Reviewerly/exHarmony huggingface dataset:
Identity-aware Graph Neural Networks

Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Altogether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks.
How does AI challenge design practice?

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
Application of Data Mining Based on Computer Algorithm in Personalized Recommendation Service of University Smart Library

Abstract Smart library is an important channel to improve the reading ability of college students. It is an important means to cultivate students’ reading interest and cultivate the reading style of the whole people. The wisdom library focuses on wisdom, including service intelligence, management intelligence, and facility intelligence. In the process of promoting smart libraries, big data computing is at an important position, and computer algorithms are the core technology support. Therefore, it is of great significance to combine computer algorithm data mining with computer algorithms to analyze the application of data mining in the personalized recommendation service of university libraries. This paper analyzes the application of computer algorithm and big data mining technology in the personalized recommendation service of smart library, analyzes readers’ reading preferences and behavior habits, and proposes three measures for reference.
FPGA Acceleration of Zstd Compression Algorithm

With the continued increase in the amount of big data generated and stored in various application domains, such as high-frequency trading, compression techniques are becoming ever more important to reduce the requirements on communication bandwidth and storage capacity. Zstandard (Zstd) is emerging as an important compression algorithm for big data sets capable of achieving a good compression ratio but with a higher speed than comparable algorithms. In this paper, we introduce the architecture of a new hardware compression kernel for Zstd that allows the algorithm to be used for real-time compression of big data streams. In addition, we optimize the proposed architecture for the specific use case of streaming high-frequency trading data. The optimized kernel is implemented on a Xilinx Alveo U200 board. Our optimized implementation allows us to fit ten kernel blocks on one board, which is able to achieve a compression throughput of about 8.6GB/s and compression ratio of about 23.6%. The hardware implementation is open source and publicly available at https://github.com/ChenJianyunp/Hardware-Zstd-Compression-Unit.
Dual Online Stein Variational Inference for Control and Dynamics

Model predictive control (MPC) schemes have a proven track record for delivering aggressive and robust performance in many challenging control tasks, coping with nonlinear system dynamics, constraints, and observational noise.Despite their success, these methods often rely on simple control distributions, which can limit their performance in highly uncertain and complex environments.MPC frameworks must be able to accommodate changing distributions over system parameters, based on the most recent measurements.In this paper, we devise an implicit variational inference algorithm able to estimate distributions over model parameters and control inputs on-the-fly.The method incorporates Stein Variational gradient descent to approximate the target distributions as a collection of particles, and performs updates based on a Bayesian formulation.This enables the approximation of complex multi-modal posterior distributions, typically occurring in challenging and realistic robot navigation tasks.We demonstrate our approach on both simulated and realworld experiments requiring real-time execution in the face of dynamically changing environments.
Research on The Method of Grass Mouse Hole Target Detection Based on Deep Learning

Abstract Due to climate warming, increasing soil erosion and overloading of grassland grazing, grasslands around Taipusi Banner in Inner Mongolia have been degraded to varying degrees, causing widespread rodent damage. Aiming at the problem of rodent damage in the grassland surrounding Taipusi Banner, the UAV visible light image is the main research object, and the precise detection of grass mouse holes is the research goal. Computer vision technology is used to study grass mouse hole detection methods, in order to achieve rapid and accurate mouse detection. Hole function.
A multi-criteria comparison of mobile applications built with the use of Android and Flutter Software Development Kits

This publication presents a multi-criteria comparison of two mobile applications built with the use of Android and Flutter SDK. The former has been implemented with Kotlin and the latter with Dart. The benchmarking process examines factors such as execution time and CPU usage during data and disk operations. During the analysis, attention was paid to the length and volume the source code, community support and the availability of libraries. The comparative analysis shows that a mobile application written using Android SDK is often not only faster and more efficient, but also has greater community support and the number of libraries available. In addition, an analysis of the source code volume showed that Flutter has more concise but more difficult to navigate code than Android.
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learning models. In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. We materialize the supervisory signals through determining whether a pair of samples is from one frame or from one video, and whether a triplet of samples is in the correct temporal order. We uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo). SeCo shows superior results under the linear protocol on action recognition (Kinetics), untrimmed activity recognition (ActivityNet) and object tracking (OTB-100). More remarkably, SeCo demonstrates considerable improvements over recent unsupervised pre-training techniques, and leads the accuracy by 2.96% and 6.47% against fully-supervised ImageNet pre-training in action recognition task on UCF101 and HMDB51, respectively. Source code is available at \url{https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning}.
A Zero-Watermark Hybrid Algorithm for Remote Sensing Images Based on DCT and DFT

