Reviewerly/exHarmony huggingface dataset:
A Tale of Two Boards: On the Influence of Microarchitecture on Side-Channel Leakage

Advances in cryptography have enabled the features of confidentiality, security, and integrity on small embedded devices such as IoT devices. While mathematically strong, the platform on which an algorithm is implemented plays a significant role in the security of the final product. Side-channel attacks exploit the variations in the system’s physical characteristics to obtain information about the sensitive data. In our scenario, a software implementation of a cryptographic algorithm is flashed on devices from different manufactures with the same instruction set configured for identical execution. To analyze the influence of the microarchitecture on side-channel leakage, we acquire thirty-two sets of power traces from four physical devices. While we notice minor differences in the leakage behavior for different physical boards from the same manufacturer, our results confirm that the difference in microarchitecture implementations of the same core will leak different side-channel information. We also show that TVLA leakage prediction should be treated with caution as it is sensitive to both false positives and negatives.
Troubled Worlds

The goal of this syllabus is to interrogate the material, and socioeconomic processes which underpin our everyday information work. In particular, we examine the relationships developing between contemporary information practices and what problematically gets configured as “nature”—that messy world of non-human entanglements that often exists beyond the purview of innovation work, whether digital software development or industrial engineering. Much recent work on the environmental conditions of computing has sought to break down technology-nature dualisms in order to expose the implication of information technology in broader social and material ecologies. Library and information professionals and researchers are well poised to deepen this inquiry by presenting alternative nature-technology epistemologies grounded in longstanding analyses of information resources and their consumption. The “Troubled Worlds” syllabus starts with a discussion of concerns most obviously germane to the work of most library and information science professionals: practices at the intersection of structuring information and computing. Building on this attention, we turn to humanistic approaches to thinking through the era of dominant human activities widely known as the “Anthropocene” by introducing poetic, artistic, and activist lenses. We explore how artistic objects representing an increasingly troubled natural world raise awareness of the challenges facing it, as well as how they may incorporate and reshape information for aesthetic ends. We then look to questions of disability justice and how it works in blended built and natural spaces as well as the many different ways in which bodies respond to the toxic environments produced by information technologies. We next consider the newer design approaches to library and information research, specifically asking how design perspectives on digital information objects get inscribed in the Anthropocene. Lastly, we consider paradigms of repair and making and analyze the different valences through which information researchers and professionals categorize and contextualize what is possible with them. This compilation does not provide a comprehensive review of the literature on the environment within the information fields. Instead, it extends this literature to promote experimental research and practice. The modules construct an interdisciplinary and provisional path through the related literature in a form that we hope may be continually adjusted, rearranged, and augmented.&#x0D; Pre-print first published online 03/15/2020
Transfer of Pretrained Model Weights Substantially Improves Semi-supervised Image Classification

Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires considerable resources, time, and effort. If labeling new data is not feasible, so-called semi-supervised learning can achieve better generalisation than purely supervised learning by employing unlabeled instances as well as labeled ones. The work presented in this paper is motivated by the observation that transfer learning provides the opportunity to potentially further improve performance by exploiting models pretrained on a similar domain. More specifically, we explore the use of transfer learning when performing semi-supervised learning using self-learning. The main contribution is an empirical evaluation of transfer learning using different combinations of similarity metric learning methods and label propagation algorithms in semi-supervised learning. We find that transfer learning always substantially improves the model's accuracy when few labeled examples are available, regardless of the type of loss used for training the neural network. This finding is obtained by performing extensive experiments on the SVHN, CIFAR10, and Plant Village image classification datasets and applying pretrained weights from Imagenet for transfer learning.
Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models

Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement.However, it remains unclear if such an inductive bias would also improve language models' ability to learn grammatical dependencies in typologically different languages.Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English.We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformerparameterized generative parsing models on two Mandarin Chinese datasets of different sizes.We evaluate the models' ability to learn different aspects of Mandarin grammar that assess syntactic and semantic relationships.We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.
Union and Intersection of All Justifications

