Reviewerly/exHarmony huggingface dataset:
Development and Evaluation of an Android-based Yoruba Language Proverbs Preservatory and Repository System

Yoruba language in Nigeria is one of the several languages of the world that face extinction, and proverbs are an important component of languages, especially Yoruba language.A good mastery of Yoruba proverbs is an indication that the speaker has an in-depth knowledge of the language.However, the current generation of Yoruba youths do not understand the proverbs in the language let alone how to use them in their writings and speeches.Hence, there is a need to preserve the proverbs in the language for these young and future generations in a bid to ensure continuity and prevent the extinction that looms over these proverbs.Towards preserving these proverbs and ultimately preserving the language, an Android-based Yoruba language proverbs preservatory and repository system was developed and its success evaluated majorly among the age groups that need to learn them based on DeLone and Mclean Success Model.The results show that the system was successful.This means that systems of this nature can be used as a platform for the young and future generations to learn Yoruba proverbs and their usage towards ultimately preserving the language and removing the extinction that looms over it.
Jump Point Search with Temporal Obstacles

In 4-connected grid-based path planning one often needs to account for temporal and moving obstacles: ones that appear, disappear and which can prevent the agent from reaching its target. Such problems are common in a variety of settings (games, robotics etc.) and they can be surprisingly challenging to solve. First, because the temporal aspect increases the size of the search space; second because the search space contains many symmetric paths, indistinguishable from one another except by the order in which grid moves appear. To tackle such problems we consider a new optimal algorithm – in the style of Jump Point Search – which can identify and break these symmetries and thus improves performance; from several factor to more than one order of magnitude vs. SIPP, arguably the gold standard baseline in the area.
Chatbot with Facemask Detection Technique

Artificial intelligence is now in full swing, and chatbots are just a blip on the radar of a massive wave of advancement. Virtual chatbots that simulate human discussions for performing various tasks are becoming increasingly popular as messengers become more widely used. Some chatbots can schedule medical appointments, order a taxi, transfer money to pals, check into a flight, and perform a variety of other functions. Chatbots assist consumers as well, and they are becoming increasingly interested in this technology. Given the current COVID-19 situation, it's critical to be informed about the growing number of cases and the many precautions that must be followed. This information will be obtained with the assistance of the chatbot. Our chatbot not only converses with users, but it also informs them about Covid-19 and the measures that must be taken. We also understand the importance of wearing masks to prevent the infection from spreading. Using image processing, we will determine if people are wearing their masks or not in this section of our research. To determine if people are wearing a face mask or not, we will develop this section of the project using a simple and basic Convolutional Neural Network (CNN) model, TensorFlow with Keras library, and OpenCV. In this project, we will use a dataset that contains two types of images: images with masks and images without masks. Then, using the webcam on your PC, we'll utilize these images to create a CNN model using TensorFlow to detect if you're wearing a face mask.
Hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation

Software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation. The proposed approach was implemented and tested on two well-known datasets of software projects. The performance of the proposed model was compared with other optimization algorithms based on MMRE, MDMRE, and PRED(0.25) measures. The results showed that weighted ABE models provide more accurate and better effort estimates relative to unweighted ABE models and that the PSO-SA hybrid approach has led to better and more accurate results compared with the other weighting approaches in both datasets.
Lightweight Cross-Lingual Sentence Representation Learning

Large-scale models for learning fixeddimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks.However, further increases and modifications based on such large-scale models are usually impractical due to memory limitations.In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations.We explore different training tasks and observe that current cross-lingual training tasks leave a lot to be desired for this shallow architecture.To ameliorate this, we propose a novel cross-lingual language model, which combines the existing single-word masked language model with the newly proposed cross-lingual token-level reconstruction task.We further augment the training task by the introduction of two computationally-lite sentence-level contrastive learning tasks to enhance the alignment of cross-lingual sentence representation space, which compensates for the learning bottleneck of the lightweight transformer for generative tasks.Our comparisons with competing models on cross-lingual sentence retrieval and multilingual document classification confirm the effectiveness of the newly proposed training tasks for a shallow model. 1
An Efficient Analysis of Web Search Personalization Using Fuzzy Based Approach

The paper is intended to explain a process by using the web browsing behaviour of the user to personalize aggregate results from different search engines in accordance with the users' interests. Under the existing taxonomy the various dimensions of Web mining, such as clustering, association rules, navigation, customization, semantic web, recovery of information, text, and image mining, are considered. The role of Fuzzy is highlighted in handling the various types of uncertainty. Classifying web users in a personalised search setup is cumbersome due to the nature of dynamism in user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted within a fuzzy setting. Prior to analysing user behaviour, nature of user interests must be collected. A fuzzy user classification model for a custom web search environment is provided here. A custom browser that is designed for personalization is used to collect user browsing data. Data is flouted by the application of decision trees and fuzzy rules are generated. Here, the search pages are labelled to help user search groups using fuzzy rules.
Do Language Models Perform Generalizable Commonsense Inference?

Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).However, there is a lack of understanding on their generalization to multiple CKGs, unseen relations, and novel entities.This paper analyzes the ability of LMs to perform generalizable commonsense inference, in terms of knowledge capacity, transferability, and induction.Our experiments with these three aspects show that: (1) LMs can adapt to different schemas defined by multiple CKGs but fail to reuse the knowledge to generalize to new relations.(2) Adapted LMs generalize well to unseen subjects, but less so on novel objects.Future work should investigate how to improve the transferability and induction of commonsense mining from LMs. 1
An Analysis of Identification of Students’ Need in Learning English at Marketing Department of SMK Negeri 1 Kolaka

This research is aimed to answer the problem of the research “What are the students’ needs in the Marketing Department at SMK Negeri 1 Kolaka in learning English?” This research uses a qualitative research design where the data are explained in the description form. The instruments of the research are questionnaires and interview guidelines. The participants of the research are students of the Marketing Department in the second grade in SMK Negeri 1 Kolaka. The result showed the student needs, the first is for the student's necessities who are more likely to learn speaking skills, and also for the material they wanted is online marketing material, which means that they considered their needs in the future when they enter the world of work, another thing that students’ needs in their English learning is they need a material that is appropriate to their major so that what they learn in school can be applied when they are working later, while the students’ lack is their speaking skill and also vocabulary deficiencies. Where they have no space or place to use their English skills that they have learned and for the desires of students in terms of learning English, students are more likely want to learn English by going outside. And for the learning methods that used by the teacher, students are more likely want to study in pairs or groups with their friends rather than study alone. Besides, students also want the teachers who can become facilitators and guides in the English learning process.
Exploring Blockchain in the Realm of a Network of Positive Energy Buildings

Positive Energy Blocks (PEBs) is a paradigm useful in the transition to a low carbon economy. This novel approach makes use the collective ownership of renewable energy generation and storage system to optimise their use. In addition to the networking of devices, PEBs also require a suite of managerial systems that can be used to integrate data with the network. One possible solution to this integration is by using Blockchain technology, which has the potential to resolve several issues in data management, security, and community integration. However, the use of Blockchains within PEBs has not been fully engineered or tested, and this paper presents the results of an exploration into possible solutions. The methods adopted in this research are descriptive and exploratory approaches in modelling a network of PEBs. This research points out how Blockchain technology would be used in the organisation process within a network of PEBs and would be useful for academics and professionals interested in delivering a new generation of buildings and services and in promoting collaborations.
CHALLENGES OF DATA INTEGRATION AND INTEROPERABILITY FOR HETEROGENEOUS BIG DATA ARCHITECTURES

Information Communication and Technology (ICT) utilization has become a backbone of e-government implementation.Six components of information system (hardware, software, people, data, process and network) are combined to deliver information from government to their citizen as part of public services.However, there is an interoperability challenge on e-government implementation related to information system.These systems due to different vendors and their use of different storage types, data formats, languages and middleware platform have become diverse, hence the challenge of interoperability due to heterogeneity of e-government information systems.This has led to the need for organizations to share information and services through interoperability platforms.Currently most e-government platforms are independent thus result in lack of integration, inconsistency of meaningful data and redundancy of effort.The main aim of this study was to develop an interoperability framework for egovernment information system which will improve efficiency and effectiveness of government services through accurate sharing of information among various information systems while the specific objectives were to identify the factors that affected egovernment information system interoperability, determine the critical factors that influenced e-government information system interoperability.
AI4GEO: A DATA INTELLIGENCE PLATFORM FOR 3D GEOSPATIAL MAPPING

Abstract. The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives.
DWT based Multimedia Compression

Current multimedia compression methods utilize techniques which are computationally resource intensive. Despite using high resources, presence of noise content is significant. To limit the unnecessary components, developing a different method which consumes comparatively lesser resources is essential. There is also a need to reduce the time latency of such algorithms for time critical applications like online gaming or automated monitoring of manufacturing facilities. In order to counter the problem of time latency, various operating system level optimisation techniques were employed. Thus a method is developed using Wavelets which helps reduce the noise content suitable for any Intel architecture.
Societal impact through lingual plurality

Recent tendency towards internationalization of academic publishing has reinforced the dominant role of English language in scholarly debates and discussions. The monolinguistic development has led to several drawbacks: it risks to sustain global inequalities in knowledge production, limits the access of non-native English speakers to international publishing, and disengages place-specific knowledge from national and local contexts, not only in scholarly communities but also among decision-makers and within the civil society. In response, Fennia seeks ways towards multicultural publishing, including lingual plurality. The journal has a long history in multilingual publishing yet, in its present form – following international standards of journal publishing, with modest resources – its content is solely in English. The editorial briefly introduces this linguistic development, since 1889, and presents ideas for further activities. Fennia’s current multilingual strategy emphasizes the popularization of the peer reviewed content in different languages, which is implemented through collaboration with the online popular science forum Versus. This serves two ends in broadening the audience, beyond the academy and the primarily English-speaking world. The collaboration of Fennia and Versus has already yielded multilingual popular science articles accessible in the contexts that the research concerns and in the societies where the authors work. Based on positive experiences and feedback, we are eager to continue similar efforts promoting linguistic plurality. As achieving these aims requires notable extra effort – from authors, editors, and the publisher – we call for support and commitment from the funding agencies and academic institutions that we rely on, along with the scholarly community whose voluntary work forms the basis of all activities in Fennia.
Knowledge-aware Dialogue Generation with Hybrid Attention (Student Abstract)

