Reviewerly/exHarmony huggingface dataset:
A Multi-authority CP-ABE Scheme based on Cloud-Chain Fusion for SWIM

SWIM (System Wide Information Management) has become the development direction of A TM (Air Traffic Management) system by providing interoperable services to promote the exchange and sharing of data among various stakeholders. The premise of data sharing is security, and the access control has become the key guarantee for the secure sharing and exchange. The CP-ABE scheme (Ciphertext Policy Attribute-Based Encryption) can realize one-to-many access control, which is suitable for the characteristics of SWIM environment. However, the combination of the existing CP-ABE access control and SWIM has following constraints. 1. The traditional single authority CP-ABE scheme requires unconditional trust in the authority center. Once the authority center is corrupted, the excessive authority of the center may lead to the complete destruction of system security. So, SWIM with a large user group and data volume requires multiple authorities CP-ABE when performing access control. 2. There is no unified management of users' data access records. Lack of supervision on user behavior make it impossible to effectively deter malicious users. 3. There are a certain proportion of lightweight data users in SWIM, such as aircraft, users with handheld devices, etc. And their computing capacity becomes the bottleneck of data sharing. Aiming at these issues above, this paper based on cloud-chain fusion basically proposes a multi-authority CP-ABE scheme, called the MOV ATM scheme, which has three advantages. 1. Based on a multi-cloud and multi-authority CP-ABE, this solution conforms to the distributed nature of SWIM; 2. This scheme provides outsourced computing and verification functions for lightweight users; 3. Based on blockchain technology, a blockchain that is maintained by all stakeholders of SWIM is designed. It takes user's access records as transactions to ensure that access records are well documented and cannot be tampered with. Compared with other schemes, this scheme adds the functions of multi-authority, outsourcing, verifiability and auditability, but do not increase the decryption cost of users.
Classifying the Ideological Orientation of User-Submitted Texts in Social Media

With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of text articles posted to the online communities r/Liberal and r/Conservative from the social media website Reddit. Using the corpus, we develop and apply three classifiers. The baseline classifier is a Bayes model that accounts for each text article’s web domain, as such, classification is independent of content. Next, we develop a support vector machine (SVM) model with term frequency-inverse document frequency (TF-IDF) features; this approach highlight differences in language using a count-based feature-space to differentiate text articles. Last, we evaluate the context-based transformer (RoBERTa) model and discuss its under-performance relative to the baseline and SVM models.
Aspect-based Sentiment Analysis of English and Hindi Opinionated Social Media Texts

We present a lexicon-based approach for classifying opinionated social media texts in English and Hindi. The effect of conjunctions, degree modifiers, negations, emojis and emoticons in scoring the intensity of opinion expressed is further explored. Using a manually built Hindi polarity lexicon, we achieve an accuracy of 86.45% in classifying 2,717 Hindi reviews. A real-time analysis on YouTube reviews showed 86% accuracy for English review classification task.
SFMD: A Semi-Supervised Federated Malicious Traffic Detection Approach in IoT

With the increasingly widespread application of Internet of Things (IoT), network attacks has become a main threat of IoT devices' security. Due to the network traffic data is the carrier of information from users and devices, the traffic-based IoT malicious behavior detection has become an effective solution to prevent such threats. In order to identify malicious traffic in IoT while protecting users' personal privacy, researchers introduce Federated Learning (FL) into malicious network traffic detection. However, most of the current FL frameworks need all clients to own labeled data to train a high-performance detection model jointly. In addition, they require different clients must design the same model structure to meet the requirement of parameter sharing, which is unreasonable because each client faces problems such as data heterogeneity. And it will degrade the detection performance of some clients. In this research, Semi-Supervised Federated Learning for Malicious Traffic Detection (SFMD) is proposed, aiming to assist the clients who do not have the ability to label their data to train a high-performance model with other clients together. Besides, another key feature of this framework is that it allows each client to train its personalized model according to their own situation. The experimental results indicate that SFMD can accurately identify the attack types for the unsupervised clients without labeled data. In addition, it has achieved high accuracy compared to other anomaly detection methods.
Self-Supervised Transformer Networks for Error Classification of Tightening Traces

