Reviewerly/exHarmony huggingface dataset:
A Discriminative Channel Diversification Network for Image Classification

Channel attention mechanisms in convolutional neural networks have been proven to be effective in various computer vision tasks. However, the performance improvement comes with additional model complexity and computation cost. In this paper, we propose a light-weight and effective attention module, called channel diversification block, to enhance the global context by establishing the channel relationship at the global level. Unlike other channel attention mechanisms, the proposed module focuses on the most discriminative features by giving more attention to the spatially distinguishable channels while taking account of the channel activation. Different from other attention models that plugin the module in between several intermediate layers, the proposed module is embedded at the end of the backbone networks, making it easy to implement. Extensive experiments on CIFAR-10, SVHN, and Tiny-ImageNet datasets demonstrate that the proposed module improves the performance of the baseline networks by a margin of 3% on average.
Perception of environmental information materials by youth audiences: Results of a neuromarketing study

The paper presents the results of using eye-tracking technologies to study news materials on environmental topics collected as stimulus material and classified according to their impact on audience perception. The empirical material of the study came from news websites covering environmental issues. For objectivity, we compared two regional media resources – 74.ru (Chelyabinsk) and Aktobe Times2. We present our analysis of participants’ response to the studied materials and the degree of their influence on the participants’ emotional state and behavioral reactions. The paper also supplements the data obtained from content analysis in previous studies and expands the understanding of this issue. The chosen quantitative and qualitative approach to content research allowed us to determine the semantic and stylistic features of constructing ecotexts, determine their connotative characteristics, and classify them by groups, topics, and categories. The paper also contains survey data contributing to a deeper study of the stated problem and focusing on a detailed study of the audience’s behavioral responses. The survey data allowed us to confirm the expressed hypotheses and highlight the active areas of interest of the youth readership. Alongside the presented conclusions and points of discussion, we expressed the main assumptions highlighting the features of the formation of environmental texts in the news media.
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network

The paper focuses on improving the recent plug-and-play patch rescaling module (PRM) based approaches for crowd counting. In order to make full use of the PRM potential and obtain more reliable and accurate results for challenging images with crowd-variation, large perspective, extreme occlusions, and cluttered background regions, we propose a new PRM based multi-resolution and multi-task crowd counting network by exploiting the PRM module with more effectiveness and potency. The proposed model consists of three deep-layered branches with each branch generating feature maps of different resolutions. These branches perform a feature-level fusion across each other to build the vital collective knowledge to be used for the final crowd estimate. Additionally, early-stage feature maps undergo visual attention to strengthen the later-stage channels understanding of the foreground regions. The integration of these deep branches with the PRM module and the early-attended blocks proves to be more effective than the original PRM based schemes through extensive numerical and visual evaluations on four benchmark datasets. The proposed approach yields a significant improvement by a margin of 12.6% in terms of the RMSE evaluation criterion. It also outperforms state-of-the-art methods in cross-dataset evaluations.
Geographical Information System Mapping of Temporary Landfills Using the Haversine Formula Method Based on Android

The potential for waste generation at Merauke district with population at 223,389 people reaches 111.69 tons per day. Moreover at the city of Merauke it self with population at 98,986 people, the potential for waste generation is 49.99 tons per day. However, the number of customers recorded up to 2019 is 6,428 customers, with the potential for waste generated to be only 2.88 tonnes per day. This figure is of course still very less than the total potential for existing waste generation, therefore we need a system that can help and make it easier for people to find, find out the information on the nearest dump and the distance to be traveled. The Geographical Information System for temporary landfill mapping uses the Android- based Haversine Formula method, the design uses Eclipse with the Java programming language, and the database uses MySQL. The results that obtained from this application were the system can map the closest dump, provide information on the condition of the dump and can display the distance between the start point and the dump so it can help the public to finding the information.
A Content-adaptive Visibility Predictor for Perceptually Optimized Image Blending

