Reviewerly/exHarmony huggingface dataset:
Customs Fraud Detection in the Presence of Concept Drift

Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current paper proposes ADAPT, an adaptive selection method that controls the balance between exploitation and exploration strategies used for customs fraud detection. ADAPT makes use of the model performance trends and the amount of concept drift to determine the best exploration ratio at every time. Experiments on data from four countries over several years show that each country requires a different amount of exploration for maintaining its fraud detection system. We find the system with ADAPT can gradually adapt to the dataset and find the appropriate amount of exploration ratio with high performance.
Stubber: Compiling Source Code into Bytecode without Dependencies for Java Code Clone Detection

A lot of clone detection tools for Java have been introduced in recent years. On the one hand, many of these tools work on Java source code and can thus be conveniently evaluated with the state-of-the-art benchmark BigCloneBench and evaluation framework BigCloneEval. On the other hand, certain clone detectors deliberately address the Bytecode and cannot be applied to Java source code. Simple compilation often does not solve the problem due to missing required dependencies, as, e.g., in case of BigCloneBench. In this work, we therefore present the tool STUBBER for compiling Java source code into Bytecode without dependencies. We can show that using Stubber, Bytecode for over 95% of all Java source files and 92.5% of all code clones contained in BigCloneBench can be successfully generated. As a consequence, the evaluation of Bytecode-based clone detectors on BigCloneBench becomes possible and such tools can also be applied to Java source files, even beyond BigCloneBench.
Using Transfer Learning to contextually Optimize Optical Character Recognition (OCR) output and perform new Feature Extraction on a digitized cultural and historical dataset

Understanding handwritten and printed text is easier for humans but computers do not have the same level of accuracy. While there are many Optical Character Recognition (OCR) tools like PyTesseract <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , Abbyy FineReader <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> which extract the text as digital characters from handwritten or printed text images, none of them are without unrecognizable characters or misspelled words. Spelling correction is one of the well-known tasks in Natural Language Processing. Spelling correction of an individual word could be performed through existing tools, however, correcting a word based on the context of the sentence is a challenging task that requires a human-level understanding of the language. In this paper, we introduce a novel experiment of applying Natural Language Processing using a machine learning concept called Transfer Learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> on the text extracted by OCR tools, thereby optimizing the output text by reducing misspelled words. This experiment is conducted on the OCR output of a sample of newspaper images published between the late 18th century to 19th century. These images were obtained from the Maryland State Archives <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> digital archives project named, the Legacy of Slavery <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> . This Natural Language Processing approach uses pre-trained language transformer models like BERT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> and RoBERTa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> which are used as word-prediction software for spelling correction based on the context of the words in the OCR output. We compare the performance of BERT and RoBERTa on two OCR tool outputs, namely PyTesseract and Abbyy FineReader. A comparative evaluation shows that both the models work fairly well on correcting misspelled words considering the irregularities in the text data from the OCR output. Additionally, with the Transfer Learning output text, a special process is conducted to create a new feature that originally did not exist in the original dataset dataset using Spacy’s Entity Recognizer (ER) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> . This new extracted values are added to the dataset as a new feature. Also, an existing feature’s values are compared to Spacy’s ER output and the original hand transcribed data.
Automatic Satellite Image Stitching Based on Speeded Up Robust Feature

The process of merging multiple images those having overlapping areas and covering different views of the same scene into a composite image is called image stitching. Image stitching can be used in variety of applications such as computer vision, medical image analysis, satellite imaging, photogrammetry etc. Image stitching also used for creating mosaic images which is useful for extending the field of view of the image. Image stitch are generally classified into direct image stitching and feature based image stitching. In this paper we proposed a feature-based satellite image stitching using Speeded Up Robust Features (SURF) algorithm. SURF mainly involves two steps, feature point detection and feature description. Detected feature points of input images are compared using nearest neighbourhood matching for identifying the overlapping points. Affine transformation-based image warping is used for aligning the input images to the mosaic frame. Finally, alpha blending is used for making the final mosaic as perfect smooth image.
Part of Speech Tagging for Tamil Language Using Deep Learning

