Reviewerly/exHarmony huggingface dataset:
Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.
Online human action recognition with spatial and temporal skeleton features using a distributed camera network

Online action recognition is an important task for human-centered intelligent services. However, it remains a highly challenging problem due to the high varieties and uncertainties of spatial and temporal scales of human actions. In this paper, the following core ideas are proposed to deal with the online action recognition problem. First, we combine spatial and temporal skeleton features to represent human actions, which include not only geometrical features, but also multiscale motion features, such that both spatial and temporal information of the actions are covered. We use an efficient one-dimensional convolutional neural network to fuse spatial and temporal features and train them for action recognition. Second, we propose a group sampling method to combine the previous action frames and current action frames, which are based on the hypothesis that the neighboring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Third, the skeletons from multiview cameras are fused in a distributed manner, which can improve the human pose accuracy in the case of occlusions. Finally, we propose a Restful style based client-server service architecture to deploy the proposed online action recognition module on the remote server as a public service, such that camera networks for online action recognition can benefit from this architecture due to the limited onboard computational resources. We evaluated our model on the data sets of JHMDB and UT-Kinect, which achieved highly promising accuracy levels of 80.1% and 96.9%, respectively. Our online experiments show that our memory group sampling mechanism is far superior to the traditional sliding window.
Levels of critique in models and concepts of human information behaviour research

Purpose To elaborate the nature of critique presented in the models and concepts of human information behaviour (HIB) research by identifying the issues to which the critique is directed and the ways in which the critique is conducted. Design/methodology/approach Conceptual analysis focusing on 58 key studies on the topic. First, the objects and ways of conducting the critique were identified. Thereafter, three levels of depth at which the critique is conducted were specified. The conceptual analysis is based on the comparison of the similarities and differences between the articulations of critique presented at these levels. Findings At the lowest level of depth, critique of HIB research is directed to the lack of research by identifying gaps and complaining the neglect or paucity of studies in a significant domain. At the level of critiquing the shortcomings of existing studies, the attention is focused on the identification and analysis of the inadequacies of concepts and models. Finally, constructive critiques of research approaches dig deeper in that they not only identify weaknesses of existing studies but also propose alternative in which the shortcomings can be avoided, and the conceptualizations of HIB enhanced. Research limitations/implications As the study focuses on critiques addressed to HIB models and concepts, the findings cannot be generalized to concern the field of Library and Information Science (LIS) as a whole. Moreover, due to the emphasis of the qualitative research approach, the findings offer only an indicative picture of the frequency of the objects critiqued in HIB research. Originality/value The study pioneers by providing an in-depth analysis of the nature of critiques presented in a LIS research domain.
Correcting the Eccentricity Error of Projected Spherical Objects in Perspective Cameras

Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views.
Marketing of higher education institutes through the creation of positive learning experiences – analyzing the role of teachers’ caring behaviors

The higher education sector faces challenges in attracting students due to increasing competition, disruptive forces of online education, social media and employment orientation of education. A differentiating factor can be created by focusing on student-teacher relationships by creating positive learning experiences inside the classroom. The present study indicates that caring behaviors displayed by teachers impact students positive learning experiences. A multi-dimensional construct incorporating relevant dimensions from Watson's caring behavior model was used for this exploratory study with a sample size of 267 millennial students belonging to higher education. Nurturing, direction setting, and supportive environment were significant dimensions of caring behavior that impacted students’ positive learning experiences. While most institutes differentiate themselves based on cost, career opportunities, campus amenities, showcasing teachers’ caring behaviors can give institutes the edge to market themselves since educational journeys experienced by students are at the crossroads of technology and human connection.
PNRank: Unsupervised ranking of person name entities from noisy OCR text

Text databases have grown tremendously in number, size, and volume over the last few decades. Optical Character Recognition (OCR) software is used to scan the text and make them available in online repositories. The OCR transcription process is often not accurate resulting in large volumes of garbled text in the repositories. Spell correction and other post-processing of OCR text often prove to be very expensive and time-consuming. While it is possible to rely on the OCR model to assess the quality of text in a corpus, many natural language processing and information retrieval tasks prefer the extrinsic evaluation of the effect of noise on the task at hand. This paper examines the effect of noise on the unsupervised ranking of person name entities by first populating a list of person names using an out-of-the-box Named Entity Recognition (NER) software, extracting content-based features for the identified entities, and ranking them using a novel unsupervised Kernel Density Estimation (KDE) based ranking algorithm. This generative model has the ability to learn rankings using the data distribution and therefore requires limited manual intervention. Empirical results are presented on a carefully curated parallel corpus of OCR and clean text and “in the wild” using a large real-world corpus. Experiments on the parallel corpus reveals that even with a reasonable degree of noise in the dataset, it is possible to generate ranked lists using the KDE algorithm with a high degree of precision and recall. Furthermore, since the KDE algorithm has comparable performance to state-of-the-art unsupervised rankers, using it on real-world corpora is feasible. The paper concludes by reflecting on other methods for enhancing the performance of the unsupervised algorithm on OCR text such as cleaning entity names, disambiguating names concatenated to one another and correcting OCR errors that are statistically significant in the corpus.
Handwritten Text Recognition with Convolutional Prototype Network and Most Aligned Frame Based CTC Training

