Reviewerly/exHarmony huggingface dataset:
STPD: Defending against <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1091" altimg="si25.svg"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>-norm attacks with space transformation

The human imperceptible adversarial examples crafted by ℓ0-norm attacks, which aims to minimize ℓ0 distance from the original image, thereby misleading deep neural network classifiers into the wrong classification. Prior works of tackling ℓ0 attacks can neither eliminate perturbed pixels nor improve the performance of the classifier in the recovered low-quality images. To address the issue, we propose a novel method, called space transformation pixel defender (STPD), to transform any image into a latent space to separate the perturbed pixels from the normal pixels. In particular, this strategy uses a set of one-class classifiers, including Isolation Forest and Elliptic Envelope, to locate the perturbed pixels from adversarial examples. The value of the neighboring normal pixels is then used to replace the perturbed pixels, which hold more than half of the votes from these one-class classifiers. We use our proposed strategy to successfully defend against well-known ℓ0-norm adversarial examples in the image classification settings. We show experimental results under the One-pixel Attack (OPA), the Jacobian-based Saliency Map Attack (JSMA), and the Carlini Wagner (CW) ℓ0-norm attack on CIFAR-10, COVID-CT, and ImageNet datasets. Our experimental results show that our approach can effectively defend against ℓ0-norm attacks compared with the most popular defense techniques.
Latency-Driven Model Placement for Efficient Edge Intelligence Service

Deep learning services based on cloud computing have deficiencies in latency, privacy, etc. To meet the requirements of low latency, researchers have begun to consider the deployment of deep learning services in edges, i.e., edge intelligence service. Deploying deep learning models on multiple processors or devices so that the computation of a deep learning model can be conducted in parallel is a possible solution to improve the efficiency of edge intelligence services. In this article, we propose a novel latency-driven deep learning model placement method for efficient edge intelligence service. Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert the model into execution graphs and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned sub-models are heuristically assigned to available processors. To the best of our knowledge, it is the first work that proposes latency-driven graph partition algorithms for model placement. Extensive experiments on several commonly used DNN (deep neural network) models and synthetic datasets show that our method can achieve the lowest execution latency with low complexity compared with other state-of-the-art model placement methods.
Complexity Thresholds in Inclusion Logic

Logics with team semantics provide alternative means for logical characterization of complexity classes. Both dependence and independence logic are known to capture non-deterministic polynomial time, and the frontiers of tractability in these logics are relatively well understood. Inclusion logic is similar to these team-based logical formalisms with the exception that it corresponds to deterministic polynomial time in ordered models. In this article we examine connections between syntactical fragments of inclusion logic and different complexity classes in terms of two computational problems: maximal subteam membership and the model checking problem for a fixed inclusion logic formula. We show that very simple quantifier-free formulae with one or two inclusion atoms generate instances of these problems that are complete for (non-deterministic) logarithmic space and polynomial time. Furthermore, we present a fragment of inclusion logic that captures non-deterministic logarithmic space in ordered models.
Current challenges of the batik industry in Malaysia and proposed solutions

Batik is an art or craft that is produced through a variety of textile designing techniques. In Malaysia, batik is considered as a national heritage art that is the pride of the country. The World-Asia Pacific Craft Council recognizes Malaysian batik as one of the most well-known local crafts globally. This study aims to examine the challenges faced by the batik industry in Malaysia. The literature review method is fully utilized to collect the required data. The results of the study found that the Batik Industry in Malaysia faces the challenge of competition from cheaper and modern external products and even more worrying is the original batik design imitated by irresponsible foreign entrepreneurs by offering lower prices. The lack of skilled workers from young people should also be taken into consideration when many young people voices that they are not interested in working in this industry. A more detailed research study proposal is needed by paying attention to the role of entrepreneurs and designers in the batik industry in Malaysia. They are the leading movers in addressing the challenges and problems that exist in this industry in line with efforts to develop the local batik industry and elevate the art of batik in Malaysia.
Decentralized and expressive data publish-subscribe scheme in cloud based on attribute-based keyword search