Transformers have shown remarkable results in the domains of Natural Language Processing and Computer Vision. This naturally raises the question of whether the success could be replicated in other domains. However, due to Transformers being inherently data-hungry and sensitive to weight initialization, applying the Transformer to new domains is quite a challenging task. Previously, the data demands have been met using large-scale supervised or self-supervised pre-training on a similar task before supervised fine-tuning on a target downstream task. We show that Transformers are applicable for the task of multi-label error classification of trace data and that masked data modelling based on self-supervised learning methods can be used to leverage unlabelled data to increase performance compared to a baseline supervised learning approach.
Evaluating Checkpointing Capability against Eclipse-based Stake-Bleeding Attack in PoS Blockchain

Eclipse-based Stake-Bleeding (ESB) attack is a kind of long-range attacks in a Proof-of-Stake (PoS) blockchain system, which can shorten the attack completion time. Researchers have quantitatively analyzed ESB attack but ignoring checkpointing defense technique, which solidifies the history of blockchain and then prevents an adversary to change the blockchain. This paper aims to investigate the capability of the checkpoint-based defense scheme in resisting ESB attack. We develop a Monte Carlo simulator to capture the dynamics of a PoS blockchain system, which is subject to ESB attack and deploys checkpoint-base defense scheme. We also develop the methods for calculating the probability of successful attack and attack profit. This simulator can be applied to evaluate the influence of ESB attack and the checkpointing capability from the perspective of honest verifiers, adversaries and victims. Our experiment results can help enhance PoS blockchain security.
Are Post-Hoc Explanation Methods for Prostate Lesion Detection Effective for Radiology End Use?

Deep learning has demonstrated impressive performance for medical tasks such as cancer classification and lesion detection. While it has achieved impressive performance, it is a black-box algorithm and therefore is difficult to interpret. Interpretation is especially important in fields that are high-risk in nature such as the medical field. There recently has been various methods proposed to interpret deep learning algorithms. However, there are limited studies evaluating these explanation methods in clinical settings such as radiology. To that end, we conduct a pilot study that evaluates the effectiveness of explanation methods for radiology end use. We evaluate if explanation methods improve diagnosis performance and what method is preferred by radiologists. We also glean insight into what characteristics radiologists deem explainable. We found that explanation methods increase diagnosis performance however it is dependent on the individual method. We also find that the radiology cohort deem the themes insight, visualization, and accuracy to be the most sought after explainable characteristics. The insights garnered in this study have the potential to guide future developments and studies of explanation methods for clinical use.
Threat Modeling and Reasoning for Industrial Control System Assets

With the evolution of the industry from automation to intelligence, the control, and management of industry have gradually become intelligent and simple, and the Industrial Internet of Things has emerged. The most important industrial control system in the industrial Internet of Things has improved the control efficiency of managers to deal with emergencies, but it also makes hackers available. To improve the response-ability of the industrial control system to attack, a threat modeling and reasoning base on industrial control system assets is proposed. The model constructs the asset structure of the industrial control system through the description of assets and the Purdue model in A TT &CK, and then establishes a threat model through attackers and the consequences of attackers. Thus, it can effectively deal with the attack of the attacker. On this basis, the asset security ontology of the industrial control system(ASOICS) is constructed and based on ASOICS, the asset security inference rules under ICS are designed. The semantic relationship between asset security of industrial control system is established by using the expression ability of ontology which can improve the response speed of security personnel to ICS attacks. Finally, this method is applied to the real industrial control system attack case scenario for analysis, and the attack and assets are mapped to the knowledge map. The potential risks and actual attack scenarios of the attacked assets are successfully detected, which proves the effectiveness of the proposed method.
Predicting Clinical Events via Graph Neural Networks

Timely detection of clinical events would provide healthcare providers the opportunity to make meaningful interventions that can result in improved health outcomes. This work describes a methodology developed at a large U.S. healthcare insurance company for predicting clinical events using administrative claims data. Most of the existing literature for predicting clinical events leverage historical data in Electronic Health Records (EHR). EHR data however has limitations making it undesirable for real-time use-cases. It is inconsistent, expensive, inefficient and sparsely available. In contrast, administrative claims data is relatively consistent, efficient and readily available. In this work, we introduce a novel modeling workflow: First, we learn custom embeddings for medical codes within claims data in order to uncover the hidden relationships between them. Second, we introduce a novel way of representing a member’s health history with a graph such that the relationships between various diagnosis and procedure codes is captured. Finally, we apply Graph Neural Networks (GNN) to perform a multi-label graph classification for clinical event prediction. Our approach produces more accurate predictions than any other standard classification approaches and can be easily generalized to other clinical prediction tasks.
Rethinking of Domain Users Control in Computer Vision Pipelines by Customized Attention

