Reviewerly/exHarmony huggingface dataset:
An Intralingual Parallel Corpus of Translations into German Easy Language (Geasy Corpus): What Sentence Alignments Can Tell Us About Translation Strategies in Intralingual Translation

Parallel corpora are traditionally interlingual and contain source and target texts in different languages. However, intralingual translations into Easy Language (EL) become more and more common in various countries. First intralingual corpora have been built up and investigated in terms of linguistic and structural features, but a translation-driven corpus linguistic approach is still missing to empirically describe the strategies of Easy Language translation, the characteristics of translated texts as well as to make these parallel corpora usable for professionalising and automatising translation processes. In this paper, we introduce an intralingual parallel corpus of translations into German Easy Language (Geasy Corpus). It contains published professional translations from Standard German into German Easy Language, including different text types and various formulation guidelines for German Easy Language. Currently, the corpus contains 1,087,643 words of source text and 292,552 words of Easy Language translations. So far, 93 (of 276) texts have been sentence aligned. We compare descriptive values, investigate the alignments, and describe which translation strategies are revealed to give first empirical evidence on the characteristics of Easy Language translations. Finally, we will discuss the potentials of tree annotations for Easy Language corpora, summarise our findings and give on outlook on future research.
"Learn, Use, and (Intermittently) Abandon": Exploring the Practices of Early Smart Speaker Adopters in Urban India

Since smart speakers were introduced to the Indian markets at the end of 2017, they have been adopted by hundreds of thousands households in India. While the scholarship has examined the long-term use of voice-based devices in western contexts, little is known about user behavior in India -- one of the fastest-growing smart speaker markets in the Asian Pacific region. Therefore, this study aims to explore how members families in India integrate Google Home into their daily lives. To this end, we collected long-term Google Home activity logs from 20 households and conducted interviews with one member from each generation in every household. Our findings shed light on the unique daily use patterns of adults and children, the users' approaches to, and challenges in learning to use, the devices, and factors that impact users' continued use or abandonment of the devices. We conclude the paper by discussing the implications of our findings and by proposing relevant design recommendations.
A High Fidelity Authentication Scheme for AMBTC Compressed Image Using Reference Table Encoding

In this paper, we propose a high-quality image authentication method based on absolute moment block truncation coding (AMBTC) compressed images. The existing AMBTC authentication methods may not be able to detect certain malicious tampering due to the way that the authentication codes are generated. In addition, these methods also suffer from their embedding technique, which limits the improvement of marked image quality. In our method, each block is classified as either a smooth block or a complex one based on its smoothness. To enhance the image quality, we toggle bits in bitmap of smooth block to generate a set of authentication codes. The pixel pair matching (PPM) technique is used to embed the code that causes the least error into the quantization values. To reduce the computation cost, we only use the original and flipped bitmaps to generate authentication codes for complex blocks, and select the one that causes the least error for embedment. The experimental results show that the proposed method not only obtains higher marked image quality but also achieves better detection performance compared with prior works.
Exploiting Long- and Short-Term Preferences for Deep Context-Aware Recommendations

With the tremendous growth in online information, capturing dynamic users’ preferences based on their historical interactions and providing a few desirable items to users have become an urgent service for all businesses. Recurrent neural networks (RNNs) and item-based collaborative filtering (CF) models are commonly used in industries due to their simplicity and efficiency. However, they fail to different contexts that could differently influence current users’ decision-making. Also, they are not sufficient to capture multiple users’ interests based on features of the interacting items. Besides, they have a limited modeling capability for the evolution of diversity and dynamic user preferences. In this article, we exploit long- and short-term preferences for deep context-aware recommendations (LSCAR) to enhance the next item recommendation’s performance by introducing three novel components as follows: 1) the user–contextual interaction module is proposed to capture and differentiate the interaction between contexts and users; 2) the encoded multi-interest module is introduced to capture various types of user interests; and 3) the integrator fusion gate module is used to effectively fuse the related long-term interests to the current short-term part, and the module returns the final user interest representation. Extensive experiments and results for two public datasets demonstrate that the proposed LSCAR outperforms the state-of-the-art models in almost all metrics and could provide interpretable recommendation results.
Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model

