Reviewerly/exHarmony huggingface dataset:
Toward the identification of social signatures in ceramic production – An archaeological case study

Ceramic analysis has been concerned with categorizing types according to vessel shape and size for describing a given material culture at a particular time. This analysis’ long tradition has enabled archaeologists to define cultural units across time. However, going into the analysis of sub-typological variations is rarely done, although their meanings bear significant consequences on the understanding of ties between individuals and social units. This study, aiming to assess whether it is possible to identify social signatures, focuses on a single archaeological ceramic type. For this propose, we selected a corpus of 235 storage jars from two distinct periods: storage jars from the Intermediate Bronze Age (the 25 th -20 th century BCE); and the Oval Storage Jar type (hereafter: OSJ) from the Iron Age II (the late 9 th –early 6 th century BCE). The vessels selected were 3-D scanned to extract accurate geometric parameters and analyzed through an advanced shape analysis. The study results show that integrating computational and objective analysis methods, focusing on the “minute variation” within a single ceramic type, yields substantial insights regarding the relationship between variability and social units. In addition to the methodological guidelines and the suggested “work protocol” for further studies, the results shed light on the social organization of the Intermediate Bronze Age and the Iron Age II in Southern Levant.
A trustful monad for axiomatic reasoning with probability and nondeterminism

Abstract The algebraic properties of the combination of probabilistic choice and nondeterministic choice have long been a research topic in program semantics. This paper explains a formalization in the Coq proof assistant of a monad equipped with both choices: the geometrically convex monad. This formalization has an immediate application: it provides a model for a monad that implements a nontrivial interface, which allows for proofs by equational reasoning using probabilistic and nondeterministic effects. We explain the technical choices we made to go from the literature to a complete Coq formalization, from which we identify reusable theories about mathematical structures such as convex spaces and concrete categories, and that we integrate in a framework for monadic equational reasoning.
Fidelity measurement of a multiqubit cluster state with minimal effort

The size of the Hilbert space for a multiqubit state scales exponentially with the number of constituent qubits. Often this leads to a similar exponential scaling of the experimental resources required to characterize the state. Contrary to this, we propose a physically motivated method for experimentally assessing the fidelity of an important class of entangled states known as cluster states. The proposed method always yields a lower bound of the fidelity with a number of measurement settings scaling only linearly with the system size, and is tailored to correctly account for the errors most likely to occur in experiments. For one-dimensional cluster states, the constructed fidelity measure is tight to lowest order in the error probability for experimentally realistic noise sources and thus closely matches the true fidelity. Furthermore, it is tight for the majority of higher-order errors, except for a small subset of certain nonlocal multiqubit errors irrelevant in typical experimental situations. The scheme also performs very well for higher-dimensional cluster states, assessing correctly the majority of experimentally relevant errors.
Economic Growth, Governance and Educational Sustainability: A VAR Analysis

Quality educational institutions are strategic tools for accelerating the attainment of Sustainable Development Goals (SDGs). All the 17 SDGs are interlinked. For instance, quality education (SDG4) reduces poverty (SDG 1,2) and inequalities (SDG10) and stimulates good health and wellbeing (SDG3). The paper applied unorthodox theoretical postulations such as convergence models, intergovernmentalism, neofunctionalism and neorealism in explaining how functional (educational) institutions are a necessary enabling environment in accelerating the attainment of SDGs. Empirically, the paper identified unclear modus operandi, lack of political will, political instability, small and fragmented markets and economies with heterogeneous characteristics, and lack of standardization of product and procedures, among other factors, as constraints to sustainability in tertiary education. A Vector Autoregressive (VAR) model was employed using data from 51 Sub-Saharan countries. The three variables were gross domestic product per capita (GDPP), governance and tertiary education expenditure. Results indicated significant short-run unidirectional causality from gross domestic product per capita and tertiary education expenditure to governance, but joint short-run causality was not established. However, transmission effects across the three variables became significant as the number of years increased to ten years. The study recommends a holistic approach from policymakers in order to ensure sustainability in tertiary education due to interlinkages, with emphasis placed on direction of causality.
Optimized Quantum Networks

