Reviewerly/exHarmony huggingface dataset:
DAPSTEP: Deep Assignee Prediction for Stack Trace Error rePresentation

The task of finding the best developer to fix a bug is called bug triage. Most of the existing approaches consider the bug triage task as a classification problem, however, classification is not appropriate when the sets of classes change over time (as developers often do in a project). Furthermore, to the best of our knowledge, all the existing models use textual sources of information, i.e., bug descriptions, which are not always available. In this work, we explore the applicability of existing solutions for the bug triage problem when stack traces are used as the main data source of bug reports. Additionally, we reformulate this task as a ranking problem and propose new deep learning models to solve it. The models are based on a bidirectional recurrent neural network with attention and on a convolutional neural network, with the weights of the models optimized using a ranking loss function. To improve the quality of ranking, we propose using additional information from version control system annotations. Two approaches are proposed for extracting features from annotations: manual and using an additional neural network. To evaluate our models, we collected two datasets of real-world stack traces. Our experiments show that the proposed models outperform existing models adapted to handle stack traces. To facilitate further research in this area, we publish the source code of our models and one of the collected datasets.
Framework for Blockchain Technology Adoption in Supply Chain for Small and Medium Indonesian Urban Farming: A Case Study

This research focuses on designing a tracking system with blockchain technology to address the lack of transparency in supply chain flow in urban farming. Unlike the original distribution channel (farmers to consumers), this study proposes two distribution channels: a distribution channel with farmers, wholesalers, retailers, and consumers, and a distribution channel consisting of farmers, retailers, and consumers. A tracking system for suppliers and producers in an urban farming company is designed and implemented with peer-to-peer private blockchain technology using RFID, Raspberry Pi, Golang, and BitTorrent protocols. The study result helps to narrow the gap in theoretical and practical literature research on blockchain technology in supply chain management. It shows how blockchain technology supports tracking systems for suppliers and producers to minimize data falsification and maximize transparency between related parties for small and medium enterprises (SMEs) supply chains in Indonesia. Further study can be done to create a real-time system where researchers can develop a real-time peer-to-peer system.
Linguistic Extension of UTAU Singing Voice Synthesis and Its Application from Japanese to Mandarin

The Mandarin singing voice synthesis by UTAU is investigated. The UTAU software is a Japanese singing voice synthesis system that is widely used to create variable musical content. As a state-of-the-art singing voice synthesis system, UTAU synthesizes singing voices of reasonable quality. However, many efforts are still required to obtain multilingual singing synthesis. Japanese pitch changes only between syllables, while Mandarin pitch changes within the syllables. In addition, Mandarin needs pinyin to look up words in the dictionary. To use UTAU for Mandarin, we propose a linguistic extension of UTAU. The extension contains modules in the front and back ends. The front-end GUI module makes the lyrics more readable. The back-end pitch bend editor adds four tones of Mandarin to a melody. The application of UTAU software especially for Mandarin singing is presented and evaluated in subjective experiments. Then, Japanese and Mandarin singing voice syntheses are compared. As a result, the tonal production accuracy and naturalness of synthesized singing in Mandarin is significantly improved. Therefore, the extension is to offer a direct way for other languages.
Chatbot Script Design for Programming Language Learning

Chatbots have been used to teach programming languages for novice students of introductory courses. Code Tutor helps students learn JAVA online with coding suggestions and error corrections. ProgBot provides three language modules including C++, JAVA, and Python, and adaptively selects quizzes according to students&#x2019; progress. Python-Bot assists students with programming concepts and predefined questions. If necessary, it makes an appointment with teaching assistants or teachers for a face-to-face consultation. A chatbot with a Facebook Messenger interface using the IBM Watson service was also used for non-technical students of the Multimedia Design Department at Chihlee University of Technology in Taiwan. According to our survey after the test, the feature that students need most is to help them learn programming logic. To facilitate the completion of the chatbot, the scripts are redesigned based on the supportive information requirements of the four-component instructional design (4C/ID). A framework is also developed for the scripts of a chatbot for novice students. Hopefully, the results is helpful for others who wish to build their chatbots to teach students programming languages.
Data Security, Privacy, and Regulatory Issues: A Conceptual Approach to Digital Transformation to Smart Cities

