Reviewerly/exHarmony huggingface dataset:
Sensor-Independent Illumination Estimation for DNN Models.

While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space retains the linear property of the original sensor raw-RGB space and allows unseen camera sensors to be used on a single DNN model trained on this working space. We demonstrate the effectiveness of this approach on several different camera sensors and show it provides performance on par with state-of-the-art methods that were trained per sensor.
Smart KYC Using Blockchain and IPFS

Know your customer, also known as know your client or simply KYC, is the process that businesses and financial institutes must employ to identify their clients and assessing any kind potential risk due to illegal intentions and foul play for the business relationship in compliance with a national regulating body. The term KYC is often used to refer the bank regulations and the anti-money laundering regulations which are in place to govern such activities. Also due to bribery and foul play, companies of all sizes are compelled to employ KYC for the purpose of ensuring their consultants, agents, or distributors follow rules set by anti-bribery compliant. With population of India around 1.3 billion, a secure and faster system for sharing sensitive information like KYC document which may contain personal document, capable of handling this vast amount of this data is of high demand. While the implementation of such a system isn’t new, the present systems have drawbacks. The proposed system will replicate the functionality of the legacy KYC system. By using the immutable property of Distributed Ledger Technology (DLT) and Inter Planetary File System (IPFS), a tamper-proof system can be formed. This paper aims to address some of the shortcomings of the current system and propose implementation of innovative features to develop a more secure and comprehensive system. The proposed system will allow customer and business institute to verify and record the customer KYC document into the DLT. The proposed system will use IPFS which will greatly improve the storage efficiency of DLT.
Mixed image denoising using weighted coding and non-local similarity

Denoising an image is a heuristic and objective process. Still, underlying noise that is predominant in the images reduces the quality. Additive white Gaussian noise (AWGN) and impulse noise are the most exploited types of noise. For a specified amount of density, a combination of AWGN and impulse noise may distract the entire signal causing a loss in the magnitude. This paper presents a denoising model by exploiting such a combination that uses an overcomplete dictionary by sparse based denoising scheme with suitable regularization terms. A weight matrix is defined to optimize the operation at specific locations of the image. Finally, the use of non-local similarity features improves the quality of reconstructed images. The weight matrix maps the regions where the effect of multiple noise sources is present. The results proved the superiority of the proposed technique. Simulation of the proposed technique on many images with different quantities of noise produced an improvement of up to 2 dB when the noise effect is more when compared to the state-of-the-art techniques.
MultiMix: A Robust Data Augmentation Framework for Cross-Lingual NLP

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised natural language processing tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose MultiMix, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios. In particular, MultiMix targets to solve cross-lingual adaptation problems from a source language distribution to an unknown target language distribution, assuming no training labels are available for the target language task. At its core, MultiMix performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on zero-resource cross-lingual transfer tasks for Named Entity Recognition and Natural Language Inference. MultiMix achieves SoTA results in both tasks, outperforming the baselines by a good margin. With an in-depth model dissection, we demonstrate the cumulative contributions of different components to MultiMix's success.
Single-Trace Attacks on Keccak

Hybrid Public Key Encryption (HPKE) is a cryptographicprimitive being standardized by the Crypto Forum Research Group (CFRG)within the Internet Research Task Force (IRTF). HPKE schemes combineasymmetric and symmetric cryptographic primitives for efficient authenti-cated encryption of arbitrary-sized plaintexts under a given recipient publickey. This document presents a mechanized cryptographic analysis done withCryptoVerif, of all four HPKE modes, instantiated with a prime-order-groupDiffie-Hellman Key Encapsulation Mechanism (KEM).
Optimal early warning defense of N-version programming service against co-resident attacks in cloud system

