Reviewerly/exHarmony huggingface dataset:
Cognitive Task Analysis: Eliciting Management Cognition

Cognitive Task Analysis (CTA) is a powerful qualitative methodological approach which has the potential to add to management researchers’ knowledge of in-depth interviewing techniques, whilst addressing the importance of producing rigorous research that has relevance for management practice. Developed within the Naturalistic Decision-Making community, this set of semi-structured interviewing techniques have much to offer to management researchers. Distinct from traditional semi-structured interview methods, CTA is designed to identify the knowledge requirements that underpin expertise in complex work domains. CTA is underutilised by management scholars, contrary to other applied social science disciplines in cognitive psychology, human factors, and ergonomics where it is widely used. This paper aims to advance understanding of these pragmatic techniques which are theoretically rigorous and easy to use, with results which have high face validity with practitioners. First, we provide an overview of CTA as a robust methodological approach to research oriented around knowledge elicitation. Second, we provide two examples of how the CTA approach has been used to research management; specifically using the Critical Decision Method to explore management in high-stakes environments; and an Applied Cognitive Task Analysis to explore global strategic leadership. In ending, we highlight potential avenues for using CTA for innovative management research.
Corporate Investment in the Context of Rising Populism: Domestic Firms, MNEs and Emerging Markets

This paper analyzes the impact of growing populism on national and multinational investments. Combining insights from international business and political science, various hypotheses are developed about how a populist government and firm’s geographic scope shapes corporate investment decisions. By testing these hypotheses on a global dataset of firms from 1994 to 2020, we show that firms in populist countries reduce investment more than those in non-populist countries. However, our findings highlight that the type of populism matters, as left-wing populism amplifies this negative effect on investment compared to right-wing populism. Furthermore, the effect of populism on business investment is exacerbated in emerging markets compared to developed economies. Finally, the investment activity of multinationals is less sensitive to the presence of populism in their own country than domestic firm´s investment. However, multinationals withdraw their investments when a populist government arrives in the host country. This paper, in addition to considering institutional frameworks in the study of political uncertainty and firm's investments, also recognizes the ability of actors to exacerbate uncertainty by generating institutional volatility. Overall, our results provide new evidence in the literature on the influence of populism on corporate investment activities.
Optimized tongue driven system using artificial intelligence

This paper presents a cost-effective design of a wearable wireless tongue drive system (TDS) for disabled individuals, particularly with spinal cord injuries. We propose a basic TDS whose language is specifically designed for issuing movement-based commands, be it a gadget or a self-governing transport chair. While the overall industrial penetration of TDS is low, we offer a cheap and straightforward design for a TDS that consists of items which amount to as low as US$30. A data-driven-based approach has been used to classify the tongue-gestures with a response time of less than 0.25 s. Compared with other machine learning algorithms, the proposed TDS is equipped with shallow neural networks that govern all the decisions when it comes to classifying the tongue gesture. This has been achieved after a thorough data analysis and rigorous comparison with other classification techniques along with hardware deployment. With its open architecture, the newly designed TDS can be an ideal tool to control other movement-based peripherals. The final test accuracy amounts to 93.4% when the TDS is tested on human subjects.
Distinctive characteristics of Cheniah river sand from Dungun, Terengganu Malaysia

Mineral ilmenite sand deposit in the Hulu Dungun, Terengganu, Malaysia, has prompted a huge interest in their viability as a new resource survey of titanium dioxide feedstock in Malaysia. In accordance with this necessity, this study is executed to conduct mineral characterisation on the ilmenite sand (IS) located at the catchment area, Cheniah River, Kelip Hill, Hulu Dungun, Malaysia. Characterization of raw ilmenite sand was done by X-Ray Fluorescence (XRF) for exploring elements in the sample. Digital microscopy (DM) for optical imaging purpose with polarizing filterand Scanning Electron Microscope (SEM) analyses were developed for surface morphological studies. As determined through this data research, chemical analysis reveals that IS has the highest amount of 44.0% TiO2, and come after with 37.8% Fe2O3, 12.0% SiO2 and other impurity elements such as Mn, V, Cr and Mg. DM images has the same point of view regarding to the mineral constituents from XRF data wherein majority of the IS grains display black dark to steel grey colour as the ilmenite and few of colourless quartz. Meanwhile, the surface morphology from both analyses, DM and SEM can be concluded that IS has heterogenous surface. Most of the IS grains have angular shape with moderate sphericity and few subrounded and high sphericity. Nearly all of the IS are found to be associated with iron oxide as displayed through Backscattered image (BSE) from SEM-EDX analysis. This characterization study's data and information assist in the ilmenite sand discovery and creates a design of a viable treatment procedure which will be developed in future work for the local mineral exploitation.
Spatiotemporal regularization correlation filter with response feedback

