Reviewerly/exHarmony huggingface dataset:
Fast Counterfactual Explanation for Solar Flare Prediction

Solar flare prediction has become essential in space weather research due to its potential adverse space-weather ramifications. Over recent years, a set of machine learning models on solar flare prediction have been proposed and significant improvement has been made over the previous state of the art. However, most existing research work focuses on the prediction task and ignores the interpretability behind the prediction task. In this paper, we provide a post-hoc explanation method based on solar flare prediction, FAST-CF. In particular, we incorporate the nearest unlike neighbor for guiding the counterfactual search, which is fast to search for the optimal result. In addition, FAST-CF encapsulates the desirable properties of a counterfactual explanation for solar flare prediction. We use different evaluation metrics to compare the performance of FAST-CF with the other two baselines and verify the superiority of our method to existing state-of-the-art.
A Novel Approach for Synthetic Reduced Nearest-Neighbor Leveraging Neural Networks

Synthetic Reduced Nearest Neighbor is a nearest neighbor model that is constrained on synthetic samples (i.e., prototypes). The body of work on such models includes proposals for improving the interpretability and optimization of SRNN models using expectation maximization. Motivated by the promise of this paradigm, we propose a novel Expectation Maximization approach for Synthetic Reduced Nearest Neighbors leveraging neural networks. Furthermore, we compare the performance of our proposed technique to classical state-of-the-art machine learning methods such as random forest and ensemble models. The empirical results demonstrate the advantages of using neural networks in lieu of an expectation maximization algorithm.
HeteroGenius: An Improvised ‘Intelligence’ in Heterogeneous Graph Transformers

Heterogeneous graphs can capture pragmatic relations between entities (or nodes) better than homogeneous graphs. Heterogeneous graphs are crucial in search and classification problems and can correlate with social network graphs. However, this increases complexity and demands a clear understanding of the relationships, and rankings of the network. The ranking can take the form of various scoring-based systems, or finding the importance of the relations between two entities. In this research, we practice the use of incorporating a meta-edge definition, ‘Force’, between nodes to embed meaning to its dimensionality. We add this ‘Force’ to the Heterogeneous Graph Transformer, which we term as ‘HeteroGenius’, and experimentally demonstrate that the addition increases the overall accuracy by 2%.
Zero-shot Topic Classification via Automatic Tagging on Chinese Text Datasets

Data scarcity problem is often encountered for topic classification in many real-world applications. Zero-shot classification aims to deal with this problem by conducting a classification without any previously labelled data. However, only a few studies work on zero-shot topic classification on Chinese text. In this paper, we focus on providing an automatic tagging structure for zero-shot topic classification, which adopts labelled data for training based on a transformer-based model from external corpuses. Moreover, we show the effectiveness of fine-tuning large dataset in a downstream task, where the training data labels are not aligned with the test data labels in advance. Our experiments shows that the results outperform the performance of the benchmark approaches on two standard Chinese text datasets for the zero-shot setting.
Recurrent Nerual Imaging: An Evolutionary Approach for Mixed Possion-Gaussian Image Denoising

Recurrent neural networks (RNNs) are traditionally used for machine learning applications for temporal sequences such as natural language processing. Its application to image processing is relatively new. In this paper, we apply RNNs to denoise images corrupted by mixed Poisson and Gaussian noise. The motivation for using an RNN comes from viewing the denoising of the Poisson-Gaussian realization as a temporal process. The network then attempts to trace back the steps that create the noisy realization in order to arrive at the noiseless reconstruction. Numerical experiments demonstrate that our proposed RNN approach outperforms convolutional autoen-coder methods for denoising and upsampling low-resolution images from the CIFAR-10 dataset.
A Secure and Efficient USB-based In-band Communication Interface between Host and BMC

In data centers, the server monitoring and man-agement functions are offloaded to the baseboard management controller integrated in the server. New requirements emerge with the development of cloud computing technology. Deep communication between the host and the baseboard management controller through the in-band communication interface is nec-essary. However, the existing interfaces have limitations in terms of security and performance. In this paper, we propose a secure and efficient USB-based in-band communication interface. We separate it into the transport layer and the protocol layer. The transport model for the transport layer uses USB as the hardware connection, abstracts the physical USB channel into multiple virtual channels, and provides hardware-independent interfaces for the protocol layer. The message protocol for the protocol layer can perform authentication and securely transport management messages. We verify the effectiveness of the transport model using SPIN and the security of the message protocol using BAN logic. We also implement the proposed interface to a server with an AST2400 baseboard management controller and implement three management functions. The security analysis and performance evaluation show that the proposed interface is secure and efficient, which meets the in-band management requirements.
FAWS: Fault-Aware Weight Scheduler for DNN Computations in Heterogeneous and Faulty Hardware

