Reviewerly/exHarmony huggingface dataset:
Retrieval Augmented Code Generation and Summarization

Software developers write a lot of source code and documentation during software development.Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them.To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models.REDCODER has a couple of uniqueness.First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries.Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs).We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.
Adapting intelligent information services in libraries: a case of smart AI chatbots

Purpose The recent COVID-19 pandemic affects all segments of the population in all possible fields of life. To encounter this critical situation and to safeguard the health of the library users &amp; library staff, the library services have been forced to adapt and accept the “new normal” and give a greater use and reliance on virtual space than ever before with physical spaces. This paper aims to demonstrate an artificial intelligence (AI)-driven solution that can be practically implemented in the form of a “InfoBot” or “Chatbot” to fulfil user needs 24/7 with minimal or without human intervention. Design/methodology/approach To give a general overview of the use of AI Chatbot in libraries and its multitasking features followed by the practical implementation, a versatile AIchatbot service, Engati is used as a target bot with its basic free plan. Findings The study findings reveal that AI Chatbots offer a dependable solution for the libraries to initiate virtual assistance, thus augmenting the reference service while adding a newer dimension to virtual reference service. Social implications Innovative and intelligent information service would be overcoming the time and location barriers reaffirming the concept of “library without walls” while falling in line with the five laws of library science as propounded by Dr S.R. Ranganathan, the father of library science in India. Originality/value Though the implementation of bots and chatbots is not a purely novel concept to explore, no such research has been conducted focusing particularly on its exceptional benefits in the library in reference to the recent pandemic.
Distributed Ledger Technologies for Network Slicing: A Survey

Network slicing is one of the fundamental tenets of Fifth Generation (5G)/Sixth Generation (6G) networks. Deploying slices requires end-to-end (E2E) control of services and the underlying resources in a network substrate featuring an increasing number of stakeholders. Beyond the technical difficulties this entails, there is a long list of administrative negotiations among parties that do not necessarily trust each other, which often requires costly manual processes, including the legal construction of neutral entities. In this context, Blockchain comes to the rescue by bringing its decentralized yet immutable and auditable lemdger, which has a high potential in the telco arena. In this sense, it may help to automate some of the above costly processes. There have been some proposals in this direction that are applied to various problems among different stakeholders. This paper aims at structuring this field of knowledge by, first, providing introductions to network slicing and blockchain technologies. Then, state-of-the-art is presented through a global architecture that aggregates the various proposals into a coherent whole while showing the motivation behind applying Blockchain and smart contracts to network slicing. And finally, some limitations of current work, future challenges and research directions are also presented.
Using Virtual Reality and Augmented Reality with ICT Tools for Enhancing Quality in the Changing Academic Environment in COVID-19 Pandemic: An Empirical Study

The use of ICT has contributed significantly to improving the quality of educational programs offered by institutions. In this everchanging technologically advanced world, educators must cope with the inventions and advancements to cater to the students of this generation. With the help of ICT, individuals can generate awareness amongst students apart from augmenting their knowledge level in different aspects. The unexpected breakout of Covid-19, a fatal illness induced by a Corona Virus (SARS-CoV-2), terrified the whole world. Education is one of the notable sectors that is affected by the pandemic. The world is after ICT tools like remote meeting applications, online exams, Virtual Reality, Augmented Reality, etc., to improve the quality of education. Information communications technology has become an integral part of our life, and it is one of the necessities of the modern education system in the COVID-19 pandemic. However, teachers have a mixed opinion to adopt ICT or integrate Virtual Reality or Augmented reality in their routine. Several pieces of research have been carried out to highlight the significance of the use of ICT, VR, or AR tools in education. This study was carried out with a sample of 140 teaching professionals from different parts of India to ascertain the significance of using Virtual Reality and Augmented Reality with ICT tools for improving the quality of academic environments. The outcome of this study shows that the teachers agree and welcome the integration of Virtual Reality and Augmented reality to enhance the quality of education. At the same time, the analysis also showed that it is difficult for the students in rural areas to acquaint themselves with the technology. There is no significant moderating effect of gender noticed in this study.
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation

As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic data streams, which suffer from concept drift problems where the data distribution changes overtime. The performance of GBDT model is degraded when applied to predict data streams with concept drift. Although incremental learning can help to alleviate such degrading, finding a perfect learning rate (i.e., the iteration in GBDT) that suits all time periods with all their different drift severity levels can be difficult. In this paper, we convert the issue of determining an optimal learning rate into the issue of choosing the best adaptive iterations when tuning GBDT. We theoretically prove that drift severity is closely related to the convergence rate of model. Accordingly, we propose a novel drift adaptation method, called adaptive iterations (AdIter), that automatically chooses the number of iterations for different drift severities to improve the prediction accuracy for data streams under concept drift. In a series of comprehensive tests with seven state-of-the-art drift adaptation methods on both synthetic and real-world data, AdIter yielded superior accuracy levels.
Confidence-Aware Multi-Teacher Knowledge Distillation

Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training. To boost the student performance, some recent variants attempt to exploit diverse knowledge sources from multiple teachers. However, existing studies mainly integrate knowledge from diverse sources by averaging over multiple teacher predictions or combining them using other label-free strategies, which may mislead student in the presence of low-quality teacher predictions. To tackle this problem, we propose Confidence-Aware Multi-teacher Knowledge Distillation (CA-MKD), which adaptively assigns sample-wise reliability for each teacher prediction with the help of ground-truth labels, with those teacher predictions close to one-hot labels assigned large weights. Besides, CA-MKD incorporates features in intermediate layers to stable the knowledge transfer process. Extensive experiments show our CA-MKD consistently outperforms all compared state-of-the-art methods across various teacher-student architectures. Code is available: https://github.com/Rorozhl/CA-MKD.
Making New Worlds – Transformative Becomings with Soma Design

Soma design is intended to increase our ability to appreciate through all our senses and lead to more meaningful interactions with the world. We contribute a longer-term study of soma design that shows evidence of this promise. Using storytelling approaches we draw on qualitative data from a three-month study of the soma mat and breathing light in four households. We tell stories of people’s becomings in the world as they learn of new possibilities for their somas; and as their somas transform. We show how people drew on their somaesthetic experiences with the prototypes to find their way through troubled times; and how through continued engagement some felt compelled to make transformations in how they live their lives. We discuss the implications for the overarching soma design program, focusing on what is required to design for ways of leading a better life.
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition

Language models (LMs) pre-trained on massive amounts of text, in particular bidirectional encoder representations from Transformers (BERT), generative pre-training (GPT), and GPT-2, have become a key technology for many natural language processing tasks. In this paper, we present results using fine-tuned GPT, GPT-2, and their combination for automatic speech recognition (ASR). Unlike unidirectional LM GPT and GPT-2, BERT is bidirectional whose direct product of the output probabilities is no longer a valid language prior probability. A conversion method is proposed to compute the correct language prior probability based on bidirectional LM outputs in a mathematically exact way. Experimental results on the widely used AMI and Switchboard ASR tasks showed that the combination of the fine-tuned GPT and GPT-2 outperformed the combination of three neural LMs with different architectures trained from scratch on the in-domain text by up to a 12% relative word error rate reduction (WERR). Furthermore, on the AMI corpus, the proposed conversion for language prior probabilities enables BERT to obtain an extra 3% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.
An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering

A fundamental task in machine learning involves visualizing high-dimensional data sets that arise in high-impact application domains. When considering the context of large imbalanced data, this problem becomes much more challenging. In this paper, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to reduce the dimensions of an earthquake engineering related data set for visualization purposes. Since imbalanced data sets greatly affect the accuracy of classifiers, we employ Synthetic Minority Oversampling Technique (SMOTE) to tackle the imbalanced nature of such data set. We present the result obtained from t-SNE and SMOTE and compare it to the basic approaches with various aspects. Considering four options and six classification algorithms, we show that using t-SNE on the imbalanced data and SMOTE on the training data set, neural network classifiers have promising results without sacrificing accuracy. Hence, we can transform the studied scientific data into a two-dimensional (2D) space, enabling the visualization of the classifier and the resulting decision surface using a 2D plot.
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation

Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models.However, low-resource languages are mostly left out in these datasets.This is primarily because many widely spoken languages are not well represented on the web and therefore excluded from the large-scale crawls used to create datasets.Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training.This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages.We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training?and 2) How can the resulting translation models effectively transfer to new domains?To answer these questions, we create a new African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset.We demonstrate that the most effective strategy for transferring both to additional languages and to additional domains is to fine-tune large pre-trained models on small quantities of highquality translation data.
A queuing theory model for e-health cloud applications

A healthcare application plays a significant role in people's healthier life in recent times. Cloud computing is an ease technology that provides resources to users on demand. In high-performance computing, cloud has been outgrew technology for providing its services to e-health applications by pay-as-you-go model. Workload management for e-health applications in the cloud is one of the major areas to focus on to achieve availability in e-health cloud. To manage the workload, elasticity is the key characteristics where the workloads are scaled up and down on demand. This can be achieved by effectively allocating and de-allocating virtual machines (VM). Henceforth VM allocation and de-allocation are the major issues in e-health cloud. In this paper, a Markovian-based queueing model is presented to manage the elasticity of an e-health cloud. There are two conditions were analysed in which 'virtual machine is failed' and the 'virtual machine can be recovered' and 'cannot be recovered'. The proposed model helps to improve the virtual machine scaling without changing the type of machine.
Understanding the adoption of cryptocurrencies for financial transactions within a high-risk context

