Reviewerly/exHarmony huggingface dataset:
The Convex Information Bottleneck Lagrangian

The information bottleneck (IB) problem tackles the issue of obtaining relevant compressed representations T of some random variable X for the task of predicting Y. It is defined as a constrained optimization problem that maximizes the information the representation has about the task, I ( T ; Y ) , while ensuring that a certain level of compression r is achieved (i.e., I ( X ; T ) ≤ r ). For practical reasons, the problem is usually solved by maximizing the IB Lagrangian (i.e., L IB ( T ; β ) = I ( T ; Y ) − β I ( X ; T ) ) for many values of β ∈ [ 0 , 1 ] . Then, the curve of maximal I ( T ; Y ) for a given I ( X ; T ) is drawn and a representation with the desired predictability and compression is selected. It is known when Y is a deterministic function of X, the IB curve cannot be explored and another Lagrangian has been proposed to tackle this problem: the squared IB Lagrangian: L sq − IB ( T ; β sq ) = I ( T ; Y ) − β sq I ( X ; T ) 2 . In this paper, we (i) present a general family of Lagrangians which allow for the exploration of the IB curve in all scenarios; (ii) provide the exact one-to-one mapping between the Lagrange multiplier and the desired compression rate r for known IB curve shapes; and (iii) show we can approximately obtain a specific compression level with the convex IB Lagrangian for both known and unknown IB curve shapes. This eliminates the burden of solving the optimization problem for many values of the Lagrange multiplier. That is, we prove that we can solve the original constrained problem with a single optimization.
On the relationship between design discussions and design quality: a case study of Apache projects

Open design discussion is a primary mechanism through which open source projects debate, make and document design decisions. However, there are open questions regarding how design discussions are conducted and what effect they have on the design quality of projects. Recent work has begun to investigate design discussions, but has thus far focused on a single communication channel, whereas many projects use multiple channels. In this study, we examine 37 Apache projects and their design discussions, the project’s design quality evolution, and the relationship between design discussion and design quality. A mixed method empirical analysis (data mining and a survey of 130 developers) shows that: I) 89.51% of all design discussions occur in project mailing list, II) both core and non-core developers participate in design discussions, but core developers implement more design related changes (67.06%), and III) the correlation between design discussions and design quality is small. We conclude the paper with several observations that form the foundation for future research and development.
Feasible platform to study negative temperatures

We afford an experimentally feasible platform to study Boltzmann negative temperatures.Our proposal takes advantage of well-known techniques of engineering Hamiltonian to achieve steady states with highly controllable population inversion.Our model is completely general and can be applied in a number of contexts, such as trapped ions, cavity-QED, quantum dot coupled to optical cavities, circuit-QED, and so on.To exemplify, we use Hamiltonian models currently used in optical cavities and trapped ion domain, where the level of precision achieved the control of the freedom degrees of a single atom inside a cavity/trapped ion.We show several interesting effects such as absence of thermalization between systems with inverted population and cooling by heating in these unconventional systems.
Jackknife covariance matrix estimation for observations from mixture

A general jackknife estimator for the asymptotic covariance of moment estimators is considered in the case when the sample is taken from a mixture with varying concentrations of components.Consistency of the estimator is demonstrated.A fast algorithm for its calculation is described.The estimator is applied to construction of confidence sets for regression parameters in the linear regression with errors in variables.An application to sociological data analysis is considered.
Performance Comparison of Pre-trained Residual Networks for Classification of the Whole Mammograms with Smaller Dataset

The false-positive breast cancer cases detected by radiologists and Computer-aided Detection (CAD) systems increase the medical cost and patient discomfort due to the unnecessary breast biopsies. These available CAD systems were developed using traditional machine learning techniques for breast cancer diagnosis. A noteworthy progress is happening in cancer diagnosis after the introduction of deep learning in Convolutional Neural Networks (CNNs) for CAD development. This paper compares the performance of three pre-trained Residual Networks (ResNets), i.e., ResNet18, ResNet50, and ResNet101 with increased image input layer size of $512\times 512\times 3$ for the classification of the pre-processed whole mammograms into normal, benign, and malignant categories. INbreast dataset was pre-processed and then these pre-processed whole breast images were segregated into three categories based on the ground truths. Original and modified networks were developed by replacing the last three layers of the selected ResNets to match the output category along with the image input layer. Data augmentation and transfer learning were applied to overcome the overfitting issue due to smaller dataset. The developed models were tested and the attained training and testing accuracies, sensitivity, and specificity were compared to evaluate their performances. It was observed that ResNet50 with an image input layer of size $512\times 512\times 3$ provided best results after five-fold training and the test accuracy was 79.27% with the average sensitivity and specificity of 0.76, and 0.89, respectively for three categories. This experimental work is significant as it proves that the increased image input layer size has a considerable effect in classifying the whole mammograms. Further development will be done with a balanced dataset and other pre-trained deep networks will also be tried.
Statistical machine translation outperforms neural machine translation in software engineering: why and how