Part of Speech (POS) tagging is the process of marking up a word in a sentence to a corresponding part of speech. POS tagging is considered one of the pre-processing steps in Natural Language Processing (NLP) applications such as speech recognition, machine translation and sentiment analysis. A few works have been conducted to determine the POS tags for the Tamil words. However, the performance of the POS tagger with unknown words (words that do not appear in the lexicon) is not explored in the literature. The appearance of unknown words is a frequently occurring problem in POS tagging because, in real-world use, the NLP application will encounter words that are not in its lexicon. This paper proposes a deep learning-based POS tagger for the Tamil language using Bi-directional Long Short Term Memory (BLSTM). Our experiments use two corpora, one is AU-KBC annotated corpus, and the other is MeitY corpus. We also analysed the performance of the POS tagger with unknown words. Test results show that the POS tags for Tamil words determined by this approach have 99.8%, 99.5% and 96.5% accuracies for only known words, around 9.8% unknown words and 47.6% unknown words in test sentences respectively.
On the Generalization Abilities of Fine-Tuned Commonsense Language Representation Models

Recently, transformer-based methods such as RoBERTa and GPT-3 have led to significant experimental advances in natural language processing tasks such as question answering and commonsense reasoning. The latter is typically evaluated through multiple benchmarks framed as multiple-choice instances of the former. According to influential leaderboards hosted by the Allen Institute (evaluating state-of-the-art performance on commonsense reasoning benchmarks), models based on such transformer methods are approaching human-like performance and have average accuracy well over 80% on many benchmarks. Since these are commonsense benchmarks, a model that generalizes on commonsense reasoning should not experience much performance loss across multiple commonsense benchmarks. In this paper, we study the generalization issue in detail by designing and conducting a rigorous scientific study. Using five common benchmarks, multiple controls and statistical analysis, we find clear evidence that fine-tuned commonsense language models still do not generalize well, even with moderate changes to the experimental setup, and may, in fact, be susceptible to dataset bias.
Orientation-Aware Feature Fusion Network for Ship Detection in SAR Images

Recently, deep learning methods have been successfully applied to the ship detection in synthetic aperture radar (SAR) images. It is still a great challenge to detect SAR ships, due to the extremely poor image quality and complex background. To solve the problems, a novel method named orientation-aware feature fusion network (OFF-Net) for ship detection in SAR images is proposed in this letter. OFF-Net consists of global context path aggregation (GCPA) module and local rotated contrast enhance (LRCE) module, which fuses the global and local information in feature extraction. First, GCPA module is explored to integrate the global context block with path aggregation network (PAN) to learn the global background information. Second, by designing a rotation scheme based on feature map cyclic shift with four directions, LRCE module is developed to enhance the targets and suppress the background clutters in SAR images. Finally, a decoupled orientation-aware head is proposed to handle the arbitrarily rotated ships more robustly and alleviate the conflict between classification and regression tasks during detection. In addition, a high-resolution SAR-ship detection dataset (OBB-HRSDD) with rotatable bounding boxes is provided. The detection results on the SAR ship detection dataset (SSDD+) and OBB-HRSDD illustrate that our method outperforms all the compared methods. The code and OBB-HRSDD are released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SJX152/papercode</uri>
A Display-Independent Quality Assessment for HDR Images

High Dynamic Range (HDR) images improve visual quality by representing a wide range of luminance in real world. HDR Image Quality Assessment (IQA) is essential in HDR processing technologies. This work focus on the IQA for the HDR applications represented by HDR compression. Towards designing IQA for these tasks, user-end display information should not be considered, given that these tasks only focus on the media intrinsic features. However, most existing HDR metrics are associated with display luminance, hence they can not handle IQA during HDR processing. In this work, we find that it is possible to evaluate HDR image quality without prior knowledge of luminance for display. Based on this, we proposed a low complexity Display-Independent Gradient Magnitude Similarity (DIGMS) metric. Experimental results demonstrate that the proposed DIGMS outperforms the state-of-the-arts in HDR IQA. This work opens up a new perspective for designing IQA algorithms for HDR images.
On the High-Energy Consumption of Bitcoin Mining

A well-known fact is that Bitcoin’s blockchain maintenance using proof-of-work mining consumes a lot of energy. This article explains how this energy expense is inherent to Bitcoin’s design whose purpose is to induce a truly distributed peer-to-peer currency ledger (the blockchain) that is nevertheless trustable.
Multi-scale and multi-channel neural network for click-through rate prediction

