Reviewerly/exHarmony huggingface dataset:
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering

Multi-hop reasoning requires aggregation and inference from multiple facts.To retrieve such facts, we propose a simple approach that retrieves and reranks set of evidence facts jointly.Our approach first generates unsupervised clusters of sentences as candidate evidence by accounting links between sentences and coverage with the given query.Then, a RoBERTa-based reranker is trained to bring the most representative evidence cluster to the top.We specifically emphasize on the importance of retrieving evidence jointly by showing several comparative analyses to other methods that retrieve and rerank evidence sentences individually.First, we introduce several attention-and embedding-based analyses, which indicate that jointly retrieving and reranking approaches can learn compositional knowledge required for multi-hop reasoning.Second, our experiments show that jointly retrieving candidate evidence leads to substantially higher evidence retrieval performance when fed to the same supervised reranker.In particular, our joint retrieval and then reranking approach achieves new state-of-the-art evidence retrieval performance on two multi-hop question answering (QA) datasets: 30.5 Recall@2 on QASC, and 67.6% F1 on MultiRC.When the evidence text from our joint retrieval approach is fed to a RoBERTa-based answer selection classifier, we achieve new state-ofthe-art QA performance on MultiRC and second best result on QASC.
Assessing Reference-Free Peer Evaluation for Machine Translation

Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains.It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric.We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU.We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.
Design of a Virtual Tour as a Solution for Promoting the Tourism Sector in the Pandemic Period

The COVID-19 pandemic has had a significant impact on the tourism sector. Tourism businesses must adapt to new conditions and rearrange their promotional strategies so that they can survive in the new normal era. Since the pandemic started spreading in early 2020, the Government has recommended a new promotional initiative for the tourism sector through the use of technology that can be accessed remotely via the internet. One of these approaches is the presentation of virtual tourism in various ways to strengthen the brand and promote tourism, for example, through virtual reality. The aim of this research was to increase tourist visits to Kajoetangan Heritage Village through applying technology to create a virtual tourism portal application, thereby raising the attractiveness of thematic village tourist destinations in Malang City. Using the virtual reality design as a form of marketing communication could be an alternative solution to tourism promotion during the pandemic. A Research and Development approach was used in this study in the following order: (1) conducting product analysis to be developed; (2) developing initial products; (3) expert validation and revision; (4) small-scale field trials and product revisions; (5) field trials and final product. The results from the field test showed that users can gain awareness about tourism activities through this virtual reality product, which can therefore be effective in supporting tourism promotion during the pandemic period.&#x0D; Keywords: virtual reality, COVID-19
GPT Perdetry Test: Generating new meanings for new words

Human innovation in language, such as inventing new words, is a challenge for pretrained language models.We assess the ability of one large model, GPT-3, to process new words and decide on their meaning.We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions.We find GPT-3 produces plausible definitions that align with human judgments.Moreover, GPT-3's definitions are sometimes preferred to those invented by humans, signaling its intriguing ability not just to adapt, but to add to the evolving vocabulary of the English language.
Boosted Supervised Intensional Learning Supported by Unsupervised Learning

Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure mode is not well understood. This lack of explainability also increases the risk of malicious attacks by adversarial actors because these actions can become obscured in the decision-making process that lacks transparency. This paper describes an intensional learning approach which uses boosting to enhance prediction performance while minimizing reliance on availability of annotated data. The intensional information is derived from an unsupervised learning preprocessing step involving clustering. Preliminary evaluation on the MNIST data set has shown encouraging results. Specifically, using the proposed approach, it is now possible to achieve similar accuracy result as extensional learning alone while using only a small fraction of the original training data set.
Adaptive harmony search algorithm utilizing differential evolution and opposition-based learning

<abstract> An adaptive harmony search algorithm utilizing differential evolution and opposition-based learning (AHS-DE-OBL) is proposed to overcome the drawbacks of the harmony search (HS) algorithm, such as its low fine-tuning ability, slow convergence speed, and easily falling into a local optimum. In AHS-DE-OBL, three main innovative strategies are adopted. First, inspired by the differential evolution algorithm, the differential harmonies in the population are used to randomly perturb individuals to improve the fine-tuning ability. Then, the search domain is adaptively adjusted to accelerate the algorithm convergence. Finally, an opposition-based learning strategy is introduced to prevent the algorithm from falling into a local optimum. The experimental results show that the proposed algorithm has a better global search ability and faster convergence speed than other selected improved harmony search algorithms and selected metaheuristic approaches. </abstract>
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs.To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier.The latent code guides the pair representations and influences sentence reconstruction.Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits.Additional exploration of employing Knowledge Base priors into the VAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results 1 .
21st Century Literacy Skill of Information Technology and Computer Education Students

