Reviewerly/exHarmony huggingface dataset:
Visualizing Inter-Team Coordination

As agile ways of working are implemented in larger settings, inter-team coordination becomes increasingly important. Some dependencies between teams can be identified in advance, but much of the dependencies in software development emerge as the system evolve. A way to identify, manage, assess, and evaluate the status of dependencies is to visualize dependencies between teams. New artifacts and routines for visualizing dependencies are suggested from the increasingly popular Scaled Agile Framework, SAFe. This multiple case study focuses on how inter-team coordination is visualized by providing a rich description of two years of implementation and tailoring of the boundary object called the program board at the (anonymized) organizations A-Gov and B-Bank. A total of 183 hours of observations and 14 interviews were used for triangulation purposes. The findings present the difficulties of keeping visualizations up to date. By inventing and tailoring the routines of managing the Program Board, the current situation of dependencies could be visualized and kept track of. The findings also indicate the importance of how dependencies are defined.
A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only on the intensity information, which makes it sensitive to noise. Lots of GMM-based segmentation algorithms have the low segmentation accuracy because of the influence caused by noise and intensity inhomogeneity. To further improve the segmentation accuracy, a robust spatial information-theoretic GMM algorithm is proposed in this paper to simultaneously estimate the intensity inhomogeneity and segment brain MR images. First, a novel spatial factor containing the non-local spatial information is incorporated to reduce the impact of noise. The proposed spatial factor not only takes into account the local neighboring information, but also considers the spatial structure information of pixels. Thus, our algorithm can retain more image details while reducing the influence of noise. Second, the mutual information (MI) maximization method is used to identify and eliminate outliers. Finally, to overcome the impact of intensity inhomogeneity, we use a linear combination of a set of orthogonal polynomials to approximate the bias field. The objective function is integrated with the bias field estimation model to segment the images and estimate the bias field simultaneously. The experimental results on both synthetic and clinical brain MR images show that the proposed algorithm can overcome the influence of noise and intensity inhomogeneity, and achieve more accurate segmentation results.
Statistical Modeling of Harassment against Reddit Moderators

Despite the dedication that some volunteer moderators of online communities display when performing their moderation duties, they become targets of hate and harassment by other users. To understand what causes the change in moderator role from heroes to victims, we analyze the responses of 1,818 moderators on Reddit to an online survey about moderation practices and harassment. We built a statistical model and found 6 significant independent variables that affect harassment on moderators, such as the knowledge of community norms, which increases harassment on moderators the most. Our findings imply that vulnerable moderators in toxic communities need countermeasures against harassment.
An Exploratory Study Towards Understanding Lambda Expressions in Python

Lambda expressions are anonymous functions in Python. It is one of the alternatives to write a function definition. Syntactically, it is a single expression and defined using the keyword lambda. Lambda expression is a functional programming feature, currently in use, in many mainstream programming languages such as Python, Java8, C++11. There are few studies in C++ and Java to understand the impact of lambda expressions on programmers. These studies are focusing on the developer's adaptability to use a functional style of construct and the benefit they gain from using it. However, we are not aware of any literature on the use of lambda expressions in Python. Thus, there is a need to study lambda expressions in Python projects. In this paper, we examine 15 GitHub repositories out of 760 from our dataset, that are using Python as their primary language. In this study, we are classifying the uses of lambda expressions based on varying scenarios. We identified 13 different usages of lambda expressions from these Python repositories. This catalog is an attempt to support programmers to use lambda expressions more effectively and efficiently.
Bidirectional Gated Recurrent Unit Neural Networks for Relation Extraction of Chinese Enterprises

With the improvement of people's entrepreneurial awareness and the encouragement of policies, the number of enterprises is gradually increasing. However, the profit-seeking nature of enterprises has increased the number of criminal activities. In order to help financial practitioners effectively identify high-risk enterprises, legal persons or shareholders, domestic and foreign scholars construct enterprise knowledge graphs for risk warning. Extracting enterprise relations from unstructured data such as financial news plays an important role in constructing enterprise knowledge graphs, but its irregular data structure and scarce processing tools bring challenges to relation extraction. To solve this problem, this paper proposes SDP-BGRU model to extract enterprise relations from unstructured data and treats enterprise relations extraction as a classification problem. The model uses the shortest dependency path (SDP) between two entities, obtains feature vectors through Bidirectional Gated Recurrent Unit (BGRU) networks and support vector machines as classifier. Experimental results show that the model can achieve good results on test data.
Development of seismic acquisition instrumentation system based MEMS accelerometer using SPI communication with Raspberry Pi