The visibility of an image semi-transparently overlaid on another image varies significantly, depending on the content of the images. This makes it difficult to maintain the desired visibility level when the image content changes. To tackle this problem, we developed a perceptual model to predict the visibility of the blended results of arbitrarily combined images. Conventional visibility models cannot reflect the dependence of the suprathreshold visibility of the blended images on the appearance of the pre-blended image content. Therefore, we have proposed a visibility model with a content-adaptive feature aggregation mechanism, which integrates the visibility for each image feature (i.e., such as spatial frequency and colors) after applying weights that are adaptively determined according to the appearance of the input image. We conducted a large-scale psychophysical experiment to develop the visibility predictor model. Ablation studies revealed the importance of the adaptive weighting mechanism in accurately predicting the visibility of blended images. We have also proposed a technique for optimizing the image opacity such that users can set the visibility of the target image to an arbitrary level. Our evaluation revealed that the proposed perceptually optimized image blending was effective under practical conditions.
Features of the conduction of investigative actions in the investigation of the sale of counterfeit banknotes through Internet resources

The article analyzes the features of the investigation of the sale of counterfeit banknotes through Internet resources. The materials of forensic investigative practice in this category of criminal cases are considered, as well as the specifics of the production of individual investigative actions, such as inspection, appointment of a forensic examination and an investigative experiment. In the course of the study, a positive experience of countering the considered variety of counterfeiting was revealed and recommendations for the investigation of these crimes were formed.
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning

We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewards and transitions are modeled using parametric bilinear exponential families. We propose an algorithm, BEF-RLSVI, that a) uses penalized maximum likelihood estimators to learn the unknown parameters, b) injects a calibrated Gaussian noise in the parameter of rewards to ensure exploration, and c) leverages linearity of the exponential family with respect to an underlying RKHS to perform tractable planning. We further provide a frequentist regret analysis of BEF-RLSVI that yields an upper bound of $\tilde{\mathcal{O}}(\sqrt{d^3H^3K})$, where $d$ is the dimension of the parameters, $H$ is the episode length, and $K$ is the number of episodes. Our analysis improves the existing bounds for the bilinear exponential family of MDPs by $\sqrt{H}$ and removes the handcrafted clipping deployed in existing \RLSVI-type algorithms. Our regret bound is order-optimal with respect to $H$ and $K$.
DOOML: A New Database &amp; Object-Oriented Modeling Language for Database-Driven Web Application Design and Development

A database driven web application is a very common software solution to rising business problems. Modeling the database and the software architecture can be challenging, hence there not being one combined modeling language for database and software architecture, specifically suited for web application development. In this paper we present Database object-oriented Modeling Language (DooML) and its primary Archetype Diagram: a notation for specifying the design of a database schema and corresponding object-oriented software architecture. It combines the syntax for drawing Entity Relationship Diagrams, the Relational Model and Universal Modeling Language Class Diagrams as well to create a mixed diagram, stating database design as well as software design specifications. By default, DooML ensures that the approach of advanced web application development is model-driven and both database-oriented as well as object-oriented.
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation

Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning. With hundreds of self-supervised pretext tasks proposed over the past few years, the research community has greatly developed, and the key is no longer to design more powerful but complex pretext tasks, but to make more effective use of those already on hand. This paper studies the problem of how to automatically, adaptively, and dynamically learn instance-level self-supervised learning strategies for each node from a given pool of pretext tasks. In this paper, we propose a novel multi-teacher knowledge distillation framework for Automated Graph Self-Supervised Learning (AGSSL), which consists of two main branches: (i) Knowledge Extraction: training multiple teachers with different pretext tasks, so as to extract different levels of knowledge with different inductive biases; (ii) Knowledge Integration: integrating different levels of knowledge and distilling them into the student model. Without simply treating different teachers as equally important, we provide a provable theoretical guideline for how to integrate the knowledge of different teachers, i.e., the integrated teacher probability should be close to the true Bayesian class-probability. To approach the theoretical optimum in practice, two adaptive knowledge integration strategies are proposed to construct a relatively "good" integrated teacher. Extensive experiments on eight datasets show that AGSSL can benefit from multiple pretext tasks, outperforming the corresponding individual tasks; by combining a few simple but classical pretext tasks, the resulting performance is comparable to other leading counterparts.
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One reason for this academic-industrial gap is the neighborhood-fetching latency incurred by data dependency in GNNs, which make it hard to deploy for latency-sensitive applications that require fast inference. Conversely, without involving any feature aggregation, MLPs have no data dependency and infer much faster than GNNs, but their performance is less competitive. Motivated by these complementary strengths and weaknesses, we propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce the gap between GNNs and MLPs. Specifically, the GSDN framework is based purely on MLPs, where structural information is only implicitly used as prior to guide knowledge self-distillation between the neighborhood and the target, substituting the explicit neighborhood information propagation as in GNNs. As a result, GSDN enjoys the benefits of graph topology-awareness in training but has no data dependency in inference. Extensive experiments have shown that the performance of vanilla MLPs can be greatly improved with self-distillation, e.g., GSDN improves over stand-alone MLPs by 15.54% on average and outperforms the state-of-the-art GNNs on six datasets. Regarding inference speed, GSDN infers 75X-89X faster than existing GNNs and 16X-25X faster than other inference acceleration methods.
Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks

This paper addresses the communication issues when estimating hyper-gradients in decentralized federated learning (FL). Hyper-gradients in decentralized FL quantifies how the performance of globally shared optimal model is influenced by the perturbations in clients' hyper-parameters. In prior work, clients trace this influence through the communication of Hessian matrices over a static undirected network, resulting in (i) excessive communication costs and (ii) inability to make use of more efficient and robust networks, namely, time-varying directed networks. To solve these issues, we introduce an alternative optimality condition for FL using an averaging operation on model parameters and gradients. We then employ Push-Sum as the averaging operation, which is a consensus optimization technique for time-varying directed networks. As a result, the hyper-gradient estimator derived from our optimality condition enjoys two desirable properties; (i) it only requires Push-Sum communication of vectors and (ii) it can operate over time-varying directed networks. We confirm the convergence of our estimator to the true hyper-gradient both theoretically and empirically, and we further demonstrate that it enables two novel applications: decentralized influence estimation and personalization over time-varying networks.
Using Conflict-free Replicated Data Types to support Block Editing

Recent times once more informed us on the relevance of capable online collaborative tools.For our online collaborative XML editor, we have looked into technologies for constrained block editing which, obeying schemas such as with XML, permit on-and off-line users or agents to add, delete, copy, move, split and merge blocks of text.To that end, we studied the current state of Operational Transformations (OT) and Conflict-free Replicated Data Types (CRDT).Furthermore, after selection of the best-ranking enabling technology, we studied existing CRDT implementations for unstructured texts, and extended a Logootbased CRDT to implement on-and offline split and merge block support.We designed a generic concept and created a scientific prototype to test the concept on its correctness.For now, we neglected undo and redo operations.Within these limitations, we show that our prototype converges under most circumstances.We verify causality and assess the experience of user editing intent.Finally, we give an outlook and design recommendations for production implementations, and suggestions for tackling the problem of cyclic references in block mergers.
Denotational semantics of general store and polymorphism

We contribute the first denotational semantics of polymorphic dependent type theory extended by an equational theory for general (higher-order) reference types and recursive types, based on a combination of guarded recursion and impredicative polymorphism; because our model is based on recursively defined semantic worlds, it is compatible with polymorphism and relational reasoning about stateful abstract datatypes. We then extend our language with modal constructs for proof-relevant relational reasoning based on the "logical relations as types" principle, in which equivalences between imperative abstract datatypes can be established synthetically. What is new in relation to prior typed denotational models of higher-order store is that our Kripke worlds need not be syntactically definable, and are thus compatible with relational reasoning in the heap. Our work combines recent advances in the operational semantics of state with the purely denotational viewpoint of synthetic guarded domain theory.
Common Vulnerability Scoring System Prediction based on Open Source Intelligence Information Sources

The number of newly published vulnerabilities is constantly increasing. Until now, the information available when a new vulnerability is published is manually assessed by experts using a Common Vulnerability Scoring System (CVSS) vector and score. This assessment is time consuming and requires expertise. Various works already try to predict CVSS vectors or scores using machine learning based on the textual descriptions of the vulnerability to enable faster assessment. However, for this purpose, previous works only use the texts available in databases such as National Vulnerability Database. With this work, the publicly available web pages referenced in the National Vulnerability Database are analyzed and made available as sources of texts through web scraping. A Deep Learning based method for predicting the CVSS vector is implemented and evaluated. The present work provides a classification of the National Vulnerability Database's reference texts based on the suitability and crawlability of their texts. While we identified the overall influence of the additional texts is negligible, we outperformed the state-of-the-art with our Deep Learning prediction models.
Differentiable Mathematical Programming for Object-Centric Representation Learning

