Reviewerly/exHarmony huggingface dataset:
Personalised instructional feedback in a mobile-assisted language learning application using fuzzy reasoning

This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalisation to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a database of categories of errors and misconceptions which have been reported as common in the related literature. The system is also responsible for conducting model-based error diagnosis using machine learning techniques and identifying errors such as knowledge transfer, spelling or verb mistakes. In conjunction with error diagnosis, the system employs fuzzy logic to automatically model these misconceptions and errors and then provide personalised feedback to students based on their personal learning needs. The system has been fully evaluated, using the CIAO! framework and t-test. The evaluation results are positive and encouraging regarding the educational effectiveness.
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances

Dialogue State Tracking (DST) is primarily evaluated using Joint Goal Accuracy (JGA) defined as the fraction of turns where the groundtruth dialogue state exactly matches the prediction.Generally in DST, the dialogue state or belief state for a given turn contains all the intents shown by the user till that turn.Due to this cumulative nature of the belief state, it is difficult to get a correct prediction once a misprediction has occurred.Thus, although being a useful metric, it can be harsh at times and underestimate the true potential of a DST model.Moreover, an improvement in JGA can sometimes decrease the performance of turn-level or non-cumulative belief state prediction due to inconsistency in annotations.So, using JGA as the only metric for model selection may not be ideal for all scenarios.In this work, we discuss various evaluation metrics used for DST along with their shortcomings.To address the existing issues, we propose a new evaluation metric named Flexible Goal Accuracy (FGA).FGA is a generalized version of JGA.But unlike JGA, it tries to give penalized rewards to mispredictions that are locally correct i.e. the root cause of the error is an earlier turn.By doing so, FGA considers the performance of both cumulative and turn-level prediction flexibly and provides a better insight than the existing metrics.We also show that FGA is a better discriminator of DST model performance.
Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder

Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications. Attributed network embedding aims to learn the low-dimensional representation of network nodes by integrating network architecture and attribute information. The network architectures of many real-world applications are complex, and the relations between network architectures and their attributed nodes are opaque. Thus, shallow models fail to capture deep nonlinear information when an attributed network is embedded, leading to unreliable embedding. In the present paper, a Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder (DANE-WLA) is proposed in order to capture high nonlinearity and preserve the many proximities in the network attribute information of nodes and structures. Weisfeiler-Lehman proximity schema was used to capture the node dependency between both node edges and node attributes based on information sequences. Then, a deep autoencoder was applied to invest complex nonlinear information. Extensive experiments were conducted on benchmark datasets to verify that DANE-WLA is computationally efficient for various tasks requiring network embedding. The experimental results show that our model outperforms the state-of-the-art network embedding models.
A Review of JavaScript Object Notation in Data Analysis

JSON is an extremely popular data format in the 2000s.It is used for transforming the data type.It is not different for reading and writing, and it is simple for machines.As time goes through the age of big data, there appears a group of applications, for example, the two that will be discussed in this essay: NoSQL and NewSQL, for data analysis and big data.The SQL database is always a popular database since the 1980s.In recent years, key-value storage, which always means NoSQL, became more and more popular.Also, the NewSQL database type was developed to solve a similar challenge more efficiently.This essay aims to find the application of these data formats in the real world.According to the research results, JSON could be used as the data format when the data are shifting between the Android application and the Web server.
An Intelligent Scanning Vehicle for Waste Collection Monitoring

While many industries have adopted digital solutions to improve ecological footprints and optimize services, new technologies have not yet found broad acceptance in waste management. In addition, past efforts to motivate households to improve waste separation have shown limited success. To reduce greenhouse gas emissions as part of a greater plan for fighting climate change, institutions like the European Union (EU) undertake strong efforts. In this context, developing intelligent digital technologies for waste management helps to increase the recycling rate and as a consequence reduces greenhouse gas emissions. Within this work, we propose an innovative computer vision system that is able to assess the residential waste in real-time and deliver individual feedback to the households and waste management companies with the aim of increasing recycling rates and thus reducing emissions. It consists of two core components: A compact scanning hardware designed specifically for rugged environments like the innards of a garbage truck and an intelligent software that applies a convolutional neural network (CNN) to automatically identify the composition of the waste which was dumped into the truck and subsequently delivers the results to a web portal for further analysis and communication. We show that our system can impact household separation behavior and result in higher recycling rates leading to noticeable reduction of CO2 emissions in the long term.
Do Dependency Relations Help in the Task of Stance Detection?

