Reviewerly/exHarmony huggingface dataset:
Curve-based Approach for Optimal Trajectory Planning with Optimal Energy Consumption: application to Wheeled Mobile Robots

Wheeled transportation systems have been used since the dawn of civilization. The great amount of experience we have with wheeled transportation systems gives the Wheeled Mobile Robots (WMRs) an edge over other types of robots. This has allowed WMRs to be the prime candidates for many applications including, but not limited to, military and space applications. Sometimes these vehicles have to work in complex environments with complicated constraints to finish their assigned tasks. This paper presents a methodology to generate a smooth feasible trajectory for WMR using Pythagorean Hodograph (PH) curves. Generated trajectory not only avoids the static obstacles but also conforms to minimum energy consumption and travel time possible within the kinematic constraints of the WMR. Simulations are presented to check the performance of the proposed approach. The proposed method is experimentally validated using a WMR ‘Robotino’.
TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning

Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.
Optical designs for realization of a set of schemes for quantum cryptography

Several quantum cryptographic schemes have been proposed and realized experimentally in the past. However, even with an advancement in quantum technology and escalated interest in the designing of direct secure quantum communication schemes there are not many experimental implementations of these cryptographic schemes. In this paper, we have provided a set of optical circuits for such quantum cryptographic schemes, which have not yet been realized experimentally by modifying some of our theoretically proposed secure communication schemes. Specifically, we have proposed optical designs for the implementation of two single photon and one entangled state based controlled quantum dialogue schemes and subsequently reduced our optical designs to yield simpler designs for realizing other secure quantum communication tasks, i.e., controlled deterministic secure quantum communication, quantum dialogue, quantum secure direct communication, quantum key agreement, and quantum key distribution. We have further proposed an optical design for an entanglement swapping based deterministic secure quantum communication and its controlled counterpart.
Towards Causal Federated Learning For Enhanced Robustness and Privacy.

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.
The use of Augmented Reality for the teaching of dosimetry and metrology of ionizing radiation at IRD

Abstract It can be said that ionizing radiation are of great importance to society, considering the range of applications of nuclear technology. In this sense, the Institute for Radiation Protection and Dosimetry (IRD) acts in favor of the health and safety of the population and workers exposed to radiation, promoting the preservation of the environment. Therefore, safety is extremely important in the construction of knowledge in Ionizing Radiation Metrology (IRM), due to the use of radioactive sources in its practice. Then, the adoption of safe methodologies is fundamental in the teaching-learning process. Thus, technological tools such as Augmented Reality (AR) are being increasingly used to present, in real environment, virtual elements such as equipment, spaces, concepts and practices, avoiding contact with radioactive sources. In this case, AR is a technology that dramatically displaces the location and time of learning and training. New information and communication technologies are not only powerful and compact tools for delivering AR experiences through personal computers and mobile devices, but also developed and sophisticated in order to combine the real world with improved information interactively. The objective of this work is to demonstrate the possibility of using AR as a pedagogical tool for the learning of radiation protection in IRD courses, as well as assisting in the training of professionals and students in the nuclear safety area.
A fast and effective image steganalysis model based on convolutional neural network

Abstract In recent years, the performance of deep learning in image steganalysis applications has become more and more outstanding, but at the same time the training time has also greatly increased. Some models need to be trained for several days, and the research efficiency is very low. In this article, we propose an image steganalysis model in spatial domain based on a three-layer convolutional neural network. The model does not use a pooling layer, and uses the global average pooling layer instead of the fully connected layer. Experimental results show that the training time of the model is greatly shortened, and the accuracy of detecting the three steganography algorithms with an embedding rate of 0.4bpp exceeds 85%.
Factors Influencing Student Satisfaction in Higher Education. The Case of a Georgian State University

