Reviewerly/exHarmony huggingface dataset:
<scp>RoSGAS</scp> : Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search

Social bots are referred to as the automated accounts on social networks that make attempts to behave like humans. While Graph Neural Networks (GNNs) have been massively applied to the field of social bot detection, a huge amount of domain expertise and prior knowledge is heavily engaged in the state-of-the-art approaches to design a dedicated neural network architecture for a specific classification task. Involving oversized nodes and network layers in the model design, however, usually causes the over-smoothing problem and the lack of embedding discrimination. In this article, we propose RoSGAS , a novel R einf o rced and S elf-supervised G NN A rchitecture S earch framework to adaptively pinpoint the most suitable multi-hop neighborhood and the number of layers in the GNN architecture. More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task. We exploit the heterogeneous information network to present the user connectivity by leveraging account metadata, relationships, behavioral features, and content features. RoSGAS uses a multi-agent deep reinforcement learning (RL), 31 pages. mechanism for navigating the search of optimal neighborhood and network layers to learn individually the subgraph embedding for each target user. A nearest neighbor mechanism is developed for accelerating the RL training process, and RoSGAS can learn more discriminative subgraph embedding with the aid of self-supervised learning. Experiments on five Twitter datasets show that RoSGAS outperforms the state-of-the-art approaches in terms of accuracy, training efficiency, and stability and has better generalization when handling unseen samples.
Contrasting random and learned features in deep Bayesian linear regression

Understanding how feature learning affects generalization is among the foremost goals of modern deep learning theory. Here, we study how the ability to learn representations affects the generalization performance of a simple class of models: deep Bayesian linear neural networks trained on unstructured Gaussian data. By comparing deep random feature models to deep networks in which all layers are trained, we provide a detailed characterization of the interplay between width, depth, data density, and prior mismatch. We show that both models display samplewise double-descent behavior in the presence of label noise. Random feature models can also display modelwise double descent if there are narrow bottleneck layers, while deep networks do not show these divergences. Random feature models can have particular widths that are optimal for generalization at a given data density, while making neural networks as wide or as narrow as possible is always optimal. Moreover, we show that the leading-order correction to the kernel-limit learning curve cannot distinguish between random feature models and deep networks in which all layers are trained. Taken together, our findings begin to elucidate how architectural details affect generalization performance in this simple class of deep regression models.
A Study on the 3D Hopfield Neural Network Model via Nonlocal Atangana–Baleanu Operators

Hopfield neural network (HNN) is considered as an artificial model derived from the brain structures and it is an important model that admits an adequate performance in neurocomputing. In this article, we solve a dynamical model of 3D HNNs via Atangana–Baleanu (AB) fractional derivatives. To find the numerical solution of the considered dynamical model, the well-known Predictor-Corrector (PC) method is used. A number of cases are taken by using two different sets of values of the activation gradient of the neurons as well as six different initial conditions. The given results have been perfectly established using the different fractional-order values on the given derivative operator. The objective of this research is to investigate the dynamics of the proposed HNN model at various values of fractional orders. Nonlocal characteristic of the AB derivative contains the memory in the system which is the main motivation behind the proposal of this research.
Robot Learning of Mobile Manipulation With Reachability Behavior Priors

Mobile Manipulation (MM) systems are ideal candidates for taking up the role of a personal assistant in unstructured real-world environments. Among other challenges, MM requires effective coordination of the robot's embodiments for executing tasks that require both mobility and manipulation. Reinforcement Learning (RL) holds the promise of endowing robots with adaptive behaviors, but most methods require prohibitively large amounts of data for learning a useful control policy. In this work, we study the integration of robotic reachability priors in actor-critic RL methods for accelerating the learning of MM for reaching and fetching tasks. Namely, we consider the problem of optimal base placement and the subsequent decision of whether to activate the arm for reaching a 6D target. For this, we devise a novel Hybrid RL method that handles discrete and continuous actions jointly, resorting to the Gumbel-Softmax reparameterization. Next, we train a reachability prior using data from the operational robot workspace, inspired by classical methods. Subsequently, we derive Boosted Hybrid RL (BHyRL), a novel algorithm for learning Q-functions by modeling them as a sum of residual approximators. Every time a new task needs to be learned, we can transfer our learned residuals and learn the component of the Q-function that is task-specific, hence, maintaining the task structure from prior behaviors. Moreover, we find that regularizing the target policy with a prior policy yields more expressive behaviors. We evaluate our method in simulation in reaching and fetching tasks of increasing difficulty, and we show the superior performance of BHyRL against baseline methods. Finally, we zero-transfer our learned 6D fetching policy with BHyRL to our MM robot TIAGo++. For more details and code release, please refer to our project site: irosalab.com/rlmmbp
Composition and Style Attributes Guided Image Aesthetic Assessment

