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Verifiable machine learning Models in Industrial IoT via Blockchain 14th

Verifiable Machine Learning Models in Industrial IoT via B...

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12th international Advanced Computing conference, IACC 2022

作者： Stodt, Jan Ghajar, Fatemeh Ghovanlooy Reich, Christoph Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen Germany Offenburg University of Applied Sciences Offenburg Germany Plymouth University Portland Square PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9783031356438

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model. © 2023, Springer Nature Switzerland AG.

关键词： Blockchain

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Accelerating Random Forest Classification on GPU and FPGA 22

Accelerating Random Forest Classification on GPU and FPGA

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Proceedings of the 51st international conference on Parallel Processing

作者： Milan Shah Reece Neff Hancheng Wu Marco Minutoli Antonino Tumeo Michela Becchi Electrical and Computer Engineering North Carolina State University United States of America Pacific Northwest National Laboratory United States of America

ISBN: (纸本)9781450397339

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that, while reporting the best performance on GPU, our code variants outperform the CSR baseline both on GPU and FPGA. For high accuracy targets, our GPU implementation yields a 5-9 × speedup over CSR, and up to a 2 × speedup over Nvidia’s cuML library.

关键词： GPU random forest classification FPGA

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A Hybrid Approach for Inference between Behavioral Exception API Documentation and Implementations, and Its Applications 22

A Hybrid Approach for Inference between Behavioral Exception...

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Proceedings of the 37th IEEE/ACM international conference on Automated software engineering

作者： Hoan Anh Nguyen Hung Dang Phan Samantha Syeda Khairunnesa Son Nguyen Aashish Yadavally Shaohua Wang Hridesh Rajan Tien Nguyen Amazon United States of America Computer Science Iowa State University United States of America Bradley University USA Computer Science University of Texas at Dallas United States of America New Jersey Institute of Technology United States of America University of Texas at Dallas United States of America

ISBN: (纸本)9781450394758

Automatically producing behavioral exception (BE) API documentation helps developers correctly use the libraries. The state-of-the-art approaches are either rule-based, which is too restrictive in its applicability, or deep learning (DL)-based, which requires large training dataset. To address that, we propose StatGen, a novel hybrid approach between statistical machine translation (SMT) and tree-structured translation to generate the BE documentation for any code and vice versa. We consider the documentation and source code of an API method as the two abstraction levels of the same intent. StatGen is specifically designed for this two-way inference, and takes advantage of their structures for higher accuracy. We conducted several experiments to evaluate StatGen. We show that it achieves high precision (75% and 75%), and recall (81% and 84%), in inferring BE documentation from source code and vice versa. StatGen achieves higher precision, recall, and BLEU score than the state-of-the-art, DL-based baseline models. We show StatGen’s usefulness in two applications. First, we use it to generate the BE documentation for Apache APIs that lack of documentation by learning from the documentation of the equivalent APIs in JDK. 44% of the generated documentation were rated as useful and 42% as somewhat useful. In the second application, we use StatGen to detect the inconsistency between the BE documentation and corresponding implementations of several JDK8 packages.

关键词： Behavioral Exception Specification Hybrid machine Translation Inference between Documentation and Code

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A Novel Dataset for Fake News Detection in Tamil Regional Language 1st

A Novel Dataset for Fake News Detection in Tamil Regional ...

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1st international conference on Speech and Language Technologies for Low-resource Languages, SPELLL 2022

作者： Mirnalinee, T.T. Jayaraman, Bhuvana Anirudh, A. Jagadish, R. Karthik Raja, A. Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9783031332302

Tamil is one of the very few ancient languages that have survived the passage of time. And yet even though a lot of pieces of literature are available for this language, not a lot of data is labeled. With the Internet boom and digitization in all mediums, it is important to build classifiers for data analysis and prediction. But the available labeled data is from little to none in each domain. With the internet being used in all walks of life, the news spread via this medium quickly. Misleading and distorted information will not only affect the individual but also impact on the public. This research work elaborates on the creation of one such corpus meant for fake new detection. News snippets were scrapped from the news media and are annotated into fake and real news. The news of two classes are further labelled manually as 5 classes namely Sports, Politics, Science, Entertainment and Miscellaneous. The corpus has a collection of 2949 fake news and 2324 samples of genuine news was also added to the corpus to provide for a balanced dataset. One of the main observations was that a major chunk of the fake news data was political. For bench-marking this dataset we have built 5 baseline models with our corpus, each model showed improvement in different areas. Four machine learning and one deep learning model were trained on this new corpus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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machine learning-Aided Aerial Surveillance: Enhancing Trespasser Detection Using UAVs 4th

Machine Learning-Aided Aerial Surveillance: Enhancing Trespa...

