Reviewerly/exHarmony huggingface dataset:
Design of Parallel Algorithm of Transfer Learning based on Weak Classifier

In the TrAdaBoost method that based on sample transfer learning, weak classifier is the kernel of its design. Aiming at the problem of high time cost for the serial algorithm of weak classifier, a parallel algorithm of weak classifier is designed and applied to the prediction of diabetes in this paper. Experiments are performed on a computer with an 8-core processor, the speedup ratio of the TrAdaBoost algorithm after the parallelization of the weak classifier is 5.14, and the classification accuracy of diabetes prediction is 79%, which means this design can make an auxiliary diagnosis for patients.
Transforming Quantum Programs in Kdm to Quantum Design Models in Uml

Quantum computing has come to stay in our lives. Companies and national governments are investing billions of dollars in it because of the potential benefits that it can achieve, providing promising applications in almost every business sector. Although quantum computing is evolving at an exponential rate, the development of tools, techniques, or frameworks for the evolution of current information systems towards quantum software systems is still proving to be a challenge. This research attempts to manage the evolution of current information systems towards hybrid information systems (combining the classical and quantum computing paradigm). We propose a software modernization process, by following model-driven engineering principles, adapted to the quantum paradigm, based on modified versions of standards like KDM (for reverse engineering of classical, quantum software assets) and UML (for the design of the target system). In particular, this paper focuses on the restructuring transformation from KDM to UML models, where KDM models have been generated from Q# code. This proposal has been validated through a case study which involves 17 programs transformed into KDM. The results obtained show optimistic values regarding the complexity of the UML models generated, their expressiveness and scalability. The main implication of this research is that UML models can indeed help the software evolution of/toward hybrid information systems.
Report on the 1st workshop on recommender systems for human resources (RecSys in HR 2021) at RecSys 2021

Recommender systems are increasingly used in more high risk application domains, including in the domain of Human Resources (HR). These recommender systems help end-users find relevant vacancies out of an abundant overload of available vacancies, but also support other important objectives such as job mobility. Despite the use in industry applications, there are several research challenges associated to such objectives that have not yet been addressed in detail in this context, such as supporting end-users to steer the recommendation process with input and feedback and increasing diversity of recommendations. The goal of our workshop is to build a strong research capacity around recommender systems for HR to address these challenges. This paper describes the goal and activities of the Workshop on Recommender Systems for Human Resources (RecSys in HR 2021), organized at the 15th ACM Conference on Recommender Systems. Date : 10--14 September, 2021. Website : https://foo2021.net.
A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage Systems

The use of the Cloud computing has been constantly on the rise. However, there are many challenges associated with the Cloud, such as high bandwidth requirements, data security, vendor lock-in and others. The recent rise of decentralized file systems (DFSs) can help mitigate some of these challenges. However, they have some limitations of their own and the current solutions do not provide any mechanism for implementing access control policies. This becomes a hurdle for migrating sensitive data from the Cloud as the associated authorization policies cannot be migrated to the DFSs. In this paper, the authors address the problem of migrating data, and associated authorization policies, from the Cloud to the DFS. They have applied the approach on the content and policies from an actual Cloud provider and it migrates data from AWS S3 to the IPFS and the resource-based authorization policies specified at AWS are added to a custom blockchain solution. The authors have provided implementation details to justify the practicality of the approach.
New Extensions of Fuzzy Cognitive Maps for Sequential Multistage Decision-Making Problems: Application in Project Management

From a systematic review on the use of FCMs and their extensions, it is identified that there are shortcomings in the works reported in the consulted bibliography regarding the treatment of indeterminacy and the solution of multistage sequential decision-making problems. In this paper, two new extensions of Fuzzy Cognitive Maps (FCMs) for multistage sequential decision-making problems are proposed. The Multistage Sequential Triangular Neutrosophic Cognitive Map (MSTrNCM) combines neutrosophic theory with computer with words techniques to represent the map’s relationships and the inference process. This extension improves the modeling of indeterminacy and the interpretability of results. The second map, which is called Neutrosophic Cognitive Map based on linguistic Data Summarization (NCM-LDS), uses linguistic summaries to represent the map’s relations and to carry out the inference process. One of the main advantages of this extension is that it facilitates the maps construction and interpretability. Furthermore, the suggested extensions are applied as a decision-making support tool for projects evaluation using a dataset with 1011 projects records. In experimental analysis, the two proposed extensions MSTrNCM and NCM_LDS report better results than the traditional FCM and NCM_Indeterminacy reported in bibliography.
Computational Intelligence Techniques for Big Data Analytics: A Contemplative Perspective

