Reviewerly/exHarmony huggingface dataset:
AI Health Care Chatbot

Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.
Computational Modeling of Affixoid Behavior in Chinese Morphology

The morphological status of affixes in Chinese has long been a matter of debate.How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear.Issues involving polysemy and diachronic dynamics further blur the boundaries.In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese.The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix's status in an isolating language like Chinese.A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.
Prediction of Life Expectancy in Aceh Province by District City Using the Cyclical Order Algorithm

Indicators to evaluate the role of the local and central government in the welfare of its population, especially in the health sector, can be seen from the life expectancy of the community. Therefore the aim of this study is to predict the life expectancy of the Acehnese using the Cyclical Order Weight / Bias Algorithm. This study uses life expectancy data for the population of Aceh in 2010-2019 based on city districts consisting of 23 regions, which were obtained from the Aceh Central Statistics Agency. This study uses 6 architectural models, including: 8-5-1, 8-6-1, 8-7-1, 8-8-1, 8-9-1 and 8-10-1. After analyzing and calculating the 6 architectural models, the 8-9-1 model was chosen as the best with an accuracy rate of 91% and MSE testing 0.0010800577. The results of this study are in the form of the best architectural model that can be used to predict the life expectancy of the Acehnese people.
EXPLORING EFL STUDENTS’ PROBLEMS IN LISTENING TO ENGLISH NEWS BROADCASTS

Listening is central for language learning and for daily communication. However, in foreign language learning, students still encounter problems in understanding listening materials. This present study is aimed to reveal the students’ problems in listening English news broadcasts. The subjects of this study were two classrooms at the Department of English Language Education at an acknowledged university in Banda Aceh. The samples were made of 50 students in their third semester. This study employed a quantitative analysis approach. A close-ended questionnaire was used as the instruments in collecting the data. The findings showed that the difficulties faced by the students in listening to English news broadcast were mainly caused by unfamiliar topics, unfamiliar vocabulary, different accents, fast speech rate, unintelligible pronunciation, difficulty in inference making, excessively long passages, complicated grammatical structure, as well as complex ideas.
Agile information technology service management with DevOps: an incident management case study

This research aims to investigate how DevOps culture can be applied in the incident management process. The authors believe, based on experience as practitioners, that agile software development methodologies are fair enough to be used on Incident Management process, to quickly restore the business interruption. An application management team which solves incidents and applies DevOps practices was studied. Three data collection methods were used: interviews, document analysis and observation. This research provides novel findings supported by metrics and real experience implementing DevOps practices in incident management process. The novelty of the findings brings advantages for academics, and due to the exploratory nature of this research, it extends the body of knowledge. It also provides contributions for practitioners, by showing how these practices can be applied and the result of the implementation of these practices. Directions of future work are also presented.
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings

While pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best performance with slight changes to the training data?Do the same pre-trained embeddings perform well with multiple neural network architectures?Do imputation strategies for unknown words impact reliability?In this paper, we introduce two new metrics to understand the downstream reliability of word embeddings.We find that downstream reliability of word embeddings depends on multiple factors, including, the evaluation metric, the handling of out-of-vocabulary words, and whether the embeddings are fine-tuned.
Artificial Intelligence – Game Changer in the Teratology Information Service

For various contexts it may be of great interest to measure the way in which the meaning of certain target concept changes over time. In this paper, we propose a simple information-based procedure for addressing this challenge. Our procedure involves the construction of a context-vector representing the target's meaning and the use of the Kullback-Leibler divergence measure for quantifying its semantic change. This procedure is illustrated through historical case study in which we study the semantic change of the concept “Sin” in the Corpus of Historical American English. The procedure, its benefits and limitations are discussed.
Smatgrisene at SemEval-2020 Task 12: Offense Detection by AI - with a Pinch of Real I

This paper discusses how ML based classifiers can be enhanced disproportionately by adding small amounts of qualitative linguistic knowledge.As an example we present the Danish classifier Smatgrisene, our contribution to the recent OffensEval Challenge 2020.The classifier was trained on 3000 social media posts annotated for offensiveness, supplemented by rules extracted from the reference work on Danish offensive language (Rathje 2014b).Smatgrisene did surprisingly well in the competition in spite of its extremely simple design, showing an interesting trade-off between technological muscle and linguistic intelligence.Finally, we comment on the perspectives in combining qualitative and quantitative methods for NLP. 1
A Set-theoretic Approach to Reasoning Services for the Description Logic 𝒟 ℒ D 4,×