The aesthetic quality of an image is defined as the measure or appreciation of the beauty of an image. Aesthetics is inherently a subjective property but there are certain factors that influence it such as, the semantic content of the image, the attributes describing the artistic aspect, the photographic setup used for the shot, etc. In this paper we propose a method for the automatic prediction of the aesthetics of an image that is based on the analysis of the semantic content, the artistic style and the composition of the image. The proposed network includes: a pre-trained network for semantic features extraction (the Backbone); a Multi Layer Perceptron (MLP) network that relies on the Backbone features for the prediction of image attributes (the AttributeNet); a self-adaptive Hypernetwork that exploits the attributes prior encoded into the embedding generated by the AttributeNet to predict the parameters of the target network dedicated to aesthetic estimation (the AestheticNet). Given an image, the proposed multi-network is able to predict: style and composition attributes, and aesthetic score distribution. Results on three benchmark datasets demonstrate the effectiveness of the proposed method, while the ablation study gives a better understanding of the proposed network.
Multi-Criteria Ranking: Next Generation of Multi-Criteria Recommendation Framework

Recommender systems have been served to assist decision making by recommending a list of items to the end users. Multi-criteria recommender system (MCRS) is a type of recommender systems which enhance recommendation performance by taking user preferences on multiple criteria. Traditional algorithms for MCRS usually predict user ratings on these criteria, and finally estimate the overall rating by different aggregation functions. In this paper, we propose a new multi-criteria recommendation framework in which we can take advantage of Pareto ranking based estimated preferences on multiple criteria, and infer a ranking score for top-<i>N</i> recommendations. The proposed framework is general enough and all existing algorithms in MCRS can be reused to be integrated with our framework. We demonstrate the effectiveness of the proposed framework by evaluating top-<i>N</i> recommendations over four real-world data sets.
A Systematic Review of Architectural Design Collaboration in Immersive Virtual Environments

Emerging applications of immersive virtual technologies are providing architects and designers with powerful interactive environments for virtual design collaboration, which has been particularly beneficial since 2020 while the architecture, engineering and construction (AEC) industry has experienced an acceleration of remote working. However, there is currently a lack of critical understanding about both the theoretical and technical development of immersive virtual environments (ImVE) for supporting architectural design collaboration. This paper reviewed recent research (since 2010) relating to the topic in a systematic literature review (SLR). Through the four steps of identification, screening, eligibility check, and inclusion of the eligible articles, in total, 29 journal articles were reviewed and discussed from 3 aspects: ImVE in the AEC industry, ImVE for supporting virtual collaboration, and applications of ImVE to support design collaboration. The results of this review suggest that future research and technology development are needed in the following areas: (1) ImVE support for design collaboration, particularly at the early design stage; (2) cognitive research about design collaboration in ImVE, toward the adoption of more innovative and comprehensive methodologies; (3) further enhancements to ImVE technologies to incorporate more needed advanced design features.
Prevention of Shoulder-Surfing Attack Using Shifting Condition with the Digraph Substitution Rules

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method).The proposed method was tested and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password.
From BERT to GPT-3 codex