We present new algorithms for computing the union and intersection of all justifications for a given ontological consequence without first computing the set of all justifications. Our goal is to use these sets to explain the consequences and, if needed, repair them. Through an empirical evaluation, we show that our approach behaves well in practice for expressive description logics. In particular, the union of all justifications can be computed much faster than with existing justification-enumeration approaches.
Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior

While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality. To address some of these practical challenges, we develop a framework of generalized dynamic cognitive hierarchy for both modelling naturalistic human driving behavior as well as behavior planning for autonomous vehicles (AV). This framework is built upon a rich model of level-0 behavior through the use of automata strategies, an interpretable notion of bounded rationality through safety and maneuver satisficing, and a robust response for planning. Based on evaluation on two large naturalistic datasets as well as simulation of critical traffic scenarios, we show that i) automata strategies are well suited for level-0 behavior in a dynamic level-k framework, and ii) the proposed robust response to a heterogeneous population of strategic and non-strategic reasoners can be an effective approach for game theoretic planning in AV.
Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding

Code understanding is an increasingly important application of Artificial Intelligence. A fundamental aspect of understanding code is understanding text about code, e.g., documentation and forum discussions. Pre-trained language models (e.g., BERT) are a popular approach for various NLP tasks, and there are now a variety of benchmarks, such as GLUE, to help improve the development of such models for natural language understanding. However, little is known about how well such models work on textual artifacts about code, and we are unaware of any systematic set of downstream tasks for such an evaluation. In this paper, we derive a set of benchmarks (BLANCA - Benchmarks for LANguage models on Coding Artifacts) that assess code understanding based on tasks such as predicting the best answer to a question in a forum post, finding related forum posts, or predicting classes related in a hierarchy from class documentation. We evaluate performance of current state-of-the-art language models on these tasks and show that there is significant improvement on each task from fine tuning. We also show that multi-task training over BLANCA tasks help build better language models for code understanding.
Internet of Samples

Abstract Material samples are indispensable data sources in many natural science, social science, and humanity disciplines. More and more researchers recognize that samples collected in one discipline can be of great value for another. This has motivated organizations that manage a large number of samples to make their holdings accessible to the world. Currently, multiple projects are working to connect natural history and other samples managed by individual institutions or individuals into a universe of samples that follow FAIR principles. This poster reports the progress of the US NSF‐funded iSamples project, in the context of other efforts initiated by US DOE, DiSCCo, BCoN, and GBIF. By October 2021, we will also be able to present an iSamples prototype. We encourage individual organizations that hold material samples to get to know these projects and help shape these projects to realize the goal of a global linked sample cloud that connects all material samples and is accessible to all.
Medical Data Classification Based on SMOTE and Recurrent Neural Network

Medical data classification analysis the medical data of the patients to predict the diseases risk. Data mining techniques were highly used in the medical data classification and predicted the diseases. Many existing methods were use the various classifier and feature selection to improve the performance of the classification. Although data imbalance problem is need to be solved for increases the performance. In this research, Synthetic Minority Over-sampling TEchnique (SMOTE) techniques is used for solving the data imbalance problem and Recurrent Neural Network (RNN) was used for the classification. The SMOTE method based on the k Nearest Neighbor (kNN) for the over-sample and under-sample the attributes. The RNN process the instance independent of the previous instance for the classification. Four medical datasets of University of California, Irvine (UCI) were used to evaluate the effectiveness of the proposed SMOTE-RNN method. The proposed SMOTE-RNN method has the accuracy of 85 % while existing method has 82 % accuracy.
Understanding holographic error correction via unique algebras and atomic examples

We introduce a fully constructive characterisation of holographic quantum error-correcting codes. That is, given a code and an erasure error we give a recipe to explicitly compute the terms in the RT formula. Using this formalism, we employ quantum circuits to construct a number of examples of holographic codes. Our codes have nontrivial holographic properties and are simpler than existing approaches built on tensor networks. Finally, leveraging a connection between correctable and private systems we prove the uniqueness of the algebra satisfying complementary recovery. The material is presented with the goal of accessibility to researchers in quantum information with no prior background in holography.
Efficiency Comparison in Prediction of Normalization with Data Mining Classification

