Reviewerly/exHarmony huggingface dataset:
Semantically Similarity-Wise Dual-Branch Network for Scene Graph Generation

Scene graph generation aims to detect visual entities and relationships between them from an image. The object-level visual information is of vital importance for predicting accurate relationships. However, most existing methods essentially encode visual information with coarse supervised information, since they regard different relationships as mutually exclusive semantics with equal-distance labels by taking cross-entropy function as the main training loss. Intuitively, different relationship semantics naturally have their own similarity and dissimilarity with different level distances, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the topological information of relationship semantics. It can serve as an inspiring hint to aid learning to grasp the key related visual information. Accordingly, we propose a Semantically Similarity-wise Dual-branch Network (SSDN) which introduces topological information of relationship semantics as extra supervision to aid learning extracting and encoding relationship-related visual information. To avoid possible chaotic feature learning and enable the introduced knowledge to be better absorbed during inference, we design a dual-branch framework consisting of an auxiliary branch and an inference branch. The topological information extracted from the groundtruth is introduced at the front end of the auxiliary branch which then generates a soft embedding to be propagated to the inference branch in a knowledge distillation manner. Extensive experiments show that our model averagely outperforms state-of-the-art approaches on benchmark Visual Genome and VRD significantly, which demonstrates its effectiveness and superiority.
74‐3: Image Deblurring of Camera Under Display by Deep Learning

A deep learning method is provided in this paper to deblur images captured behind OLED screen named Camera Under Display (CUD). Because of optical diffraction of OLED pixel pattern, images captured by front camera under the display panel are unfavorably blurred. This paper proposed a modified GAN training network to deblur these images, with result showing perceptually clearer image with better restored high spatial frequency information.
Extraction of Opinion Target Using Syntactic Rules in Urdu Text

Opinion target or aspect extraction is the key task of aspect-based sentiment analysis.This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion.Although, opinion target extraction has been studied extensively in the English language domain, with notable work in other languages such as Chinese, Arabic etc., other regional languages have been neglected.One of the reasons is the lack of resources and available texts for these languages.Urdu is one, with millions of native and non-native speakers across the globe.In this paper, the Urdu language domain is focused on to identify opinion targets from written Urdu texts.To accomplish this task, several syntactic rules are crafted to identify users' opinions and associated target words.These rules are crafted using the grammatical and linguistic context of the words in the sentence.To the best of our knowledge, there is no existing work available in the Urdu domain for opinion target extraction.The proposed methodology is evaluated on an Urdu language dataset and compared with an existing approach for the English language by applying the same technique.The experiments have demonstrated that the proposed approach achieves promising performance as compared to the applied English language domain approach.
Best Selection of Generative Adversarial Networks Hyper-Parameters Using Genetic Algorithm

Generative adversarial networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other. In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digit numbers 1–9 with generator and train discriminator to decide whereas the generated images are fake or true. Genetic algorithm (GA) technique was used to adapt GAN hyper-parameters, the resulted algorithm is named GANGA: generative adversarial network with genetic algorithm. The resulted algorithm has achieved high performance; it was able to get zero value of loss function for the generator and discriminator separately. Anaconda environment with tensorflow library facilitates was used; python as programming language was adapted with needed libraries. The implementation was done using MNIST dataset to validate the work. The proposed method is to let genetic algorithm choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function used to find best values of learning rate, batch normalization, number of neurons and a parameter of dropout layer.
Emerging Threats for the Human Element and Countermeasures in Current Cyber Security Landscape

The chapter presents an overview of emerging issues in the psychology of human behaviour and the evolving nature of cyber threats. It reflects on the role of social engineering as the entry point of many sophisticated attacks and highlights the relevance of the human element as the starting point of implementing cyber security programmes in organisations as well as securing individual online behaviour. Issues associated with the emerging trends in human behaviour research and ethics are presented for further discussion. The chapter concludes with a set of open research questions warranting immediate academic attention to avoid the exponential growth of information breaches in the future.
Toward Communication-Learning Trade-Off for Federated Learning at the Network Edge

In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that 1) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and 2) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.
A tissue‐fraction estimation‐based segmentation method for quantitative dopamine transporter SPECT

