Reviewerly/exHarmony huggingface dataset:
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection

We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which converts a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is data independent. This paper shows that Isolation Distributional Kernel (IDK), which is based on a data dependent point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection. Without explicit learning, using IDK alone outperforms existing kernel based anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. We reveal for the first time that an effective kernel based anomaly detector based on kernel mean embedding must employ a characteristic kernel which is data dependent.
Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms

Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging gender-related information encoded in WEs. Through the critical lens of construct validity, we validate the proposed measure on synthetic and real collections. Subsequently, we use GSR to compare widely-used Information Retrieval ranking algorithms, including lexical, semantic, and neural models. We check if and how ranking algorithms based on WEs inherit the biases of the underlying embeddings. We also consider the most common debiasing approaches for WEs proposed in the literature and test their impact in terms of GSR and common performance measures. To the best of our knowledge, GSR is the first specifically tailored measure for IR, capable of quantifying representational harms.
A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms

Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.
Master-slave chain based trusted cross-domain authentication mechanism in IoT

With the rapid development of the Internet of Thing (IoT), more and more IoT devices have accessed to the IoT system, and the IoT architecture has evolved from vertical integration to horizontal structure, which causes the devices from different systems or different IoT domains can not trust each other in process of interacting. How to ensure the authentication between different system and domain in IoT has become an urgent problem to be solved. This paper proposes a master-slave chain based trusted cross-domain authentication mechanism in IoT to improve the efficiency and credibility of authentication. In this system, a master-slave blockchain architecture supporting distributed cross-domain authentication is designed. Then, an improved Byzantine Fault Tolerance based on reputation value model (RIBFT)is proposed for trusted authentication and data traceability. The security and performance analysis show that our scheme could effectively improve the efficiency and credibility of authentication for IoT devices. In addition, the model is also used in practical applications.
Edge Computing-Enabled Deep Learning for Real-time Video Optimization in IIoT

Real-time multimedia applications have gained immense popularity in the industrial Internet of Things (IIoT) paradigm. Due to the impact of the complex industrial environment, the transmission of video streaming is usually unstable. In the duration of a low bandwidth transmission, existing optimization methods often reduce the original resolution of some frames in a random way to avoid the video interruption. If the key frames with some important content are selected to be transmitted with a low resolution, it will greatly reduce the effect of industrial supervision. In view of this challenge, a real-time video streaming optimization method by reducing the number of video frames transmitted in the IIoT environment is proposed. Concretely, a deep learning-based object detection algorithm is recruited to effectively select the key frames in our method. The key frames with the original resolution will be transmitted along with audio data. As some nonkey frames are selectively discarded, it is helpful for smooth network transmitting with fewer bandwidth requirements. Moreover, we employ edge servers to run the object detection algorithm, and adjust video transmission flexibly. Extensive experiments are conducted to validate the effectiveness, and dependability of our method.
Firefly optimization-based segmentation technique to analyse medical images of breast cancer

Nature-inspired algorithms emulate the mathematical and innovative techniques for non-linear and real-life problems worldwide. Imaging technology is emerging out as one of the most prominent and widely used domain in medical field such as cancerous cell nuclei detection, blood vessel segmentation, study of organs or structure of tissues and many more. Nature-inspired algorithms emulate the mathematical and innovative techniques for non-linear and real-life problems and can be applied to segment or analyse the images. An efficient image segmentation technique may help the subject experts such as radiologist and pathologist for early and effective examination or diagnosis of disease. The authors proposed a firefly-based segmentation technique that can be employed to segment the breast cancer image regardless the type or modality of the image. The effectiveness of the proposed technique is validated by comparing the procured results with the existing state-of-art techniques.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings

Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology that not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric, Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).
Multi-GPU Design and Performance Evaluation of Homomorphic Encryption on GPU Clusters

We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme.Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs.The design is put to address space and runtime requirements of FHE computations.It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols.Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing.We implement and evaluate the performance of our design on 2 homogeneous and heterogeneous NVIDIA GPU clusters: K80, and a customized P100.We also provide a comparison with a recent shared-memory based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication.Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the MNIST and CIFAR-10 datasets.Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations.In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.
Street View Text Recognition With Deep Learning for Urban Scene Understanding in Intelligent Transportation Systems

