Reviewerly/exHarmony huggingface dataset:
Dependability Engineering in Isabelle

In this paper, we introduce a process of formal system development supported by interactive theorem proving in a dedicated Isabelle framework. This Isabelle Infrastructure framework implements specification and verification in a cyclic process supported by attack tree analysis closely inter-connected with formal refinement of the specification. The process is cyclic: in a repeated iteration the refinement adds more detail to the system specification. It is a known hard problem how to find the next refinement step: this problem is addressed by the attack based analysis using Kripke structures and CTL logic. We call this cyclic process the Refinement-Risk cycle (RR-cycle). It has been developed for security and privacy of IoT healthcare systems initially but is more generally applicable for safety as well, that is, dependability in general. In this paper, we present the extensions to the Isabelle Infrastructure framework implementing a formal notion of property preserving refinement interleaved with attack tree analysis for the RR-cycle. The process is illustrated on the specification development and privacy analysis of the mobile Corona-virus warning app.
The Influence of the Form of Teaching Video on Learning Effect

The main goal of this study is to introduce panoramic video and two-dimensional video into daily learning as a form of teaching video, and to explore whether panoramic video can promote learners' learning compared with two-dimensional video. The theme of this study is "AIDS prevention", using 360 degree camera equipment to shoot the panoramic video of real learning scene and the two-dimensional video of the same knowledge content. The participants of this study were 50 undergraduates from central China Normal University (including 15 males and 35 females, aged from 19 to 21). The students in the experimental group watched panoramic video to learn "HIV self help guide", while the students in the control group watched two-dimensional video. The results show that there is a significant difference between the experimental group and the control group. The students who watch panoramic video have better learning effect than those who watch two-dimensional video.
CGNN: Caption-Assisted Graph Neural Network for Image-Text Retrieval

Image-text retrieval has drawn much attention in recent years, where similarity measure between image and text plays an important role. Most existing works focus on learning global coarse-grained or local fine-grained features for similarity computation. However, the large domain gap between different modalities is often neglected, which makes it difficult to match the images and texts effectively. In order to deal with this problem, we propose to use auxiliary information to release the domain gap, where the image captions are generated. Then, a Caption-Assisted Graph Neural Network(CGNN) is designed to learn the structured relationships among images, captions, and texts. Since the captions and the texts are from the same domain, the domain gap between images and texts can be effectively released. With the help of caption information, our model achieves excellent performance on two cross-modal retrieval datasets, Flickr30K and MS-COCO, which shows the effectiveness of our framework.
Evaluating Contrastive Learning on Wearable Timeseries for Downstream  Clinical Outcomes

Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical. This paper discusses ways in which self-supervised approaches that use contrastive losses, such as SimCLR and BYOL, previously applied to the vision domain, can be applied to high-dimensional health signals for downstream classification tasks of various diseases spanning sleep, heart, and metabolic conditions. To this end, we adapt the data augmentation step and the overall architecture to suit the temporal nature of the data (wearable traces) and evaluate on 5 downstream tasks by comparing other state-of-the-art methods including supervised learning and an adversarial unsupervised representation learning method. We show that SimCLR outperforms the adversarial method and a fully-supervised method in the majority of the downstream evaluation tasks, and that all self-supervised methods outperform the fully-supervised methods. This work provides a comprehensive benchmark for contrastive methods applied to the wearable time-series domain, showing the promise of task-agnostic representations for downstream clinical outcomes.
Reaction SPL – extension of a public document markup standard to chemical reactions