The idea of using inexact computation for overprovisioned DNNs (Deep Neural Networks) to decrease power and la-tency at the cost of minor accuracy degradation has become very popular. However, there is still no general method to schedule DNN computations on a given hardware platform to effectively implement this idea without loss in computational efficiency. Most contemporary methods require specialized hardware, ex-tensive retraining and hardware-specific scheduling schemes. We present FAWS: Fault-Aware Weight Scheduler for scheduling DNN computations in heterogeneous and faulty hardware. Given a trained DNN model and a hardware fault profile, our scheduler is able to recover significant accuracy during inference even at high fault rates. FAWS schedules the computations such that the low priority ones are allocated to inexact hardware. This is achieved by shuffling (exchanging) the rows of the matrices. The best shuffling order for a given DNN model and hardware fault profile is determined using Genetic Algorithms (GA). We simulate bitwise errors on different model architectures and datasets with different types of fault profiles and observe that FAWS can recover up to 30% of classification accuracy even at high fault rates (which correspond to approximately 50 % power savings).
Performance Benchmark of Machine Learning-Based Methodology for Swahili News Article Categorization

As data increases at unprecedented rates, so does the need to classify this data, including news article data. Unfortunately, most news article categorization research utilizes global languages such as English or Spanish, and not much research considers low-resource languages like Swahili. Testing multiple classifiers and preprocessing methods, we show that the SVM model with tokenization and stop word removal has the highest accuracy (85.13%) scores for Swahili news article categorization. These results from the first publicly available peer-reviewed Swahili news article dataset provide benchmark performance for Swahili news article categorization and contribute to lean Swahili text classification research.
Flexible Exploration Strategies in Multi-Agent Reinforcement Learning for Instability by Mutual Learning

A fundamental challenge in multi-agent reinforcement learning is an effective exploration of state-action spaces because agents must learn their policies in a non-stationary environment due to changing policies of other learning agents. As the agent’s learning progresses, different undesired situations may appear one after another and agents have to learn again to adapt them. Therefore, agents must learn again with a high probability of exploration to find the appropriate actions for the exposed situation. However, existing algorithms can suffer from inability to learn behavior again on the lack of exploration for these situations because agents usually become exploitation-oriented by using simple exploration strategies, such as ε-greedy strategy. Therefore, we propose two types of simple exploration strategies, where each agent monitors the trend of performance and controls the exploration probability, ε, based on the transition of performance. By introducing a coordinated problem called the PushBlock problem, which includes the above issue, we show that the proposed method could improve the overall performance relative to conventional ε-greedy strategies and analyze their effects on the generated behavior.
Checking Composition-Aware Service Substitutability

In many applications involving composite Web services, one or more component services may become unavailable, or no longer satisfy the quality requirements. This leads to the problem of identifying other components that can substitute the faulty component, while maintaining the overall functionality of the composite service. Given a candidate service that offers the desired functionality, it is often necessary to select the most preferred substitution based on the correctness of the global behavior of the composite service. Such a candidate is selected from a repository of public representations of possible components where internal and private behavior are hidden. In this paper, we present and implement an approach to determine whether a possible candidate can substitute a part of a composite service while preserving the deadlock freeness property. At the same time, we propose a behavioral abstraction of web services, namely the symbolic observation graph, that preserves their privacy while allowing the modular verification of their correction. The approach is illustrated on a realistic use case involving three services (a provider, a distributor and a client).
Point Cloud-based Variational Autoencoder Inverse Mappers (PC-VAIM) - An Application on Quantum Chromodynamics Global Analysis

