Reviewerly/exHarmony huggingface dataset:
20 Years of the Agile Manifesto: on Agile Project Management

Agile Project Management is a topic that has become popular both in business and academia, since the publication of the Agile Manifesto -a historic landmark in this subject.In the next 20 years, there was a relevant scientific production that must be analyzed to provoke reflection about the knowledge built up in this period.In this sense, this study aims to analyze the relevant scientific literature on Agile Project Management through a systematic review and a bibliometric analysis of articles published in scientific journals with Digital Object Identifier, in English, from the Web of Science and Scopus databases, from 2001 to 2021.The research results enable us to gain insights into the characteristics of this knowledge domain, regarding its volume and evolutionary trend, main contributors (i.e.scientific journals, authors, and their affiliations), main studies, methods used, and its central thematic axes.
Probing the symmetry breaking of a light–matter system by an ancillary qubit

Hybrid quantum systems in the ultrastrong, and even more in the deep-strong, coupling regimes can exhibit exotic physical phenomena and promise new applications in quantum technologies. In these nonperturbative regimes, a qubit-resonator system has an entangled quantum vacuum with a nonzero average photon number in the resonator, where the photons are virtual and cannot be directly detected. The vacuum field, however, is able to induce the symmetry breaking of a dispersively coupled probe qubit. We experimentally observe the parity symmetry breaking of an ancillary Xmon artificial atom induced by the field of a lumped-element superconducting resonator deep-strongly coupled with a flux qubit. This result opens a way to experimentally explore the novel quantum-vacuum effects emerging in the deep-strong coupling regime.
TriBoDeS: A Tri-Blockchain-Based Detection and Sharing Scheme for Dangerous Road Condition Information in Internet of Vehicles

Bad weather or environmental factors, particularly in remote mountain areas, may result in unsafe driving conditions and, consequently, road traffic accidents. As the deployment of large-scale sensing nodes for reporting road conditions is too expensive, the crowdsourcing method or reporting by sensors in vehicles themselves will be easier to deploy and more practical. However, those participant sensing methods impose some difficulties, such as fake information, reporter misbehavior, and timeliness. Thus, we propose a tri-blockchain-based Internet of Vehicles system, called TriBoDeS, to facilitate real-time information detection and sharing. It is designed to guarantee concurrency and security to dynamically store, manage, and share information uploaded by vehicles with great efficiency. Such information will be announced on the blockchain under the autonomous identification of vehicles in low-trust conditions. In order to ensure the software’s security, TriBoDeS can monitor the software’s state, detect identified malicious activities, and respond accordingly. To ensure data security, a role-based management mechanism is introduced to achieve fine-grained control over permissions, and confidence rules are established to guarantee the authenticity of the data. To demonstrate the applicability of the proposed scheme, we evaluate its performance (e.g., computing and communication overheads) and security (e.g., resiliency against common attacks) over a consortium blockchain. The experimental results demonstrate that, under the conditions of a sufficient number of vehicles, the TriBoDeS system is capable of real-time information sharing while ensuring the security of user information. Compared to conventional single-chain systems, the TriBoDeS system achieves a 2.75-time improvement in efficiency.
Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle's nature to the "vehicle management system." C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction.
Deep Sparse Depth Completion Using Joint Depth and Normal Estimation

Depth completion densifies sparse depth images obtained from LiDAR and is a great challenge due to the given extremely sparse information. In this paper, we propose deep sparse depth completion using joint depth and normal estimation. There exists a mutually convertible geometric relationship between depth and surface normal in 3D coordinate space. Based on the geometric relationship, we build a novel adversarial model that consists of one generator and two discriminators. We adopt an encoder-decoder structure for the generator. The encoder extracts features from RGB image, sparse depth image and its binary mask that represent the inherent geometric relationship between depth and surface normal, while two decoders with the same structure generate dense depth and surface normal based on the geometric relationship. We utilize two discriminators to generate guide information for sparse depth completion from the input RGB image while imposing an auxiliary geometric constraint for depth refinement. Experimental results on KITTI dataset show that the proposed method generates dense depth images with accurate object boundaries and outperforms state-of-the-art ones in terms of visual quality and quantitative measurements.
Tyr: Finding Consensus Failure Bugs in Blockchain System with Behaviour Divergent Model