Abstract The background of this research concerned to the operational cost and human resources to conduct seismic data surveys. In seismic surveys, the tools and cables are very heavy and need very long cables, so they spend a lot of cost and resources. Based on those reasons, an improvement of Geophone technology for data acquisition was needed in this study to reduce cost and resources. Accelerometer based on MEMS technology was proposed to replace a conventional geophone to reduce costs and human resources. In previous research, a study of seismic signal acquisition instrumentation based on MEMS accelerometer sensor could acquire only two geophones. In this study has renewed seismic signal accelerometer by communication aspect to acquire more geophones or seismic signal acquisition instrumentation. So, it would be more efficient and reduce cost and need fewer resources. The hypothesis is Output data of this system is almost the same with output from conventional geophone. This system uses SPI Extender module to make one Raspberry Pi to acquire more MEMS geophone and with further cable reach. Respond result or output of geophone is saved into Raspberry Pi first so that it can be taken wirelessly by the host computer. Trial test is done at Universitas Indonesia. Based on the test results, the response of the MEMS Geophone, when given a wave explosion, is almost the same as a conventional Geophone.
Cancer Detection Based on Image Classification by Using Convolution Neural Network

Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. The tumor is malignant (cancer) if the cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body. The challenge of this project was to build an algorithm by using a neural network to automatically identify whether a patient is suffering from breast cancer by looking at biopsy images. The algorithm must be accurate because the lives of people are at stake.
Research on Face Attribute Recognition Based on Multi-task CNN Network

In recent years, the research on face attribute recognition has received special attention due to its research and practical value. Face attribute recognition with multi-task convolutional neural network achieves higher reliability and accuracy than traditional feature extraction methods. In this paper, we propose a hard parameter sharing-channel split network(HPS-CS) to enhance channel information sharing and reduce the number of parameters for the face attribute recognition task. HPS-CS is employed in this work to identify 7 types of face attributes on the CelebA dataset. The average accuracy for the experiment is 94.59% and the model size is only 2.88 MiB. The competitive result proves that the network has advantages in multi-task face attribute recognition.
Bowlesite, PtSnS, a new platinum group mineral (PGM) from the Merensky Reef of the Bushveld Complex, South Africa

Abstract Bowlesite is a new mineral discovered in the Merensky Reef of the Rustenburg Platinum Mine, Bushveld complex, South Africa. Bowlesite forms tiny grains (maximum dimension 20 μm). It is associated with sulfides including chalcopyrite, pyrrhotite and pentlandite, in contact with silicates including plagioclase, pyroxene- and minor serpentine-subgroup and amphibole-supergroup minerals. Bowlesite is brittle and has a metallic lustre. In plane-polarised light, bowlesite has a light bluish grey colour. It shows weak bireflectance, no pleochroism and has weak anisotropism. Internal reflections were not observed. Reflectance values of bowlesite in air ( R 1 , R 2 in %) are: 50.3–51.4 at 470 nm, 48.5–48.9 at 546 nm, 47.9–48.6 at 589 nm and 47.8–48.7 at 650 nm. Ten spot analyses of bowlesite give the average composition: Pt 56.85, Pd 0.02, Sn 34.03 and S 9.15, total 100.05 wt.%, corresponding to the empirical formula (Pt 1.001 Pd 0.001 ) Σ1.002 Sn 0.997 S 1.001 , based on 3 atoms per formula unit. The simplified formula is PtSnS. Due to the small size of bowlesite, the crystal structure was solved and refined from the powder X-ray-diffraction data of synthetic PtSnS. The calculated density is 10.06 g⋅cm –3 . The mineral is orthorhombic, space group: Pca 2 1 (#29) with a = 6.11511(10), b = 6.12383(10), c = 6.09667(11) Å, V = 228.31(1) Å 3 and Z = 4. Bowlesite is isotypic with cobaltite, CoAsS. The origin of bowlesite is probably related to low- T exsolution of Pt–Sn phases from high- T sulfides crystallised from the sulfide melt. The mineral honours Dr. John Bowles (Manchester University, UK) for his contributions to ore mineralogy and mineral deposits related to mafic–ultramafic rocks.
“Ellos no están entendiendo nada” [“They are not understanding anything”]: embodied remembering as complex narrative in a Telematic Sonic Improvisation

