Reviewerly/exHarmony huggingface dataset:
TraceSim: a method for calculating stack trace similarity

Many contemporary software products have subsystems for automatic crash reporting. However, it is well-known that the same bug can produce slightly different reports. To manage this problem, reports are usually grouped, often manually by developers. Manual triaging, however, becomes infeasible for products that have large userbases, which is the reason for many different approaches to automating this task. Moreover, it is important to improve quality of triaging due to a large volume of reports that needs to be processed properly. Therefore, even a relatively small improvement could play a significant role in the overall accuracy of report bucketing. The majority of existing studies use some kind of a stack trace similarity metric, either based on information retrieval techniques or string matching methods. However, it should be stressed that the quality of triaging is still insufficient.
Real-Time Streaming Image Based PP2LFA-CRNN Model for Facial Sentiment Analysis

In modern society, the real-time emotion adaptive driving system for providing safety to drivers and emotion-based services are being researched.However, in the service process have problem of personal information might get leaked.Therefore, a robust personal information protection method is required for face recognition services based on real-time images.In this study, we propose a real-time streaming image based PingPong256 (PP2) algorithm, line-segment feature analysis (LFA), convolutional recurrent neural network (CRNN) model for facial sentiment analysis.The proposed method applied the PP2 algorithm to images for encryption and decryption for the security of the real-time images collected by image devices.For transmitting images to a server, LFA, as a dimensionality reduction algorithm, is used to extract facial information.PP2 encrypts and decrypts an image through a linear feedback shift register with a different length and sets a random value other than 0 so that inferring the initial value of encryption becomes difficult, and then executes the random operations approximately 1,000 times.The LFA analyzes the line segments of an image, assigns a different unique number depending on its type, and cumulatively adds them to generate a Line-Segment map (LS-map) with a size of 16 × 16.The LS-map is used as an input of the CRNN model designed in this study, and the facial expressions are classified.Performance evaluation compares the accuracy of face recognition by using the proposed method with the loss rate for other models.Performance evaluation renders excellence to the accuracy of face recognition and loss rate comparison.
Rethinking the Defocus Blur Detection Problem and a Real-Time Deep DBD Model

Defocus blur detection (DBD) is a classical low level vision task. It has recently attracted attention focusing on designing complex convolutional neural networks (CNN) which make full use of both low level features and high level semantic information. The heavy networks used in these methods lead to low processing speed, resulting difficulty in applying to real-time applications. In this work, we propose novel perspectives on the DBD problem and design convenient approach to build a real-time cost-effective DBD model. First, we observe that the semantic information does not always relate to and sometimes mislead the blur detection. We start from the essential characteristics of the DBD problem and propose a data augmentation method accordingly to inhibit the semantic information and enforce the model to learn image blur related features rather than the semantic features. A novel self-supervision training objective is proposed to enhance the model training consistency and stability. Second, by rethinking the relationship between defocus blur detection and salience detection, we identify two previously ignored but common scenarios, based on which we design a hard mining strategy to enhance the DBD model. By using the proposed techniques, our model that uses a slightly modified U-Net as backbone, improves the processing speed by more than 3 times and performs competitively against state of the art methods. Ablation study is also conducted to verify the effectiveness of each part of our proposed methods.
Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images

Automatic segmentation of white matter hyperintensities in magnetic resonance images is of paramount clinical and research importance. Quantification of these lesions serve as a predictor for risk of stroke, dementia and mortality. During the last years, convolutional neural networks (CNN) specifically tailored for biomedical image segmentation have outperformed all previous techniques in this task. However, they are extremely data dependent, and maintain a good performance only when data distribution between training and test datasets remains unchanged. When such distribution changes but we still aim at performing the same task, we incur in a domain adaptation problem (e.g. using a different MR machine or different acquisition parameters for training and test data). In this work, we explore the use of cycle-consistent adversarial networks (CycleGAN) to perform unsupervised domain adaptation on multicenter MR images with brain lesions. We aim at learning a mapping function to transform volumetric MR images between domains, which are characterized by different medical centers and MR machines with varying brand, model and configuration parameters. Our experiments show that CycleGAN allows us to reduce the Jensen-Shannon divergence between MR domains, enabling automatic segmentation with CNN models on domains where no labeled data was available.
Research on language analysis of English translation system based on fuzzy algorithm

