Reviewerly/exHarmony huggingface dataset:
Identifying the key factors of sustainable entrepreneurship in the Nigerian food industry: The role of media availability

Objective: The article's objective is to identify how media could facilitate the effect of sustainable entrepreneurship on consumer purchase behaviour, based on evidence from the food industry in Nigeria. Research Design & Methods:The research population was experts who have sufficient information, expertise, and experience in the field of sustainable entrepreneurship and marketing, and pay attention to online social platforms and consumer purchase behaviour.There population amounted to 33 interviewed experts.A combination of Delphi-ANP (Analytic Network Process) approaches was used.Results showed that customers' satisfaction was the key dimension that affects sustainable entrepreneurship in the Nigerian food industry.Findings: 'Access to the internet' had the highest rank (F15) (0.33741), which showed that it was the most important factor in developing sustainable entrepreneurship of food industry, regarding to media role in Nigeria.'production process and the environment' (F1) (0.33461) ranked second and was close to the 'price satisfaction' (F17) (0.32049) in importance.In addition, managers should not ignore the 'excellent functional quality of the brand,' 'personal attributes of customers' care' and 'packaging quality,' since these three factors were ranked fourth, fifth and sixth, respectively. Implications & Recommendations:The entrepreneurs should focus more on this dimension to develop a sustainable food industry in Nigeria.Meanwhile, media availability had a significant role in developing sustainable entrepreneurship in Nigeria.Contribution & Value Added: Most researchers focused on sustainable entrepreneurship without considering the impacts of media as a channel of achieving it by changing the consumers' behaviours towards consuming a particular product.Therefore, this research -investigated how media could moderate the effect of sustainable entrepreneurship on consumer purchase behaviour, taking evidence from the food industry in Nigeria.
Interpretability for Language Learners Using Example-Based Grammatical Error Correction

Grammatical Error Correction (GEC) should focus not only on correction accuracy but also on the interpretability of the results for language learners.However, existing neuralbased GEC models mostly focus on improving accuracy, while their interpretability has not been explored.Example-based methods are promising for improving interpretability, which use similar retrieved examples to generate corrections.Furthermore, examples are beneficial in language learning, helping learners to understand the basis for grammatically incorrect/correct texts and improve their confidence in writing.Therefore, we hypothesized that incorporating an example-based method into GEC could improve interpretability and support language learners.In this study, we introduce an Example-Based GEC (EB-GEC) that presents examples to language learners as a basis for correction result.The examples consist of pairs of correct and incorrect sentences similar to a given input and its predicted correction.Experiments demonstrate that the examples presented by EB-GEC help language learners decide whether to accept or refuse suggestions from the GEC output.Furthermore, the experiments show that retrieved examples also improve the accuracy of corrections.
CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction

Chinese spelling correction (CSC) models detect and correct a typo in texts based on the misspelled character and its context.Recently, Bert-based models have dominated the research of Chinese spelling correction (CSC).These methods have two limitations: (1) they have poor performance on multi-typo texts.In such texts, the context of each typo contains at least one misspelled character, which brings noise information.Such noisy context leads to the declining performance on multi-typo texts.(2) they tend to overcorrect valid expressions to more frequent expressions due to the masked token recovering task of Bert.We attempt to address these limitations in this paper.To make our model robust to contextual noise brought by typos, our approach first constructs a noisy context for each training sample.Then the correction model is forced to yield similar outputs based on the noisy and original contexts.Moreover, to address the overcorrection problem, copy mechanism is incorporated to encourage our model to prefer to choose the input character when the miscorrected and input character are both valid according to the given context.Experiments are conducted on widely used benchmarks.Our model achieves superior performance against state-of-the-art methods by a remarkable gain.We release the source code and pre-trained model for further use by the community 1 .
Job Recommendation System Using Hybrid Filtering

As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Many fresher candidates face issue while job recruitment process to undergo which field of interest. The proposed system will help the user to overcome this difficulties by matching their work experience, skills and other details with appropriate companies suitable for respective user. The system will also help experienced users in getting their intended job on the basis of their last job profile. The job recommendation algorithm developed is tedious nor complicated and will be using user-friendly approach to implement job search.The proposed system consist of user dataset with various attributes and company dataset with company details. The profile matching of user with the respective companies can be done using various recommendation algorithms such as content-based,collaborative and hybrid filtering. Since, the content-based and collaborative approach have their own disadvantages, so here implement hybrid filtering which overcomes the disadvantages of the content-based and collaborative filtering. The user can expect a well-proof recommendation from our model. The Project will focus of developing the job recommendation system using hybrid filtering. As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Here, our job recommendation system comes in picture which neither is tedious nor complicated and makes use of user-friendly approach and helps user to accomplish the task easily.The project will also be focusing on developing the android application which will add a better user interface. The Android application will be user friendly and the user just have to fill in basic details such as his past years of experiences, project, internship, etc. That’s it,the rest part of recommending the job to the users will be done safely by the recommendation model of this project.
Evaluating Factuality in Text Simplification

