Reviewerly/exHarmony huggingface dataset:
Robust Heterogeneous Graph Neural Networks against Adversarial Attacks

Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. However, despite their wide use, there is currently no understanding of their robustness to adversarial attacks. In this work, we first systematically study the robustness of HGNNs and show that they can be easily fooled by adding the adversarial edge between the target node and large-degree node (i.e., hub). Furthermore, we show two key reasons for such vulnerability of HGNNs: one is perturbation enlargement effect, i.e., HGNNs, failing to encode transiting probability, will enlarge the effect of the adversarial hub in comparison of GCNs, and the other is soft attention mechanism, i.e., such mechanism assigns positive attention values to obviously unreliable neighbors. Based on the two facts, we propose a novel robust HGNN framework RoHe against topology adversarial attacks by equipping an attention purifier, which can prune malicious neighbors based on topology and feature. Specifically, to eliminate the perturbation enlargement, we introduce the metapath-based transiting probability as the prior criterion of the purifier, restraining the confidence of malicious neighbors from the adversarial hub. Then the purifier learns to mask out neighbors with low confidence, thus can effectively alleviate the negative effect of malicious neighbors in the soft attention mechanism. Extensive experiments on different benchmark datasets for multiple HGNNs are conducted, where the considerable improvement of HGNNs under adversarial attacks will demonstrate the effectiveness and generalization ability of our defense framework.
A Sustainable Green Approach to the Virtualized Environment in Cloud Computing

Cloud technology is a dynamic industry of communication and information technology (especially the Internet) that has revolutionized current computing while also introducing new environmental concerns. The cloud is a huge improvement over the conventional computing as it has redesigned the way businesses operate. With the cloud, the information is virtualized and reduces the need of infrastructural based model. Businesses can operate through this tangible area, significantly reducing energy consumption and the need for excessive material resources. In recent years, computational technologies and ideas have transitioned to distant data centers and pay-per-use hardware and software solutions. However, as the number of information centers supporting cloud-based applications grows, vast quantities of energy are consumed, resulting to high prices and greenhouse emissions in the atmosphere. Going green emerged as a viable solution to this challenge. Green computing is a study field that intends to overcome energy and climate issues. Green sustainable cloud technology aims to accomplish not only effective processing and computing environment usage, but also to improve energy efficiency. This paper will address the possibilities of sustainable cloud technology, emerging developments, and strategies to increase data center efficiency, which also will minimize carbon emissions. This creates research hurdles when such power devices are necessary to reduce the negative environmental effect of cloud technology.
Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

Objective and Impact Statement . We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction . Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods . We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion . This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.
Categorizing and relating implementation challenges for realizing blockchain applications in government

Purpose Blockchain technology (BCT) can be used for a wide variety of applications across domains and can bring many benefits. BCT-based applications can be beneficial for the government as well as businesses. Despite the many promises, BCT implementation lags behind. The purpose of this research is to identify a roadmap of critical implementation challenges that influence BCT implementation by governments. Design/methodology/approach The study develops an ISM-based model spread across seven levels to analyze the inter-relationship among the selected BCT challenges. The MICMAC analysis further helps in evaluating the variables based on their driving power and dependencies. Findings The findings show that all challenges have a strong impact on implementing BCT. The foundation for implementation BCT is to define standards and develop appropriate regulations. Next, the findings show the need for a shared infrastructure meeting the basic technical and societal requirements and developing viable business models to advance BCT implementation. Many challenges hinder the development of blockchain applications meeting the technical and ethical requirements. Originality/value Existing research has analyzed the relationship among challenges. To the best of the authors' knowledge this is the first paper to collate these implementation challenges and incorporate them to develop a hierarchical model using interpretive structural modeling technique. The results can be used to prioritize the tackling of the challenges.
Photonic Emulator for Inverse Design

Inverse design has become a powerful tool widely used in the design of high-performance integrated photonic devices. However, current inverse design methods rely heavily on computationally intensive electromagnetic simulation or time-consuming model training. Here, we proposed an efficient inverse design strategy, called a photonic emulator, that uses light propagation instead of electromagnetic simulation. We experimentally demonstrated the application of this photonic emulator for various typical single- and multiwavelength devices and functions, such as an optical multiple-input–multiple-output (MIMO) descrambler (at the modulation rate of 10 Gbit/s), matrix computation (percentage error < 2%), and a tunable wavelength selective switch (extinction ratio > 10 dB for three-wavelength routing). The photonic emulator enables high-precision reconfiguration of the design target on the basis of precise tuning of the effective refractive index near the pixels in the design area and fast feedback of the optical response in real time. Our work shows that the concept of propagation-as-computation can be used for inverse design to provide an efficient method for designing reconfigurable integrated photonic devices.
ASSIST: Towards Label Noise-Robust Dialogue State Tracking