INTIMAL is a physical-virtual system for relational listening, exploring the role of the body as interface that keeps memory of place in migratory contexts.The system is developed to integrate the body movements of performers (and their voices) with an oral archive.The system has been informed and tested by nine Colombian migrant women in Europe in a telematic performance between the cities of Oslo, Barcelona and London.In the performance a "complex narrative" emerged, for both the improvisers and the audiences.In this paper, we describe the conditions of the narrative environment, and the embodied expressions that emerged.We reflect on how this distributed embodied expression-through technological mediated sound and movement interactions-might further aid processes of collective remembering and catharsis, in a context of conflict and gendered migration.
Optimizing Interrupt Handling Performance for Memory Failures in Large Scale Data Centers

Intermittent hardware failures are generally non-catastrophic and typical large-scale service infrastructures are designed to tolerate them while still serving user traffic. However, intermittent errors cause performance aberrations if they are not handled appropriately. System error reporting mechanisms send hardware interrupts to the Central Processing Unit (CPU) for handling the hardware errors. This disrupts the CPU's normal operation, which impacts the performance of the server. In this paper, we describe common intermittent hardware errors observed on server systems in a large-scale data center environment. We discuss two methodologies of handling interrupts in server systems - System Management Interrupt (SMI) and Corrected Machine Check Interrupt (CMCI). We characterize the performance of these methods in live environments as compared to prior studies that used error injection to simulate error behavior. Our experience shows that error injection methods are not reflective of production behavior. We also present a hybrid approach for handling error interrupts that achieves better performance, while preserving monitoring granularity, in large scale data center environments.
Local Binary Pattern based Technique for Content Based Image Copy Detection

Now a day's there is a significant increase in the in the duplicate copies of large original images. One of the main reason for such duplication is due to the availability of large number of image editing software. In this paper, we have proposed a significant image hashing technique based on Local Binary Pattern (LBP) for image identification. Here, the input image is initially pre-processed to remove any kind of minor effects. Local binary pattern (LBP) is then applied to the processed image to produce features which is used for identification. The calculated experimental values show that proposed hashing is giving extraordinary performance against `Histogram equalization' attack. Receiver operating characteristics (ROC) curve shows that proposed hashing gives better results as compared to other referenced techniques. Keeping in view the results obtained for the proposed technique, one can say that we can use such a copy detector for online detection of image copies.
An Empirical Model for n-gram Frequency Distribution in Large Corpora

Statistical multiword extraction methods can benefit from the knowledge on the n-gram (\(n \ge 1\)) frequency distribution in natural language corpora, for indexing and time/space optimization purposes. The appearance of increasingly large corpora raises new challenges on the investigation of the large scale behavior of the n-gram frequency distributions, not typically emerging on small scale corpora. We propose an empirical model, based on the assumption of finite n-gram language vocabularies, to estimate the number of distinct n-grams in large corpora, as well as the sizes of the equal-frequency n-gram groups, which occur in the lower frequencies starting from 1. The model was validated for n-grams with \(1 \le n \le 6\), by a wide range of real corpora in English and French, from 60 million up to 8 billion words. These are full non-truncated corpora data, that is, their associated frequency data include the entire range of observed n-gram frequencies, from 1 up to the maximum. The model predicts the monotonic growth of the numbers of distinct n-grams until reaching asymptotic plateaux when the corpus size grows to infinity. It also predicts the non-monotonicity of the sizes of the equal-frequency n-gram groups as a function of the corpus size.
Design of a Sentiment Lexicon for the Greek Food and Beverage Sector

Sentiment Analysis is a computational method aiming to extract opinions/evaluations of individuals for an entity such as a product, a service etc. In social media networks and other online sources, as for example the food websites, sentiment analysis is able to identify all possible terms, such as simple words, combinations of words, or phrases (pre-processing stage) that can be used to express the feelings of a user for a specific entity. Then, by considering the characteristics of these media, such as time sensitivity, text size limitation and unstructured expressions, converts them giving a positive or negative significance. For the analysis, a set of linguistic, statistical and machine learning techniques are usually considered to structure the information contained in text sources. The main purpose of this paper is twofold. First is to provide a literature review of the sentiment analysis techniques and second is to design a sentiment lexicon as a preliminary step to further analyze the customers’ reviews of some leading companies in the Greek Food and Beverage industry as they uploaded in the most common Opinion Social Networks in Greece (fb). Existing research has focused mainly on the recognition on English characters, while to our knowledge, limited research papers have been published so far concerning the Greek language, concentrating mainly on the banking and financial sector, neglecting contributions on food industry. Note that since significant portion of online text-based Greek communications ignore the rules of spelling and grammar the study takes into account this trend and improves the calculation of a sentiment score accordingly. As appears, the findings are expected to contribute in the design issues of a sentiment lexicon particularly devoted to the Greek food and beverage industry and to be used for further analysis.
Method of Image Style Transfer Based on Edge Detection

