Reviewerly/exHarmony huggingface dataset:
Hide and Seek: An Adversarial Hiding Approach Against Phishing Detection on Ethereum

With the wide application and development of blockchain technology, the past years have witnessed the emergence of various cybercrimes, which have caused a huge amount of economic loss. Among them, phishing scams on the blockchain are regarded as a serious threat to the trading security of the blockchain ecosystem. By modeling the transaction data of blockchain as a network, a series of graph-based phishing detection frameworks have been proposed. Enlightened by adversarial attacks of graph data, we propose to verify the robustness of current phishing detection frameworks under intentional attackers aiming to hide phishing behaviors. In this study, we first propose a general phishing detection framework based on feature engineering and then propose a phishing hiding framework combing the greedy selection mechanism with four phishing hiding strategies to measure the robustness of the proposed general detection models. Extensive experiments evaluate the detective performance of the phishing detection model and its robustness against the hiding framework. The experimental results indicate that the detective model based on feature engineering is rather fragile under adversarial attacks.
Lightweight Authentication Scheme for Data Dissemination in Cloud-Assisted Healthcare IoT

Recent advancements in the Internet of Things (IoT) and cloud computing technologies have accelerated the development of various practical applications, including healthcare systems. Adequately revealing the collected healthcare data in a cloud-assisted healthcare IoT system brings a huge potential for improving the safety, quality, and efficiency of healthcare services. However, often the data collected in a healthcare IoT system is vital and sensitive. The dissemination of such data is also vulnerable to malicious attacks such as tampering, eavesdropping, and forgery. Thus, disseminated data's integrity, authenticity, and privacy are elementary security demands to end-users and owners. Also, the resource-constrained nature of healthcare IoT devices invalidates the existing solutions. To address the above challenges, we propose a lightweight and secure redactable signature scheme with coarse-grained additional redaction control (CRS) for secure dissemination of healthcare data in a cloud-assisted healthcare IoT system. The security analysis indicates our CRS is secure against signature forgery, additional redaction attacks, and redacted version linkability. Compared to other existing solutions, our scheme can achieve some level of security but less computational complexity and communication overhead.
Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning

Previous efforts to learn histology features that correlate with specific genetic/molecular traits resort to tile-level multi-instance learning (MIL) which relies on a fixed pretrained model for feature extraction and an instance-bag classifier. We argue that such a two-step approach is not optimal at capturing both fine-grained features at tile level and global features at slide level optimal to the task. We propose a self-interactive MIL that iteratively feedbacks training information between the fine-grained and global context features. We validate the proposed approach on 4 subtyping tasks: EMT status (ovarian), KRAS mutation (colon and lung), EGFR mutation (colon), and HER2 status (breast). Our approach yields an average improvement of $$7.05\%-8.34\%$$ (in terms of AUC) over the baseline.
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views. These multiple related images provide complementary diagnostic information and can improve the radiologist's classification accuracy. Unfortunately, most existing deep learning systems, trained with globally-labelled images, lack the ability to jointly analyse and integrate global and local information from these multiple views. By ignoring the potentially valuable information present in multiple images of a screening episode, one limits the potential accuracy of these systems. Here, we propose a new multi-view global-local analysis method that mimics the radiologist's reading procedure, based on a global consistency learning and local co-occurrence learning of ipsilateral views in mammograms. Extensive experiments show that our model outperforms competing methods, in terms of classification accuracy and generalisation, on a large-scale private dataset and two publicly available datasets, where models are exclusively trained and tested with global labels.
A Fast, Accurate, and Comprehensive PPA Estimation of Convolutional Hardware Accelerators