Blockchain is a decentralized distributed system on which a large number of financial applications have been deployed. The consensus process in it plays an important role, which guarantees that legal transactions on the chain can be executed and recorded fairly and consistently. However, because of Consensus Failure Bugs (CFBs), many blockchain systems do not provide even this basic guarantee. The validity and consistency of blockchain systems rely on the soundness of complex consensus logic implementation. Any bugs which cause the blockchain consensus failure can be crucial.In this work, we introduce Tyr, an open-source tool for detecting CFBs in blockchain systems with a large number of abnormal divergent consensus behaviors. First, we design four oracle detectors to monitor the behaviors of nodes and analyze the violation of consensus properties. To trigger these oracles effectively, Tyr harnesses a behavior divergent model to constantly generate consensus messages and make nodes behave as differently as possible. We implemented and evaluated Tyr on six widely used commercial blockchain consensus systems, including IBM Fabric, WeBank FISCO-BCOS, ConsenSys Quorum, Facebook Diem, Go-Ethereum, and EOS. Compared with the state-of-the-art tools Peach, Fluffy, and Twins, Tyr covers 27.3%, 228.2%, and 297.1% more branches, respectively. Furthermore, Tyr has detected 20 serious previously unknown vulnerabilities, all of which have been repaired by the corresponding maintainers.
Quality indices for topic model selection and evaluation: a literature review and case study

Abstract Background Topic models are a class of unsupervised machine learning models, which facilitate summarization, browsing and retrieval from large unstructured document collections. This study reviews several methods for assessing the quality of unsupervised topic models estimated using non-negative matrix factorization. Techniques for topic model validation have been developed across disparate fields. We synthesize this literature, discuss the advantages and disadvantages of different techniques for topic model validation, and illustrate their usefulness for guiding model selection on a large clinical text corpus. Design, setting and data Using a retrospective cohort design, we curated a text corpus containing 382,666 clinical notes collected between 01/01/2017 through 12/31/2020 from primary care electronic medical records in Toronto Canada. Methods Several topic model quality metrics have been proposed to assess different aspects of model fit. We explored the following metrics: reconstruction error, topic coherence, rank biased overlap, Kendall’s weighted tau, partition coefficient, partition entropy and the Xie-Beni statistic. Depending on context, cross-validation and/or bootstrap stability analysis were used to estimate these metrics on our corpus. Results Cross-validated reconstruction error favored large topic models (K ≥ 100 topics) on our corpus. Stability analysis using topic coherence and the Xie-Beni statistic also favored large models (K = 100 topics). Rank biased overlap and Kendall’s weighted tau favored small models (K = 5 topics). Few model evaluation metrics suggested mid-sized topic models (25 ≤ K ≤ 75) as being optimal. However, human judgement suggested that mid-sized topic models produced expressive low-dimensional summarizations of the corpus. Conclusions Topic model quality indices are transparent quantitative tools for guiding model selection and evaluation. Our empirical illustration demonstrated that different topic model quality indices favor models of different complexity; and may not select models aligning with human judgment. This suggests that different metrics capture different aspects of model goodness of fit. A combination of topic model quality indices, coupled with human validation, may be useful in appraising unsupervised topic models.
Classifying Code Comments via Pre-trained Programming Language Model