In recent years, with the rapid development of computer technology, the need for barrier-free communication between people of all countries have become more and more urgent. Therefore, it is extremely important to establish a high translation accuracy and high-quality English translation system. At present, Although the various English translation systems on the market have solved the communication problems between different languages to a certain extent, there are a series of problems such as language translation ambiguity and inaccurate use of words in translation methods, in order to improve English The translation accuracy of the translation system can improve the quality of the English translation system. This paper proposes a language analysis study of the English translation system based on fuzzy algorithms. The research of this paper firmly grasps the analysis and understanding of the language, analyzes it from the corpus, vocabulary, syntax, and translation characteristics, and fully understands its language characteristics, so as to eliminate the semantic understanding ambiguity in the translation process to a certain extent. Thereby improving the accuracy of the translation. The English translation system designed in this paper includes an image input module and an image recognition module, so the Gaussian blur algorithm is used for processing. The Gaussian blur algorithm can retain edge information in the edge area where the pixel value of the image changes sharply, and can effectively remove noise and enhance the image effect. Therefore, this article uses fuzzy algorithm-based English translation system language analysis research, first analyze the English language characteristics, and then use Gaussian fuzzy algorithm to denoise the image in the translation system, and then display the image recognition results.
Numerical wavelets scheme to complex partial differential equation arising from Morlet continuous wavelet transform

Abstract In this article, a new wavelets approximation scheme based on operational matrices has been discussed for the solutions of proposed initial value complex partial differential equation (CPDE) which is arising from continuous wavelet transform. We developed an algorithm using transformation to obtain the numerical solution of CPDE with a numerical wavelet collocation method based on two‐dimensional Legendre wavelet (TDLW) and two‐dimensional Bernoulli wavelet (TDBW) operational matrices. We converted CPDE into a system of partial differential equations (PDEs) and this system of PDEs converted into couple PDEs in terms of the real and imaginary component. Finally, we obtained a system of partial integro‐differential equations (PIDEs) with nonlocal boundary conditions. After implementation of wavelets scheme on PIDEs, PIDEs reduced into a system of algebraic equations. In the sequence of this, we also investigated the convergence analysis of TDLW, TDBW, and error analysis, respectively. Finally, illustrative examples are included to demonstrate the validity and applicability of the presented wavelet scheme for the proposed problem.
Employee Retention in light of Green HRM practices through the Intervening role of Work Engagement

The current research examined the relationship of green human resource management practices with employees’ retention in the pharmaceutical industry of Oman. The study also checked the mediating effect of work engagement in the relationships of Green HRM practices with employee retention. Following the current purpose, the study targeted employees in the pharmaceutical industry as the unit of analyses. Five hundred seventy-six respondents were selected through simple random sampling. Data were collected through a self-administrative questionnaire. Collected data was screened through SPSS23, which concluded with 349 usable questionnaires for data analysis and assessment. Structural equation modeling via Smart PLS 3.2.8 was employed to test the proposed model. The findings of the study show a positive but insignificant association of green HRM practices toward employee retention. However, the study found a significant positive influence of green HRM practices with work engagement and work engagement with employee retention. The study also reported the mediation of work engagement in the green HRM and employee retention relationship. The findings of the study have contributed theoretically and practically.
Sprinkling Diversity: Hurdles on the Way to Inclusiveness in Makerspaces

