Reviewerly/exHarmony huggingface dataset:
Template-Free Nonrevisiting Uniform Coverage Path Planning on Curved Surfaces

A novel mechanism to generate nonrevisiting uniform coverage (NUC) paths on arbitrarily shaped object surfaces is presented in this work. Given a nonplanar surface, nonzero curvature makes traditional homeomorphic fitting of regular template coverage paths from planar regions onto the object surface non-distance-preserving. Any coverage path with a realistic tooling size derived in this way will suffer from overlaps and missing gaps when transformed onto the object surfaces, unable to uniformly cover the target. To overcome this, a discretization process is adopted to represent the object surface as a uniform unstructured mesh, with resolution set in accordance to the tool size. It is proven that a coverage skeleton path must exist by mesh subdivision refinement which, after a local optimization step to improve overlap, missing gaps, and smoothness, gives rise to template-free superior NUC paths. Extensive simulation examples are presented to prove the validity of the proposed strategy in realistic settings. The proposed scheme is able to achieve 95.9% coverage on benchmark surface tests, outperforming comparable coverage algorithms, such as a homeomorphic boustrophedon mapping, which can at best achieve 80.9% coverage, or more recent state-of-the-art methods able to reach 94.7% coverage. An accompanying video is supplied with examples, including a real-world implementation of an NUC path tracked by a manipulator. An open-source implementation has been made available.
Security Based Object Detector Using YOLO Algorithm in Neural Networks

Object detection is the main issue in the security field. In general, entity detection is based on a combination of categorization and localization algorithms. Today, AI and neural network methods detect instances of entities of a particular class in an image or video. An existing loss-based system and a recursive detector are used to detect instances of the abounding object. One of the main disadvantages of real-time object finding algorithms is very slow image processing. To overcome them, we use Security based You Only Look Once (SYOLO), one of the most important model structural designs and object finding algorithms. It uses the best neural network structural designs to produce high accuracy and overall processing speed, which is the main reason why it is so popular. It is compatible for applications such as CCTV, self-driving motors and increased authenticity. Furthermore, SYOLO has a simple structural design and entails nominal training information, making it simple to deploy and adjust to novel tasks.
Healthcare Monitoring System with Fall Detection Mechanism

HealthCare has to be given the at most significance as we have suffered a lot during the epidemic and have learnt how to continue our lives and use the technology as for our convenience. When we see various technologies, we may understand that IOT can provide the superior support which is required at a crucial time like the pandemic. There are many devices that use IOT and provide an easy life so that we can maintain our health with least worries. There are many devices like smart watches that have sensors and could show the data related to our health in our mobile phones. When we monitor people using such devices it will bring the spreading of diseases to a minimum and a human does not need to be present at all times to monitor the health conditions. The devices can be used to capture the data regarding the patient’s health and give insights without needing them to be in front of the doctor every time.
Keeping Track of Coal Mine Safety using IoT Technology

The wireless sensor network (WSN) and Internet of Things (IoT) based system for monitoring coal mine safety that you mentioned can be a valuable tool in enhancing the safety and health of coal miners. The use of microsensor nodes that are connected wirelessly can make it easier and less expensive to monitor various environmental factors in a coal mine, including temperature, humidity, gas concentrations, fire and light. The system can detect the presence of toxic gases, extreme temperatures, and dust, and send alerts to the miners, giving them the opportunity to take action and avoid potential hazards. Furthermore, the wireless network of sensors can provide real-time data and instant communication between the sensors and the monitoring station, allowing for quick responses to any detected issues. This kind of system can help improve the degree of surveillance of the mine and ensure that miners are alerted to any potential dangers promptly. It is worth noting that while such a system can help to enhance the safety and health of miners, it is not a substitute for proper mining practices and adherence to safety regulations. Employers should ensure that miners are well-trained and equipped with appropriate personal protective equipment to minimize exposure to hazards, and that they follow established safety procedures. Overall, the use of WSN and IoT-based systems for monitoring coal mine safety can be a promising approach to improve the working conditions of coal miners and reduce the risks of accidents and illnesses associated with coal mining.
Facilitators and inhibitors of Agile methods adoption: Practitioners view