Due to the virtual machine co-resident architecture, cloud computing systems are vulnerable to co-resident attacks (CRAs) where a malicious attacker may access and corrupt information of a target user through co-locating their virtual machines on the same physical server. To defend against cyber threats such as the CRA, early warning mechanisms have been developed with the aim to detect and block an attack at a nascent stage. In this paper, we study the optimal strategy of allocating early warning resources to defend against CRAs for the voting-based N-version programming (NVP) service running in the cloud. A probabilistic model is proposed to evaluate the failure probability of the NVP service program and further the expected cost of loss for the considered service. Optimization problems of co-determining the optimal numbers of service program versions and early warning agents are further solved to minimize the expected cost of loss. As demonstrated through examples, the resultant optimal strategies can effectively allocate service and defense resources to defend the NVP cloud service against CRAs.
AUGMENTED REALITY IN TEACHER EDUCATION. A FRAMEWORK TO SUPPORT TEACHERS' TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE

In this article the use of Augmented Reality (AR) as an educational technology in classroom applications and teacher training is suggested. AR is a combination of real-world information with digital information overlaid. A review of the research literature on AR learning environments reveals that in most studies AR contributes to successful learning and increased motivation. An evaluation study reveals that teachers’ ability of self-concept for implementing AR within their classrooms is closely related to educational technology and the technology itself in general. Here, teachers need orchestrated support for implementing AR in classrooms. Subsequently, a model for design and implementation of AR classroom application is suggested. These suggestions are illustrated by some exemplary AR classroom learning environments. This paper closes with recommendations for future research, in which more basic and controlled experimental research is needed in order to get a valid impression about the benefits of AR as classroom technology.
Phase-change photonic crystal ring resonator for reconfigurable directional-coupler switching

A novel compact photonic crystal ring resonator is designed based on phase-change material (PCM) to realize reconfigurable directional-coupler switching for the two wavelengths of coarse wavelength division multiplexing (CWDM) system at 1530 nm and 1550 nm. The working mechanism of this PCM-based device is analyzed from the perspective of the coupled optical field in the inner and outer resonator rings during the phase transition of selected photonic crystal rods. This kind of PCM-based directional-coupler switches will play a strong role in the reconfigurable lattice mesh-based integrated intelligent photonic system.
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree

Cree is one of the most spoken Indigenous languages in Canada. From a speech recognition perspective, it is a low-resource language, since very little data is available for either acoustic or language modeling. This has prevented development of speech technology that could help revitalize the language. We describe our experiments with available Cree data to improve automatic transcription both in speaker- independent and dependent scenarios. While it was difficult to get low speaker-independent word error rates with only six speakers, we were able to get low word and phoneme error rates in the speaker-dependent scenario. We compare our phoneme recognition with two state-of-the-art open-source phoneme recognition toolkits, which use end-to-end training and sequence-to-sequence modeling. Our phoneme error rate (8.7%) is significantly lower than that achieved by the best of these systems (15.1%). With these systems and varying amounts of transcribed and text data, we show that pre-training on other languages is important for speaker-independent recognition, and even small amounts of additional text-only documents are useful. These results can guide practical language documentation work, when deciding how much transcribed and text data is needed to achieve useful phoneme accuracies.
A novel quadruple generative adversarial network for semi-supervised categorization of low-resolution images

In order to make utilization of unlabeled low-resolution (LR) images to shape discriminative models, we present quadruple generative adversarial network (Q-GAN), a game-theoretical framework for implementing semi-supervised categorization of LR images. It can realize photo-realistic image super-resolution (SR) and semi-supervised pattern recognition simultaneously. We consider our pipeline as a four-player optimization-based formulation, which consists of four vital components, i.e., a refiner for image SR and generation, a discriminator for identifying high-resolution (HR) samples and another for identifying true (original) samples, a classifier for label prediction. The refiner and two discriminators characterize the conditional distributions between images and labels, whilst the classifier solely focuses on predicting real image-label pairs. We select those high-quality super-solved images with ground-truth labels for data supplement. We customize the global optimization objective function as well as the training procedure to ensure model approximates the posterior distribution of latent variables given true data in a semi-supervised manner. Experimental results demonstrate that Q-GAN can simultaneously (1) deliver the promising categorization performance among state-of-the-arts, i.e., validation accuracy achieves 92.18% and testing accuracy achieves 90.63%, and (2) recover fine-grained textures with high peak signal-to-noise ratios (PNSRs) and structural similarities (SSIMs) from heavily downsampled testing images of hand-crafted dataset and public benchmarks.
Shapley-based explainability on the data manifold.