Although makerspaces are becoming more and more prevalent worldwide, users of these spaces are predominantly male. Having access to makerspaces provides the users of these spaces with communities to learn from and network in. It is therefore important to research the reasons behind male dominance in makerspaces, as well as possible solutions that can be designed to increase diversity in makerspace users by improving access to makerspaces for underrepresented groups (i.e., women, girls, queer, non-binary, transgender, and intersex people alike). This paper describes a workshop with experts in the areas of making and STEM activities for women and girls. This expert workshop was guided by the discussion of three utopian design fictions, and three dystopian design fictions, prepared by the researchers prior to the workshop. Based on discussions and mindmaps that followed from this expert workshop, we present considerations for inclusiveness in makerspaces, the use of design fictions in expert workshops, and the creation of women-only spaces for expert workshops.
Extension-Deformation Approach to Extended Object Tracking

This article presents a new approach, named extension-deformation approach (EDA), to extended object tracking (EOT). It models the extension of an object as being deformed from a simple reference extension by moving several control points. Then, a complex object extension can be characterized by these control points given the reference extension. This greatly simplifies the problem of shape description. Thus, modeling and estimation of the extension reduce to those of the control points, which are treated as state components of the extension. A noiseless measurement is a point on the deformed extension that corresponds to a source on the reference extension. Once the source is given, the corresponding measurement model is linear in the state, making state estimation simple and direct. However, the source is unknown and hard to estimate accurately. To handle this problem, a multiple-source approach to kinematic state and extension estimation is proposed. Overall the proposed approach is simple and it can effectively track an object with an extension that can be nonstar-convex. Simulation results are given to illustrate the effectiveness of the proposed approach.
Real-Time Family Member Recognition Using Raspberry Pi for Visually Impaired People

This research work firstly describes the development and estimation of raspberry pi based smart glass system to recognize the family member of a blind man by using image processing. This system helps the blind man by giving the name of the family member as audio information. Raspberry Pi is a very powerful processor where machine learning can be integrated very easily like SVM, KNN, and Tensor Flow, etc. Blind People can easily identify their native family members by using their voice. When family members stop talking, it&#x0027;s quite impossible to identify the man near the blind man. So, here we introduce a Raspberry pi based system that can easily identify the person in front of the blind man and give audio name information. In this system, we use raspberry pi face recognition, face encoding, and face database creation for family member face recognition. So this device can be a great assistive device for the blind man. Our proposed smart glass system is good, low weight, extremely economical, and highly efficient.
Letters to José: A Design Case for Building Tangible Interactive Narratives

The field of interactive digital storytelling has been largely focused on screen-based, algorithm-driven narrative systems. While a considerable number of tools, models and experimental cases have been built to explore different aspects of the narrative and system design, very few of them are applicable for the category of tangible narratives. This paper presents a design case, Letters to José, to contribute to the body of works in tangible storytelling. Conducted through a research through design process, the building and evaluation of this case reveals key design aspects and considerations for authoring tangibles narratives, as well as the interactive narrative experience it brings. From the design process and the study findings, we identify a critical design category – artifacts for storytelling – whose characteristics and roles in an interactive narrative system are discussed. In reflecting the decision-makings, we address the complex design problems for building tangible interactive narratives.
Plans Versus Political Priorities

Problem, research strategy, and findings Local elected officials play a leadership role in setting plan directions and can jeopardize implementation if they are not committed to plan goals. In this research, we apply topic modeling, semantic networks, and sentiment analyses to Calgary's (Canada) plans and candidates' social media communications in the 2017 Calgary municipal election to assess alignments or divergences between plans' and candidates' priorities. Though the mayor, ward representatives, incumbents, and challengers prioritized different topics, we find overall support for transit infrastructure, development, and improving the downtown and the municipal tax base. However, candidates showed little interest in environmental issues, growth management, and regional cooperation, which are important plan goals that may not be addressed. The methodology has limitations: Using social media posts underrepresents the views of some candidates; text data processing may miss metaphorical phrases; elected officials' priorities during campaigns may not determine their actual votes once in office; and this cross-sectional analysis does not capture the ever-changing relations between officials' priorities, plan-making, and implementation.Takeaway for practice Candidates focused mainly on transit and taxes to the detriment of regional and environmental issues (energy, watershed, and growth management), revealing the incoming municipal administration's priorities and its potential blind spots. Planners may use this methodology to analyze large text data from both online and offline sources, understand local implementation barriers, explain shifts in municipal policy directions, and engage elected officials to build support for important plan components.
Learning to Scale Multilingual Representations for Vision-Language Tasks

Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we-9*6 propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5th the training parameters compared to other word embedding methods.
Security Information Sharing in Smart Grids: Persisting Security Audits to the Blockchain

With the transformation in smart grids, power grid companies are becoming increasingly dependent on data networks. Data networks are used to transport information and commands for optimizing power grid operations: Planning, generation, transportation, and distribution. Performing periodic security audits is one of the required tasks for securing networks, and we proposed in a previous work autoauditor, a system to achieve automatic auditing. It was designed according to the specific requirements of power grid companies, such as scaling with the huge number of heterogeneous equipment in power grid companies. Though pentesting and security audits are required for continuous monitoring, collaboration is of utmost importance to fight cyber threats. In this paper we work on the accountability of audit results and explore how the list of audit result records can be included in a blockchain, since blockchains are by design resistant to data modification. Moreover, blockchains endowed with smart contracts functionality boost the automation of both digital evidence gathering, audit, and controlled information exchange. To our knowledge, no such system exists. We perform throughput evaluation to assess the feasibility of the system and show that the system is viable for adaptation to the inventory systems of electrical companies.
Learning to Segment Anatomical Structures Accurately from One Exemplar

Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotations in a scalable manner. Methods that permit to produce accurate anatomical structure segmentation without using a large amount of fully annotated training images are highly desirable. In this work, we propose a novel contribution of Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism. Segmentation is formulated by learning a contour evolution behavior process based on graph convolutional networks (GCN). Training of our CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. We demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning approaches. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved and tailored towards the observer desired outcomes. This can facilitate the clinician designed imaging-based biomarker assessments (to support personalized quantitative clinical diagnosis) and outperforms fully supervised baselines.
Exploiting Reuse for GPU Subgraph Enumeration

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to 95 percent of the total processing time. (Un)surprisingly, a significant portion (as high as 99 percent) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this article, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to 5 times speedups compared with the state-of-the-art GPU solutions.
How Does Lipschitz Regularization Influence GAN Training?

Despite the success of Lipschitz regularization in stabilizing GAN training, the exact reason of its effectiveness remains poorly understood. The direct effect of K-Lipschitz regularization is to restrict the L2-norm of the neural network gradient to be smaller than a threshold K (e.g., \(K=1\)) such that \(\Vert \nabla f\Vert \le K\). In this work, we uncover an even more important effect of Lipschitz regularization by examining its impact on the loss function: It degenerates GAN loss functions to almost linear ones by restricting their domain and interval of attainable gradient values. Our analysis shows that loss functions are only successful if they are degenerated to almost linear ones. We also show that loss functions perform poorly if they are not degenerated and that a wide range of functions can be used as loss function as long as they are sufficiently degenerated by regularization. Basically, Lipschitz regularization ensures that all loss functions effectively work in the same way. Empirically, we verify our proposition on the MNIST, CIFAR10 and CelebA datasets.
Modeling the Effects of Windshield Refraction for Camera Calibration

In this paper, we study the effects of windshield refraction for autonomous driving applications. These distortion effects are surprisingly large and can not be explained by traditional camera models. Instead of using a generalized camera approach, we propose a novel approach to jointly optimize a traditional camera model, and a mathematical representation of the windshield's surface. First, using the laws of geometric optics, the refraction is modeled using a local spherical approximation to the windshield's geometry. Next, a spline-based model is proposed as a refinement to better adapt to deviations from the ideal shape and manufacturing variations. By jointly optimizing refraction and camera parameters, the projection error can be significantly reduced. The proposed models are validated on real windshield observations and custom setups to compare recordings with and without windshield, with accurate laser scan measurements as 3D ground truth.