Purpose Cryptocurrencies such as bitcoins represent a novel method of conducting financial transactions and exchanging money. However, their adoption by the general public remains low. Within countries facing financial distress and characterized by a high level of risk, cryptocurrency adoption might offer opportunities for countering crises. The purpose of this study is to explore the factors that influence individuals' adoption of cryptocurrencies for financial transactions within a high-risk context. Design/methodology/approach To do so, it presents a behavioral model, which is tested using data collected from a survey of 255 respondents residing in Lebanon. The causal relationships between the different factors and individuals' willingness to use cryptocurrencies were then analyzed through Structural Equation Modeling. Findings Findings show that financial technology awareness and social influence contribute to reducing perceived risk and increasing individuals' willingness to use cryptocurrencies, while individuals' risk aversion and the presence of regulatory support increase the perceived risk of cryptocurrencies. Originality/value The study is among the first to use a human-centered approach to understanding cryptocurrency adoption and takes place within a country that is facing a deep financial crisis. Its outcomes contribute to existing theories of cryptocurrency adoption and provide policymakers with insight into how adoption is unfolding namely in developing countries.
Brightness as an Augmentation Technique for Image Classification

Augmentation techniques are crucial for accurately training convolution neural networks (CNNs). Therefore, these techniques have become the preprocessing methods. However, not every augmentation technique can be beneficial, especially those that change the image’s underlying structure, such as color augmentation techniques. In this study, the effect of eight brightness scales was investigated in the task of classifying a large histopathology dataset. Four state-of-the-art CNNs were used to assess each scale’s performance. The use of brightness was not beneficial in all the experiments. Among the different brightness scales, the [0.75–1.00] scale, which closely resembles the original brightness of the images, resulted in the best performance. The use of geometric augmentation yielded better performance than any brightness scale. Moreover, the results indicate that training the CNN without applying any augmentation techniques led to better results than considering brightness augmentation. Therefore, experimental results support the hypothesis that brightness augmentation techniques are not beneficial for image classification using deep-learning models and do not yield any performance gain. Furthermore, brightness augmentation techniques can significantly degrade the model’s performance when they are applied with extreme values. Doi: 10.28991/ESJ-2022-06-04-015 Full Text: PDF
Traits of Complex Thinking: A Bibliometric Review of a Disruptive Construct in Education

The purpose of this research is to contextualize the behavior of publications on complex thinking in education. A total of 428 documents compiled in Scopus from 1937 to 2022 were analyzed with a bibliometric study considering criteria such as "complex thinking", "complex thought", and "reasoning for complexity", all combined with education. The results show 153, 47, and 5 publications for each criterion with their related disciplines, citations, types of documents, universities, prominent authors, researching countries, and the general diachronic evolution of the subject, this allows to establish an idea about the implications of the present study according to one of the most important databases in the world. It is concluded that complex thinking and its relationship with education awakens a greater interest in the academy, not only because of its incidence in diverse fields that are nourished by it for the generation of new multidisciplinary knowledge but also because of the published research that demonstrates its transcendence.
Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion

Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images.
Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions

Obstacle avoidance between polytopes is a chal-lenging topic for optimal control and optimization-based tra-jectory planning problems. Existing work either solves this problem through mixed-integer optimization, relying on simpli-fication of system dynamics, or through model predictive control with dual variables using distance constraints, requiring long horizons for obstacle avoidance. In either case, the solution can only be applied as an offline planning algorithm. In this paper, we exploit the property that a smaller horizon is sufficient for obstacle avoidance by using discrete-time control barrier function (DCBF) constraints and we propose a novel optimization formulation with dual variables based on DCBFs to generate a collision-free dynamically-feasible trajectory. The proposed optimization formulation has lower computational complexity compared to existing work and can be used as a fast online algorithm for control and planning for general nonlinear dynamical systems. We validate our algorithm on different robot shapes using numerical simulations with a kinematic bicycle model, resulting in successful navigation through maze environments with polytopic obstacles.
A Blockchain Ontology for DApps Development

Decentralized Applications, or DApps, provide distributed trusted applications that use blockchains. They are often composed of several services, such as transaction scalability protocols, decentralized storage and distributed computing solutions. In order to help formalize these applications, facilitate their development and improve their interoperability, we propose a novel blockchain Ontology focused on the concepts involving DApps. This ontology extends the existing EthOn ontology. It defines several key concepts related to DApps development, as well as the relations between these concepts. It features the formalization of known use cases and design patterns of blockchain technology through blockchain patterns. We use Semantic Web Rule language (SWRL) in order to define rules that express constraints on the formalized concepts. We then execute an inference engine and obtain new constraints on the properties of a defined DApp, such as its cost, based on the DApp characteristics and the services it uses. For illustration we show the inference of constraints between the Ethereum blockchain and its sidechain Polygon. We apply our research work in the field of blockchain video games. This application shows how to use the ontology to model DApps, and can be adapted to other fields.