Abstract This study provides empirical evidence to the body of knowledge in Agile methods adoption in small, medium, and large organizations in the global context. This research explores facilitators and inhibitors of Agile methods adoption in software development organizations. A survey was conducted among Agile professionals to gather survey data from 52 software organizations in seven countries across the world. This study found many facilitators of Agile adoption to be significant such as customers’ dominant issues, encouragement, project champion, highly competent team, use of tools, etc. Similarly a correlation analysis revealed multiple inhibitors as significant: absence of a full set of right Agile practices, absence of customer presence, absence of tracking mechanisms during Agile progress, and failure to determine the role of the client. The present study identifies that an Agile team with high expertise and competence leads to higher quality in software, customer satisfaction along with return on investment (ROI) while a small Agile team increases ease in handling changing requirements, customer satisfaction, reduced delivery time, and increased ROI. Frequent delivery accelerates better control over work, adds to software quality, customer satisfaction, and in shortening delivery time along with increase ROI. It has also been observed that providing essential features early leads to increase in software quality and customer satisfaction. This study confirms that active customer focus leads to better control over work. Further, absence of customer decreases dealing with changing requirements, and customer satisfaction while absence of progress tracking lowers customer satisfaction.
Severity Factor (<i>SF</i>): An aid to developers for application of refactoring operations to improve software quality

Abstract Bad smells are certain flaws in the structure of the code that might not disturb the normal functioning of a program but negatively affects the software quality. Developers use refactoring as a corrective measure for the treatment of bad smells. The current study aids the developers in the application of refactoring by identifying the critical classes, that is, classes that are challenging to maintain and are of degraded quality. In this study, 10 quality metrics and 10 bad smells have been selected to conduct an investigation on different releases of five open‐source systems. A new metric, severity factor ( SF ) has been introduced that categorizes the classes of the selected systems into four criticality levels—severe, major, mid, and low. Also, the relationship between SF , criticality levels, and the refactoring operations has been analyzed. The findings show that 60% of the total classes have been affected by bad smells, and long statement is the most dominant smell present in 27.6% of the classes. The results show 84% of the refactoring operations have been performed on highly critical classes. Thus, the SF metric plays a crucial role in driving the developer's attention to the critical classes that need to be treated urgently.
A Chatbot System for Education NLP Using Deep Learning

One of the key study topics in human-computer interaction is the design of intuitive and natural interaction modalities. Much effort has been put into developing systems that can interact with people in their native tongue. Chat-boats are the conventional means of communicating with computers in natural language. A chatbot is a computer software that tries to mimic the conversational style of a real person by simulating an intelligent discussion having one or many people in natural language that are humans. Most chatbots use straightforward text interfaces to converse with their human partners, while some also include speech recognition and text-to-speech capabilities. Due to the commercial relevance of chatbots and their entertainment value, research on them has attracted much attention.The proposed work envisions the creation of a chatbot framework that attempts to improve the interest skills of chatbots by intelligently recognising and gathering lacking data from the users that are required for the answer generation. This study primarily examines chatbots that can serve the demands of small to medium-sized organisations. It is envisaged that such systems will be sufficiently interactive, easy maintenance, and inexpensive. Using a mixed knowledge base, this chatbot system combines an external knowledge base into an integrated big data framework, a modified AIML system, and an RDBMS. Researchers have also looked at the feasibility of a chatbot tailored to a certain region.
Towards a FAIR Dataset for non-functional requirements

In the last years, the application of supervised Machine Learning (ML) algorithms in Requirements Engineering (RE) has allowed increasing the performance (e.g. accuracy, precision) and scalability of automatic requirements classification. However, the lack of publicly labeled datasets is still one concern when conducting ML experiments. Few publicly labeled datasets for non-functional requirements classification are available, and even less in the Spanish language. Moreover, most of the available datasets present some limitations, such as imbalanced classes (e.g. PROMISE NFR). This study aims to generate a FAIR dataset of non-functional requirements in the Spanish language for facilitating reuse in ML classification experiments. 109 non-functional requirements were collected from final degree projects from the University of A Coruña. We conducted a pilot quasi-experiment for non-functional requirements labeling in the categories and subcategories of the ISO/IEC 25010 quality model. The labeling process was accomplished by 7 annotators. The inter-annotator agreement using a Fleiss' Kappa test obtained a substantial agreement in the category level (0.78) and a moderate agreement (0.48) when the classification is per subcategory.
Handwritten Character Recognition Using Directed Acyclic Graph