Automated simplification models aim to make input texts more readable. Such methods have the potential to make complex information accessible to a wider audience, e.g., providing access to recent medical literature which might otherwise be impenetrable for a lay reader. However, such models risk introducing errors into automatically simplified texts, for instance by inserting statements unsupported by the corresponding original text, or by omitting key information. Providing more readable but inaccurate versions of texts may in many cases be worse than providing no such access at all. The problem of factual accuracy (and the lack thereof) has received heightened attention in the context of summarization models, but the factuality of automatically simplified texts has not been investigated. We introduce a taxonomy of errors that we use to analyze both references drawn from standard simplification datasets and state-of-the-art model outputs. We find that errors often appear in both that are not captured by existing evaluation metrics, motivating a need for research into ensuring the factual accuracy of automated simplification models.
∞-former: Infinite Memory Transformer-former: Infinite Memory Transformer

Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length.While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information.In this paper, we propose the ∞-former, which extends the vanilla transformer with an unbounded longterm memory.By making use of a continuousspace attention mechanism to attend over the long-term memory, the ∞-former's attention complexity becomes independent of the context length, trading off memory length with precision.In order to control where precision is more important, ∞-former maintains "sticky memories," being able to model arbitrarily long contexts while keeping the computation budget fixed.Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the ∞-former's ability to retain information from long sequences. 1
Few-Shot Fine-Grained Ship Classification With a Foreground-Aware Feature Map Reconstruction Network

Fine-grained ship classification plays an important part in many military and civilian applications. However, it is often costly to obtain images of ships, making it difficult to procure large numbers of such images. This difficulty poses challenges to machine learning procedures that require ship images. Commonly, only a few input images are available for certain types of ships, which leads to the poor generalization of trained models. Therefore, few-shot fine-grained ship classification is an important (but significantly challenging) task in machine learning. In this study, we propose a novel foreground-aware feature map reconstruction network (FRN) that is simple, effective, and scalable. We reconstruct the query features from support features using ridge regression and predict the distribution of the categories of query images between the reconstructed and real query features by comparing the weighted distances with foreground weights. The foreground weights indicate the percentages of foreground information in the feature map locations. We propose two methods for calculating the foreground weights: a non-parametric method and a parametric method. Our proposed network achieves state-of-the-art results on both the fine-grained ship classification dataset Fine-Grained Ship Classification in Remote sensing images (FGSCR) and the natural fine-grained bird classification dataset Caltech UCSD Birds (CUB).
MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction

Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings.Many recent works use BERT-based language models to directly correct each character of the input sentence.However, these methods can be sub-optimal since they correct every character of the sentence only by the context which is easily misled by the misspelled characters.Some other works propose to use an error detector to guide the correction by masking the detected errors.Nevertheless, these methods dampen the visual or phonological features from the misspelled characters which could be critical for correction.In this work, we propose a novel general detectorcorrector multi-task framework where the corrector uses BERT to capture the visual and phonological features from each character in the raw sentence and uses a late fusion strategy to fuse the hidden states of the corrector with that of the detector to minimize the misleading impact from the misspelled characters.Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform the state-of-theart methods in the CSC task.
Remote Sensing Image Dehazing Based on an Attention Convolutional Neural Network

Haze may affect the quality of optical remote sensing images, thus limiting the scope of their application. Remote sensing image dehazing has become important in remote sensing image preprocessing, promoting the use of remote sensing data and the precision of target recognition. Existing remote sensing dehazing methods based on simplified atmospheric degradation models are not suitable for the removal of heterogeneous haze that exist in remote sensing images. For this purpose, this study proposes an end-to-end convolutional neural network based on attention mechanism, in which the residual block structure combines both channel and spatial attention mechanisms, and establishes a synthetic high-resolution haze image dataset for full training. Thus, it obtains the desired dehazing model. Finally, this study investigates the dehazing model using a GF-1 image and compares it with existing dehazing methods. The results show that the proposed method improved the image similarity, color authenticity, and haze residue level.
Nibbling at the Hard Core of Word Sense Disambiguation

With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models.And yet, if we look below the surface of raw figures, it is easy to realize that current approaches still make trivial mistakes that a human would never make.In this work, we provide evidence showing why the F1 score metric should not simply be taken at face value and present an exhaustive analysis of the errors that seven of the most representative state-of-the-art systems for English all-words WSD make on traditional evaluation benchmarks.In addition, we produce and release a collection of test sets featuring (a) an amended version of the standard evaluation benchmark that fixes its lexical and semantic inaccuracies, (b) 42D, a challenge set devised to assess the resilience of systems with respect to least frequent word senses and senses not seen at training time, and (c) hardEN, a challenge set made up solely of instances which none of the investigated state-of-the-art systems can solve.We make all of the test sets and model predictions available to the research community at https://github.com/SapienzaNLP/wsd-hard-benchmark.
Reachability Analysis of Neural Networks Using Mixed Monotonicity