Explainability in machine learning is crucial for iterative model development, compliance with regulation, and providing operational nuance to model predictions. Shapley values provide a general framework for explainability by attributing a model's output prediction to its input features in a mathematically principled and model-agnostic way. However, practical implementations of the Shapley framework make an untenable assumption: that the model's input features are uncorrelated. In this work, we articulate the dangers of this assumption and introduce two solutions for computing Shapley explanations that respect the data manifold. One solution, based on generative modelling, provides flexible access to on-manifold data imputations, while the other directly learns the Shapley value function in a supervised way, providing performance and stability at the cost of flexibility. While the commonly used ``off-manifold'' Shapley values can (i) break symmetries in the data, (ii) give rise to misleading wrong-sign explanations, and (iii) lead to uninterpretable explanations in high-dimensional data, our approach to on-manifold explainability demonstrably overcomes each of these problems.
Particle Swarm Optimisation Based Feature Selection for Software Effort Prediction Using Supervised Machine Learning and Ensemble Methods: A Comparative Study

An essential goal of software cost prediction is predicting accurate costs, which is a vital part of the software process. Individuals with various skills carry out most software development tasks, so efforts are measured by person-months and time. The overestimation and underestimation of developing costs is a future disappointment. Additionally, software venture managers will determine the cost of software development. The prevailing expense for any software is the cost of ascertaining efforts, making software cost estimation a crucial task. The past three decades have seen various approaches to software cost estimations based on software metrics. However, it is difficult to decide which model offers a more precise estimate on the data set. This article uses three different data sets: Albrecht, Desharnais and COCOMONASA. Classification often includes many features in a data set, but not all are useful for classification. Abusive and repetitive symptoms reduce performance, so this article uses particle swarm optimisation as a feature selection algorithm for different machine learning methods like Random Forest, REPTree, SMOReg, Linear Regression, M5Rule and MLP. At the same time, bagging is also used with said base learners. The bagging M5Rule gives the best performance with an MMRE value of 28.78%.
Conceptions of digital libraries: an African perspective

Purpose The purpose of this paper was to explore African conceptions of digital libraries from the perspective of the historical literature. This paper argues that the concept of digital libraries is a western creation and that there was a need for developing societies to develop their own conceptions to guide their own digital library development agenda. Design/methodology/approach The paper is based on a literature review. The paper makes use of publicly-available literature on the theme of digital libraries from both the Western and African perspectives. The search terms used were “digital libraries”, “Africa digital libraries”, “electronic libraries”, “information communication technologies/libraries” and “institutional repositories”. A total of 89 publications were examined for this purpose. Findings The analysis revealed that most of the initial digital library initiatives in Africa emanated from the west with African countries benefiting from international initiatives to expand access to information resources to bridge the global digital divide. However, due to a number of contextual challenges such as lack of sustainable funding and inadequate capacity and strategy, the development of digital libraries was hampered. Thus, even though digital libraries enjoy considerable goodwill, there remain negative conceptions of digital libraries in Africa. Practical implications Information institutions in African countries must evolve a unified conception of digital libraries as this would largely drive the direction of digital library development towards achieving the developmental goals of the continent. Originality/value The study applies the attributes of innovation to explain contextual factors shaping African conceptions of digital libraries.
Characterizing Peaks in Acceleration Signals–Application to Physical Activity Detection Using Wearable Sensors

The world is getting older by the minute due to rising life expectancy, leading to an urgent need for the continuous monitoring of patients. Tracking human activities without hospitalization has been tackled in the past decade thanks to the advancement of sensing technologies and wearable devices. However, the limited power resources of microcontrollers and the power consumption due to the use of different sensors are two issues that make the recognition process via embedded systems an open research topic to date. Consequently, this paper proposes a low-cost machine learning-based human activity recognition algorithm. It detects cyclic activities from wrist-worn tri-axial accelerometer data and classifies them into four classes. Specifically, a novel and smart peak detection technique is proposed, followed by the extraction of small handcrafted feature vectors, representing the input of a novel machine-learning architecture. These three contributions are approved by experimental results on a 3300-file dataset, showing an accuracy of 99.21% with a low computational cost thanks to an efficient implementation for feature extraction. The effectiveness of the proposed recognition process is validated in real world conditions.
Perception of Researchers &amp; Academicians of Parul University towards Research Data Management System &amp; Role of Library