End-to-end Frameworks with Connectionist Temporal Classification (CTC) have achieved great success in text recognition. Despite high accuracies with deep learning, CTC-based text recognition methods also suffer from poor alignment (character boundary positioning) in many applications. To address this issue, we propose an end-to-end text recognition method based on robust prototype learning. In the new CTC framework, we formulate the blank as the rejection of character classes and use the one-vs-all prototype classifier as the output layer of the convolutional neural network. For network learning, based on forced alignment between frames and character labels, the most aligned frame is up-weighted in CTC training strategy to reduce estimation errors in decoding. Experiments of handwritten text recognition on four benchmark datasets of different languages show that the proposed method consistently improves the accuracy and alignment of CTC-based text recognition baseline.
Open-Set Recognition With Gradient-Based Representations

Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes. This closed-set assumption is challenged in real-world applications where models may encounter inputs of unknown classes. Open-set recognition aims to solve this problem by rejecting unknown classes while classifying known classes correctly. In this paper, we propose to utilize gradient-based representations obtained from a known classifier to train an unknown detector with instances of known classes only. Gradients correspond to the amount of model updates required to properly represent a given sample, which we exploit to understand the model’s capability to characterize inputs with its learned features. Our approach can be utilized with any classifier trained in a supervised manner on known classes without the need to model the distribution of unknown samples explicitly. We show that our gradient-based approach outperforms state-of-the-art methods by up to 11.6% in open-set classification.
Research on Abnormal Pedestrian Trajectory Detection of Dynamic Crowds in Public Scenarios

In public scenes such as stations and hospitals, the crowds are intensive and abnormal pedestrian often causes group hazards. The recognition of abnormal pedestrian is an important security problem, which is generally solved by inspection robots. Traditional visual feature methods pay much attention to the inherent attributes of pedestrians (such as gender and age), which ignores the complex semantic information displayed by pedestrian trajectories. This article uses scene monitoring visual sensors to analyze pedestrian trajectories in public scenes. We propose an abnormal trajectory recognition framework, which analyzes the pedestrian trajectories from clusters, deviation and trajectory entropy. In this framework, the convergence condition of the K-Means method is optimized to cluster the pedestrian destinations and trajectories; the Mahalanobis distance is used to evaluate the trajectory deviation; the dimensional feature is established through the velocity and angle difference of the trajectory. In the end, the results can prove that the methods in this article can successfully identify abnormal pedestrians.
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Clustering has been applied in machine learning, data mining and so on, and has received extensive attention. However, since some data has noise or outliers, these noise or outliers easily bring about the objective function with large errors. In this paper, a robust clustering model with ℓ2,1, ℓ1 norm and Laplacian regularization (RCLR) is proposed, on which, sparse error matrix is introduced to express sparse noise, and ℓ1 norm is introduced to alleviate the sparse noise. In addition, the ℓ2,1 norm is also introduced to achieves space robust by virtue of its nice rotation invariance property. Therefore, our RCLR is insensitive to data noise and outliers. More importantly, the Laplacian regularization is introduced into the RCLR to improve the clustering accuracy. In order to solve the optimization objective of clustering problem, we propose an iterative updating algorithm, named alternating direction method of multipliers (ADMM), to update each optimization variable alternatively, and the convergence of the proposed algorithm is also proved in theory. Finally, experimental results on a total of eleven datasets of three types of datasets, elaborate the superiority of this method over six existing classical clustering methods. Three types of datasets include face images dataset, handwritten recognition dataset, and UCI dataset. In particular, our RCLR clustering approach has the best effect on face image dataset.
Krushi – The Farmer Chatbot

As per the reports of November 2020, around 58% of India's population earns a primary source of living from agriculture. But, close to 10,000 farmers every year succumb to the harsh conditions in the agricultural sector. These conditions arise when the product of crops is not as desired, spoilt crops, which leads to large loans on the farmers. In this paper, we present an Artificial Intelligence (Al) chatbot that assists the farmers by providing solutions to agricultural queries. Some of the questions are concerning crop sowing, crop diseases, seasons related issues. Thus, benefit the farmers in making the right decisions regarding their crops, thereby increasing their yield. Krushi - The Farmer Chatbot is an end-to-end trainable learning model to create a conversational system with minimum error and answer questions about current conditions. The chatbot is build using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The dataset for the chatbot is used from Kisan Call Centre (KCC). The proposed system, answers queries related to weather, plant protection, animal husbandry, market price, fertilizer uses, government schemes, soil testing with an overall accuracy of 96.1% using RASA X.
Text-based multi-dimensional medical images retrieval according to the features-usage correlation