This study aims to determine student perceptions related to technology and media literacy in the Information Technology and Computer Education (ITC Education) Program and to compare technology and media literacy based on the students’ level and gender. The method used in this research is a survey method with data collection tools in the form of questionnaires and analysis using descriptive and comparative analysis. The population of this study were all 696 students of Information Technology and Computer Education and the sample of this study was 236 taken using the Proportionate Stratified Random Sampling technique. The results showed that the technology and media literacy of ITC Education students belong to the quite good category. In this research, it is known that there are significant differences in technology and media literacy based on gender, while based on the level of students there are significant differences in media literacy but there is no difference in technology literacy.
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI

We examine the effect of domain-specific external knowledge variations on deep large scale language model performance.Recent work in enhancing BERT with external knowledge has been very popular, resulting in models such as ERNIE (Zhang et al., 2019a).Using the ERNIE architecture, we provide a detailed analysis on the types of knowledge that result in a performance increase on the Natural Language Inference (NLI) task, specifically on the Multi-Genre Natural Language Inference Corpus (MNLI).While ERNIE uses general TransE embeddings, we instead train domain-specific knowledge embeddings and insert this knowledge via an information fusion layer in the ERNIE architecture, allowing us to directly control and analyze knowledge input.Using several different knowledge training objectives, sources of knowledge, and knowledge ablations, we find a strong correlation between knowledge and classification labels within the same polarity, illustrating that knowledge polarity is an important feature in predicting entailment.We also perform classification change analysis across different knowledge variations to illustrate the importance of selecting appropriate knowledge input regarding content and polarity, and show representative examples of these changes.
BERT based Adverse Drug Effect Tweet Classification

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks (Magge et al., 2021).Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions.Our best performing model utilizes BERTweet followed by a single layer of BiL-STM.The system achieves an F-score of 0.45 on the test set without using any supplementary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.
A corpus of K’iche’ annotated for morphosyntactic structure

This article describes a collection of sentences in K'iche' annotated for morphology and syntax.K'iche' is a language in the Mayan language family, spoken in Guatemala.The annotation is done according to the guidelines of the Universal Dependencies project.The corpus consists of a total of 1,433 sentences containing approximately 10,000 tokens and is released under a free/open-source licence.We present a comparison of parsing systems for K'iche' using this corpus and describe how it can be used for mining linguistic examples.
Named Entity Recognition on Software Requirements Specification Documents

Software Requirements Specifications (SRS) documents set the requirements and expectations of software development projects.This textual information is considered as a guideline for identifying various requirements for the developers.The content of an SRS document is concentrated around software requirement specific entities which can be used to improve software-based information retrieval systems.In this paper, we consider Named Entity Recognition (NER) over SRS documents and classify the software requirement specific entities using ML-based methods.Existing NER methods are typically restricted to four basic sets of entities that are not pertinent to NER in the software requirement domain.We define ten software requirement-based entities which cover the essence of the SRS documents.In order to predict the appropriate entity tags, we first create a limited feature set and experiment with various NER models including MLbased probabilistic models and DL models.We conduct a detailed numerical study to evaluate the effectiveness of the NER models over the SRS datasets.NER models with a basic feature set demonstrate promising performance with an F1-macro score of 81%.Our analysis shows that the entities can be extracted with high accuracy from the SRS documents, which then can be used for various purposes in practice such as extractive summarization of large SRS documents and effectively grouping the requirements.
Deep Convolutional Neural Network Ensembles Using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance.
Clone-Seeker: Effective Code Clone Search Using Annotations

Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. With various experiments, we show that (1) Clone-Seeker is effective in finding clones from BigCloneBench dataset by applying code queries and natural language queries; 2) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; 3) Clone-Seeker is a generalized technique as it is effective in finding clones in Project CodeNet dataset by applying code queries and natural language queries. 4) Clone-Seeker with manual annotation outperforms other variants in finding clones on the basis of natural language queries
Social Media use and its Effects on writing Ability among Moroccan University EFL Students

Social Media is part and parcel of our everyday lives.And since millennials have been interacting on social media on a constant basis, writing is one of the skills by means of which they interact.The intent of this paper is to measure students' social media usage and explore the relationship between these technologies and writing ability among Moroccan university EFL students.In particular, this study employs the explanatory sequential mixed-methods design as a means of researching the reciprocity between social media use and its effects on the writing skill.The findings have indicated that Moroccan university students' social media use habits exert significant impacts on the writing skill and that most of them are susceptible to potential risks pertaining to the use of social media.The reported findings gave sufficient proof that social media is a convenient tool to develop better writing skills.Hence, social media literacy should be integrated in the curriculum.
An Agent-Oriented, Blockchain-Based Design of the Interbank Money Market Trading System

