Reviewerly/exHarmony huggingface dataset:
AdaFlow: A Framework for Adaptive Dataflow CNN Acceleration on FPGAs

To meet latency and privacy requirements, resource-hungry deep learning applications have been migrating to the Edge, where IoT devices can offload the inference processing to local Edge servers. Since FPGAs have successfully accelerated an increasing number of deep learning applications (especially CNN-based ones), they emerge as an effective alternative for Edge platforms. However, Edge applications may present highly unpredictable workloads, requiring runtime adaptability in the inference processing. Although some works apply model switching on CPU and GPU platforms by exploiting different pruning rates at runtime, so the inference can adapt according to some quality-performance trade-off, FPGA-based accelerators refrain from this approach since they are synthesized to specific CNN models. In this context, this work enables model switching on FPGAs by adding to the well-known FINN accelerator an extra level of adaptability (i.e., flexibility) and support to the dynamic use of pruning via fast model switch on flexible accelerators, at the cost of some extra logic, or via FPGA reconfigurations of fixed accelerators. From that, we developed AdaFlow: a framework that automatically builds, at design time, a library from these new available versions (flexible and fixed, pruned or not) that will be used, at runtime, to dynamically select a given version according to a user-configurable accuracy threshold and current workload conditions. We have evaluated AdaFlow under a smart Edge surveillance application with two CNN models and two datasets, showing that AdaFlow processes, on average, 1.3× more inferences and increases, on average, 1.4× the power efficiency over state-of-the-art statically deployed dataflow accelerators.
BTS-GAN: Computer-aided segmentation system for breast tumor using MRI and conditional adversarial networks

Breast tumor is one of the most prominent indicators for the diagnosis of breast cancer. The precise segmentation of tumors is crucial for enhancing the accuracy of breast cancer detection. A physician's assessment of the MRI scan is time-consuming and require a lot of human effort and expertise. Furthermore, traditional medical segmentation approaches frequently need prior information or manual feature extraction, resulting in a subjective diagnosis. Therefore, the development of an automated image segmentation approach is essential for clinical applications. This work presents BTS-GAN, an automatic breast tumor segmentation process using conditional GAN (cGAN) in Magnetic Resonance Imaging (MRI) scans. First, we used an encoder-decoder deep network with skip connections between encoder and decoder for the generator to increase the localization efficiency. Second, we utilized a parallel dilated convolution (PDC) module to retain the features of various sizes of masses and to effectively extract information about the masses' edges and interior texture. Third, an extra classification-related constraint is included to the loss function of the cGAN for mitigating the hard-to-converge challenge in image-to-image (I2I) translation tasks based on classification. The generator side of our proposed model learns to detect the tumor and construct a binary mask, while the discriminator learns to distinguish between ground truth and synthetic masks, driving the generator to produce masks as genuine as possible. The experimental results demonstrate that our BTS-GAN is more efficient and reliable for breast tumor segmentation and outperform other segmentation techniques in terms of the IoU and Dice coefficient on the publicly available RIDER breast cancer MRI dataset. Our proposed model achieved an average IoU and Dice scores of 77% and 85% respectively.
Gaze as an Indicator of Input Recognition Errors

Input recognition errors are common in gesture- and touch-based recognition systems, and negatively affect user experience and performance. When errors occur, systems are unaware of them, but the user's gaze following an error may provide valuable cues for error detection. A study was conducted using a manual serial selection task to investigate whether gaze could be used to discriminate user-initiated selections from injected false positive selection errors. Logistic regression models of gaze dynamics could successfully identify injected selection errors as early as 50 milliseconds following a selection, with performance peaking at 550 milliseconds. A two-phase gaze pattern was observed in which users exhibited high gaze motion immediately following errors, and then decreased gaze motion as the error was noticed. Together, these results provide the first demonstration that gaze dynamics can be used to detect input recognition errors, and open new possibilities for systems that can assist with error recovery.
Mining commit messages to enhance software refactorings recommendation: A machine learning approach

