Reviewerly/exHarmony huggingface dataset:
Correlation-Based Transformer Tracking

In recent studies on object tracking, Siamese tracking has achieved state-of-the-art performance due to its robustness and accuracy. Cross-correlation which is responsible for calculating similarity plays an important role in the development of Siamese tracking. However, the fact that general cross-correlation is a local operation leads to the lack of global contextual information. Although introducing transformer into tracking seems helpful to gain more semantic information, it will also bring more background interference, thus leads to the decline of the accuracy especially in long-term tracking. To address these problems, we propose a novel tracker, which adopts transformer architecture combined with cross-correlation, referred as correlation-based transformer tracking (CTT). When capturing global contextual information, the proposed CTT takes advantage of cross-correlation for more accurate feature fusion. This architecture is helpful to improve the tracking performance, especially long-term tracking. Extensive experimental results on large-scale benchmark datasets show that the proposed CTT achieves state-of-the-art performance, and particularly performs better than other trackers in long-term tracking.
A Unified Multiple Inducible Co-attentions and Edge Guidance Network for Co-saliency Detection

The learning-based methods have improved the performances of co-salient object detection (CoSOD). Mining the intra-image saliency individuals and exploring the inter-image co-attention are two challenges. In this paper, we propose a unified Multiple INducible co-attentions and Edge guidance network (MineNet) for CoSOD. Firstly, a classified inducible co-attention (CICA) is designed to model the classification interactions from a group of images. Secondly, a focal inducible co-attention (FICA) is employed to adaptively suppress and aggregate inter-image saliency features. CICA and FICA are jointly embedded into the network to predict the co-attention. The co-attentions of CICA and FICA are collaborative calibration and mutual optimization. Thirdly, we put forward an edge guidance module (EGM) to mine the intra-image saliency individuals, which aims to keep the consistency of co-attention during the feature transfer and refine the object edges. Finally, these three modules are merged into a unified and end-to-end network to predict the fine-grained boundary-preserving salient objects. Experimental results on three prevailing benchmarks show that our MineNet outperforms other competitors in terms of the evaluation metrics. In addition, the proposed method runs at the speed of more than 30 fps on a single GPU.
Towards Interdependent Safety Security Assessments Using Bowties

We present a way to combine security and safety assessments using Bowtie Diagrams. Bowties model both the causes leading up to a central failure event and consequences which arise from that event, as well as barriers which impede events. Bowties have previously been used separately for security and safety assessments, but we suggest that a unified treatment in a single model can elegantly capture safety-security interdependencies of several kinds. We showcase our approach with the example of the October 2021 Facebook DNS shutdown, examining the chains of events and the interplay between the security and safety barriers which caused the outage.
Context-Assisted Attention for Image Captioning

Temporal attention has demonstrated its crucial role with regard to modelling the relationships between semantic queries and image regions in current image captioning task. Nevertheless, most existing attention-based methods ignore the potential effect of the previously attended information on the generation of current attention context. In this paper, we propose a simple but effective Context-Assisted Attention (CA $$^2$$ ) for image captioning, which considers the temporal coherence of the attention contexts in the process of sequence prediction. Specifically, CA $$^2$$ combines the attention contexts from previous time steps with the features of image regions to serve as the input key-value pairs of the attention module for current context generation, which enables the sentence decoder to not only attend to the image regions by tradition but also focus on the historical attention contexts when necessary. Furthermore, we present a regularization method tailored to our CA $$^2$$ , namely Weight Transferring Constraint (WTC), to restrict the total weight assigned to the historical contexts in each decoding step. Experiments on the popular MS COCO dataset demonstrate that our method consistently improves LSTM-based baselines and achieves a competitive performance with 38.7 BLEU-4 and 128.5 CIDEr-D scores.
Addressing Contradiction Between Reconstruction and Correlation Maximization in Deep Canonical Correlation Autoencoders