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4th international conference on Internet of Things, ICIoT 2023

作者： Phang, Swee King Ma’amor, Farah Hani Azman, Afizan School of Engineering Taylor’s University Selangor Subang Jaya Malaysia School of Computer Science Taylor’s University Selangor Subang Jaya Malaysia

ISBN: (纸本)9783031856655

Since unmanned aerial vehicle (UAV) has gained popularity in the current era, UAV has been used as a form of surveillance. UAVs are known for its portable, easy to control, and unmanned features, which makes it the finest choice to install a surveillance system on. With the high rates of the property occurring in Malaysia, the idea of creating a newly improved and state-of-the-art CCTV pose as a solution to combat the stated issue. The obtained system integrates both human detection and tracking algorithm as well as face recognition system to detect trespassers caught on footage and to recognise a person’s facial attributes. The underlying motivation for the project had emerged due to the high number of cases for property crimes occurring locally and uprising surveillance applications of UAV. Upon implementing the algorithm, the obtained system must be able to fulfil the objectives, such as to critically investigate the potential of using UAV for human tracking and detection in lowering property crime cases, modify an algorithm for detecting or classifying human with an improvement of 1.14% from aerial images captured by a UAV, and apply face recognition system within the acceptable range of distance on aerial images captured. The project research would be split into 3 stages, which are code implementation for human tracking and detection, code implementation for facial recognition, and improvement of code implemented in the previous stages. During these stages, the results of the existing algorithms would be recorded on a spreadsheet software and issues emerging from the algorithm would be aimed to be resolved. In stage 1, the tested algorithm is expected to run with an improvement of 1.14%. In stage 2, testing with the algorithm built to output the acceptable distance range for the project. In stage 3, the combined algorithm is expected to work well with the footage taken in CC lab at Taylor’s Block E. The expected outcome of the proposed system is to be able to d

关键词： Unmanned aerial vehicles (UAV)

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Linear and Non-Linear Filter-based Counter-Forensics Against Image Splicing Detection 7th

Linear and Non-Linear Filter-based Counter-Forensics Against...

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Das, Debjit Bhunia, Banalata Naskar, Ruchira Chakraborty, Rajat Subhra Department of Information Technology Indian Institute of Engineering Science and Technology West Bengal Shibpur Howrah711103 India Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India

ISBN: (纸本)9783031314063

Digital images are widely used as primary sources of evidence in today’s world, spanning security, forensics, and legal domains. However, image tampering poses a shallow technical skill barrier with the wide availability of sophisticated, easy-to-use image manipulation software. Tampered images are often used intentionally for unlawful and malicious purposes. One of the most common forms of image manipulation attack is image splicing, which is performed by combining regions from multiple source images to synthesize an artificial image that looks natural. Digital forensic measures have been widely explored in the literature to detect such type of image forgery. However, the recent growth of counter-forensics poses a threat to such forensic/security measures. Forensic techniques can be easily deceived by adopting counter-forensic manipulation of forged images. In this work, we explore different linear and non-linear filtering-based counter-forensic modifications to digital images and hence investigate the after-effects of those, in terms of severity of such manipulations in rendering state-of-the-art forensic splicing detection methods useless. In this paper, we implement two forensic image splicing detection techniques based on feature extraction from image along with machine learning and deep CNN with transfer learning. Then, different filtering techniques have been applied to the image dataset, investigating their effectiveness as a counter-forensic attack against image splicing detection. Experimental results show that the Gaussian filter and Average filter are the two most effective counter-forensic filtering methods against image splicing detection, suggesting the need for further strengthening the existing family of forensic techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Median filters

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Intelligent System for Crime and Insecurity Management

Intelligent System for Crime and Insecurity Management

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Multidisciplinary engineering and Applied Science (ICMEAS), international conference on

作者： Wilson Nwankwo Kingsley C. Ukaoha Eti Friday Irikefe Ovilli Peter Duke Oghorodi Esemena Jeroh Okpu Okpomo Eterighoi Edafe John Atajeromavwo Oyewale Mojeed Adebowale Ubrurhe Ogheneochuko Department of Cyber Security Delta State University of Science & Technology Ozoro Nigeria Department of Computer Science Pan Atlantic University Lekki Nigeria Department of Information Systems & Technology Delta State University of Science & Technology Ozoro Nigeria Dept. of Information Technology Delta State University of Science & Technology Ozoro Nigeria Department of Computer Science Delta State University of Science & Technology Ozoro Nigeria Department of Library and Information Science Delta State University of Science & Technology Ozoro Nigeria Dept. of Cyber Security Delta State University of Science & Technology Ozoro Nigeria Department of Software Engining Delta State University of Science & Technology Ozoro Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