Convolutional Neural Networks (CNN) are widely adopted for Machine Learning (ML) tasks, such as classification and computer vision. GPUs became the reference platforms for both training and inference phases of CNNs due to their tailored architecture to the CNN operators. However, GPUs are power-hungry architectures. A path to enable the deployment of CNNs in energy-constrained devices is adopting hardware accelerators for the inference phase. However, the literature presents gaps regarding analyses and comparisons of these accelerators to evaluate Power-Performance-Area (PPA) trade-offs. Typically, the literature estimates PPA from the number of executed operations during the inference phase, such as the number of MACs, which may not be a good proxy for PPA. Thus, it is necessary to deliver accurate hardware estimations, enabling design space exploration (DSE) to deploy CNNs according to the design constraints. This work proposes a fast and accurate DSE approach for CNNs using an analytical model fitted from the physical synthesis of hardware accelerators. The model is integrated with CNN frameworks, like TensorFlow, to generate accurate results. The analytic model estimates area, performance, power, energy, and memory accesses. The observed average error comparing the analytical model to the data obtained from the physical synthesis is smaller than 7%.
DuMSE: Toward Practical and Dynamic Multiuser Search Over Encrypted Cloud Data Against Keyword Guessing Attack

For multiuser search scenarios over encrypted cloud data, the multiuser searchable encryption (MUSE) technology is a viable solution for incorporating privacy protection and favorable operability functions together. However, current MUSE technologies suffer from three practical limitations: 1) a secure channel between data owner and users will greatly increase the burden of owner; 2) a trusted third party (TTP) is required in some solutions; and 3) some solutions cannot resist keyword guessing attack (KGA), which means that the information of search trapdoor is easily leaked. In order to solve the above three problems, our first practical solution (DuMSE) without secure channel and without TTP is proposed in this article, which also can resist KGA. With inverted indexes, the search time complexity of DuMSE can be reduced to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is the number of corresponding data. To support dynamically updating data, our second dynamic solution based on DuMSE is also proposed in this article, named as dynamic DuMSE (dDuMSE). The construction of dDuMSE includes not only the above features of DuMSE but also a new feature. Our analysis and experimental results show that the proposed schemes are secure and practical.
Lifelong Visual-Tactile Spectral Clustering for Robotic Object Perception

This work presents a novel visual-tactile fused clustering framework, called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> ifelong <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> isual- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> actile <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> pectral <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> lustering (i.e., LVTSC), to effectively learn consecutive object clustering tasks for robotic perception. Lifelong learning has become an important and hot topic in recent studies on machine learning, aiming to imitate “human learning” and reduce the computational cost when consecutively learning new tasks. Our proposed LVTSC model explores the knowledge transfer and representation correlation from a local modality-invariant perspective under modality-consistent constraint guidance. For the modality-invariant part, we design a set of modality-invariant basis libraries to capture the latent clustering centers of each modality and a set of modality-invariant feature libraries to forcibly embed the manifold information of each modality. A modal-consistent constraint reinforces the correlation between visual and tactile modalities by maximizing the feature manifold correspondences. When the object clustering task comes continuously, the overall objective is optimized by an effective alternating direction method with guaranteed convergence. Our proposed LVTSC framework has been extensively validated for its effectiveness and efficiency on the three challenging real-world robotic object perception datasets.
Lexicon-based vs. Lexicon-free ASR for Norwegian Parliament Speech Transcription

Norwegian is a challenging language for automatic speech recognition research because it has two written standards (Bokmål and Nynorsk) and a large number of distinct dialects, from which none has status of an official spoken norm. A traditional lexicon-based approach to ASR leads to a huge lexicon (because of the two standards and also due to compound words) with many spelling and pronunciation variants, and consequently to a large (and sparse) language model (LM). We have built a system with 601k-word lexicon and an acoustic model (AM) based on several types of neural networks and compare its performance with a lexicon-free end-to-end system developed in the ESPnet framework. For evaluation we use a publically available dataset of Norwegian parliament speeches that offers 100 h for training and 12 h for testing. In spite of this rather limited training resource, the lexicon-free approach yields significantly better results (13.0% word-error rate) compared to the best system with the lexicon, LM and neural network AM (that achieved 22.5% WER).
End-to-End Learning for Image-Based Detection of Molecular Alterations in Digital Pathology