When studying the interbank money market (IMM), it is common to model banks as agents interacting through loans to tackle its complexity. However, the use of agent abstraction in the IMM is mostly limited to some specific cases. Besides, recent advancements show that it is promising to use blockchain technology to improve its security in a decentralized way. Based on this observation, this paper proposes an agent-oriented, blockchain-based design of the IMM trading systems, where the main objective is to decide on the times and methods of liquidity supply and demand by various market players based on what has been learned from the information available. The models in this paper are suitable for use by both academics and practitioners in this field.
Mobile, Open and Social Language Learning Designs and Architectures

The emerging paradigm of mobile open social learning for languages (MOSL4L) integrates the three elements of mobile, open and social, and in so doing it creates the idea of a conceptually different language learning space. It is a space full of opportunity and challenge, relevant to a post-pandemic world in which we are looking for ways to build back better. The paper discusses tensions between formal and informal language learning and the nature of learning outcomes in MOSL4L. It focuses on the needs of individuals while also considering the characteristics of the virtual spaces in which they participate. It highlights the potential of micro experiences and learning moments as structures that are well aligned with MOSL4L. It suggests developments in language curricula to take account of communication challenges being identified in the contemporary world. Many more new learning designs and software architectures will have to be developed to match the possibilities generated by the MOSL4L space.
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media

This paper presents our contribution to the ProfNER shared task.Our work focused on evaluating different pre-trained word embedding representations suitable for the task.We further explored combinations of embeddings in order to improve the overall results.
QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval

The embedding-based large-scale querydocument retrieval problem is a hot topic in the information retrieval (IR) field.Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a Quadru-pletBERT model for effective and efficient retrieval in this paper.Unlike most existing BERT-style retrieval models, which only focus on the ranking phase in retrieval systems, our model makes considerable improvements to the retrieval phase and leverages the distances between simple negative and hard negative instances to obtaining better embeddings.Experimental results demonstrate that our QuadrupletBERT achieves state-of-the-art results in embedding-based large-scale retrieval tasks.
Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Abstract Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation, such as artificial intelligence and hybrid models. Recently, the Gaussian process regression (GPR) algorithm has been used successfully in remote sensing and Earth sciences. In this paper, a wavelet-coupled Gaussian process regression (W–GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). For this purpose, 3 years of data (2013–15) have been used in model training while the data of 2016 were used to validate the model. In this work, different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid W–GPR model compared with the classical GPR model in terms of root mean square error (RMSE), relative root mean square error (rRMSE), mean absolute error (MAE) and determination coefficient (R2).
TBSN: Sparse-Transformer Based Siamese Network for Few-Shot Action Recognition

Few-shot learning(FSL) problem is challenging task, which aims to recognize novel categories with only a few labeled samples. It has aroused significant attentions in both industry and academia. Most existing few-shot learning methods focus on image classification, only few works focus on few-shot video classification. For few-shot video classification problem, obtaining temporal features and designing a good distance measurement are two main challenges. In this work, we address these challenges by proposing a Sparse-Transformer Based Siamese Network termed as TBSN for few-shot action recognition which can lever-age the relative relationship and importance of frames to mine temporal characteristics of video. A relation module based on alignment and feedforward network is designed to learn a good distance measurement. In TBSN, we propose two novel modules: (1) an embedding module based on Sparse-Transformer for fusing information from different video clips to effectively capture temporal information of frames, and (2) a relation module based on alignment and feedforward network, which can discover subtle differences between samples. We conduct extensive experiments on two challenging real-world dataset(UCF101 and Kinetics 400) and compared with other state-of-the-art methods, the results demonstrate its superior performance.
Non-Gaussianity of Four-Photon Superpositions of Fock States

We study two families of four-photon superpositions of the Fock states: even vacuum squeezed states (EVSS) and orthogonal-even coherent states (OECS). These families are distinguished due to several properties: for certain values of parameters, they give the fourth-order uncertainty products close to the known minimal value (which is lower than for the Gaussian states); they have equal dimensionless values of the second- and fouth-order moments of the coordinate and momentum for all values of parameters; they possess zero covariances for all values of parameters. Since these states are obviously non-Gaussian, we consider them as good candidates to compare several different measures of non-Gaussianity proposed by different authors for the past fifteen years. The reference Gaussian states in all examples are thermal states dependent on a single parameter (an effective temperature or the coordinate variance). We analyze the measures based on the normalized Hilbert–Schmidt distance and the relative entropy (introduced by Genoni–Paris–Banaszek), the fidelity measure (Ghiu–Marian–Marian) and its logarithmic analog (Baek–Nha), as well as the Mandilara–Karpov–Cerf “Gaussianity parameter”. These measures are compared with the kurtosis of the coordinate probability density and with the non-Gaussian behavior of the Wigner function.