Previous studies have categorized code comments for various programming languages to produce high-quality code comments that can improve code readability and benefit maintenance. However, it still requires more effort to identify the main information contained in code comments. Pre-trained language model has shown promising results for solving software engineering tasks. In this paper, we propose a model for code comment classified using the recent pre-trained language model specialized for code-specific tasks (i.e., CodeT5). We introduce expert-predefined features to enhance the model's classification performance. Our evaluation on the official dataset shows that it outperforms the baseline by improving the precision (+65.9 %), recall (+147.3%) and the Fl-score (+112.5%) of the classification.
Correlated Oscillations in Kerr Parametric Oscillators with Tunable Effective Coupling

We study simultaneous parametric oscillations in a system composed of two distributed-element-circuit Josephson parametric oscillators in the single-photon Kerr regime coupled via a static capacitance. The energy of the system is described by a two-bit Ising Hamiltonian with an effective coupling whose amplitude and sign depend on the relative phase between parametric pumps. We demonstrate that the binary phases of the parametric oscillations are correlated with each other, and that the parity and strength of the correlation can be controlled by adjusting the pump phase. The observed correlation is reproduced in our simulation taking pure dephasing into account. The present result demonstrates the tunability of the Hamiltonian parameters by the phase of external microwave, which can be used in the Ising machine hardware composed of the Kerr parametric oscillator network.
Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized. While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase (LMuMOGWO) for tackling feature selection problems. The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer (MOGWO): a Lévy flight and a mutation operator. The Lévy flight, a type of random walk with jump size determined by the Lévy distribution, enhances the global search capability of MOGWO, with the objective of maximizing classification accuracy while minimizing the number of selected features. The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy. As feature selection is a binary problem, the continuous search space is converted into a binary space using the sigmoid function. To evaluate the classification performance of the selected feature subset, the proposed approach employs a wrapper-based Artificial Neural Network (ANN). The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO, BMOGWO-S (based sigmoid), BMOGWO-V (based tanh) as well as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (BMOPSO). The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem. Moreover, the proposed approach outperforms existing approaches in most cases in terms of classification error rate, feature reduction, and computational cost.
What does Chinese BERT learn about syntactic knowledge?

Pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have been applied to a wide range of natural language processing (NLP) tasks and obtained significantly positive results. A growing body of research has investigated the reason why BERT is so efficient and what language knowledge BERT is able to learn. However, most of these works focused almost exclusively on English. Few studies have explored the language information, particularly syntactic information, that BERT has learned in Chinese, which is written as sequences of characters. In this study, we adopted some probing methods for identifying syntactic knowledge stored in the attention heads and hidden states of Chinese BERT. The results suggest that some individual heads and combination of heads do well in encoding corresponding and overall syntactic relations, respectively. The hidden representation of each layer also contained syntactic information to different degrees. We also analyzed the fine-tuned models of Chinese BERT for different tasks, covering all levels. Our results suggest that these fine-turned models reflect changes in conserving language structure. These findings help explain why Chinese BERT can show such large improvements across many language-processing tasks.
Sports Video Classification with Deep Convolution Neural Network: A Test on UCF101 Dataset

In the present era, Deep Learning has been applied on a variety of problems from image processing to speech recognition. Convolution Neural Network (CNN) has been extensively used as a powerful classification model for image recognition problems. Video classification presents unique challenges but the problem related to video data is similar to image classification or an object detection problem. The main purpose of video classification in sports is to help the viewers to find the video of their own interest for training and improve the performance. The proposed work is a preliminary attempt to evaluate the performance of deep convolution neural network architectureson the ordered sequence of frames of the sports video. Video classification and video content analysis is one of the ongoing research areas in the field of computer vision. The classification of each frames are recorded and the majority vote of the frames are used to classify the video. UCF101 Video action database has been used for the classification problem.
Towards CRISP‐BC: 3TIC specification framework for Blockchain use‐cases