As the global law enforcement workforce faces unprecedented challenges such as declining numbers and shifting demographics, the need for innovative solutions to manage crime and insecurity is becoming increasingly critical. This paper presents a multifaceted approach aimed at harnessing the power of intelligent systems to address the complex and evolving landscape of crime in societies with diminishing Police workforce in Nigeria. The study provides a comprehensive overview of the demographic shifts leading to a declining workforce and their far-reaching implications on crime rates, public safety, and law enforcement resources. It underscores the urgency of adapting to this new reality and the need for intelligent systems to assist in this endeavor. The paper adopts the object-oriented methodology in the design and development of an intelligent system tailored for crime management and insecurity mitigation. Drawing from the fields of artificial intelligence, machine learning, and data analytics, this system employs cutting-edge techniques for predictive policing, resource allocation optimization, and risk assessment. It integrates data from various sources, including crime statistics, demographic data, economic indicators, and social media sentiment analysis, to provide a holistic understanding of the crime landscape. Furthermore, this paper emphasizes the ethical considerations and safeguards built into the intelligent system to prevent bias, discrimination, and privacy infringements. It discusses the importance of transparency, accountability, and community engagement in the deployment and operation of such systems. The study also highlights real-world case studies and pilot implementations of the intelligent system in select regions facing workforce decline. It showcases how the system has contributed to more efficient law enforcement practices, reduced response times, and enhanced crime prevention strategies. The paper outlines a roadmap for future research, emp

关键词： Ethics Law enforcement Social networking (online) Sociology Time factors Intelligent systems Statistics

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Is this Change the Answer to that Problem?: Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness 22

Is this Change the Answer to that Problem?: Correlating Desc...

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Proceedings of the 37th IEEE/ACM international conference on Automated software engineering

作者： Haoye Tian Xunzhu Tang Andrew Habib Shangwen Wang Kui Liu Xin Xia Jacques Klein TegawendÉ F. BissyandÉ University of Luxembourg Luxembourg National University of Defense Technology China Huawei China

ISBN: (纸本)9781450394758

Patch correctness has been the focus of automated program repair (APR) in recent years due to the propensity of APR tools to generate overfitting patches. Given a generated patch, the oracle (e.g., test suites) is generally weak in establishing correctness. Therefore, the literature has proposed various approaches of leveraging machine learning with engineered and deep learned features, or exploring dynamic execution information, to further explore the correctness of APR-generated patches. In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that “answer” to a problem posed by buggy behavior. Concretely, we turn the patch correctness assessment into a Question Answering problem. To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer). Specifically, we consider as inputs the bug reports as well as the natural language description of the generated patches. Our approach, Quatrain, first considers state-of-the-art commit message generation models to produce the relevant inputs associated to each generated patch. Then we leverage a neural network architecture to learn the semantic correlation between bug reports and commit messages. Experiments on a large dataset of 9 135 patches generated for three bug datasets (Defects4j, *** and Bears) show that Quatrain achieves an AUC of 0.886 on predicting patch correctness, and recalling 93% correct patches while filtering out 62% incorrect patches. Our experimental results further demonstrate the influence of inputs quality on prediction performance. We further perform experiments to highlight that the model indeed learns the relationship between bug reports and code change descriptions for the prediction. Finally, we compare against prior work and discuss the benefits of our approach.

关键词： Question Answering Program Repair machine learning Patch Correctness

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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Textual Entailment Recognition with Semantic Features from Empirical Text Representation 1st

Textual Entailment Recognition with Semantic Features from...

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1st international conference on Speech and Language Technologies for Low-resource Languages, SPELLL 2022

作者： Shajalal, Md Atabuzzaman, Md. Baby, Maksuda Bilkis Karim, Md. Rezaul Boden, Alexander Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany Bangladesh University of Engineering and Technology Dhaka Bangladesh Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Bonn-Rhein-Sieg University of Applied Science Sankt Augustin Germany RWTH Aachen University Aachen Germany University of Siegen Siegen Germany

ISBN: (纸本)9783031332302

Textual entailment recognition is one of the basic natural language understanding (NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural language processing (NLP) techniques to automatically recognize the textual entailment. A text entails a hypothesis if and only if the true meaning and intent of the hypothesis follows the text. Classical approaches generally utilize the feature value of each word from word embedding to represent the sentences. In this paper, we propose a new framework to identify the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair. We carried out several experiments on a benchmark entailment classification (SICK-RTE) dataset. We train several machine learning (ML) algorithms applying both semantic and lexical features to classify the text-hypothesis pair as entailment, neutral, or contradiction. Our empirical sentence representation technique enriches the semantic information of the texts and hypotheses found to be more efficient than the classical ones. In the end, our approach significantly outperforms known methods in understanding the meaning of the sentences for the textual entailment classification task. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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