Reviewerly/exHarmony huggingface dataset:
Agile Work Practices: Scale Development and Validation

Building on team effectiveness and work design perspectives, the present study develops a novel Agile Work Practices Instrument (AWPI). This instrument measures the most common agile practices and further distinguishes them along the taskwork-teamwork continuum. Using data of 269 teams, we establish the psychometric properties and construct validity of the AWPI. Tests of measurement models and nomological networks supported the distinction between agile taskwork and agile teamwork. As hypothesized, agile taskwork related uniquely to emergent taskwork planning and team autonomy, while agile teamwork related uniquely to emergent teamwork planning and feedback. The AWPI sets the stage for more empirical research on agile working, beyond the software development context, where most agile concepts originated. By integrating team effectiveness and work design perspectives, we aim to clarify the meaning of the term “agile” for management researchers and practitioners.
Digital Tedious Work and Developing Novel Outcomes with Digitization

Digital technology is pervasive in creative work, yet how it shapes the process of making novel outcomes remains underexamined. Drawing from a comparative ethnography of two settings in which digitization prevails—Nashville music production and systems biology cancer research—we present a model of digital tedious work in developing novel outcomes. We discover that exploiting digitization's potential for unlimited experimentation and refinement amplifies digital tedious work. Digital tedious work is highly repetitive, time-intensive, requires substantial expertise, and is inherent to creating with digitization. If not managed deftly, digital tedious work undermines the generation and synthesis of novel ideas. We find that actors engage in three curbing practices—truncating technology, automating, and zooming in and out—to limit digital tedious work and move the creative process forward. Our study offers three contributions: First, we develop a model of how digital tedious work arises throughout the creative process, why it becomes problematic, and how actors engage in curbing to respond. Second, we demonstrate the essential role of curbing practices as a necessary complement to exploiting digitization. Third, we provide evidence of how digital technology not only supports but also undermines the development of novel outcomes by exposing the pitfalls of digitization’s unlimited possibilities.
RAKSHA - The Smart Security System

Web-based technology has improved drastically in the past decade. As a result, security technology has become a major help to protect our daily life. In this project, we propose a robust security based on face recognition system with security for gas and fire. In particular, we develop this system to give access into a room for authenticated users and help us in the condition where there is LPG gas leak or fire catch. The classifier is trained by using a new adaptive learning method. The training data are initially collected from live images. The accuracy of the classifier is incrementally improved as the user starts using the system. A novel method has been introduced to improve the classifier model by human interaction and social media. By using a deep learning framework- TensorFlow, it will be easy to reuse the framework to adopt with many devices and applications. In addition to face Security system, we are going with Pin conformation, OTP and Finger print series.
DIGITAL EDUCATION IN HIGHER EDUCATION INSTITUTIONS IN PORTUGAL AND BRAZIL – CHALLENGES AND TRANSFORMATIONS

"This Communication aims to promote a reflection on Digital Education in Higher Educations Institutions in Portugal and Brazil, and the challenges and transformations perceived, when it is implemented, on the occasion of remote teaching during the pandemic of the new corona virus. From this study, a greater exchange of experiences in terms of methodological proposals in relation to Digital Education could be a great positive differential. And yet, different ways of facing the different challenges to which the researched institutions were and are being submitted, tends to promote valuable cooperation between the researched institutions, promoting the necessary transformations, in the face of the adversities encountered. In Higher Education, Digital Education is seen as a trend in education, as well as in citizenship. What reinforces the 2030 agenda of the UN (United Nations Organization), in its SDGs – Sustainable Development Goals, for a more inclusive world. Digital Education, for some higher education institutions, was previously adopted as a beginner, today it is seen as a trend that allows broad access to higher education, expanding the notion of citizenship. The challenges are numerous and need to be overcome, mainly because of its originality. And, as we are not in a controlled situation, neither in relation to the virus, technological conditions, nor behavioral, psychological conditions, efforts must be scaled in order to make cooperation between institutions the true legacy of this context. In this way, it is believed that Digital Education will be increasingly fulfilling its transforming role of promoting a more inclusive society."
Achieving deep clustering through the use of variational autoencoders and similarity-based loss

&lt;abstract&gt;&lt;p&gt;Clustering is an important and challenging research topic in many fields. Although various clustering algorithms have been developed in the past, traditional shallow clustering algorithms cannot mine the underlying structural information of the data. Recent advances have shown that deep clustering can achieve excellent performance on clustering tasks. In this work, a novel variational autoencoder-based deep clustering algorithm is proposed. It treats the Gaussian mixture model as the prior latent space and uses an additional classifier to distinguish different clusters in the latent space accurately. A similarity-based loss function is proposed consisting specifically of the cross-entropy of the predicted transition probabilities of clusters and the Wasserstein distance of the predicted posterior distributions. The new loss encourages the model to learn meaningful cluster-oriented representations to facilitate clustering tasks. The experimental results show that our method consistently achieves competitive results on various data sets.&lt;/p&gt;&lt;/abstract&gt;
Deep Convolutional K-Means Clustering