The topology of classical networks is determined by physical links between nodes, and after a network request the links are used to establish the desired connections. Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests, which can substitute links and allow one to fulfill multiple network requests with the same resource state. We utilize this to design entanglement-based quantum networks tailored to their desired functionality, independent of the underlying physical structure. The kind of entanglement to be stored is chosen to fulfill all desired network requests (i.e. parallel bipartite or multipartite communications between specific nodes chosen from some finite set), but in such a way that the storage requirement is minimized. This can be accomplished by using multipartite entangled states shared between network nodes that can be transformed by local operations to different target states. We introduce a clustering algorithm to identify connected clusters in the network for a given desired functionality, i.e. the required network topology of the entanglement-based network, and a merging algorithm that constructs multipartite entangled resource states with reduced memory requirement to fulfill all desired network requests. This leads to a significant reduction in required time and resources, and provides a powerful tool to design quantum networks that is unique to entanglement-based networks.
A Survey on Cat Swarm Optimization Algorithm

Swarm based optimization algorithms are a collection of intelligent techniques in the field of Artificial Intelligence (AI) were developed for simulating the intelligent behavior of animals. Over the years ago, problems complexity increased in a means that it is very difficult for basic mathematical approaches to obtain an optimum solution in an optimal time, this leads the researchers to develop various algorithms base on the natural behaviors of living beings for solving problems. This paper present a review for Cat Swarm Optimization (CSO), which is a powerful metaheuristic swarm-based optimization algorithm inspired by behaviors of cats in the Nature for solving optimization problems. Since its first appearances in 2006, CSO has been improved and applied in different fields by many researchers. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed.
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset

Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text-and visual-modalities.To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synthetic examples generalize to 'memes in the wild'.In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset.We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than 'traditional memes', including screenshots of conversations or text on a plain background.This paper thus serves as a reality check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.
Effectiveness of Automatic Formative Assessment for learning Mathematics in Higher Education

The health emergency due to COVID-19 has highlighted the need of new and flexible digital methodologies for learning and teaching Mathematics, which can support the individual student’s needs and help shape education. In this paper, we propose to use Automatic Formative Assessment (AFA) activities designed according to an innovative model, framed on the theories of formative assessment and feedback. The model includes: availability of the activities and multiple attempts; algorithmic questions; open mathematical answers; contextualized tasks; immediate and interactive feedback. We analyzed an experience using AFA in a blended module in Mathematics for 96 first-year students in Biotechnology. We collected data from the platform on grades and attempts of AFA activities and cross-checked them with the final exam grades. The results show that the feedback gained from AFA helped students improve their performance and supported them in the exam preparation. In light of these results, we can conclude that similar activities can be an effective solution to support students’ self-study during and after the pandemic.
Efficient Optimization of Cutoffs in Quantum Repeater Chains

Quantum communication enables the implementation of tasks that are unachievable with classical resources. However, losses on the communication channel preclude the direct long-distance transmission of quantum information in many relevant scenarios. In principle, quantum repeaters allow one to overcome losses. However, realistic hardware parameters make long-distance quantum communication a challenge in practice. For instance, in many protocols an entangled pair is generated that needs to wait in quantum memory until the generation of an additional pair. During this waiting time the first pair decoheres, impacting the quality of the final entanglement produced. At the cost of a lower rate, this effect can be mitigated by imposing a cutoff condition. For instance, a maximum storage time for entanglement after which it is discarded. In this article, we optimize the cutoffs for quantum repeater chains. First, we develop an algorithm for computing the probability distribution of the waiting time and fidelity of entanglement produced by repeater chain protocols which include a cutoff. Then, we use the algorithm to optimize cutoffs in order to maximize the secret-key rate between the end nodes of the repeater chain. In this article, we find that the use of the optimal cutoff extends the parameter regime for which secret key can be generated and, moreover, significantly increases the secret-key rate for a large range of parameters.
An Enhanced Approach of Image Steganographic Using Discrete Shearlet Transform and Secret Sharing