Research data management is a system that helps in archiving and retrieving of research data to reuse and preserving them for long term use. Many universities in developed countries have already started providing RDM services to their researchers and academicians. In India, it is still in the initial stage. The purpose of the present study is to investigate the perceptions of researchers and academicians of Parul University on research data management and research data sharing. It also explores the ways the researchers preserved their research data for future use. It also explores the ways the library can take initiatives to encourage and extend support to the researchers and academicians to the organisation, preservation, and sharing of research data. To investigate and study the problem 100 questionnaires were distributed. There are 88 responses we received out of 100. The study revealed that the majority of respondents were agreeing about the research data sharing and free accessibility of research data to browse and reuse. Researchers are very much interested and agreed in the library’s involvement in organizing and preservation of research data. Researchers and faculty members are more concerned about their intellectual property rights while sharing the data on the public domain.
THE IMPACTS OF COVID-19 THROUGH ONLINE LEARNING USAGE FOR TERTIARY EDUCATION IN MALAYSIA

During Movement Control Order (MCO), where most schools and universities have to shut down their operations due to Covid-19, online learning has been seen as a platform where educators can share their knowledge with the students. This paper explains about what is coronavirus, online learning in Malaysia, the advantages and disadvantages of online learning, how to overcome the disadvantages of online learning and why online learning is the good choice to conduct class for tertiary education during Movement Control Order in Malaysia.
Follow The Curve: Arbitrarily Oriented Scene Text Detection Using Key Points Spotting And Curve Prediction

Detecting arbitrarily oriented text in natural images is still a challenging and unsolved problem in multimedia. In this work, we propose an efficient and accurate scene text detector. The detector first detects key points which are carefully designed and semantically meaningful. Then the key points are learnt to be associated together to form a hexagon for each text instance. Starting from a key point, the detector then predicts curves alongside the border of the text region. Simple heuristic post-processing followed by the predicted curve resulting in more accurate text region prediction. The predicted key points are used as anchors to correct errors from the search process. The proposed method is efficient since it is a single stage key point detection method with simple post-processing. It is also effective and shows state-of-the-art or comparable performance on several benchmark datasets.
A reconceptualization of learning space as schools reopen amid and after COVID-19 pandemic

As educational systems begin to resume operations amid and after the Coronavirus Disease 2019 (COVID-19) crisis, there is a complicated imagination of how learning will take place in what kind of space. Drawing ideas from the experiences of selected countries as well as relevant theory, this article attempts to reconceptualize learning space as schools reopen in this new age. The discussion shows that physical distancing measures and emergency remote teaching are the two powerful forces that reconceptualize the learning space. The theory of transactional distance, with its three factors of dialogue, structure, and autonomy, further provides a better understanding. This paper recommends that shared efforts among governments, educational systems, and researchers should be placed to fully realize the benefits of education in this new age as far as reconceptualized learning space is concerned.
Differentially Private Set Union

We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe U of items, possibly of infinite size, and a database D of users. Each user i contributes a subset W⊆U of items. We want an (ϵ,δ)-differentially private algorithm which outputs a subset S of the union of all sets W such that the size of S is as large as possible. The problem arises in countless real world applications; it is particularly ubiquitous in natural language processing (NLP) applications as vocabulary extraction. For example, discovering words, sentences, n-grams etc., from private text data belonging to users is an instance of the set union problem. Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold. Crucially, in the above process, the contribution of each individual user is always independent of the items held by other users, resulting in a wasteful aggregation process, where some item counts happen to be way above the threshold. We deviate from the above paradigm by allowing users to contribute their items in a dependent fashion, guided by a policy. In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain contractive properties would result in a differentially private algorithm. We design two new algorithms, one using Laplace noise and other Gaussian noise, as specific instances of policies satisfying the contractive properties. Our experiments show that the new algorithms significantly outperform previously known mechanisms for the problem.