Reviewerly/exHarmony huggingface dataset:
A Machine Learning guided Rewriting Approach for ASP Logic Programs

Answer Set Programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in general, for designing an ASP encoding that can be efficiently evaluated by an actual ASP system. A common way for trying to reduce the burden of manually tweaking an ASP program consists in automatically rewriting the input encoding according to suitable techniques, for producing alternative, yet semantically equivalent, ASP programs. However, rewriting does not always grant benefits in terms of performance; hence, proper means are needed for predicting their effects with this respect. In this paper we describe an approach based on Machine Learning (ML) to automatically decide whether to rewrite. In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information. To this end, a Multilayer Perceptrons model has then been trained to guide the ASP grounder I-DLV on rewriting input rules. We report and discuss the results of an experimental evaluation over a prototypical implementation.
Probabilistic Label Trees for Extreme Multi-label Classification

Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently handled by organizing labels as a tree, like in hierarchical softmax used for multi-class problems. In this paper, we thoroughly investigate probabilistic label trees (PLTs) which can be treated as a generalization of hierarchical softmax for multi-label problems. We first introduce the PLT model and discuss training and inference procedures and their computational costs. Next, we prove the consistency of PLTs for a wide spectrum of performance metrics. To this end, we upperbound their regret by a function of surrogate-loss regrets of node classifiers. Furthermore, we consider a problem of training PLTs in a fully online setting, without any prior knowledge of training instances, their features, or labels. In this case, both node classifiers and the tree structure are trained online. We prove a specific equivalence between the fully online algorithm and an algorithm with a tree structure given in advance. Finally, we discuss several implementations of PLTs and introduce a new one, napkinXC, which we empirically evaluate and compare with state-of-the-art algorithms.
Opponent-Pruning Paranoid Search

This paper proposes a new search algorithm for fully observable, deterministic multiplayer games: Opponent-Pruning Paranoid Search (OPPS). OPPS is a generalization of a state-of-the-art technique for this class of games, Best-Reply Search (BRS+). Just like BRS+, it allows for Alpha-Beta style pruning through the paranoid assumption, and both deepens the tree and reduces the pessimism of the paranoid assumption through pruning of opponent moves. However, it introduces three parameters that allow for more fine-grained control over the resulting search. Empirically, we show the effectiveness of OPPS in Chinese Checkers variants with three, four, and six players, where it outperforms its special case BRS+ as well as classic maxn and Paranoid search. We conclude that OPPS opens a promising research direction for search in multiplayer board and video games, and beyond.
Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic Covid-19 Era with Support Vector Machine

Online transportation in Indonesia is a new trend of transportation that is currently used among the lower to upper society.The change in behavior began in 2011 and is growing to this day, the comments that are growing on social media are very important for the online transportation company the negative comments lower the level of users while the good comments increase the users' level.Thus, the comments influence the overall trust of their customers.Among social media, Twitter is a place where many people convey feelings of pleasure and displeasure timely, especially at a time when the COVID-19 pandemic is becoming a serious outbreak.Through these "tweets," many customers express their experience with the service.In this paper, we aim to analyze the experience of online transportation consumers using Support Vector Machine.The data were taken in two periods, i.e.April 2019 ("the normal era") and June 2020 ("the COVID-19 pandemic outbreak.Class logging is done based on 3 categories namely positive, negative and normal, while in mining the data we labeled with the keywords @grabID and @gojekindonesia, 1618 data were obtained with a ratio of 1183 is normal era data and 435 data in the era of COVID-19 pandemic, The highest accuracy results occurred in the normal era with a ratio of 10% as test data and 90% as its training data on linear and sigmoid kernels of 0.8060 while the COVID-19 era only got the highest accuracy of 0.59 in linear kernels with a ratio of 60:40.This is a sign that the COVID-19 pandemic does not contribute to decreasing trust in the service.
Surface modified bentonite mineral as a sorbent for Pb2+ and Zn2+ ions removal from aqueous solutions

