Reviewerly/exHarmony huggingface dataset:
Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm

The modern urban environment is becoming more and more complex. In helping us identify surrounding objects, vehicle vision sensors rely more on the semantic segmentation ability of deep learning networks. The performance of a semantic segmentation network is essential. This factor will directly affect the comprehensive level of driving assistance technology in road environment perception. However, the existing semantic segmentation network has a redundant structure, many parameters, and low operational efficiency. Therefore, to reduce the complexity of the network and reduce the number of parameters to improve the network efficiency, based on the deep learning (DL) theory, a method for efficient image semantic segmentation using Deep Convolutional Neural Network (DCNN) is deeply studied. First, the theoretical basis of the convolutional neural network (CNN) is briefly introduced, and the real-time semantic segmentation technology of urban scenes based on DCNN is recommended in detail. Second, the atrous convolution algorithm and the multi-scale parallel atrous spatial pyramid model are introduced. On the basis of this, an Efficient Symmetric Network (ESNet) of real-time semantic segmentation model for autonomous driving scenarios is proposed. The experimental results show that: (1) On the Cityscapes dataset, the ESNet structure achieves 70.7% segmentation accuracy for the 19 semantic categories set, and 87.4% for the seven large grouping categories. Compared with other algorithms, the accuracy has increased to varying degrees. (2) On the CamVid dataset, compared with segmentation networks of multiple lightweight real-time images, the parameters of the ESNet model are around 1.2 m, the highest FPS value is around 90 Hz, and the highest mIOU value is around 70%. In seven semantic categories, the segmentation accuracy of the ESNet model is the highest at around 98%. From this, we found that the ESNet significantly improves segmentation accuracy while maintaining faster forward inference speed. Overall, the research not only provides technical support for the development of real-time semantic understanding and segmentation of DCNN algorithms but also contributes to the development of artificial intelligence technology.
Baggage Threat Recognition Using Deep Low-Rank Broad Learning Detector

In the last two decades, monitoring contraband data concealed within baggage has become one of the most pressing security issues. Manual screening of baggage is a time-consuming and error-prone procedure that also compromises the privacy of the passengers. To address this problem, many researchers have proposed X-ray based threat detection models. However, these frameworks involve considerable training efforts on large-scale and well annotated datasets, to accurately detect the prohibited items. This paper presents a novel broad learning detector that is driven via deep low-rank features to identify and localize concealed and cluttered baggage threats from the X-ray imagery. More precisely, the proposed system first extracts the contours of the suspicious baggage items by analyzing the transitional information of the baggage content across multiple orientations. These contours yield a series of proposals which are passed to the CNN backbones to extract distinct features for the objects contained within the proposals. The extracted features are then decomposed through subspace learning and are passed to the broad learning system to identify the respective categories. Moreover, the proposed framework is trained only once using few-shot learning, and it outperforms its competitors by achieving 19.85%, and 8.33% improvements in terms of mean intersection-over-union and mean average precision scores on the highly imbalanced datasets.
A feature selection method based on term frequency difference and positive weighting factor

Firstly, a new concept of term frequency difference factor is proposed to balance the influences of term frequency and document frequency on feature selection. Secondly, the idea of positive weighting factor is advanced to balance the roles of the document frequency in the positive and negampared with six popular algorithms on six datasets using two classifiers of Naive Bayes and Support tive categories. And finally, a new feature selection algorithm based on term frequency difference and positive weighting factor, PWTF-TCM, is presented based on the two above concepts. In the experiments, PWTF-TCM is coVector Machines. The experimental results show that PWTF-TCM outperforms by 75% for Macro-F1 and 58.33% for Micro-F1. In addition, PWTF-TCM improves the classification accuracy by 4.58% compared with Trigonometric comparison measure.
A Loyalty System Incorporated with Blockchain and Call Auction

A loyalty program is a type of incentive to reward customers’ perceived value and enhance their purchasing behavior. The key to the success of a loyalty program is to allow customers to more actively participate in the program. One possible solution is to allow customers to sell out idle loyalty points and buy in the points that they need. On the basis of a call auction, this study designs a peer-to-peer exchange mechanism for customers to realize the above trade. In addition, a blockchain-based system is developed to support the issuance, redemption, and exchange of loyalty points. In this study, Hyperledger Fabric is adopted as the underlying blockchain technology because it has some features that are beneficial to a cross-organizational coalition loyalty program. This study also proposes a feasible multi-host deployment scheme for the Hyperledger Fabric blockchain network that is suitable for our application scenario. Finally, some implementation results are given to demonstrate the system process from the perspective of the application layer. The mechanism proposed in this study is helpful to improve the likelihood of successfully exchanging points, thus accelerating the circulation and use of loyalty points.
Attention <scp>Mask R‐CNN</scp> with edge refinement algorithm for identifying circulating genetically abnormal cells