The worldwide network of computers moves a huge amount of electronic information and, therefore, it has become an integral part of our daily lives. The most diverse types of companies, such as hospitals, banks, stock exchanges, educational institutions, and power generation industries use this network to efficiently operate their processes, which range from collecting, processing, storing, and sharing large amounts of digital information. As more digital information is collected and shared, protecting that information becomes even more essential for national security and economic stability. Information security is the ongoing effort to protect these networked systems and all data from unauthorized or harmful use. As an individual, we have a responsibility to protect our identity, our data, and our computing devices. At the corporate level, it is everyone’s responsibility to protect the company’s reputation, data, and customers. As a state, national security and the well-being of citizens are at stake. Therefore, consolidating data and privacy competencies, integrating available technologies, can contribute to the better implementation, operation, and maintenance of compliance and privacy programs in organizations. In addition to being up-to-date with new technologies, ensuring that information security management methods and infrastructure are essential for a free society that has its privacy rights guaranteed.
Thermal Image-Based Dense Containers and Packaging Garbage Classification for Recycling Robots

Due to the current labor shortage, automation by robots has been expected in society. In manual sorting of garbage in recycling factories, there is a risk of injury due to sharp garbage, and robots are needed to replace them. In this paper, we propose an improved method of garbage sorting using thermal images. Previously, we classified three types of beverage container garbage from thermal images, but it could not cope with dense garbage. In this work, material classification is performed for each pixel of the thermal image, followed by clustering to correctly separate and classify garbage, even dense garbage. In the experiment, we collected thermal images of heated garbage on hot conveyor for classification, and verified the accuracy of the computed material and object maps, and compared them with previous work.
Design of methods for impact detection in geotechnical protection fences using Machine Learning

The paper is focused on the design of methods for classifying events on the protection fence. Two methods for the classification of data obtained from accelerometers based on LSTM neural networks are proposed. Own data sets are used for training and testing. The method of evaluation of individual methods using F-score is also described here. A hypothesis is formulated to compare the proposed methods, based on a comparison of the proposed methods based on the evaluation results. The proposed methods can be implemented directly into individual IoT sensor modules within monitoring systems.
What Happens in Crowd Scenes: A New Dataset About Crowd Scenes for Image Captioning

Making machines endowed with eyes and brains to effectively understand and analyze crowd scenes is of paramount importance for building a smart city to serve people. This is of far-reaching significance for the guidance of dense crowds and accident prevention, such as crowding and stampedes. As a typical multimodal scene understanding task, image captioning has always attracted widespread attention. However, crowd scene understanding captioning is rarely studied due to the unobtainability of related datasets. Therefore, it is difficult to know what happens in crowd scenes. In order to fill this research gap, we propose a crowd scenes caption dataset named CrowdCaption which has the advantages of crowd-topic scenes, comprehensive and complex caption descriptions, typical relationships and detailed grounding annotations. The complexity and diversity of the descriptions and the specificity of the crowd scenes make this dataset extremely challenging to most current methods. Thus, we propose a Multi-hierarchical Attribute Guided Crowd Caption Network (MAGC) based on crowd objects, actions, and status (such as position, dress, posture, etc.) aiming to generate crowd-specific detailed descriptions. We conduct extensive experiments on our CrowdCaption dataset, and our proposed method reaches the state-of-the-art (SoTA) performance. We hope the CrowdCaption dataset can assist future studies related to crowd scenes in the multimodal domain.
An Efficient Method for Calculating the Doppler Spectrum of an Arbitrarily Shaped Object in Uniform Motion

In the article, an efficient numerical method for calculating the spectrum of the scattering fields of an arbitrarily shaped object in uniform motion is proposed. This method relies on the frame hopping technique stemming from the special theory of relativity (STR). Firstly, the incident monochromatic plane wave is transformed to the comoving inertial frame in which the object is stationary. Next, the scattering fields of the moving object at the sampling time points are obtained in the comoving frame by solving the electromagnetic (EM) scattering problem with the conventional numerical method in the frequency domain. Then, the spectrum of the scattering fields in the frame in which the observer remains stationary is obtained using the discrete Fourier transform (DFT). The asymptotic expressions for the scattering fields when the moving object is far away from the observation point are derived, by applying which the computation efficiency could be greatly improved.
A Decoder for a Lightweight McEliece Cryptosystem Based on Concatenated Codes

