Reviewerly/exHarmony huggingface dataset:
Of Pandemic and Pedagogic Reconstruction: An Epistemological Enquiry into Digital Education in India

With the outbreak of COVID-19 pandemic, the year 2020 witnessed an unprecedented challenge and change in all known structures of existence. In the field of education, radical adjustments had to be made following the shutting down of schools, colleges, and all forms of tangible sites of learning. The classroom underwent a digital metamorphosis with educators across the globe attempting to modify the existing curriculum and reconstruct pedagogic practices around the digital domain. However, such revisions and modifications faced challenges due to the lack of any practiced models or theories of education that completely substitutes the classroom in a regular mode of learning. Educators and academic administrators in India already face immense challenges in designing a technology-mediated learning curriculum and implementing updated digital tools and techniques in the classrooms owing to Indian education being deeply enmeshed in the socio-politics of the nation. Under the present circumstances, with the outbreak of the pandemic and subsequent lockdown of public spaces, digital learning is no longer a choice but a necessity. Although academic institutions, especially those that are privately owned and those in the urban quarters of the nation have been implementing digital tools in their curriculum over the span of the last decade, in most cases it has been utilized as a supplement to the ongoing offline classroom interaction-based education. With this change and challenge, the article seeks to explore the following issues: (a) the way the teachers are adapting to this new mode of teaching–learning systems, (b) the effectiveness of the online learning management systems (LMS) in designing the course content and implementing a methodology grounded in theories of connectivism and collaborativism, and (c) the challenges that teachers are facing and the way they are mutating their pedagogic practices both in teaching and evaluating. The article aims to explore the above issues through an inquiry into the theoretical foundations of digital learning, understanding the rationale and impact of pedagogic alterations in the context of the pandemic, and drawing from the shared experiences of teachers and educators of graduate and postgraduate levels of education in the Indian context.
Spatial search via an interpolated memoryless walk

The defining feature of memoryless quantum walks is that they operate on the vertex space of a graph and therefore can be used to produce search algorithms with minimal memory. We present a memoryless walk that can find a unique marked vertex on a two-dimensional lattice. Our walk is based on the construction proposed by Falk, which tessellates the lattice with squares of size $2\ifmmode\times\else\texttimes\fi{}2$. Our walk uses minimal memory, $O(\sqrt{NlogN})$ applications of the walk operator, and outputs the marked vertex with vanishing error probability. To accomplish this, we apply a self-loop to the marked vertex---a technique we adapt from interpolated walks. We prove that with our explicit choice of self-loop weight, this forces the action of the walk asymptotically into a single rotational space. We characterize this space and as a result show that our memoryless walk produces the marked vertex with a success probability asymptotically approaching one.
Design of digital image encryption based on elliptic curve cryptography (ECC) algorithm and Radix-64 conversion

The billions of bits of information are transferred each second through the internet every day. The information may be text, image, audio or video etc, accordingly, we need some protection mechanism while sharing confidential data. Generally, RSA algorithm is used for encrypting the Secret images. However, the security provided by Elliptic Curve Cryptography (ECC) is higher with lower sized key than the RSA algorithm. So, this article proposes an extended Elliptic Curve encryption approach for encrypting the secret images. In this system, the secret image is partitioned into three color image planes such as Red, Green and Blue. By applying Radix-64 encoding and Mapping table, these planes are converted into elliptic curve points and then these points are encrypted using ECC algorithm. Again, these points are applied to the Radix-64 decoding and the mapping table to get ciphered-image. At last, the key parameters such as a, b, p and Generator point (G) are embedded in the last four pixel positions of the ciphered-image. In order to get the original secret image, the recipient must extract these key parameters from the encrypted image and then apply the remaining processes to the encrypted image in the opposite order. Experimental results tested using MATLAB R2021b and it shows that the NPCR and UACI values are 99.54% and 28.73 % and better quality feature is attained since the entropy value is almost closer to eight. So, the proposed image encryption has robust capacity to fight against the differential attack.
Delegate contract signing mechanism based on smart contract

In this paper, a delegate contract signing solution is proposed to eliminate the potential risk of contract fraud caused by information and interest asymmetry. By utilizing the functional properties of the Ethereum blockchain and smart contracts, a delegate contract signing mechanism is established. By running the mechanism, the delegate contract signing information is received and processed, and the information is broadcast to the blockchain network nodes. By designing the algorithms of "requesting contract signing", "successful contract signing" and "contract fraud dispute resolution", the delegate contract signing is realized. By proposing algorithms and their calling processes, the smart contracts are completed. Finally, the smart contracts based on the solution are tested and verified. The source code of the smart contracts has been published on GitHub.
A multiple instance learning approach for detecting COVID-19 in peripheral blood smears

