Reviewerly/exHarmony huggingface dataset:
Neural Network Pruning by Cooperative Coevolution

Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for 63.42% FLOPs on CIFAR10 with -0.24% accuracy drop, and ResNet50 for 44.56% FLOPs on ImageNet with 0.07% accuracy drop.
Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation

The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questions regarding their robustness. In this paper, we employ a rule-based architecture called Tsetlin Machine (TM) that learns both simple and complex correlations by ANDing features and their negations. As such, it generates explainable AND-rules using negated and non-negated reasoning. Here, we explore how non-negated reasoning can be more prone to distribution biases than negated reasoning. We further leverage this finding by adapting the TM architecture to mainly perform negated reasoning using the specificity parameter s. As a result, the AND-rules becomes robust to spurious correlations and can also correctly predict counterfactual data. Our empirical investigation of the model's robustness uses the specificity s to control the degree of negated reasoning. Experiments on publicly available Counterfactually-Augmented Data demonstrate that the negated clauses are robust to spurious correlations and outperform Naive Bayes, SVM, and Bi-LSTM by up to 20 %, and ELMo by almost 6 % on counterfactual test data.
Deep-Learning-Based Financial Message Sentiment Classification in Business Management

A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Specifically, in the classification process, the seq2seq model is firstly adopted to extract the abstract from the financial message, which can reduce the influence of invalid information and speed up processing. In the process of sentiment classification, a bidirectional LSTM model is adopted for classification, which can more comprehensively make use of context information. Experiments are carried out to testify the proposed method through the open-source data set. It can be seen that the proposed method can effectively transfer from the reduced Amazon data set to the StockTwits financial text data set. Compared with the parameter-frozen-based method and the SDA-based method, the recognition rates have improved by 0.5% and 6.8%, respectively. If the target domain data set can be directly adopted for training, the recognition rate of the proposed method is higher than that of the SVM method and the LSTM method by 8.3% and 4.5%, respectively.
GASiam: Graph Attention Based Siamese Tracker for Infrared Anti-UAV

Unmanned aerial vehicle (UAV) is widely used in all walks of life, which not only brings convenience but also poses a threat to security. Although infrared UAV tracking has many advantages, it still faces many challenges such as target rapid movement, scale variation, etc. In order to realize the accurate tracking of UAV targets, this paper proposes a graph attention based Siamese tracker, called GASiam. Specifically, aiming at the loss caused by occlusion or rapid movement of UAV targets, SiamR-CNN is used as the baseline because of its three-stage redetection mechanism. Aiming at the lack of semantic information of infrared UAV targets, a graph attention module (GATM) is designed to enhance the information embedding ability of the local tracking network. This module can reduce the influence of background while retaining more foreground information, and improve the ability of the tracker to face the challenges of cluttered background and scale variation. Finally, a switching strategy is designed to select local tracking and global redetection adaptively according to the target state, in order to realize the robust and stable tracking of the infrared UAV target. Experiments on the IR-anti-UAV dataset show that GASiam has achieved leading performance in the field of anti-UAV.
SoK: SCT Auditing in Certificate Transparency

The Web public key infrastructure is essential to providing secure communication on the Internet today, and certificate authorities play a crucial role in this ecosystem by issuing certificates. These authorities may misissue certificates or suffer misuse attacks, however, which has given rise to the Certificate Transparency (CT) project. The goal of CT is to store all issued certificates in public logs, which can then be checked for the presence of potentially misissued certificates. Thus, the requirement that a given certificate is indeed in one (or several) of these logs lies at the core of CT. In its current deployment, however, most individual clients do not check that the certificates they see are in logs, as requesting a proof of inclusion directly reveals the certificate and thus creates the clear potential for a violation of that client’s privacy. In this paper, we explore the techniques that have been proposed for privacy-preserving auditing of certificate inclusion, focusing on their effectiveness, efficiency, and suitability in a near-term deployment. In doing so, we also explore the parallels with related problems involving browser clients. Guided by a set of constraints that we develop, we ultimately observe several key limitations in many proposals, ranging from their privacy provisions to the fact that they focus on the interaction between a client and a log but leave open the question of how a client could privately report any certificates that are missing.
Collaborative Design Review Sessions in Virtual Reality: Multi-Scale and Multi-User

The use of Virtual Reality (VR) for design reviews in projects is becoming more common in construction.However, the use of VR in these processes has been limited to been used more as a complementary reviewing tool alongside information medias such as 2D drawings and 3D models.Furthermore, immersive VR has been argued to have limitations when it comes to orientation and understanding and reasoning about functional links between physical layouts in a facility.This paper presents a case study of a VR system used during design reviews that support end-users to switch between different representations and scale i.e., miniature model/bird-eye view, and a 1:1 scale experience of the facility.The data gathered, consisted of recorded observation of the VR based design review process and study what type of discussion and design errors that was found during two VR-workshops connected to a new elementary school.The result shows, that by supporting switching between miniature model and 1:1 scale VR experience facilitated spatial orientation and understanding and collaboration across disciplines in the project.The study also show how collaborative immersive VR can be used as an efficient communication-tool during the design process in a real-world project.
Projective Manifold Gradient Layer for Deep Rotation Regression

Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem.The gap between the Euclidean network output space and the non-Euclidean SO(3) manifold imposes a severe challenge for neural network learning in both forward and backward passes.While several works have proposed different regression-friendly rotation representations, very few works have been devoted to improving the gradient backpropagating in the backward pass.In this paper, we propose a manifold-aware gradient that directly backpropagates into deep network weights.Leveraging Riemannian optimization to construct a novel projective gradient, our proposed regularized projective manifold gradient (RPMG) method helps networks achieve new state-of-the-art performance in a variety of rotation estimation tasks.Our proposed gradient layer can also be applied to other smooth manifolds such as the unit sphere.Our project page is at https://jychen18.github.io/RPMG.
Exploring and Distilling Posterior and Prior Knowledge for Radiology  Report Generation

Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.
Exploring diversity in back translation for low-resource machine translation

Back translation is one of the most widely used methods for improving the performance of neural machine translation systems.Recent research has sought to enhance the effectiveness of this method by increasing the 'diversity' of the generated translations.We argue that the definitions and metrics used to quantify 'diversity' in previous work have been insufficient.This work puts forward a more nuanced framework for understanding diversity in training data, splitting it into lexical diversity and syntactic diversity.We present novel metrics for measuring these different aspects of diversity and carry out empirical analysis into the effect of these types of diversity on final neural machine translation model performance for low-resource English↔Turkish and mid-resource English↔Icelandic.Our findings show that generating back translation using nucleus sampling results in higher final model performance, and that this method of generation has high levels of both lexical and syntactic diversity.We also find evidence that lexical diversity is more important than syntactic for back translation performance.
A Dependency Treebank of Spoken Second Language English

In this paper, we introduce a dependency treebank of spoken second language (L2) English that is annotated with part of speech (Penn POS) tags and syntactic dependencies (Universal Dependencies).We then evaluate the degree to which the use of this treebank as training data affects POS and UD annotation accuracy for L1 web texts, L2 written texts, and L2 spoken texts as compared to models trained on L1 texts only.
FRUIT: Faithfully Reflecting Updated Information in Text

Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent.While automated writing assistants could potentially ease this burden, the problem of suggesting edits grounded in external knowledge has been under-explored.In this paper, we introduce the novel generation task of faithfully reflecting updated information in text (FRUIT) where the goal is to update an existing article given new evidence.We release the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data produced from pairs of Wikipedia snapshots, along with our data generation pipeline and a gold evaluation set of 914 instances whose edits are guaranteed to be supported by the evidence.We provide benchmark results for popular generation systems as well as EDIT5-a T5-based approach tailored to editing we introduce that establishes the state of the art.Our analysis shows that developing models that can update articles faithfully requires new capabilities for neural generation models, and opens doors to many new applications.
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model.In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting.Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template.Our experiments demonstrate that AMu-LaP achieves competitive performance on the GLUE benchmark without human effort or external resources.
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models

The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision.However, it remains unclear how these models learn to generalise across languages.In this work, we conjecture that multilingual pretrained models can derive language-universal abstractions about grammar.In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages.We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe.Our findings show that the cross-lingual overlap between neurons is significant, but its extent may vary across categories and depends on language proximity and pre-training data size.
Text Style Transfer for Bias Mitigation using Masked Language Modeling

It is well known that textual data on the internet and other digital platforms contain significant levels of bias and stereotypes.Various research findings have concluded that biased texts have significant effects on target demographic groups.For instance, masculine-worded job advertisements tend to be less appealing to female applicants.In this paper, we present a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data.Our style transfer model improves on the limitations of many existing text style transfer techniques such as the loss of content information.Our model solves such issues by combining latent content encoding with explicit keyword replacement.We will show that this technique produces better content preservation whilst maintaining good style transfer accuracy.
Towards Toxic Positivity Detection

Over the past few years, there has been a growing concern around toxic positivity on social media which is a phenomenon where positivity is used to minimize one's emotional experience.In this paper, we create a dataset for toxic positivity classification from Twitter and an inspirational quote website.We then perform benchmarking experiments using various text classification models and show the suitability of these models for the task.We achieved a macro F1 score of 0.71 and a weighted F1 score of 0.85 by using an ensemble model.To the best of our knowledge, our dataset is the first such dataset created.
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations

The ability for a human to understand an Artificial Intelligence (AI) model's decision-making process is critical in enabling stakeholders to visualize model behavior, perform model debugging, promote trust in AI models, and assist in collaborative human-AI decision-making. As a result, the research fields of interpretable and explainable AI have gained traction within AI communities as well as interdisciplinary scientists seeking to apply AI in their subject areas. In this paper, we focus on advancing the state-of-the-art in interpreting multimodal models - a class of machine learning methods that tackle core challenges in representing and capturing interactions between heterogeneous data sources such as images, text, audio, and time-series data. Multimodal models have proliferated numerous real-world applications across healthcare, robotics, multimedia, affective computing, and human-computer interaction. By performing model disentanglement into unimodal contributions (UC) and multimodal interactions (MI), our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models while maintaining generality across arbitrary modalities, model architectures, and tasks. Through a comprehensive suite of experiments on both synthetic and real-world multimodal tasks, we show that DIME generates accurate disentangled explanations, helps users of multimodal models gain a deeper understanding of model behavior, and presents a step towards debugging and improving these models for real-world deployment.
A Global Harmony Search Algorithm Based on Tent Chaos Map and Elite Reverse Learning

To improve the performance of the harmony search algorithm and enable the processing of increasingly complicated optimization problems, a global harmony search algorithm based on tent chaos map and elite reverse learning (HS-TE) has been proposed. The algorithm uses the tent chaos map to initialize the population and adopts the elite reverse learning strategy to optimize the iterative process. The method reduces the algorithm’s dependence on the initial solution, improves the search optimization ability, enhances the diversity of the population, and establishes adaptive parameters to control the development and exploration of the iterative process, which is beneficial to improving the algorithm’s search ability. Create test experiments: Various HS algorithms perform classic benchmark function tests. The experimental test data shows that the algorithm is better than the current five improved harmony search algorithms and has better convergence and accuracy. The algorithm is used to improve the penalty parameters and kernel function parameters of SVR, and then use the optimized SVR to perform regression prediction on the daily opening number of the Shanghai Stock Exchange. According to the experimental results, the upgraded SVR provides better prediction performance. It works both in theory and in real life and can be used to predict the Shanghai Securities Composite Index.
High-Secured Data Communication for Cloud Enabled Secure Docker Image Sharing Technique Using Blockchain-Based Homomorphic Encryption

In recent years, container-based virtualization technology for edge and cloud computing has advanced dramatically. Virtualization solutions based on Docker Containers provide a more lightweight and efficient virtual environment for Edge and cloud-based applications. Because their use is growing on its own and is still in its early phases, these technologies will face a slew of security issues. Vulnerabilities and malware in Docker container images are two serious security concerns. The risk of privilege escalation is increased because Docker containers share the Linux kernel. This study presents a distributed system framework called Safe Docker Image Sharing with Homomorphic Encryption and Blockchain (SeDIS-HEB). Through homomorphic encryption, authentication, and access management, SeDIS-HEB provides secure docker image sharing. The SeDIS-HEB framework prioritizes the following three major functions: (1) secure docker image upload, (2) secure docker image sharing, and (3) secure docker image download. The proposed framework was evaluated using the InterPlanetary File System (IPFS). Secure Docker images were uploaded using IPFS, preventing unauthorized users from accessing the data contained within the secure Docker images. The SeDIS-HEB results were transparent and ensured the quality of blockchain data access control authentication, docker image metadata denial-of-service protection, and docker image availability.
Passive Remote Monitoring and Aging in Place: A Scoping Review

Abstract Passive remote monitoring is a relatively new technology that may support older adults to age in place. However, current knowledge about the effectiveness of this technology in extending older adults’ independence is lacking. Therefore, we conducted a scoping review of studies examining passive remote monitoring to systematically synthesize evidence about the technology’s effectiveness as an intervention. Our initial search of Embase, CINAHL, PubMed, and Scopus databases identified 486 unique articles. Of these, 14 articles met our inclusion criteria. Results show that passive remote monitoring technologies are being used in innovative and diverse ways to support older adults aging in place and their caregivers. More high-quality research on this topic is needed.
Cloud Computing: Legal Issues and Provision

Micro, Small, and Medium Enterprises (MSMEs) are gradually adopting cloud-based solutions using Information, Communication, and Technologies (ICT). Due to the lack of awareness regarding the legal challenges arising from jurisdictional issues of the Cloud Service Provider (CSP), MSMEs often get entangled in expensive litigations. The paper throws light on such legal conflicts and proposes new innovative ways (such as bilateral treaties, US CLOUD Act, Mutual Law Assistance Treaties, and In-house Capability) to tackle the same. Any domestic law governing data privacy must align with the globally acceptable standards prevalent in the international perspective. In the era of industrial revolution 4.0, data is the new oil, wherein any country having control and understanding of the intersectoral dimensions of the cloud-based data indeed gains an edge over the competition. Due to the prominence of the global private sector in providing local public services, there is a need for government authorities to take flexible approaches while formulating domestic laws in the realm of cloud computing.