Canonical correlation analysis (CCA) and its nonlinear extensions have shown promising performance in multi-view representation learning. One of the most representative methods is Deep Canonical Correlation Analysis Autoencoders (DCCAE), which combines CCA with a reconstruction loss to reserve more information in representations. However, the contradiction between reconstruction and correlation maximization hinders the optimization of them. Here we propose a multi-view representation learning method named Full Reconstruction based Deep Canonical Correlation Analysis (FR-DCCA), which not only addresses this contradiction but also enables the reconstructing and correlation maximization to benefit from each other. In FR-DCCA, Split Encoder models the information in each view as shared information and specific information; CCA layer maintains consistency through maximizing the canonical correlation between shared information of views; Full Reconstruction module guarantees completeness and complementarity by reconstructing each view with both the shared and specific information. In FR-DCCA, reconstructing and correlation maximization mutually improve each other and yield complete, compact, and discriminative view representations. Experiments and analysis on multiple datasets demonstrate: 1) FR-DCCA significantly outperforms the comparison methods. 2) FR-DCCA effectively addresses the contradiction between reconstruction and correlation maximization. To facilitate future research, we release the codes at https://github.com/FR-DCCA/FR-DCCA .
Nesterov Adam Iterative Fast Gradient Method for Adversarial Attacks

Deep Neural Networks (DNNs) are vulnerable to adversarial examples that mislead DNNs with imperceptible perturbations. Existing adversarial attacks often exhibit weak transferability under the black-box setting, especially when attacking the models with defense mechanisms. In this work, we regard the adversarial example generation problem as the problem of optimizing DNNs, and propose Nesterov Adam Iterative Fast Gradient Method (NAI-FGM) which applies Nesterov accelerated gradient and Adam to iterative attacks to improve the transferability of the gradient-based attack method so as to adjust the attack step size by itself and avoid local optimum more effectively. Empirical results on ImageNet dataset demonstrate that NAI-FGM could improve transferability of adversarial examples. Under the setting of ensemble model, the integrated method of NAI-FGM with various input transformations could achieve an average attack success rate of 91.88% against six advanced defense models, 1.78%–3.3% higher than the benchmarks. Code is available at https://github.com/NinelM/NAI-FGM.
Targeted Aspect-Based Sentiment Analysis by Utilizing Dynamic Aspect Representation

Aspect-Based Sentiment Analysis (ABSA) task can analyze the sentiment of social reviews on different aspects. On the basis of ABSA, Targeted Aspect-Based Sentiment Analysis (TABSA) task additionally takes into account target entities. However, current studies on these tasks usually represent aspects based on word-level vectors, as static aspects and may not strongly correlated with review semantics, thus the effectiveness of sentiment prediction may be affected. Based on pre-trained BERT, we propose a sentiment analysis model using dynamic aspect representation. Specifically, we supplement external knowledge for limited review information, and integrate dynamic review features into static aspect. We also build a hierarchical interactive attention to fully explore the relationship among target, context and aspect. Several experiments are conducted on SentiHood dataset (for TABSA) and SemEval-2014 dataset (for ABSA). Our model has the best results in aspect detection and sentiment classification subtasks. Especially in contrast with BERT-pair, the accuracy of our model improves 1.2 $$\%$$ and 0.6 $$\%$$ in two subtasks on SentiHood dataset, and 1.9 $$\%$$ and 1.4 $$\%$$ on SemEval dataset.
Learning to Generate Textual Adversarial Examples

Word substitution based textual adversarial attack is actually a combinatorial optimization problem. Existing greedy search methods are time-consuming due to extensive unnecessary victim model calls in word ranking and substitution. In this work, we propose a learnable attack method which uses neural networks to guide the greedy search to reduce victim model calls. Specifically, we use one network to predict the importance of each word, without accessing the victim model. It avoids the victim model calls proportional to the length of the text, which may be in hundreds. Moreover, we use the other network to score each substitute word for a specific substitute position and filter out the attack-inefficient and out-of-context ones, so as to reduce substitution attempts. We evaluate our method on sentiment analysis, natural language inference and paraphrase identification tasks. Experimental results show that our method can achieve higher attack success rate and adversarial example quality than the baseline methods, while requiring less computation overhead. The code is public at https://github.com/CMACH508/PredictiveTextualAttack .
CSDM: A Cross-Scale Decomposition Method for Low-Light Image Enhancement

Inadequate lighting condition is one of the main factors affecting image quality. For high-quality image display and vision applications, low-light image enhancement is an extremely challenging task. However, most existing methods are difficult to remove heavy noise in the low-light enhancement process effectively. To solve this problem, this paper presents a novel cross-scale decomposition method (CSDM) for low-light image enhancement. The main idea is to capture the small-scale texture and heavy noise, and preserve the large-scale structure by designing a Gaussian regularization based on a cross-scale relative relationship. An optimization objective function is established to transform image decomposition, low-light enhancement and noise suppression into a unified optimization framework. The framework uses sequential decomposition to estimate texture and structure layers. Firstly, the structure layer of the image is estimated, and an illumination prior is introduced to improve the contrast. Then the texture layer is estimated, and a low-rank prior is applied to eliminate noise. Finally, the structure and texture layers are combined to obtain a final enhanced image. Experiment results on public datasets and an industrial site demonstrate that the advantages of our method from both qualitative and quantitative perspectives.
Euler vs Hasse Diagrams for Reasoning About Sets: A Cognitive Approach