We propose topology-aware feature partitioning into $k$ disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a partitioning method which is represented as a linear program. The method is topologically aware since it explicitly encodes neighborhood relationships in the image graph. To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation. Optimizations specific to cut problems allow us to solve the quadratic programs and compute their gradients significantly more efficiently compared with the general quadratic programming approach. Our results show that our approach is scalable and outperforms existing methods on object discovery tasks with textured scenes and objects.
LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models

Research in vision-language models has seen rapid developments off-late, enabling natural language-based interfaces for image generation and manipulation. Many existing text guided manipulation techniques are restricted to specific classes of images, and often require fine-tuning to transfer to a different style or domain. Nevertheless, generic image manipulation using a single model with flexible text inputs is highly desirable. Recent work addresses this task by guiding generative models trained on the generic image datasets using pretrained vision-language encoders. While promising, this approach requires expensive optimization for each input. In this work, we propose an optimization-free method for the task of generic image manipulation from text prompts. Our approach exploits recent Latent Diffusion Models (LDM) for text to image generation to achieve zero-shot text guided manipulation. We employ a deterministic forward diffusion in a lower dimensional latent space, and the desired manipulation is achieved by simply providing the target text to condition the reverse diffusion process. We refer to our approach as LDEdit. We demonstrate the applicability of our method on semantic image manipulation and artistic style transfer. Our method can accomplish image manipulation on diverse domains and enables editing multiple attributes in a straightforward fashion. Extensive experiments demonstrate the benefit of our approach over competing baselines.
New results of $0$-APN power functions over $\mathbb{F}_{2^n}$

Partially APN functions attract researchers' particular interest recently. It plays an important role in studying APN functions. In this paper, based on the multivariate method and resultant elimination, we propose several new infinite classes of $0$-APN power functions over $\mathbb{F}_{2^n}$. Furthermore, two infinite classes of $0$-APN power functions $x^d$ over $\mathbb{F}_{2^n}$ are characterized completely where $(2^k-1)d\equiv 2^m-1~({\rm mod}\ 2^n-1)$ or $(2^k+1)d\equiv 2^m+1~({\rm mod}\ 2^n-1)$ for some positive integers $n, m, k$. These infinite classes of $0$-APN power functions can explain some examples of exponents of Table $1$ in \cite{BKRS2020}.
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning

Learning a good state representation is a critical skill when dealing with multiple tasks in Reinforcement Learning as it allows for transfer and better generalization between tasks. However, defining what constitute a useful representation is far from simple and there is so far no standard method to find such an encoding. In this paper, we argue that distillation -- a process that aims at imitating a set of given policies with a single neural network -- can be used to learn a state representation displaying favorable characteristics. In this regard, we define three criteria that measure desirable features of a state encoding: the ability to select important variables in the input space, the ability to efficiently separate states according to their corresponding optimal action, and the robustness of the state encoding on new tasks. We first evaluate these criteria and verify the contribution of distillation on state representation on a toy environment based on the standard inverted pendulum problem, before extending our analysis on more complex visual tasks from the Atari and Procgen benchmarks.
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains

Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e.g., IID data). We generalize conformal prediction to the Hidden Markov Model (HMM) framework where the assumption of exchangeability is not valid. The key idea of the proposed method is to partition the non-exchangeable Markovian data from the HMM into exchangeable blocks by exploiting the de Finetti's Theorem for Markov Chains discovered by Diaconis and Freedman (1980). The permutations of the exchangeable blocks are viewed as randomizations of the observed Markovian data from the HMM. The proposed method provably retains all desirable theoretical guarantees offered by the classical conformal prediction framework in both exchangeable and Markovian settings. In particular, while the lack of exchangeability introduced by Markovian samples constitutes a violation of a crucial assumption for classical conformal prediction, the proposed method views it as an advantage that can be exploited to improve the performance further. Detailed numerical and empirical results that complement the theoretical conclusions are provided to illustrate the practical feasibility of the proposed method.