Software refactoring is the common practice that is applied to improve the internal structure of software systems without altering their external behaviors. Software developers sometimes apply refactoring to prepare software systems for further extensions of requirements or adaptation to new requirements often presented as feature requests. However, in such context, identifying where and what type of refactoring to use is very challenging and mostly relies on developer's intuition and experience. To facilitate refactorings selection during feature requests implementation, existing studies have relied on the past software change history to predict and recommend future refactorings. However, none of these approaches have attempted to exploit the potential of commit messages to drive refactoring recommendation. To this end, this paper proposes a machine-learning approach trained with the past history of previously applied refactorings detected using both traditional refactoring detectors and analysis of commit messages. The approach implements binary classifier to predict the need for refactoring, and a multi-label classifier to recommend required refactorings. The evaluation of the proposed approach based on the dataset comprised of commit messages of 65 open source projects suggest that, the approach significantly outperforms the state-of-the-art approach.
Joint prediction of monocular depth and structure using planar and parallax geometry

Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data. However, LiDAR can only generate sparse 3D maps which causes losing information. Obtaining high-quality ground-truth depth data per pixel is difficult to acquire. In order to overcome this limitation, we propose a novel approach combining structure information from a promising Plane and Parallax geometry pipeline with depth information into a U-Net supervised learning network, which results in quantitative and qualitative improvement compared to existing popular learning-based methods. In particular, the model is evaluated on two large-scale and challenging datasets: KITTI Vision Benchmark and Cityscapes dataset and achieve the best performance in terms of relative error. Compared with pure depth supervision models, our model has impressive performance on depth prediction of thin objects and edges, and compared to structure prediction baseline, our model performs more robustly.
SceneSketcher-v2: Fine-Grained Scene-Level Sketch-Based Image Retrieval Using Adaptive GCNs

Sketch-based image retrieval (SBIR) is a long-standing research topic in computer vision. Existing methods mainly focus on category-level or instance-level image retrieval. This paper investigates the fine-grained scene-level SBIR problem where a free-hand sketch depicting a scene is used to retrieve desired images. This problem is useful yet challenging mainly because of two entangled facts: 1) achieving an effective representation of the input query data and scene-level images is difficult as it requires to model the information across multiple modalities such as object layout, relative size and visual appearances, and 2) there is a great domain gap between the query sketch input and target images. We present SceneSketcher-v2, a Graph Convolutional Network (GCN) based architecture to address these challenges. SceneSketcher-v2 employs a carefully designed graph convolution network to fuse the multi-modality information in the query sketch and target images and uses a triplet training process and end-to-end training manner to alleviate the domain gap. Extensive experiments demonstrate SceneSketcher-v2 outperforms state-of-the-art scene-level SBIR models with a significant margin.
Creating standards for Canadian health data protection during health emergency – An analysis of privacy regulations and laws

Health emergencies require unprecedented measures to protect the public from the health disaster. Such measures may require limiting the exercise of personal freedom and other rights like the right to data privacy. The limitation, however, should be temporary and proportionate so that privacy rights are not compromised superfluously. In this aspect, the European Union (EU) implemented better data protection measures and guided the government and various entities on the acceptable ways of handling data during a pandemic, though the measures taken were not very comprehensive. Canadian privacy laws in general are sector driven and not harmonised at the national level and there is no new guidance on the usage of data during emergencies. Hence, this research will analyse laws and regulation in EU and Canada with a view to understanding the necessity of amending privacy laws in Canada to make it relevant, up-to-date and in compliance with EU data protection requirements so data sharing from EU countries could be made easy. It further encapsulates appropriate standards for Canada health data protection for better management of health data privacy.
Keyword expansion techniques for mining social movement data on social media

Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.
The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg"><mml:mi>k</mml:mi></mml:math>-sparse LSR for subspace clustering via 0-1 integer programming