Conventional Convolutional Neural Network (CNN) based clustering formulations are based on the encoder-decoder based framework, where the clustering loss is incorporated after the encoder network. The problem with this approach is that it requires training an additional decoder network; this, in turn, means learning additional weights which can lead to over-fitting in data constrained scenarios. This work introduces a Deep Convolutional Transform Learning (DCTL) based clustering framework. The advantage of our proposed formulation is that we do not require learning the additional decoder network. Therefore our formulation is less prone to over-fitting. Comparison with state-of-the-art deep learning based clustering solutions on benchmark image datasets shows that our proposed method improves over the rest in challenging scenarios where there are many clusters with limited samples.
A Survey Paper on Sketch to Face Recognition by using Machine Learning

Our goal is to create a tool that assists forensic investigators in accurately identifying victims. However, police sketching tactics are a common component of law enforcement investigations and are frequently used to identify suspects from the recollection of an eyewitness. This time-honored method of identification is typically laborious and slow, and it could not result in the appropriate criminal being apprehended. In order to accurately identify the criminal from a collection of face photos, an automatic face sketch identification system is needed. Because faces and sketches are produced from many sources and have varied gaps to be filled at low and high levels, such technology design presents an open research problem. Although other approaches have been suggested, we are not aware of any surveys on this specific subject. Measuring the performance of new systems and of current systems becomes extremely critical as we propose new algorithms and construct more systems. Below is an overview of systematic data collecting and evaluation of facial recognition systems. It can be difficult to identify a 3D item from its 2D representation. For appearance- or image-based techniques, the posture and illumination concerns are two significant problems. To address these difficulties, numerous strategies have been put forth, the bulk of which focus on domain knowledge.
Automatic Face Mask Detection using PCA

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. This paper represents an implementation of Principal Component Analysis (PCA) on masked and no masked face recognition. Security is an essential term in our today’s life. In various Biometric technology, face recognition is widely used to secure any system because it is better than any other traditional techniques like PIN, password, fingerprint etc. and most reliable to identify or verify a person efficiently. In recent years, face recognition is a very challenging task because of different occlusion or masks like the existence of sunglasses, scarves, hats and different types of make-up or disguise ingredients. The accuracy rate of face recognition is influenced by these types of masks. Many algorithms have been developed recently for non-masked face recognition which are widely used and give better performance. Still in the field of masked face recognition, few contributions has been done. Therefore, in this work a statistical procedure has been selected which is applied in non-masked face recognition and also apply in the masked face recognition technique. PCA is more effective and successful statistical technique and widely used. For this reason in this work, PCA algorithm has been chosen. Finally, a comparative study also done here for a better understanding.
Automated Search for Concepts of Cognitive Map in the Field of Strategic Management

The cognitive map provides an integral idea of complicated economic situation. The article proposes to identify cognitive map concepts not on the basis of experts’ reports, as it is usually done, but on the basis of thematic modeling methods – a fast developing line in statistic text analysis. It will give an opportunity to increase confidence in forecasts made on the basis of cognitive modeling methods. For each year (from 2016 to 2020) a model of similarity of subjects found in the sphere of strategic management was plotted on the basis of key publications in ScienceDirect for this period, which allowed to find key topics by years through using measures of centrality based on own vector. The author identified semantic structures of academic publications and key topics that excited scientific community in the field of strategic management from 2016 to 2020 and their dynamics, which is considered an important step towards automated plotting of the cognitive map being a tool of analyzing and forecasting the condition of complicated systems.
Challenges for Digital University

The term digital university is the need of the hour however bringing the education system easily available and accessible is a sea change in this globalized world. The digital university is the concept of implementing teaching and learning anywhere at ease. The union budget 2022-2023 proposes the establishment of a digital university. Currently, India occupies 2 digital universities with a vision of digitization of knowledge and skill with proper training in Artificial Intelligence, Virtual Reality, and Augmented Reality. But every task needs planning, execution &amp; follow-up. But the path is bundled with ifs and but, does and don’ts, and hurdled with blockages. For an extra strong future, we need to be extra predictable, extra manageable, and extra implementable. So this study talks about risks to be covered in order to set up digital universities throughout India, how the hub &amp; spoke model works and danger of information manipulation, and many more.
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel  Attribute Synthesis

Most of the existing algorithms for zero-shot classification problems typically rely on the attribute-based semantic relations among categories to realize the classification of novel categories without observing any of their instances. However, training the zero-shot classification models still requires attribute labeling for each class (or even instance) in the training dataset, which is also expensive. To this end, in this paper, we bring up a new problem scenario: "Are we able to derive zero-shot learning for novel attribute detectors/classifiers and use them to automatically annotate the dataset for labeling efficiency?" Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i.e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner. Our proposed method, Zero Shot Learning for Attributes (ZSLA), which is the first of its kind to the best of our knowledge, tackles this new research problem by applying the set operations to first decompose the seen attributes into their basic attributes and then recombine these basic attributes into the novel ones. Extensive experiments are conducted to verify the capacity of our synthesized detectors for accurately capturing the semantics of the novel attributes and show their superior performance in terms of detection and localization compared to other baseline approaches. Moreover, with using only 32 seen attributes on the Caltech-UCSD Birds-200-2011 dataset, our proposed method is able to synthesize other 207 novel attributes, while various generalized zero-shot classification algorithms trained upon the dataset re-annotated by our synthesized attribute detectors are able to provide comparable performance with those trained with the manual ground-truth annotations.
COCOPLOT: COlor COllapsed PLOTting software : Using color to view 3D  data as a 2D image