Big data analytics has become an area of great research potential with the emergence of enabling technologies of Web 3.0 and Web 4.0. The proliferation of mobile devices, sensor devices, cloud computing and cyber-physical systems has generated such huge amount of data that if analysed properly are an asset to any organisation or institution. Traditional methods for data analysis are no longer sufficient to handle this data. So, newer and more efficient tools and technologies need to be developed to tap this potential. Computational intelligence techniques like artificial neural networks, evolutionary intelligence, genetic algorithms, federated machine learning, etc., are some of the methods used for big data analytics. In this regard, this paper aims to address the details of different computational intelligence techniques used for big data analytics and provide an in-depth detail of each technique. This study will provide insights and knowledge to the research community to have a one-stop solution to identify the pros and cons of various computational techniques. Further researchers will be able to identify which technique will suit their particular analytics problem at hand.
Coherent Image Animation Using Spatial-Temporal Correspondence

Recent studies have achieved remarkable success using deep generative models for the image animation of an arbitrary object.However, previous methods synthesize animated results in a frame-by-frame manner, which is prone to producing flickering and temporally inconsistent results. In this paper, we propose a novel self-supervised framework leveraging temporal information for image animation. Our framework processes a video clip directly instead of processing each frame independently. To achieve coherence in the animated video, we design a spatial-temporal correspondence network (STCN) to maintain the consistency of the keypoints. Specifically, the STCN takes full advantage of temporal information to propagate the keypoints between adjacent frames, and it can be trained with consistent keypoints during the forward and backward process. Furthermore, we apply a 3D-CNN-based generator and discriminator in our framework to ensure coherence in the final output video. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method.
Web Content Extraction by Weighing the Fundamental Contextual Rules

Nowadays, data access, data sharing, data extraction and data usage have become a vital issue for technology experts. With the rapid growth of content on the Web, humans need new and up-to-date approaches for data extraction from the Web. However, there is much useless and unrelated information such as navigation panel, content table, propaganda, service catalogue, and menus in these pages. Thus, the web content is considered useful (original) and useless (secondary) content. Most receivers and final users search for useful content. This research presents a new approach to extract useful content from the Web. For this purpose, child nodes are selected as the original content by weighing the fundamental contextual rules method to DOM Tree&#x0027;s nodes. Overall, after standardizing web page and developing DOM Tree, the best child node of the parent node are selected according to a weighing algorithm; then, the best path and the best sample node are selected. The presented solution applied on several datasets shows high accuracy rate such as Precision, Recall and F factor are 0.992, 0.983 and 0.988, respectively.
Farmers Agriculture Assistance Chatbot

India is known for the apprehension of global powerhouse of agriculture in the world and has been mainly an agriculture-based economy, in spite of the fact that a lot of farmers and respective individuals face a lot of problems in agriculture due to inadequate knowledge of agriculture production such as poor farming techniques, inadequate materials for the crop, proper crop planning, maintaining of fertilizer balance. This project represents a prototype of a chatbot structure which helps assist individuals or farmers in crop management and predicts the need of the crop. Prediction such as the fertilizer dosage, nutrients in the crop and assists in providing sufficient knowledge to the individual to work on the basic needs. This structure uses Natural Language Processing and takes feed from the predetermined data to give responses on based queries. For the Structure to be accurate the chatbot analyzes factors such as past feed and the sources of the data from "The Indian Council of Agricultural Research"
Collaborative Robots Sim: A Simulation Environment Of Air-Ground Robots With Strong Physical Interactivity

