Reviewerly/exHarmony huggingface dataset:
Extracting Semantic Relationships in Greek Literary Texts

In the era of Big Data, the digitization of texts and the advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) are enabling the automatic analysis of literary works, allowing us to delve into the structure of artifacts and to compare, explore, manage and preserve the richness of our written heritage. This paper proposes a deep-learning-based approach to discovering semantic relationships in literary texts (19th century Greek Literature) facilitating the analysis, organization and management of collections through the automation of metadata extraction. Moreover, we provide a new annotated dataset used to train our model. Our proposed model, REDSandT_Lit, recognizes six distinct relationships, extracting the richest set of relations up to now from literary texts. It efficiently captures the semantic characteristics of the investigating time-period by finetuning the state-of-the-art transformer-based Language Model (LM) for Modern Greek in our corpora. Extensive experiments and comparisons with existing models on our dataset reveal that REDSandT_Lit has superior performance (90% accuracy), manages to capture infrequent relations (100%F in long-tail relations) and can also correct mislabelled sentences. Our results suggest that our approach efficiently handles the peculiarities of literary texts, and it is a promising tool for managing and preserving cultural information in various settings.
Bad law or implementation flaws? Lessons from the implementation of the new law on epidemics during the response to the first wave of COVID-19 in Switzerland

After the 2009-2010 H1N1 pandemic, Switzerland overhauled its 1970 law on epidemics. The reform aimed at improving early detection, surveillance, and preparedness for future outbreaks of infectious diseases. Notably, the law introduced stronger coordination between Federal and Cantonal authorities, better management tools and international cooperation. The new law entered into force in 2016 after a long legislative process. During the process, the law survived a referendum fuelled by concerns about vaccine safety and pharmaceutical industry interference. The law was first applied during the COVID-19 pandemic in early 2020. The epicentre of the outbreak in Europe was in Lombardy, a large Italian region adjacent to Switzerland and with strong economic ties with its southern region of Ticino. The first months of pandemic response highlighted two major weaknesses. Firstly, the mechanisms introduced by the new law did not ease the tension between Cantonal autonomy and central coordination of the pandemic response. Central and Cantonal authorities will need to put in place new rules and arrangements to avoid dangerous delayed responses to foreseeable problems related to the spread of infectious diseases. Secondly, relevant stakeholders excluded from the policymaking process (trade unions, firms, large industries) should be involved to allow the introduction of harsh restrictions when needed, both internally and in relation to cross-border workers.
Investigation on Privacy-Preserving Techniques For Personal Data

Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.
Differentiable Ranking Metric Using Relaxed Sorting for Top-K Recommendation

Most recommenders generate recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K-items of high scores.Since sorting is not differentiable and is difficult to optimize with gradient descent, it is nontrivial to incorporate it in recommendation model training despite its relevance to top-K recommendations.As a result, inconsistency occurs between existing learning objectives and ranking metrics of recommenders.In this work, we present the Differentiable Ranking Metric (DRM) that mitigates the inconsistency between model training and generating top-K recommendations, aiming at improving recommendation performance by employing the differentiable relaxation of ranking metrics via joint learning.Using experiments with several real-world datasets, we demonstrate that the joint learning of the DRM objective and existing factor based recommenders significantly improves the quality of recommendations.
Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images

We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. Different from existing methods that represent an indoor scene with the type, location, and other properties of objects in the room and learn the scene layout from a collection of complete 3D indoor scenes, our method models each indoor scene as a 3D semantic scene volume and learns a volumetric generative adversarial network (GAN) from a collection of 2.5D partial observations of 3D scenes. To this end, we apply a differentiable projection layer to project the generated 3D semantic scene volumes into semantic-segmented depth images and design a new multiple-view discriminator for learning the complete 3D scene volume from 2.5D semantic-segmented depth images. Compared to existing methods, our method not only efficiently reduces the workload of modeling and acquiring 3D scenes for training, but also produces better object shapes and their detailed layouts in the scene. We evaluate our method with different indoor scene datasets and demonstrate the advantages of our method. We also extend our method for generating 3D indoor scenes from semantic-segmented depth images inferred from RGB images of real scenes. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
An efficient chaotic salp swarm optimization approach based on ensemble algorithm for class imbalance problems