Environmental pollution by lead (Pb 2+ ) and zinc (Zn 2+ ) ions has become an important issue due to its harmful effects on human health and environment.This work aims to evaluate the application of surface modified Egyptian bentonite mineral by acid activation using H2SO4 and thermal treatment as an adsorbent to remove lead (Pb 2+ ) and zinc (Zn 2+ ) ions from aqueous solution.X-ray diffraction (XRD), Fourier Transformed Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) techniques were used to investigate the modified bentonite.The impact of organic and inorganic dispersants on rheological characteristics of bentonite suspensions was investigated.Adsorption of Pb 2+ and Zn 2+ ions using modified bentonite mineral was performed with different adsorbent doses and pH values.Removal efficiencies of lead and zinc are 99.67% and 99%, respectively with adsorbent dose of 25 g/l at pH of 6.2.
Attribute Propagation Network for Graph Zero-shot Learning

The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the attribute propagation network (APNet), which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.
Adaptive Mechanism Model for the Prevention of SLA Violation in the Context of COPD Patient Monitoring

In this paper, we introduce a new kind of Service Level Agreement(SLA) Template to better control dynamically quality of medical monitoring platform service. Our approach is based on Health care system and Health Information Technology (HIT) research area, specifically the field of telemonitoring system for patients who suffer from chronic obstructive pulmonary disease (COPD). According to WHO statistics, COPD is the third leading cause of death worldwide. To this end, several solutions or platforms exist today to monitor COPD. Most of these platforms manage large volume of patient data. This can bring about quality and lost data problems. To address these issues, control mechanisms must be proposed and designed to improve the quality of service (QoS) on these platforms. A platform with continuously monitored QoS can save patients’ lives and reduce data quality risk. In this article, we propose an ontology that uses SLAs data from COPD monitoring platforms with dynamic data from a patient context. We dynamically calculate the number of patient data incidents and the number of service request incidents from two dynamic contexts: SLA and the patient context. If the number of incidents is higher than what is expected in the SLA, then alerts are sent to the interface parties in real time. Finally, the contribution of this article is the proposed virtual SLA template to better control SLA violation and improve quality of medical monitoring platforms services.
Augmented reality in libraries

To design information space, to attract users and to develop collections, the libraries have to implement new technologies, and augmented reality (AR) is among them. This technology combines the real and virtual worlds which enables to visualize demanded information and to facilitate access to this information. Several applications are based on AR principles. However, there are still no popular AR solutions to support library processes. In her study, the author examines the AR technology with the view of its implementation and use in libraries. The study of foreign experience demonstrates AR relevance for the libraries. Both experts and users are interested in implementing AR library solutions.Tours, displays and exhibitions, search navigation and AR-books make the most advisable ideas for AR projects. The positives of AR implementation in libraries are specified, i.e. almost free implementation, high adaptiveness to technologies, and the higher interactive character of libraries. However, AR implementation in libraries is limited due to lacking unified apps and insufficient technological support.
Dynamic Graph Repartitioning: From Single Vertex to Vertex Group

With the increase of large graph data arising in applications like Web, social network, knowledge graph, and so on, there is a growing need for partitioning and repartitioning large graph data in graph data systems. However, the existing graph repartitioning methods are known for poor efficiency in the dynamic environment. In this paper, we devise an efficient lightweight method to identify and move the candidate vertices to achieve graph repartitioning in the dynamic environment. Different from previous approaches that just focus on the case of moving a single vertex as a basic unit, we show that the movement of some closely connected vertices as a group can further improve the quality of graph repartitioning result. We conduct experiments on a large set of real and synthetic graph data sets, and the results showed that the proposed method is more efficient comparing with existing method in several aspects.
Quantum soundness of the classical low individual degree test

