Reviewerly/exHarmony huggingface dataset:
Investigating Undergraduate Students Discourse Markers Used in Final Project Article of English Department at Universitas Negeri Semarang

Students' writing quality in academic writing must still be investigated. Especially in terms of coherence and arrangement between sentences, sometimes inaccuracies cause sentences written to be less meaningful. This study aims to: 1) describe the use of discourse markers in the final undergraduate student article projects, especially in the discussion section, and 2) explain how far the quality of undergraduate students' writing is in applying discourse markers. Discourse Markers are conjunctions used as hooks between sentences so that the linkages between sentences are maintained, and the meaning of the sentences can be easily understood. This study applies the theory of Discourse Markers by Fraser (2009), which is complemented by an explanation of material on Academic Writing based on the book Writing Academic English by Oshima and Hogue (2006). The instrument is in the form of documentation. This study's qualitative data for analysis came from 11 undergraduate students' final project articles focused on the discussion part only. Furthermore, the data is categorized into three types of Discourse Markers: contrastive, elaborative, and inferential. After that, the analysis results are also provided in the form of the quality of student writing which has been categorized as appropriate and less appropriate.
ANALISIS PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM DETEKSI KATARAK

One of the most vital senses for humans is sight. Humans utilize their eyes to take in visual information that is used for a variety of tasks, but vision problems are prevalent, ranging from minor disorders to serious disorders that can result in blindness. Cataracts are one of the factors contributing to this vision loss. In Indonesia, cataracts alone account for 81 percent of cases of blindness, and 40 percent of those affected don't even realize they have them. A technique for early cataract identification using digital photographs is one way to lower the incidence of cataract-related blindness. We used Support Vector Machine (SVM) and Convolutional Neural Network (CNN) techniques to create a cataract detection system. These two techniques are used to categorize normal eye classes, immature cataracts, and mature cataracts. A digital image that has been downsized to 64x64 pixels in Joint Photographic Group (JPG) format that was taken from earlier study serves as the input data. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) methods used in the cataract detection process provide optimal results. The Support Vector Machine (SVM) method itself produces an accuracy value of 96.67%, while the Convolutional Neural Network method produces a better accuracy value of 98.89%.&#x0D; Keywords: Cataract, Digital Image, Machine Learning, Support Vector Machine, Convolutional Neural Network
Improving Population-Based Algorithms with Fitness Deterioration

This work presents a new hybrid approach for supporting sequential niching strategies called Cluster Supported Fitness Deterioration (CSFD). Sequential niching is one of the most promising evolutionary strategies for analyzing multimodal global optimization problems in the continuous domains embedded in the vector metric spaces. In each iteration CSFD performs the clustering of the random sample by OPTICS algorithm and then deteriorates the fitness on the area occupied by clusters. The selection pressure pushes away the next-step sample (population) from the basins of attraction of minimizers already recognized, speeding up finding the new ones. The main advantages of CSFD are low memory an computational complexity even in case of large dimensional problems and high accuracy of deterioration obtained by the flexible cluster definition delivered by OPTICS. The paper contains the broad discussion of niching strategies, detailed definition of CSFD and the series of the simple comparative tests.
The Design of an Objective Metric and Construction of a Prototype System for Monitoring Perceived Quality (QoE) of Video Sequences

The paper presents different no reference (NR) objective metrics addressing the most important artefacts for raw (source) video sequences (noise, blur, exposure) and thoseintroduced by compression (blocking, flickering) which can be used for assessing quality of experience. The validity of all metrics was verified under subjective tests.
Personalized Knowledge Mining in Large Text Sets

The paper starts with a discussion of the concept of knowledge engineering, in particular ontological engineering. Consequently, the paper presents assumptions accepted as a basis for a group research on a radically personalized system of ontological knowledge mining, relying on the perspective of human centered computing and combining ontological concepts of the user with an ontology resulting from an automatic classification of a given set of textual data. The paper presents a pilot system PrOnto that supports research work in two aspects: searching for information interesting for a user according to her/his personalized ontological profile, and supporting research cooperation in a group of users (Virtual Research Community) according, e.g., to a comparison of such personalized ontological profiles. The paper concludes with suggestions concerning diverse applications of ontological engineering tools and future work.
A Human-Intelligence-Imitating Question-Asking Strategy for Propositional Knowledge-Based Systems

When initial data are not sufficient to accomplish inference in a knowledge-based system, more information may be needed. A question-asking strategy is to select questions ask about. We present a heuristic question-asking strategy for propositional knowledge-based systems. Our computational experiments showed that the new strategy is significantly better than the strategy which is currently used in many knowledge-based systems.
Online course: Win business cases

This course takes a generic approach to business case formulation that can be applied in any organisation.
Deterministic Generation of Large-Scale Entangled Photonic Cluster State from Interacting Solid State Emitters