A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient’s COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.
Improved quantitative testing of a non-symmetric convex surface using a conical null-screen

In this work, we show the design of a conical null-screen for evaluating non-symmetric convex surfaces, such as toroidal and biconical convex surfaces. We propose a customized evaluation algorithm to compute the shape of the surface. The data obtained by our algorithm are fitted to a customized non-symmetric shape surface, considering orthogonal polynomials, to obtain the geometrical parameters such as the radius of curvature and the conic constant. The advantages and disadvantages of applying this algorithm to the quantitative test results are also presented.
Deep Orientation-Guided Gender Recognition from Face Images

In the recent decade, gender recognition and face analysis has been one of the most researched issues in computer vision. Although several solutions have been provided to the problem of gender recognition from face images, nonetheless, it is regarded as a difficult issue. Deep learning has been proven to solve challenging problems. On the other hand, several existing works have proven their ability to accurately predict the head orientation angles. The remaining error in gender prediction models requires novel solutions to try to improve it further. In this work, we present a novel deep learning-based method to predict gender using both the face image and the head orientation angles. We show that the use of head orientation information consistently boosts the accuracy of gender prediction models. We achieve this by increasing the representational power of deep neural networks by introducing a head orientation adapter. It takes the head angles as input and outputs a vector that is used to recalibrate the deep learning neural networks. The proposed method was tested on a large-scale dataset called AutoPOSE, which has sub-millimeter-accurate head orientation angles. We show that using the head orientation adapter consistently boosts the gender prediction models&#x0027; accuracy, and reduces the error by 20&#x0025;.
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results

Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at our disposal. This survey covers theoretical results for this setting and maps out the benefits of unlabeled data in classification and regression tasks. Most methods that use unlabeled data rely on certain assumptions about the data distribution. When those assumptions are not met, including unlabeled data may actually decrease performance. For all practical purposes, it is therefore instructive to have an understanding of the underlying theory and the possible learning behavior that comes with it. This survey gathers results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods. Specifically, it aims to answer the following questions: what are, in terms of improving supervised methods, the limits of semi-supervised learning? What are the assumptions of different methods? What can we achieve if the assumptions are true? As, indeed, the precise assumptions made are of the essence, this is where the survey's particular attention goes out to.
Electrocardiogram Based Group Device Pairing for Wearables

The widespread usage of wearables to provide healthcare services prompts the need for secure group communication among multiple devices using group keys. Gait-based group key establishment schemes are either vulnerable to video attacks, or fail to offer a secure group key update mechanism when group device changes. In this paper, we present an electrocardiogram (ECG) signals based group device pairing protocol, which can strengthen the security and reduce the overhead of wearables. Specifically, we first design a robust and lightweight fuzzy extractor that supports secure and efficient group device association between wearables. Meanwhile, we propose Improved Martingale Randomness Extraction (IMRE) algorithm, which utilizes the trend of InterPulse Interval (IPI) from ECG signal to extract high-entropy keys. Then we present a membership management mechanism that enables group key dynamic update when group device changes. Finally, we simulate our protocol and evaluate the accuracy and efficiency by various experiments. The experimental results demonstrate that the proposed work is robust and efficient, and the threat model-based security analysis shows that the proposed protocol can prevent both active and passive attacks.
Discriminative Self-Paced Group-Metric Adaptation for Online Visual Identification

Existing solutions to instance-level visual identification usually aim to learn faithful and discriminative feature extractors from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe distribution shifting issue between training and testing samples. Therefore, we propose a novel online group-metric adaptation model to adapt the offline learned identification models for the online data by learning a series of metrics for all sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples that share strong visual similarity relationships to each other. Furthermore, to handle potentially large-scale testing samples, we introduce self-paced learning (SPL) to gradually include samples into adaptation from easy to difficult which elaborately simulates the learning principle of humans. Unlike existing online visual identification methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration. Our method is generally suitable to any off-the-shelf offline learned visual identification baselines for online performance improvement which can be verified by extensive experiments on several widely-used visual identification benchmarks.
A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (STGA) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed Node-Filter to generate enough topological nodes, and simplified the node set by Node-Selection. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by STGA had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. Note to Practitioners—Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.
Wrapped Particle Filtering for Angular Data

Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution. In this paper, we propose using a wrapped normal distribution as a proposal distribution for angular data, <i>i.e</i>. data within finite range (-&#x03C0;,&#x03C0;]. We then use the same method to derive the proposal density for a particle filter, in place of a standard assumed Gaussian density filter such as the unscented Kalman filter. The numerical integrals with respect to wrapped normal distribution are evaluated using Rogers-Szeg&#x0151; quadrature. Compared to using the unscented filter and similar approximate Gaussian filters to produce proposal densities, we show through examples that wrapped normal distribution gives a far better filtering performance when working with angular data. In addition, we demonstrate the trade-off involved in particle filters with local sampling and global sampling (<i>i.e</i>. by running a bank of approximate Gaussian filters vs running a single approximate Gaussian filter) with the former yielding a better filtering performance than the latter at the cost of increased computational load.
Model and Procedure for Assessing the Qualification of a Software Developer

Developers’ qualifications and their professional development are the important aspects of IT project’s success. Software developers are interested in advancing their careers and improving their skills in order to maintain their level of professional value both for organizations and for the labor market. However, the evaluation of developers' qualifications is frequently based on the subjective opinion of managers, which can negatively affect the further professional development of employees. Quantitative metrics become particularly relevant in the assessment, because they can reduce the level of subjectivity. Therefore, the objective of this work is to develop data model for assessing the qualifications of software developers. The proposed model is based on data from version control system, knowledge management system and task management system. In order to collect information from these systems, as well as to carry out calculations, a decision support system based on the Node.js software platform was developed. The designed model and system were implemented in a software development company. For the assessment of relations between selected metrics and developers’ qualifications a multiple regression analysis was carried, where the employee's qualification rating assessed by the manager was the dependent variable. As a result, 2 models were prepared to assess the responsibility and the level of technical skills. Together, these models will provide a more comprehensive picture of developer’s qualification.
Research on Lossless Compression Coding Algorithm of N-Band Parametric Spectral Integer Reversible Transformation Combined With the Lifting Scheme for Hyperspectral Images

After considering the spectral correlation of structure and statistics of the hyperspectral image, the redundancy reduction is optimized using reversible transformation of integer matrices in conjunction with the N-band integer reversible transformation of spectral matrices. This paper proposes a new quantization algorithm, XCJRCT, that uses invertible matrix transformation to remove spectral redundancy, optimize bit allocation in structure, the lifting scheme of the discrete wavelet transform (DWT) is used in conjunction with other algorithms to reduce redundancy and set partitioning in hierarchical trees (SPIHT) coding. The experimental results show that lossless compression is significantly better than JPEG-LS, WinZip, ARJ, and DPCM. Using the Jet Propulsion Laboratory (JPL) Canal test image (Band Sequential) as an example data set, the average compression ratio increases by about 73.691598%, 67.713276%, 65.175242%, and 59.107580%, respectively, compared to the above algorithms.
High-Dimensional Multi-Label Data Stream Classification With Concept Drifting Detection

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts .<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label>1.</label> Source codes and data sets are available at below. https://github.com/peipeilihfut/MLStreamClassification </fn> In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Secondly, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency.
PaFPN-SOLO: A SOLO-based Image Instance Segmentation Algorithm

In order to improve the image instance segmentation algorithm due to the long propagation path of the underlying location information and the slow speed of convolutional operations in the process of capturing long-distance dependencies due to low computational efficiency, a PaFPN-SOLO algorithm is proposed in this paper. By adding Non-local operation to the ResNet backbone, the feature information of the image in the feature extraction process is better preserved; by using the bottom-up path augmentation method, more accurate position information is extracted on the lower feature layers, which not only improves the feature structure localization ability of the network model, but also shortens the information propagation path between the feature layers. The experimental results show that the algorithm in this paper has good segmentation effect on both COCO2017 and Cityscapes datasets, and the average segmentation accuracy reaches 56&#x0025; and 47.3&#x0025;, respectively, which improves 4.4&#x0025; and 7.4&#x0025; compared with the original SOLO network, respectively.
Minimally Modified Balanced Codes

We present and analyze a new construction of bipolar balanced codes where each codeword contains equally many −1’s and +1’s. The new code is minimally modified as the number of symbol changes made to the source word for translating it into a balanced codeword is as small as possible. The balanced codes feature low redundancy and time complexity. Large look-up tables are avoided.
Attention mechanics for improving online Multi-Object Tracking

In recent years, FairMOT is a known online one-shot model for tracking pedestrians with a focus on fairness between detection and re-identification (re-ID) tasks with remarkable performance. In this paper, we integrate some attention modules with more relating-object information to improve the performance of FairMOT. Firstly, we propose a spatial attention module which is the proper combination between deformable convolution and key-content factors to improve detection accuracy. Furthermore, we introduce a channel attention module in re-ID branch that can enhance efficient tracking capability. Our experimental evaluation shows that our extensions increase IDF1 and MOTA in tracking challenges MOT17, MOT20 with provided training data only.