Data publish-subscribe scheme is a valuable approach to share and retrieve data from cloud platforms selectively. However, the common practice of standard encryption hinders the effective keyword-based data subscription over outsourced data. Attribute-based keyword search (ABKS), which fulfills the functionality of attribute-based encryption and searchable encryption, is a promising cryptographic primitive to construct secure data publish-subscribe scheme. In this paper, for the first time, we propose a decentralized and expressive CP-ABKS scheme with computation outsourcing and collusion resistance. Then we construct a novel data publish-subscribe scheme based on the proposed CP-ABKS to realize secure and flexible data sharing among multiple users. Both the access and subscription policies are expressed with the Linear Secret Sharing Scheme structure that can be derived from any monotonic Boolean formula and any threshold gate. To protect the keyword privacy, the subscription policy is partially publicized, and a keyword localization method is designed to enable the server to reconstruct the subscription policy and execute the search protocol in linear time. Additionally, the proposed scheme adopts online/offline encryption and outsourced decryption in a fully decentralized setting. Security analysis and performance evaluation show that our scheme achieves a good balance between security goals and practical efficiency.
Learning under Distribution Mismatch and Model Misspecification

We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm. The effect of this mismatch on the generalization error and model misspecification are quantified. Moreover, we provide a connection between the generalization error and the rate-distortion theory, which allows one to utilize bounds from the rate-distortion theory to derive new bounds on the generalization error and vice versa. In particular, the rate-distortion-based bound strictly improves over the earlier bound by Xu and Raginsky even when there is no mismatch. We also discuss how “auxiliary loss functions” can be utilized to obtain upper bounds on the generalization error. A full version of this paper is accessible at [1].
Image-to-Image Transfer Makes Chaos to Order

GAN-based image-to-image transfer tools have achieved remarkable results in image generation. However, most of the research efforts focus on changing the style features, e.g., color and texture. The spatial features, e.g., the locations of objects in input and output images, always keep consistent. If the above tools are employed to translate locations, such as organizing objects from a chaotic scene to an orderly scene in images (i.e., chaos to order), can these tools work well? Therefore, we investigate the issue of image-to-image location transfer and receive a preliminary conclusion that it is hard to manipulate spatial features of objects in raw images automatically. In this paper, we propose a novel framework called LT-GAN to address the above issue. Specifically, a multi-stage generation structure is designed, where the location translation is performed based on semantic labels as a bridge to enhance the effect of automatically manipulate the spatial features of raw images. Experimental results demonstrate the effectiveness of the proposed multi-stage generation strategy. Meanwhile, a Color Histogram Loss is explored to evaluate the similarity of color distribution between a chaotic scene image and the corresponding orderly scene image. The quality of orderly scene images generated by the final stage is improved significantly in LT-GAN by using the combination of feature extraction and the Color Histogram Loss. Moreover, to break through the limitation of public datasets in image-to-image transfer tasks, a new dataset named M2C is constructed for this new application scenario of location transfer, including more than 15,000 paired images and the corresponding semantic labels in total. The dataset is available at \urlhttps://drive.google.com/open?id=1amr9ga9wvhnIzeZ48OHbLapHGqOb4-Up
Demonstration of generating explanations for black-box algorithms using Lewis

Explainable artificial intelligence (XAI) aims to reduce the opacity of AI-based decision-making systems, allowing humans to scrutinize and trust them. Unlike prior work that attributes the responsibility for an algorithm's decisions to its inputs as a purely associational concept, we propose a principled causality-based approach for explaining black-box decision-making systems. We present the demonstration of Lewis, a system that generates explanations for black-box algorithms at the global, contextual, and local levels, and provides actionable recourse for individuals negatively affected by an algorithm's decision. Lewis makes no assumptions about the internals of the algorithm except for the availability of its input-output data. The explanations generated by Lewis are based on probabilistic contrastive counterfactuals, a concept that can be traced back to philosophical, cognitive, and social foundations of theories on how humans generate and select explanations. We describe the system layout of Lewis wherein an end-user specifies the underlying causal model and Lewis generates explanations for particular use-cases, compares them with explanations generated by state-of-the-art approaches in XAI, and provides actionable recourse when applicable. Lewis has been developed as open-source software; the code and the demonstration video are available at lewis-system.github.io.
Observing Virtual Avatars: The Impact of Avatars’ Fidelity on Identifying Interactions

