Reviewerly/exHarmony huggingface dataset:
A Multi-Channel and Multi-Spatial Attention Convolutional Neural Network for Prostate Cancer ISUP Grading

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&amp;E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading.
Task Aware Multi-Task Learning for Speech to Text Tasks

In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks. However, the issues with the current joint learning strategies inhibit the knowledge transfer across these tasks. We propose a task modulation network which allows the model to learn task specific features, while learning the shared features simultaneously. This proposed approach removes the need for separate finetuning step resulting in a single model which performs all these tasks. This single model achieves a performance of 28.64 BLEU score on ST MuST-C English-German, WER of 11.61% on ASR TEDLium v3, 23.35 BLEU score on MT WMT’15 English-German task. This sets a new state-of-the-art performance (SOTA) on the ST task while outperforming the existing end-to-end ASR systems.
PPTAM<sup>λ</sup>: What, Where, and How of Cross-domain Scalability Assessment

Software development ecosystems vary significantly among different industrial domains. Therefore, it is challenging to establish quality assurance processes that can be deployed seamlessly to multiple domains. In this paper, we extend our previous work on performance and scalability assessment by identifying the architecture variability points in our PPTAM tooling infrastructure. The goal is to design a modifiable software architecture that enables low cost deployment of our performance and scalability assessment approach. We present the scalability assessment context, architecture modifiability, and lessons learned that were derived from our experience with scalability assessment in several business domains. Specifically, we describe our experience with the application of the proposed approach to a large complex telecom system at Ericsson.
Quality assessment of image deburring algorithms

Abstract Camera shake during video recording is a major source of image defocus. Restoring these defocuses is one of the most important tasks in image processing. This article examines and evaluates the most commonly used algorithms in practice, namely: Richardson-Lucy algorithm, Tikhonov regularization, and Blind deconvolution. The comparisons were made using structural similarity, image sharpness, and root mean square error.
An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization

XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models either have data leakage issues or are only applicable to the two-party setting with heavy communication and computation overheads. In this article, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost’s split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. Remarkably, a thorough analysis of model security is provided as well, and multiple numerical results showcase the superiority of the proposed FedXGB compared with the state-of-the-art models on benchmark datasets.
An ethical defense of cryptocurrencies

Abstract The growing importance of the cryptocurrency phenomenon has raised concerns about the ethical implications of a hypothetical widespread use of these new forms of digital money. In this paper, we undertake an ethical assessment of cryptocurrencies drawing upon two specific ethical theories: private property ethics and utilitarianism. Particularly, we focus on three distinctive aspects. First, we examine the advantages and disadvantages of cryptocurrencies vis‐à‐vis central bank fiat money. Second, we analyze cryptocurrencies as facilitators of tax evasion and the ethical implications arising therefrom. Finally, we explore the use of cryptocurrencies for nefarious consumption. We conclude that, were cryptocurrencies to become widespread media of exchange, governments capacity to undertake monetary, fiscal, and drug policy would be undermined. We argue that this would be an ethically desirable outcome from both a private‐property‐rights and a utilitarian perspective since it would force governments to reduce their size and scope in these three areas.
DV-DCNN: Dual-view deep convolutional neural network for matching detected masses in mammograms

Background and Objective: Mammography is an X-ray imaging technique used for breast cancer screening. Each breast is usually screened at two different angles generating two views known as mediolateral oblique (MLO) and craniocaudal (CC), which are clinically used by radiologists to detect suspicious masses and diagnose breast cancer. Previous studies applied deep learning models to process each view separately and concatenate the features from the two views to detect and classifying masses. However, direct concatenation is not enough to uncover the relationship between the masses that appear in the two views because they can substantially vary in terms of shape, size, and texture. The relationship between the two views should be established by matching correspondence between their extracted masses. This paper presents a dual-view deep convolutional neural network (DV-DCNN) model for matching masses detected from the two views by establishing correspondence between their extracted patches, which leads to more robust mass detection. Methods: Given a pair of patches as input, the presented model determines whether these patches represent the same mass or not. The network contains two parts: a feature extraction part using tied dense blocks, and a neighborhood patch matching part with three consecutive layers, i.e., a cross-input neighborhood differences layer to find the relationship between the two patches, a patch summary features layer to define a summary of the neighborhood differences and an across-patch features layer to learn a higher-level representation across neighborhood differences. Results: To evaluate the model's performance in diverse cases, several experimental scenarios were followed for training and testing using two public datasets, i.e., CBIS-DDSM and INbreast. We also evaluate the contribution of our mass-matching model within a mass detection framework. Experiments show that DV-DCNN outperforms other related deep learning models and demonstrate that the detection results improve when using our model. Conclusions: Matching potential masses between two different views of the same breast leads to more robust mass detection. Experimental results demonstrate the efficacy of a dual-view deep learning model in matching masses, which helps in increasing the accuracy of mass detection and decreasing the false positive rates.
Iterative confidence relabeling with deep ConvNets for organ segmentation with partial labels