Abstract The literature on diagrammatic reasoning includes theoretical and experimental work on the effectiveness of diagrams for conveying information. One influential theoretical contribution to this field proposes that a notation that is more effective than another would have an observational advantage over it; that is, it would make certain pieces of information observable—by means of some visual, meaning-carrying relationships—that were not observable in the other. Although the notion of observational advantage captures a relevant aspect of the benefit of using one notation over another, we present here an example where this notion is not sufficient to distinguish between a more and a less effective diagram. We suggest to take the theory of observational advantage one step further by linking it to cognitive theories of human conceptualisation and reasoning. Following our previous work, we propose that the act of observing facts about set theory from the geometry of a diagram can be modeled as a conceptual blend of image schemas with parts of the geometric configuration of a diagram. Image schemas are elementary mental structures that crystallize early embodied experiences, allowing agents to make sense of what they perceive by conceptualising it in terms of these structures (e.g., container , link , scale etc.). With our approach, we can extend the theory of observational advantage to take into account the cognitive complexity of the act of observation. Concretely, we present an example of an Euler and a Hasse diagram, and we posit that, while their observational advantage is equivalent, the Hasse diagram requires a much more complex network of conceptual blends to model certain observations made from it. Thus, to reason about certain set-theoretic claims, a Hasse diagram is less cognitively effective than an Euler diagram with equivalent observational advantage. We believe our approach contributes to the theoretical discussion on what factors affect the effectiveness of a diagram, and provides new avenues for the exploration of how the embodied experiences of the users contribute to the way they reason with diagrams.
From Euler Diagrams to Aristotelian Diagrams

Euler and Aristotelian diagrams are both among the most well-studied kinds of logical diagrams today. Despite their central status, very little research has been done on relating these two types of diagrams. This is probably due to the fact that Euler diagrams typically visualize relations between sets, whereas Aristotelian diagrams typically visualize relations between propositions. However, recent work has shown that Aristotelian diagrams can also perfectly be understood as visualizing relations between sets, and hence it becomes natural to ask whether there is any kind of systematic relation between Euler and Aristotelian diagrams. In this paper we provide an affirmative answer: we show that every Euler diagram for two non-trivial sets gives rise to a well-defined Aristotelian diagram. Furthermore, depending on the specific relation between the two sets visualized by the Euler diagram, the resulting Aristotelian diagram will also be fundamentally different. We will also link this with well-known notions from logical geometry, such as the information ordering on the seven logical relations between non-trivial sets, and the notion of Boolean complexity of Aristotelian diagrams.
Visually Analyzing Universal Quantifiers in Photograph Captions

Universal quantifiers have been the subject of much work in symbolic and diagrammatic logic. However, little attention is paid to the question of how they can be visually grounded, that is, depicted in real images such as photographs. To investigate this question, we focus on universal quantifiers such as "all" and "every" used in an image captioning dataset and present a qualitative analysis of these expressions. The analysis revealed that although the use of universal quantifiers in image captions is rare, there are interesting patterns in their usage in terms of the semantics of visual representations. We distinguish two ways in which universal quantifiers are used in image captions. One is object-based quantification, which involves quantification over multiple discrete objects in a definite domain. The other is region-based quantification, where some property is ascribed to a salient continuous region in an image. We compare these two ways of visually representing universal quantification with two major representation systems studied in diagrammatic logic.
Visualizing Polymorphisms and Counter-Polymorphisms in S5 Modal Logic

We present a graphical representation that allows us to easily determine if a certain modal function is or is not a polymorphism of a given relation. While doing so, we provide a comparison between two ways (a calculative and a diagrammatic one) to analyze a claim about the Sheferness criterion in the theory of clones of (S5) modal functions.
VID2META: Complementing Android Programming Screencasts with Code Elements and GUIs