Designing Computer Vision (CV) algorithms for production applications requires extensive knowledge of Machine Learning (ML), programming, and software architecture. Accordingly, keeping humans, especially domain users, in the loop of model decisions is a challenge. The algorithms created by the CV community usually omit to provide clear interpretable information on the finally selected model, except for numerical metrics. This leads to a transparency challenge, known as black-box ML, which may negatively impact the users’ trust in the sensitive use cases. Furthermore, the majority of interpretable methods for giving feedback to domain users are usually made after training, i.e., proposing a final model without the user participation.In this paper, we present a Visual Interpretation-based Control (VIC) technique, which is a simple but principled model evaluation criteria. VIC offers a decision-making strategy for enabling non-experts to dictate their intuition from the most important areas in an image and incorporate this within CV pipelines. Specifically, we supervise a generative adversarial network to penalize its generator for the specified region of interest. We further validate our method through an architecture selection strategy in one of the common AutoML benchmarks.
A Vision Transformer Architecture for Open Set Recognition

Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the training data. However, in a more realistic open set scenario, traditional classifiers with incomplete knowledge cannot tackle test data that are not from the training classes. Open set recognition (OSR) aims to address this problem by both identifying unknown classes and distinguishing known classes simultaneously. In this paper, we propose a novel approach to OSR that is based on the vision transformer (ViT) technique. Specifically, our approach employs two separate training stages. First, a ViT model is trained to perform closed set classification. Then, an additional detection head is attached to the embedded features extracted by the ViT, trained to force the representations of known data to class-specific clusters compactly. Test examples are identified as known or unknown based on their distance to the cluster centers. To the best of our knowledge, this is the first time to leverage ViT for the purpose of OSR, and our extensive evaluation against several OSR benchmark datasets reveals that our approach significantly outperforms other baseline methods and obtains new state-of-the-art performance.
Structure-Noise-Aware Anchor Link Prediction Across Social Networks

Anchor Link Prediction (ALP) across social networks plays a pivotal role in inter-network applications. The difficulty of anchor link prediction across social networks lies in how to consider the factors affecting nodes alignment comprehensively. In recent years, predicting anchor links based on network embedding has become the main trend. For social networks, previous anchor link prediction methods first integrate neighbourhood nodes associated with a user node to obtain a fusion embedding vector from global perspective, and then predict anchor links based on the similarity between fusion vectors corresponding with different user nodes. However, the social network structure contains a lot of structural noise, which is ignored by the fusion vector. To address the challenge, we propose a novel structure-noise-aware anchor link prediction framework across social networks (SNALP), which models all the edges of the node from a fine-grained perspective and evaluates the contribution of them. Then, each edge is given a different weight according to their contribution. While the trusted edges are enhanced, the effects of noisy edges are reduced. SNALP can solve the network embedding and structure-noise-aware alignment under a unified optimization framework. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.
An Ontology-based transfer learning method improving classification of medical documents

Automatic classification of documents is a well known problem and can be solved with Machine Learning methods. However, such approaches require large sets of training data which are not always available. Moreover, in data protection sensitive domains, e.g. electronic health records, Machine Learning models often cannot directly be transferred to other environments. We present a transfer learning method which uses ontologies to normalise the feature space of text classifiers. With this we can guarantee that the trained models do not contain any person related data and can therefore be widely reused without raising General Data Protection Regulation (GDPR) issues. Furthermore, we describe a process with which the ontologies can be enriched so that the classifiers can be reused in different contexts with deviating terminology without any additional training of the classifiers. Our transfer learning method follows a combined paradigm of transfer by copy and transfer by enrichment. As proof of concept we apply classifiers trained on hospital medical documents together with appropriately enriched ontologies to medical texts written in colloquial language. The promising results show the potential of our transfer learning approach that respects GDPR requirements and can flexibly be adapted to drifting terminology.
Self-Supervised Learning in the Twilight of Noisy Real-World Datasets

