Reviewerly/exHarmony huggingface dataset:
Scrum: An Agile Process Reengineering In Software Engineering

BPR (Business Process Re-engineering) is an organizational mechanism leading organization towards change management as and when it is required. In the form BPR, Agile practices have had a remarkable impact on Software Engineering Management (SEM) in software development organizations across the world. It has enhanced collaboration and productivity of Software Engineering (SE) teams and improved the level of quality of software products. Nowadays Scrum has been trending into software development organizations as a standard SDLC approach. Scrum is the framework of the Agile methodology, built on empiricism control theory, asserts that experience brings knowledge and increases decision making capabilities. The empiricism control theory is built on three pillars: Adaptation, Inspection and Transparency. This research study presents Scrum as a trending SDLC framework using empirical analysis. We have analyzed literature reviews, case studies, and research surveys; and implemented Scrum in our software development unit and carried out the reasons, why Scrum is trending in software development organizations. How the scrum artifacts, events and values play a vital role in upholding the Scrum pillars and strengthen the capabilities of Scrum team members to address the software engineering management challenges; compared to traditional software development approaches.
Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer

Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer
Finance, Growth, and Inequality

Finance and growth emerged as a distinct field of economics during the last three decades as economists integrated the fields of finance and economic growth and then explored the ramifications of the functioning of financial systems on economic growth, income distribution, and poverty.In this paper, I review theoretical and empirical research on the connections between the operation of the financial system and economic growth and inequality.While subject to ample qualifications, the preponderance of evidence suggests that (1) financial development-both the development of banks and stock markets-spurs economic growth and (2) better functioning financial systems foster growth primarily by improving resource allocation and technological change, not by increasing saving rates.Some research also suggests that financial development expands economic opportunities and tightens income distribution, primarily by boosting the incomes of the poor.This work implies that financial development fosters growth by expanding opportunities.Finally, and more tentatively, financial innovation-improvements in the ability of financial systems to ameliorate information and transaction costs-may be necessary for sustaining growth.
Rydberg quantum wires for maximum independent set problems

One application of near-term quantum computing devices1–4 is to solve combinatorial optimization problems such as non-deterministic polynomial-time hard problems5–8. Here we present an experimental protocol with Rydberg atoms to determine the maximum independent set of graphs9, defined as an independent set of vertices of maximal size. Our proposal is based on a Rydberg quantum wire scheme, which exploits auxiliary atoms to engineer long-ranged networks of qubits. We experimentally test the protocol on three-dimensional Rydberg atom arrays, overcoming the intrinsic limitations of two-dimensional arrays for tackling combinatorial problems and encode high-degree vertices. We find the maximum independent set solutions with our programmable quantum-wired Rydberg simulator for Kuratowski subgraphs10 and a six-degree graph, which are paradigmatic examples of non-planar and high-degree graphs, respectively. Our protocol provides a way to engineer the complex connections of high-degree graphs through many-body entanglement, taking a step towards the demonstration of quantum advantage in combinatorial optimization. Combinatorial optimization is one of the areas for which quantum computing promises to overcome classical devices. An experiment with arrays of Rydberg atoms now shows how to solve combinatorial graph problems with auxiliary atomic wires.
Analysis of Blockchain Solutions for E-Voting: A Systematic Literature Review

Today, the abstention rate continues to rise, largely due to the need to travel to vote. This is why remote e-voting will increase the turnout by allowing everyone to vote without the need to travel. It will also minimize the risks and obtain results in a faster way compablack to a traditional vote with paper ballots. In fact, given the high stakes of an election, a remote e-voting solution must meet the highest standards of security, reliability, and transparency to gain the trust of citizens. In literature, several remote e-voting solutions based on blockchain technology have been proposed. Indeed, the blockchain technology is proposed today as a new technical infrastructure for several types of IT applications because it allows to remove the TTP and decentralize transactions while offering a transparent and fully protected data storage. In addition, it allows to implement in its environment the smart-contracts technology which is used to automate and execute agreements between users. In this paper, we are interested in reviewing the most revealing e-voting solutions based on blockchain technology.
SynWoodScape: Synthetic Surround-View Fisheye Camera Dataset for Autonomous Driving

Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$190^{\circ }$</tex-math></inline-formula> field of view cover the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> around the vehicle. Due to its high radial distortion, the standard algorithms do not extend easily. Previously, we released the first public fisheye surround-view dataset named WoodScape. In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it. Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth. Secondly, WoodScape did not have all four cameras annotated simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed to obtain a unified output in birds-eye space, which is enabled in the new dataset. We implemented surround-view fisheye geometric projections in CARLA Simulator matching WoodScape’s configuration and created SynWoodScape. We release 80 k images from the synthetic dataset with annotations for 10+ tasks. We also release the baseline code and supporting scripts.
Decentralized Collaborative Learning Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel d ecentralized c ollaborative l earning framework for POI r ecommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models’ dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.
Mid-Circuit Cavity Measurement in a Neutral Atom Array

Subsystem readout during a quantum process, or mid-circuit measurement, is crucial for error correction in quantum computation, simulation, and metrology. Ideal mid-circuit measurement should be faster than the decoherence of the system, high-fidelity, and nondestructive to the unmeasured qubits. Here, we use a strongly coupled optical cavity to read out the state of a single tweezer-trapped ^{87}Rb atom within a small tweezer array. Measuring either atomic fluorescence or the transmission of light through the cavity, we detect both the presence and the state of an atom in the tweezer, within only tens of microseconds, with state preparation and measurement infidelities of roughly 0.5% and atom loss probabilities of around 1%. Using a two-tweezer system, we find measurement on one atom within the cavity causes no observable hyperfine-state decoherence on a second atom located tens of microns from the cavity volume. This high-fidelity mid-circuit readout method is a substantial step toward quantum error correction in neutral atom arrays.
Cross-modal hierarchical interaction network for RGB-D salient object detection

How to effectively exchange and aggregate the information of multiple modalities (e.g. RGB image and depth map) is a big challenge in the RGB-D salient object detection community. To address this problem, in this paper, we propose a cross-modal Hierarchical Interaction Network (HINet), which boosts the salient object detection by excavating the cross-modal feature interaction and progressively multi-level feature fusion. To achieve it, we design two modules: cross-modal information exchange (CIE) module and multi-level information progressively guided fusion (PGF) module. Specifically, the CIE module is proposed to exchange the cross-modal features for learning the shared representations, as well as the beneficial feedback to facilitate the discriminative feature learning of different modalities. Besides, the PGF module is designed to aggregate the hierarchical features progressively with the reverse guidance mechanism, which employs the high-level feature fusion to guide the low-level feature fusion and thus improve the saliency detection performance. Extensive experiments show that our proposed model significantly outperforms the existing nine state-of-the-art models on five challenging benchmark datasets. Codes and results are available at: https://github.com/RanwanWu/HINet.
Stitch it in Time: GAN-Based Facial Editing of Real Videos

The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their success with videos has proven challenging. Applying StyleGAN editing to real videos introduces two main challenges: (i) StyleGAN operates over aligned crops. When editing videos, these crops need to be pasted back into the frame, resulting in a spatial inconsistency. (ii) Videos introduce a fundamental barrier to overcome — temporal coherency. To address the first challenge, we propose a novel stitching-tuning procedure. The generator is carefully tuned to overcome the spatial artifacts at crop borders, resulting in smooth transitions even when difficult backgrounds are involved. Turning to temporal coherence, we propose that this challenge is largely artificial. The source video is already temporally coherent, and deviations arise in part due to careless treatment of individual components in the editing pipeline. We leverage the natural alignment of StyleGAN and the tendency of neural networks to learn low-frequency functions, and demonstrate that they provide a strongly consistent prior. These components are combined in an end-to-end framework for semantic editing of facial videos. We compare our pipeline to the current state-of-the-art and demonstrate significant improvements. Our method produces meaningful manipulations and maintains greater spatial and temporal consistency, even on challenging talking head videos which current methods struggle with. Our code and videos are available at https://stitch-time.github.io/.
Third Time’s the Charm? Image and Video Editing with StyleGAN3

StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this work, we explore the recent StyleGAN3 architecture, compare it to its predecessor, and investigate its unique advantages, as well as drawbacks. In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery. Next, our analysis of the disentanglement of the different latent spaces of StyleGAN3 indicates that the commonly used $$\mathcal {W}/\mathcal {W}+$$ spaces are more entangled than their StyleGAN2 counterparts, underscoring the benefits of using the StyleSpace for fine-grained editing. Considering image inversion, we observe that existing encoder-based techniques struggle when trained on unaligned data. We therefore propose an encoding scheme trained solely on aligned data, yet can still invert unaligned images. Finally, we introduce a novel video inversion and editing workflow that leverages the capabilities of a fine-tuned StyleGAN3 generator to reduce texture sticking and expand the field of view of the edited video.
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.
Classical Planning in MDP Heuristics: with a Little Help from Generalization

Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space robotics to epilepsy management, it may be possible to have an initial training period when suboptimal performance is permitted. For such problems it is important to be able to identify when this training period is complete, and the computed policy can be used with high confidence in its future performance. A simple principled criteria for identifying when training has completed is when the error bounds on the value estimates of the current policy are sufficiently small that the optimal policy is fixed, with high probability. We present an upper bound on the amount of training data required to identify the optimal policy as a function of the unknown separation gap between the optimal and the next-best policy values. We illustrate with several small problems that by estimating this gap in an online manner, the number of training samples to provably reach optimality can be significantly lower than predicted offline using a Probably Approximately Correct framework that requires an input epsilon parameter.
A Latent-Class Model for Clustering Incomplete Linear and Circular Data in Marine Studies

Identification of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specification of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values.We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions.Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data.The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.
Designer Modeling for Personalized Game Content Creation Tools

With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer's preferences, goals and processes from their interaction with a computer-aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed-initiative content creation, and envision future directions which focus on personalizing the processes to a designer's particular wishes.
Draft-Analysis of the Ancients: Predicting Draft Picks in DotA 2 using Machine Learning

Analysing strategic decision-making in eSports is an increasingly important problem -- for players, for teams, for commentators, for viewers and for broadcasters. Such analysis is extremely difficult, however, because of the comparatively small quantities of data, the ever-shifting state of competitive play, and the huge complexity of the game. In this paper we describe a system for predicting drafting decisions in DOTA 2, and evaluate both how the system performs compared to human experts, as well as the new kinds of analysis made possible by automation.
SDN-Based Privacy Preserving Cross Domain Routing

Today's large-scale enterprise networks, data center networks, and wide area networks can be decomposed into multiple administrative or geographical domains. Domains may be owned by different administrative units or organizations. Hence protecting domain information is an important concern. Existing general-purpose Secure Multi-Party Computation (SMPC) methods that preserves privacy for domains are extremely slow for cross-domain routing problems. In this paper we present PYCRO, a cryptographic protocol specifically designed for privacy-preserving cross-domain routing optimization in Software Defined Networking (SDN) environments. PYCRO provides two fundamental routing functions, policy-compliant shortest path computing and bandwidth allocation, while ensuring strong protection for the private information of domains. We rigorously prove the privacy guarantee of our protocol. To improve time efficiency we design the QuIck Pathing (QIP) technique. QIP only requires one-time offline preprocessing and very fast online computation. We have implemented a prototype system that runs PYCRO and QIP on servers in a campus network. Experimental results using real ISP network topologies show that PYCRO and QIP are very efficient in computation and communication costs.
Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks

Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.
Linking gold mineralization to regional-scale drivers of mineral systems using in situ U–Pb geochronology and pyrite LA-ICP-MS element mapping

Proterozoic orogens commonly host a range of hydrothermal ores that form in diverse tectonic settings at different times. However, the link between mineralization and the regional-scale tectonothermal evolution of orogens is usually not well understood, especially in areas subject to multiple hydrothermal events. Regional-scale drivers for mineral systems vary between the different classes of hydrothermal ore, but all involve an energy source and a fluid pathway to focus mineralizing fluids into the upper crust. The Mount Olympus gold deposit in the Proterozoic Capricorn Orogen of Western Australia, was regarded as an orogenic gold deposit that formed at ca. 1738 Ma during the assembly of Proterozoic Australia. However, the trace element chemistry of the pyrite crystals closely resembles those of the Carlin deposits of Nevada, with rims that display solid solution gold accompanied by elevated As, Cu, Sb, Hg, and Tl, surrounding gold-poor cores. New SHRIMP U–Pb dating of xenotime intergrown with auriferous pyrite and ore-stage alteration minerals provided a weighted mean 207Pb*/206Pb* date of 1769 ± 5 Ma, interpreted as the age of gold mineralization. This was followed by two discrete episodes of hydrothermal alteration at 1727 ± 7 Ma and 1673 ± 8 Ma. The three ages are linked to multiple reactivation of the crustal-scale Nanjilgardy Fault during repeated episodes of intracratonic reworking. The regional-scale drivers for Carlin-like gold mineralization at Mount Olympus are related to a change in tectonic regime during the final stages of the intracratonic 1820–1770 Ma Capricorn Orogeny. Our results suggest that substantial sized Carlin-like gold deposits can form in an intracratonic setting during regional-scale crustal reworking.