Correctly mapping the experimental data to quantum probability distributions is a critical step to characterize nucleon structure and the emergence of hadrons, in terms of quark and gluon degrees of freedom. Since the actual parameters of interest are not directly measurable, but instead are inferred from experimental observables, this is fundamentally an inverse problem of recovering parameters from observables. In addition to the well-known challenges such as ill-posedness in general inverse problems, an application specific issue here is that the experimental data are observed on kinematics bins which are usually irregular and varying. In this paper, to address this ill- defined, varying observable space problem, we represent the observables together with their kinematics bins as an unstructured, high-dimensional point cloud. We incorporate a permutation invariant neural network framework to handle the observables in unstructured and unordered point cloud representations. We incorporate the point cloud representation into the Variational Autoencoder Inverse Mapper (VAIM) framework. The point cloud-based VAIM (PC-VAIM) enables the underlying deep neural networks to learn how the observables are distributed across kinematics. We demonstrate the effectiveness of PC-VAIM on a toy inverse problem, and then on constructing the inverse function mapping Quantum Correlation Functions (QCF) to observables in a Quantum Chromodynamics (QCD) analysis of nucleon structure.
On Label Quality in Class Imbalance Setting -A Case Study

Producing high-quality labeled data is a challenge in any supervised learning problem, where in many cases, human involvement is necessary to ensure the label quality. However, human annotations are not flawless, especially in the case of a challenging problem. In nontrivial problems, the high disagreement among annotators results in noisy labels, which affect the performance of any machine learning model. In this work, we consider three noise reduction strategies to improve the label quality in the Article-Comment Alignment Problem, where the main task is to classify article-comment pairs according to their relevancy level. The first considered labeling disagreement reduction strategy utilizes annotators’ background knowledge during the label aggregation step. The second strategy utilizes user disagreement during the training process. In the third and final strategy, we ask annotators to perform corrections and relabel the examples with noisy labels. We deploy these strategies and compare them to a resampling strategy for addressing the class imbalance, another common supervised learning challenge. These alternatives were evaluated on ACAP, a multiclass text pairs classification problem with highly imbalanced data, where one of the classes represents at most 15% of the dataset’s entire population. Our results provide evidence that considered strategies can reduce disagreement between annotators. However, data quality improvement is insufficient to enhance classification accuracy in the article-comment alignment problem, which exhibits a high-class imbalance. The model performance is enhanced for the same problem by addressing the imbalance issue with a weight loss-based class distribution resampling. We show that allowing the model to pay more attention to the minority class during the training process with the presence of noisy examples improves the test accuracy by 3%.
CityLifeSim: A High-Fidelity Pedestrian and Vehicle Simulation with Complex Behaviors

Simulations are a powerful tools particularly in the case of safety critical scenarios. However, simulating complex temporal events in multi-agent scenarios with vehicles and pedestrians, such as those that exist in urban environments, is challenging. We present CityLifeSim, a simulation for the research community that focuses on rich pedestrian behavior, such as the one that arises when different personalities, environmental events, and group goals are simulated. In our simulations we can see cases of people jay walking a red light, sitting on a bench, waiting for the bus, or calling on the phone, but also more complex creation and management of crowds that might even line up or just keep moving while observing interpersonal distances. CityLifeSim is configurable and can create unlimited scenarios with detailed logging capabilities. As a demonstration we have run CityLifeSim to create a demo dataset for training setups that includes 17 different cameras, views from a moving vehicle in the street under different weather conditions (rain, snow, sun), and from a drone with frontal and downward views. All content is released with the corresponding original configuration files, ground truth pedestrian segmentation, and RGB-D frames. We evaluate our dataset on a pedestrian detection and identification task with state of the art Multi-Object Tracker (MOT), showing the limitations and opportunities for synthetic data in this use case.
Bayesian Rule Ontologies For XAI Classification and Regression

A random foam defines a modular rule-based ontology after sampling from a neural or other input-output system. A random foam combines several rule-based systems and averages the systems. It gives a Bayesian posterior over the subsystems. It also gives separate Bayesian posteriors over the rules of each subsystem. The shape of the rules controls how well the random-foam ontology performs in classification and regression. We found that a heterogenous ontology that mixes different rule shapes can perform better than a homogenous ontology based on a single Gaussian or other rule type. Random foams are also universal function approximators. So they can train on a neural black box and act as its explainable proxy system. We prove this uniform approximation theorem for the important case of bump-function random foams with throughput combination. Random foams also measure their output’s uncertainty through the conditional variance. Bump function rules performed better than Cauchy rules at classification while Cauchy rules performed better at regression. Gaussian rules performed best in both classification and regression. A homogeneous Gaussian random foam that trained on a 96.7% accurate neural classifier was itself 95.96% accurate on the MNIST data set. A heterogeneous random foam with two-thirds Gaussian rules and one-third Laplacian rules did better than did the all-Gaussian foam ontology.
A Blockchain- Based Copyright Protection System for Short Videos