With the increasing importance, higher education is considered as a major asset for any nation’s socio-economic and technological development. The quality of education offered by the higher education institutions directly impacts country’s performance. That is why, number of scientific researches and public reports and debates agrees on the importance of the quality management in higher education. There is also an important debate on a link between the service quality and students’ satisfaction. On the one hand, there is a logical dependence of the degree of satisfaction to the perceived quality. On the other hand, number of studies have also identified a link between students’ satisfaction and their loyalty. This study looks at the determinants of student satisfaction in Georgian Higher Education Institutions. With the objective to detect the main components of service quality influencing students’ satisfaction, the article uses HEdPERF as a measuring instrument of higher education service quality. Data is collected from 793 students of one of the largest universities of the country. An exploratory factor analysis six factors for the further examination. After scale development, a multiple regression analysis is used to test the research hypothesis. The results of the study show that the administrative factors are the most sensitive and have a positive influence on the students’ satisfaction. Also, there is a positive relation between academic programs and student’s satisfaction. The disproportionate attitude is observed between the importance of academic factors and satisfaction.
Lossy Compression for Lossless Prediction

Most data is automatically collected and only ever seen by algorithms. Yet, data compressors preserve perceptual fidelity rather than just the information needed by algorithms performing downstream tasks. In this paper, we characterize the bit-rate required to ensure high performance on all predictive tasks that are invariant under a set of transformations, such as data augmentations. Based on our theory, we design unsupervised objectives for training neural compressors. Using these objectives, we train a generic image compressor that achieves substantial rate savings (more than $1000\times$ on ImageNet) compared to JPEG on 8 datasets, without decreasing downstream classification performance.
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models

Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer learning, and self-supervised learning (SSL). Following the success of these models in the general domain, the biomedical research community has developed various in-domain PLMs starting from BioBERT to the latest BioMegatron and CoderBERT models. We strongly believe there is a need for a survey paper that can provide a comprehensive survey of various transformer-based biomedical pretrained language models (BPLMs). In this survey, we start with a brief overview of foundational concepts like self-supervised learning, embedding layer and transformer encoder layers. We discuss core concepts of transformer-based PLMs like pretraining methods, pretraining tasks, fine-tuning methods, and various embedding types specific to biomedical domain. We introduce a taxonomy for transformer-based BPLMs and then discuss all the models. We discuss various challenges and present possible solutions. We conclude by highlighting some of the open issues which will drive the research community to further improve transformer-based BPLMs.
Rethinking Search: Making Experts out of Dilettantes

When experiencing an information need, users want to engage with an expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Large pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and large pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of expert advice.
Sentiment Analysis of the Nigerian Nationwide Lockdown Due to COVID19 Outbreak

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Ocean Sensors Record Rare Triple Tsunami near New Zealand

A new suite of DART buoys in the South Pacific Ocean spotted waves set in motion by three tsunamigenic earthquakes that occurred within hours of one another.
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals

We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend to underestimate it. To obtain the predictive uncertainty inherent in an observation, we propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. By decoupling target estimation and uncertainty estimation, we also control the balance between signal estimation and uncertainty estimation. We conduct three types of experiments: regression with simulation data, age estimation, and depth estimation. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
QueryInst: Parallelly Supervised Mask Query for Instance Segmentation

Recently, query based object detection frameworks achieve comparableperformance with previous state-of-the-art object detectors. However, how tofully leverage such frameworks to perform instance segmentation remains an openproblem. In this paper, we present QueryInst, a query based instancesegmentation method driven by parallel supervision on dynamic mask heads. Thekey insight of QueryInst is to leverage the intrinsic one-to-one correspondencein object queries across different stages, as well as one-to-one correspondencebetween mask RoI features and object queries in the same stage. This approacheliminates the explicit multi-stage mask head connection and the proposaldistribution inconsistency issues inherent in non-query based multi-stageinstance segmentation methods. We conduct extensive experiments on threechallenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate theeffectiveness of QueryInst in instance segmentation and video instancesegmentation (VIS) task. Specifically, using ResNet-101-FPN backbone, QueryInstobtains 48.1 box AP and 42.8 mask AP on COCO test-dev, which is 2 points higherthan HTC in terms of both box AP and mask AP, while runs 2.4 times faster. Forvideo instance segmentation, QueryInst achieves the best performance among allonline VIS approaches and strikes a decent speed-accuracy trade-off. Code isavailable at \url{https://github.com/hustvl/QueryInst}.
Learning from Łukasiewicz and Meredith: Investigations into Proof Structures

