Reviewerly/exHarmony huggingface dataset:
Applying Soft Computing Techniques for Software Project Effort Estimation Modelling

Software project development phase is a crucial one; under this, calculating the accurate effort and cost is challenging and tedious task, and for that, one has to undergo several hit and trial software practices. It is the need of every software organization to complete their software project development within the stipulated schedule and budget effectively; to accomplish this, traditional approaches are quite insufficient to model their current needs. Overestimation and underestimation of software development effort may cause financial implications in the form of cost of staffing, resources and the budget of the software project. Thus, there is always a constant need of a suitable and defined model of soft computing which can combined to estimate accurately and predict the effort or cost in time. In presented article, the soft computing approaches or machine learning (ML) approaches, e.g., linear regression (LR), support vector regression (SVR), multilayer perceptron or artificial neural networks (ANNs), ensemble learning viz. decision tree (DT), random forest, etc., are used and compared for evaluating the accurate effort estimation model, and various error evaluation metrics are computed and documented. For this, we have taken a prominent dataset into consideration which is Chinese dataset (499) standard project instances. After training and testing the datasets, it was obtained that the mean absolute error (MAE), magnitude of error relative to the estimate (MER), median of MRE (MdMRE), mean magnitude of relative error (MMRE) and PRED (25) for decision tree and random forest were significantly better than the existing listed techniques. Thus, these techniques could be taken into consideration for enhancing and improving the estimation accuracy of traditional effort estimation models while calculating effort, so that software organization meets their budget accordingly.
Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis

Motivated by the need to identify erroneous disparity estimates, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years. Especially, the introduction of deep learning based methods and the accompanying significant improvement in accuracy have greatly increased the popularity of this field. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, neglecting the corresponding 3-dimensional cost volumes. However, conventional hand-crafted methods have already demonstrated that the additional information contained in such cost volumes are beneficial for the task of uncertainty estimation. In this paper, we combine the advantages of deep learning and cost volume based features and present a new Convolutional Neural Network (CNN) architecture to directly learn features for the task of aleatoric uncertainty estimation from volumetric 3D data. Furthermore, we discuss and apply three different uncertainty models to train our CNN without the need to provide ground truth for uncertainty. In an extensive evaluation on three datasets using three common dense stereo matching methods, we investigate the effects of these uncertainty models and demonstrate the generality and state-of-the-art accuracy of the proposed method.
Fast and Effective Well-Definedness Checking

Well-Definedness is important for many formal methods. In B and Event-B it ensures that certain kinds of errors (e.g., division by 0) cannot appear and that proof rules based on two-valued logic are sound. For validation tools such as ProB, well-definedness is important for constraint solving. B and Event-B establish well-definedness by generating dedicated proof obligations (POs). Unfortunately, the standard provers are not always very good at discharging them. In this paper, we present a new integrated technique to simultaneously generate and discharge well-definedness POs. The implementation contains a dedicated rule-based prover written in Prolog supporting B, Event-B and extensions thereof for data validation. We show that the generation and discharging is significantly faster than existing implementations in rodin and Atelier-B and that a large number of POs are automatically discharged. The POs are fine-grained enough to provide precise source code feedback, and allow inspection of problematic POs within various editors.
Value-Dependent Session Design in a Dependently Typed Language

Session Types offer a typing discipline that allows protocol specifications to be used during type-checking, ensuring that implementations adhere to a given specification. When looking to realise global session types in a dependently typed language care must be taken that values introduced in the description are used by roles that know about the value. We present Sessions, a Resource Dependent EDSL for describing global session descriptions in the dependently typed language Idris. As we construct session descriptions the values parameterising the EDSLs' type keeps track of roles and messages they have encountered. We can use this knowledge to ensure that message values are only used by those who know the value. Sessions supports protocol descriptions that are computable, composable, higher-order, and value-dependent. We demonstrate Sessions expressiveness by describing the TCP Handshake, a multi-modal server providing echo and basic arithmetic operations, and a Higher-Order protocol that supports an authentication interaction step.
Global Data Exchange Standards: The Basis for Future Smart Container Digital Services