Low degree tests play an important role in classical complexity theory, serving as basic ingredients in foundational results such as $\mathsf{MIP} = \mathsf{NEXP}$ [BFL91] and the PCP theorem [AS98,ALM+98]. Over the last ten years, versions of these tests which are sound against quantum provers have found increasing applications to the study of nonlocal games and the complexity class~$\mathsf{MIP}^*$. The culmination of this line of work is the result $\mathsf{MIP}^* = \mathsf{RE}$ [arXiv:2001.04383]. One of the key ingredients in the first reported proof of $\mathsf{MIP}^* = \mathsf{RE}$ is a two-prover variant of the low degree test, initially shown to be sound against multiple quantum provers in [arXiv:1302.1242]. Unfortunately a mistake was recently discovered in the latter result, invalidating the main result of [arXiv:1302.1242] as well as its use in subsequent works, including [arXiv:2001.04383]. We analyze a variant of the low degree test called the low individual degree test. Our main result is that the two-player version of this test is sound against quantum provers. This soundness result is sufficient to re-derive several bounds on~$\mathsf{MIP}^*$ that relied on [arXiv:1302.1242], including $\mathsf{MIP}^* = \mathsf{RE}$.
The Design of X-Ray Film Reader with Film Presence Detector

X-Ray viewer is a tool for observing the results of X-Ray films using ray lighting. It aims to get clearer readings of X-Ray films by radiographers and doctors. X-Ray viewers in hospitals generally cannot be carried anywhere because they use fluorescent lamps as a source of radiation and use 220 Volt AC voltage directly. So that its use is less effective and efficient because it must be connected directly to a 220 Volt AC power source and requires large power. In this regard the author wants to design an X-Ray viewer tool that can be used to read the results of X-Ray films clearly and is portable so that the device can be used anywhere because it uses a battery as a voltage source and is equipped with a presence detection sensor film in order to save energy so that the use of tools is more effective and efficient.
Superpixel Based Hierarchical Segmentation for Color Image

In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.
Predictive Model for Risk of 30-Day Rehospitalization Using a Natural Language Processing/Machine Learning Approach Among Medicare Patients with Heart Failure

Background: The COVID-19 pandemic disrupted the way care is delivered to patients with chronic conditions such as heart failure (HF).Many outpatient encounters are now conducted virtually via telehealth.Whether virtual visit for HF results in similar type of interventions as when the patient is seen in person is not known.Methods: Starting on March 15, 2020, all non-time sensitive outpatient in-person appointments at our institution were cancelled and transitioned to virtual appointments where possible.We included all patients seen in a tertiary care HF clinic from February 18 to March 13 (pre-Covid) and from March 16 to May 15 (post-COVID).We examined the volume of in-person and virtual visits and compared medication titration rates pre-and post-COVID.Results: The study cohort included 745 patients, mean age 60.7+/-15.3years, 65.2% male, 80.9% Caucasian, 7.7% Hispanic/Latino.Of these, 227 patients were seen pre-COVID and 518 post-COVID.All appointments were in-person pre-COVID.After the change, only 18% of appointments were in-person while 82% were virtual.Outpatient volume decreased after March 15, but gradually increased, eventually to volumes that exceeded pre-COVID (Figure).Detailed results on medication titration are shown in Table .Diuretic titration took place in 33/227 (14.5%) of patients pre-COVID and 83/518 (16.0%) post-COVID (p=NS).Among 567 patients with HF with reduced ejection fraction (HFrEF), titration of guideline-directed medical therapy (GDMT) took place in 86/172 (50.0%) of patients pre-COVID and 159/395 (40.2%) post-COVID (p=0.03).Among the 395 HFrEF patients seen post-COVID, GDMT was titrated in 33/68 (48.5%) patients seen in person and 126/327 (38.5%) seen virtually -p=0.13.Barriers to medication titration in virtual visits were lack of blood pressure readings and lack of recent laboratory results.Conclusion: Telehealth has become an essential method of outpatient care delivery for chronic HF.Once implemented, it offered efficiencies including improved access to the HF clinic thanks to higher throughput capacity compared to physical clinic space.However, we identified that GDMT titration took place less frequently than during in-person visits.Since it is anticipated that telehealth use will continue into the future, approaches to maximize GDMT in the absence of traditional direct physical contact with HF patients are needed.
Hand Pose Estimation for Hand-Object Interaction Cases using Augmented Autoencoder

