Reviewerly/exHarmony huggingface dataset:
Contribution of Low, Mid and High-Level Image Features of Indoor Scenes in Predicting Human Similarity Judgements

Human judgments can still be considered the gold standard in the assessment of image similarity, but they are too expensive and time-consuming to acquire. Even though most existing computational models make almost exclusive use of low-level information to evaluate the similarity between images, human similarity judgements are known to rely on both high-level semantic and low-level visual image information. The current study aims to evaluate the impact of different types of image features on predicting human similarity judgements. We investigated how low-level (colour differences), mid-level (spatial envelope) and high-level (distributional semantics) information predict within-category human judgements of 400 indoor scenes across 4 categories in a Four-Alternative Forced Choice task in which participants had to select the most distinctive scene among four scenes presented on the screen. Linear regression analysis showed that low-level (t = 4.14, p < 0.001), mid-level (t = 3.22, p< 0.01) and high-level (t = 2.07, p < 0.04) scene information significantly predicted the probability of a scene to be selected. Additionally, the SVM model that incorporates low-mid-high level properties had 56% accuracy in predicting human similarity judgments. Our results point out: 1) the importance of including mid and high-level image properties into computational models of similarity to better characterise the cognitive mechanisms underlying human judgements, and 2) the necessity of further research in understanding how human similarity judgements are done as there is a sizeable variability in our data that it is not accounted for by the metrics we investigated.
EDTECH START-UPS IN THE EDUCATION ECOSYSTEM IN THE POST-COVID-19 ERA IN INDIA

The widespread and growing use of digital technology is reshaping the teaching-learning process in India. Covid-19 pandemic accelerated such online learning and gave birth to several Education Technology (EdTech) start-ups in India and across the globe. Due to the Covid-19 pandemic, the forced online has created a digital divide in developing countries like India. The digital divide is not restricted to developing countries but also some developed countries. EdTech companies promise cost-saving, flexible and a personal learning experience (LeX). It is also scalable to reach the unreached population of the country to improve the gross enrollment ratio (GER). These Edtech companies have existed for close to two decades and promised a great future in the education space. The sudden growth of EdTech companies in India also tapped learners' skilling and up-skilling or learning new skills while still enrolling with the colleges and university institutions. As per the funding pattern, India is the most preferred country for Edtech funding by foreign investors. The Government of India has been giving a digital push in the education sector through its various regulatory agencies, fueling growth for Edtech companies in India. The present study evaluates the changing nature of the education ecosystem because of the unexpected impetus of EdTech start-ups in India. The study also explores the potential growth opportunities and sustainability of the Edtech start-ups in India.
MoCaNet: Motion Retargeting In-the-Wild via Canonicalization Networks

We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. In particular, our method is capable of retargeting body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure. It is designed to leverage massive online videos for unsupervised training, needless of 3D annotations or motion-body pairing information. The proposed method is built upon two novel canonicalization operations, structure canonicalization and view canonicalization. Trained with the canonicalization operations and the derived regularizations, our method learns to factorize a skeleton sequence into three independent semantic subspaces, i.e., motion, structure, and view angle. The disentangled representation enables motion retargeting from 2D to 3D with high precision. Our method achieves superior performance on motion transfer benchmarks with large body variations and challenging actions. Notably, the canonicalized skeleton sequence could serve as a disentangled and interpretable representation of human motion that benefits action analysis and motion retrieval.
A Deep Feature Selection Method for Tumor Classification in Breast Ultrasound Images

Breast cancer is a serious concern worldwide amongst women. The malignant growth in breast is a life-threatening issue which must be taken care of and treated at an early stage of its formation. Identification of such tumors in the breast is very crucial and important. In the present work, we introduce a deep learning based method for breast tumor classification from breast ultrasound tumor images. We combine the concept of transfer learning (TL) along with a statistical method which involves a total of three phases. At first, we extract features from ultrasound tumor images with a fine-tuned TL model, next we rank these features based on the correlation coefficient values that we get after applying the Spearman Rank Correlation Coefficient method in the second phase. At the last phase, we select the top-ranked features which are most important and relevant, and classify the unseen breast ultrasound tumors using machine learning based classifiers. The proposed method achieves state-of-the-art classification accuracy of 98.72% on the test dataset with only 40% top-ranked extracted features by the TL model.
Key Elements of On-Line Cyber Security Exercise and Survey of Learning During the On-Line Cyber Security Exercise