The ability to create large highly entangled "cluster" states is crucial for measurement-based quantum computing. We show that deterministic multiphoton entanglement can be created from coupled solid state quantum emitters without the need for any two-qubit gates and regardless of whether the emitters are identical. In particular, we present a general method for controlled entanglement creation by making direct use of the always-on exchange interaction, in combination with single-qubit operations. This is used to provide a recipe for the generation of two-dimensional, cluster-state entangled photons that can be carried out with existing experimental capabilities in quantum dots.
Evaluating ‘Graphical Perception’ with CNNs

Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.
Improving the Visual Quality of Size-Invariant Visual Cryptography for Grayscale Images: An Analysis-by-Synthesis (AbS) Approach

In visual cryptography (VC) for grayscale image, size reduction leads to bad perceptual quality to the reconstructed secret image. To improve the quality, the current efforts are limited to the design of VC algorithm for binary image, and measuring the quality with metrics that are not directly related to how the human visual system perceives halftone images. We propose an analysis-by-synthesis (AbS) framework to integrate the halftoning process and the VC encoding: the secret pixel/block is reconstructed from the shares in the encoder and the error between the reconstructed secret and the original secret images is fed back and compensated concurrently by the error diffusion process. In doing so, the error between the reconstructed secret and original secret is pushed to high frequency band, thus producing visually pleasing reconstructed secret image. This framework is simple and flexible in that it can be combined with many existing size-invariant VC algorithms, including probabilistic VC, random grid VC, and vector/block VC. More importantly, it is proved that this AbS framework is as secure as the traditional VC algorithms. Experimental results demonstrate the effectiveness of the proposed AbS framework.
Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling

Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs are complex models that require the use of powerful hardware, both for training and deploying them. To this end, a quantization-based pooling method is proposed in this paper. The proposed method is inspired from the bag-of-features model and can be used for learning more lightweight deep neural networks. Trainable radial basis function neurons are used to quantize the activations of the final convolutional layer, reducing the number of parameters in the network and allowing for natively classifying images of various sizes. The proposed method employs differentiable quantization and aggregation layers leading to an end-to-end trainable CNN architecture. Furthermore, a fast linear variant of the proposed method is introduced and discussed, providing new insight for understanding convolutional neural architectures. The ability of the proposed method to reduce the size of CNNs and increase the performance over other competitive methods is demonstrated using seven data sets and three different learning tasks (classification, regression, and retrieval).
Opportunistic Entanglement Distribution for the Quantum Internet

Abstract Quantum entanglement is a building block of the entangled quantum networks of the quantum Internet. A fundamental problem of the quantum Internet is entanglement distribution. Since quantum entanglement will be fundamental to any future quantum networking scenarios, the distribution mechanism of quantum entanglement is a critical and emerging issue in quantum networks. Here we define the method of opportunistic entanglement distribution for the quantum Internet. The opportunistic model defines distribution sets that are aimed to select those quantum nodes for which the cost function picks up a local minimum. The cost function utilizes the error patterns of the local quantum memories and the predictability of the evolution of the entanglement fidelities. Our method provides efficient entanglement distributing with respect to the actual statuses of the local quantum memories of the node pairs. The model provides an easily-applicable, moderate-complexity solution for high-fidelity entanglement distribution in experimental quantum Internet scenarios.
ME R-CNN: Multi-Expert R-CNN for Object Detection

We introduce Multi-Expert Region-based Convolutional Neural Network (ME R-CNN) which is equipped with multiple experts (ME) where each expert is learned to process a certain type of regions of interest (RoIs). This architecture better captures the appearance variations of the RoIs caused by different shapes, poses, and viewing angles. In order to direct each RoI to the appropriate expert, we devise a novel "learnable" network, which we call, expert assignment network (EAN). EAN automatically learns the optimal RoI-expert relationship even without any supervision of expert assignment. As the major components of ME R-CNN, ME and EAN, are mutually affecting each other while tied to a shared network, neither an alternating nor a naive end-to-end optimization is likely to fail. To address this problem, we introduce a practical training strategy which is tailored to optimize ME, EAN, and the shared network in an end-to-end fashion. We show that both of the architectures provide considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

In this paper, we present a novel technique for a non-parallel voice conversion (VC) with the use of cyclic variational autoencoder (CycleVAE)-based spectral modeling. In a variational autoencoder(VAE) framework, a latent space, usually with a Gaussian prior, is used to encode a set of input features. In a VAE-based VC, the encoded latent features are fed into a decoder, along with speaker-coding features, to generate estimated spectra with either the original speaker identity (reconstructed) or another speaker identity (converted). Due to the non-parallel modeling condition, the converted spectra can not be directly optimized, which heavily degrades the performance of a VAE-based VC. In this work, to overcome this problem, we propose to use CycleVAE-based spectral model that indirectly optimizes the conversion flow by recycling the converted features back into the system to obtain corresponding cyclic reconstructed spectra that can be directly optimized. The cyclic flow can be continued by using the cyclic reconstructed features as input for the next cycle. The experimental results demonstrate the effectiveness of the proposed CycleVAE-based VC, which yields higher accuracy of converted spectra, generates latent features with higher correlation degree, and significantly improves the quality and conversion accuracy of the converted speech.
Residual Learning for Salient Object Detection