The MultiWOZ 2.0 dataset has greatly boosted the research on dialogue state tracking (DST).However, substantial noise has been discovered in its state annotations.Such noise brings about huge challenges for training DST models robustly.Although several refined versions, including MultiWOZ 2.1-2.4,have been published recently, there are still lots of noisy labels, especially in the training set.Besides, it is costly to rectify all the problematic annotations.In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.ASSIST first generates pseudo labels for each sample in the training set by using an auxiliary model trained on a small clean dataset, then puts the generated pseudo labels and vanilla noisy labels together to train the primary model.We show the validity of ASSIST theoretically.Experimental results also demonstrate that AS-SIST improves the joint goal accuracy of DST by up to 28.16% on MultiWOZ 2.0 and 8.41% on MultiWOZ 2.4, compared to using only the vanilla noisy labels.
GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition

In the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing three various metrics: energy (E), energy-delay product (EDP) as well as energy-delay sum (EDS) which resulted in considerable energy savings, with a low to medium performance loss for EDP and EDS. Specifically, for Quadro 6000 and minimization of E we obtained energy savings of 28.5%–32.5%, for EDP 25%–28% of energy was saved with average 4.5%–15.4% performance loss, for EDS (k = 2) 22%–27% of energy was saved with 4.5%–13.8% performance loss. For V100 we found average energy savings of 24%–33%, for EDP energy savings of 23%–27% with corresponding performance loss of 13%–21% and for EDS (k = 2) 23.5%–27.3% of energy was saved with performance loss of 4.5%–13.8%.
Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning

Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training epoch processing data samples in a random access fashion. Because this puts enormous pressure on the I/O subsystem, the most common approach to distributed SGD in HPC environments is to replicate the entire dataset to node local SSDs. However, due to rapidly growing data set sizes this approach has become increasingly infeasible. Surprisingly, the questions of why and to what extent random access is required have not received a lot of attention in the literature from an empirical standpoint. In this paper, we revisit data shuffling in DL workloads to investigate the viability of partitioning the dataset among workers and performing only a partial distributed exchange of samples in each training epoch. Through extensive experiments on up to 2,048 GPUs of ABCI and 4,096 compute nodes of Fugaku, we demonstrate that in practice validation accuracy of global shuffling can be maintained when carefully tuning the partial distributed exchange. We provide a solution implemented in PyTorch that enables users to control the proposed data exchange scheme.
The impact of social media marketing on building brand loyalty through customer engagement in Jordan

The following study was conducted to determine the impact of social media marketing on building brand loyalty through customer engagement in Jordan. The current trend of using social media has revealed insights about customer trends and brands use them to engage with them and create different strategies. This study aims to discover whether or not customers' engagement mediates the impact of social media marketing on brand loyalty in Jordan. This study population consists of college students in Jordan. A sample of 214 respondents was once obtained; the survey for statistical analysis was 208. Structural equation modelling (SEM) was used as an inferential statistical analysis technique to test the hypotheses of the research. With R value of 0.686, it indicates that there is statistically strong relation between social media marketing and brand loyalty. The results of the study showed that social media marketing dimensions affect brand loyalty. Also, it was noticed that there was an impact of social media marketing on building brand loyalty through customer engagement. Based on the obtained results, the authors recommend managers and decision-makers to exert effort in using social media's word-of-mouth to be able to reap additional benefits.
Blockchain and cloud computing-based secure electronic healthcare records storage and sharing

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.
Cross-Domain Detection of GPT-2-Generated Technical Text