Class imbalance problems have attracted the research community, but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an AdaBoost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization, and AdaBoost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of AdaBoost.
Towards a formal modelling, analysis and verification of a clone node attack detection scheme in the internet of things

A clone node attack is one of the severe attacks on the Internet of Things (IoT) network. In a clone node attack, an adversary aims to physically capture the secret credentials of the deployed IoT devices to make the clone of devices look similar to the original devices. In recent years, several solutions for detecting clone node attacks have been designed; however, the focus of such designed solutions is on system design discussion, specific feature sets, high-level abstraction of underlying system architecture and fulfilling the performance analysis requirements. While the designed solutions efficiently detect clone attacks, critical features such as modelling, analysis, and verification of such solutions are frequently overlooked, necessitating that users verify their internal behaviour and claimed properties to ensure that no problematic scenarios or anomalies exist. Taking into account the rationale for formal verification and the limitations of previous works, this paper aims to perform formal modelling, analysis, and verification of our existing proposed clone node attack detection scheme for IoT. We used the High-Level Petri Nets (HLPNs) and Z-specification language to model our proposed scheme and analyse the logic correctness of our scheme using incidence markings and confidence interval matrices. To validate the functional correctness, we utilised the Satisfiability Modulo Theories Library (SMT-Lib) and the Z3 Solver and verified the functional satisfiable properties by analysing sat/unsat verification results and their execution time. Finally, we extend our modelling work by utilising the Coloured Petri Nets (CPNs), taking into consideration their both timed and untimed models with the aim of validating functional correctness and logical correctness, respectively. To analyse both correctnesses of CPN models, we used logging facility statistics such as count, sum, average, minimum, and maximum as data packet observations.
Multi-type decision fusion network for visual Q&amp;A

The existing VQA task contains questions of various complexity, which can be divided into the object-level and relation-level categories. The former requires the model to focus on individual objects of images to answer the question, such as “Is a man in the image?”, denoted by object-QA, while the latter depends on a better understanding in the relation of pairwise objects to infer the answer, such as “What is the man doing now?”, denoted by relation-QA. However, existing methods indiscriminately rely on the single network to deal with multi-type question categories, which cannot meet the comprehensive nature of VQA task. To this end, we introduce the novel multi-type decision fusion model, which can adaptively leverage object- and relation-level decision networks to infer the answer. Our approach consists of three key components: 1) the object-QA network designs the attention layer to search for object regions in an image that are associated with the answer. 2) the relation-QA network captures the question aware relation information from the visual scene graph, for the answer inference; 3) the proposed balance gate involves the outputs of object- and relation-QA networks to adaptively predict the final output. Extensive experiments on two benchmarks demonstrate that the proposed method can achieve competing performance against the state-of-the-art methods. Additional ablation studies further validate its effectiveness.
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning

While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully de-signed adversarial attacks. We propose a novel adversarial training-based model by Attention Guided Knowledge Distillation and Bi-directional Metric Learning (AGKD-BML). The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model. In this way, the student model is able to focus on the correct region, as well as correcting the intermediate features corrupted by AEs to eventually improve the model accuracy. Moreover, to efficiently regularize the representation in feature space, we propose a bidirectional metric learning. Specifically, given a clean image, it is first attacked to its most confusing class to get the forward AE. A clean image in the most confusing class is then randomly picked and attacked back to the original class to get the backward AE. A triplet loss is then used to shorten the representation distance between original image and its AE, while enlarge that between the forward and backward AEs. We conduct extensive adversarial robustness experiments on two widely used datasets with different attacks. Our proposed AGKD-BML model consistently outperforms the state-of-the-art approaches. The code of AGKD-BML will be available at: https://github.com/hongw579/AGKD-BML.
GRNet: Graph-based remodeling network for multi-view semi-supervised classification