In order to overcome the problem of edge information loss in the process of neural network processing, a method of neural network style transfer based on edge detection is presented. The edge information of the content image is extracted, and the edge information image is processed in the neural network together with the content image and the style image to constrain the edge information of the content image. Compared with Gatys algorithm and markov random field neural network algorithm, the content image edge structure after image style transfer is successfully retained.
Using Radial Basis Function for Water Quality Events Detection

The current chapter demonstrates utilization of radial basis function (RBF) as a tool for detection and classification of abnormal events in water quality. The methodology is based on calibration of a RBF based on historical true events classified by human experts. The aim of the process is selection of parameters that ensure zero false negative events. The chapter describes the main method of using RBF and then compares four different kernel functions which are used for implementing the RBF. The case study part of the chapter illustrates actual analysis of real-world data as well as an illustrative example. The chapter concludes with some practical advice on how kernel functions should be selected for this task.
An Automatic Blind Image Deblurring Method via Label Confidence

Image deblurring has been an active research topic in the computer vision and image processing. Generally, the existing deep learning methods can get high quality deblurred image while the quality depends on the variety of training dataset for blurred images. On the other side, the blur kernel estimation methods can work well on wider range images without training dataset for blurred images while the quality depends on the manual tuning of parameters. Label confidence is the production of classification result by deep learning method. We found that the image is in the process from blur to clear, the label confidence of the image increases. In our paper we introduced a new blur kernel estimation method that uses label confidence and Laplace variance to perform auto tuning, so that it can select the best deblurred image from the outputs of the blur kernel estimation method automatically. The experimental results show that our method can achieve automatic image deblurring and generate higher quality images than the existing state-of-art deblurring method.
Collaborative topological filtering with multi-hop recurrent pathological aggregation

Learning vectorial representations of users and items from their interaction data is a core approach for Collaborative Filtering. While a user’s or an item’s representation is commonly built upon low-hop features such as IDs and interaction history, some recent works argue the existence of higher-hop interactions, thereby motivating the use of multi-hop topological knowledge in representation learning. However, existing methods in this area explore only the local pathological connections and thus ignore the overall semantics along paths. To this end, this paper introduces a new CF approach that learns to explicitly inject the multi-hop topological features of a user or an item as a whole into its representation in an end-to-end manner. Specifically, we explore the multi-hop topology via the paths connecting a user or an item to its neighbors at different hops. To capture the entire topological information, we seamlessly integrate aggregator function with a recurrent neural network to jointly extract salient neighborhood information and detect the pathological semantics. We develop two neural network models, DF-CTF and DW-CTF, where the former focuses on modeling each individual path and the latter focuses on adapting to the path entanglement in multi-hop structures. Furthermore, we evaluate our proposed approach on three real-world benchmark datasets and demonstrate its superior performance against state-of-the-art methods.
Secure Speculative Core

The recent attacks on speculative cores have led people to believe that high-performance cores and security demands contradict each other. This work demonstrates that if the core is designed while considering security demands as a first-class citizen, it can support high-performance computing via out-of-order architectures and remain secure. We propose a new methodology for secure and speculative core (SCC) architecture. The design uses an enhanced out-of-order core architecture to achieve the required high-performance and implements a unique secure-wrapper that guarantees the required security properties. The methodology provides a set of mechanisms to implement the SSC architecture. Our experiments indicate that adding these new features to out-of-order cores such as the Intel Coffee Lake can immunize the system against side-channel attacks, with only minor performance degradation.
Enhancing User-Engagement in the Design Process through Augmented Reality Applications

Augmented Reality (AR) technologies are often perceived as the most impactful method to enhance the communication between the designer and the client during the iterative design process.However, the significance of designing the User Interface (UI) and the User Experience (UX) are often underestimated.To intercede, this research aims to employ new and existing techniques to develop UI's, and comparatively assess ``the accuracy and completeness with which specified users can achieve specified goals in particular environments'' (Stone, 2005) -a notion this research delineates as `effectiveness'.Prompted by the work of key scholars, the developed UI's were assessed through the lens of existing UI evaluation techniques, including: Usability Heuristics (Nielsen, 1994) and Visual and Cognitive Heuristics (Zuk and Carpendale, 2006).In partnership with PTW Architects, characteristics such as the rapidity and complexity of interactions, in conjunction with the interface's simplicity and intuitiveness, were extracted from 15 trials underwent by architectural practitioners.The outcomes of this research highlights strategies for the effective development of user interface design for mobile augmented reality applications.