Machine-generated text presents a potential threat not only to the public sphere, but also to the scientific enterprise, whereby genuine research is undermined by convincing, synthetic text.In this paper we examine the problem of detecting GPT-2-generated technical research text.We first consider the realistic scenario where the defender does not have full information about the adversary's text generation pipeline, but is able to label small amounts of in-domain genuine and synthetic text in order to adapt to the target distribution.Even in the extreme scenario of adapting a physicsdomain detector to a biomedical detector, we find that only a few hundred labels are sufficient for good performance.Finally, we show that paragraph-level detectors can be used to detect the tampering of full-length documents under a variety of threat models.
Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to “spin” their outputs so as to support an adversary-chosen sentiment or point of view—but only when the input contains adversary-chosen trigger words. For example, a spinned <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> summarization model outputs positive summaries of any text that mentions the name of some individual or organization.Model spinning introduces a “meta-backdoor” into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary.Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims.To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task (e.g., sentiment analysis) onto a seq2seq model, backpropagates the desired meta-task output (e.g., positive sentiment) to points in the word-embedding space we call “pseudo-words,” and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary’s meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models.Finally, we propose a black-box, meta-task-independent defense that, given a list of candidate triggers, can detect models that selectively apply spin to inputs with any of these triggers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We use “spinned” rather than “spun” to match how the word is used in public relations.
Optimization of Network-Based Earthquake Early Warning Systems on the Korean Peninsula

Most countries and local governments provide earthquake services in the public domain, and they must have high accuracy.If a missed alarm of the Earthquake Early Warning (EEW) system causes many casualties, or if the industrial system is temporarily suspended owing to a false alarm, causing economic losses, it inevitably becomes the responsibility of the government.Therefore, most countries approach the technological improvement of EEW systems carefully by performing simulations and conducting long-term tests to ensure their reliability.In this study, we extract characteristics of the initial P-wave amplitude from an earthquake on the Korean Peninsula and perform trend analysis.We found a common optimal threshold on the Korea Meteorological Administration's seismic observatory network from trend analysis.We then evaluated the performance of the optimized algorithm based on the simulation.The performance evaluated the actual events recorded corresponding to the number of matched, missed, and false events.As the result of the evaluation, the optimized module combination had a significantly lower occurrence of false events than the previous version.Therefore, we expected that the proposed optimization should contribute to improving alarm stability in real-time EEW.
Critical Discourse Analysis in the Classroom: A Critical Language Awareness on Early Children’s Critical Thinking

By using CDA as a tool in the classroom, this study visually and verbally analyses the children's growth. This is based on the understanding that learning is not content-oriented, but rather about the learning process of knowledge gained. Students who can think well will be successful in life. Therefore, this study aims to determine the app implementing critical discourse analysis on early children's development in the classroom. The method used is a qualitative approach with a literature review using sources from the internet, books, and related research outputs. Based on the study's findings, the critical thinking structure is explicit to make clear what is to be learned in order to facilitate writing skills, systematic to provide a consistent framework for focusing on both language and context, needs-based to ensure that course objectives and content are derived from students' needs, supportive to give teachers a central role in scaffolding students' learning and creativity, and empowering to give access to the pattern and possibilities.
A Machine Learning Model for Spam Reviews and Spammer Community Detection

People’s choices to purchase a product are influenced by its internet ratings and recommendations. Spammers manipulate product sales by creating fraudulent ratings on online social media platforms. The majority of current research on online review has concentrated on supervised learning algorithms, which require labelled data.. This is an insufficient requirement for online review. In this piece, we will be concentrating on identifying any misleading text reviews that we come across. The goal of this study is to discover spam comments and spammer groups. Various spam detection strategies have been proposed in the literature, including Review-Linguistic (RL) based features, User-Behavioral (UB) based features, and Review-Behavioural (RB) based features, but none of them include a simultaneous detection of these characteristics and relative importance of the features while also defining the communication between spam users. The suggested work establishes a diverse network of users and feedback nodes, and then applies the spam detection methodology to the issue of the communication environment. A feature weighting approach is presented to determine the relative value of features. Our solution uses an attention mechanism to discover the spamming hints hidden within the material and determines the relevance of each word in the text by computing its weight. We used the CNN algorithm to classify the reviews and compared the results with the usual Naive Bayes and Support Vector Machine algorithms.
Multi-Target Markov Boundary Discovery: Theory, Algorithm, and Application

Markov boundary (MB) has been widely studied in single-target scenarios. Relatively few works focus on the MB discovery for variable set due to the complex variable relationships, where an MB variable might contain predictive information about several targets. This paper investigates the multi-target MB discovery, aiming to distinguish the common MB variables (shared by multiple targets) and the target-specific MB variables (associated with single targets). Considering the multiplicity of MB, the relation between common MB variables and equivalent information is studied. We find that common MB variables are determined by equivalent information through different mechanisms, which is relevant to the existence of the target correlation. Based on the analysis of these mechanisms, we propose a multi-target MB discovery algorithm to identify these two types of variables, whose variant also achieves superiority and interpretability in feature selection tasks. Extensive experiments demonstrate the efficacy of these contributions.