In this paper we consider the most common TBox and ABox reasoning services for the description logic 𝒟ℒ〈4LQSR,x〉(D) ( 𝒟 ℒ D 4,× , for short) and prove their decidability via a reduction to the satisfiability problem for the set-theoretic fragment 4LQSR. 𝒟 ℒ D 4,× is a very expressive description logic. It combines the high scalability and efficiency of rule languages such as the SemanticWeb Rule Language (SWRL) with the expressivity of description logics. In fact, among other features, it supports Boolean operations on concepts and roles, role constructs such as the product of concepts and role chains on the left-hand side of inclusion axioms, role properties such as transitivity, symmetry, reflexivity, and irreflexivity, and data types. We further provide a KE-tableau-based procedure that allows one to reason on the main TBox and ABox reasoning tasks for the description logic 𝒟 ℒ D 4,× . Our algorithm is based on a variant of the KE-tableau system for sets of universally quantified clauses, where the KE-elimination rule is generalized in such a way as to incorporate the γ-rule. The novel system, called KEγ-tableau, turns out to be an improvement of the system introduced in [1] and of standard first-order KE-tableaux [2]. Suitable benchmark test sets executed on C++ implementations of the three mentioned systems show that in several cases the performances of the KEγ-tableau-based reasoner are up to about 400% better than the ones of the other two systems.
Extending Recommendations for Creative Visualization-Opportunities Workshops

Participatory design is an approach in human-computer interaction that involves all relevant stakeholders coequally in the design process. A recent participatory method for visualization design is the creative visualization-opportunities (CVO) workshop, which is used to efficiently develop visualization design requirements in the early stages of applied visualization work. In this paper we report on our experiences of running four CVO workshops in different domains with diverse participants to explore new methods and variations of workshop variables. Through reflection on our experiences, we propose two contributions that extend existing guidance for planning, executing, and analyzing CVO workshops: a set of 12 pragmatic recommendations that extend and complement existing ones; and a recommended method for analyzing workshop results, called user stories. Additionally, we report on the outcomes of our successful workshops to provide evidence for the efficacy of CVO workshops.
Generalized Shortest-Paths Encoders for AMR-to-Text Generation

For text generation from semantic graphs, past neural models encoded input structure via gated convolutions along graph edges. Although these operations provide local context, the distance messages can travel is bounded by the number of encoder propagation steps. We adopt recent efforts of applying Transformer self-attention to graphs to allow global feature propagation. Instead of feeding shortest paths to the vertex self-attention module, we train a model to learn them using generalized shortest-paths algorithms. This approach widens the receptive field of a graph encoder by exposing it to all possible graph paths. We explore how this path diversity affects performance across levels of AMR connectivity, demonstrating gains on AMRs of higher reentrancy counts and diameters. Analysis of generated sentences also supports high semantic coherence of our models for reentrant AMRs. Our best model achieves a 1.4 BLEU and 1.8 chrF++ margin over a baseline that encodes only pairwise-unique shortest paths.
Measuring Class Cohesion Based on Client Similarities Between Method Pairs: An Improved Approach That Supports Refactoring

Class cohesion is an important quality attribute that has an impact on other quality attributes such understandability, testability, and maintainability. Therefore, classes with low cohesion should be refactored in order to improve their overall qualities. Many cohesion metrics have been introduced in the literature to automatically assess the quality of the class and support refactoring activities. Most existing metrics measure the class cohesion based on how the methods of a class are internally related to each other, while a few metrics measure the class cohesion based on how the methods are externally used by the clients of the class. Unfortunately, the existing client-based cohesion metrics cannot automatically support refactoring techniques such as the Extract Class refactoring. Therefore, this article proposes a new client-based cohesion metric that can be used to automatically identify Extract Class refactoring opportunities. The proposed metric is theoretically evaluated by proving the compliance of the metric to the mathematical cohesion properties, while it is empirically evaluated by conducting a large case study on three systems to compare the metric with other cohesion metrics. Finally, the paper introduces and evaluates an Extract Class refactoring approach based the proposed cohesion metric.
Attn-Eh ALN: complex text-to-image generation with attention-enhancing adversarial learning networks