Recently, the internet has made the users able to transmit the digital media in the easiest manner. In spite of this facility of the internet, this may lead to several threats that are concerned with confidentiality of transferred media contents such as media authentication and integrity verification. For these reasons, data hiding methods and cryptography are used to protect the contents of digital media. In this paper, an enhanced method of image steganography combined with visual cryptography has been proposed. A secret logo (binary image) of size (128x128) is encrypted by applying (2 out 2 share) visual cryptography on it to generate two secret share. During the embedding process, a cover red, green, and blue (RGB) image of size (512x512) is divided into three layers (red, green and blue). The blue layer is transformed using Discrete Shearlet Transform (DST) to obtain its coefficients. The first secret share is embedded at the coefficients of transformed blue layer to obtain a stego image. At extraction process, the first secret share is extracted from the coefficients of blue layer of the stego image and XORed with the second secret share to generate the original secret logo. According to the experimental results, the proposed method is achieved better imperceptibility for the stego image with the payload capacity equal to (1 bpp). In addition, the secret logo becomes more secured using (2 out 2 share) visual cryptography and the second secret share as a private key.
Active Curriculum Learning

This paper investigates and reveals the relationship between two closely related machine learning disciplines, namely Active Learning (AL) and Curriculum Learning (CL), from the lens of several novel curricula.This paper also introduces Active Curriculum Learning (ACL) which improves AL by combining AL with CL to benefit from the dynamic nature of the AL informativeness concept as well as the human insights used in the design of the curriculum heuristics.Comparison of the performance of ACL and AL on two public datasets for the Named Entity Recognition (NER) task shows the effectiveness of combining AL and CL using our proposed framework.
Radio frequency mixing modules for superconducting qubit room temperature control systems

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost, and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing boards to address this challenge by integrating I/Q quadrature mixing, intermediate frequency/LO (local oscillator)/RF power level adjustments, and direct current bias fine tuning on a 40 × 80 mm2 four-layer printed circuit board with electromagnetic interference shielding. The RF mixing module is designed to work with RF and LO frequencies between 2.5 and 8.5 GHz. The typical image rejection and adjacent channel isolation are measured to be ∼27 dBc and ∼50 dB. By scanning the drive phase in a loopback test, the module short-term amplitude and phase linearity are typically measured to be 5 ×10−4 (Vpp/Vmean) and 1 ×10−3 radian (pk–pk). The operation of the RF mixing board was validated by integrating it into the room temperature control system of a superconducting quantum processor and executing randomized benchmarking characterization of single and two qubit gates. We measured a single-qubit process infidelity of 9.3(3) × 10−4 and a two-qubit process infidelity of 2.7(1) × 10−2.
Engaging Student Teachers in An Online Teaching Pedagogies Module during COVID-19

COVID 19 represented a major challenge for many educators, with teachers needing to pivot rapidly to using online learning tools in order to stay connected with their students. This was particularly relevant for teachers in the process of completing their Initial Teacher Education (ITE), whose programmes of study did not include online teaching components. The objective of this study was to develop and evaluate a module for teaching in online and blended learning environments for 244 post-primary teachers in ITE. This study begins by examining the impact of COVID-19 and the resulting pivot to online learning, this includes an overview of the challenges associated with online teaching. Next, the authors explain the module in detail, outlining the tools, strategies and activities provided for student teachers. This included peer-evaluated online micro-teaching components - which formed a major part of the learning. Data collection involved a questionnaire which gathered student teachers’ perceptions of the module and its approaches, the impact it had on their ability to teach during the COVID-19 pandemic, and the knowledge and skills useful for future practice. Findings suggested that the implementation of the developed module was successful in preparing student teachers to teach online, providing them with the tools and confidence necessary for success. Improvements suggested the development of differentiated pathways for student teachers who are more experienced with online teaching.
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer

Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the external language model (LM) fusion. In HAT, the blank probability and the label probability are estimated using two separate probability distributions, which provides a more accurate solution for internal LM score estimation, and thus works better when combining with an external LM. Previous work mainly focuses on HAT model training with the negative log-likelihood loss, while in this paper, we study the minimum word error rate (MWER) training of HAT — a criterion that is closer to the evaluation metric for speech recognition, and has been successfully applied to other types of end-to-end models such as sequence-to-sequence (S2S) and RNN-T models. From experiments with around 30,000 hours of training data, we show that MWER training can improve the accuracy of HAT models, while at the same time, improving the robustness of the model against the decoding hyper-parameters such as length normalization and decoding beam during inference.
KnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge Graph for NLP Research

Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers.Finding relevant research papers and their contribution to the domain is a challenging problem.In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContri-butionGraph, by developing a system for a research paper contributions-focused knowledge graph over Natural Language Processing literature.The task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases.The proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area.We found that transformer-based language models can significantly improve existing techniques and utilized the SciBERT-based model.Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM.The third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases.Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.
Science communicators intimidated: researchers' freedom of expression and the rise of authoritarian populism

In this article, we explore scientists' freedom of expression in the context of authoritarian populism. Our particular case for this analysis is Finland, where the right-wing populist Finns Party entered the government for the first time in 2015. More recently, after leaving the government in 2017, the party has been the most popular party in opinion polls in 2021. We illustrate the current threats to Finnish researchers' freedom of expression using their responses on three surveys, made in 2015, 2017 and 2019. We focus on politically motivated disparagement of scientists and experts, and the scientists' experiences with online hate and aggressive feedback. Further, we relate these findings to the recent studies on authoritarian populism and science-related populism. We argue that this development may affect researchers' readiness to communicate their research and expertise in public.
Does virtual threat harm VR experience?: Impact of threat occurrence and repeatability on virtual embodiment and threat response

This paper is an extended version of a previous paper [1] published at ICAT-EGVE 2020. We explored the potential impact of threat occurrence and repeatability on users' Sense of Embodiment (SoE) and threat response. To that aim, we conducted an experiment in which participants were embodied in a virtual avatar, and performed a task in which a threat towards the virtual body was introduced a first time, then repeated several times through the experiment (in total 5 times). The SoE of participants as well as their subjective response to the threat were assessed through subjective questionnaires before the introduction of the threat, after a first introduction of the threat and at the end of the experiment. A control group did the same experiment with no threat introduced during the task. The main findings of our experiment are that the introduction of a threat does not alter users' SoE but might change their behaviour while performing a task after the threat occurrence. In addition, threat repetitions did not show any effect on users' subjective SoE, or subjective and objective responses to threat. Taken together, our results suggest that embodiment studies should expect potential change in participants behaviour while doing a task after a threat was introduced, but that threat introduction and repetition do not seem to impact the subjective measure of the SoE (subective ratings) nor the objective measure of the SoE (physical reaction to threat).
Exploiting Auxiliary Data for Offensive Language Detection with Bidirectional Transformers

Offensive language detection (OLD) has received increasing attention due to its societal impact.Recent work shows that bidirectional transformer based methods obtain impressive performance on OLD.However, such methods usually rely on large-scale well-labeled OLD datasets for model training.To address the issue of data/label scarcity in OLD, in this paper, we propose a simple yet effective domain adaptation approach to train bidirectional transformers.Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain.Experimental results on benchmark datasets show that our approach, AL-BERT (DA), obtains the state-of-the-art performance in most cases.Particularly, our approach significantly benefits underrepresented and under-performing classes, with a significant improvement over ALBERT.
An effective feature set for enhancing printed Tamil character recognition

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.