Most existing trackers based on correlation filtering try to introduce different regularization items to improve the learning process of the tracking object. However, the parameters of the corresponding regularization items need to be adjusted dynamically to meet the corresponding tracking process. The regularization parameters are often fixed, so they cannot be updated for more accurate tracking. We propose a method to adaptively adjust the spatiotemporal regularization parameters during the tracking process. We make use of the important correlation between the response map and tracking quality in the tracking process of correlation filtering and introduce the confidence description index of the response map to optimize the spatiotemporal regularization parameters, so the training of correlation filtering can focus on the part with high confidence. In this way, the overfitting situation in training is reduced, and the accuracy and robustness of the tracker are enhanced. In performance evaluation experiments on the OTB2015 and DTB70 datasets, our algorithm outperforms the other comparative algorithms in terms of tracking success rate and distance accuracy, especially in complex scenarios, such as illumination variation, background clutter, out-of-view, and low resolution.
Towards Label-Free Few-Shot Learning: How Far Can We Go?

Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes. Since data annotation is time-consuming and costly, reducing the label requirement even further is an important goal. To that end, our paper presents a more challenging few-shot setting with almost no class label access. By leveraging self-supervision to learn image representations and similarity for classification at test time, we achieve competitive baselines while using almost zero (0–5) class labels. Compared to existing state-of-the-art approaches which use 60,000 labels, this is a four orders of magnitude (10,000 times) difference. This work is a step towards developing few-shot learning methods that do not depend on annotated data. Our code is publicly released at https://github.com/adbugger/FewShot . (This work was supported by the IMPRINT program.)
Soft Biometric Based Person Retrieval for Burglary Investigation

Burglar investigations increase day by day and growing up as a never-ending issue in the world. Surveillance systems are deployed in the general public for security, and many urban communities and nations are currently equipped with such systems. However, this is hard to recognize the face from the surveillance footage for authentication because of the lower resolution and the distance present between the sensor and the person. In such situations, existing problems utilize many traits from the human being to distinguish the Person. Height, attire color, gender, attire type, and build usually describe a person. Such characteristics of a person are said to be soft biometrics traits; these attributes are trained in such a manner to recognize the robber. To resolve this issue, we build-up a Deep Learning-based approach, Soft Bio-metric based-Person Retrieval (SB-PR), which uses Mask Region-based Convolutional Neural Network (R-CNN) and You Only Look Once (YOLO V4) to recognize the suspect using height and attire color, and gender. A joint framework addressing various challenges namely low-resolution, view and pose-variance are proposed. On the typical person recovery dataset of the Jewelry Shop Theft Dataset (JSTD), we achieve an average Intersection of Union (IoU) of 0.506. Thus, We hope our proposed framework will reduce the burden of manual query search of person recovery in the closed-circuit television (CCTV) security system.
A Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth Images

Nowadays, the trend is to use hand gestures to interact with digital devices such as computers, robots, drones, VR interfaces, etc. While interacting with digital devices, selection, pick and place, and navigation are important tasks which can be performed using pointing gestures. Thus, detection of pointing gestures is an important step for pointing gesture based interaction. In computer vision-based analysis of gestures, depth images of the hand region have been predominantly used. Currently, the only existing method to detect pointing gesture from depth images of the hand region has sub optimal performance as shown in our experiments. This can be attributed to the lack of a large data-set that could be used to detect pointing gestures. To overcome this limitation, we create a new large data-set (1,00,395 samples) for pointing gesture detection using depth images of the hand region. The data-set has a large variation in the hand poses and in the depth of the hand with respect to the depth sensor. The data-set will be made publicly available. We also propose a 3D convolutional neural network based real-time technique for pointing gesture detection from depth images of the hand region. The proposed technique performs much better than the existing technique with respect to various evaluation measures.
Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World