In this paper, a Directed Acyclic Graph (DAG) neural network is proposed for the recognition of handwritten Manipuri characters. Handwritten character recognition is one of the exciting and challenging research areas for pattern recognition. A particular method may work on some datasets; however, there is no absolute solution to this problem, and researchers are still finding solutions, especially for regional languages. It may be due to unavailability of the standard dataset, compounding characters, presence of similar-looking symbols, or curvature of characters in these languages. The objective of this paper is to recognize handwritten Meitei Mayek (Manipuri character) using Directed Acyclic Graph (DAG) deep neural network.
Deep Learning Assisted Cervical Cancer Detection based on Artificial Neural Network

After widespread breast cancer, cervical cancer has risen to the status of the third most frequent malignancy in the in-universe population. The majority of cancer cases have a correlation to the risk of infection with human papillomavirus. About 37% of cancer cases may be prevented with preventive care, making it the most expensive method of cancer treatment. Nevertheless, due to human error, many false-positive results are produced by this manual testing technique. Many scientists have looked at deep learningmodels for cervical cell classification. When it comes to predicting characteristics from a cancer image collection, the Random Forest (RF) method is by far the most often used approach. If too many decision trees are utilized, nevertheless, the RF approach might become too sluggish and ineffective for real-time predictions. In order to forecast the occurrence of cervical cancer, the authors of this study suggest a deep learning model based on ANN. Python is used to implement the suggested ANN model, along with other common ML models, and a wide range of performance metrics are collected and analyzed. However, the suggested ANNmodel performed superior to the state-of-the-art approaches.
Track the Success of Your Press Releases

Nonprofit Communications ReportVolume 21, Issue 7 p. 3-3 Media Relations Track the Success of Your Press Releases First published: 05 June 2023 https://doi.org/10.1002/npcr.32202Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume21, Issue7July 2023Pages 3-3 RelatedInformation
A logical framework to model software development by multiple agents following a common specification

In this paper, we address program development by multiple different programmers (or programming teams), each working in different settings (programming languages or reasoning frameworks), but following a common specification; in particular, we examine at an abstract level the problem of translatability between their produced programs. To this end, after consideration of some philosophical issues regarding program development, including its similarities and dissimilarities with scientific theorising, we extend a logical framework built to describe scientific theorising in relativist settings: our extensions add the ability of reasoning about programs, the iterative process of their generation, and their specifications. We are thus able to define a notion of translation between the outputs of program generators and prove that there is a (trivial) such translation when two program generators follow the same specification reliably (in a specific sense of reliability).
Some techniques for reasoning automatically on co-inductive data structures

Abstract Some techniques are proposed for reasoning on co-inductive structures. First, we devise a sound axiomatization of (conservative extensions) of such structures, thus reducing the problem of checking whether a formula admits a co-inductive model to a first-order satisfiability test. We devise a class of structures, called regularly co-inductive, for which the axiomatization is complete (for other co-inductive structures, the proposed axiomatization is sound, but not complete). Then, we propose proof calculi for reasoning on such structures. We first show that some of the axioms mentioned above can be omitted if the inference rules are able to handle rational terms. Furthermore, under some conditions, some other axioms may be replaced by an additional inference rule that computes the solutions of fixpoint equations. Finally, we show that a stronger completeness result can be established under some additional conditions on the signature.
Don’t PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer’s Disease

Alzheimer’s disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep learning approaches combined image and tabular information to improve diagnostic performance. However, the black-box nature of such neural networks is still a barrier for clinical applications, in which understanding the decision of a heterogeneous model is integral. We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data. It is interpretable by design and, thus, avoids the need for post-hoc explanations that try to approximate the decision of a network. Our results demonstrate that PANIC achieves state-of-the-art performance in AD classification, while directly providing local and global explanations. Finally, we show that PANIC extracts biologically meaningful signatures of AD, and satisfies a set of desirable desiderata for trustworthy machine learning. Our implementation is available at https://github.com/ai-med/PANIC .
Improving the Identification of Abusive Language Through Careful Design of Pre-training Tasks

The use of Deep Learning-based solutions has become popular in Natural Language Processing due to their remarkable performance in a wide variety of tasks. Specifically, Transformer-based models (e.g. BERT) have become popular in recent years due to their outstanding performance and their ease of adaptation (fine-tuning) in a large number of domains. Despite their outstanding results, the fine-tuning of these models under the presence of informal language writing, especially the one that contains offensive words and expressions, remains a challenging task, due to the lack of vocabulary coverage and proper task contextual information. To overcome this issue, we proposed the domain adaptation of the BERT language model to the abusive language detection task. In order to achieve this, we constrain the language model with the adaptation of two default pre-trained tasks, through the retraining of the model parameters. The obtained configurations were evaluated in six abusive language datasets, showing encouraging results; a remarkable improvement was achieved with the use of the proposed approaches in comparison with its base model. In addition to this, competitive results were obtained with respect to state-of-the-art approaches, thus obtaining a robust and easy-to-train model for the identification of abusive language.
Remarks on uniform interpolation property