In this paper we present a set of multilingual experiments tackling the task of Stance Detection in five different languages: English, Spanish, Catalan, French and Italian.Furthermore, we study the phenomenon of stance with respect to six different targets -one per language, and two different for Italian -employing a variety of machine learning algorithms that primarily exploit morphological and syntactic knowledge as features, represented throughout the format of Universal Dependencies.Results seem to suggest that the methodology employed is not beneficial per se, but might be useful to exploit the same features with a different methodology.
E-Render: Enabling UHD-Quality Cloud Gaming Through Edge Rendering

Cloud gaming services enable users to enjoy various high-quality computer games without installing them in their devices which may have poor resources. However, it consumes high bandwidth in backbone networks as game videos have to be constantly streamed to client devices, which can be a hindering factor for providing Ultra High Definition (UHD)-quality cloud gaming services whose demands are ever increasing. In this paper, we present a novel cloud gaming architecture, <i>E-Render</i>, in which the graphics engine is separated from the cloud game server and is deployed in the edge for rendering. In the architecture, since video frames are streamed from the edge server to the client device via highbandwidth subscriber networks, we can greatly save the backbone network bandwidth. To realize it, however, we need to effectively reduce the amount and latency of graphics commands transfer from the game engine in the cloud to the graphics engine in the edge server. E-render solves the problem by using novel techniques including multi-granular command deduplication and asynchronous buffer binding. We implement a prototype cloud gaming platform based on the E-Render architecture, and evaluate its performance using four real world games. The evaluation results confirm that E-Render can provide UHD-quality cloud gaming services.
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

In this paper, we propose a mixture modelbased end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL).Semantic dependencies in SRL are modeled as a distribution over semantic dependency labels conditioned on a predicate and an argument word.The semantic label distribution varies depending on Shortest Syntactic Dependency Path (SSDP) hop patterns.We target the variation of semantic label distributions using a mixture model, separately estimating semantic label distributions for different hop patterns and probabilistically clustering hop patterns with similar semantic label distributions.Experiments show that the proposed method successfully learns a cluster assignment reflecting the variation of semantic label distributions.Modeling the variation improves performance in predicting short distance semantic dependencies, in addition to the improvement on long distance semantic dependencies that previous syntax-aware methods have achieved.The proposed method achieves a small but statistically significant improvement over baseline methods in English, German, and Spanish and obtains competitive performance with state-of-the-art methods in English. 1
R3 : Refined Retriever-Reader pipeline for Multidoc2dial

In this paper, we present our submission to the DialDoc shared task based on the Multi-Doc2Dial dataset.MultiDoc2Dial is a conversational question answering dataset that grounds dialogues in multiple documents.The task involves grounding a user's query in a document followed by generating an appropriate response.We propose several improvements over the baseline's retriever-reader architecture to aid in modeling goal-oriented dialogues grounded in multiple documents.Our proposed approach employs sparse representations for passage retrieval, a passage re-ranker, the fusion-in-decoder architecture for generation, and a curriculum learning training paradigm.Our approach shows a 12 point improvement in BLEU score compared to the baseline RAG model.
Transformer based ensemble for emotion detection

Detecting emotions in languages is important to accomplish a complete interaction between humans and machines.This paper describes our contribution to the WASSA 2022 shared task which handles this crucial task of emotion detection.We have to identify the following emotions: sadness, surprise, neutral, anger, fear, disgust, joy based on a given essay text.We are using an ensemble of ELECTRA and BERT models to tackle this problem achieving an F1 score of 62.76%.Our codebase 1 and our WandB project 2 is publicly available.
On the Complementarity of Images and Text for the Expression of Emotions in Social Media