Using commonsense knowledge to assist dialogue generation is a big step forward for dialogue generation task. However, how to fully utilize commonsense information is always a challenge. Furthermore, the entities generated in the response do not match the information in the post most often. In this paper, we propose a dialogue generation model which uses hybrid attention to better generate rational entities. When a user post is given, the model encodes relevant knowledge graphs from a knowledge base with a graph attention mechanism. Then it will encode the user post and graphs with a co-attention mechanism, which effectively encodes complex related data. Through the above mechanism, we can get a better mutual understanding of post and knowledge. The experimental results show that our model is more effective than the current state-of-the-art model (CCM).
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction

In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting.Firstly, We evaluate our pretrained model on various pronoun resolution datasets without any finetuning.Our method outperforms all previous unsupervised methods on all datasets by large margins.Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately.Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande.
Quick Response Code Based on Least Significant Bit

Quick response (QR) code design based on Least Significant Bit (LSB) image is studied. The basic principle of the research is to put the required information into the QR code first, and embed the QR code into the lowest bit of the pixels in the background image for hiding the information. If the picture information is necessary to read by obtaining the QR code picture, the lowest bit of the picture pixel is extracted through the program language, and the hidden information can be obtained by parsing the QR code picture. In this scheme, the information is put into the QR code picture, and the QR code is merged with the least significant bit of the picture. It is shown that cannot be recognized by the naked eye. This method can ensure the safety and security of the information during the transmission process. Accuracy, and greatly improve the visual aesthetics of the QR code.
Structure, Security Attacks, and Countermeasures in the Blockchain Network

Blockchain uses several cryptographic techniques to validate and store information, thereby making it extremely difficult to hack. Some have even argued that a blockchain is impossible to hack. The blockchain network has gained quite the reputation over the last decade because of its seemingly impenetrable security and owing to this, several organizations have considered or are considering adopting the use of blockchain technology. However, like every other technology, blockchain is also susceptible to attacks. In thischapter, we will take a deep dive into the structure, security attacks, and countermeasures in the blockchain network, with a focus on blockchain vulnerabilities, precautions, and countermeasures.
Evolutionary game analysis of Chinese cross border logistics enterprises adopting blockchain technology in supply chain

Blockchain technology is considered to be the representative technology of the fourth technological revolution, and logistics and supply chain field has been considered as the main application direction in the next step by industry and academia. In order to study the behavior and intention of Chinese cross-border logistics enterprises to introduce blockchain into internal supply chain, this paper constructs a model of cross-border logistics enterprises adopting blockchain technology on the basis of bounded rationality based on the game model, this paper attempts to find out the factors that affect the introduction of blockchain into the internal supply chain through mathematical derivation and numerical analysis. The game results show that the willingness of cross-border logistics enterprises to adopt blockchain is closely related to the technical ability of blockchain, the incremental performance after the introduction of blockchain, the conversion cost of blockchain technology, and other factors. Enterprises should coordinate various factors and choose to adopt blockchain technology according to their own situation.
Teaching Active Human Learners

Teaching humans is an important topic under the umbrella of machine teaching, and its core problem is to design an algorithm for selecting teaching examples. Existing work typically regards humans as passive learners, where an ordered set of teaching examples are generated and fed to learners sequentially. However, such a mechanism is inconsistent with the behavior of human learners in practice. A real human learner can actively choose whether to review a historical example or to receive a new example depending on the belief of her learning states. In this work, we propose a model of active learners and design an efficient teaching algorithm accordingly. Experimental results with both simulated learners and real crowdsourcing workers demonstrate that our teaching algorithm has better teaching performance compared to existing methods.
On Estimating Recommendation Evaluation Metrics under Sampling

Since the recent studies (KDD'20) done by Krichene and Rendle on the sampling based top-k evaluation metric for recommendation, there have been a lot of debate on the validity of using sampling for evaluating recommendation algorithms. Though their work and the recent work done by Li et. al. (KDD'20) have proposed some basic approach for mapping the sampling based metrics to their counter-part in the global evaluation which uses the entire dataset, there is still lack of understanding how sampling should be used for recommendation evaluation, and the proposed approaches either are rather ad-hoc or can only work on simple metrics, like Recall/Hit-Ratio. In this paper, we introduce some principled approach to derive the estimators of top-k metric based on sampling. Our approaches utilize the weighted MLE and maximal entropy approach to recover the global rank distribution and then utilize that for estimation. The experimental results shows significant advantages of using our approaches for evaluating recommendation algorithms based on top-k metrics.