Neural Machine Translation (NMT) is the current trend approach in Natural Language Processing (NLP) to solve the problem of auto- matically inferring the content of target language given the source language. The ability of NMT is to learn deep knowledge inside lan- guages by deep learning approaches. However, prior works show that NMT has its own drawbacks in NLP and in some research problems of Software Engineering (SE). In this work, we provide a hypothesis that SE corpus has inherent characteristics that NMT will confront challenges compared to the state-of-the-art translation engine based on Statistical Machine Translation. We introduce a problem which is significant in SE and has characteristics that challenges the abil- ity of NMT to learn correct sequences, called Prefix Mapping. We implement and optimize the original SMT and NMT to mitigate those challenges. By the evaluation, we show that SMT outperforms NMT for this research problem, which provides potential directions to optimize the current NMT engines for specific classes of parallel corpus. By achieving the accuracy from 65% to 90% for code tokens generation of 1000 Github code corpus, we show the potential of using MT for code completion at token level.
A Systematic Literature Review on Support Vector Machines Applied to Classification

This paper aims to identify the current state of the art of the latest research related to support vector machines through a literature review system according to the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas are they used? Q2: What are the main applications related with classification? Q3: What optimization methods or algorithms are used in SVMs?
SRARNet: A Unified Framework for Joint Superresolution and Aircraft Recognition

Aircraft recognition in high-resolution remote sensing images has rapidly progressed with the advance of convolutional neural networks (CNNs). However, the previous CNN-based methods may not work well for recognizing aircraft in low-resolution remote sensing images because the blurred aircraft in these images offer insufficient details to distinguish them from similar types of targets. An intuitive solution is to introduce superresolution preprocessing. However, conventional superresolution methods mainly focus on reconstructing natural images with detailed texture rather than constructing a high-resolution object with strong discriminative information for the recognition task. To address these problems, we propose a unified framework for joint superresolution and aircraft recognition (Joint-SRARNet) that tries to improve the recognition performance by generating discriminative, high-resolution aircraft from low-resolution remote sensing images. Technically, this network integrates superresolution and recognition tasks into the generative adversarial network (GAN) framework through a joint loss function. The generator is constructed as a joint superresolution and refining subnetwork that can upsample small blurred images into high-resolution ones and restore high-frequency information. In the discriminator, we introduce a new classification loss function that forces the discriminator to distinguish between real and fake images while recognizing the type of aircraft. In addition, the classification loss function is back-propagated to the generator to obtain high-resolution images with discriminative information for easier recognition. Extensive experiments on the challenging multitype aircraft of remote sensing images (MTARSI) dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a small blurred image and significant improvement in the recognition performance. To our knowledge, this is the first work on joint superresolution and aircraft recognition tasks.
Cryptocurrency Constellations Across the Three-Dimensional Space: Governance Decentralization, Security, and Scalability

In the post-Bitcoin era, many cryptocurrencies with a variety of goals and purposes have emerged in the digital arena. This article aims to map cryptocurrency protocols across three main defining dimensions, which are governance decentralization, security, and scalability. We theorize about the organizational and technological features that impact these three dimensions. Such features encompass roles permissiveness, validation network size, resource expenditure, and number of transactions per second. We map the different cryptocurrency constellations based on their consensus mechanisms, discussing the organizational and technological features of the various protocols applications and how they experience and play with the tradeoffs among governance decentralization, security, and scalability.
Comparison of zero downtime based deployment techniques in public cloud infrastructure