Abstract Background Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter–single‐photon emission computed tomography (DaT‐SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT‐SPECT images is challenging, a major reason being partial‐volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite‐sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT‐SPECT images. Purpose Design and objectively evaluate a fully automated tissue‐fraction estimation‐based segmentation method that segments the caudate, putamen, and GP from DaT‐SPECT images. Methods The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three‐dimensional DaT‐SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross‐entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep‐learning‐based approach. Results Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed () all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) &lt; 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen. Conclusions The proposed tissue‐fraction estimation‐based segmentation method for DaT‐SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical‐phantom and patient studies.
Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale

Session-based recommendation predicts the next item with which a user will interact, given a sequence of her past interactions with other items. This machine learning problem targets a core scenario in e-commerce platforms, which aim to recommend interesting items to buy to users browsing the site. Session-based recommenders are difficult to scale due to their exponentially large input space of potential sessions. This impedes offline precomputation of the recommendations, and implies the necessity to maintain state during the online computation of next-item recommendations.
A universal adversarial policy for text classifiers

Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts. However, unlike the vision domain, adding the same perturbation to different inputs results in noticeably unnatural inputs. Therefore, we introduce a new universal adversarial setup – a universal adversarial policy, which has many advantages of other universal attacks but also results in valid texts – thus making it relevant in practice. We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts. This formulation is universal in that the policy is successful in finding adversarial examples on new texts efficiently. Our approach uses text perturbations which were extensively shown to produce natural attacks in the non-universal setup (specific synonym replacements). We suggest a strong baseline approach for this formulation which uses reinforcement learning. Its ability to generalise (from as few as 500 training texts) shows that universal adversarial patterns exist in the text domain as well.
Single-step implementation of a hybrid controlled-not gate with one superconducting qubit simultaneously controlling multiple target cat-state qubits

Hybrid quantum gates have recently drawn considerable attention. They play significant roles in connecting quantum information processors with qubits of different encoding and have important applications in the transmission of quantum states between a quantum processor and a quantum memory. In this work, we propose a single-step implementation of a multi-target-qubit controlled-NOT gate with one superconducting (SC) qubit simultaneously controlling $n$ target cat-state qubits. In this proposal, the gate is implemented with $n$ microwave cavities coupled to a three-level SC qutrit. The two logic states of the control SC qubit are represented by the two lowest levels of the qutrit, while the two logic states of each target cat-state qubit are represented by two quasi-orthogonal cat states of a microwave cavity. This proposal operates essentially through the dispersive coupling of each cavity with the qutrit. The gate realization is quite simple because it requires only a single-step operation. There is no need of applying a classical pulse or performing a measurement. The gate operation time is independent of the number of target qubits, thus it does not increase as the number of target qubits increases. Moreover, because the third higher energy level of the qutrit is not occupied during the gate operation, decoherence from the qutrit is greatly suppressed. As an application of this hybrid multi-target-qubit gate, we further discuss the generation of a hybrid Greenberger-Horne-Zeilinger (GHZ) entangled state of SC qubits and cat-state qubits. As an example, we numerically analyze the experimental feasibility of generating a hybrid GHZ state of one SC qubit and three cat-state qubits within present circuit QED technology.
RES: A Robust Framework for Guiding Visual Explanation

Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e.g., "whether the explanations are accurate") and improve the explanation quality (e.g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored. To guide the model toward better explanations, techniques in explanation supervision - which add supervision signals on the model explanation - have started to show promising effects on improving both the generalizability as and intrinsic interpretability of Deep Neural Networks. However, the research on supervising explanations, especially in vision-based applications represented through saliency maps, is in its early stage due to several inherent challenges: 1) inaccuracy of the human explanation annotation boundary, 2) incompleteness of the human explanation annotation region, and 3) inconsistency of the data distribution between human annotation and model explanation maps. To address the challenges, we propose a generic RES framework for guiding visual explanation by developing a novel objective that handles inaccurate boundary, incomplete region, and inconsistent distribution of human annotations, with a theoretical justification on model generalizability. Extensive experiments on two real-world image datasets demonstrate the effectiveness of the proposed framework on enhancing both the reasonability of the explanation and the performance of the backbone DNNs model.
BEAT: Blockchain-Enabled Accountable and Transparent Infrastructure Sharing in 6G and Beyond