Abstract A logic $\mathcal{L}$ is said to satisfy the descending chain condition, DCC, if any descending chain of formulas in $\mathcal{L}$ with ordering induced by $\vdash _{\mathcal{L}};$ eventually stops. In this short note, we first establish a general theorem, which states that if a propositional logic $\mathcal{L}$ satisfies both DCC and has the Craig Interpolation Property, CIP, then it satisfies the Uniform Interpolation Property, UIP, as well. As a result, by using the Nishimura lattice, we give a new simply proof of uniform interpolation for $\textbf{IPL}_2$, the two-variable fragment of Intuitionistic Propositional Logic; and one-variable uniform interpolation for $\textbf{IPL}$. Also, we will see that the modal logics $\textbf{S}_4$ and $\textbf{K}_4$ do not satisfy atomic DCC.
Feature distillation network based on enhanced full-channel attention for lightweight image super-resolution

In recent years, deep learning-based methods have made a big splash in the field of single image super-resolution (SISR), and a lot of excellent research works on lightweight image super-resolution networks have emerged. This series of approaches point a way forward for lightweight SR network design, but they generally suffer from two problems: 1) slow convergence of the network 2) poor image reconstruction quality. In this paper, we firstly propose a simple and effective learning rate decay scheme without additional cost, and the new strategy can accelerate the convergence speed of the network, and the model obtained in the same training time is about 0.05 dB higher in PSNR test results. Secondly, we think about the performance bottleneck of RFDN and propose an enhanced full-channel attention (EFA) module with a mixture of channel and spatial attention, which can improve about 0.085 dB in PSNR compared to the CCA attention module. Finally, we propose a new feature distillation network based on the EFA module, called EFAFDN. With only 523k parameters, the EFAFDN(x2) is lighter than the RFDN with 534k parameters and has better SR performance than the RFDN-L with 626k parameters. Extensive experimental results show that EFAFDN performs slightly better than RLFN, the winning model of the main track of the NTIRE 2022 Efficient Super-Resolution Challenge.
Multi-scale reconstruction generative networks for thermal to visible image transformation

In recent years, the field of thermal to visible image transformation has made great progress with Generative Adversarial Network(GAN). Since GANs own the ability to fit the signal frequency, these methods are limited to using more GANs or designing sub-tasks to improve the quality of the generated image. Due to the large difference in the information frequency of individual objects in the image, GANs pay less attention to low-frequency information, which leads to edge distortion of dynamic objects such as people and vehicles in the generated image. Edge distortion has become an important issue for practical applications We propose a new structure and two new loss functions to complete the task of thermal to visible image transformation. Our network takes one thermal image as input and generates a visible RGB image as output. First, we down-sample inputs for three times to get multi-scale inputs. Then we build outputs in multi-scale form small size to large size and fuse the multi-scale outputs. In train stage, our two new losses better direct the network to converge. In order to prove the effectiveness of the proposed thermal to visible image transformation algorithm, we use the KASIT-MPD dataset, which includes real paired thermal and visible images.
Objective cognitive functioning and mood in older adults with subjective cognitive impairment

Spacecraft telemetry data are real-time data as the only basis for ground operation station and management system to judge the working performance and health status of spacecrafts in orbit. Telemetry data are high dimension, strong-dependent, and pseudo-periodic series, which bring great challenges to traditional anomaly detection methods of multivariate parameters. In this case, with the advantages of strong feature extraction and space injection ability, Mahalanobis distance (MD)-based approach has been a strong foundation for industrial system health monitoring. However, the typical MD-based method performs anomaly detection with a fixed threshold for MD series without capturing temporal evolution which cause high false alarms or missing alarms for complex abnormal modes. In this work, the temporal dependence Mahalanobis distance (TDMD) is realized based on multi-factors prediction which can effectively detect contextual and collective anomalies in multivariate telemetry series. Upper and lower limits with time series correlation and dynamic characteristics for the MD of each arriving multivariate point are constructed for online testing. Adequate experiments on simulated and real telemetry series verify the effectiveness and applicability of the proposed method.