The ever-metamorphosing nature of modern businesses demand changes to be rapidly deployed to the production environments without causing any major outages. Cloud computing in conjunction with containerization and orchestration technologies has undoubtedly contributed to the development and proliferation of quicker and light weight deployment strategies. Of late, Micro service architecture (MSA) has become the de-facto standard for building such lightweight containers, which made the deployments even quicker. Though public cloud technologies and MSA based containers helped in rapid deployments, continuous deployment with zero downtime is not something which comes out of the box. It needs the implementation of Blue Green (BG) deployment techniques to achieve the zero downtime with no disruption to end users while applying the patches. But there are multiple ways to implement the BG deployment pattern. This research paper aims to address three things - (i) Explore Blue Green, Canary and Rolling deployment techniques possible in the cloud platforms (ii) Perform a research project, to simulate the BG deployment strategies using DNS routing swap versus Load Balancer swap versus newer image switch techniques(iii) Draw the comparisons and elucidate which deployment strategy is suitable for which scenario.
Motivations and Methods for Replication in Geography: Working with Data Streams

The article begins with a historical account of Hudson’s rural settlement theory and the various attempts to replicate Hudson’s research. Harvey’s exhortation “by our theories you shall know us” is discussed as a motivation for replication. Motivations not considered are the detection of fraud, mendacity, and incompetence, because these are the domain of reproducible research. Replication research in medicine, psychology, economics, and criminology is reviewed. The varying distinctions between replication and reproducible (R&R) research in each discipline are described. In each discipline the essential papers that have defined the “replication crisis” and the strategies that researchers have presented are discussed. These strategies include recommendations for systematic reviews and the standardization of research protocols, including the PRISMA and STROBE protocols that are now the accepted format for research in medicine. All four disciplines recommend the use of a formal meta-analysis following the systematic reviews of previous research contributions. There follows a brief discussion of a case study of a meta-analysis in geography that represents a model for others to follow and, second, the suggestion that geographically weighted regression analyses can be seen as a method for replicating the validity of a model across space. The article concludes with a review of the recently developed technology of computer and Jupyter Notebook as a way of facilitating research replication.
Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.
Lossless data hiding in JPEG images with segment coding

This study examines the lossless compression of an original JPEG bitstream such that spare space is reserved to embed secret data in a file header. After an original image is transformed using discrete cosine transform (DCT), the nonzero DCT coefficients at high frequencies are relatively sparse and their absolute values are small. To improve encoding efficiency, we propose a segment encoding algorithm that defines a high frequency in DCT blocks as the termination point and recodes coefficients separately. The coefficients above this point are encoded in the proposed Rxy code, and the coefficients below it are encoded in JPEG code. Then, to further preserve the embedding capacity’s (EC) robustness to different quality factors, we cluster blocks into large 32 × 32 blocks and record special block indexes that are not suitable for Rxy encoding in appended data. Hence, space is reserved for inserting secret data and appended data after the comment marker in a file header. A receiver can obtain the termination point through data analysis, extract additional data, and recover the original JPEG image in a lossless manner. Encoding redundancy is substantially reduced such that a large EC can be achieved with no file expansion. Moreover, only coefficients at high frequencies are recoded; hence, concealment is computationally simple. Experimental results demonstrate that the proposed method has a larger EC than state-of-the-art methods.
Multiview Feature Aggregation for Facade Parsing

Facade image parsing is essential to the semantic understanding and 3-D reconstruction of urban scenes. Considering the occlusion and appearance ambiguity in single-view images and the easy acquisition of multiple views, in this letter, we propose a multiview enhanced deep architecture for facade parsing. The highlight of this architecture is a cross-view feature aggregation module that can learn to choose and fuse useful convolutional neural network (CNN) features from nearby views to enhance the representation of a target view. Benefitting from the multiview enhanced representation, the proposed architecture can better deal with the ambiguity and occlusion issues. Moreover, our cross-view feature aggregation module can be straightforwardly integrated into existing single-image parsing frameworks. Extensive comparison experiments and ablation studies are conducted to demonstrate the good performance of the proposed method and the validity and transportability of the cross-view feature aggregation module.
Analysing Microsoft Access Projects: Building a Model in a Partially Observable Domain