Abstract There are numerous formats and data models for describing reaction-related data. However, each offers only a limited coverage of the multitude of information that can be of interest to a broad user base in the context of chemical reactions. Structured Product Labeling (SPL) is a robust yet fairly light public XML document standard. It uses a highly generic but usefully refinable data schema, which is, like a language, highly expressive. We are therefore presenting an extension of SPL to chemical reactions (“Reaction SPL”). This extension is designed to support chemical manufacturing processes, which include as a minimum the chemical reaction and the procedures and conditions to run it. We provide an overview of the SPL reaction specification structures followed by some examples of documents with reaction data: predicted single-step reactions, a two-step synthesis, an enzymatic reaction, an example how to represent a reaction center, a patent, and a fully annotated reaction with by-products. Special attention is given to a mechanism for atom-atom mapping of reactions as well as to the possibility to integrate Reaction SPL with laboratory automation equipment, in particular automated synthesis devices.
Computational Argumentation and Cognition

This paper examines the interdisciplinary research question of how to integrate Computational Argumentation, as studied in AI, with Cognition, as can be found in Cognitive Science, Linguistics, and Philosophy. It stems from the work of the 1st Workshop on Computational Argumentation and Cognition (COGNITAR), which was organized as part of the 24th European Conference on Artificial Intelligence (ECAI), and took place virtually on September 8th, 2020. The paper begins with a brief presentation of the scientific motivation for the integration of Computational Argumentation and Cognition, arguing that within the context of Human-Centric AI the use of theory and methods from Computational Argumentation for the study of Cognition can be a promising avenue to pursue. A short summary of each of the workshop presentations is given showing the wide spectrum of problems where the synthesis of the theory and methods of Computational Argumentation with other approaches that study Cognition can be applied. The paper presents the main problems and challenges in the area that would need to be addressed, both at the scientific level but also at the epistemological level, particularly in relation to the synthesis of ideas and approaches from the various disciplines involved.
Mesoscopic and macroscopic quantum correlations in photonic, atomic and  optomechanical systems

This paper reviews the progress that has been made in our knowledge of quantum correlations at the mesoscopic and macroscopic level. We begin by summarizing the Einstein-Podolsky-Rosen (EPR) argument and the Bell correlations that cannot be explained by local hidden variable theories. It was originally an open question as to whether (and how) such quantum correlations could occur on a macroscopic scale, since this would seem to counter the correspondence principle. The purpose of this review is to examine how this question has been answered over the decades since the original papers of EPR and Bell. We first review work relating to higher spin measurements which revealed that macroscopic quantum states could exhibit Bell correlations. This covers higher dimensional, multi-particle and continuous-variable EPR and Bell states where measurements on a single system give a spectrum of outcomes, and also multipartite states where measurements are made at multiple separated sites. It appeared that the macroscopic quantum observations were for an increasingly limited span of measurement settings and required a fine resolution of outcomes. Motivated by this, we next review correlations for macroscopic superposition states, and examine predictions for the violation of Leggett-Garg inequalities for dynamical quantum systems. These results reveal Bell correlations for coarse-grained measurements which need only distinguish between macroscopically distinct states, thus bringing into question the validity of certain forms of macroscopic realism. Finally, we review progress for massive systems, including Bose-Einstein condensates and optomechanical oscillators, where EPR-type correlations have been observed between massive systems. Experiments are summarized, which support the predictions of quantum mechanics in mesoscopic regimes.
Partnering to Provide Electronic Access to Life-Sciences Serials: The Experience of Journal Selection for the Electronic Library Project

Academic Information Services and Research (AISR) oversees and directs Scott Memorial Library (SML), and supports a broad spectrum of services to the three colleges of Thomas Jefferson University. The Jefferson community no longer consists solely of on-campus constituents who need only walk across a street to use the Library’s collections. An electronic journal might be part of a collection of electronic resources, like Academic Press’s “IDEAL,” or may be the sole title released by a publisher. An electronic journal might be part of a collection of electronic resources, like Academic Press’s “IDEAL,” or may be the sole title released by a publisher. The primary criteria for journal selection were the circulation and cost-per-use data. The single most important criterion for continued provision of serial titles at SML is the evaluation of internal and external circulation use data. The library system compiled internal statistics from bound and unbound journal issues scanned as they are reshelved by library staff.
Generative Dynamic Patch Attack