Cyber security exercises have experienced broad evolution in their relatively short lifetime. Cyber security exercises have been changing from individual technical skill based trainings or even competitions to the team based organisational learning experiences where different work-roles are trained and exercised during the cyber security incidents. Nowadays the modern requirements for cyber security exercises are collaboration between different training platforms and on-line remote participation of the learning audience. In the domain of cyber security, the most valuable assets are skills and know-how, so the basic ambition for conducting the cyber security exercises for individuals and for organisations is the learning. In this research, the learning experience during the state-of-the-art on-line remote cyber security exercise is studied. NIST NICE cyber security framework is used as a base for knowledge categories of used questionnaire. The results from the on-line cyber security exercise are analysed with and concluded with future research topics.
A UML-Style Visual Query Environment Over DBPedia

We describe and demonstrate a prototype of a UML-style visual query environment over DBPedia that allows query seeding with any class or property present in the data endpoint and provides for context-sensitive query growing based on class-to-property and property-to-property mappings. To handle mappings that connect more than 480 thousand classes and more than 50 thousand properties, a hybrid approach of mapping pre-computation and storage is proposed, where the property information for “large” classes is stored in a database, while for “small” classes and for individuals the matching property information is retrieved from the data endpoint on-the-fly. The created schema information is used to back the query seeding and growing in the ViziQuer tool. The schema server and the schema database contents can be re-used also in other applications that require DBPedia class and property linking information.
Comprehensive Study on Machine Learning-Based Container Scheduling in Cloud

Containers are considered the best lightweight application in virtualization technology, and it is promising in enhancing cloud computing services quality. Due to cloud workload diversity, the scheduler module is considered the central part of the containers framework that optimizes resource utilization and reduces cost and energy consumption. Container scheduling algorithms can be classified into four main types: heuristic, metaheuristic, mathematical modeling, and machine learning. Machine Learning, with its high ability to analyze data and train the system to predict outputs based on previous data considered the best choice for predicting workloads and performance metrics. Such a vision allows schedulers to improve the quality of resource allocation with changing user requests rates in complicated work environments. This paper presents a comprehensive literature review for the current container orchestration machine learning-based algorithms. A detailed study is proposed for the main features, advantages, and disadvantages of existing algorithms.
An Intelligent Online Attendance Tracking System Through Facial Recognition Technique Using Edge Computing

Recently, due to COVID-19 pandemic, the classes, seminars, meetings are scheduled on virtual platform. It is a need to keep track of the presence of attendees. Earlier online attendance involved extracting the list of attendees, which was inconvenient as a lot of people mute themselves and leave the meeting altogether. Therefore, a tool is required to capture attendance through facial recognition which can effectively identify the attendees who remain online for the whole duration of the lecture. In this paper, a method has been proposed to completely automate the attendance tracking system using the concept of edge computing. The tool runs alongside any video conference platform and tracks the faces of attendees in a random interval, and using face recognition technique, find out the people who remain present for the complete duration of the class. This novel method acts as a fail-proof method to monitor attendance and improve digital transparency.
Enabling Parity Authenticator-Based Public Auditing With Protection of a Valid User Revocation in Cloud

The significance of the cloud enables the data owners (DOs) to store data remotely in cloud server (CS). The external and internal attacks on the stored data at CS can deliberately remove data. Furthermore, the CS removes the stored data to make empty location for the user's upcoming new data. However, it is a legal expectation of DOs to know whether their data are correctly stored or altered in CS. In this article, we propose a novel privacy-aware and hash-parity-bits-based public auditing (PA-HPPA) framework to secure full data, left half of the data, and the right half of the data, generated by a DO. DO generates two private key pairs with the assistance of a virtual key and a user ID (IP). The virtual key is the sequence number of DO who is registered and provided by the trusted data manager (TDM) while IP is the sequence number of DO working in an organization. Subsequently, DO blinds the categorized data and generates their signatures and hashes. In addition, DO generates the parity bits using xor and assigns to each hard drive (HD) in CS, which assistants to TDM in public auditing. Second, how to identify the error in the stored data and how to securely recover the error/missed data? Extension to the framework, the novel proposed data error identification and secure data recovery produce tags for installed HDs of CS using truth table and recover the altered/missed data via a authenticator, which is produced using xor function. Third, how to protect a valid user from revocation and, in case a user has revoked on merit basis, then how to securely access the stored data of it? This novel work has proposed three conditions to meet the validity of the valid user from revocation and securely generating the public-private key pairs to access the stored data of the revoked user securely from CS. Fourth, there is an efficient novel proposed dynamic operation scheme to insert, update, or delete the stored data at CS without regenerating the signatures, hashes, and tags for the whole stored data in cloud. The security analysis and the performance evaluation of the proposed solutions are provably efficient and secure with reduced communication costs.
SAER: Sentiment-Opinion Alignment Explainable Recommendation