Training deep ConvNets requires large labeled datasets. However, collecting pixel-level labels for medical image segmentation is very expensive and requires a high level of expertise. In addition, most existing segmentation masks provided by clinical experts focus on specific anatomical structures. In this paper, we propose a method dedicated to handle such partially labeled medical image datasets. We propose a strategy to identify pixels for which labels are correct, and to train Fully Convolutional Neural Networks with a multi-label loss adapted to this context. In addition, we introduce an iterative confidence self-training approach inspired by curriculum learning to relabel missing pixel labels, which relies on selecting the most confident prediction with a specifically designed confidence network that learns an uncertainty measure which is leveraged in our relabeling process. Our approach, INERRANT for Iterative coNfidencE Relabeling of paRtial ANnoTations, is thoroughly evaluated on two public datasets (TCAI and LITS), and one internal dataset with seven abdominal organ classes. We show that INERRANT robustly deals with partial labels, performing similarly to a model trained on all labels even for large missing label proportions. We also highlight the importance of our iterative learning scheme and the proposed confidence measure for optimal performance. Finally we show a practical use case where a limited number of completely labeled data are enriched by publicly available but partially labeled data.
Empirical Effort and Schedule Estimation Models for Agile Processes in the US DoD

Estimating the cost and schedule of agile software projects is critical at an early phase to establish baseline budgets and schedules for the selection of competitive bidders. The challenge is that common agile sizing measures such as story points and user stories are not practical for early estimation as these are often reported after contract award in DoD. This study provides a set of effort and schedule estimation models for agile projects using a sizing measure that is available before proposal evaluation based on data from 36 DoD agile projects. The results suggest that initial software requirements, defined as the sum of functions and external interfaces, is an effective sizing measure for early estimation of effort and schedule of agile projects. The models’ accuracy improves when application domain groups and peak staff are added as inputs.
Feature selection algorithm for usability engineering: a nature inspired approach

Abstract Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.
Improving Transformer-Based Neural Machine Translation with Prior Alignments

Transformer is a neural machine translation model which revolutionizes machine translation. Compared with traditional statistical machine translation models and other neural machine translation models, the recently proposed transformer model radically and fundamentally changes machine translation with its self-attention and cross-attention mechanisms. These mechanisms effectively model token alignments between source and target sentences. It has been reported that the transformer model provides accurate posterior alignments. In this work, we empirically prove the reverse effect, showing that prior alignments help transformer models produce better translations. Experiment results on Vietnamese-English news translation task show not only the positive effect of manually annotated alignments on transformer models but also the surprising outperformance of statistically constructed alignments reinforced with the flexibility of token-type selection over manual alignments in improving transformer models. Statistically constructed word-to-lemma alignments are used to train a word-to-word transformer model. The novel hybrid transformer model improves the baseline transformer model and transformer model trained with manual alignments by 2.53 and 0.79 BLEU, respectively. In addition to BLEU score, we make limited human judgment on translation results. Strong correlation between human and machine judgment confirms our findings.
Floating Point and Fixed Point 32-bits Quantizers for Quantization of Weights of Neural Networks

Floating Point 32-bits (FP32) representation format is proposed by IEEE Standard 754, being widely used in neural networks (NN), signal processing and numerical computation. Also, Fixed Point 32-bits format is widely used for data representation. This paper describes those standard 32-bits formats (Fixed Point 32 and FP32) as quantization schemes, defining quantizers based on them and providing in this way references for comparison of other quantization schemes used in neural networks. Quantization of data with the Laplacian distribution is considered, in a wide range of variance. Theoretical results are proven by an experiment, applying those quantization schemes on weights of a neural network.
A Decentralized ETC Architecture Based on Blockchain Technology

Electronic toll collection (ETC) has been widely spread in China, providing efficient, comfortable, and convenient toll services. However, the nature of centralization of those ETC systems has incurred serious security threatens. Given blockchain’s features of self-trust, decentralized architecture, and traceability, we propose a decentralized ETC architecture based on blockchain technology. After implementing our architecture in a test network, we verify its feasibility. A thorough discussion indicates that our architecture can effectively operate normal ETC services while avoiding most attacks that threaten traditional ETC systems. The proposed architecture provides a brand new way of conventional ETC system optimization.
Average Performance of Adaptive Streaming

This paper analyzes the average behavior of video streaming systems with adaptation to network bandwidth and player sizes. The main results are formulae for average system performance parameters such as average system bandwidth consumption, average delivered quality, etc. Derived expressions are used to study performance limits achievable by adaptive streaming, and to pose several related optimization problems. Numerical simulations, illustrating the usefulness of the proposed formulae and techniques are also provided.
Wigglears

Human-Computer Integration is an extension of the Human-Computer Interaction paradigm that explores systems in which the boundary between user and computer is blurred. We build on this and explore its intersection with the fields of biodata and playful expression by presenting “Wigglears”, a wearable system that will wiggle the wearer's ears based on skin conductance, aiming to explore playful solutions towards integrated biodata-based self-expression. Through an autobiographical study, we demonstrate the system's ability to fuel social dialogue, amplify positive emotions, and triggering refocus. We intend for our system to be a novel solution to expressing emotions within social interactions, and hope to offer insights towards the social implications of biodata-based integration as a social cue, to help further the research within Human-Computer Integration.
Optimal Tilt and Orientation Angles in Fixed Flat Surfaces to Maximize the Capture of Solar Insolation: A Case Study in Ecuador