In research project, efficiency comparison study in prediction of normalization with data mining classification.The purpose of the research was to compare three normalization methods in term of classification accuracy that the normalized data provided: Z-Score, Decimal Scaling and Statistical Column.The six known classifications: K-Nearest Neighbor, Decision Tree, Artificial Neural Network, Support Vector Machine, Naïve Bayes, and Binary Logistic Regression were used to evaluate the normalization methods.The six studied data sets were into two groups.Those data sets were data sets of White wine quality, Pima Indians diabetes, and Vertebral column of which data were 1-5 variables of the outlier coefficient of variation and data sets of Indian liver disease, Working hours, and Avocado of which data were 6-10 variables of the outlier coefficient of variation.The result of comparison White wine quality and Vertebral column, the best efficiency method had many methods in a non-systematic way.For the data set of Pima Indians diabetes and Indian liver disease, Statistical Column and classification by K-Nearest Neighbor was the best efficiency.For the data set of Working hours, Decimal Scaling and classification by K-Nearest Neighbor was the best efficiency.For the data set of Avocado, Statistical Column and classification by K-Nearest Neighbor, Z-Score and Decimal Scaling and classification by Binary Logistic Regression were the best efficiency.All of normalization and classification methods, Statistical Column and classification by K-Nearest Neighbor was the best efficiency by precision.
Community Information System for Ladprao Canal Development

Preparation of information for community development along the Ladprao canal. The objective is to survey and create a database of the Ladprao canal community. And developing the community information system at Ladprao canal. Systematically collecting data according to the dimensions of the community Consists of 6 basic basic information. General health, economic, environmental information The relation of the household to politics and government and communication. Analyze information for quality of life development in 3 issues which are economic, environmental and health. The results of the research showed that Ladprao canal community that has completed housing construction. And there are already 22 communities in 2717 households. The total population is 3164 people, 47% males and 53% females. The majority of the population are aged 40-59 years. In the economic aspect, it is found that the main cause of the economy in every household's household is low and unstable. Because most of them are professionals earn daily but very expensive. Regarding the environment, the majority of the population have good waste collection and care for the environment within the household. For health, most problems are chronic illnesses. With non-communicable diseases. Therefore, all 3 issues are relevant and clearly linked. Especially the economic aspect which directly affects the well-being of the people The circumstances of each person are different. Affecting the care for the environment and take care of one's own health of people in the community, both directly and indirectly.
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations

Adversarial attacks curated against NLP models are increasingly becoming practical threats.Although various methods have been developed to detect adversarial attacks, securing learning-based NLP systems in practice would require more than identifying and evading perturbed instances.To address these issues, we propose a new set of adversary identification tasks, Attacker Attribute Classification via Textual Analysis (AACTA), that attempts to obtain more detailed information about the attackers from adversarial texts.Specifically, given a piece of adversarial text, we hope to accomplish tasks such as localizing perturbed tokens, identifying the attacker's access level to the target model, determining the evasion mechanism imposed, and specifying the perturbation type employed by the attacking algorithm.Our contributions are as follows: we formalize the task of classifying attacker attributes, and create a benchmark on various target models from sentiment classification and abuse detection domains.We show that signals from BERT models and target models can be used to train classifiers that reveal the properties of the attacking algorithms.We demonstrate that adversarial attacks leave interpretable traces in the feature space of both of pre-trained language models and target models, making AACTA a promising direction towards more trustworthy NLP systems.
A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

pddl+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised pddl+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. We define the subclass of problems where the variant is safely applicable, and we assess the advantages of such a translation.
The masking condition for the quantum state in two-dimensional Hilbert space

This paper focuses on quantum information masking for quantum state in two-dimensional Hilbert space. We present a system of equations as the condition of quantum information masking. It is shown that quantum information contained in a single qubit state can be masked, if and only if the coefficients of quantum state satisfy the given system of equations. By observing the characteristics of non-orthogonal maskable quantum states, we obtain a related conclusion, namely, if two non-orthogonal two-qubit quantum states can mask a single qubit state, they have the same number of terms and the same basis. Finally, for maskable orthogonal quantum states, we analyze two special examples and give their images for an intuitive description.
On implicit multiparadigmality of parallel programming