Authors of posts in social media communicate their emotions and what causes them with text and images.While there is work on emotion and stimulus detection for each modality separately, it is yet unknown if the modalities contain complementary emotion information in social media.We aim at filling this research gap and contribute a novel, annotated corpus of English multimodal Reddit posts.On this resource, we develop models to automatically detect the relation between image and text, an emotion stimulus category and the emotion class.We evaluate if these tasks require both modalities and find for the imagetext relations, that text alone is sufficient for most categories (complementary, illustrative, opposing): the information in the text allows to predict if an image is required for emotion understanding.The emotions of anger and sadness are best predicted with a multimodal model, while text alone is sufficient for disgust, joy, and surprise.Stimuli depicted by objects, animals, food, or a person are best predicted by image-only models, while multimodal models are most effective on art, events, memes, places, or screenshots.My everyday joy is to see my adorable cat smiles.And I've just realized, my cat can "dance with music".Amazing!(a) joy/complementary/animal.https://www.reddit.com/r/happy/comments/j76dog/my_everyday_joy_ is_to_see_my_adorable_cat_smiles/ Don't move to Australia unless you can handle these bad boys
An Integrated Approach to Research Paper and Expertise Recommendation in Academic Research

Research papers and expertise recommendation systems have been operating independently of one another, resulting in avoidable search queries and unsatisfactory recommendations.This study developed an integrated recommendation system to harmonize research papers and expertise recommendations in the academic domain for enhanced research collaboration.It aims to address issues related to the isolated existence of these systems.A recommendation algorithm was developed to synergize the research paper and expertise recommendation models.The Cosine similarity function between user query and available research papers as well as experts, was combined with selected criteria to achieve recommendation.The synergized model was simulated and evaluated using Precision, Recall, F-measure and Root Mean Square Error as performance metrics.The findings showed that the harmonization of research paper and expertise recommendation approaches provides a holistic and enhanced approach towards research paper and expertise recommendation.Thus, academic researchers now have a reliable way to recommend experts and research papers, which will lead to more collaborative research activities.
Fair and Argumentative Language Modeling for Computational Argumentation

Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy.In this paper, we address this research gap and conduct a thorough investigation of bias in argumentative language models.To this end, we introduce AB BA , a novel resource for bias measurement specifically tailored to argumentation.We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameterefficient than full fine-tuning.Finally, we analyze the potential impact of language model debiasing on the performance in argument quality prediction, a downstream task of computational argumentation.Our results show that we are able to successfully and sustainably remove bias in general and argumentative language models while preserving (and sometimes improving) model performance in downstream tasks.We make all experimental code and data available at https://github.com/umanlp/FairArgumentativeLM.
QoS based task scheduling algorithm in cloud computing

Task scheduling is a critical topic that has a significant impact on the performance of the Cloud computing environment where cloud service providers and users have conflicting objectives and requirements. A good scheduler must provide an acceptable trade-off between these goals. Thus, cloud task scheduling becomes a multi-objective optimization problem. In this paper, our contribution is to address the problem of task scheduling in the Cloud computing environments in order to orchestrate the suitable assignment of the submitted tasks to the set of virtual machines. For this purpose, we have adapted the Flower Pollination Algorithm (FPA) metaheuristic by evaluating its objective function in term of three metrics which are makespan, cost and reliability. The simulation results achieved using CloudSim framework are very satisfactory and clearly show the interest of our FPA-based approach.
Extended Max-Occurrence with Normalized Non-Occurrence as MONO Term Weighting Modification to Improve Text Classification

The increased volume of data due to advancements in the internet and relevant technology makes text classification of text documents a popular demand.Providing better representations of the feature vector by setting appropriate term weight values using supervised term weighting schemes improves classification performance in classifying text documents.A stateof-the-art term weighting scheme MONO with variants TF-MONO and SRTF-MONO improves text classification considering the values of non-occurrences.However, the MONO strategy suffers setbacks in weighting terms with non-uniformity values in its term's interclass distinguishing power.In this study, extended max-occurrence with normalized non-occurrence (EMONO) with variants TF-EMONO and SRTF-EMONO are proposed where EMO value is determined as MO interclass extensions as improvements to address its problematic weighting behavior of MONO as it neglected the utilization of the occurrence of the classes with short-distance document frequency in non-uniformity values.The proposed schemes' classification performance is compared with the MONO variants on the Reuters-21578 dataset with the KNN classifier.Chi-square-max was used to conduct experiments in different feature sizes using micro-F1 and macro-F1.The results of the experiments explicitly showed that the proposed EMONO outperforms the variants of MONO strategy in all feature sizes with an EMO parameter value of 2 sets number of classes in MO extension.However, the SRTF-EMONO showed better performance with Micro-F1 scores of 94.85% and 95.19% for smallest to largest feature size, respectively.Moreover, this study also emphasized the significance of interclass document frequency values in improving text classification aside from non-occurrence values in term weighting schemes.
Variable Length Black Hole for Optimization and Feature Selection