The post COVID world has completely disrupted our lifestyle, where wearing a mask is necessary to protect ourselves and others from contracting the virus. However, face masks have proved to be challenging for facial biometric systems, in the sense that these systems do not work as expected when wearing masks as nearly half of the face is covered, thus reducing discriminative features that the model can leverage. Most of the existing frameworks rely on the entire face as the input, but as the face is covered, these frameworks do not perform up to the mark. Moreover, training another facial recognition system with mask images is challenging as the availability of datasets is limited, both qualitatively and quantitatively. In this paper, we propose a framework that shows better results without significant training. In the proposed work, firstly we extracted the face using SSD, then by obtaining Facial Landmarks for utilizing the cues from other dis-criminative parts for facial recognition. The proposed framework is able to out-perform other frameworks on facial mask images and also found ~4.5% increment in accuracy.
Evaluation of Detection and Segmentation Tasks on Driving Datasets

Object detection, semantic segmentation, and instance segmentation form the bases for many computer vision tasks in autonomous driving. The complexity of these tasks increases as we shift from object detection to instance segmentation. The state-of-the-art models are evaluated on standard datasets such as pascal-voc and ms-cococ, which do not consider the dynamics of road scenes. Driving datasets such as Cityscapes and Berkeley Deep Drive (bdd) are captured in a structured environment with better road markings and fewer variations in the appearance of objects and background. However, the same does not hold for Indian roads. The Indian Driving Dataset (idd) is captured in unstructured driving scenarios and is highly challenging for a model due to its diversity. This work presents a comprehensive evaluation of state-of-the-art models on object detection, semantic segmentation, and instance segmentation on-road scene datasets. We present our analyses and compare their quantitative and qualitative performance on structured driving datasets (Cityscapes and bdd) and the unstructured driving dataset (idd); understanding the behavior on these datasets helps in addressing various practical issues and helps in creating real-life applications.
AB-net: Adult- Baby Net

The problem of estimating and classifying age from a given input im- age is age old. With the advancement of modern technology and recent progress in the field of deep learning it has been made possible to gain success for this particular application. We present a novel algorithm inspired by the Multi-Task Cascaded Convolutional Neural Networks architecture and a subsequent pipeline to a Prediction architecture thus defining an end - to - end pipeline, which can predict with great certainty the age group, from a given input image. We introduce approaches in data preparation techniques such as Complex Negatives and IOU based segregation which proves beneficial to the reduction of false-positive classification of samples. Thus significantly proving on benchmarks to achieve robust performance with regard to the age classification problem. The proposed method is able to accurately predict and has been benchmarked on the FDDB dataset [7].
Signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures

Online signature verification is an open research problem. The research in this field has widely evolved in the last decade, starting from the traditional approaches such as dynamic time warping, hidden markov models, support vector machines during the early part of the decade to modern approaches involving deep learning methodologies in the last few years. Online signature verification using deep neural networks is the state of the art. However training and retraining a deep learning model is computationally expensive. We report here Signature2Vec algorithm that has 3 main features - (i) coordinate reference frame agnosticism for translation and rotation, (ii) horizontal and vertical scale invariance and (iii) high quality and lightweight forgery detection classifier. The Signature2Vec algorithm generates a vector embedding for a handwritten signature purely based on the time series of coordinates sampled while the signature is written. Our algorithm has performed at par with much better computational cost reductions over time aligned recurrent neural networks (TA-RNN) based approaches. We also propose an method of generating synthetic forgery data based on random strokes as subsequences of time series data of coordinate trace of a given signature. The synthetic forgery data assists in building personalized signature verification models only based on normal signatures which is usually the case. The Signature2Vec algorithm has yielded an average accuracy of 86% on the SVC-2004 dataset. Moreover the vector embedding generated by Signature2Vec when used for TA-RNN, the performance has improved compared to usage of raw features.
Segmentation of Unstructured Scanned Devanagari Newspaper Documents

Newspapers are a great source of information. Very large projects are running worldwide to preserve the repository of this pile of data. One way is to digitize the pages and store them on a disk. But digitization preserves only the image, no searchable option can be made available on an image to search a particular headline or event. So, the appropriate way is to develop an OCR that could convert data into the process-able form. In this paper, a very novel technique is presented to completely segment the Devanagari news article. News documents may consist of images related to the event along with the text. So, images or graphics need to be separated first for further segmentation of blocks. If the newspaper article is only textual then the segmentation process will start by segmenting the body text (columns etc.) from the headline section. There could be many hurdles in the development of OCR for the newspaper due to the complex layout. Generally, the biggest problem is caused to poor paper and printing quality. Many problems have also been discussed in this article. This paper presents a complete segmentation of text/graphics, headlines, columns, lines, and characters. Proposed algorithms have achieved good accuracies on both textual as well non-textual document images.
On the Prospects of Latent MasterPrints