Explainable recommendation systems not only provide users with recommended results but also explain why they are recommended. Most existing explainable recommendation methods leverage sentiment analysis to help users understand reasons for recommendation results. They either convert particular preferences into sentiment scores or simply introduce the rating as the overall sentiment into the model. However, the simple rating information cannot provide users with more detailed reasons for recommendations in the explanation. To encode more sentiment information, some methods introduce user opinions into the explanations. As the opinion-based explainable recommendation system does not utilize supervision from sentiment, the generated explanations are generally limited to templates. To solve these issues, we propose a model called Sentiment-opinion Alignment Explainable Recommendation (SAER), which combines sentiment and opinion to ensure that the opinion in the explanation is consistent with the user’s sentiment to the product. Moreover, SAER provides informative explanations with diverse opinions for recommended items. Experiments on real datasets demonstrate that the proposed SAER model outperforms state-of-the-art explainable recommendation methods.
SpeechTyper: From Speech to Typographic Composition

Many authors consider typography as what language looks like. Over time, designers explored connections between type design and sound, trying to bridge the gap between the two areas. This paper describes SpeechTyper, an ongoing system that generates typographic compositions based on speech. Our goal is to create typographic representations that convey aspects of oral communication expressively. The system takes a pre-processed analysis of speech recordings and uses it to affect the glyph design of the recited words. The glyphs’ structure is generated using a system we developed previously that extracts skeletons from existing typefaces.
Blockchain-Driven and IoT-Assisted Chemical Supply-Chain Management

Blockchain (BC) technologies have gained prominence in the scientific and industrial sectors, as they have the potential to benefit a wide range of sectors. The shift is attributed owing to the key benefits of immutability, chronology, and ownership at every point in the supply chain. Thus, through BC, and smart contracts (SCs), every point in the supply chain can capture and exchange transactional data, and thus, it enables efficient and automated chain operations. Thus, BC adoption introduces traceability and transparency and lowers costs. Owing to the potential benefits of BC, promising solutions can be adopted in the chemical industry sector, where BC-based solutions can be potentially used to manage inventory management, to logistics, and ensure that fake chemicals are not sold by malicious entities, without the requirement for third-party validation or authorization. Moreover, chemical products can be assigned low-powered Internet-of-Things (IoT) digital tags that simplify the chemical inventory operations. Thus, in this paper, we propose a BC-based IoT-assisted reference architecture for chemical industries and present the entire supply-chain operation. To support the claim, a practical case study, ChemBlocks is presented, that presents the potential benefits of integration of IoT tagging on chemical products, with ledger state information managed via BC. At every supply-chain point, SCs are executed that automated the transfer of funds, and asset transfer is achieved. The proposed case study eliminates cost and effort duplication and increases the return of investment (ROI) of chemical industries.
Unification of random dynamical decoupling and the quantum Zeno effect

Abstract Periodic deterministic bang–bang dynamical decoupling and the quantum Zeno effect are known to emerge from the same physical mechanism. Both concepts are based on cycles of strong and frequent kicks provoking a subdivision of the Hilbert space into independent subspaces. However, previous unification results do not capture the case of random bang–bang dynamical decoupling, which can be advantageous to the deterministic case but has an inherently acyclic structure. Here, we establish a correspondence between random dynamical decoupling and the quantum Zeno effect by investigating the average over random decoupling evolutions. This protocol is a manifestation of the quantum Zeno dynamics and leads to a unitary bath evolution. By providing a framework that we call equitability of system and bath , we show that the system dynamics under random dynamical decoupling converges to a unitary with a decoupling error that characteristically depends on the convergence speed of the Zeno limit. This reveals a unification of the random dynamical decoupling and the quantum Zeno effect.
Now Is the Time to Build a National Data Ecosystem for Materials Science and Chemistry Research Data

Research organizations are critically in need of directed growth toward future interoperability and federation. The purpose of this Viewpoint is to alert the government, academia, professional societies, foundations, and industries of a further need for consideration of data in chemistry and materials as a long-term and sustained development in the US. This paper is a call for coordinated action from the government, academia, and industry to establish a national strategy and concomitant infrastructure focused on research data.
Optimization of CNN in Capsule Networks for Alzheimer’s Disease Prediction Using CT Images

