Reviewerly/exHarmony huggingface dataset:
A Study of Automated Evaluation of Student’s Examination Paper using Machine Learning Techniques

The written exam is a universal tool for evaluating student performance in the field of education. The written exam provides a mechanism by which instructors and organizations ensure the consistency of the assessment process. Human effort required for the assessment is very high and it depends on several factors such as knowledge of the teacher, application level understanding of the teacher, criteria of the marking and time allotted. However, traditional evaluation processes consume very costly efforts and take huge time for the completion of the complete evaluation, verification and publishing of the result process. This research introduces the design and implementation of Handwritten Answer Evaluation (HAES) system for student exam papers. The HAES is an automatic response assessment system that enables the identification of text in answer sheets and can evaluate the grade of each answer based on the previous knowledge of the model. In this study, Optical Character Recognition (OCR) tool is used to extract the text from human written scanned answer script and machine learning/natural language processing (NLP) techniques are used for grading the answer sheets. The scores are based on cosine set similarity measures, where each sentence in the evaluated answer paper carries their corresponding mark. The developed model can be used to evaluate the marks of the unscored descriptive answers.
Circle detection in images: A deep learning approach

Circle detection is a well-known application in computer vision. The Hough transform has been the traditional algorithm applied to detect circular objects in images. In this paper, we are concerned with detecting circular objects for an autonomous underwater robotics application using computer vision. One of the task of the autonomous robot is to identify circular objects underwater. However, circular objects in the image appear in different sizes depending on the depth of the robot. Our experimental studies show that using conventional algorithms have limitations as the environmental light and reflections significantly affect the performance. Furthermore, the algorithm performance depends on parameters used in preprocessing and it has a considerable amount of computational complexity and large memory space requirements. There are various techniques introduced in computer vision literature aiming to reduce computational complexity or to improve its accuracy. Heuristic optimization techniques such as genetic algorithms and simulated annealing are used for large or noisy images for the advantage of less computation time compared to Hough transform. Nevertheless, deep learning algorithms recently become very popular in computer vision due to their remarkable performance in object detection. In this paper, we experimented detecting circular objects in images using latest deep learning algorithms and studied their performance. It showed significant advantage in underwater images compared to conventional algorithms.
CRUDyLeaf: A DSL for Generating Spring Boot REST APIs from Entity CRUD Operations

Abstract Domain-Specific Languages (DSLs) are programming languages designed specifically to express solutions to problems in a particular domain. It is said they foster productivity and quality. In this work we describe CRUDyLeaf, a DSL focused on the generation of Spring Boot REST APIs from entity CRUD operations. Spring Boot is an open source Java-based framework used to implement the REST architecture style. It has gained popularity among developers mainly because it allows to build stand-alone and production ready software applications (avoiding the use of an application server). Through seven proposed stages (domain immersion, golden application implementation, syntax definition, meta model generation, code generator implementation, deployment, and refinement) we describe the development of this DSL. We also exemplify and evaluate the proposed DSL. Our findings suggest a yield automation rate of 32.1 LOC (Lines Of Code) for each LOC written in this DSL, among other observed benefits.
A Workflow Analysis of Context-driven Conversational Recommendation

A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.
Transformer-based Machine Translation for Low-resourced Languages embedded with Language Identification

Recent research on the development of machine translation (MT) models has resulted in state-of-the-art performance for many resourced European languages. However, there has been a little focus on applying these MT services to low-resourced languages. This paper presents the development of neural machine translation (NMT) for low-resourced languages of South Africa. Two MT models, JoeyNMT and transformer NMT with self-attention are trained and evaluated using BLEU score. The transformer NMT with self-attention obtained state-of-the-art performance on isiNdebele, SiSwati, Setswana, Tshivenda, isiXhosa, and Sepedi while JoeyNMT performed well on isiZulu. The MT models are embedded with language identification (LID) model that presets the language for translation models. The LID models are trained using logistic regression and multinomial naive Bayes (MNB). MNB classifier obtained an accuracy of 99% outperforming logistic regression which obtained the lowest accuracy of 97%.
A concept of mirror world for radioactive working environment by interactive fusion of radiation measurement in real space and radiation visualization in virtual space