This chapter describes the importance of global international standards to support the future of global trade. The United Nations Centre for Trade Facilitation and Electronic Business (UN/CEFACT) is developing the needed data exchange standards to promote and simplify the deployment of smart container solutions. Through its Transport and Logistics Domain Smart Container Project, the methodology and deliverables were designed to encourage adoption of these international multimodal standards for value-added smart container digital services. The focus of the UN/CEFACT Smart Container Project is to define the data elements via varied use cases applicable to smart container usage. Currently, the data model is being developed, which will provide the basis for the smart container standard messaging and application programming interfaces (APIs). The smart container API catalogue will be the source code-based interface specification enabling software components (services) to communicate with each other. It is crucial to first determine and align the required data elements and their semantics. The UN/CEFACT Smart Container Project is a step-by-step methodology enabling to the compilation of standard APIs. The standard API definitions are based on the standardised data elements' semantic descriptions (defined meanings of objects or information) and on a syntactic description, that captures the functional behaviour of API operations. Leading shipping companies are investing heavily in smart containers to digitalise their fleet, operations, and administration. CMA CGM, MSC have invested in a French start-up called TRAXENS to deploy smart containers across their fleets. The ecosystem actors are collaborating to define enabling technologies including standards and competing in value-added services definitions.
A comparative analysis of distributional term representations for author profiling in social media

Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability.
Federated Multi-View Spectral Clustering

Multi-view spectral clustering (MVSC) has become a popular approach to harvest knowledge about group information from multiple views of data, owned by different parties.A high quality MVSC approach usually requires collecting massive amount of data from each view party, in order to perform MVSC algorithm in a centralized manner.However, such centralized MVSC approach raises serious privacy concerns, not only in terms of the sensitivity property of many real-world data such as medical or financial records, but also in terms of the regulations from authorities to preclude centralized operations.Hence it is crucial to design new paradigm for training spectral clustering model on multi-view data in industrial scenarios.In this article, we propose a distributed and secure framework named Federated Multi-view Spectral Clustering (FMSC), in which a group of view parties collaboratively perform a MVSC model, but couldn't learn the data of other participants.FMSC is inspired by the concept of federated learning, and utilize Homomorphic Encryption (HE) and Differential Privacy (DP) to achieve secure and private clustering.We conduct a series of extensive experiments to verify the effectiveness of FMSC on both synthetic and real-world datasets.Evaluations show that FMSC achieves respectable clustering results over conventional centralized approaches.
Collaborative robot and mixed reality assisted microgravity assembly for large space mechanism

Gravity balance has a great impact on the assembly and deployment experiment of the large space mechanism to achieve the perfect performance in space. Traditional gravity balance methods have their advantages in the deployment experiment, but not suitable for the assembly process which has a more stringent location requirement. This paper proposed an advanced microgravity assembly method for a large space mechanism. The architecture of the system for microgravity assembly is proposed. The collaborative robot and mixed reality (MR) are integrated into this system, which can realize human-robot collaboration assembly, improve assembly efficiency and reduce errors. The microgravity assembly is realized by the six-dimensional force sensor which makes the robot work in force control and installed at the end of the collaborative robot. The prototype system is verified in the laboratory environment, which provides a new and feasible assembly mode for intelligent collaborative assembly for large complex space products.
Testing practices for infrastructure as code

Infrastructure as code (IaC) helps practitioners to rapidly deploy software services to end-users. Despite reported benefits, IaC scripts are susceptible to defects. Defects in IaC scripts can cause serious consequences, for example, creating large-scale outages similar to the Amazon Web Services (AWS) incident in 2017. The prevalence of defects in IaC scripts necessitates practitioners to implement IaC testing and be aware of IaC testing practices. A synthesis of IaC testing practices can enable practitioners in early mitigation of IaC defects and also help researchers to identify potential research avenues. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying a set of testing practices for IaC scripts. We apply open coding on 50 Internet artifacts, such as blog posts to derive IaC testing practices. We identify six testing practices that include behavior-focused test coverage, the practice of measuring coverage of IaC test cases in terms of expected behavior. We conclude our paper by discussing how practitioners and researchers can leverage our derived list of testing practices for IaC.
Improved Traffic Sign Recognition Using Deep ConvNet Architecture