Large language models have recently advanced the state of the art on many natural language processing benchmarks. The newest generation of models can be applied to a variety of tasks with little to no specialized training. This technology creates various opportunities for applications in the context of data management. The tutorial will introduce participants to basic background on language models, discuss different methods to use language models, and give an overview and short demonstration of available libraries and APIs. Models for generating natural language will be considered as well as models, such as GPT-3 Codex, which complete program code or generate code from natural language instructions. Finally, the tutorial will discuss recent research in the database community that exploits language models in the context of traditional database systems or proposes novel system architectures that are based on them. The tutorial is targeted at database researchers. No prior background on language models is required. The goal of the tutorial is to introduce database researchers to the latest generation of language models, and to their use cases in the domain of data management.
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts

Inspired by the strong ties between vision and language, the two intimate human sensing and communication modalities, our paper aims to explore the generation of 3D human full-body motions from texts, as well as its reciprocal task, shorthanded for text2motion and motion2text, respectively. To tackle the existing challenges, especially to enable the generation of multiple distinct motions from the same text, and to avoid the undesirable production of trivial motionless pose sequences, we propose the use of motion token, a discrete and compact motion representation. This provides one level playing ground when considering both motions and text signals, as the motion and text tokens, respectively. Moreover, our motion2text module is integrated into the inverse alignment process of our text2motion training pipeline, where a significant deviation of synthesized text from the input text would be penalized by a large training loss; empirically this is shown to effectively improve performance. Finally, the mappings in-between the two modalities of motions and texts are facilitated by adapting the neural model for machine translation (NMT) to our context. This autoregressive modeling of the distribution over discrete motion tokens further enables non-deterministic production of pose sequences, of variable lengths, from an input text. Our approach is flexible, could be used for both text2motion and motion2text tasks. Empirical evaluations on two benchmark datasets demonstrate the superior performance of our approach on both tasks over a variety of state-of-the-art methods. Project page: https://ericguo5513.github.io/TM2T/
Fiber-based photon-pair generation: tutorial

The purpose of this tutorial paper is to present a broad overview of photon-pair generation through the spontaneous four wave mixing (SFWM) process in optical fibers. Progress in optical fiber technology means that today we have at our disposal a wide variety of types of fiber, which, together with the fact that SFWM uses two pump fields, implies a truly remarkable versatility in the resulting possible photon-pair properties. We discuss how the interplay of frequency, transverse mode, and polarization degrees of freedom—the first linked to the latter two through fiber dispersion—leads to interesting entanglement properties both in individual degrees of freedom and also permitting hybrid and hyper entanglement in combinations of degrees of freedom. This tutorial covers methods for photon-pair factorability, frequency tunability, and SFWM bandwidth control, the effect of frequency non-degenerate and counterpropagating pumps, as well as methods for characterizing photon pairs generated in optical fibers.
Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking

Attributing to material identification ability powered by a large number of spectral bands, hyperspectral videos (HSVs) have great potential for object tracking. Most hyperspectral trackers employ manually designed features rather than deeply learned features to describe objects due to limited available HSVs for training, leaving a huge gap to improve the tracking performance. In this paper, we propose an end-to-end deep ensemble network (SEE-Net) to address this challenge. Specifically, we first establish a spectral self-expressive model to learn the band correlation, indicating the importance of a single band in forming hyperspectral data. We parameterize the optimization of the model with a spectral self-expressive module to learn the nonlinear mapping from input hyperspectral frames to band importance. In this way, the prior knowledge of bands is transformed into a learnable network architecture, which has high computational efficiency and can fast adapt to the changes of target appearance because of no iterative optimization. The band importance is further exploited from two aspects. On the one hand, according to the band importance, each frame of HSVs is divided into several three-channel false-color images which are then used for deep feature extraction and location. On the other hand, based on the band importance, the importance of each false-color image is computed, which is then used to assemble the tracking results from individual false-color images. In this way, the unreliable tracking caused by false-color images of low importance can be suppressed to a large extent. Extensive experimental results show that SEE-Net performs favorably against the state-of-the-art approaches. The source code will be available at https://github.com/hscv/SEE-Net.
Building computational thinking through the incorporation of STEM education into the secondary curriculum in the Quindío department (Colombia)

