Reviewerly/exHarmony huggingface dataset:
Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach

Customer satisfaction is very important for public service providers, customer satisfaction can be delivered with a survey application or writing criticism that can be used to evaluate and improve service. Unfortunately, there are only a few customers who are willing to give an assessment. The survey application cannot represent the overall feeling of the customer, so it is necessary to analyze the content of the conversation between the customer and the service personnel to determine the level of customer satisfaction. In small amounts, it can be done manually, but in large quantities it is more effective to use the system. A solution is needed in the form of a system that converts voice conversations into text and analyzes customer satisfaction to obtain information for evaluation and improvement of services. This research uses K-nearest neighbors (KNN) and term frequency-inverse document frequency (TF-IDF) algorithm with natural language processing (NLP) approach to classify conversations into 2 classes, and  dissatisfied . The results of this study received 74.00% accuracy, 76.00% precision and 73.08% recall. In conversations with the label shows customers with the service and fulfillment of customer desires, while in conversations with the label not satisfied customers are less with the waiting time.
Mining of Social Media on Covid-19 Big Data Infodemic in Indonesia

is an unprecedented disaster that is still difficult to contain.During the pandemic, there were a lot of cases that were reported to increase exponentially.In this situation, the dissemination of messages and information was very important.The social media platform has contributed as a channel of communication with unprecedented speed.However, the uncontrolled and irresponsible dissemination of information will result in new problems that can be detrimental to many parties.A lot of information may trigger panic, fear and result in lose hope and even paranoia.The provision of correct and timely information as well as any curative and preventive effort to stop the disease are very important.This study aims to present a method in finding out public opinion through Twitter social media mining in the Indonesian context.We are particularly interested in finding out what people's stance with the pandemic.Some people may fully aware of this threat, but the remaining could be careless about what is going on.It is assumed that this stance could lead to people's obedience to the government's policy on COVID 19 Protocol.It is believed that the opinion is hidden behind the comments in the media.By scrapping the tweets on Twitter during March 2020 using Corona and COVID keywords, we obtained as many as 31,003 tweets.We manually classified the tweets into 3 classes, positive, negative and neutral stances.Predictive models are derived using Support Vector Machine, Random Forest and Naïve Bayes algorithms.Random Forest-based model gives the highest accuracy level as high as 89%, followed by Support Vector Machine as high as 87% and Naïve Bayes as high as 68%.The model can further be used to classify opinions in the future giving valuable information for the government in making policies and steps in overcoming the pandemic.
Throughput prediction based on ExtraTree for stream processing tasks

In the era of big data, as the amount of streaming data continues to increase, stream processing tasks (SPTs) face serious challenges in real-time processing scenarios with low latency and high throughput. However, much of the current literature on the performance of SPTs pays attention to the reactive approach, which cannot well avoid the problem of system crashes due to the inherent performance volatility. In this paper, a novel throughput prediction method based on ExtraTree for SPTs is presented to address these challenges. A volatility detection algorithm was proposed to obtain the reasonable metric values after the performance volatility of SPTs was studied. Moreover, a selection algorithm of regression function was proposed to output the performance values of SPTs under a relative stead state. Furthermore, a ExtraTree-based algorithm was proposed to predict the throughput of SPTs. The experimental results from two open-source benchmarks running on Apache Flink, a popular stream processing system (SPS), indicated that the average of the accuracy and efficiency of the proposed method could achieve 90.535% and 0.835 s/10,000 samples, which proved the effectiveness of the proposed method on the task of predicting the throughput of SPTs.
Fast End-to-end Coreference Resolution for Korean

Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution.However, such advancements came at a cost of computational complexity and recent works have not focused on tackling this problem.Hence, in this paper, to cope with this issue, we propose BERT-SRU-based Pointer Networks that leverages the linguistic property of head-final languages.Applying this model to the Korean coreference resolution, we significantly reduce the coreference linking search space.Combining this with Ensemble Knowledge Distillation, we maintain state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time.
Photonic hybrid state entanglement swapping using cat state superpositions

We propose the use of hybrid entanglement in an entanglement swapping protocol, as means of distributing a Bell state with high fidelity to two parties. The hybrid entanglement used in this work is described as a discrete variable (Fock state) and a continuous variable (cat state super- position) entangled state. We model equal and unequal levels of photonic loss between the two propagating continuous variable modes, before detecting these states via a projective vacuum-one-photon measurement, and the other mode via balanced homodyne detection. We investigate homodyne measurement imperfections, and the associated success probability of the measurement schemes chosen in this protocol. We show that our entanglement swapping scheme is resilient to low levels of photonic losses, as well as low levels of averaged unequal losses between the two propagating modes, and show an improvement in this loss resilience over other hybrid entanglement schemes using coherent state superpositions as the propagating modes. Finally, we conclude that our protocol is suitable for potential quantum networking applications which require two nodes to share entanglement separated over a distance of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>5</mml:mn> <mml:mtext>--</mml:mtext> <mml:mn>10</mml:mn> <mml:mo> </mml:mo> <mml:mrow> <mml:mtext>km</mml:mtext> </mml:mrow> </mml:math> , when used with a suitable entanglement purification scheme.
Rubrication of text information based on the voting of intellectual classifiers