Short video making industry has boomed along with the advent of the wireless network in recent years. However, fre-quent copyright infringements with short videos might seriously jeopardize the development of short video making industry. To avoid this, it is required that the copyright infringement could be detected efficiently and accurately when an end-user uploads his/her video clips to an online platform. Copyright infringement detection for short videos is challenging as there might be a great number of videos in the repository and complicated copyright trading needs to be taken into account. In this paper, we propose a novel framework based on blockchain to automate the detection of copyright infringement for short videos. In the proposed framework, a similar video retrieval algorithm is used to check whether the uploaded video is too similar to any one in the repository. And if it is, the copyright ownership will be examined through a novel data structure called IP (Intellectual Property) Tree, which is used to manage the copyright authorization via blockchain, so that it can be determined whether infringement occurs. The evaluation results indicate the effectiveness of our framework on infringement detection for short videos.
Transfer Learning for Bayesian Optimization with Principal Component Analysis

Bayesian Optimization has been widely used for black-box optimization. Especially in the field of machine learning, BO has obtained remarkable results in hyperparameters optimization. However, the best hyperparameters depend on the specific task and traditionally the BO algorithm needs to be repeated for each task. On the other hand, the relationship between hyperparameters and objectives has similar tendency among tasks. Therefore, transfer learning is an important technology to accelerate the optimization of novel task by leveraging the knowledge acquired in prior tasks. In this work, we propose a new transfer learning strategy for BO. We use information geometry based principal component analysis (PCA) to extract a low-dimension manifold from a set of Gaussian process (GP) posteriors that models the objective functions of the prior tasks. Then, the low dimensional parameters of this manifold can be optimized to adapt to a new task and set a prior distribution for the objective function of the novel task. Experiments on hyperparameters optimization benchmarks show that our proposed algorithm, called BO-PCA, accelerates the learning of an unseen task (less data are required) while having low computational cost.
An adaptive traffic sign recognition scheme based on deep learning in complex environment

Since detection and recognition of traffic signs is one of three modules of autonomous driving, it has been concerned by many researchers. But the traffic sign images, which are gathered in complex environments such as bright or foggy weather, are often obscured or blurred. This may bring about serious challenge to recognize traffic sign. In this paper, a novel and adaptive traffic sign recognition scheme is proposed, which addresses the issue of traffic sign recognition in complex environments. First, according to the environmental conditions, the traffic sign images are classified into four categories by Swin Transformer. Then, based on INDANE and ACE algorithms, an adaptive dataset enhancement algorithm is proposed to enhance the classified dataset and strengthen the image features. Finally, yolov5 algorithm is improved to effectively accomplish traffic sign recognition by reducing the maximum down sampling multiple, adjusting the stacking proportion of residual blocks, increasing the convolution kernel, and replacing the normalization method. Based on the GTSRB dataset, the experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the mAP of our proposed scheme is 99.86% when traffic sign is recognized in complex environment. Compared with the existing schemes, our proposed scheme has higher recognition accuracy and wider application.
Access control method for air ticket distribution system based on blockchain

The air ticket distribution system is a platform to integrate the resources of ticket agents around the ticket sales. It is responsible for connecting ticket suppliers and passengers, and mainly undertakes the services of flight inquiries, air ticket reservation and ticket issuance. However, due to the insufficient access control authority and lax internal supervision and other reasons, passengers' privacy information is frequently leaked, which seriously damage the interests of passengers and airlines. Based on the analysis of ticket distribution process, this paper proposes a blockchain-based access control model for ticket distribution system, aiming at protecting passengers' privacy information and property security. The model is based on Hyperledger Fabric 1.0, which uses the SM2 cryptographic algorithm to replace the default algorithm of the ledger. Experimental results show that the access control method based on blockchain can realize distributed access decision and prevent passenger information from being tampered with illegally. Compared with RSA algorithm, SM2 algorithm is used in the test to generate employee transactions, generate PNR transaction and permission query transaction. The total time is decreased by 17.13%, which is more efficient in each single transaction. Therefore, it is feasible and necessary to the reform of the ledger cryptographic algorithm.