Abstract The application of Blockchain and augmented technologies such as IoT, AI, and Big Data platforms present a feasible approach for resolving the implementation challenges of trusted, decentralized platforms. This article proposes a DevOps framework for the specification of Blockchain use‐cases that enables evaluation, replication, and benchmarking. Specifically, it could be applied to specify the requirements and design characteristics of Blockchain applications in terms of key attributes such as: (i) transparency; (ii) traceability; (iii) tamper‐resistance; (iv) immutability; and (v) compliance. The article first introduces the design characteristics of Blockchain as a Platform and then examines successful use‐cases for its implementation using the above attributes. It may be conjectured that the 3TIC framework would serve as the basis of a cross industry process for Blockchain. The intended contribution is that such a standard process will support industry‐academia collaboration in the development of Blockchain platforms and services of relevance and utility as it can be applied by firms to structure their requirements and design specifications.
Quantum Haecceity

There is an extensive philosophical literature on the interrelated issues of identity, individuality and distinguishability in quantum systems. A key consideration is whether quantum systems are subject to a strong form of individuality termed 'haecceity' (from the Latin for 'this-ness'). I argue that the traditional, strong form of haecceity does not apply at the quantum level, but that in order to properly account for the need for symmetrization in quantum systems, a weaker kind of haecceity must be involved, which I call quantum haecceity. In the process, I also question some generally accepted tenets of the current debate, such as the idea that symmetrization of states for identical quanta must be postulated and reflects permutation invariance. Instead, I note that a perturbative Hamiltonian is required for exchange effects, which suggests that the need for symmetrization arises from specific physical conditions. This article is part of the theme issue 'Identity, individuality and indistinguishability in physics and mathematics'.
Artificial intelligence as a tool for improving oral cancer outcomes

Oral cancer detection at an early stage remains a challenging situation for health professionals in the rural setup. This is due to lack of training and infrastructure facilities. In the era of modernization, when tools like artificial intelligence and machine learning are gaining acceptance, this article throws insight into the role of AI as a tool for improving oral cancer outcomes.
Logical Fallacy Detection

Reasoning is central to human intelligence.However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change.In this paper, we propose the task of logical fallacy detection, and provide a new dataset (LOGIC) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LOGICCLIMATE).Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument.We find that existing pretrained large language models perform poorly on this task.In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% F 1 scores on LOGIC and 4.51% on LOGICCLIMATE.We encourage future work to explore this task since (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. 1
Readability Controllable Biomedical Document Summarization

Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge.However, existing biomedical document summarization systems have paid little attention to readability control, leaving users with summaries that are incompatible with their levels of expertise.In recognition of this urgent demand, we introduce a new task of readability controllable summarization for biomedical documents, which aims to recognise users' readability demands and generate summaries that better suit their needs: technical summaries for experts and plain language summaries (PLS) for laypeople.To establish this task, we construct a corpus consisting of biomedical papers with technical summaries and PLSs written by the authors, and benchmark multiple advanced controllable abstractive and extractive summarization models based on pre-trained language models (PLMs) with prevalent controlling and generation techniques.Moreover, we propose a novel masked language model (MLM) based metric and its variant to effectively evaluate the readability discrepancy between lay and technical summaries.Experimental results from automated and human evaluations show that though current control techniques allow for a certain degree of readability adjustment during generation, the performance of existing controllable summarization methods is far from desirable in this task.
Is Anisotropy Truly Harmful? A Case Study on Text Clustering

In the last few years, several studies have been devoted to dissecting dense text representations in order to understand their effectiveness and further improve their quality.Particularly, the anisotropy of such representations has been observed, which means that the directions of the word vectors are not evenly distributed across the space but rather concentrated in a narrow cone.This has led to several attempts to counteract this phenomenon both on static and contextualized text representations.However, despite this effort, there is no established relationship between anisotropy and performance.In this paper, we aim to bridge this gap by investigating the impact of different transformations on both the isotropy and the performance in order to assess the true impact of anisotropy.To this end, we rely on the clustering task as a means of evaluating the ability of text representations to produce meaningful groups.Thereby, we empirically show a limited impact of anisotropy on the expressiveness of sentence representations both in terms of directions and L 2 closeness.