The complexity of software projects and the rapid technological evolution make it such that developers often need additional help and knowledge to tackle their daily tasks. For this purpose, they often refer to online resources, which are easy to access and contain a wealth of information in various formats. Programming screencasts hosted on platforms such as YouTube are one such online resource that has seen a growth in popularity and adoption over the past decade. These screencasts usually have some metadata such as a title, a short description, and a set of tags that should describe what the main concepts captured in the video are. Unfortunately, metadata are often generic and do not contain detailed information about the code showcased in the tutorial, such as the API calls or graphical user interface (GUI) elements employed, which could lead to developers missing useful tutorials. Having a quick overview of the main code elements and GUIs used in a video tutorial can be very helpful for developers looking for code examples involving specific API calls, or looking to design applications with a specific GUI in mind. The aim is to make this information easily available to developers, and propose VID2META, a technique that automatically extracts Java import statements, class names, method information, GUI elements, and GUI screens from videos and makes them available to developers as metadata. VID2META is currently designed to work with Android screencasts. It analyzes video frames using a combination of computer vision, deep learning, optical character recognition, and heuristic-based approaches to identify the needed information in a frame, extract it, and present it to the developer. VID2META has been evaluated in an empirical study on 70 Android programming videos collected from YouTube. The results revealed that VID2META can accurately detect and extract Java and GUI elements from Android programming videos with an average accuracy of 90%.
On Data Augmentation for Models Involving Reciprocal Gamma Functions

In this article, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears in full conditional densities. Our approach is to approximate full conditional densities of shape parameters by using Gauss’s multiplication formula and Stirling’s formula for the gamma function, where the approximation error can be made arbitrarily small. We use the techniques to construct efficient Gibbs and Metropolis–Hastings algorithms for a variety of models that involve the gamma distribution, Student’s t-distribution, the Dirichlet distribution, the negative binomial distribution, and the Wishart distribution. The proposed sampling method is numerically demonstrated through simulation studies. Supplementary materials for this article are available online.
CAEFL: composable and environment aware federated learning models

Federated Learning allows multiple distributed agents to contribute to a global machine learning model. Each agent trains locally and contributes to a global model by sending gradients to a central parameter server. The approach has some limitations: 1) some events may only occur in the local environment, so a global model may not perform as well as a specialized model; 2) changes in the local environment may require an agent to use some dedicated model, that is not available in a single global model; 3) a single global model approach is unable to derive new models from dealing with complex environments.
Partial type constructors in practice

Type constructors in functional programming languages are total: a Haskell programmer can equally readily construct lists of any element type. In practice, however, not all applications of type constructors are equally sensible: collections may only make sense for orderable elements, or embedded DSLs might only make sense for serializable return types. Jones et al. proposed a theory of partial type constructors, which guarantees that type applications are sensible, and extends higher-order abstractions to apply equally well to partial and total type constructors. This paper evaluates the practicality of partial type constructors, in terms of both language design and implementation. We extend GHC, the most widely used Haskell compiler, with support for partial type constructors, and test our extension on the compiler itself and its libraries. We show that introducing partial type constructors has a minimal impact on most code, but raises important questions in language and library design.
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Differential uniformity and non-linearity of functions are the properties of great interest in cryptographic world. In this article, both the properties have been explored when the function is a permutation over Zn and some interesting results have been obtained. Also, some differential n-uniform non-affine permutations have been constructed over Zn, n being composite. Inversion permutation over GF(2n) is known to have good non-linearity and differential uniformity. Its behaviour with respect to these properties has been studied over another domain, the ring of integer modulo p, where p is a prime. Further, conditions for maximum possible value of differential uniformity and non-linearity have been derived for permutations obtained by swapping two positions in the inversion permutation.
Designing for Value Creation: Principles, Methods, and Case Insights from Embedding Designing-as-Performance in Digital Health Education and Research

Designing for Value Creation in healthcare entails engaging stakeholders in both academia and industry. Therefore, education and training on value creation for students and professionals are central to designing better healthcare systems. In this chapter, we explore the state of digital health design education, particularly how value creation can be rigorously explored and implemented. A Designing-as-Performance (MEDGI + PretoVids) approach augments current healthcare innovation approaches in increasing customer engagement, reducing risk, and improving profit. Here we review the literature on current Design Thinking (DT) in healthcare and posit Designing for Value Creation as a way forward based on our 3+ years of action research in this emerging field. Our insights are relevant for design educators, medical practitioners, and industry actors looking to leverage Design for Value Creation in healthcare in the digital transformation era.