The report is devoted to the results of a paradigmatic analysis of problems, means and methods of organizing parallel computing and multi-threaded programs for multiprocessor complexes and distributed systems. Paradigmatic analysis of programming language and systems allows decomposing the complexity of the tasks being solved into autonomously developed components, assessing their similarities and differences, which must be taken into account when predicting the course of application processes, as well as when planning the study and organizing the development of programs. A variety of paradigmatic characteristics inherent in the preparation and debugging of long-lived parallel computing programs are shown. A sketch of a multi-paradigm parallel programming language for educational purposes is presented.
Improved Lattice-Based Mix-Nets for Electronic Voting

Mix-networks were first proposed by Chaum in the late 1970s – early 1980s [10] as a general tool for building anonymous communication systems. Classical mix-net implementations rely on standard public key primitives (e.g. ElGamal encryption) that will become vulnerable when a sufficiently powerful quantum computer will be built. Thus, there is a need to develop quantum-resistant mix-nets. This paper focuses on the application case of electronic voting where the number of votes to be mixed may reach hundreds of thousands or even millions. We propose an improved architecture for lattice-based post-quantum mix-nets featuring more efficient zero-knowledge proofs while maintaining established security assumptions. Our current implementation scales up to 100000 votes, still leaving a lot of room for future optimisation.
Blockchain in the Biopharmaceutical Industry

Biopharmaceutical companies and health authorities continuously exchange information to provide safe and effective therapeutics. The interactions between the two require transparency and extensive documentation exchange concerning the processes which extend from the development through the manufacturing phase. The current processes rely on paper documentation, notebooks, and point-to-point electronic data interchange (EDI) for the storage of data. Thereby, generating challenges of data integrity within the internal siloed structures and the traceability of the medicinal products in the pursuit to avoid counterfeiting. With Industry 4.0 and blockchain, the authors envisioned a reinvented workflow that helps to 1) manage data integrity with decentralized trust and 2) improve the track and trace capabilities. Hence, biopharmaceutical companies can manage data in a more trustable manner while maintaining security and privacy, further enabling the external ecosystem with track and trace to ensure complete transparency until the therapeutics reach patients.
Linking theory and practice in teacher training: pandemic lessons from three countries (Russia, Kazakhstan and Slovakia)

The objective of this comparative study devoted to the theoretical and practical training of future teachers (as e.g. French as a foreign language) in the context of Covid-19 is conditioned by the challenge that the pandemic represents for social institutions, by the global trends of the evolution of teacher training, by national priorities in the implementation of professional training in Russia, Kazakhstan, Slovakia as well as by the institutional educational strategies of the universities of the above-mentioned countries (Federal University of Kazan, National University Eurasian named after LN Goumilev and Comenius University of Bratislava). In this article, we analyze the institutional contexts of the training of future French teachers under the conditions of Covid-19 and from the point of view of changes in content and procedural aspects as well as the evaluative component of their professional training. The research priority was to assess the influence of pandemic conditions on the quality of the training of future French teachers and on the degree of training of their teaching skills, as well as to identify and justify a certain number of principles of the future training of French teachers. The study is based on systemic and task-based approaches. Research data was provided by 20 teachers and 44 university students from Russia and Kazakhstan; and by 11 students from Comenius University in Bratislava. The authors performed a reflexive-systematic analysis of educational activity and a qualitative factor analysis of unstructured interviews. The research results indicate the potential of integrating the theoretical and practical training of a future teacher of French in a pandemic environment, respecting a number of principles: 1) modularity; 2) polymodality (multimedia); 3) continuity of the contents of disciplines and practices; 4) dialogue between educational actors; 5) combination of assessment tools from theoretical and practical disciplines. The transition from face-to-face to distance training as well as the presence of gaps in digital teaching constitute a particular challenge in the training of future French teachers. The results of the study can be used for the development of educational programs for the training of French teachers and the implementation of joint research in the field of comparative pedagogy.