The collaborative robot peers that are able to perform a variety of missions get attention. The current simulations or research platforms only focus on the information interaction between the robots leading to great limitations in the simulation of collaborative robots. In this paper, a simulation environment for collaborative robots with strong physical interaction is proposed. We analyzed the characteristics of various robot simulation platforms and built the main "control-driving-rectify" framework of the environment based on Gazebo. For a better integration of UAV, UGV, manipulators and the physical simulation framework, we modelled the dynamics simulation system of UAV by propeller dynamics and refine the definition of UAV in the physical simulation after an accurate configuration of them. In the process of environment construction, some plain but innovative and valuable physical interaction modules were created which play a major role in the success of the simulation. Finally, the efficiency of this simulation environment is illustrated and verified through the experiments and specific examples.
Accurate Kidney Tumor Segmentation Using Weakly-Supervised Kidney Volume Segmentation in CT images

Recent leading approaches to medical image segmentation rely on deep convolutional networks trained with human-annotated, pixel-level segmentation labels. However, these methods require training images with pixel-level annotations, which are expensive and time-consuming to obtain. Weakly-supervised approaches have therefore emerged as a solution to address this issue. In CT scan images, due to the large number of slices, pixel-level labeling is very tedious, so applying weakly supervised techniques becomes more necessary. We propose a weakly supervised semantic segmentation approach based on image-level labels for kidney tumor segmentation. Experiments on KITS2019 dataset illustrate that our approach achieves Promising results (Dice score of 0.823 for kidney segmentation and 0.583 for tumor segmentation) with only image-level annotations compared to its fully supervised counterpart in KITS19 challenge (Dice score of 0.974 for kidney segmentation and 0.851 for tumor segmentation).
Predictive synthesis of API-centric code

Today’s programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, and the like. Program synthesizers can provide significant coding assistance to this community of users; however program synthesis also can be slow due to enormous search spaces.
Learning Bidirectional Translation Between Descriptions and Actions With Small Paired Data

This study achieved bidirectional translation between descriptions and actions using small paired data from different modalities. The ability to mutually generate descriptions and actions is essential for robots to collaborate with humans in their daily lives, which generally requires a large dataset that maintains comprehensive pairs of both modality data. However, a paired dataset is expensive to construct and difficult to collect. To address this issue, this study proposes a two-stage training method for bidirectional translation. In the proposed method, we train recurrent autoencoders (RAEs) for descriptions and actions with a large amount of non-paired data. Then, we finetune the entire model to bind their intermediate representations using small paired data. Because the data used for pre-training do not require pairing, behavior-only data or a large language corpus can be used. We experimentally evaluated our method using a paired dataset consisting of motion-captured actions and descriptions. The results showed that our method performed well, even when the amount of paired data to train was small. The visualization of the intermediate representations of each RAE showed that similar actions were encoded in a clustered position and the corresponding feature vectors were well aligned.
Veritas: A Sign Language-To-Text Translator Using Machine Learning and Computer Vision

research-article Veritas: A Sign Language-To-Text Translator Using Machine Learning and Computer Vision Share on Authors: Shaun Njazi Faculty of Arts and Science, International University of Malaya-Wales, Malaysia Faculty of Arts and Science, International University of Malaya-Wales, MalaysiaSearch about this author , Sokchoo Ng Faculty of Arts and Science, International University of Malaya-Wales, Malaysia Faculty of Arts and Science, International University of Malaya-Wales, MalaysiaSearch about this author Authors Info & Claims CIIS 2021: 2021 The 4th International Conference on Computational Intelligence and Intelligent SystemsNovember 2021 Pages 55–60https://doi.org/10.1145/3507623.3507633Online:11 April 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-feature Similarity Based Deep Learning Framework for Semantic Segmentation

Liver tumor is one of the significant causes of death among men and women, but it is confirmed that early detection of the disease ensures the long survival of the patient. In our research, a hybrid of Multi-feature pyramid based U-Net, short skip connections and a Feature similarity module are proposed for early tumor detection. The proposed algorithm focuses on improving the tumor segmentation performance with fewer training parameters. The robustness of the proposed algorithm is claimed on the basis of the dice score coefficient of tumor segmentation. We have achieved a dice score of 0.753 and 0.950 on tumor and liver, respectively on the Liver Tumor Segmentation (LiTS) dataset. In comparison with earlier models, our model has achieved a higher dice coefficient with less training time with nearly 6 million learnable parameters.
Information Technology Architects in Agile Organizations