Large-scale quantum computers threaten the security of today's public-key cryptography. The McEliece cryptosystem is one of the most promising candidates for postquantum cryptography. However, the McEliece system has the drawback of large key sizes for the public key. Similar to other public-key cryptosystems, the McEliece system has a comparably high computational complexity. Embedded devices often lack the required computational resources to compute those systems with sufficiently low latency. Hence, those systems require hardware acceleration. Lately, a generalized concatenated code construction was proposed together with a restrictive channel model, which allows for much smaller public keys for comparable security levels. In this work, we propose a hardware decoder suitable for a McEliece system based on these generalized concatenated codes. The results show that those systems are suitable for resource-constrained embedded devices.
Trip-A Complete Tourism Solution

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Semantic Equalization Learning for Semi-Supervised SAR Building Segmentation

Synthetic aperture radar (SAR) building segmentation, which is one of the fundamental tasks in the remote sensing community, has been achieved remarkable performance using convolutional neural networks (CNNs). Since most methods do not consider distinctive characteristics of SAR images, they tend to be biased towards simple and large buildings while ignoring small and complex-shaped ones. To build a general and powerful SAR building segmentation model, in this letter, we introduce a semi-supervised learning (SSL) framework with semantic equalization learning (SEL). Concretely, we leverage labeled SAR and EO image pairs and unlabeled SAR images for SSL to extract representative SAR features with the help of context-rich EO features. Moreover, SEL aims to balance the training of well and poor-performing samples via our purposed data augmentation technique and the objective functions. It consists of a semantic proportional CutMix (SP-CutMix) module to increase the sampling probability of under-performed samples during the training phase, and an equalized segmentation loss (ESL) to adjust the loss contribution depending on difficulties. By doing so, our method prevents the model from being biased to easy samples and increases the performance of difficult building samples. Experimental results on the SpaceNet-6 benchmark demonstrate the effectiveness of our framework, especially by significantly improving the most challenging scenarios, that is less labeled data available.
Efficient Region of Interest Based Metric Learning for Effective Open World Deep Face Recognition Applications

Face Recognition (FR) has recently gained traction as a widely used biometric for security-based applications such as facial recognition payment. The widespread use is due to improvements in deep convolutional neural networks (CNN) and large datasets. However, FR is still an ill-posed problem, especially in an open world scenario. Existing FR methods require finetuning, classifier retraining, or global metric learning to improve the performance for effective domain adaptation. It incurs an undesirable downtime. Open world FR must identify the persons for whom the FR model is not trained. It also produces imbalanced pairs, giving a false sense of high performance. The popular fixed threshold strategies, such as &#x03C3; values, also lead to sub-optimal performance. This paper proposes a fast and efficient threshold adapter algorithm using an effective Region of Interest (ROI) setting for metric learning. It uses five different ROI schemes to find an adaptive threshold in real-time. The algorithm also determines the FR model quality and usability after new enrolments. To establish the effectiveness, we investigated various threshold finding strategies for five state-of-the-art face recognition algorithms for open world adaptation on different datasets. We also proposed a novel performance evaluation metric for FR algorithms on imbalanced datasets. Experimental results demonstrated that the proposed metric learning is up to 12 times faster than the nearest competitor while reporting higher accuracy and fewer errors. The study suggests that the F1-score is vital as a performance indicator for imbalanced pair evaluation, and accuracy at the highest reported F1-score is the desired metric for benchmarking FR algorithms in open world.
CNN Models Acceleration Using Filter Pruning and Sparse Tensor Core

Convolutional neural network (CNN) is a state-of-the-art technique in machine learning and has achieved high accuracy in many computer vision applications. The number of the parameters of the CNN models is fast increasing for improving accuracy; therefore, it requires more computation time and memory space for both training and inference. As a result, reducing the model size and improving the inference speed have become critical issues for CNN. This paper focuses on filter pruning and special optimization for NVIDIA sparse tensor core. Filter pruning is a model compression technique that evaluates the importance of filters in the CNN model and removes the less critical filters. NVIDIA sparse tensor core is special hardware for CNN computation from NVIDIA Ampere GPU architecture, which can speed up a matrix multiplication if the matrix has a structure that manifests as a 2:4 pattern. This paper proposed hybrid pruning to prune the CNN models. The hybrid pruning combines filter pruning and 2:4 pruning. We apply filter pruning to remove the redundant filters to reduce the model size. Next, we use 2:4 pruning to prune the model according to a 2:4 pattern to utilize the sparse tensor core hardware for speedup. In this hybrid pruning scenario, we also proposed two hybrid metrics to decide the filter’s importance during filter pruning. The hybrid ranking metrics preserve the essential filters for both pruning steps and achieve higher accuracy than traditional filter prunings by considering both metrics. We test our hybrid pruning algorithm on MNIST, SVHN, CIFAR-10 datasets using AlexNet. Our experiments concluded that our hybrid metrics achieve better accuracy than the classic L1-norm metric and the output L1-norm metric. When we prune away 40 percent of filters in the model, our methods have 2.8% to 3.3%, 2.9% to 3.5%, 2.5% to 2.7% higher accuracy than the classic L1-norm metric and the output L1-norm metric on these three datasets. We also evaluate the inference speed of the model from our hybrid pruning. We compare the hybrid pruning model with the models that result from either filter pruning or 2:4 pruning. We find that a hybrid pruning model runs up to 1.3x faster than the traditional filter pruning model with similar accuracy.
CAA-Net: End-to-End Two-Branch Feature Attention Network for Single Image Dehazing