Robot path planning is difficult to solve due to the contradiction between the optimality of results and the complexity of algorithms, even in 2D environments. To find an optimal path, the algorithm needs to search all the state space, which costs many computation resources. To address this issue, we present a novel recurrent generative model (RGM), which generates efficient heuristic to reduce the search efforts of path planning algorithms. This RGM model adopts the framework of general generative adversarial networks (GAN), which consists of a novel generator that can generate heuristic by refining the outputs recurrently and two discriminators that check the connectivity and safety properties of heuristic. We test the proposed RGM module in various 2D environments to demonstrate its effectiveness and efficiency. The results show that, compared with a model without recurrence, the RGM successfully generates appropriate heuristic in both seen and new unseen maps with higher accuracy, demonstrating the good generalization ability of the RGM model. We also compare the rapidly-exploring random tree star (RRT*) with generated heuristic and the conventional RRT* in four different maps, showing that the generated heuristic can guide the algorithm to efficiently find both initial and optimal solutions in a faster and more efficient way.
Gated Information Bottleneck for Generalization in Sequential Environments

Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution (OOD) detection. Meanwhile, we also establish the connection between IB theory and invariant causal representation learning, and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization (IRM) fails.
Design and Evaluation of a High-performance Support System for Credibility Tracing of Agricultural Products

The data credibility tracing for agricultural product transactions is affected by all organizations in the entire supply chain. For traditional centralized product transaction management, the data are only stored in one organization in the supply chain, making transaction data management non-transparent and not credible. However, blockchain connects transaction information blocks into a tamper-proof and distributed chain ledger by Hash Function, which can help solve the problem. But the data access performance in blockchain still needs improvement. With Hyperledger Fabric as the underlying technology of the decentralized tracing system for agricultural products, and through improving system performance by Redis-based data structuring method, this study designed and implemented a complete chain tracing system from production, processing, package and logistics to sales of agricultural products. The study program not only introduced the design program and technological architecture of this system, but also evaluated the system performance under single-node and multi-node server environments. The experiment results show that the program integrating Hyperledger Fabric and Redis has a data access speed at least four times faster than that without Redis, especially for multi-node server, in which the performance improvement is over 20 times. Therefore, this design program can offer a feasible approach for improving system performance of the credibility tracing system of agricultural products.
A Decade of Dependent Session Types

invited-talk Share on A Decade of Dependent Session Types Authors: Bernardo Toninho NOVA School of Science and Technology and NOVA-LINCS, Portugal NOVA School of Science and Technology and NOVA-LINCS, PortugalView Profile , Luís Caires NOVA School of Science and Technology and NOVA-LINCS, Portugal NOVA School of Science and Technology and NOVA-LINCS, PortugalView Profile , Frank Pfenning Carnegie Mellon University, USA Carnegie Mellon University, USAView Profile Authors Info & Claims PPDP 2021: 23rd International Symposium on Principles and Practice of Declarative ProgrammingSeptember 2021 Article No.: 3Pages 1–3https://doi.org/10.1145/3479394.3479398Online:07 October 2021Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Novel hybrid ANN and clustering inspired load balancing algorithm in cloud environment

Load balancing in the cloud environment is a NP-hard problem. To address this problem, a new hybridized soft computing inspired technique named Clustering based Artificial Neural Network for Dynamic Load Balancing (CANN-DLB) is introduced. Artificial Neural Network aims to achieve an optimized load of VMs by training the cloud environment. Back-propagation ANN is performed to calculate the optimized Virtual Machine (VM) load in cloud systems for improving QoS. The CANN-DLB technique uses K-means clustering algorithm over calculated VM loads and clusters them into under-loaded and over-loaded VMs. The scheduling of homogeneous independent non-preemptive tasks is performed using Particle Swarm Optimization (PSO) technique. CloudSim (Cloud Simulator) tool has been used to implement the proposed algorithm. The performance of the CANN-DLB technique has been achieved for Space Shared and Time Shared VM task scheduler methods. The simulation results of the CANN-DLB algorithm are compared with existing load balancing and scheduling algorithms with the objectives to improve load balance fairness. Results of CANN-DLB have achieved 69.5%, 96.9%, 96.0% and 97.4% less processing cost and 87.12%, 87.12%, 87.57% and 81.78% less degree of imbalance compared to CM-eFCFS, Static RR, MinMin and MaxMin method respectively. The system fairness with respect to system load is calculated to analyze CANN-DLB algorithm performance on heavy load. The proposed algorithm achieved 12% higher load fairness than the existing Two-level method and proves that the introduced model works better in dynamic scenarios. Plotted graphs show that the proposed idea is innovative for load balancing in a dynamic cloud environment. This hybridization of the ANN and K-means clustering method produces remarkable results as compared to existing algorithms for different cases.
Focal and Composed Vision-semantic Modeling for Visual Question Answering