Alzheimer’s disease is a cumulative and irreparable disorder that affects remembrance and some other mental functionality, commonly observed in elderly and ageing population. This disease involves the gradual deterioration of brain cells, gradually shattering memory and other important brain related functions. Amnesia and uncertainty are the common indicators. No cure is known to exist for Alzheimer’s disease, but medication and management action plans may provisionally act upon the symptoms giving the patient more time before severity is reached. Medications can occasionally help people having Alzheimer's disease magnify brain functions for a particular duration of time. The use of innovative computer aided detection or diagnosis (CAD) approach with Content-Based Image Retrieval (CBIR) system has drastically opened up novel possibilities in the area of image retrieval, classification and training for detection of Alzheimer's disease during the starting stages using magnetic resonance imaging (MRI) technology. In this paper, we have proposed a CBIR system using the following modes: 3D Capsule Network, 3D Convolution Neural Network and a sparse 3D autoencoder. A 3D Capsule Network (CapsNet) has a capability of learning fast, for very small datasets as well. It can conclusively control image transitions and rotations. It was found that, a collaborative model using 3D CapsNet and 3D CNN with 3D sparse autoencoder, multiplied the prediction performance when compared with the traditional deep CNN.
Transcribing Natural Languages for the Deaf via Neural Editing Programs

This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses. Previous sequence-to-sequence language models trained with paired sentence-gloss data often fail to capture the rich connections between the two distinct languages, leading to unsatisfactory transcriptions. We observe that despite different grammars, glosses effectively simplify sentences for the ease of deaf communication, while sharing a large portion of vocabulary with sentences. This has motivated us to implement glossification by executing a collection of editing actions, e.g. word addition, deletion, and copying, called editing programs, on their natural spoken language counterparts. Specifically, we design a new neural agent that learns to synthesize and execute editing programs, conditioned on sentence contexts and partial editing results. The agent is trained to imitate minimal editing programs, while exploring more widely the program space via policy gradients to optimize sequence-wise transcription quality. Results show that our approach outperforms previous glossification models by a large margin, improving the BLEU-4 score from 16.45 to 18.89 on RWTH-PHOENIX-WEATHER-2014T and from 18.38 to 21.30 on CSL-Daily.
Interval Privacy: A Framework for Privacy-Preserving Data Collection

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data that are transparent and acceptable to data owners. We present a new concept of privacy and corresponding data formats, mechanisms, and theories for privatizing data during data collection. The privacy, named Interval Privacy, enforces the raw data conditional distribution on the privatized data to be the same as its unconditional distribution over a nontrivial support set. Correspondingly, the proposed privacy mechanism will record each data value as a random interval (or, more generally, a range) containing it. The proposed interval privacy mechanisms can be easily deployed through survey-based data collection interfaces, e.g., by asking a respondent whether its data value is within a randomly generated range. Another unique feature of interval mechanisms is that they obfuscate the truth but do not perturb it. Using narrowed range to convey information is complementary to the popular paradigm of perturbing data. Also, the interval mechanisms can generate progressively refined information at the discretion of individuals, naturally leading to privacy-adaptive data collection. We develop different aspects of theory such as composition, robustness, distribution estimation, and regression learning from interval-valued data. Interval privacy provides a new perspective of human-centric data privacy where individuals have a perceptible, transparent, and simple way of sharing sensitive data.
Evaluating Performance on Covid-19 Tweet Sentiment Analysis Outbreak Using Support Vector Machine

Sentiment analysis is a perfect machine learning process to analyze text and returns the text whether in positive or negative. The machine is trained with the emotions in text, then the machine can automatically understand text and predict the sentiment analysis. Sentiment analysis is an information extraction task that gives the result based on users writing emotions such as positive and negative thoughts, feelings. The emotions can be categorized as positive or negative words. Now, natural language processing (NLP) is an upcoming field in machine learning which gives hybrid applications in daily life. For example, the keyword which is taken from the text will undergo for intelligent learning. The output of the NLP algorithm enables sentiment analysis report daily activities. In this paper, we exposed the Covid-19 tweets from social media and did sentiment analysis using support vector machine (SVM). We trained the system using sentiment model and found the emotions from the Covid-19 tweets. Based on the trained system, we found the emotions in terms of negative, positive, and neutral emotions from Covid-19 tweet messages.