In this paper, we propose an end-to-end two-branch feature attention network. The network is mainly used for single image dehazing. The network consists of two branches, we call it CAA-Net: 1) A U-NET network composed of different-level feature fusion based on attention (FEPA) structure and residual dense block (RDB). In order to make full use of all the hierarchical features of the image, we use RDB. RDB contains dense connected layers and local feature fusion with local residual learning. We also propose a structure which called FEPA.FEPA structure could retain the information of shallow layer and transfer it to the deep layer. FEPA is composed of serveral feature attention modules (FPA). FPA combines local residual learning with channel attention mechanism and pixel attention mechanism, and could extract features from different channels and image pixels. 2) A network composed of several different levels of FEPA structures. The network could make feature weights learn from FPA adaptively, and give more weight to important features. The final output result of CAA-Net is the combination of all branch prediction results. Experimental results show that the CAA-Net proposed by us surpasses the most advanced algorithms before for single image dehazing.
Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information

To enhance trustworthiness of AI systems, a number of solutions have been proposed to document how such systems are built and used. A key facet of realizing trust in AI is how to make such systems accountable - a challenging task, not least due to the lack of an agreed definition of accountability and differing perspectives on what information should be recorded and how it should be used (e.g., to inform audit). Information originates across the life cycle stages of an AI system and from a variety of sources (individuals, organizations, systems), raising numerous challenges around collection, management, and audit. In our previous work, we argued that semantic Knowledge Graphs (KGs) are ideally suited to address those challenges and we presented an approach utilizing KGs to aid in the tasks of modelling, recording, viewing, and auditing accountability information related to the design stage of AI system development. Moreover, as KGs store data in a structured format understandable by both humans and machines, we argued that this approach provides new opportunities for building intelligent applications that facilitate and automate such tasks. In this paper, we expand our earlier work by reporting additional detailed requirements for knowledge representation and capture in the context of AI accountability; these extend the scope of our work beyond the design stage, to also include system implementation. Furthermore, we present the <i>RAInS</i> ontology which has been extended to satisfy these requirements. We evaluate our approach against three popular baseline frameworks, namely, Datasheets, Model Cards, and FactSheets, by comparing the range of information that can be captured by our KGs against these three frameworks. We demonstrate that our approach subsumes and extends the capabilities of the baseline frameworks and discuss how KGs can be used to integrate and enhance accountability information collection processes.
Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed.Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence was segmented according to the recognition results.Secondly, the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method, and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors, and the monitoring sequence correlation was further used to verify the assessment.Finally, the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.
Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble

Gliomas are the most common primary malignant brain tumors. Accurate segmentation and quantitative analysis of brain tumor are critical for diagnosis and treatment planning. Automatically segmenting tumors and their subregions is a challenging task as demonstrated by the annual Multimodal Brain Tumor Segmentation Challenge (BraTS). In order to tackle this challenging task, we trained 2D non-local Mask R-CNN with 814 patients from the BraTS 2021 training dataset. Our performance on another 417 patients from the BraTS 2021 training dataset were as follows: DSC of 0.784, 0.851 and 0.817; sensitivity of 0.775, 0.844 and 0.825 for the enhancing tumor, whole tumor and tumor core, respectively. By applying the focal loss function, our method achieved a DSC of 0.775, 0.885 and 0.829, as well as sensitivity of 0.757, 0.877 and 0.801. We also experimented with data distillation to ensemble single model’s predictions. Our refined results were DSC of 0.797, 0.884 and 0.833; sensitivity of 0.820, 0.855 and 0.820.