Visual Question Answering (VQA) is a vital yet challenging task in the field of multimedia comprehension. In order to correctly answer questions about an image, a VQA model requires to sufficiently understand the visual scene, especially the vision-semantic reasonings between the two modalities. Traditional relation-based methods allow to encode the pairwise relations of objects to boost the VQA model performance. However, this simple strategy is deficient to exploit the abundant concepts expressed by the composition of diverse image objects, leading to sub-optimal performance. In this paper, we propose a focal and composed vision-semantic modeling method, which is a trainable end-to-end model, for better vision-semantic redundancy removal and compositionality modeling. Concretely, we first introduce the LENA cell, a plug-and-play reasoning module, which removes redundant semantic by a focal mechanism in the first step, followed by the vision-semantic compositionality modeling for better visual reasoning. We then incorporate the cell into a full LENA network, which progressively refines multimodal composed representations, and can be leveraged to infer the high-order vision-semantic in a multi-step learning way. Extensive experiments on two benchmark datasets, i.e., VQA v2 and VQA-CP v2, verify the superiority of our model as compared with several state-of-the-art baselines.
Fixation prediction for advertising images: Dataset and benchmark

Existing saliency prediction methods focus on exploring a universal saliency model for natural images, relatively few on advertising images which typically consists of both textual regions and pictorial regions. To fill this gap, we first build an advertising image database, named ADD1000, recording 57 subjects’ eye movement data of 1000 ad images. Compared to natural images, advertising images contain more artificial scenarios and show stronger persuasiveness and deliberateness, while the impact of this scene heterogeneity on visual attention is rarely studied. Moreover, text elements and picture elements express closely related semantic information to highlight product or brand in ad images, while their respective contribution to visual attention is also less known. Motivated by these, we further propose a saliency prediction model for advertising images based on text enhanced learning (TEL-SP), which comprehensively considers the interplay between textual region and pictorial region. Extensive experiments on the ADD1000 database show that the proposed model outperforms existing state-of-the-art methods.
When Art Meets Technology or Vice Versa: Key Challenges at the Crossroads of AI-Generated Artworks and Copyright Law

This paper discusses the intersection of Artificial Intelligence (AI) and copyright law in the art domain, stressing the dominant problems and polemics that arise, such as authorship, originality and creativity, use of copyrightable content as input data to train and teach AI to generate artwork and the liability for AI-induced copyright infringements. In the first section, this paper provides an overview of the current advancement of AI and the necessary human contribution to generate artwork. The second section analyses doctrinal aspects of originality and creativity, aiming to exemplify how AI-generated artwork cannot be subsumed under traditional interpretations of these concepts, which should be reinterpreted to fit AI-generated artworks. This paper further discusses the issue of authorship of AI-generated artworks and aims to point out that this issue should be the extent to whom, of the many humans involved, the authorship should be attributed. The fourth section analyses the use of copyright content to train and teach AI to generate artwork, possible infringements of the right of reproduction and the right of adaptation, and the applicability of exceptions in that respect, and also proposes a collective licensing scheme to tackle this issue. The fifth section analyses the loophole of who is liable for AI-induced copyright infringements, concepts of copyright liability and sanctions, and suggests the assignment of liability to human(s) with decisive influence over the infringing algorithm(s). Finally, this paper discusses whether AI should be granted (limited) legal personhood and, if opting for this and if enforceable, aims to tackle the foregoing issues. The European Union perspective on copyright is in the focus of this paper, referring to the Croatian legal system, where needed or appropriate. The United States’ copyright system is also referenced.
Violent Scene Detection of Film Videos Based on Multi-Task Learning of Temporal-Spatial Features