This paper presents a new reachability analysis approach to compute interval over-approximations of the output set of feedforward neural networks with input uncertainty.We adapt to neural networks an existing mixedmonotonicity method for the reachability analysis of dynamical systems and apply it to each partial network within the main network.This ensures that the intersection of the obtained results is the tightest interval over-approximation of the output of each layer that can be obtained using mixed-monotonicity on any partial network decomposition.Unlike other tools in the literature focusing on small classes of piecewise-affine or monotone activation functions, the main strength of our approach is its generality: it can handle neural networks with any Lipschitz-continuous activation function.In addition, the simplicity of our framework allows users to very easily add unimplemented activation functions, by simply providing the function, its derivative and the global argmin and argmax of the derivative.Our algorithm is compared to five other interval-based tools (Interval Bound Propagation, RELUVAL, NEURIFY, VERINET, CROWN) on both existing benchmarks and two sets of small and large randomly generated networks for four activation functions (ReLU, TanH, ELU, SiLU).
Blockchain–Cloud Integration: A Survey

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain–cloud services being offered by existing Cloud Service providers.
Dual Contrastive Learning for Spatio-temporal Representation

Contrastive learning has shown promising potential in self-supervised spatio-temporal representation learning. Most works naively sample different clips to construct positive and negative pairs. However, we observe that this formulation inclines the model towards the background scene bias. The underlying reasons are twofold. First, the scene difference is usually more noticeable and easier to discriminate than the motion difference. Second, the clips sampled from the same video often share similar backgrounds but have distinct motions. Simply regarding them as positive pairs will draw the model to the static background rather than the motion pattern. To tackle this challenge, this paper presents a novel dual contrastive formulation. Concretely, we decouple the input RGB video sequence into two complementary modes, static scene and dynamic motion. Then, the original RGB features are pulled closer to the static features and the aligned dynamic features, respectively. In this way, the static scene and the dynamic motion are simultaneously encoded into the compact RGB representation. We further conduct the feature space decoupling via activation maps to distill static- and dynamic-related features. We term our method as \textbf{D}ual \textbf{C}ontrastive \textbf{L}earning for spatio-temporal \textbf{R}epresentation (DCLR). Extensive experiments demonstrate that DCLR learns effective spatio-temporal representations and obtains state-of-the-art or comparable performance on UCF-101, HMDB-51, and Diving-48 datasets.
Training of future primary school teacher for use digital educational resources in their professional activities

The article reveals the relevance of training future specialists in primary education to use digital educational resources in professional activities. After analysing the scientific works on this problem, the authors identified similar approaches to its solution in different countries and mostly low and medium levels of the pre-service primary school teachers’ readiness to use digital educational resources in their professional activities. Based on the results of empirical research on the state of development of operational and activity and projective components of the readiness of prospective bachelors and masters of Primary Education to use digital educational resources in professional activities and the dynamics of this personal phenomenon under traditional conditions of higher education, the research methodology consisted in the gradual implementing the quasi-professional technology, the method of expert evaluation, and the further distribution of the respondents according to the levels of development of the readiness components: intuitive and receptive, reproductive, productive, and research and creative. The results of the study were subjected to the quantitative and qualitative analysis, as a result of which the authors concluded that within the current system of professional training of future primary school teachers the level of development of operational and activity and projective components of their readiness to use digital educational resources is insufficient. In addition, the authors found that among the methods and techniques of working with presentation slides, the reproductive patterns predominate: information method, structural and graphical techniques of information processing; respondents are not sufficiently oriented in the types of slides, as well as in software environments for designing the presentations. Prospective bachelors and masters are limited to the use of graphic and textual information in presentations; future specialists in primary education mainly use animation and creolisation of the text for their e-learning tools, and there are almost no infographics, interactive posters, educational comics, tasks on online learning platforms, etc.
Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

A blood count is one of the most important diagnostic tools in medicine and one of the most common procedures. It can reveal important changes in the body and is commonly used as the first stage in the process of evaluating patients' health. Even though this is a common practice, delivering examinations in laboratories can be difficult due to the availability of expensive technology that requires frequent maintenance. This study is developing an alternative deep learning computational model capable of automatically detecting cells in images of blood samples. Using object detection libraries, it was possible to train a model that was focused on this task and capable of detecting cells in images with adequate accuracy. When the identification of cells in images of blood samples was taken into account in the best results obtained, it was possible to count white cells with an accuracy of one hundred percent, red cells with an accuracy of 89%, and platelets with an accuracy of 96%, which generated subsidies to develop the primary components of a blood count. The components that were supposed to classify the various types of white cells were not carried out due to the limits of the dataset provided. On the other hand, the study can be broadened to include further works that deal with this issue because it produced satisfactory results.