Everyday more and more accidents between vehicles accrue mainly because of poorly visible road signs, that is why it is important to have a system that can help drivers for safer driving. In order to improve road safety and significantly reduce the risk of these accidents, numerous works have been undertaken in computer vision. In this paper, we propose a contribution to this issue by taking advantage of the remarkable results of deep convolutional neural networks in computer vision. We propose an automatic recognition system of road signs based on a modified model inspired by LeNet model. The results obtained by comparison of LeNet model and two proposed modified models on the German traffic dataset is about 99% accuracy which is promising compared to the state-of-the-art results.
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains

One approach to matching texts from asymmetrical domains is projecting the input sequences into a common semantic space as feature vectors upon which the matching function can be readily defined and learned.In realworld matching practices, it is often observed that with the training goes on, the feature vectors projected from different domains tend to be indistinguishable.The phenomenon, however, is often overlooked in existing matching models.As a result, the feature vectors are constructed without any regularization, which inevitably increases the difficulty of learning the downstream matching functions.In this paper, we propose a novel match method tailored for text matching in asymmetrical domains, called WD-Match.In WD-Match, a Wasserstein distance-based regularizer is defined to regularize the features vectors projected from different domains.As a result, the method enforces the feature projection function to generate vectors such that those correspond to different domains cannot be easily discriminated.The training process of WD-Match amounts to a game that minimizes the matching loss regularized by the Wasserstein distance.WD-Match can be used to improve different text matching methods, by using the method as its underlying matching model.Four popular text matching methods have been exploited in the paper.Experimental results based on four publicly available benchmarks showed that WD-Match consistently outperformed the underlying methods and the baselines.
Quantum statistical properties of multiphoton hypergeometric coherent states and the discrete circle representation

We review the definition of hypergeometric coherent states, discussing some representative examples. Then, we study mathematical and statistical properties of hypergeometric Schrödinger cat states, defined as orthonormalized eigenstates of kth powers of nonlinear f-oscillator annihilation operators, with f of the hypergeometric type. These “k-hypercats” can be written as an equally weighted superposition of hypergeometric coherent states ∣zl⟩, l = 0, 1, …, k − 1, with zl = ze2πil/k a kth root of zk, and they interpolate between number and coherent states. This fact motivates a continuous circle representation for high k. We also extend our study to truncated hypergeometric functions (finite dimensional Hilbert spaces), and a discrete exact circle representation is provided. We also show how to generate k-hypercats by amplitude dispersion in a Kerr medium and analyze their generalized Husimi Q-function in the super- and sub-Poissonian cases at different fractions of the revival time.
Improving Personal Health Mention Detection on Twitter Using Permutation Based Word Representation Learning

Social media has become a substitute for social interaction, thus the amount of medical and clinical-related information on the web is increasing. Monitoring of Personal Health Mentioning (PHM) on social media is an active area of research that predicts whether a given piece of text contains a health condition or not. To this end, the main idea is to consider the usage of disease or symptom words in the text. However, due to their usage in a figurative sense, disease or symptom words may not always indicate the presence of the health condition. Prior work attempts to address this by considering contextual word representations along with the utilization of the sentiment information. However, these methods are unable to capture the complete context in which symptom word is used. In this work, we incorporate permutation-based contextual word representation for the task of health mention detection which captures the context of disease words efficiently, in the given piece of text, and hence improves the performance of the classifier. To evaluate the integrity of the proposed method, we perform experimentation on the public benchmark dataset that shows an improvement of 5.5% in F-score in comparison to the state of the art health mention detection classifier. (Code is available at https://github.com/pervaizniazi/Figurative-Mention ).
Visibility Detection Based on the Recognition of the Preceding Vehicle’s Taillight Signals