Understanding the surrounding scenes is one of the fundamental tasks in intelligent transportation systems (ITS), especially in unpredictable driving scenes or in developing regions/cities without digital maps. Street view is the most common scene during driving. Since streets are often full of shops with signboards, scene text recognition over the shop sign images in street views is of great significance and utility to urban scene understanding in ITS. To advance research in this field, (1) we build ShopSign, which is a large-scale scene text dataset of Chinese shop signs in street views. It contains 25,770 natural scene images, and 267,049 text instances. The images in ShopSign were captured in different scenes, from downtown to developing regions, and across 8 provinces and 20 cities in China, using more than 50 different mobile phones. It is very sparse and imbalanced in nature. (2) we carry out a comprehensive empirical study on the performance of state-of-the-art DL based scene text reading algorithms on ShopSign and three other Chinese scene text datasets, which has not been addressed in the literature before. Through comparative analysis, we demonstrate that language has a critical influence on scene text detection. Moreover, by comparing the accuracy of four scene text recognition algorithms, we show that there is a very large room for further improvements in street view text recognition to fit real-world ITS applications.
Survey on JPEG compatible joint image compression and encryption algorithms

In recent years, image encryption has been broadly researched. Since a large proportion of images on Internet are compressed, and JPEG is the most widely adopted standards for image compression, a variety of joint image compression and encryption algorithms have been proposed. JPEG image encryption uses signal processing techniques as well as cryptographic techniques in different stages of the JPEG compression process. Based on the location where the encryption is taken place, these algorithms can be generally classified into three major categories: pre-compression encryption algorithms, in-compression encryption algorithms, and post-compression encryption algorithms, which correspond to conduct encryption operations before, during, and after the compression process. From this perspective of classification, the authors give a comprehensive survey on representative image encryption algorithms of each type, and show their properties and limitations. Some of the most recent encryption schemes that achieve protection at various positions of the compression process are selected for comparison, which are two pre-compression encryption algorithms, four in-compression encryption algorithms, and two post-compression encryption algorithms. Possible future research directions on designing joint JPEG compression and encryption schemes are provided in the end, which may facilitate solving application scenario-oriented JPEG security problems with new technologies.
Generation of multi-channel chaotic signals with time delay signature concealment and ultrafast photonic decision making based on a globally-coupled semiconductor laser network

We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers (SLs) that generate triple-channel chaotic signals with time delayed signature (TDS) concealment. The effects of the coupling strength and bias current on the concealment of the TDS are investigated. The generated chaotic signals are further applied to reinforcement learning, and a parallel scheme is proposed to solve the multiarmed bandit (MAB) problem. The influences of mutual correlation between signals from different channels, the sampling interval of signals, and the TDS concealment on the performance of decision making are analyzed. Comparisons between the proposed scheme and two existing schemes show that, with a simplified algorithm, the proposed scheme can perform as well as the previous schemes or even better. Moreover, we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes. This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement. The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.
Single image dehazing via atmospheric scattering model-based image fusion

Scene visibility in outdoor images is often deteriorated by bad weather conditions such as snow, haze, and rain. Especially, degradation due to haze is typically observed in the form of faded color and low contrast of images. To overcome image degradation, dehazing algorithms based on the atmospheric scattering model employ transmission map estimation, which is related to the haze density across the depth of scenes. However, estimating the depth of the outdoor scene without additional information is challenging, and an erroneously estimated depth leads to a dehazed image of poor quality. In this paper, we propose a fusion-based dehazing algorithm that does not require direct estimation of the transmission map. The intermediate latent images are obtained by restoring the scene radiance with several globally constant transmission values based on the atmospheric scattering model. Using this approach, only the region where the particular transmission corresponds to the ground truth is dehazed successfully in each image. These images are merged into a haze-free result via the fusion algorithm, which selectively uses the information of the patches from the latent images. Experimental results show that the proposed algorithm effectively removes haze and outperforms the several dehazing methods based on quantitative and qualitative evaluations.
On the appropriateness of Platt scaling in classifier calibration