Due to the technology evolution, every IT Company migrates their software systems at least once. Reengineering tools build system models which are used for running software analysis. These models are traditionally built from source code analysis and information accessible by data extractors (that we call such information observable). In this article we present the case of Microsoft Access projects and how this kind of project is partially observable due to proprietary storing formats. We propose a novel approach for building models that allows us to overcome this problem by reverse engineering the development environment runtime through the usage of Microsoft COM interface. We validate our approach and implementation by fully replicating 10 projects, 8 of them industrial, based only on our model information. We measure the replication performance by measuring the errors during the process and completeness of the product. We measure the replication error, by tracking replication operations. We used the scope and completeness measure to enact this error. Completeness is measured by the instrumentation of a simple and scoped diff based on a third source of information. We present extensive results and interpretations. We discuss the threats to validity, the possibility of other approaches and the technological restrictions of our solution.
Generating Codebooks to Ensure the Independent Use of Research Data

1 We are aware that both legal and ethical aspects of data sharing are far
A novel Face Recognition System based on Jetson Nano developer kit

Abstract Real_time face recognition became more important in the last two decade, it is adopted universally for attacking crime, stopping fraud, ensuring public safety. It proves that it is one of the most reliable biometrics security systems because frames can be taken from cameras without touching or interacting, and those images recorded and spontaneously validated with existing databases. The methodology achieved in this work will deal with several challenges like real_time recognition, cheap, portable, and reliable system. NVIDIA Jetson Nano is used in this work. It is a tiny, powerful AI computer that delivers the estimated performance to run advanced AI workloads with a small size, low-power, and low-cost. The main goal of this research is to deploy a deep learning model for face recognition in Jetson Nano, with the intention of real-time performance and high accuracy. The results of this study have shown great real-time performance in face recognition using Jetson Nano, Where it was processing 8.9 FPS in comparison with the latest raspberry pi edition 2.6 FPS.
F-2D-QPCA: A Quaternion Principal Component Analysis Method for Color Face Recognition

Two-dimensional quaternion principal component analysis (2D-QPCA) is one of the successful dimensionality reduction methods for color face recognition. However, 2D-QPCA is sensitive to outliers. For solving this shortcoming, an efficient robust method(F-2D-QPCA) is presented by means of Frobenius norm(F-norm). The goal of F-2D-QPCA is to find the projection matrix such that the projected data has the maximum variance based on F-norm, and it is more robust to outliers and has higher recognition accuracy than other methods, such as 2D-QPCA, R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -2-DPCA, F-norm 2DPCA and 2D-PCA, etc. Also, we study in detail a quaternion optimization problem, propose a nongreedy iterative algorithm and prove its convergence. Experiments on several color face databases illustrate the superiority of our proposed method.
Quantum-Inspired Hierarchy for Rank-Constrained Optimization

Many problems in information theory can be reduced to optimizations over matrices, where the rank of the matrices is constrained. We establish a link between rank-constrained optimization and the theory of quantum entanglement. More precisely, we prove that a large class of rank-constrained semidefinite programs can be written as a convex optimization over separable quantum states and, consequently, we construct a complete hierarchy of semidefinite programs for solving the original problem. This hierarchy not only provides a sequence of certified bounds for the rank-constrained optimization problem, but also gives pretty good and often exact values in practice when the lowest level of the hierarchy is considered. We demonstrate that our approach can be used for relevant problems in quantum information processing, such as the optimization over pure states, the characterization of mixed unitary channels and faithful entanglement, and quantum contextuality, as well as in classical information theory including the maximum cut problem, pseudo-Boolean optimization, and the orthonormal representation of graphs. Finally, we show that our ideas can be extended to rank-constrained quadratic and higher-order programming.
A Boundary Assembling Method for Nested Biomedical Named Entity Recognition

Biomedical named entity recognition (BNER) is an important task in biomedical natural language processing, in which neologisms (new terms, words) are coined constantly. Most of the existing work can only identify biomedical named entities with flattened structures and ignore nested biomedical named entities and discontinuous biomedical named entities. Because biomedical domains often use nested structures to represent semantic information of named entities, existing methods fail to utilize abundant information when processing biomedical texts. This paper focuses on identifying nested biomedical named entities using a boundary assembly (BA) model, which is a cascading framework consisting of three steps. First, start and end named entity boundaries are identified and then assembled into named entity candidates. Finally, a classifier is implemented for filtering false named entities. Our approach is effective in handling nesting and discontinuous problems in biomedical named entity recognition tasks. It improves the performance considerably, achieving an F1-score of 81.34% on the GENIA dataset.