Adversarial patch attack is a family of attack algorithms that perturb a part of image to fool a deep neural network model. Existing patch attacks mostly consider injecting adversarial patches at input-agnostic locations: either a predefined location or a random location. This attack setup may be sufficient for attack but has considerable limitations when using it for adversarial training. Thus, robust models trained with existing patch attacks cannot effectively defend other adversarial attacks. In this paper, we first propose an end-to-end patch attack algorithm, Generative Dynamic Patch Attack (GDPA), which generates both patch pattern and patch location adversarially for each input image. We show that GDPA is a generic attack framework that can produce dynamic/static and visible/invisible patches with a few configuration changes. Secondly, GDPA can be readily integrated for adversarial training to improve model robustness to various adversarial attacks. Extensive experiments on VGGFace, Traffic Sign and ImageNet show that GDPA achieves higher attack success rates than state-of-the-art patch attacks, while adversarially trained model with GDPA demonstrates superior robustness to adversarial patch attacks than competing methods. Our source code can be found at https://github.com/lxuniverse/gdpa.
Research frontiers of pre-training mathematical models based on BERT

Natural language processing (NLP) is a popular technology after the rise of big data and machine learning in recent years. With the development of deep learning, the field of natural language processing has also undergone a landmark transformation, including the emergence of the BERT large-scale language training model. The emergence of this model makes text mining a qualitative leap, meets more practical needs, and solves the related problems of feature vectorization of unstructured data. This article will sort out the connotation, task application, and main optimization and improvement methods of the BERT pre-training model released by Google, and provide a reference for subsequent related research and development based on BERT.
A Contrastive Distillation Approach for Incremental Semantic  Segmentation in Aerial Images

Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning current deep neural architectures is known as catastrophic forgetting, namely the inability to faithfully maintain past knowledge once a new set of data is provided for retraining. Over the years, several techniques have been proposed to mitigate this problem for image classification and object detection. However, only recently the focus has shifted towards more complex downstream tasks such as instance or semantic segmentation. Starting from incremental-class learning for semantic segmentation tasks, our goal is to adapt this strategy to the aerial domain, exploiting a peculiar feature that differentiates it from natural images, namely the orientation. In addition to the standard knowledge distillation approach, we propose a contrastive regularization, where any given input is compared with its augmented version (i.e. flipping and rotations) in order to minimize the difference between the segmentation features produced by both inputs. We show the effectiveness of our solution on the Potsdam dataset, outperforming the incremental baseline in every test. Code available at: https://github.com/edornd/contrastive-distillation.
WLAA*: A Time-efficient Method for Path Planning in Warehouse Environments

The path planning problem is to find a feasible path between two given locations. Search-based methods, like A* and its variants, have shown effectiveness for solving such problems in some specific environments, for example, in automated warehouses. However, their search time always increases exponentially with the growing number of extra visited nodes, which is inconsistent with the requirements of warehouse applications. To address this issue, we first analyze the generation of extra visited nodes. On this basis, we then propose warehouse-layout-aware A* (WLAA*) that can lessen such generation according to the layout of a warehouse. We also present an algorithm that identifies the layout automatically. Finally, we perform simulation experiments on different instances. Experimental results show that WLAA* is able to effectively reduce the number of extra visited nodes thus speeding up the search.
The Narrow Channel of Quantitative Easing: Evidence from Ycc Down Under

We study the recent Australian experience with yield curve control (YCC) of government bonds as perhaps the best evidence of how this policy might work in other developed economies. We interpret the evidence with a simple model in which YCC affects prices of both government and other bonds via “broad” transmission channels, but only government bond prices through “narrow” liquidity channels. YCC seemingly worked well in 2020 while the market expected short rates to stay at zero for long. But as the global recovery and inflation gained momentum in 2021, liftoff expectations moved up, the Reserve Bank of Australia purchased most of the outstanding amount of the targeted government bond, and its yield dislocated from other financial market instruments. The model and empirical evidence point to narrow transmission channels playing more prominent roles than broad channels considered in prior studies of quantitative easing (QE), such as portfolio balance effects and signaling about short term rates. We argue that asset-specific narrow channels may be primary transmission mechanisms of quantity-based QE policies as well.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL

Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-RL approaches struggle to learn in sparse reward environments. Although existing meta-RL algorithms can learn strategies for adapting to new sparse reward tasks, the actual adaptation strategies are learned using hand-shaped reward functions, or require simple environments where random exploration is sufficient to encounter sparse reward. In this paper, we present a formulation of hindsight relabeling for meta-RL, which relabels experience during meta-training to enable learning to learn entirely using sparse reward. We demonstrate the effectiveness of our approach on a suite of challenging sparse reward goal-reaching environments that previously required dense reward during meta-training to solve. Our approach solves these environments using the true sparse reward function, with performance comparable to training with a proxy dense reward function.
Adaptive Steganography Based on bargain Game

The capacity and security of the confidential message on the channel are two important challenges in steganography. In this paper, a new block steganography model is presented using the bargain method so that a competitive model is introduced. In this game, the blocks are the same players. The bargain is provided with the aim of embedding information without reducing capacity as well as increasing security. The proposed model shows that it can be used both of the special domain and the transform domain, which are two important methods of steganography. For this purpose, an example of a special domain model is introduced in which, In the first step, the image is divided into $n \times n$ blocks, and in the second step using the graph coloring algorithm, pixels are considered to embed confidential information in each block. In the third step, regarding the bargaining method in game theory, each block plays the role of a player, that the competition between players is based on the defined goal function, and in the best blocks in terms of two criteria of capacity and security, which here means each block has a higher security-to-capacity ratio, so it has a higher priority, which is determined based on the bargaining model. Also, information embedded in LSB two bits. An example of a conversion domain method is also shows that security increases without decreasing in capacity. The conclusion is evaluated by three criteria: PSNR, histogram, and $\epsilon-secure$ also, 2000 standard images were evaluated and observed that the proposed method improves the block methods of embedding information.
Application of Artificial Intelligence and Machine Learning in  Libraries: A Systematic Review

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed to summarize on the application of AI and ML domain and techniques which are most often used in libraries. Findings show that the current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.
Top-Down Deep Clustering with Multi-generator GANs

Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven remarkably successful for high dimensional data spaces. Some DC methods employ Generative Adversarial Networks (GANs), motivated by the powerful latent representations these models are able to learn implicitly. In this work, we propose HC-MGAN, a new technique based on GANs with multiple generators (MGANs), which have not been explored for clustering. Our method is inspired by the observation that each generator of a MGAN tends to generate data that correlates with a sub-region of the real data distribution. We use this clustered generation to train a classifier for inferring from which generator a given image came from, thus providing a semantically meaningful clustering for the real distribution. Additionally, we design our method so that it is performed in a top-down hierarchical clustering tree, thus proposing the first hierarchical DC method, to the best of our knowledge. We conduct several experiments to evaluate the proposed method against recent DC methods, obtaining competitive results. Last, we perform an exploratory analysis of the hierarchical clustering tree that highlights how accurately it organizes the data in a hierarchy of semantically coherent patterns.
Learning Transferable Reward for Query Object Localization with Policy Adaptation

We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time policy adaptation to new environments where the reward signals are not readily available, and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach.
Resurgence or retrofit? A chance to humanise student teachers in crisis-ravaged Chile

This prediction piece reports on challenges and opportunities for better understanding student teachers in Chile under the double stressors of political dysfunction and the COVID-19 pandemic. Immediately prior to October, 2019 protests and violence that would bring Chile to a standstill in the lead-in to major coronavirus waves from March 2020, Chile’s annual student engagement survey would collect data on university experiences, including those of student teachers preparing to serve a troubled schooling system. As online modalities have brought tensions between the technological and human elements of teaching and learning to the fore, a more explicit premium might now be placed on understanding student teachers not simply as filling educational functions, but as individuals with their own human needs.