Many applications using data mining and machine learning techniques require posterior probability estimates besides often highly accurate predictions. Classifier calibration is a separate branch of machine learning that aims at transforming classifier predictions into posterior class probabilities and thus are useful additional extensions in the respective applications. Among the existing state-of-the-art classifier calibration techniques, Platt scaling (sometimes also called sigmoid or logistic calibration) actually is the only parametric one while almost all of its competing methods do not rely on parametric assumptions. Platt scaling is controversially discussed in the classifier calibration literature, despite good empirical results reported in many domains there are many authors criticizing it. Interestingly, none of these criticisms properly deal with the underlying parametric assumptions. Instead, even incorrect statements exist. Thus, the first contribution of this work is to review such criticism and to present a proof of the true parametric assumptions. In fact, these are more general and valid for different probability distributions, which is an immediate consequence. Next, the relationship between Platt scaling and a different, relatively new classifier calibration technique called beta calibration is analyzed and it is shown that these two are actually equivalent: Their only difference lies in the characteristics of the classifier whose predictions are calibrated. Thus, the proven validity of Platt scaling additionally translates directly into a proven optimality of beta calibration. Furthermore, evaluating classifier calibration techniques is a highly non-trivial problem as the true posteriors cannot be used as a reference. Hence, the existing evaluation metrics are reviewed as well because there exist relatively popular evaluation criteria that should not be used at all. Finally, the theoretical findings are supported by a simulation study.
G <sup>3</sup>

This paper demonstrates G 3 , a framework for &lt;u&gt;G&lt;/u&gt;raph Neural Network (GNN) training, tailored from &lt;u&gt;G&lt;/u&gt;raph processing systems on &lt;u&gt;G&lt;/u&gt;raphics processing units (GPUs). G 3 aims at improving the efficiency of GNN training by supporting graph-structured operations using parallel graph processing systems. G 3 enables users to leverage the massive parallelism and other architectural features of GPUs in the following two ways: building GNN layers by writing sequential C/C++ code with a set of flexible APIs (Application Programming Interfaces); creating GNN models with essential GNN operations and layers provided in G 3 . The runtime system of G 3 automatically executes the user-defined GNNs on the GPU, with a series of graph-centric optimizations enabled. We demonstrate the steps of developing some popular GNN models with G 3 , and the superior performance of G 3 against existing GNN training systems, i.e., PyTorch and TensorFlow.
Dremel

Google's Dremel was one of the first systems that combined a set of architectural principles that have become a common practice in today's cloud-native analytics tools, including disaggregated storage and compute, in situ analysis, and columnar storage for semistructured data. In this paper, we discuss how these ideas evolved in the past decade and became the foundation for Google BigQuery.
Document Processing: Methods for Semantic Text Similarity Analysis

The document text similarity measurement and analysis is a growing application of Natural Language Processing. This paper presents the results of using different techniques for semantic text similarity measurements in documents used for safety-critical systems. The research objective of this work is to measure the degree of semantic equivalence of multi-word sentences for rules and procedures contained in the documents on railway safety. These documents, with unstructured data and different formats, need to be preprocessed and cleaned before the set of Natural Language Processing toolkits, and Jaccard and Cosine similarity metrics are applied. The results demonstrate that it is feasible to automate the process of identifying equivalent rules and procedures and measure similarity of disparate safety-critical documents using Natural language processing and similarity measurement techniques.
Just-In-Time Test Smell Detection and Refactoring

Test smells represent sub-optimal design or implementation solutions applied when developing test cases. Previous research has shown that these smells may decrease both maintainability and effectiveness of tests and, as such, researchers have been devising methods to automatically detect them. Nevertheless, there is still a lack of tools that developers can use within their integrated development environment to identify test smells and refactor them. In this paper, we present DARTS (Detection And Refactoring of Test Smells), an Intellij plug-in which (1) implements a state-of-the-art detection mechanism to detect instances of three test smell types, i.e., General Fixture, Eager Test, and Lack of Cohesion of Test Methods, at commit-level and (2) enables their automated refactoring through the integrated APIs provided by Intellij.
Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.
Comparing Non-Visual and Visual Guidance Methods for Narrow Field of View Augmented Reality Displays

Current augmented reality displays still have a very limited field of view compared to the human vision. In order to localize out-of-view objects, researchers have predominantly explored visual guidance approaches to visualize information in the limited (in-view) screen space. Unfortunately, visual conflicts like cluttering or occlusion of information often arise, which can lead to search performance issues and a decreased awareness about the physical environment. In this paper, we compare an innovative non-visual guidance approach based on audio-tactile cues with the state-of-the-art visual guidance technique EyeSee360 for localizing out-of-view objects in augmented reality displays with limited field of view. In our user study, we evaluate both guidance methods in terms of search performance and situation awareness. We show that although audio-tactile guidance is generally slower than the well-performing EyeSee360 in terms of search times, it is on a par regarding the hit rate. Even more so, the audio-tactile method provides a significant improvement in situation awareness compared to the visual approach.