorption Spectroscopy

Quantum information science is driving progress in a vast number of scientific and technological areas that cover molecular spectroscopy and matter-light interactions in general. In these fields, the ability to generate quantum mechanically-entangled photons is opening avenues to explore the interaction of molecules with quantum light. This work considers an alternative way of correlating photons by including energy superpositions. We study how the multichromatic quantum superposition, or color superposition of photon-pair states, influences the optical properties of organic chromophores. This work uses electronic structure calculations based on time-dependent density functional theory, and a simple modification of the standard entangled two-photon absorption theory. Our calculations show that it is possible to substantially modify the optical absorption cross section of molecules, where constructive and destructive interferences are computed. The quantum interference effects are more pronounced than the constructive ones. These quantum effects, or related ones, could be observed in quantum spectroscopic experiments where qudit photon states are generated.
MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

Imitation learning has been widely applied to various autonomous systems thanks to recent development in interactive algorithms that address covariate shift and compounding errors induced by traditional approaches like behavior cloning. However, existing interactive imitation learning methods assume access to one perfect expert. Whereas in reality, it is more likely to have multiple imperfect experts instead. In this paper, we propose MEGA-DAgger, a new DAgger variant that is suitable for interactive learning with multiple imperfect experts. First, unsafe demonstrations are filtered while aggregating the training data, so the imperfect demonstrations have little influence when training the novice policy. Next, experts are evaluated and compared on scenarios-specific metrics to resolve the conflicted labels among experts. Through experiments in autonomous racing scenarios, we demonstrate that policy learned using MEGA-DAgger can outperform both experts and policies learned using the state-of-the-art interactive imitation learning algorithm. The supplementary video can be found at https://youtu.be/pYQiPSHk6dU.
Diffusing Graph Attention

The dominant paradigm for machine learning on graphs uses Message Passing Graph Neural Networks (MP-GNNs), in which node representations are updated by aggregating information in their local neighborhood. Recently, there have been increasingly more attempts to adapt the Transformer architecture to graphs in an effort to solve some known limitations of MP-GNN. A challenging aspect of designing Graph Transformers is integrating the arbitrary graph structure into the architecture. We propose Graph Diffuser (GD) to address this challenge. GD learns to extract structural and positional relationships between distant nodes in the graph, which it then uses to direct the Transformer's attention and node representation. We demonstrate that existing GNNs and Graph Transformers struggle to capture long-range interactions and how Graph Diffuser does so while admitting intuitive visualizations. Experiments on eight benchmarks show Graph Diffuser to be a highly competitive model, outperforming the state-of-the-art in a diverse set of domains.
Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies. Unsupervised learning can provide insight into the pathophysiology of AD. A mixture of regressions can simultaneously identify clusters from multiple biomarkers while accounting for within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.
READ Avatars: Realistic Emotion-controllable Audio Driven Avatars

We present READ Avatars, a 3D-based approach for generating 2D avatars that are driven by audio input with direct and granular control over the emotion. Previous methods are unable to achieve realistic animation due to the many-to-many nature of audio to expression mappings. We alleviate this issue by introducing an adversarial loss in the audio-to-expression generation process. This removes the smoothing effect of regression-based models and helps to improve the realism and expressiveness of the generated avatars. We note furthermore, that audio should be directly utilized when generating mouth interiors and that other 3D-based methods do not attempt this. We address this with audio-conditioned neural textures, which are resolution-independent. To evaluate the performance of our method, we perform quantitative and qualitative experiments, including a user study. We also propose a new metric for comparing how well an actor's emotion is reconstructed in the generated avatar. Our results show that our approach outperforms state of the art audio-driven avatar generation methods across several metrics. A demo video can be found at \url{https://youtu.be/QSyMl3vV0pA}
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation

The effectiveness of Neural Machine Translation (NMT) models largely depends on the vocabulary used at training; small vocabularies can lead to out-of-vocabulary problems -- large ones, to memory issues. Subword (SW) tokenization has been successfully employed to mitigate these issues. The choice of vocabulary and SW tokenization has a significant impact on both training and fine-tuning an NMT model. Fine-tuning is a common practice in optimizing an MT model with respect to new data. However, new data potentially introduces new words (or tokens), which, if not taken into consideration, may lead to suboptimal performance. In addition, the distribution of tokens in the new data can differ from the distribution of the original data. As such, the original SW tokenization model could be less suitable for the new data. Through a systematic empirical evaluation, in this work we compare different strategies for SW tokenization and vocabulary generation with the ultimate goal to uncover an optimal setting for fine-tuning a domain-specific model. Furthermore, we developed several (in-domain) models, the best of which achieves 6 BLEU points improvement over the baseline.
On the universality of $S_n$-equivariant $k$-body gates

The importance of symmetries has recently been recognized in quantum machine learning from the simple motto: if a task exhibits a symmetry (given by a group $\mathfrak{G}$), the learning model should respect said symmetry. This can be instantiated via $\mathfrak{G}$-equivariant Quantum Neural Networks (QNNs), i.e., parametrized quantum circuits whose gates are generated by operators commuting with a given representation of $\mathfrak{G}$. In practice, however, there might be additional restrictions to the types of gates one can use, such as being able to act on at most $k$ qubits. In this work we study how the interplay between symmetry and $k$-bodyness in the QNN generators affect its expressiveness for the special case of $\mathfrak{G}=S_n$, the symmetric group. Our results show that if the QNN is generated by one- and two-body $S_n$-equivariant gates, the QNN is semi-universal but not universal. That is, the QNN can generate any arbitrary special unitary matrix in the invariant subspaces, but has no control over the relative phases between them. Then, we show that in order to reach universality one needs to include $n$-body generators (if $n$ is even) or $(n-1)$-body generators (if $n$ is odd). As such, our results brings us a step closer to better understanding the capabilities and limitations of equivariant QNNs.
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multi-layer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods Integrated Gradients, layer-wise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods -- gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for randomization skill. We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method.
Division algebras and MRD codes from skew polynomials

Let $D$ be a division algebra, finite-dimensional over its center, and $R=D[t;\sigma,\delta]$ a skew polynomial ring. Using skew polynomials $f\in R$, we construct division algebras and a generalization of maximum rank distance codes consisting of matrices with entries in a noncommutative division algebra or field. These include a class of codes constructed by Sheekey (in particular, generalized Gabidulin codes), as well as Jha Johnson semifields.
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization

Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally optimizing the local, pixel-wise correspondences across multiple views. We are thus motivated to make use of the complementary benefits from the two strategies, and propose a method termed Helix-shaped neural implicit Surface learning or HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the guidance to regularize the learning of the other one, and conducts such intertwined regularization iteratively during the learning process. We also propose an efficient scheme for differentiable volume rendering in HelixSurf. Experiments on surface reconstruction of indoor scenes show that our method compares favorably with existing methods and is orders of magnitude faster, even when some of existing methods are assisted with auxiliary training data. The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf.
WEARDA: Recording Wearable Sensor Data for Human Activity Monitoring

We present WEARDA, the open source WEARable sensor Data Acquisition software package. WEARDA facilitates the acquisition of human activity data with smartwatches and is primarily aimed at researchers who require transparency, full control, and access to raw sensor data. It provides functionality to simultaneously record raw data from four sensors -- tri-axis accelerometer, tri-axis gyroscope, barometer, and GPS -- which should enable researchers to, for example, estimate energy expenditure and mine movement trajectories. A Samsung smartwatch running the Tizen OS was chosen because of 1) the required functionalities of the smartwatch software API, 2) the availability of software development tools and accessible documentation, 3) having the required sensors, and 4) the requirements on case design for acceptance by the target user group. WEARDA addresses five practical challenges concerning preparation, measurement, logistics, privacy preservation, and reproducibility to ensure efficient and errorless data collection. The software package was initially created for the project "Dementia back at the heart of the community", and has been successfully used in that context.