Text-to-image generation can be widely applied in various fields, such as scene retrieval and computer-aided design. The existing approaches can generate realistic images from simple text descriptions, whereas rendering images from complex text descriptions is still not satisfactory for practical applications. To generate accurate high-resolution images from given complex texts, we proposed an attention-enhancing adversarial learning network (Attn-Eh ALN) based upon conditional generative adversarial networks and the attention mechanism. This model consists of an encoding module and a generative module. In the encoding module, we proposed a local attention driven encoding network that allows words in the text with different weights to enhance the semantic representation of specific object features. The attention mechanism is employed to capture more details while ensuring global information. This enables the details in the generated images to be more fine-grained. In the discriminating stage, we take multiple discriminators to distinguish the realness of the generated images, avoiding the bias caused by a single discriminator. Moreover, a semantic similarity judgment module is introduced to improve the semantic consistency between the text description and visual content. Experimental results on benchmark datasets indicate that Attn-Eh ALN generates favorable results in comparison with other state-of-the-art methods from qualitative and quantitative assessments.
[Spatial Distribution, Contamination Assessment, and Sources of Heavy Metals in the Urban Green Space Soils of a City in North China].

To study the condition of urban green space soils in the central parts of a city in North China, the spatial distribution, sources, and pollution levels of heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni) within green space soils in the central urban districts of the city were investigated. The results showed that the soil quality was high overall. The mean concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were 0.172, 0.202, 9.02, 34.7, 57.0, 31.2, 85.7, and 26.3 mg·kg-1, respectively. The mean concentrations of Cd, Hg, Pb, and Zn in urban soils exceeded the background value of the Beijing-Tianjin-Tangshan region. All of the samples' heavy metal concentrations were lower than the risk screening values for soil contamination of development land in the national soil environment quality standards. With respect to the spatial distribution, the concentrations of As, Cr, and Ni were higher in the northwest of the study area, the concentrations of Cd and Zn were higher in the northeast, and the concentrations of Hg, Pb, and Cu were higher in the urban core area. As for the different land use types of the soils, the concentrations of Cd, Zn, and Ni were higher in the enterprise soils, while the concentrations of Hg, Pb, and Cu were higher in park and residential soils. Assessments of soil quality showed that 97.2% of soil samples' Nemerow integrated indices were less than 1, indicating that the soils were clean. Indices of potential ecological risk for all soil samples were less than 80, indicating that they posed a slight ecological risk. Multivariate statistical analysis (correlation and principle component analyses) showed that Cu, Pb, and Hg may originate from an anthropogenic source via the painting of ancient buildings and pesticides used to protect ancient trees. Chromium may originate from natural sources via geochemical activity and soil parent material; Cr, Zn, Ni, and As were derived from mixed sources through human and geochemical activities. The receptor model was used for identification and apportionment of pollution sources of elements over the standard. The contribution rates of sources were as follows:source 2(46.1%), source 3(33.1%), source 1(17.7%), and others (3.1%) for Cd, source 1(93.0%) for Cu, source 1(52.4%), source 3(24.2%), source 2(20.0%), and others (3.4%) for Zn, source 1(56.3%), source 2(37.8%), and source 3(5.8%) for Ni. Sources 1 and 3 were anthropogenic, while source 2 was natural.
Instance Segmentation Model for Substation Equipment Based on Mask R-CNN

Accurate instance segmentation of substation equipment scene image is beneficial to eliminating background interference and completing more efficient fault detection tasks. However, it is difficult to segment complex substation scenes with a large number of substation equipment. In this paper, we propose a substation equipment image dataset. On this dataset, we train and evaluate substation equipment segmentation models based on mask-RCNN. The experimental results show that our model has more than 69.1% mAp in the verification set, and has good segmentation effect in different scenes and lighting conditions. We also try to introduce the automatic data augmentation into the model training to expand the dataset and further improve the model performance, but the experimental results show that using more data augmentation methods cannot improve the model's mAP. In addition, based on a smaller bimodal dataset of visible light and temperature map, we compare the effect of the instance segmentation models based on visible light and temperature map. The experimental results show that the segmentation model based on visible light is more accurate than the temperature map model.
Video super-resolution network via enhanced deep feature extraction and residual up-down block