There is a need to create theoretical-practical models that, based on existing conceptual contributions, facilitate the informational and technological literacy of new generations. Incorporating STEM Education (science, technology, engineering, and mathematics) into the curriculum has proven helpful for secondary school students. The study aimed to generate the first approach to a model for understanding computational thinking (CT) from a STEM approach. This article displays the results of a conceptual review of topics related to the object of study in the last five years and the critical analysis of the found research. The main results highlight the importance of balancing technical, educational, and pedagogical aspects. Furthermore, critical components for model design are proposed. It is concluded that while achieving a conceptual scheme to guide STEM education is vital, it must be updated and reviewed frequently so that emerging disciplines and processes can be incorporated.
Analysis of the Dynamics of Reasoning Using Multiple Representations

This paper presents a formalisation and analysis method for the dynamics of a reasoning process in which multiple representations play a role. Dynamics of reasoning processes are described by reasoning traces consisting of sequences of reasoning states over time. Reasoning states have a compositional structure; they are composed of different parts, for example, for different representations. Transitions between two reasoning states model reasoning steps. In relation to the compositional structure of the states, transitions are classified into a number of types. An example reasoning process involving multiple representations is used to illustrate how its dynamics can be formalised and analysed using the approach.
Interjections in literary readings and artistic performance

Numerosity and privileges of occurrence of various types of interjections (primary conventional, primary non-conventional, secondary, and onomatopoeic) were investigated in three different literary readings of Winnie-the-Pooh (Milne 1926), in one reading of Ulysses (Joyce 1960), and in an artistic performance by actors (the film The third man , Korda, Selznik, &amp; Reed 1949). The spoken corpora, based on printed texts as source, consisted of 667 interjections. Ameka’s (1992 b, 1994) hypothesis that, parallel to their independence from ambient grammar, interjections would also be isolated temporally by preceding and following pauses, was not confirmed; for the entire corpus, only 39% of all interjections were thus isolated. However, an alternative hypothesis, that interjections serve an initializing function, was confirmed: Altogether, 77% of the interjections were found to be initializing, i.e., were preceded by a pause, introduced a speaking turn, introduced an utterance, and/or introduced a citation. Primary conventional interjections constituted the majority of interjections (overall 56%), but only two of these were common to all the corpora ( oh and ah ). By far the highest percentage (28 %) of primary non- conventional interjections occurred in the artistic performance of The third man . None of these occurred in either the novel or the screenplay of The third man , unlike the primary non-conventional interjections throughout the text of the literary readings. Functions of interjections are discussed in terms of Goffman’s (1981: 226) animators (literary readers, 26% of whose spoken interjections were added to those in the printed text) and principals (actors, 79% of whose spoken interjections were added to those in the printed text), in terms of literacy and orality, and in terms of the emotional stance and perspective of a speaker at the very moment of utterance.
Identity-Based Revocation From Subset Difference Methods Under Simple Assumptions

Identity-based revocation (IBR) is a specific kind of broadcast encryption that can effectively send ciphertext to a set of receivers. In IBR, a ciphertext is associated with a set of revoked users instead of a set of receivers and the maximum number of users in the system can be an exponential value in the security parameter. In this paper, we reconsider the general method of Lee et al. (ESORICS 2014) that constructs a public-key revocation (PKR) scheme by combining the subset difference (SD) method of Naor, Naor, and Lotspiech (CRYPTO 2001) and a single revocation encryption (SRE) scheme. Lee et al. left it as an open problem to construct an SRE scheme under the standard assumption without random oracles. In this paper, we first propose a selectively secure SRE scheme under the standard assumption without random oracles. We also propose a fully secure SRE scheme under simple static assumptions without random oracles. Next, we present an efficient IBR scheme that supports fast decryption by combining the SD method and our SRE scheme. The security of our IBR scheme depends on that of the underlying SRE scheme. Finally, we implemented our SRE and IBR schemes and measured the performance.
Model checking and validity in propositional and modal inclusion logics

Abstract Propositional and modal inclusion logic are formalisms that belong to the family of logics based on team semantics. This article investigates the model checking and validity problems of these logics. We identify complexity bounds for both problems, covering both lax and strict team semantics. By doing so, we come close to finalizing the programme that aims to completely classify the complexities of the basic reasoning problems for modal and propositional dependence, independence and inclusion logics.