In the high dimensional space, the problem of feature selection (FS) can be regarded as combinatorial optimization problem with high complexity due to the huge number of candidate features. In this article, a novel type of meta-heuristic searching based on variable length of solution space is proposed in order to solve the high dimensionality issue of the FS and to obtain more optimal results. The proposed algorithm uses the original black hole optimization as baseline for development. Blackhole optimization assumes in fixed solution space which decreases the efficiency when the number of features is high. Furthermore, the algorithm is subject to stagnation due to the single exemplar or black hole selection. Hence, or novel variable length black hole modifies the original black hole algorithm with various aspects, namely, it enables decomposing the solution space into subset of dimensions and searching within each dimension separately with selecting an exemplar for each dimension which represents the black hole of the corresponding dimension. In addition, it enables length changing of the solutions based on stagnation criterion. Furthermore, it proposes new concept of energy to the black hole which indicates to the decrease in the effectiveness of black hole with time in an exponential way and use it to replace the black hole when it is not effective anymore. The proposed algorithm which is designated as variable length black hole optimization VLBHO is compared with the variable length particle swarm optimization. The approach has increased the accuracy from 50% to 67% for forest cover dataset and from 38% to 80% for wine dataset.
Pattern Analysis Software Tools (PAST) for Written Artefacts

The research of ancient written artefacts results in an ever-increasing amount of digital data in different forms, ranging from raw images of artefacts, to automatically generated data from advanced acquisition techniques. The manual analysis of this data is typically time consuming, and can be subject to human error and bias. Therefore, we present in this work a set of Pattern Analysis Software Tools (PAST), which are dedicated to the automatic analysis of visual and tabular patterns in the research data from the study of ancient written artefacts. These software tools have been developed to facilitate a more efficient study of written artefacts and to help the scholars benefit from the rapid advancements in the fields of pattern analysis and artificial intelligence. Furthermore, these tools can provide new insights which can only be emerged from the statistical analysis of research data. Each tool in PAST is developed and tested in close collaboration with experts from relevant fields of research, and presented here with actual use cases in order to demonstrate its usability and applicability to real research questions.
Who Are We Talking About? Handling Person Names in Speech Translation

Recent work has shown that systems for speech translation (ST) -similarly to automatic speech recognition (ASR) -poorly handle person names.This shortcoming does not only lead to errors that can seriously distort the meaning of the input, but also hinders the adoption of such systems in application scenarios (like computerassisted interpreting) where the translation of named entities, like person names, is crucial.In this paper, we first analyse the outputs of ASR/ST systems to identify the reasons of failures in person name transcription/translation.Besides the frequency in the training data, we pinpoint the nationality of the referred person as a key factor.We then mitigate the problem by creating multilingual models, and further improve our ST systems by forcing them to jointly generate transcripts and translations, prioritising the former over the latter.Overall, our solutions result in a relative improvement in token-level person name accuracy by 47.8% on average for three language pairs (en→es,fr,it).
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation

Intrinsic evaluations of OIE systems are carried out either manually-with human evaluators judging the correctness of extractionsor automatically, on standardized benchmarks.The latter, while much more cost-effective, is less reliable, primarily because of the incompleteness of the existing OIE benchmarks: the ground truth extractions do not include all acceptable variants of the same fact, leading to unreliable assessment of the models' performance.Moreover, the existing OIE benchmarks are available for English only.In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.In contrast to existing OIE benchmarks, BenchIE is fact-based, i.e., it takes into account informational equivalence of extractions: our gold standard consists of fact synsets, clusters in which we exhaustively list all acceptable surface forms of the same fact.Moreover, having in mind common downstream applications for OIE, we make BenchIE multi-faceted; i.e., we create benchmark variants that focus on different facets of OIE evaluation, e.g., compactness or minimality of extractions.We benchmark several state-of-the-art OIE systems using BenchIE and demonstrate that these systems are significantly less effective than indicated by existing OIE benchmarks.We make BenchIE (data and evaluation code) publicly available. 1