There are many cases where observing the interactions of a virtual avatar can be useful. However, it is unclear to what extent the avatar fidelity affects bystanders' performance when observing gestures performed by avatars. We, therefore, conducted an online study (N=28) with different avatars performing touch, mid-air, and eye gaze gestures. Our avatars range from an abstract avatar to two more realistic ones. Our study shows that an abstract avatar provides bystanders with the same interaction information as more realistic avatars. This implies that it is sufficient to use abstract avatar designs when observing interaction behaviour in virtual environments and researchers do not necessarily need to go through time-consuming and expensive implementations of highly realistic avatars to answer their research questions, making Virtual Reality (VR) studies more accessible to the broader research community. Finally, we discuss studying VR security systems as a potential research application for which abstract avatars can be leveraged.
Improvement of image enhancement for mammogram images using Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL)

(2021). Improvement of image enhancement for mammogram images using Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL). Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. Ahead of Print.
A New Quantitative Approach for Element-Mineral Determination Based on “EDS (Energy Dispersive Spectroscopy) Method”

With the continuous development of hydrocarbon exploration, how to efficiently, economically, accurately, and comprehensively obtain mineral species, composition, and structure and diagenesis information has become one of the hot topics in both the academia and industry. By scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), a new method of qualitative mineral identification and quantitative measurement is established. Typical tight sandstone reservoir rock samples in the Ordos Basin are selected; through the element surface scanning image of “mineral element composition” and “pixel element combination”, mineral types are distinguished, and mineral parameters such as types, characteristics, and content are rapidly and accurately determined. Meanwhile, such results achieved via the new method are compared with conventional XRD and TIMA methods. The results show that the new method exhibits several advantages: cost advantages compared to XRD experiment analysis technology and TIMA system and ability to analyze low content minerals which XRD techniques are hard to identify; it allows quantitative characterization on the phenomenon of mineral miscibility, which is of great significance to explore the mineral diagenetic evolution.
HyperFuzzer: An Efficient Hybrid Fuzzer for Virtual CPUs

In this cloud computing era, the security of hypervisors is critical to the overall security of the cloud. In particular, the security of CPU virtualization in hypervisors is paramount because it is implemented in the most privileged CPU mode. Blackbox and graybox fuzzing are limited to finding shallow virtual CPU bugs due to its huge search space. Whitebox fuzzing can be used for systematic analysis of CPU virtualization, but existing implementations rely on slow hardware emulators to enable dynamic symbolic execution.
Sliding-Window Informed RRT*: A Method for Speeding Up the Optimization and Path Smoothing

Path planning plays a vital role in robot navigation and manipulation, and multiple types of algorithms have been introduced to address this problem. Rapidly-exploring Random Tree (RRT) based algorithms have many advantages over other path planning algorithms. For example, RRT is suitable to solve the path planning problem in high dimensional space and can easily handle robot differential constraints. Informed RRT* is a method that uses the prolate hyper-spheroid to speed up the optimization process, but its efficiency will decrease to the same level as RRT* when the hyper-spheroid covers most of the state space. To overcome this drawback, we further propose a Sliding-Window Informed RRT* (SWIRRT*), which combines the sliding-window thought into the Informed RRT*, taking the advantage of the initial path and make the path optimization much faster. Simulations in 2D space have been carried out to demonstrate that our proposed method can improve the RRT-like algorithm's convergence speed.
Blockchain for Transformation in Digital Marketing