Multi-view semi-supervised classification (MSSC) focuses on exploring information from multiple views of labeled and unlabeled data to boost classification performance. However, most of the existing methods build models for each view individually, therefore, the potential relationship between different views can’t be fully explored. Additionally, they either focus on the correlations for consistency or maximize the independence for complementarity. Consistency and complementarity are equally important for multi-view learning. Therefore, this work proposes a novel Graph-based Remodeling Network for MSSC (GRNet), which can explore the potential relationship of multiple views and balance the consistency and complementation adaptively. Specifically, this model integrates multiple views and then generates reformed pseudo views by an attention-based ensemble learning strategy. Moreover, to exploit the information of unlabeled data, this work introduces graph regularization for such a goal. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed method.
Blackthorn: Latency Estimation Framework for CNNs on Embedded Nvidia Platforms

With more powerful yet efficient embedded devices and accelerators being available for Deep Neural Networks (DNN), machine learning is becoming an integral part of edge computing. As the number of such devices increases, finding the best platform for a specific application has become more challenging. A common question for application developers is to find the most cost-effective combination of a DNN and a device while still meeting latency and accuracy requirements. In this work, we propose Blackthorn, a layer-wise latency estimation framework for embedded Nvidia GPUs based on analytical models. We provide accurate predictions for each layer, helping developers to find bottlenecks and optimize the architecture of a DNN to fit target platforms. Our framework can quickly evaluate and compare large amounts of network optimizations without needing to build time-consuming execution engines. Our experimental results on Jetson TX2 and Jetson Nano devices show a per-layer estimation error of 6.104% Root-Mean-Square-Percentage-Error (RMSPE) and 5.888% RMSPE, which significantly outperforms current state-of-the-art methods. At network level, the average latency error is below 3% for the tested DNNs.
Skipping the binder bureaucracy with mixed embeddings in a semantics course (functional pearl)

Rigorous reasoning about programs calls for some amount of bureaucracy in managing details like variable binding, but, in guiding students through big ideas in semantics, we might hope to minimize the overhead. We describe our experiment introducing a range of such ideas, using the Coq proof assistant, without any explicit representation of variables, instead using a higher-order syntax encoding that we dub "mixed embedding": it is neither the fully explicit syntax of deep embeddings nor the syntax-free programming of shallow embeddings. Marquee examples include different takes on concurrency reasoning, including in the traditions of model checking (partial-order reduction), program logics (concurrent separation logic), and type checking (session types) -- all presented without any side conditions on variables.
Review of Blockchain Forensics Challenges

Blockchain technology has in many ways shown a promising technology where trust can be created between parties. With blockchain, trusted parties can easily transact or exchange information over a cryptographically secured distributed environment. However, based on the blockchain architecture, conducting digital forensic processes faces several problems and challenges. This chapter, therefore, explores the key open problems and challenges experienced while conducting digital forensic processes in blockchain technologies. The authors have leveraged design science research (DSR) to achieve the objectives of this study. Furthermore, the authors have also proposed high-level solutions to the identified problems and challenges.
OVANA: An Approach to Analyze and Improve the Information Quality of Vulnerability Databases

Vulnerability databases are one of the main information sources for IT security experts. Hence, the quality of their information is of utmost importance for anyone working in this area. Previous work has shown that machine readable information is either missing, incorrect, or inconsistent with other data sources. In this paper, we introduce a system called Overt Vulnerability source ANAlysis (OVANA), which analyzes the information quality of vulnerability databases utilizing state-of-the-art machine learning (ML) and natural language processing (NLP) techniques, searches the free-form description for relevant information missing from structured fields, and updates it accordingly. Our paper exemplifies that on the National Vulnerability Database, showing that OVANA is able to improve the information quality by 51.23% based on the indicators of accuracy, completeness, and uniqueness. Moreover, we present information which should be incorporated into the structured fields to increase the uniqueness of vulnerability entries and improve the discriminability of different vulnerability entries. The identified information from OVANA enables a more targeted vulnerability search and provides guidance for IT security experts in finding relevant information in vulnerability descriptions for severity assessment.
Hash Bit Selection Based on Collaborative Neurodynamic Optimization