Recent deep learning based salient object detection methods improve the performance by introducing multi-scale strategies into fully convolutional neural networks (FCNs). The final result is obtained by integrating all the predictions at each scale. However, the existing multi-scale based methods suffer from several problems: 1) it is difficult to directly learn discriminative features and filters to regress high-resolution saliency masks for each scale; 2) rescaling the multi-scale features could pull in many redundant and inaccurate values, and this weakens the representational ability of the network. In this paper, we propose a residual learning strategy and introduce to gradually refine the coarse prediction scale-by-scale. Concretely, instead of directly predicting the finest-resolution result at each scale, we learn to predict residuals to remedy the errors between coarse saliency map and scale-matching ground truth masks. We employ a Dilated Convolutional Pyramid Pooling (DCPP) module to generate the coarse prediction and guide the the residual learning process through several novel Attentional Residual Modules (ARMs). We name our network as Residual Refinement Network (R2Net). We demonstrate the effectiveness of the proposed method against other state-of-the-art algorithms on five released benchmark datasets. Our R2Net is a fully convolutional network which does not need any post-processing and achieves a real-time speed of 33 FPS when it is run on one GPU.
Improving Short Text Classification Through Global Augmentation Methods

We study the effect of different approaches to text augmentation. To do this we use three datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2Vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of mixup further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.
Optical analog computing of two-dimensional spatial differentiation based on the Brewster effect

Optical analog computing has attracted widespread attention in recent decades due to its advantages of lower consumption, higher efficiency, and real-time imaging in image processing. Here, we propose a two-dimensional optical analog computing scheme based on the Brewster effect. We experimentally demonstrate two-dimensional edge detection with high efficiency. By combining microscopy, our approach may develop some significant applications in cellular and molecular imaging.
Neural Network Inference on Mobile SoCs

The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. Mobile SoCs house several different types of ML capable components on-die, such as CPU, GPU, and accelerators. These different components are capable of independently performing inference but with very different power-performance characteristics. In this article, we provide a quantitative evaluation of the inference capabilities of the different components on mobile SoCs. We also present insights behind their respective power-performance behavior. Finally, we explore the performance limit of the mobile SoCs by synergistically engaging all the components concurrently. We observe that a mobile SoC provides up to 2x improvement with parallel inference when all its components are engaged, as opposed to engaging only one component.
MaxCut quantum approximate optimization algorithm performance guarantees for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math>

We obtain worst-case performance guarantees for $p=2$ and 3 QAOA for MaxCut on uniform 3-regular graphs. Previous work by Farhi et al. obtained a lower bound on the approximation ratio of 0.692 for $p=1$. We find a lower bound of 0.7559 for $p=2$, where worst-case graphs are those with no cycles $\ensuremath{\le}5$. This bound holds for any 3-regular graph evaluated at particular fixed parameters. We conjecture a hierarchy for all $p$, where worst-case graphs have with no cycles $\ensuremath{\le}2p+1$. Under this conjecture, the approximation ratio is at least 0.7924 for all 3-regular graphs and $p=3$. In addition, using an indistinguishable argument we find an upper bound on the worst-case approximation ratio for all $p$, which indicates classes of graphs for which there can be no quantum advantage for at least $p&lt;6$.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering

Knowledge base question answering (KBQA) is an important task in Natural Language Processing.Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training datasets.In this work, we propose Neuro-Symbolic Question Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question understanding; (2) a simple yet effective graph transformation approach to convert AMR parses into candidate logical queries that are aligned to the KB; (3) a pipeline-based approach which integrates multiple, reusable modules that are trained specifically for their individual tasks (semantic parser, entity and relationship linkers, and neuro-symbolic reasoner) and do not require end-to-end training data.NSQA achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD 1.0).Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems.
Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.
Looks Good To Me: Visualizations As Sanity Checks

Famous examples such as Anscombe's Quartet highlight that one of the core benefits of visualizations is allowing people to discover visual patterns that might otherwise be hidden by summary statistics. This visual inspection is particularly important in exploratory data analysis, where analysts can use visualizations such as histograms and dot plots to identify data quality issues. Yet, these visualizations are driven by parameters such as histogram bin size or mark opacity that have a great deal of impact on the final visual appearance of the chart, but are rarely optimized to make important features visible. In this paper, we show that data flaws have varying impact on the visual features of visualizations, and that the adversarial or merely uncritical setting of design parameters of visualizations can obscure the visual signatures of these flaws. Drawing on the framework of Algebraic Visualization Design, we present the results of a crowdsourced study showing that common visualization types can appear to reasonably summarize distributional data while hiding large and important flaws such as missing data and extraneous modes. We make use of these results to propose additional best practices for visualizations of distributions for data quality tasks.