Current approaches for classification of whole slide images (WSI) in digital pathology predominantly utilize a two-stage learning pipeline. The first stage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requirement for task-specific auxiliary labels which are not acquired in clinical routine. We propose a novel learning pipeline for WSI classification that is trainable end-to-end and does not require any auxiliary annotations. We apply our approach to predict molecular alterations for a number of different use-cases, including detection of microsatellite instability in colorectal tumors and prediction of specific mutations for colon, lung, and breast cancer cases from The Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to be competitive with state of the art two-stage pipelines. We believe our approach can facilitate future research in digital pathology and contribute to solve a large range of problems around the prediction of cancer phenotypes, hopefully enabling personalized therapies for more patients in future.
Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0

Prosodic boundaries in speech are of great relevance to both speech synthesis and audio annotation. In this paper, we apply the wav2vec 2.0 framework to the task of detecting these boundaries in speech signal, using only acoustic information. We test the approach on a set of recordings of Czech broadcast news, labeled by phonetic experts, and compare it to an existing text-based predictor, which uses the transcripts of the same data. Despite using a relatively small amount of labeled data, the wav2vec2 model achieves an accuracy of 94% and F1 measure of 83% on within-sentence prosodic boundaries (or 95% and 89% on all prosodic boundaries), outperforming the text-based approach. However, by combining the outputs of the two different models we can improve the results even further.
S$$^3$$R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification

Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression (S $$^3$$ R) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. Then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1) S $$^3$$ R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies; (2) S $$^3$$ R-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, S $$^3$$ R converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.
Making FAIR Practices Accessible and Attractive

Facing rapidly growing volumes of research datasets, scientists and research funding agencies are putting forward new principles of data management, such as data Findability, Accessibility, Interoperability, and Reusability (FAIR). To this end, data science experts are developing FAIR data policies, methods, protocols, and repositories, while actual research practices are lagging behind because FAIR compliance remains a burden for many researchers. Here we present a prototype data management infrastructure deployed at the National High Magnetic Field Laboratory (NHMFL/MagLab) aimed at helping scientists efficiently annotate and manage experimental data produced by their MagLab projects and making FAIR practices accessible and attractive. The infrastructure incorporates the Open Science Framework (OSF) data repository platform. We will describe infrastructure elements such as the data formats, the metadata schema, the repository integration, the naming conventions, the templates to organize the data, and the automated data pipeline from measurement stations to the FAIR repository objects.
Carbon emission and cost of blockchain mining in a case of peer-to-peer energy trading

Blockchain is an emerging technology that has demonstrated great uptake potential in Peer-to-Peer (P2P) energy trading. The revolution of blockchain brings substantial benefits and innovation to sustainability energy transitions through P2P trading. Blockchain enables energy commodities to be traded. However, perceptions of this technology’s impact on the environment and its associated costs have garnered recent adverse publicity. This paper aims to look at the linkages between blockchain technology and energy systems in terms of blockchain power consumption against blockchain advantage over renewable energy transitions via peer-to-peer energy trading. The amount of energy used and carbon released during the blockchain validation process is estimated, and the cost of blockchain is computed to assess its economic benefit in a peer-to-peer energy trading scenario. Real data from running peer-to-peer energy trading systems are used, and numerous insights on the transformation of peer-to-peer energy trading utilising various blockchain scaling methods are provided. Based on the analysis, this paper concludes that the cost of processing trading transactions is lower using blockchain than current coordination costs. Also, blockchain-based energy can be traded more frequently than current regulations allow in order to reap the full benefits of renewable energy. A secure blockchain-enabled P2P trading environment would lead to fair rates for energy providers and prosumers resulting in stimulating the renewable energy market.
Developing an E-College Monitoring System as a Web-Based Monitoring Tool Application