Latent fingerprints are the prime evidence for forensic officers investigating a criminal case. Subsequently, the legislative procedure considers the forensic department’s reports as authentic document to support their judgment. Hence, an automated latent fingerprint identification system must produce accurate results to ensure only culprits are punished instead of innocent individuals. Recent research confirmed the existence of a MasterPrint as a partial fingerprint identifying more than four distinct subjects enrolled with the database. Usually, latent fingerprints are partial impressions, i.e. fingerprints cover small portion of full finger. Hence, it presents a scope to examine the possibility of a Latent MasterPrint. We investigate the feasibility of the Latent MasterPrint in this article using the Multi-sensor Optical and Latent Fingerprint (MOLF) DB4 dataset. The identification results using the NIST Biometric Image Software (NBIS) reveal the possibility of Latent MasterPrints.
Structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated Images

Images acquired in poor illumination conditions are characterized by low brightness and considerable noise which constrain the performance of computer vision systems. Image enhancement thus remains crucial for improving the efficiency of such systems. To improve the visibility of low-light images, a novel image enhancement framework based on the structure-texture decomposition is proposed in this paper. Firstly, the low-light image is split into structure and texture layers using the total-variation (TV) based image decomposition approach. The structure layer is initially diffused using Perona-Malik (PM) diffusion model and the local and global luminance enhancement is incorporated in the structure-pathway using an expanded model of biological normalization for visual adaptation. In the texture pathway, the suppression of local noise and the enhancement of image details are attained with the estimation of the local energy of the texture layer and the strategy of energy weighting. Eventually, the final enhanced image of improved quality is obtained by merging the modified structure and texture layers. The effectiveness of the proposed framework is validated using no-reference image quality metrics including IL-NIQE, BRISQUE, PIQE, and BLIINDS-II. The experimental results show that the proposed method outperforms the state-of-the-art approaches.
A Platform for Large Scale Auto Annotation of Scanned Documents Featuring Real-Time Model Building and Model Pooling

Document digitization is an active area of research especially involving handwritten manuscripts. While the most common use cases involve digital libraries, there are other important applications in the area of electronic health records where handwritten text is predominant in developing worlds. The state-of-the-art approaches are domain-specific, and scaling across domains is still an open research problem. We report here a platform for real-time annotation and training of sub-region models in scanned documents using model pools and plug-n-play of annotation services. Given a document, sub-regions are annotated with textual labels. The textual regions themselves may correspond to characters or words or any other pattern of interest. For a given sub-region category, several sub-regions may be present in a given page or across pages. In the proposed system, a user needs to annotate only some of the sub-regions. A convolutional neural network (CNN) model is built for each of the sub-region categories, and named sets or pools of such models are prepared for application on any new document. We observe that a sub-region label may be provided by an existing optical character recognition system instead of a human annotator. In this regard, we have provisioned annotation as a service where any third-party system can be integrated into a plug-n-play mechanism. The state-of-the-art systems focused on having a pre-trained monolithic model which suffers from the problem of catastrophic forgetting when new sub-region classes are added over time. In our approach, due to sub-region specific models, the previous data models are not touched and hence providing a truly incremental learning solution. We have carried out the validation by choosing handwritten data sets belonging to different languages such as Devanagari, Kannada, Telugu, English that span diverse text patterns and the models produced by our sub-region detection algorithm were evaluated on documents containing hundreds of handwritten scripts by several authors. With respect to the performance of our models on the validation data sets, we found mAP scores for different data sets as follows: Devanagari words (96.18); Telugu words (93.20); Devanagari letters (100); Kannada letters (99.83); Tesseract English word-level annotations (90). We have also presented a single page annotation as proof of concept for annotation as a service for Kannada, Telugu, Malayalam, and English recognition to learn from Tesseract annotations.
Improved Periocular Recognition Through Blend of Handcrafted and Deep Features

Periocular region is the area surrounding human eye. It has emerged as an effective biometric trait due to its robust nature, requirement of less user cooperation and relative invariance to aging effects. Owing to these advantages, we intend to present a more secure and safe periocular recognition approach by fusing the matching scores of handcrafted and deep learning features. The handcrafted descriptor is made to analyze the periocular images at multiresolution level and understand the relationship within local pixel neighborhood. On the contrary, deep learning features are extracted using the strong potential of transfer learning. This research is completed on a publicly available standard periocular database to substantiate the performance of proposed method.