Due to the increasing attention of PV generation, and to the uncertainty of the real PV electrical power output, power system are facing planning challenges. Hence, this case report proposes a computer application that determines the optimal tilt and orientation angles of flat surfaces to maximize solar insolation capture based on existing mathematical models. The study uses data from different meteorological variables measured and estimated through a typical year from the most populated Ecuador cities. The computer application considers the global, diffuse, direct, reflected radiation, and the angle of incidence of the sun. This analysis reveals the angle of inclination and orientation where the highest percentage of insolation is obtained in Ecuador’s main cities. The results suggest that, in Ecuador, flat solar capture surfaces can be oriented between 20° and 60° concerning north, and with tilt angles between 12° and 19°. This contrasts with the literature, which recommends an orientation to the equator and an inclination equal to the location’s latitude.
On learning Random Forests for Random Forest-clustering

In this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context. In particular, we investigated the suitability of Gaussian Density Forests, Random Forests specifically designed for density estimation. Further, we introduce a novel variant of Random Forest, based on an effective non parametric by-pass estimator of the Rényi entropy, which can be useful when the parametric assumption is too strict. An empirical evaluation involving different datasets and different RF-clustering strategies confirms that the learning step is crucial for RF-clustering. We also present a set of practical guidelines useful to determine the most suitable variant of RF-clustering according to the problem under examination.
Mobile. Egocentric Human Body Motion Reconstruction Using Only Eyeglasses-mounted Cameras and a Few Body-worn Inertial Sensors

We envision a convenient telepresence system available to users anywhere, anytime. Such a system requires displays and sensors embedded in commonly worn items such as eyeglasses, wristwatches, and shoes. To that end, we present a standalone real-time system for the dynamic 3D capture of a person, relying only on cameras embedded into a head-worn device, and on Inertial Measurement Units (IMUs) worn on the wrists and ankles. Our prototype system egocentrically reconstructs the wearer's motion via learning-based pose estimation, which fuses inputs from visual and inertial sensors that complement each other, overcoming challenges such as inconsistent limb visibility in head-worn views, as well as pose ambiguity from sparse IMUs. The estimated pose is continuously re-targeted to a prescanned surface model, resulting in a high-fidelity 3D reconstruction. We demonstrate our system by reconstructing various human body movements and show that our visual-inertial learning-based method, which runs in real time, outperforms both visual-only and inertial-only approaches. We captured an egocentric visual-inertial 3D human pose dataset publicly available at https://sites.google.com/site/youngwooncha/egovip for training and evaluating similar methods.
Relation-based multi-type aware knowledge graph embedding

Knowledge graph (KG) embedding projects the graph into a low-dimensional space and preserves the graph information. An essential part of a KG is the ontology, which always is organized as a taxonomy tree, depicting the type (or multiple types) of each entity and the hierarchical relationships among these types. The importance of considering the ontology during KG embedding lies in its ability to provide side-information, improving the downstream applications' accuracy (e.g., link prediction, entity alignment or recommendation). However, the ontology has yet to receive adequate attention during the KG embedding, especially for instances where each entity may belong to multiple types. This ontology-enhanced KG embedding's main challenges are twofold: determining how to discover the relationships among these types and how to integrate them with the entities' relationship network. Although it is common to see attention-based models used in KG embedding, they cannot settle the issues raised simultaneously. Only a single type is assigned to each entity and the correlation among types are ignored in those models, leading to information loss and encumbered downstream tasks. To overcome these challenges, we propose a composite multi-type aware KG embedding model, whose main components are a multi-type layer and entity embedding layer. We model it as a natural language processing task at the multi-type layer to discover each entity's multi-type feature and automatically capture their correlations. Additionally, a relation-based attention mechanism is conducted at the entity embedding layer, which aggregates neighborhoods' information and integrates the multi-type layer's information through common entities of these two layers. Through extensive experiments on two real KGs, we demonstrate that, compared to several state-of-the-art baselines, our Multi-Type aware Embedding (MTE) model achieves substantial gain in both Mean Rank and [email protected] for the link prediction task and accuracy for multi-type classification.
Decoding Grounded Theory for Software Engineering

Grounded Theory, while becoming increasingly popular in software engineering, is also one of the most misunderstood, misused, and poorly presented and evaluated method in software engineering. When applied well, GT results in dense and valuable explanations of how and why phenomena occur in practice. GT can be applied as a full research method leading to mature theories and also in limited capacity for data analysis within other methods, using its robust open coding and constant comparison procedures. This technical briefing will go through the social origins of GT, present examples of grounded theories developed in SE, discuss the key challenges SE researchers face, and provide a gentle introduction to socio-technical grounded theory, a variant of GT for software engineering research.