The material presented in this paper contributes to establishing a basis deemed essential for substantial progress in Automated Deduction. It identifies and studies global features in selected problems and their proofs which offer the potential of guiding proof search in a more direct way. The studied problems are of the wide-spread form of "axiom(s) and rule(s) imply goal(s)". The features include the well-known concept of lemmas. For their elaboration both human and automated proofs of selected theorems are taken into a close comparative consideration. The study at the same time accounts for a coherent and comprehensive formal reconstruction of historical work by {\L}ukasiewicz, Meredith and others. First experiments resulting from the study indicate novel ways of lemma generation to supplement automated first-order provers of various families, strengthening in particular their ability to find short proofs.
High-accurate camera calibration in three-dimensional visual displacement measurements based on ordinary planar pattern and analytical distortion model

Camera calibration is crucial for geometric vision-based three-dimensional (3D) deformation measurement tasks. Among existing calibration techniques, the one based on planar targets has attracted much attention in the community due to its flexibility and reliability. Our study proposes a calibration technique to obtain high-accuracy internal and external parameters based on low-cost ordinary planar patterns. The proposed method determines the optimal internal parameters for each camera by refining 3D coordinates of planar control points, where an analytic model of optics distortion is presented to enable lens distortion to be corrected directly in subsequent external calibration and underlying 3D reconstruction. External parameters are estimated by minimizing a bundle adjustment framework, which is carefully designed based on the proposed distortion correction model and depth parameterization. In contrast to the existing techniques, the proposed method is capable of obtaining a high-accuracy calibration with ordinary targets rather than the well-designed and fabricated ones. We experimented the proposed method with a calibration performance analysis and a displacement measurement; both results demonstrated the accuracy and robustness.
Neural-Symbolic Integration for Fairness in AI.

Deep learning has achieved state-of-the-art results in various application domains ranging from image recognition to language translation and game playing. However, it is now generally accepted that deep learning alone has not been able to satisfy the requirement of fairness and, ultimately, trust in Artificial Intelligence (AI). In this paper, we propose an interactive neural-symbolic approach for fairness in AI based on the Logic Tensor Network (LTN) framework. We show that the extraction of symbolic knowledge from LTN-based deep networks combined with fairness constraints offer a general method for instilling fairness into deep networks via continual learning. Explainable AI approaches which otherwise could identify but not fix fairness issues are shown to be enriched with an ability to improve fairness results. Experimental results on three real-world data sets used to predict income, credit risk and recidivism in financial applications show that our approach can satisfy fairness metrics while maintaining state-of-the-art classification performance.
The Effect of STEAM-Based Physical Education Classes on Middle School Students’ Attitudes toward Physical Education Classes and Self-Directed Learning Abilities

Efforts have often been made to improve physical education (PE) classes in response to rapidly changing societies. We applied science, technology, engineering, arts, and mathematics (STEAM) education to PE classes. The purpose was to examine the effect of STEAM-based PE lessons on self-directed learning abilities, a core competency of the 21st century, and on attitudes toward PE classes related to PE alienation and avoidance.To achieve this purpose, six out of eight classes at a middle school in Jeollabuk-do province, Republic of Korea were selected in 2019. The experimental and control groups, consisting of 87 and 88 students, respectively, were chosen from among 238 first-grade students by utilizing convenience sampling. The experimental group attended PE classes based on STEAM for 14 weeks, whereas the control group attended traditionally teacher-centered PE classes. We used a multivariate analysis of variance (MANOVA). Statistical significance was set at P<0.05.The experimental group displayed significant differences in all the sub-factors of attitudes toward PE classes and all the sub-factors of self-directed learning abilities, compared to the control group (P<0.05). PE classes based on STEAM appear to have a positive effect on students' attitudes toward PE classes and their self-directed learning abilities.PE is struggling to solve students' alienation and avoidance problems, despite numerous efforts. Thus, discussions have been conducted on how the STEAM philosophy can be implemented in the field of PE. Results suggest that efforts to combine STEAM education and PE are needed.
Queue Analysis for Probabilistic Cloud Workflows