Video super-resolution (VSR) is an image restoration task, aiming to reconstruct a high-resolution (HR) video from its down-sampled low-resolution (LR) version. Convolutional neural networks (CNNs) have been applied to VSR successfully. Explicit motion estimation and motion compensation (ME&MC) module is commonly used in the previous CNNs-based methods to better exploit input frames’ temporal similarity. We proposed a VSR network without an explicit ME&MC module. Our network makes full use of spatiotemporal information and can implicitly capture motion relations between frames. Specifically, we proposed an enhanced deep feature extraction module (EDFEM) to extract deep features from input frames. EDFEM exploits not only intra-frame spatial information but also inter-frame temporal information to enhance feature representation. Furthermore, we proposed a residual up-down block (RUDB) to fuse features. RUDB adopts up- and down-sampling layers as the residual branch. Compared to the common residual block, RUDB addresses mutual dependencies of LR and HR images. Visual and quantitative results show that our method achieves state-of-the-art performance.
Selective Fine-Tuning on a Classifier Ensemble: Realizing Adaptive Neural Networks With a Diversified Multi-Exit Architecture

Adaptive neural networks that provide a trade-off between computing costs and inference performance can be a crucial solution for edge artificial intelligence (AI) computing where resource and energy consumption are significantly constrained. Edge AIs require a fine-tuning technique to achieve target accuracy with less computation for pre-trained models on the cloud. However, a multi-exit network, which realizes adaptive inference costs, requires significant training costs because it has many classifiers that need to be fine-tuned. In this study, we propose a novel fine-tuning method for an ensemble of classifiers that efficiently retrain the multi-exit network. The proposed fine-tuning method exploits individualities by assembling the output of the intermediate classifiers trained with distinct preprocessed data. The evaluation results show that the proposed method achieved 0.2%-5.8%, 0.2%-4.6% higher accuracy with only 77%-93%, 73%-84% training computation compared to the entire fine-tuning of classifiers on the pre-modified CIFAR-100 and Imagenet, respectively, although it depends on assumed edge environments.
3D Object Detection and 6D Pose Estimation Using RGB-D Images and Mask R-CNN

Understanding 3D scenes have attracted significant interests in recent years. Specifically, it is used with visual sensors to provide the information for a robotic manipulator to interact with the target object. Thus, 6D pose estimation and object recognition from point clouds or RGB-D images are important tasks for visual servoing. In this paper, we propose a learning based approach to perform 6D pose estimation for robotic manipulation using Mask R-CNN and the structured light technique. The proposed technique optimizes the 6D pose between the target objects and 3D CAD models in multi-layers. Our method is evaluated on a publicly available dataset for 6D pose estimation and shows its efficiency in computation time. The experimental results demonstrate the feasibility of the random bin picking application.
Neural NLG for Methodius: From RST Meaning Representations to Texts

While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly. In this paper, we investigate whether it is beneficial to include discourse relations in the input to neural data-to-text generators for texts where discourse relations play an important role. To do so, we reimplement the sentence planning and realization components of a classic NLG system, Methodius, using LSTM sequence-to-sequence (seq2seq) models. We find that although seq2seq models can learn to generate fluent and grammatical texts remarkably well with sufficiently representative Methodius training data, they cannot learn to correctly express Methodius’s similarity and contrast comparisons unless the corresponding RST relations are included in the inputs. Additionally, we experiment with using self-training and reverse model reranking to better handle train/test data mismatches, and find that while these methods help reduce content errors, it remains essential to include discourse relations in the input to obtain optimal performance.
Legal Regulation of Virtual Currencies: Illicit Activities and Current Developments in the Realm of Payment Systems

Since Bitcoin was invented a decade ago, the phenomenon of Virtual Currencies has been hailed as an ingenious innovation and decried as the preferred transaction vehicle for illicit actors. Despite the numerous headlines discussing the virtues and vices of virtual currencies, heretofore there has been no comprehensive legal response. The present contribution elaborates on the regulation of virtual currencies in the European Legal Area. Starting with a conceptual analysis of virtual currencies and their promising potential, it identifies the financial crime risks posed by the intersection between legitimate and illegitimate users. The research shows that a fragmentary regulation would be ineffective; this promising technology will either be integrated into the lawful economy or it will be exploited by criminals. The paper attempts to fill the regulatory gap by providing a recommendation for the embeddedness of Virtual Currencies into the financial system. It achieves that by redirecting regulation towards the uniqueness of their underlying technology.