It is widely expected that future networks of 6G and beyond will deliver on the unachieved goals set by 5G. Technologies such as Internet of Skills and Industry 4.0 will become stable and viable, as a direct consequence of networks that offer sustained and reliable mobile performance levels. The primary challenges for future technologies are not just low-latency and high-bandwidth. The more critical problem Mobile Service Providers (MSPs) will face will be in balancing the inflated demands of network connections and customers' trust in the network service, that is, being able to interconnect billions of unique devices while adhering to the agreed terms of Service Level Agreements (SLAs). To meet these targets, it is self-evident that MSPs cannot operate in a solitary environment. They must enable cooperation among themselves in a manner that ensures trust, both between themselves as well as with customers. In this study, we present the BEAT (Blockchain-Enabled Accountable and Transparent) Infrastructure Sharing architecture. BEAT exploits the inherent properties of permissioned type of distributed ledger technology (i.e., permissioned distributed ledgers) to deliver on accountability and transparency metrics whenever infrastructure needs to be shared between providers. We also propose a lightweight method that enables device-level accountability. BEAT has been designed to be deployable directly as only minor software upgrades to network devices such as routers. Our simulations on a resource-limited device show that BEAT adds only a few seconds of overhead processing time -- with the latest state-of-the-art network devices, we can reasonably anticipate much lower overheads.
Few-Shot Class-Incremental Learning for Named Entity Recognition

Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes.In this work, we study a more challenging but practical problem, i.e., few-shot classincremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.To alleviate the problem of catastrophic forgetting in few-shot classincremental learning, we generate synthetic data of the old classes using the trained NER model, augmenting the training of new classes.We further develop a framework that distills from the NER model from previous steps with both synthetic data, and real data from the current training set.Experimental results show that our approach achieves significant improvements over existing baselines.
Contrastive Multi-view Hyperbolic Hierarchical Clustering

Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC). It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. First, we align sample-level representations across multiple views in a contrastive way to capture the view-invariance information. Next, we utilize both the manifold and Euclidean similarities to improve the metric property. Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss. Finally, a binary clustering tree is decoded from optimized hyperbolic embeddings. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method and its components.
Real electronic signal data from particle accelerator power systems for machine learning anomaly detection

This article describes real time series datasets collected from the high voltage converter modulators (HVCM) of the Spallation Neutron Source facility. HVCMs are used to power the linear accelerator klystrons, which in turn produce the high-power radio frequency to accelerate the negative hydrogen ions (H-). Waveform signals have been collected from the operation of more than 15 HVCM systems categorized into four major subsystems during the years 2020-2022. The data collection process occurred in the Spallation Neutron Source facility of Oak Ridge, Tennessee in the United States. For each of the four subsystems, there are two datasets. The first one contains the waveform signals, while the second contains the label of the waveform, whether it has a normal or faulty signal. A variety of waveforms are included in the datasets including insulated-gate bipolar transistor (IGBT) currents in three phases, magnetic flux in the three phases, modulator current and voltage, cap bank current and voltage, and time derivative change of the modulator voltage. The datasets provided are useful to test and develop machine learning and statistical algorithms for applications related to anomaly detection, system fault detection and classification, and signal processing.
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue

We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements.NLU++ is divided into two domains (BANKING and HOTELS) and brings several crucial improvements over current commonly used NLU datasets.1) NLU++ provides fine-grained domain ontologies with a large set of challenging multi-intent sentences, introducing and validating the idea of intent modules that can be combined into complex intents that convey complex user goals, combined with finer-grained and thus more challenging slot sets.2) The ontology is divided into domain-specific and generic (i.e., domainuniversal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples.3) The dataset design has been inspired by the problems observed in industrial ToD systems, and 4) it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data.Finally, we benchmark a series of current state-of-the-art NLU models on NLU++; the results demonstrate the challenging nature of the dataset, especially in low-data regimes, the validity of 'intent modularisation', and call for further research on ToD NLU.
Reconceptualizing cybersecurity awareness capability in the data-driven digital economy

Abstract Data breaches have become a formidable challenge for business operations in the twenty-first century. The emergence of big data in the ever-growing digital economy has created the necessity to secure critical organizational information. The lack of cybersecurity awareness exposes organizations to potential cyber threats. Thus, this research aims to identify the various dimensions of cybersecurity awareness capabilities. Drawing on the dynamic capabilities framework, the findings of the study show personnel (knowledge, attitude and learning), management (training, culture and strategic orientation) and infrastructure capabilities (technology and data governance) as thematic dimensions to tackle cybersecurity awareness challenges.