Today, an increasing number of firms are embracing blockchain as part of their efforts to achieve operational efficiency and improve performance, thereby acting as a catalyst to bring about digital transformation. Gartner listed blockchain as the most promising technology in digital marketing in the year 2019. Blockchain is driving digital transformation by forcing organizations to rethink how they operate, in terms of identifying ineffectiveness of traditional approaches to doing business, to address their business needs, promote innovation, and through establishment of standard frameworks. Blockchain shows massive disruption potential in the area of customer relationship management and enhancing consumer experience, besides improving trust, security, and privacy. Therefore, this chapter focuses on providing an enlightenment on how blockchain can specifically address the areas of transformation in digital marketing, prominent frameworks in use, and listing the benefits and challenges of implementing this technology.
A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output , Instance Relations , Measure Functionality , and Measure Valence . We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.
Computer Algebra in JULIA

Recently, the place of the main programming language for scientific and engineering computations has been little by little taken by Julia. Some users want to work completely within the Julia ecosystem like they work within the Python ecosystem. There are libraries for Julia that cover the majority of scientific and engineering computations demands. The aim of this paper is to combine the usage of Julia framework for numerical computations and for symbolic computations in mathematical modeling problems. The main functional domains determining various variants of the application of computer algebra systems are described. In each of these domains, generic representatives of computer algebra systems in Julia are distinguished. The conclusion is that it is possible (and even convenient) to use computer algebra systems within the Julia ecosystem.
An Empirical Study on Automatic Post Editing for Neural Machine Translation

Automatic post editing (APE) researches aim to correct errors in the machine translation results. Recently, APE research has mainly been conducted in two directions: noise-based APE and adapter-based APE. This study poses three questions based on existing APE studies and conducted a verification. The first is a question about the optimal APE research direction, and this has been figured out through a comparative analysis of the previous studies on noise-based APE and adapter-based APE. The second is about the substantial effectiveness of the bottleneck adapter layer (BAL) in adapter based APE. For the verification, various experiments on the different size of BAL has been conducted, and through these experiments, optimal approaches in adapter based APE has been proposed. For the last, this work raises a question about the reason why leveraging external knowledge is influential in APE. In this regard, we conducted several comparative experiments on the method of utilizing external data to APE training to achieve a better performance. The results revealed that the performance can be improved by applying the method of concatenating the external data with the existing data when training the APE model.Through deep analysis on these experiments, this work propose the optimal research direction in APE.
Conditional GAN with Effective Attention for SAR-to-Optical Image Translation

Synthetic aperture radar (SAR) is an effective observation technology, which is widely used in industry and agriculture. However, SAR images have speckle noise because of its imaging mechanism, so it is difficult to obtain useful information from them directly. Generative adversarial networks (GANs) have great performance in image translation with the development of deep learning, SAR images can be translated into optical images. However, due to the complex scene, low resolution and speckle noise, the generated images obtained by the existing methods are not satisfactory. In this paper, we propose a method based on conditional GAN (CGAN) for image translation from SAR images to optical images. We use the attention mechanism, which means that the network attaches importance to useful features and ignores unimportant ones. We apply discrete cosine transform (DCT) as loss function to extract the low frequency features in the image. Our experiments show that the quality of the images generated by our method is better than that of some famous methods.
Detection and Localisation of Struck-Out-Strokes in Handwritten Manuscripts

The presence of struck-out texts in handwritten manuscripts adversely affects the performance of state-of-the-art automatic handwritten document processing systems. The information of struck-out words (STW) are often important for real-time applications like handwritten character recognition, writer identification, digital transcription, forensic applications, historical document analysis etc. Hence, the detection of STW and localisation of struck-out strokes (SS) are crucial tasks. In this paper, we introduce a system for simultaneous detection of STWs and localisation of the SS using a single network architecture based on Generative Adversarial Network (GAN). The system requires no prior information about the type of SS stroke and it is also able to robustly handle variant of strokes like straight, slanted, cris-cross, multiple-lines, underlines and partial STW as well. However, we also present a methodology to generate STW with high variability of SS for network learning. We have evaluated the proposed pipeline on publicly available IAM dataset and also on struck-out words collected from real-world writers with high variability factors like age, gender, stroke-width, stroke-type etc. The evaluation metrics show robustness and applicability in real-world scenario.