Abstract Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell‐enriched peripheral blood samples, such as circulating genetically abnormal cells (CACs), can be used as a non‐invasive tool to detect patients with benign pulmonary nodules. These cells are identified through fluorescence signals counting by using 4‐color fluorescence in situ hybridization (FISH) technology that exhibits high stability, sensitivity, and specificity. When FISH data are analyzed, the overlapping cells and fluorescence noise is a great challenge for identifying of CACs, thereby seriously affecting the efficiency of clinical diagnosis. To address this problem, in this study, we proposed an end‐to‐end FISH‐based method (CACNET) for CAC identification. CACNET achieved nuclear segmentation and counted 4‐color staining signals through improved Mask region‐based convolutional neural network (R‐CNN), followed by cell category (normal cell, deletion cell, gain cell, or CAC) according to pathological criteria. Firstly, the segmentation accuracy of overlapping nuclei was improved by adding an edge constraint head during training. Then, the interference of fluorescence noise was reduced by fusing non‐local module to reconstruct the feature extraction network of Mask R‐CNN. We trained and tested the proposed model on a dataset comprising 700 frames with 58,083 nuclei. The Accuracy, Sensitivity, and Specificity (overall performance metric for the algorithm) of CAC identification with CACNET were 94.06%, 92.1%, and 99.8%, respectively. Moreover, the developed method exhibited approximately identification speed of approximately 0.22 s per frames. The results showed that the proposed method outperformed the existing CAC identification methods, making it a promising approach for early screening of lung cancer.
Finite Difference Gradient Approximation: To Randomize or Not?

We discuss two classes of methods of approximating gradients of noisy black box functions—the classical finite difference method and recently popular randomized finite difference methods. Despite of the popularity of the latter, we argue that it is unclear whether the randomized schemes have an advantage over the traditional methods when employed inside an optimization method. We point to theoretical and practical evidence that show that the opposite is true at least in a general optimization setting. We then pose the question of whether a particular setting exists when the advantage of the new method may be clearly shown, at least numerically. The larger underlying challenge is a development of black box optimization methods that scale well with the problem dimension.
L-Mix: A Latent-Level Instance Mixup Regularization for Robust Self-Supervised Speaker Representation Learning

Over the recent years, various self-supervised embedding learning methods for deep speaker verification were proposed. The performance of the self-supervised learning framework highly depends on the data augmentation technique, but due to the sensitive nature of speaker information within the speech signal, most speaker embedding training relies on simple augmentations such as additive noise or simulated reverberation. Thus while the conventional self-supervised speaker embedding systems can yield minimum within-utterance variability, their capability to generalize to out-of-set utterance is limited. In order to alleviate this problem, we investigate the utilization of the instance mix (i-mix) regularization for training a self-supervised speaker embedding system. Moreover, we propose a new mixup strategy that applies i-mix on the latent space, instead of the raw acoustic feature domain. In this paper, the i-mix and the proposed l-mix strategy were incorporated into the self-supervised angular prototypical and softmax-based objective functions and were evaluated on the VoxCeleb dataset. From the experimental results, we observe that the self-supervised embedding network can benefit greatly from the i-mix and l-mix strategies in terms of training stability and speaker verification performance.
Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach

Due to the massive adoption of mobile money in Sub-Saharan countries, the global transaction value of mobile money exceeded $2 billion in 2021.Projections show transaction values will exceed $3 billion by the end of 2022, and Sub-Saharan Africa contributes half of the daily transactions.SMS (Short Message Service) phishing cost corporations and individuals millions of dollars annually.Spammers use Smishing (SMS Phishing) messages to trick a mobile money user into sending electronic cash to an unintended mobile wallet.Though Smishing is an incarnation of phishing, they differ in the information available and attack strategy.As a result, detecting Smishing becomes difficult.Numerous models and techniques to detect Smishing attacks have been introduced for high-resource languages, yet few target low-resource languages such as Swahili.This study proposes a machine-learning based model to classify Swahili Smishing text messages targeting mobile money users.Experimental results show a hybrid model of Extratree classifier feature selection and Random Forest using TFIDF (Term Frequency Inverse Document Frequency) vectorization yields the best model with an accuracy score of 99.86%.Results are measured against a baseline Multinomial Naïve-Bayes model.In addition, comparison with a set of other classic classifiers is also done.The model returns the lowest false positive and false negative of 2 and 4, respectively, with a Log-Loss of 0.04.A Swahili dataset with 32259 messages is used for performance evaluation.
How to reward the Web: The social dApp Yup