Cloud applications can be modeled as workflows. These workflows are represented by Directed Acyclic Graphs (DAGs) or non-DAGs. The graph shows the relationship between tasks that compose a workflow and the dependencies between these tasks. in our previous work, we presented a method for transforming a workflow into an equivalent graph that shows all possible paths that a workflow will take. In this paper, we use the results of that method for multiple workflows coming to a queue and use the famous pollaczek-khintchine formula to estimate the average waiting and completion time for submitted workflows. Then, we use different scheduling algorithms, namely, Shortest Job First (SJF) and Longest Job First (LJF) and compare them with First Come First Serve (FCFS).
Drought hazard assessment in urban areas: A case of Bantul Regency, Special Region of Yogyakarta

Abstract Drought has a significant impact on many sectors. Urban areas also have tended to more vulnerable to the drought events since it consists of complex urban systems and high urban population. This study addresses to assess the drought hazard level in Bantul Regency and compares the results with urban development. This study used both primary and secondary data collection methods, i.e., field observation and document review. Precipitation data collected from 12 rainfall stations in Bantul Regency from 2008-2018. This study was conducted by using quantitative research methods. Drought hazard levels were analyzed using the Standardized Precipitation Index (SPI) method. While urbanization levels were assessed using a scoring technique. The results reveal that the drought hazard is not always in line with urban development but is also influenced by other factors. Drought in urban areas is more complex. It is not only affected by precipitation factors as meteorological drought in general, but also natural, population, and activity characteristics that affect the balance of water supply and demand. Further, the distribution of rainfall stations also influences the validity of rain data and its calculations. Drought disaster mitigation in urban areas requires more attention from stakeholders regarding the sustainability of cities.
A Fully Anonymous e-Voting Protocol Employing Universal Zk-SNARKs and Smart Contracts

The idea of smart contracts has been around for a long time. The introduction of Ethereum has taken the concept of smart contracts to new heights because of its integration with Blockchain technology. As a result, the applications of smart contracts have also surged in areas such as e-Voting, Insurance, Crowdfunding, etc. In this paper, we aim to present the construction of a “Fully Anonymous e-Voting” protocol using the concepts of zkHawk and Zcash. zkHawk is a novel smart contract protocol designed during this Ph.D. that improves upon the Hawk protocol by solving the underlying anonymity problem of a trusted manager. We will leverage the concept of zk-SNARKs in Zcash to carry out the voting phase of the election and the zkHawk smart contract protocol to tally the results of the election. The voting phase employing Zcash will be initially designed with Non-Universal zk-SNARKs and improved upon with Universal zk-SNARKs.
A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes

This paper provides a detailed description of how to install, setup, and use the YACCLAB benchmark to test the algorithms published in “A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes,” underlying how the parameters affect and influence experimental results.
A new secure authentication based distance bounding protocol

Numerous systems are being employed in daily life where two entities authenticate each other over a range of distance. The distance involved is relatively small, but still attacks were documented. The distance bounding (DB) protocol was introduced to cater to security requirements. The schemes, however, are still prone to several threats; mainly the Relay Attack (Terrorist and Mafia Fraud). In Mafia Fraud, an attempts are made to get accepted as the prover either by replaying of messages or by the help a malicious key. In Terrorist fraud, an attempt is made to extract the secret from the verifying entity, either by extracting the key from the message captured or by physically tempering the verifying/proving entity. Therefore the mitigation of these attacks needs to be done; as to not put computational overhead on the scheme. The paper presents a comprehensive and comparative performance analysis of twelve DB protocols based on defined metrics. It also proposes a protocol which incorporates the design elements needed for added security, is computationally easy to implement and resistant to most of the threats mentioned. Analysis of the protocol is carried out against the security requirements.