Emerging medical imaging applications in healthcare, the number and volume of medical images is growing dramatically. Information needs of users in such circumstances, either for clinical or research activities, make the role of powerful medical image search engines more significant. In this paper, a text-based multi-dimensional medical image indexing technique is proposed in which correlation of the features-usages (according to the user's queries) is considered to provide an off-the content indexing while taking users' interestingness into account. Assuming that each medical image has some extracted features (e.g., based on the DICOM standard), correlations of the features are discovered by performing data mining techniques (i.e., quantitative association pattern discovery), on the history of users' queries as a data set. Then, based on the pairwise correlation of the features of medical images (a.k.a. Affinity), set of the all features is fragmented into subsets (using method like the vertical fragmentation of the tables in distribution of relational DBs). After that, each of these subsets of the features turn into a hierarchy of the features (by applying a hierarchical clustering algorithm on that subset), subsequently all of these distinct hierarchies together make a multi-dimensional structure of the features of medical images, which is in fact the proposed text-based (feature-based) multi-dimensional index structure. Constructing and using such text-based multi-dimensional index structure via its specific required operations, medical image retrieval process would be improved in the underlying medical image search engine. Generally, an indexing technique is to provide a logical representation of documents in order to optimize the retrieval process. The proposed indexing technique is designed such that can improve retrieval of medical images in a medical image search engine in terms of its effectiveness and efficiency. Considering correlation of the features of the image would semantically improve precision (effectiveness) of the retrieval process, while traversing them through the hierarchy in one dimension would try to optimize (i.e., minimize) the resources to have a better efficiency. The proposed text-based multi-dimensional indexing technique is implemented using the open source search engine Lucene, and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier platform (available for the benchmarking of information retrieval systems) and other the most related indexing techniques. Evaluation results of memory usage and time complexity analysis, beside the experimental evaluations efficiency and effectiveness measures show that the proposed multi-dimensional indexing technique significantly improves both efficiency and effectiveness for a medical image search engine.
The forgotten role of search queries in IR-based bug localization: an empirical study

Being light-weight and cost-effective, IR-based approaches for bug localization have shown promise in finding software bugs. However, the accuracy of these approaches heavily depends on their used bug reports. A significant number of bug reports contain only plain natural language texts. According to existing studies, IR-based approaches cannot perform well when they use these bug reports as search queries. On the other hand, there is a piece of recent evidence that suggests that even these natural language-only reports contain enough good keywords that could help localize the bugs successfully. On one hand, these findings suggest that natural language-only bug reports might be a sufficient source for good query keywords. On the other hand, they cast serious doubt on the query selection practices in the IR-based bug localization. In this article, we attempted to clear the sky on this aspect by conducting an in-depth empirical study that critically examines the state-of-the-art query selection practices in IR-based bug localization. In particular, we use a dataset of 2,320 bug reports, employ ten existing approaches from the literature, exploit the Genetic Algorithm-based approach to construct optimal, near-optimal search queries from these bug reports, and then answer three research questions. We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports. However, these bug reports indeed contain high-quality search keywords in their texts even though they might not contain explicit hints for localizing bugs (e.g., stack traces). We also demonstrate that optimal queries and non-optimal queries chosen from bug report texts are significantly different in terms of several keyword characteristics (e.g., frequency, entropy, position, part of speech). Such an analysis has led us to four actionable insights on how to choose appropriate keywords from a bug report. Furthermore, we demonstrate 27%–34% improvement in the performance of non-optimal queries through the application of our actionable insights to them. Finally, we summarize our study findings with future research directions (e.g., machine intelligence in keyword selection).
Object detection in rural roads using Tensorflow API

Object detection is a challenge in the computer vision area. Traditional techniques work reasonably well for this problem in urban areas where the roads and the boundaries are clearly marked. However, in the rural areas in the developed countries, the assumption does not hold which leads to the failure of such techniques. In this paper, we propose using TensorFlow Object Detection API, based on the combination of deep convolutional neural networks (CNNs). We investigate the performance of Faster RCCN, RFCN, and SDD frameworks in the context of cars, people, bike, and motorcycle detection from rural area images. We trained and tested these models on our own dataset. The Faster RCNN Resnet 50 model provides high performance in the bike, motorcycle and, car recognition, however, the Faster RCNN inception v2 models are better at detecting bike and, motorcycle in complex scenarios.