Most modern solar observatories deliver data products formatted as 3D spatio-temporal data cubes, that contain additional, higher dimensions with spectral and/or polarimetric information. This multi-dimensional complexity presents a major challenge when browsing for features of interest in several dimensions simultaneously. We developed the COlor COllapsed PLOTting (COCOPLOT) software as a quick-look and context image software, to convey spectral profile or time evolution from all the spatial pixels ($x,y$) in a 3D [$n_x,n_y,n_\lambda$] or [$n_x,n_y,n_t$] data cube as a single image, using color. This can avoid the need to scan through many wavelengths, creating difference and composite images when searching for signals satisfying multiple criteria. Filters are generated for the red, green, and blue channels by selecting values of interest to highlight in each channel, and their weightings. These filters are combined with the data cube over the third dimension axis to produce an $n_x \times n_y \times 3$ cube displayed as one true color image. Some use cases are presented for data from the Swedish 1-m Solar Telescope (SST) and IRIS, including H$\alpha$ solar flare data, a comparison with $k$-means clustering for identifying asymmetries in the Ca II K line and off-limb coronal rain in IRIS C II slit-jaw images. These illustrate identification by color alone using COCOPLOT of locations including line wing or central enhancement, broadening, wing absorption, and sites with intermittent flows or time-persistent features. COCOPLOT is publicly available in both IDL and Python.
libdlr: Efficient imaginary time calculations using the discrete Lehmann  representation

We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsbuara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl.
Understanding Domain Randomization for Sim-to-real Transfer

Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization -- one of the most popular algorithms for sim-to-real transfer -- has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is limited. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap -- the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest.
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with  Plug-in Solver

Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning procedure. Hence the learned models may lack the ability of being re-used with more specialized planners. In this paper we address this issue and provide approaches to learn an RL model efficiently without the guidance of a reward signal. In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that \emph{any planning algorithm} on the learned model can give a near-optimal policy. Specicially, we focus on the linear mixture MDP setting, where the probability transition matrix is a (unknown) convex combination of a set of existing models. We show that, by establishing a novel exploration algorithm, the plug-in approach learns a model by taking $\tilde{O}(d^2H^3/\epsilon^2)$ interactions with the environment and \emph{any} $\epsilon$-optimal planner on the model gives an $O(\epsilon)$-optimal policy on the original model. This sample complexity matches lower bounds for non-plug-in approaches and is \emph{statistically optimal}. We achieve this result by leveraging a careful maximum total-variance bound using Bernstein inequality and properties specified to linear mixture MDP.
Unsupervised Data Selection for Data-Centric Semi-Supervised Learning

We study unsupervised data selection for semi-supervised learning (SSL), where a large-scale unlabeled dataset is available and a small subset of data is budgeted for label acquisition. Existing SSL methods focus on learning a model that effectively integrates information from given small labeled data and large unlabeled data, whereas we focus on selecting the right data to annotate for SSL without requiring any label or task information. Intuitively, instances to be labeled shall collectively have maximum diversity and coverage for downstream tasks, and individually have maximum information propagation utility for SSL. We formalize these concepts in a three-step data-centric SSL method that improves FixMatch in stability and accuracy by 8% on CIFAR-10 (0.08% labeled) and 14% on ImageNet-1K (0.2% labeled). It is also a universal framework that works with various SSL methods, delivering consistent performance gains. Our work demonstrates that small computation spent on carefully selecting data for annotation brings big annotation efficiency and model performance gain without changing the learning pipeline. Our completely unsupervised data selection can be easily extended to other weakly supervised learning settings.
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences

Transformer-based models have achieved great success in various NLP, vision, and speech tasks. However, the core of Transformer, the self-attention mechanism, has a quadratic time and memory complexity with respect to the sequence length, which hinders applications of Transformer-based models to long sequences. Many approaches have been proposed to mitigate this problem, such as sparse attention mechanisms, low-rank matrix approximations and scalable kernels, and token mixing alternatives to self-attention. We propose a novel Pooling Network (PoNet) for token mixing in long sequences with linear complexity. We design multi-granularity pooling and pooling fusion to capture different levels of contextual information and combine their interactions with tokens. On the Long Range Arena benchmark, PoNet significantly outperforms Transformer and achieves competitive accuracy, while being only slightly slower than the fastest model, FNet, across all sequence lengths measured on GPUs. We also conduct systematic studies on the transfer learning capability of PoNet and observe that PoNet achieves 95.7% of the accuracy of BERT on the GLUE benchmark, outperforming FNet by 4.5% relative. Comprehensive ablation analysis demonstrates effectiveness of the designed multi-granularity pooling and pooling fusion for token mixing in long sequences and efficacy of the designed pre-training tasks for PoNet to learn transferable contextualized language representations.