The practical implementation of the concept of electronic government is one of the priorities of Russian state policy. The organization of effective interaction between authorities and citizens is an important element of this concept. In addition to providing public services, it should include the processing of electronic appeals (applications, complaints, suggestions, etc.). Research has shown that the speed and efficiency of appeal processing largely depend on the quality of determining the thematic rubric, i.e. solving the rubrication task. The analysis of citizens' appeals received by the e-mail and official websites of public authorities has revealed several specific features (small size, errors in the text, free presentation style, description of several problems) that do not allow the successful application of traditional approaches to their rubrication. To solve this problem, it has been proposed to use various methods of intellectual analysis of unstructured text data (in particular, fuzzy logical algorithms, fuzzy decision trees, fuzzy pyramidal networks, neuro-fuzzy classifi convolutional and recurrent neural networks). The article describes the conditions of the applicability of six intellectual classifiers proposed for rubricating the electronic citizens’ appeals. They are based on such factors as the size of the document, the degree of intersection of thematic rubrics, the dynamics of their thesauruses, and the amount of accumulated statistical information. For a situation where a specific model cannot make an unambiguous choice of a thematic rubric, it is proposed to use the classifier voting method, which can significantly reduce the probability of rubrication errors based on the weighted aggregation of solutions obtained by several models selected using fuzzy inference.
Do “Undocumented Workers” == “Illegal Aliens”? Differentiating Denotation and Connotation in Vector Spaces

In politics, neologisms are frequently invented for partisan objectives.For example, "undocumented workers" and "illegal aliens" refer to the same group of people (i.e., they have the same denotation), but they carry clearly different connotations.Examples like these have traditionally posed a challenge to referencebased semantic theories and led to increasing acceptance of alternative theories (e.g., Two-Factor Semantics) among philosophers and cognitive scientists.In NLP, however, popular pretrained models encode both denotation and connotation as one entangled representation.In this study, we propose an adversarial neural network that decomposes a pretrained representation as independent denotation and connotation representations.For intrinsic interpretability, we show that words with the same denotation but different connotations (e.g., "immigrants" vs. "aliens", "estate tax" vs. "death tax") move closer to each other in denotation space while moving further apart in connotation space.For extrinsic application, we train an information retrieval system with our disentangled representations and show that the denotation vectors improve the viewpoint diversity of document rankings.
Intra-Variable Handwriting Inspection Reinforced With Idiosyncrasy Analysis

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.
Verifying the DPLL Algorithm in Dafny

Modern high-performance SAT solvers quickly solve large satisfiability instances that occur in practice. If the instance is satisfiable, then the SAT solver can provide a witness which can be checked independently in the form of a satisfying truth assignment. However, if the instance is unsatisfiable, the certificates could be exponentially large or the SAT solver might not be able to output certificates. The implementation of the SAT solver should then be trusted not to contain bugs. However, the data structures and algorithms implemented by a typical high-performance SAT solver are complex enough to allow for subtle programming errors. To counter this issue, we build a verified SAT solver using the Dafny system. We discuss its implementation in the present article.
Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles.The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks,&#x0D; thus introducing dependability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded whitin FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric powertrain system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.
Recalibrating classifiers for interpretable abusive content detection

We investigate the use of machine learning classifiers for detecting online abuse in empirical research.We show that uncalibrated classifiers (i.e.where the 'raw' scores are used) align poorly with human evaluations.This limits their use for understanding the dynamics, patterns and prevalence of online abuse.We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data.We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.
Detecting and understanding JavaScript global identifier conflicts on the web

JavaScript is widely used for implementing client-side web applications, and it is common to include JavaScript code from many different hosts. However, in a web browser, all the scripts loaded in the same frame share a single global namespace. As a result, a script may read or even overwrite the global objects or functions in other scripts, causing unexpected behaviors. For example, a script can redefine a function in a different script as an object, so that any call of that function would cause an exception at run time.
Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rule discovered on those ?big? datasets can easily exceed thousands. To produce compact, understandable and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. In this paper, we propose new methods that are able to reduce the number of class association rules produced by ?classical? class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose new associative classifiers, called DC, DDC and CDC, that use distance-based agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules, and in the rule-selection step, we use different strategies (based on database coverage and cluster center) for each algorithm. Experimental results performed on selected datasets from the UCI ML repository show that our classifiers are able to learn classifiers containing significantly fewer rules than state-of-the-art rule learning algorithms on datasets with a larger number of examples. On the other hand, the classification accuracy of the proposed classifiers is not significantly different from state-of-the-art rule-learners on most of the datasets.
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit

Morphologically rich languages seem to benefit from joint processing of morphology and syntax, as compared to pipeline architectures. We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit. Here, we extend the Energy based model framework (Krishna et al., 2020), proposed for several structured prediction tasks in Sanskrit, in 2 simple yet significant ways. First, the framework’s default input graph generation method is modified to generate a multigraph, which enables the use of an exact search inference. Second, we prune the input search space using a linguistically motivated approach, rooted in the traditional grammatical analysis of Sanskrit. Our experiments show that the morphological parsing from our joint model outperforms standalone morphological parsers. We report state of the art results in morphological parsing, and in dependency parsing, both in standalone (with gold morphological tags) and joint morphosyntactic parsing setting.
Some Languages Seem Easier to Parse Because Their Treebanks Leak

Cross-language differences in dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences.In this paper I point to a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data?I discuss how to compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explains more of the observed variation than standard explanations such as the above.
Robust Sensor Placement for Signal Extraction

This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also uncertain in this context, to achieve a robust signal extraction, we propose a new placement criterion based on the maximization of the probability that the SNR exceeds a given threshold. This criterion can be easily evaluated using the Gaussian process assumption. Moreover, to reduce the computational complexity of the joint maximization of the criterion with respect to all sensor positions, we suggest a sequential maximization approach, where the sensor positions are chosen one at a time. Finally, we present numerical results showing the superior robustness of the proposed approach when compared to standard sensor placement criteria aimed at interpolating the spatial gain and to a recently proposed criterion aimed at maximizing the average SNR.
Conversational AI : Open Domain Question Answering and Commonsense Reasoning

Our research is focused on making a human-like question answering system which can answer rationally.The distinguishing characteristic of our approach is that it will use automated common sense reasoning to truly "understand" dialogues, allowing it to converse like a human.Humans often make many assumptions during conversations.We infer facts not told explicitly by using our common sense.Incorporating commonsense knowledge in a question answering system will simply make it more robust.
A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

Dynamic Uncertain Causality Graph (DUCG) is a recently developed model for fault diagnoses of industrial systems and general clinical diagnoses. In some cases, however, when state-unknown intermediate variables are many, the variable state combination explosion may appear and result in the inefficiency or even disability in DUCG inference. Monte Carlo sampling is a typical algorithm to solve this type of problem. However, since the calculation values are very small, a huge number of samplings are needed. This paper proposes an algorithm based on conditional stochastic simulation, which obtains the final calculation result from the expectation of the conditional probability in sampling cycles instead of counting the sampling frequency. Compared with the early presented recursive algorithm, the proposed algorithm requires much less computation time in the case when state-unknown intermediate variables are many. An example for diagnosing Viral Hepatitis B shows that the new algorithm performs 3 times faster than the recursive algorithm and the error ratio is within 2.7%.
Modeling Preconditions in Text with a Crowd-sourced Dataset

Preconditions provide a form of logical connection between events that explains why some events occur together and information that is complementary to the more widely studied relations such as causation, temporal ordering, entailment, and discourse relations.Modeling preconditions in text has been hampered in part due to the lack of large scale labeled data grounded in text.This paper introduces PeKo, a crowd-sourced annotation of preconditions between event pairs in newswire, an order of magnitude larger than prior text annotations.To complement this new corpus, we also introduce two challenge tasks aimed at modeling preconditions: (i) Precondition Identification -a standard classification task defined over pairs of event mentions, and (ii) Precondition Generation -a generative task aimed at testing a more general ability to reason about a given event.Evaluation on both tasks shows that modeling preconditions is challenging even for today's large language models (LM).This suggests that precondition knowledge is not easily accessible in LM-derived representations alone.Our generation results show that fine-tuning an LM on PeKo yields better conditional relations than when trained on raw text or temporally-ordered corpora.
Evaluating the performance of PV modules in buildings (BIPV/BAPV) and the soiling effect in the UAE desert setting

This paper assesses the performance of photovoltaic (PV) technologies integrated into buildings in the desert climate and the factors that affect energy yield.Cadmium telluride (CdTe) and standard monocrystalline silicon (c-Si) modules were installed facing south, in the three more common tilt angles used in the Building Applied Photovoltaics (BAPV) and Building Integrated Photovoltaics (BIPV) applications at the Dubai latitude (90°, 25°, and 0°).We monitored the energy production, the temperature of the PV modules, irradiance on each tilt angle, and the meteorological parameters for a full year.We then calculated the performance ratio for the six modules to evaluate the different factors, including temperature and soiling losses, following IEC 61724-1.The 25° modules, usual PV rooftop angle, had the highest and more consistent energy yield throughout the year.Conversely, the energy yield of the 90° modules, typical angle for facades, vertical shading devices, and guardrails, had the lowest yield and showed wide variations.This is expected as the 90° angle is more affected by the seasonal changes of the solar altitude.The soiling losses on these modules were lower than 1%.However, at 0°, the soiling loss was more evident, with an average reduction of 10.79%.The c-Si module at 25° generated the highest normalized energy yield of 402.02 kW h/m 2 , which was 23.5% more than that of CdTe module with the same tilt angle.
Routing Enforced Generative Model for Recipe Generation

One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients.Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible.In this work, we propose a routing method to dive into the content selection under the internal restrictions.The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences.Our model yields new stateof-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.