Click-through rate (CTR) prediction aims to predict the probability of a user clicking on items and ads. Feature engineering plays an important role in improving the accuracy of CTR predict ion because features are normally multi-field. Recently, existing neural network based approaches mostly focus on the feature interactions in both low- and high-order. However, this methodology ignores salient features in both local and global contexts and diverse semantic features in various aspects. In this paper, we propose a novel framework called Multi-Scale and Multi-Channel neural network (MSMC) to learn the feature importance and feature semantics for enhancing CTR prediction. MSMC consists of two parallel modules: a salient feature encoder (SFE) and a diverse semantic feature encoder (DFE). The SFE employs an attentive global–local contexts module to extract the salient features. The DFE uses an attentive semantic module to aggregate the diverse semantic features. Then, a fusion function is adopted to adaptively combine features from the SFE and the DFE so as to obtain high-order feature interactions. Experimental results on two public real datasets illustrate the effectiveness of MSMC compared to the state-of-the-art CTR prediction methods. Our extensive analysis of MSMC shows how the salient features, diverse semantic features, and fusion function positively impact the performance of CTR prediction.
py2src: Towards the Automatic (and Reliable) Identification of Sources for PyPI Package

Selecting which libraries (‘dependencies’ or ‘packages’ in the industry’s jargon) to adopt in a project is an essential task in software development. The quality of the corresponding source code is a key factor behind this selection (from security to timeliness). Yet, how easy is it to find the ‘actual’ source? How reliable is this information? To address this problem, we developed an approach called py2src to automatically identify GitHub source code repositories corresponding to packages in PyPI and automatically provide an indicator of the reliability of such information. We also report a preliminary empirical evaluation of the approach on the top PyPI packages.
Evaluating regression algorithms at the instance level using item response theory

Algorithm evaluation is a very important task for different Machine Learning (ML) problems. In this work, we assume a different perspective in ML evaluation, in which algorithms are evaluated at the instance level. In this perspective, Item Response Theory (IRT) has recently been applied to algorithm evaluation in ML in order to identify which instances are more difficult and discriminating in a dataset, while also evaluating algorithms based on their predictions for instances with different difficulty values. In IRT, a strong algorithm returns accurate predictions for the most difficult instances, while maintaining a consistent behaviour in the easiest instances. The most common IRT models adopted in the literature only deal with dichotomous responses (i.e., a response has to be either correct or incorrect). This is suitable for evaluating classification algorithms, but not adequate in application contexts where responses are recorded in a continuous scale without an upper bound, such as regression. In this paper we propose the Γ-IRT model, particularly designed for dealing with positive unbounded responses, which we model using a Gamma distribution, parameterised according to respondent ability and item difficulty and discrimination parameters. The proposed parameterisation results in item characteristic curves with more flexible shapes compared to the traditional logistic curves adopted in IRT. We apply the proposed model to evaluate student responses (number of errors) in open-ended questions extracted from Statistics exams. Then, we use Γ-IRT to assess regression model abilities, where responses are the absolute errors in test instances. This novel application represents an alternative for evaluating regression performance and for identifying regions in a regression dataset that present different levels of difficulty and discrimination.
Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Due to the recent COVID-19 pandemic, many people have faced in-home isolation, as every suspected patient must stay at home. The behavior of such isolated people needs to be monitored to ensure that they are staying at home. Using a camera is a very practical method. However, smart bracelets are more convenient when personal privacy is a concern or when the blood oxygen value or heart rate must be monitored. In this study, a low-cost indoor positioning system that uses a Bluetooth beacon, a smart bracelet, and an embedded system is proposed. In addition to monitoring whether a person living alone is active in a specific environment and tracking the heart rate or blood oxygen value under particular conditions, this system can also send early warning signals to specific observation units or relatives through instant messaging software.
MC‐Net: Learning mutually‐complementary features for image manipulation localization

Deep learning has become an emerging technical for image manipulation localization, which can automatically recognize abnormal traces caused by manipulation. However, as manipulations mainly happens in the foreground regions, these methods largely focus on the foreground contents and neglect the background, which contain complementary signal for fully understanding the image and are meaningful for manipulation localization. We propose a Mutually-Complementary Network (MC-Net), which is a two-branch network to operate the foreground and background features, respectively. To distill complementary signals from the features, we propose a mutual attentive module composed of self-feature attentive, and cross-feature attentive components to advance the communication across the foreground and background branches. Extensive qualitative and quantitative experiments demonstrate that our proposed MC-Net distinctly improves the prediction of foreground and background, obtains consistent performance increments on four benchmark data sets, and significantly outperforms the state-of-the-art methods.
Fault Injecting Co-simulations for Safety