Agile software development is already widespread in the industry and has worked well in small project organizations. However, large-scale development in the form of roles and workflows is needed for large-scale development, especially in the area of software architecture. Software architecture, and more broadly Enterprise architecture, is critical to achieving the expected success of software projects and products. However, during agile software development, for example, Scrum, no positions are created in the team for Software architects. In this paper, we present the results of the analysis of frameworks that consider the position of Software architect, such as the SAFe method, where several positions of architects are created such as Enterprise architect, Chief architect, Administrative architect, Team architect, as well as frameworks in which the position of Software architect, for example, Scrum, is not officially created. In the second case, we examined the intersection of the activities of the Software architect with the Product owner. The results of the research show that Software architects play an important role during agile software development, despite the fact that in some agile approaches they are hidden in other position.
Stac-Da: Structure Aware Cross-Modality Domain Adaptation Framework with Image and Feature Level Adaptation for Medical Image Segmentation

In this work, we present an unsupervised Structure Aware Cross-modality Domain Adaptation (StAC-DA) framework for medical image segmentation. StAC-DA implements an image- and feature-level adaptation in a sequential two-step approach. The first step performs an image-level alignment, where images from the source domain are translated to the target domain by implementing a CycleGAN based model. The latter model includes a structure-aware network that preserves the shape of the anatomical structure during translation. The second step consist of a feature-level alignment. A U-Net network is trained with the transformed source domain images and target domain images in an adversarial manner to produce probable segmentations for the target domain. The proposed framework is evaluated on the problem of bidirectional cardiac substructure segmentation from the Multi-Modality Whole Heart Segmentation Challenge dataset. StAC-DA outperforms leading unsupervised domain adaptation approaches.
The Spoken English Practice System Based on Computer English Speech Recognition Technology

Spoken English practice requires a combination of listening, speaking, reading, and writing, among which listening and speaking are the most difficult. In order to improve the speaking ability of the practitioner, the pronunciation of spoken English needs to be corrected in time. However, the workload of manual evaluation is too large, so it is necessary to combine intelligent methods for spoken language recognition. Based on the needs of spoken English pronunciation correction, this paper combines the computer English speech recognition technology to construct the spoken English recognition and correction model and combines the coding technology to study the English speech recognition technology. Moreover, this article constructs the spoken English practice system based on the actual needs of spoken English practice. Finally, this paper verifies the reliability of this system through experimental research, which provides a reliable means for the subsequent intelligent learning of spoken English.
Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.
The Perception of Test Driven Development in Computer Science – Outline for a Structured Literature Review

Test driven development (TDD) is a practice that aims to improve product quality and maintainability by interweaving the design and implementation with its testing. It is most prominent in the software development domain. However, its usefulness is not undisputed, making it a somewhat controversial topic. Besides giving a short introduction regarding the principles of TDD, the publication at hand motivates and outlines a structured literature review to obtain new insights regarding the perception of TDD in computer science, hopefully contributing to the corresponding discourse. Furthermore, by already conducting the first steps of the review, it provides a first impression regarding the vastness of the potentially relevant literature base and gives a rough indication regarding the extend that is to be expected for the completed work.
Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

Individuals and PCs (personal computers) can be recognized using CAPTCHAs (Completely Automated Public Turing test to distinguish Computers and Humans) which are mechanized for distinguishing them.Further, CAPTCHAs are intended to be solved by the people, but are unsolvable by the machines.As a result, using Convolutional Neural Networks (CNNs) these tests can similarly be unraveled.Moreover, the CNNs quality depends majorly on: the size of preparation set and the information that the classifier is found out on.Next, it is almost unmanageable to handle issue with CNNs.A new method of detecting CAPTCHA has been proposed, which simultaneously solves the challenges like preprocessing of images, proper segmentation of CAPTCHA using strokes, and the data training.The hyper parameters such as: Recall, Precision, Accuracy, Execution time, F-Measure (H-mean) and Error Rate are used for computation and comparison.In preprocessing, image enhancement and binarization are performed based on the stroke region of the CAPTCHA.The key points of these areas are based on the SURF feature.The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.