Abstract Remote sensing image has significant advantages such as large detection range and fast acquisition speed, so it has been used in military, environmental, meteorological and other fields. In order to improve the robustness of the digital watermarking technology on the premise that the remote sensing image information is not modified, a remote sensing image zero watermarking algorithm based on Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) is proposed in this paper. First of all, the color remote sensing image is converted into grayscale image. The transformed coefficient matrix is obtained by DFT, to the grayed host image. Secondly, in order to compress the amount of information and improve the efficiency of the algorithm, the high frequency coefficient image is transformed by DCT transform. The low frequency coefficient image which is consistent with the size of the watermark image after DCT is selected as the feature image of the host image. After that, the watermark information is scrambled by Arnold transform to improve the security of the watermark information. Finally, the zero-watermark image is obtained by XOR operation between the scrambled watermark information and the feature image. The zero-watermark image is saved in a third-party database for copyright verification. In order to verify the robustness of digital watermarking, this paper carries out sharpening, histogram equalization, Karma transform, noise, filtering and geometric attacks on the host image, and compares it with other algorithms. The NC value between the extracted watermark image and the original watermark image after attacking the host image show that the algorithm has a strong ability to resist common attacks.
Warning users about cyber threats through sounds

Abstract This paper reports a formative evaluation of auditory representations of cyber security threat indicators and cues, referred to as sonifications, to warn users about cyber threats. Most Internet browsers provide visual cues and textual warnings to help users identify when they are at risk. Although these alarming mechanisms are very effective in informing users, there are certain situations and circumstances where these alarming techniques are unsuccessful in drawing the user’s attention: (1) security warnings and features (e.g., blocking out malicious Websites) might overwhelm a typical Internet user and thus the users may overlook or ignore visual and textual warnings and, as a result, they might be targeted, (2) these visual cues are inaccessible to certain users such as those with visual impairments. This work is motivated by our previous work of the use of sonification of security warnings to users who are visually impaired. To investigate the usefulness of sonification in general security settings, this work uses real Websites instead of simulated Web applications with sighted participants. The study targets sonification for three different types of security threats: (1) phishing, (2) malware downloading, and (3) form filling. The results show that on average 58% of the participants were able to correctly remember what the sonification conveyed. Additionally, about 73% of the participants were able to correctly identify the threat that the sonification represented while performing tasks using real Websites. Furthermore, the paper introduces “CyberWarner”, a sonification sandbox that can be installed on the Google Chrome browser to enable auditory representations of certain security threats and cues that are designed based on several URL heuristics. Article highlights It is feasible to develop sonified cyber security threat indicators that users intuitively understand with minimal experience and training. Users are more cautious about malicious activities in general. However, when navigating real Websites, they are less informed. This might be due to the appearance of the navigating Websites or the overwhelming issues when performing tasks. Participants’ qualitative responses indicate that even when they did not remember what the sonification conveyed, the sonification was able to capture the user’s attention and take safe actions in response.
Cross-lingual alignments of ELMo contextual embeddings

Building machine learning prediction models for a specific natural language processing (NLP) task requires sufficient training data, which can be difficult to obtain for less-resourced languages. Cross-lingual embeddings map word embeddings from a less-resourced language to a resource-rich language so that a prediction model trained on data from the resource-rich language can also be used in the less-resourced language. To produce cross-lingual mappings of recent contextual embeddings, anchor points between the embedding spaces have to be words in the same context. We address this issue with a novel method for creating cross-lingual contextual alignment datasets. Based on that, we propose several cross-lingual mapping methods for ELMo embeddings. The proposed linear mapping methods use existing Vecmap and MUSE alignments on contextual ELMo embeddings. Novel nonlinear ELMoGAN mapping methods are based on generative adversarial networks (GANs) and do not assume isomorphic embedding spaces. We evaluate the proposed mapping methods on nine languages, using four downstream tasks: named entity recognition (NER), dependency parsing (DP), terminology alignment, and sentiment analysis. The ELMoGAN methods perform very well on the NER and terminology alignment tasks, with a lower cross-lingual loss for NER compared to the direct training on some languages. In DP and sentiment analysis, linear contextual alignment variants are more successful.
Meme language, its impact on digital culture and collective thinking