Robotic systems, either operating alone or in fleets, in order to be trusted, have to provide certain safety guarantees to ensure no harm comes to neither humans, environment, other robots or equipment. Co-simulation tools enable the integration of models from different modelling applications and can be adopted to ensure the safe behaviour of such systems using simulation. Different parameters of the included models can be explored, including safety aspects, by simulating different sets of scenarios. Of particular interest are those scenarios in which different system components fail at different times, potentially leading to safety violations. The core contribution of this paper is a simple but powerful fault injection mechanism, that enables developers to seamlessly inject faults in their co-simulations. We present an initial evaluation of the proposed mechanism through a water-tank case-study, to discuss its applicability and benefits for system evaluation during the development stage. Thereafter, we discuss the value of this work in the context of autonomous agricultural robot fleets, and outline our next steps in this direction.
Performance Comparison of Several Deep Learning-Based Object Detection Algorithms Utilizing Thermal Images

The recent advancement in deep learning approaches of machine learning and computer vision technology has paved the way for many advancements in object detection prediction models used in a variety of fields. Object detection algorithms are improving continuously. Many of the common Application Program Interfaces (APIs) or libraries can be used for this purpose. The most common techniques are implemented in Google TensorFlow object detection. Each Object detection technique has its advantages and disadvantages. Utilizing such models in thermal imaging is exceptionally necessary and required in systems such as automated driving and security system at night. A direct comparison between the most common state-of-the-art standard object detection methods helps in finding the best solution for thermal image detection/recognition systems. This paper discusses these algorithms and compares them in terms of accuracy and classification loss, we compare the performance of thermal image detection for the six most suitable object detection models that are supported by TensorFlow 2.0. Since we only have a small dataset (56) images, we enlarge it by cloning new images from the original ones with some variations in order to increase the number of images in the dataset. We perform a comparison among these models to select the most suitable algorithm for head detection utilizing thermal images, to be used to predict a human temperature entering a building or a campus.
VAEPass: A lightweight passwords guessing model based on variational auto-encoder

Password guessing has attracted considerable attention in recent years. With the successful application of deep learning (DL) methods in natural language processing, password guessing models that leverage deep learning techniques by treating passwords as short texts, e.g., Recurrent Neural Network-based model and PassGAN model, have been confirmed to be more effective in terms of generalizability. However, the architectures of these existing DL-based password guessing models are typically extremely complex, which makes the process of training and password generation time-consuming. It is desirable to build a lightweight password guessing model that can reduce the time required for model training while maintaining the password guessing effect. In this study, we propose VAEPass, a lightweight password guessing model based on a Variational Auto-Encoder (VAE), which comprises of an encoder and a decoder established using Gated Convolutional Neural Network (GCNN). Furthermore, we improve the proposed VAEPass model to treat common character combinations summarized from training passwords as tokens and guess passwords, known as VAEPasstoken, at a token-level. Experiments demonstrate that the matching rate of the proposed VAEPasstoken is 2.7%∼9.3% higher than that of PassGAN method in the one-site test. Moreover, compared with the state-of-the-art PassGAN model (i.e., a DL-based model), the parameters in VAEPass are approximately 32% of that in PassGAN and the training time required by VAEPass is approximately 11% of the time required by PassGAN.
Towards Multi-class Sentiment Analysis With Limited Labeled Data

Analyzing public sentiment about an entity or issue can be of interest to governments and businesses alike. There is a growing body of research that attempt to devise new sentiment analysis techniques, especially techniques based on machine learning. These machine learning-based techniques typically require large, labeled training data with a large number of instances for training in order to provide reasonable accuracy in sentiment analysis. However, labelling large volumes of data is tedious and expensive. In this paper, we propose a multi-class sentiment analysis technique, named SG-Elect, utilizing cutting-edge transformer based pre-trained models along with more traditional machine learning based approaches in a semi-supervised setting. Our experiments demonstrate that SG-Elect outperforms a recent state-of-the-art baseline for all three datasets.