Hand pose estimation with objects is challenging due to object occlusion and the lack of large annotated datasets. To tackle these issues, we propose an Augmented Autoencoder based deep learning method using augmented clean hand data. Our method takes 3D point cloud of a hand with an augmented object as input and encodes the input to latent representation of the hand. From the latent representation, our method decodes 3D hand pose and we propose to use an auxiliary point cloud decoder to assist the formation of the latent space. Through quantitative and qualitative evaluation on both synthetic dataset and real captured data containing objects, we demonstrate state-of-the-art performance for hand pose estimation with objects, even using only a small number of annotated hand-object samples.
Ecuadorian regulatory traffic sign detection by using HOG features and ELM classifier

This article presents an algorithm for Ecuadorian regulatory traffic signs detection, under extreme lighting conditions during the day. The method is composed of the following modules, i) video stabilization to reduce vertical oscilation, ii) a method to for obtaining regions of interest (ROIs) based on color information and geometric restrictions, iii) a two-stage multi classification algorithm, based on Extreme Learning Machine (ELM) and HOG descriptor, to classify by form and content. A database of Ecuadors regulatory traffic signs has also been created. It has more than 47,000 images of Ecuadorian road signs divided into 16 classes. The experimental results, from the recognition module, generate an accuracy of 99,85 %, and a sensitivity of 99,78 % in the first stage, and an accuracy of 96,71 % and a sensitivity of 94,16 % in the second stage. In addition, its robustness was compared with two other classifiers, in order to choose the one with the best performance in terms of accuracy and low computational cost. This system works at 24 frames per second, and it was tested in real driving conditions, from 6:00 a.m. until 7:00 p.m., on the streets of several cities and highways of Ecuador.
Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost

More solar-based electricity generation stations have been established markedly in recent years as new and an important source of renewable energy.That is to ensure a more efficient, reliable integration of solar power to overcome several challenges such as, the future forecasting, the costly equipment in the metrological stations.One of the effective prediction methods is Artificial Neural Networks (ANN) and the Boruta algorithm for optimal attributes selection, to train the proposed prediction model to obtain high accurate prediction performance at a lower cost.The precise goal of this research is to predict the Global Horizontal Irradiance (GHI) by building the ANN model.Also, reducing the total number of GHI prediction attributes/features consequently reducing the cost of devices and equipment required to predict this important factor.The dataset applied in this research is real data, collected from 2015-2018 by solar and meteorological stations in KSA.It provided by King Abdullah City for Atomic and Renewable Energy (KA CARE).The findings emphasize the achievement of accurate predictions of solar radiation with a minimum cost, which is considered to be highly important in KSA and all other countries that have a similar environment.
Case-Based Approach to Automated Natural Language Generation for Obituaries

Automated generation of human readable text from structured information is challenging because grammatical rules are complex making good quality outputs difficult to achieve. Textual Case-Based Reasoning provides one approach in which the text from previously solved examples with similar inputs is reused as a template solution to generate text for the current problem. Natural Language Generation also poses a challenge when evaluating the quality of the text generated due to the high cost of human labelling and the variety in potential good quality solutions. In this paper, we propose two case-based approaches for reusing text to automatically generate an obituary from a set of input attribute-value pairs. The case-base is acquired by crawling and then tagging existing solutions published on the web to create cases as problem-solution pairs. We evaluate the quality of the text generation system with a novel unsupervised case alignment metric using normalised discounted cumulative gain which is compared to a supervised approach and human evaluation. Initial results show that our proposed evaluation measure is effective and correlates well with average attribute error evaluation which is a crude surrogate to human feedback. The system is being deployed in a real-world application with a startup company in Aberdeen to produce automated obituaries.
Few-shot Learning for Time-series Forecasting

Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a recurrent neural network with an attention mechanism. With the attention mechanism, we can retrieve useful patterns in a small number of time-series for the current situation. Our model is trained by minimizing an expected test error of forecasting next timestep values. We demonstrate the effectiveness of the proposed method using 90 time-series datasets.