Hash bit selection determines an optimal subset of hash bits from a candidate bit pool. It is formulated as a zero-one quadratic programming problem subject to binary and cardinality constraints. In this article, the problem is equivalently reformulated as a global optimization problem. A collaborative neurodynamic optimization (CNO) approach is applied to solve the problem by using a group of neurodynamic models initialized with particle swarm optimization iteratively in the CNO. Lévy mutation is used in the CNO to avoid premature convergence by ensuring initial state diversity. A theoretical proof is given to show that the CNO with the Lévy mutation operator is almost surely convergent to global optima. Experimental results are discussed to substantiate the efficacy and superiority of the CNO-based hash bit selection method to the existing methods on three benchmarks.
SlideGen

Presentation slides generated from research papers provide summary of the papers primarily to guide talks. Manually generating presentation slides is labor intensive. We propose a method to automatically generate slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites which is the largest dataset used for scholarly article summarization. We generate slides 1) extractively by selecting salient sentences from the paper and 2) abstractively by fine-tuning pre-trained language models to learn the language of slides. The results show the superiority of the extractive models in terms of ROUGE scores. However, abstractive summaries are less verbose and follow the language of the slides by generating phrases rather than full sentences.
PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management

This article introduces a Poisson multi-Bernoulli mixture (PMBM) filter in which the intensities of target birth and undetected targets are grid-based. A simplified version of the Rao-Blackwellized point mass filter is used to predict the intensity of undetected targets and to initialize tracks of targets detected for the first time. The grid approximation can efficiently represents intensities with abrupt changes with relatively few grid points compared to the number of Gaussian components needed in conventional PMBM implementations. This is beneficial in scenarios where the sensor’s field of view is limited. The proposed method is illustrated in a sensor management setting, where trajectories of sensors with limited fields of view are controlled to search for and track the targets in a region of interest.
Exploring polysemy in the Academic Vocabulary List: A lexicographic approach

The Academic Vocabulary List (AVL) (Gardner & Davies, 2014) is a valuable resource for EAP teachers and students as it identifies potential lexical learning/teaching targets. This study enhances the AVL's pedagogical usefulness by identifying polysemous lemmas in it. Polysemous AVL lemmas are operationalised as those with more than one definition in two lexicographic resources, the Collins COBUILD Advanced Learners' Dictionary and WordNet. This study also examines a theoretical issue, the relationship between the number of meaning senses of AVL lemmas and their frequency in an academic-English corpus. To this end, correlations were calculated between the numbers of AVL lemmas' meaning definitions listed in both lexicographic resources and their frequency in the COCA-Academic corpus. 34.38% of the 2673 AVL lemmas included in both lexicographic resources, excluding homonyms, are polysemous. Most (66.05%) come from the most frequent 1000 AVL lemmas. The number of meaning definitions of AVL lemmas and their frequency are positively correlated. This correlation is non-linear, i.e., low-frequency words tend to be monosemous but beyond a frequency threshold, word definitions increase as word frequency increases. Implications for future research and teaching are discussed.
Graph matching based on fast normalized cut and multiplicative update mapping

Point correspondence is a fundamental problem in pattern recognition and computer vision, which can be tackled by graph matching. Since graph matching is basically an NP-complete problem, some approximate methods are proposed to solve it. Continuous relaxation offers an effective approximate method for graph matching problem. However, the discrete constraint is not taken into consideration in the optimization step. In this paper, a fast normalized cut based graph matching method is proposed, where the discrete constraint is introduced into the optimization step. Specifically, first a semidefinite positive affinity matrix based form objective function is constructed by introducing a regularization term which is related to the discrete constraint. Then the fast normalized cut algorithm is utilized to find the continuous solution. Last, the discrete solution of graph matching is obtained by a multiplicative update algorithm. Experiments on both synthetic points and real-world images validate the effectiveness of the proposed method by comparing it with the state-of-the-art methods.