To understand radiation information, such as dose rate and the position of radioactive substances in a radioactive working environment in detail, the author proposes the construction of a mirror world of the environment. In the proposed mirror world, the work environment is reproduced in a virtual space, and the radiation information measured in a real space is projected onto the virtual space. Note that in addition to displaying the radiation information in the virtual space, the visualization result of the radiation information in the virtual space is used for decision-making in the real space. In this paper, the author introduces an example of building the mirror world based on radiation measurements performed in a laboratory using 137Cs as a radiation source. In the laboratory, the dose rate was measured using a survey meter together with a device for the simultaneous localization and mapping based on three-dimensional (3D) light detection and ranging. The measured dose rate was mapped onto the work environment in the virtual space. Technologies developed by the author, for example, the 3D visualization of the radiation source based on an integration of the Compton camera and structure-from-motion technology and the virtual-reality experience technology of the work environment that displays the source image were also used. The technology to project the radiation-source image into the real space using augmented-reality was also introduced. It is seen that radiation measurement in the real space and visualization of measurement results in the virtual space always affect the decision-making of work in each other's space.
Review of Human Decision-making during Computer Security Incident Analysis

We review practical advice on decision-making during computer security incident response. Scope includes standards from the IETF, ISO, FIRST, and the US intelligence community. To focus on human decision-making, the scope is the evidence collection, analysis, and reporting phases of response, which includes human decision-making within and connecting these phases. The results indicate both strengths and gaps. A strength is available advice on how to accomplish many specific tasks. However, there is little guidance on how to prioritize tasks in limited time or how to interpret, generalize, and convincingly report results. Future work should focus on these gaps in explication and specification of decision-making during incident analysis.
Multilabel Emotion Detection from Bangla Text Using BiGRU and CNN-BiLSTM

Bangla is a popular language spoken by millions of people worldwide. These people use Bangla to convey their emotions. In addition, many non-natives but enthusiastic people in Bangla also want to interpret or communicate these emotions using the translator service. It would be nice to have a way out of the text to detect these feelings. Aiming this target, we have introduced two approaches to identify the emotion from a Bangla text. Taking deep learning as a cornerstone, we have developed two models to predict the affection of Bangla text. These two-prediction model has accounted for six identical emotion concerned labels to predict. To accomplish this, we relied on the Bidirectional Gated Recurrent Unit called BiGRU and the combination of the most classical deep learning algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory named CNN-BiLSTM. After exploiting these two algorithms, we calculated accuracy, precision, recall, and F1-score to evaluate their output. Since the dataset was assembled using a Google translator, those built models display promising performance for any Google Translator's translated extensive Bangla text.
Retrieving Skill-Based Teams from Collaboration Networks

Given a set of required skills, the objective of the team formation problem is to form a team of experts that cover the required skills. Most existing approaches are based on graph methods, such as minimum-cost spanning trees. These approaches, due to their limited view of the network, fail to capture complex interactions among experts and are computationally intractable. More recent approaches adopt neural architectures to learn a mapping between the skills and experts space. While they are more effective, these techniques face two main limitations: (1) they consider a fixed representation for both skills and experts, and (2) they overlook the significant amount of past collaboration network information. We learn dense representations for skills and experts based on previous collaborations and bootstrap the training process through transfer learning. We also propose to fine-tune the representation of skills and experts while learning the mapping function. Our experiments over the DBLP dataset verify that our proposed architecture is able to outperform the state-of-the-art graph and neural methods over both ranking and quality metrics.
Breast Cancer Detection using Deep Neural Network

In recent years, the number of breast cancer patients has increased rapidly. To increase the survival rate, the early diagnosis of breast cancer is very important. Hence, a trustworthy diagnosis and detection process is required. Automatic detection processes will be very helpful for medical practitioners. There are numerous proposed methods for timely sensing of breast cancer This study has suggested a deep neural network with feature selection techniques to predict breast cancer. The appraisal of the suggested strategy is performed on different evaluation benchmarks like train accuracy, test accuracy, precision, recall, specificity, sensitivity, f measure and MCC. Simulation result of the proposed method is very promising (accuracy 99.42%). Based on the findings of experimental simulations and analysis of the statistical data, the suggested method is efficient, beneficial and more accurate to predict breast cancer. As stated by the presented literature review, It is like setting an example among the prevailing machine learning strategies.
Deep domain adaptation with ordinal regression for pain assessment using weakly-labeled videos

Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations of facial expressions, and to operational capture conditions, the accuracy of state-of-the-art deep learning (DL) models for recognizing facial expressions may decline. Domain adaptation (DA) has been widely explored to alleviate the problem of domain shifts that typically occur between video data captured across various source (laboratory) and target (operational) domains. Moreover, given the laborious task of collecting and annotating videos, and the subjective bias due to ambiguity among adjacent intensity levels, weakly-supervised learning (WSL) is gaining attention in such applications. State-of-the-art WSL models are typically formulated as regression problems, and do not leverage the ordinal relationship among pain intensity levels, nor the temporal coherence of multiple consecutive frames. This paper introduces a new DL model for weakly-supervised DA with ordinal regression (WSDA-OR) that can be adapted using target domain videos with coarse labels provided on a periodic basis. The WSDA-OR model enforces ordinal relationships among the intensity levels assigned to target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame). In particular, it learns discriminant and domain-invariant feature representations by integrating multiple instance learning with deep adversarial DA, where soft Gaussian labels are used to efficiently represent the weak ordinal sequence-level labels from the target domain. The proposed approach was validated using the RECOLA video dataset as fully-labeled source domain data, and UNBC-McMaster shoulder pain video dataset as weakly-labeled target domain data. We have also validated WSDA-OR on BIOVID and Fatigue (private) datasets for sequence level estimation. Experimental results indicate that our proposed approach can significantly improve performance over the state-of-the-art models, allowing to achieve a greater pain localization accuracy. Code is available on GitHub link: https://github.com/praveena2j/WSDAOR.
Consistent Sampling Through Extremal Process

The1 Jaccard similarity has been widely used in search and machine learning, especially in industrial practice. For binary (0/1) data, the Jaccard similarity is often called the "resemblance" and the method of minwise hashing has been the standard tool for computing resemblances in massive data. For general weighted data, the commonly used sampling algorithm for computing the (weighted) Jaccard similarity is the Consistent Weighted Sampling (CWS). A convenient (and perhaps also mysterious) implementation of CWS is the so-called "0-bit CWS" published in KDD 2015 [31], which, in this paper, we refer to as the "relaxed CWS" and was purely an empirical observation without theoretical justification. The difficulty in the analysis of the "relaxed CWS" is due to the complicated probability problem, which we could not resolve at this point.
A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.
Mass segmentation for whole mammograms via attentive multi-task learning framework

Mass segmentation in the mammogram is a necessary and challenging task in the computer-aided diagnosis of breast cancer. Most of the existing methods tend to segment the mass by manually or automatically extracting mass-centered image patches. However, manual patch extraction is time-consuming, wheras automatic patch extraction can introduce errors that will affect the performance of subsequent segmentation. In order to improve the efficiency of mass segmentation and reduce segmentation errors, we proposed a novel mass segmentation method based on an attentive multi-task learning network (MTLNet), which is an end-to-end model to accurately segment mass in the whole mammogram directly, without the need for extraction in advance with the center of mass image patch. In MTLNet, we applied group convolution to the feature extraction network, which not only reduced the redundancy of the network but also improved the capacity of feature learning. Secondly, an attention mechanism is added to the backbone to highlight the feature channels that contain rich information. Eventually, the multi-task learning framework is employed in the model, which reduces the risk of model overfitting and enables the model not only to segment the mass but also to classify and locate the mass. We used five-fold cross validation to evaluate the performance of the proposed method under detection and segmentation tasks respectively on the two public mammographic datasets INbreast and CBIS-DDSM, and our method achieved a Dice index of 0.826 on INbreast and 0.863 on CBIS-DDSM.
Dual-Embedding based Deep Latent Factor Models for Recommendation

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.
Web archiving of indigenous knowledge systems in South Africa

The purpose of the paper was to highlight the digitization of Indigenous Knowledge Systems (IKS) in institutional repositories in South Africa with a view to develop a framework for Web archiving IKS-related websites in South Africa. Anchored on the interpretivist paradigm, the qualitative research method was adopted for this research. The multiple case study research strategy was considered appropriate for the study. Data was gathered through face-to-face in-depth interviews and content analysis. Interviews were conducted with eight IKS staff at the IKS Documentation Centres across four provinces in South Africa. The study revealed that although there are efforts to digitize IKS and make them accessible through some channels online, there are no specific digital preservation policies guiding the project. Apart from the fact that there are policies in place to support any Web archiving initiative, the concept of Web archiving was generally unfamiliar to the respondents. The respondents admitting to the lack of a standard policy guiding the digitization project also admitted to a lack of knowledge or in-depth understanding of Web archiving and its prospect as a digital preservation measure. The research, therefore, proposes a Web archiving framework that should be incorporated in the digital preservation policy framework. This research will be useful to policymakers and all stakeholders in South Africa and other parts of Africa.