Subspace clustering is a powerful technology for clustering high-dimensional data. Least squares regression (LSR) is one of the classical subspace clustering models because of the explicit solution and grouping effect that tends to cluster highly correlated data together. However, LSR lacks sparsity, which may lead to its sensitivity to noise and dimension of data. Since the ℓ0-norm constrained optimization is difficult to solve, we introduce a new method based on the 0-1 integer programming, named k-sparse LSR (KSLSR), to directly solve the optimization of the LSR constrained by an ℓ0-norm-based constraint without any relaxation, where k represents the maximum number of non-zero elements of the representation coefficient. With the help of the 0-1 integer constraint, the ℓ0-norm-based constraint is equivalent to two continuous constraints. Thus, the obtained KSLSR method with two continuous constraints is equivalent to the ℓ0-norm constrained optimization and can be effectively solved by the alternating direction multiplier method (ADMM). In addition, the new method is proven to provide a solution with both k-sparsity and the grouping effect, which makes it robust to the noise and dimension of data. Extensive experiments are conducted to demonstrate the effectiveness and superiority of the proposed method.
A coding layer robust reversible watermarking algorithm for digital image in multi-antenna system

Although existing robust reversible watermarking (RRW) schemes are robust to image processing attacks, they do not consider the effect of noise on the encoded digital images when they are transmitted in the wireless channel. In this paper, we propose a coding layer robust reversible watermarking (CL-RRW) algorithm for digital images in multi-antenna system. Through the specially designed preprocessing steps for the encoded carrier image and the embedding method of secret information, the scheme makes full use of the error correction capability of channel coding, and makes the impact of the embedding of secret information on the carrier approximate to natural noise. We analyse the proposed scheme based on multi-antenna systems and Rayleigh fading channels, which can simulate more realistic wireless communication scenarios. The analysis shows that in this scenario, the embedding capacity (EC) can reach the upper bound more quickly as the signal-to-noise ratio (SNR) increases. Within the deduced EC, the difference in peak signal-to-noise ratio (PSNR) between the original and stego images does not exceed 10%. Experimental results show that the proposed algorithm is entirely resistant to image processing attacks, while enabling the receiver to obtain high-quality carrier images and low bit error rate (BER) secret information.
On the Road to Digital Pathology in Denmark—National Survey and Interviews

Digital pathology (DP) is changing pathology departments dramatically worldwide, yet globally, few departments are presently digitalized for the full diagnostic workflow. Denmark is also on the road to full digitalization countrywide, and this study aim to cover experiences during the implementation process in a national context. Thus, quantitative questionnaires were distributed to all pathology departments in Denmark (n = 13) and distributed to all professions including medical clinical directors, medical doctors (MD) and biomedical laboratory scientists (BLS). For a qualitative perspective, we interviewed four employees representing four professions. Data were collected in 2019–2020. From the questionnaire and interviews, we found strategies differed at the Danish departments with regards to ambitions, technological equipment, workflows, and involvement of type of professions. DP education was requested by personnel. Informants were in general positive toward the digital future but mainly had concerns regarding the political pressure to integrate DP before technological advances are sufficient for maintaining rational budgets, workflows, and for sustaining diagnostic quality. This study is a glance on the Danish implementation process in its early stages from personnel’s point of view. It shows the complexity when large new workflow processes are to be implemented countrywide and with a large diversity of stakeholders like managers, MD, BLS, IT-professionals, and authorities. To ensure best technological and economical solutions and to maintain—or even optimize—diagnostic quality with DP and workflow alignment, we suggest superior inter- and intradepartmental communication. When implementing DP countrywide, a national working group is warranted with the variety of stakeholders represented.
Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning

Abstract In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.
Resolution of the burrows-wheeler transform conjecture

research-article Open Access Share on Resolution of the burrows-wheeler transform conjecture Authors: Dominik Kempa Johns Hopkins University, Baltimore, MD Johns Hopkins University, Baltimore, MDView Profile , Tomasz Kociumaka University of California, Berkeley, CA University of California, Berkeley, CAView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 6June 2022 pp 91–98https://doi.org/10.1145/3531445Online:20 May 2022Publication History 0citation5,249DownloadsMetricsTotal Citations0Total Downloads5,249Last 12 Months5,249Last 6 weeks978 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Bengali Hate Speech Detection in Public Facebook Pages