This paper proposes a method for visibility detection based on the recognition of the preceding vehicle’s taillight signals using in-vehicle camera and millimeter-wave (mm-W) radar. First, we design two methods of vehicle identification. One is to use Haar-like features and an AdaBoost algorithm to train the vehicle classifier, which is mainly used to identify vehicles without turning on the taillights. The other is to identify vehicles with taillights on by means of taillight segmentation. The two identification methods are combined with a Discriminative Scale Space Tracker (DSST) to track the vehicle in the image acquired by vehicle camera and to measure anthropic visibility with mm-W radar. In addition, we drove a test vehicle on a foggy highway and collected experimental data through in-vehicle camera and mm-W radar. The experimenter observed the movement of the vehicle in front until it disappeared from the field of vision and recorded the distance of the vehicle in front measured by radar at that time as human visibility, which was also used as the ground truth to verify the accuracy of the proposed visibility detection method. The experimental results show that the visibility measured by the proposed algorithm is essentially consistent with the visibility obtained by human eyes, that is, the visibility of vehicles with no taillights, clearance lamp, emergency flasher, or fog lamp tends to rise, with an average accuracy of 88%, 91%, 90%, and 95%, respectively. In contrast to the traditional visibility measurement, this method mainly measures the maximum distance that the driver can observe when the front vehicle is not turned on or different taillights are turned on.
Skeleton-Based Detection of Abnormalities in Human Actions Using Graph Convolutional Networks

Human action abnormality detection has been attempted by various sensors for application domains like rehabilitation, healthcare, and assisted living. Since the release of motion sensors that ease the human body skeleton retrieval, skeleton-based human action recognition has recently been an active topic in the area of artificial intelligence. Unlike human action recognition, human action abnormality detection is an emerging field that aims to detect the incorrect action from the same action class. Graph convolutional network has been widely adopted for human action recognition. However, to the best of our knowledge, whether it could be effective for the task of human action abnormality detection has not been attempted. To advance prior work in the emerging field of human action abnormality detection, we propose a novel method that uses graph convolutional network to detect abnormal actions in skeleton data. To validate the effectiveness of our proposed method, we conduct extensive experiments on a public dataset called UI-PRMD. Based on the experimental results, our proposed method achieved superior action abnormality detection performance comparing with existing deep learning methods.
Blockchain as an Approach for Secure Data Storage on Digital Consulting Platforms

Abstract This chapter examines the concept of data security in a society increasingly shaped by digital technologies. We show how secure data storage can be optimised regarding digital documentation in the implementation of health-related service offers based on established procedures. Security and privacy of data are therefore particularly important in this subject area since highly sensitive data is stored and processed during health-related online consultations. The advent of blockchain technology provides a valuable opportunity to create trust in digital platforms. After relevant concepts and terms have been clarified, the functionality of the blockchain in general, as well as the different types, will be discussed. From this, options for the use of online consulting are developed and illustrated on the basis of three use cases.
State of the art of Fairness, Interpretability and Explainability in Machine Learning

The adoption of complex machine learning (ML) models in recent years has brought along a new challenge related to how to interpret, understand, and explain the reasoning behind these complex models' predictions. Treating complex ML systems as trustworthy black boxes without domain knowledge checking has led to some disastrous outcomes. In this context, interpretability and explainability are often used unintelligibly, and fairness, on the other hand, has become lately popular due to some discrimination problems in ML. While closely related, interpretability and explainability denote different features of prediction. In this sight, the aim of this paper is to give an overview of the interpretability, explainability and the fairness concepts in the literature and to evaluate the performance of the Patient Rule Induction Method (PRIM) concerning these aspects.
Revisiting Facial Age Estimation With New Insights From Instance Space Analysis

When demonstrating the effectiveness of a new algorithm, researchers are traditionally encouraged to compare their algorithm's performance against existing algorithms on well-studied benchmark test suites. In the absence of more nuanced methodologies, algorithm performance is typically summarized on average across the test suite examples. This paper highlights the potential bias of conclusions drawn by analyzing "on average" performance, and the opportunities offered by a recent testing methodology known as instance space analysis. To illustrate, we revisit our 2007 comparative study of algorithms for facial age estimation, and rigorously stress-test to challenge the original conclusions. The case study demonstrates how powerful visualizations offered by instance space analysis enable greater insights into unique strengths and weaknesses, and which algorithm should be used when and why. Inspired by such insights, a new algorithm is proposed, and its unique advantage is demonstrated. The bias often hidden in well-studied datasets, and the ramifications for drawing biased conclusions, are also illustrated in this case study. While focused on facial age estimation, the methodology and lessons learned from the case study are broadly applicable to any study seeking to draw conclusions about algorithm performance based on empirical results.