Memes have become an increasingly common form of modern communication, which has recently attracted great research interest. In this article we analyze "language - memes", its influence on digital culture and collective thinking. The Internet, by expanding social content, contributes to the variability of cultural codes and consequently changes an individual’s cultural identity throughout life. The culture composed of cultural groups is defined as a kind of macro-code, consisting of numerous codes that are commonly used to interpret reality among members of the cyber community. Identity is also transmitted through the use of a specific language during interaction, which is a marker of discourse, in which Memes represent a distinctive business card. Linguists, as well as specialists in other disciplines (such as philosophy, anthropology) use each other’s work to study the interrelationships and mutual influences of language and culture. The study proposed a hypothesis about the influence of the Internet on changes in society’s thinking, the formation of the Internet culture and language, using the example of meme language, which is confirmed by the analysis of Internet survey. The interdisciplinary nature of the research is due to the use of mathematical method and the logic of meme language, its impact on digital culture and collective thinking, which is represented in the responses of respondents to the questions in online questionnaires.
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring

Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary (e.g., a list of identical words) as the only form of supervision. Rather than aligning two fixed embedding spaces, our method works by fixing the target language embeddings, and learning a new set of embeddings for the source language that are aligned with them. To that end, we use an extension of skip-gram that leverages translated context words as anchor points, and incorporates self-learning and iterative restarts to reduce the dependency on the initial dictionary. Our approach outperforms conventional mapping methods on bilingual lexicon induction, and obtains competitive results in the downstream XNLI task.
DeepStochLog: Neural Stochastic Logic Programming

Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural-symbolic learning tasks.
Verb Knowledge Injection for Multilingual Event Processing

Linguistic probing of pretrained Transformerbased language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language.However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information.In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure.We investigate whether injecting explicit information on verbs' semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount.Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining.We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction.We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge.Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.
Efficient classical computation of expectation values in a class of quantum circuits with an epistemically restricted phase space representation

Abstract We devise a classical algorithm which efficiently computes the quantum expectation values arising in a class of continuous variable quantum circuits wherein the final quantum observable—after the Heisenberg evolution associated with the circuits—is at most second order in momentum. The classical computational algorithm exploits a specific epistemic restriction in classical phase space which directly captures the quantum uncertainty relation, to transform the quantum circuits in the complex Hilbert space into classical albeit unconventional stochastic processes in the phase space. The resulting multidimensional integral is then evaluated using the Monte Carlo sampling method. The convergence rate of the classical sampling algorithm is determined by the variance of the classical physical quantity over the epistemically restricted phase space distribution. The work shows that for the specific class of computational schemes, Wigner negativity is not a sufficient resource for quantum speedup. It highlights the potential role of the epistemic restriction as an intuitive conceptual tool which may be used to study the boundary between quantum and classical computations.
MAGIC: Learning Macro-Actions for Online POMDP Planning

The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required.While temporally-extended macro-actions help to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macroactions?This paper proposes Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning.Specifically, MAGIC learns a macro-action generator end-to-end, using an online planner's performance as the feedback.During online planning, the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context.We evaluated MAGIC on several long-horizon planning tasks both in simulation and on a real robot.The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions.
Towards the Development of a Digital Competency Framework for Digital Teaching and Learning

Cognizant use of technology is essential in offering a pedagogically rich, inclusive, creative and equitable digital learning experience to students. Teachers may need to develop digital skills and competencies that would propagate a constructive and cultural inclusive digital learning environment for orchestrating flexible and activity-oriented learning through synchronous and asynchronous learning tools. This study presents a digital competency framework discerned from a qualitative study on teachers' understandings of digital competencies for digital teaching and learning. Thirty-eight (n=38) teachers completed an online questionnaire as means to elicit beliefs on what constitutes digital competency development for digital teaching and learning. The results revealed six (6) digital competencies and associated sub-competencies that may be perceived as a point of departure for teachers to build on new or extend existing competencies. A reflection on implications around digital content, data and information, pedagogy, creativity and inclusion is perpetuated for optimising and leveraging teaching and learning in a connected and networked digital learning ecosystem.
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation

We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance.CTC's success on translation is counter-intuitive due to its monotonicity assumption, so we analyze its reordering capability.Kendall's tau distance is introduced as the quantitative metric, and gradientbased visualization provides an intuitive way to take a closer look into the model.Our analysis shows that transformer encoders have the ability to change the word order and points out the future research direction that worth being explored more on non-autoregressive speech translation.