The E-College Monitoring System is an online system that can manage students&rsquo; outings in UiTM and to study and understand the system application for students who live in UiTM. In addition, it will be used to test the e-college monitoring application system functioning. The methodology that we chose is by using the system development life cycle (SDLC). The qualitative findings showed that the current manual system in UiTM Machang is not reliable. This project contributes to the technological value and the impacts of the system that have been developed. Different from the prior system, this study is an exploratory attempt to investigate the manual system that has been used by some universities and uncovered that the mechanisms with technology influence the university&rsquo;s performance.
A benchmark dataset for printed Meitei/Meetei script character recognition

The Manipuri language is the official language of the Indian state of Manipur. The language belongs to the Tibeto-Burman family of languages. A benchmark Meitei/Meetei script printed document images dataset is presented in this article. The dataset contains raw 824 pages of printed documents and binarized images, text files, and XML files for each raw image. It also includes 51,460 isolated character samples, composed of 27 consonants, 7 half-consonants, 8 vowels, and 10 numerical. This dataset could be used for optical character recognition (OCR) research and in the different research areas of natural language processing (NLP).
Machine Learning for Speech Recognition

Machine learning paradigms that are used in automatic speech recognition (ASR) have been improved in the past decade. The improvement in speech recognition has been developed to help make the technology more efficient by dealing with various challenges affecting speech recognition such as speaker identification, capitalization, correct formatting, domain-specific terminology, background noise, the timing of words, and punctuation placement. Some other issues that have come up in speech recognition include data security and privacy, deployment, and language coverage. Any speech recognition system must have a noise-removal feature to perform in the best way possible. This paper gives a brief about the machine learning techniques that can be used in speech recognition. A better understanding of the models will help understand the systems, and it may help improve the technology even further for the benefit of society.
Bug Report Priority Prediction Using Developer-Oriented Socio-Technical Features

Software stakeholders report bugs in Issue Tracking System (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time-insensitive approaches, and they were producing sub-optimal results for practical usage. Moreover, they tend to ignore the developer and social aspects of ITS. Since ITS bridges users and developers, we integrate their sentiment- and community-oriented socio-technical features to perform 2- and multi-classed bug priority prediction and validate our model in within-project, cross-project, and time-wise scenarios. The proposed model outperforms the 2 baselines by up to 10% in AUC-ROC and 13% in MCC, and the significance of improvement is statistically confirmed. We reveal involving assignee and reporter features from socio-technical perspectives such as sentiment could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in socio-technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest researchers should involve contributors’ experience and sentiments in bug report priority prediction.
Evolutionary Measures for Object-oriented Projects and Impact on the Performance of Cross-version Defect Prediction

Cross-version defect prediction (CVDP) has attracted more attention of researchers in recent years. For an evolutionary project, multiple versions will be produced during the process of software evolution. However, for multiple versions of an object-oriented project, the evolution degree (e.g. class change degree) between neighboring versions could affect the performance of CVDP. Therefore, how to measure the evolution degree of neighboring versions and explore the impact on the performance of CVDP are very important. Based on the neighboring versions of evolutionary projects, this paper proposed six evolutionary measures from three aspects of class change, metric change, and label change, including ratio of new classes (RNC), ratio of deleted classes (RDC), average ratio of metric change (ARMC), ratio of label changed classes (RLCC), ratio of unchanged classes (RUC), and ratio of interference classes (RIC). Spearman's rank correlation coefficient was applied to show the correlations between evolutionary measures and the performance of CVDP. An empirical study was conducted on 40 versions of 11 projects from the PROMISE repository. The performance of CVDP was evaluated with F-measure and AUC. The statistical results show that RNC, RDC, and RUC show no correlation with F-measure and AUC. ARMC shows a medium positive correlation with F-measure. RLCC and RIC show very strong or strong negative correlations with F-measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.