Hate speech is a form of negative communication intended to harm people and communities. Hate speech is quite common in the real world, and it has reached alarming proportions on social media as well. These days our lives have become increasingly reliant on social media platforms, such as Facebook. This is due to the rapid advancement of technology and communication. In Bangladesh, the number of people using social media platforms is also rapidly increasing. In English, detecting hate speech on social media is a difficult task. Comparatively, Bengali is a complicated language with few datasets available. As a result, detection of Bengali hate speech becomes even more challenging. In this paper, we present a new dataset of 10,133 user comments. We have collected them from the comment section of various public Facebook pages. We explore the performance of various machine learning and deep learning models in detecting hate speech. Bengali pre-trained word embeddings from fastText are used to train the models. We are especially interested in Convolutional Neural Network (CNN). To our knowledge it was never used for hate speech detection in binary classification. Another goal of this research is to create a new and large dataset, which will facilitate further research of Bengali Hate Speech Detection. All machine learning and deep learning models performed very well from our experiments. But, Support Vector Machine (SVM) is the one that performed the best among them.
A hybrid CTC+Attention model based on end-to-end framework for multilingual speech recognition

Speech recognition is an important field in natural language processing. In this paper, the end-to-end framework for speech recognition with multilingual datasets is proposed. The end-to-end methods do not require complicated alignment and construction of the pronunciation dictionary, which show a promising prospect. In this paper, we implement a hybrid model of CTC and attention (CTC+Attention) model based on PyTorch. In order to compare speech recognition methods for multiple languages, we design and create three datasets: Chinese, English, and Code-Switch. We evaluate the proposed hybrid CTC+Attention model in multilingual environment. Throughout our experiments, we find that the proposed hybrid CTC+Attention model based on end-to-end framework achieves better performance compared with the HMM-DNN model in a single language and Code-Switch speaking environment. Moreover, the results of speech recognition with regard to different languages are compared in this paper. The CER(i.e., Character Error Rate) of the proposed hybrid CTC+Attention model based on the Chinese dataset defeated the traditional model and reached 10.22%.
BCvoteMDE: A Blockchain-based E-Voting Scheme for Multi-District Elections

The traditional electronic voting (e-voting) has the problems of dead vote, repetition and missing registration, which cannot reflect the voting result correctly, objectively and fairly. With the booming development of blockchain in recent years, blockchain technology provides a new solution in the field of e-voting. Multi-District election is an important method of election in real life, e.g. in US presidential election. Inspired by the electoral college system, we propose a blockchain-based e-voting scheme for multi-district election. First, we propose a blockchain-based voter registration method that all voters are authenticated by the blockchain system. Second, we design a two-layer blockchain architecture, where the lower layer records the votes of voters in each district and the higher layer records the votes of electors. Then we describe the voting process and evaluate the security and availability of the proposed scheme. The experimental results show that the proposed scheme can satisfy the needs of multi-district election.
Fine-Tuned Convolutional Neural Networks for Bangladeshi Vehicle Classification

Classification of vehicles plays an important role in the intelligent transport system. In this paper, we propose a framework for the classification of Bangladeshi vehicle images based on fine-tuned convolutional neural networks (CNNs). Using the proposed framework and an unified experimental setting, we fine-tuned fifteen popular CNN architectures, namely, AlexNet, Inception-V3, VGG-II, VGG-13, VGG-16, VGG-19, ResNet18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, DenseNet121, DenseNet-161, DenseNet-169 and DenseNet-201 pretrained on ImageNet dataset on a public Bangladeshi vehicle image dataset. We conduct a systematic and comprehensive analysis on the performance of the fine-tuned CNNs which leads to new insights. Our experimental results show that ResNet-152 and DenseNet-201 fine-tuned with our proposed strategy provide excellent performance for the classification of Bangladeshi vehicle images. Our implementation is available at https://github.con MinhajurRFahad/bd-vehicle-cnn-benchmark.