In this paper, we propose a new framework for the violent scene detection of film videos based on multi-task learning of temporal-spatial features. In the proposed framework, for the violent behavior representation of film clips, we employ a temporal excitation and aggregation network to extract the temporal-spatial deep features in the visual modality. And on the other hand, a recurrent neural network with local attention is utilized to extract the utterance-level representation of affective analysis in the audio modality. In the process of feature mapping, we concern the task of violent scene detection together with that of affective analysis and then propose a multi-task learning strategy to effectively predict the violent scene of film clips. To evaluate the effectiveness of the proposed method, the experiments are done on the task of violent scenes detection 2015. The experimental results show that our model outperforms most of the state of the art methods, validating the innovation of considering the task of violent scene detection jointly with the violence emotion analysis.
Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders

Learning dynamics of collectively moving agents such as fish or humans is an essential task in research. Due to phenomena such as occlusion or change of illumination, the multi-object methods tracking such dynamics may lose the tracks of the agents which may result in fragmentations of trajectories. Here, we present an extended deep autoencoder (DA) that we train only on the fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectory matrix of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of this HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction.
Cyberattack Ontology: A Knowledge Representation for Cyber Supply Chain Security

Cyberattacks on cyber supply chain (CSC) systems and the cascading impacts have brought many challenges and different threat levels with unpredictable consequences. The embedded networks nodes have various loopholes that could be exploited by the threat actors leading to various attacks, risks, and the threat of cascading attacks on the various systems. Key factors such as lack of common ontology vocabulary and semantic interoperability of cyberattack information, inadequate conceptualized ontology learning and hierarchical approach to representing the relationships in the CSC security domain has led to explicit knowledge representation. This paper explores cyberattack ontology learning to describe security concepts, properties and the relationships required to model security goal. Cyberattack ontology provides a semantic mapping between different organizational and vendor security goals has been inherently challenging. The contributions of this paper are threefold. First, we consider CSC security modelling such as goal, actor, attack, TTP, and requirements using semantic rules for logical representation. Secondly, we model a cyberattack ontology for semantic mapping and knowledge representation. Finally, we discuss concepts for threat intelligence and knowledge reuse. The results show that the cyberattack ontology concepts could be used to improve CSC security.
Precision aggregated local models

Abstract Large‐scale Gaussian process (GP) regression is infeasible for large training data due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide‐and‐conquer, for example, partitioning into subproblems and inducing functional (and thus computational) independence. Such approximations can be speedy, accurate, and sometimes even more flexible than ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and are thus both good and bad in this regard. Model averaging, an alternative to divide‐and‐conquer, can maintain absolute continuity but often over‐smooths, diminishing accuracy. Here we propose putting LAGP‐like methods into a local experts‐like framework, blending partition‐based speed with model‐averaging continuity, as a flagship example of what we call precision aggregated local models (PALM). Using LAGPs, each selecting from total data pairs, our scheme is at most cubic in , quadratic in , and linear in . Extensive empirical illustration shows how PALM is at least as accurate as LAGP, can be much faster, and furnishes continuous predictions. Finally, we propose sequential updating scheme that greedily refines a PALM predictor up to a computational budget.
Generative Adversarial Networks with Adaptive Semantic Normalization for text-to-image synthesis

Transforming human natural language into photo-realistic images has always been a challenging issue. Batch Normalization (BN) is used in current text-to-image models to accelerate and stabilize the training process. However, the BN ignores feature differences between individuals and semantic relationship between modalities, which is negative for text-to-image tasks. To solve the problems, a novel module called Adaptive Semantic Instance Normalization (ASIN) is proposed. The ASIN considers the individuality of generated images and introduces text semantic information to the image normalization process, establishing a consistent and semantically close correlation between generated images and given text. Extensive experiments and ablation studies are carried out on two types of datasets. The results demonstrate the superiority of the proposed method in comparison of previous methods.