Despite the effort toward benchmarking self-supervised learning (SSL) methods for image recognition transfer learning tasks, our understanding is still limited about their performance on noisy real-world datasets. This paper presents an extensive analysis of various types of SSL methods on real-world datasets containing noisy images of wildlife animals. These uncurated images are auto-captured by motion-activated cameras or camera traps installed in the wild. The camera-trap datasets exhibit different types of biases typically present in practical tasks. Using a set of variably-size biased datasets, we compare the supervised learning (SL) method to two types of SSL methods, i.e., instance discrimination and cluster discrimination. Our results reveal nuances in SSL’s performance. For example, we show that though SSL methods are often more generalizable than the SL method, the performance gain of some SSL methods diminishes with the reduction in the size of the target dataset. Also, there exists significant variability in the effectiveness of the two types of SSL methods. In addition to this, we show that, unlike SL, both types of SSL gain from increased model capacity.
Stochastic Induction of Decision Trees with Application to Learning Haar Trees

Decision trees are a convenient and established approach for any supervised learning task. Decision trees are trained by greedily splitting a leaf nodes, into two leaf nodes until a specific stopping criterion is reached. Splitting a node consists of finding the best feature and threshold that minimizes a criterion. The criterion minimization problem is solved through a costly exhaustive search algorithm. This paper proposes a novel stochastic approach for criterion minimization. The algorithm is compared with several other related state-of-the-art decision tree learning methods, including the baseline non-stochastic approach. We apply the proposed algorithm to learn a Haar tree over MNIST dataset that consists of over 200, 000 features and 60, 000 samples. The result is comparable to the performance of oblique trees while providing a significant speed-up in both inference and training times.
semiMul: Floating-Point Free Implementations for Efficient and Accurate Neural Network Training

Multiply–accumulate operation (MAC) is a fundamental component of machine learning tasks, where multiplication (either integer or float multiplication) compared to addition is costly in terms of hardware implementation or power consumption. In this paper, we approximate floating-point multiplication by converting it to integer addition while preserving the test accuracy of shallow and deep neural networks. We mathematically show and prove that our proposed method can be utilized with any floating-point format (e.g., FP8, FP16, FP32, etc.). It is also highly compatible with conventional hardware architectures and can be employed in CPU, GPU, or ASIC accelerators for neural network tasks with minimum hardware cost. Moreover, the proposed method can be utilized in embedded processors without a floating-point unit to perform neural network tasks. We evaluated our method on various datasets such as MNIST, FashionMNIST, SVHN, Cifar-10, and Cifar-100, with both FP16 and FP32 arithmetics. The proposed method preserves the test accuracy and, in some cases, overcomes the overfitting problem and improves the test accuracy.
Impact of Labeling Noise on Machine Learning: A Cost-aware Empirical Study

Since the emergence of large datasets, machine learning models have demonstrated excellent performance in a wide range of applications. This accomplishment was made possible by the availability of large amounts of labeled datasets. Finding high-quality labeled datasets, on the other hand, is difficult to obtain. Acquiring high-quality datasets with limited class label noise becomes an important task since noisy datasets can affect the performance and structure of machine learning models. However, it is extremely difficult to reduce label noise significantly in real-world datasets unless using expensive expert annotators. This work studies the influence of varying degrees of label noise on the complexity and accuracy of machine learning models, based on considerable testing and research. It also explores how to reduce labeling costs while maintaining the desired accuracy.
Real-Time Cattle Interaction Recognition via Triple-stream Network

In stock breeding of beef cattle, computer vision-based approaches have been widely employed to monitor cattle conditions (e.g. the physical, physiology, and health). To this end, the accurate and effective recognition of cattle action is a prerequisite. Generally, most existing models are confined to individual behavior that uses video-based methods to extract spatial-temporal features for recognizing the individual actions of each cattle. However, there is sociality among cattle and their interaction usually reflects important conditions, e.g. estrus, and also video-based method neglects the real-time capability of the model. Based on this, we tackle the challenging task of real-time recognizing interactions between cattle in a single frame in this paper. The pipeline of our method includes two main modules: Cattle Localization Network and Interaction Recognition Network. At every moment, the cattle localization network outputs high-quality interaction proposals from every detected cattle and feeds them into the interaction recognition network with a triple-stream architecture. Such a triple-stream network allows us to fuse different features relevant to recognizing interactions. Specifically, the three kinds of features are a visual feature that extracts the appearance representation of interaction proposals, a geometric feature that reflects the spatial relationship between cattle, and a semantic feature that captures our prior knowledge of the relationship between the individual action and interaction of cattle. In addition, to solve the problem of insufficient quantity of labeled data, we pre-train the model based on self-supervised learning. Qualitative and quantitative evaluation evidences the performance of our framework as an effective method to recognize cattle interaction in real-time.