With the advent of blockchain technology, new Online Social Networks (OSNs) were proposed under the name of Blockchain Online Social Media (BOSM). Among the most well-known BOSMs, the introduction of blockchain is geared towards providing a decentralised social platform, together with an auditable and transparent rewarding system. While some current BOSMs have gathered a large set of dedicated users, they can hardly fight against the hegemony of the most well known centralised platforms, such as Twitter or Facebook. Yup tries to overcome this issue by proposing a rewarding system that can integrate with existing platforms. Its rewarding system keeps track of any piece of content uniquely identified by an URL, so its usage is not restricted to OSNs only. Given its unique approach, its rewarding system represents an important case study that, to the best of our knowledge, is not covered in the literature. In this paper, we close this gap by presenting the rewarding system provided by Yup and understanding its implications on the social activity of the users. Our analyses uncover that the voting activity favours the creation of echo chambers, and that the rewarding system is unfair. Additionally, we identify some limitations that can help design new rewarding systems.
Privacy-Enhanced and Verification-Traceable Aggregation for Federated Learning

Federated learning (FL) is a distributed machine learning framework, which allows multiple users to collaboratively train and obtain a global model with high accuracy. Currently, FL is paid more attention by researchers and a growing number of protocols are proposed. This article first analyzes the security vulnerabilities of the VerifyNet and VeriFL protocols, and proposes a new aggregation protocol for FL. We use additive homomorphic encryption and double masking to simultaneously protect the user’s local model and the aggregated global model while most of the existing protocols only consider the privacy of the local model. Also, linear homomorphic hash and digital signature are used to achieve traceable verification, which means the users can not only verify the aggregation results, but also be able to identify the wrong epoch if the results are wrong. In summary, our protocol can realize the privacy of the local model and global model and achieve verification traceability even if the cloud server colludes with malicious users. The experimental results show that the proposed protocol improves the security of FL without decreasing the efficiency of the users and classification accuracy of the training model.
MAB-OS: Multi-Armed Bandits Metaheuristic Optimizer Selection

Metaheuristic algorithms are derivative-free optimizers designed to estimate the global optima for optimization problems. Keeping balance between exploitation and exploration and the performance complementarity between the algorithms have led to the introduction of quite a few metaheuristic methods. In this work, we propose a framework based on Multi-Armed Bandits (MAB) problem, which is a classical Reinforcement Learning (RL) method, to intelligently select a suitable optimizer for each optimization problem during the optimization process. This online algorithm selection technique leverages on the convergence behavior of the algorithms to find the right balance of exploration–exploitation by choosing the update rule of the algorithm with the most estimated improvement in the solution. By performing experiments with three armed-bandits being Harris Hawks Optimizer (HHO), Differential Evolution (DE), and Whale Optimization Algorithm (WOA), we show that the MAB Optimizer Selection (named as MAB-OS) framework has the best overall performance on different types of fitness landscapes in terms of both convergence rate and the final solution. The data and codes used for this work are available at: https://github.com/BaratiLab/MAB-OS.
Attribute-Based Encryption in Securing Big Data from Post-Quantum Perspective: A Survey

Attribute-based encryption (ABE) cryptography is widely known for its potential to solve the scalability issue of recent public key infrastructure (PKI). It provides a fine-grained access control system with high flexibility and efficiency by labeling the secret key and ciphertext with distinctive attributes. Due to its fine-grained features, the ABE scheme is a protection layer in securing users’ data and privacy in big data processing and analytics. However, quantum computing, new technology on the horizon that will transform the security and privacy environment, has begun to appear. Like the conventional ABE schemes, present cryptography is not excluded from the impacts of quantum technology as they are not made to be quantum-resistant. While most recent surveys generally touched on the generic features of attribute-based encryption schemes such as user revocation, scalability, flexibility, data confidentiality, and scope in pairing-based ABE schemes, this survey investigated quantum-resistant ABE schemes in securing big data. This survey reviews the challenges faced by the recent ABE cryptography in the post-quantum era and highlights its differences from the conventional pairing-based ABE schemes. Subsequently, we defined the criteria of an ideal quantum-resistant ABE scheme. Additionally, existing works on quantum-resistant ABE schemes are reviewed based on their algorithms design, security and functionalities. Lastly, we summarized quantum-resistant ABE schemes’ ongoing challenges and future works.
Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders

We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs employs a judiciously designed objective function based on robust divergences, including a, ß, and, -divergence, making it possible to separate anomalies from normal data without the reliance on anomaly labels, thus achieving robustness and fully unsupervised training. To better capture temporal dependencies in time series data, VQRAEs are built upon quasi-recurrent neural networks, which employ convolution and gating mechanisms to avoid the inefficient recursive computations used by classic recurrent neural networks. Further, VQRAEs can be extended to bi-directional Bi VQRAEs that utilize bi-directional information to further improve the accuracy. The above design choices make VQRAEs not only robust and thus accurate, but also efficient at detecting anomalies in streaming settings. Experiments on five real-world time series offer insight into the design properties of VQRAEs and demonstrate that VQRAEs are capable of outperforming state-of-the-art methods.
From Batch Processing to Real Time Analytics: Running Presto® at Scale

Presto is an open source distributed query engine used widely at Facebook, Uber, Twitter, Pinterest, and many other internet companies. Since open sourced in 2013, the Presto community has made several rounds of design and implementations, to support a variety of use cases, including interactive analytics, real time reporting and dashboard, ETL workloads, A/B testing, monitoring and alerts, etc. In this paper, we'd like to introduce some of the most important features and performance improvements the open source Presto community made in recent years, which enables companies running Presto at scale, supporting millions of queries per day, with hundreds of thousands of machines. Specifically, how Presto provides unified SQL on heterogeneous storage systems without data copy; how Presto deals with complex data, including nested columnar data and schema evolution; How Presto supports geospatial queries efficiently, and how file list cache works in Presto. We also talk about cluster federation, and Presto on cloud. Experimental results and our production experience could help others running interactive SQL systems at scale.
Time‐varying, ray tracing irradiance simulation approach for photovoltaic systems in complex scenarios with decoupled geometry, optical properties and illumination conditions

Abstract The accurate computation of the irradiance incident on the surface of photovoltaic modules is crucial for the simulation of the energy yield of a photovoltaic system. Depending on the geometrical complexity of the surroundings, different approaches are commonly employed to calculate the irradiance on the photovoltaic system. In this article, we introduce a backward ray tracing simulation approach to calculate the irradiance on photovoltaic systems in geometrically complex scenarios. We explain how the repetition of time‐consuming simulation steps can be avoided with the proposed approach by storing a selection of the results from the most computationally expensive parts of the problem, and we show that the irradiance calculated with the proposed approach is in good agreement with the results of Radiance, a well‐established irradiance simulation tool. Furthermore, we present an experimental validation carried out using a pyranometer and a reference cell over a period of 6 months in a complex scenario, which shows errors lower than 5% in the calculation of the daily irradiation. Finally, we compare high‐resolution spectral simulations with measurements taken with a spectroradiometer under different sky conditions. The proposed approach is particularly well‐suited for the simulation of bifacial and tandem photovoltaic modules in complex urban environments, for it enables the efficient simulation of high‐resolution spectral irradiance in scenarios with time‐varying reflectance properties.
PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.
Shadow detection via multi-scale feature fusion and unsupervised domain adaptation

Shadow detection is significant for scene understanding. As a common scenario, soft shadows have more ambiguous boundaries than hard shadows. However, they are rarely present in the available benchmarks since annotating for them is time-consuming and needs expert help. This paper discusses how to transfer the shadow detection capability from available shadow data to soft shadow data and proposes a novel shadow detection framework (MUSD) based on multi-scale feature fusion and unsupervised domain adaptation. Firstly, we set the existing labeled shadow dataset (i.e., SBU) as the source domain and collect an unlabeled soft shadow dataset (SSD) as the target domain to formulate an unsupervised domain adaptation problem. Next, we design an efficient shadow detection network based on the double attention module and multi-scale feature fusion. Then, we use the global–local feature alignment strategy to align the task-related feature distributions between the source and target domains. This allows us to obtain a robust model and achieve domain adaptation effectively. Extensive experimental results show that our method can detect soft shadows more accurately than existing state-of-the-art methods.