Reviewerly/exHarmony huggingface dataset:
Evaluating factual accuracy in complex data-to-text

It is essential that data-to-text Natural Language Generation (NLG) systems produce texts which are factually accurate. We examine accuracy issues in the task of generating summaries of basketball games, including what accuracy means in this context, how accuracy errors can be detected by human annotators, as well as the types of accuracy mistakes made by both neural NLG systems and human authors. We also look at the effectiveness of automatic metrics in measuring factual accuracy.
Pedagogy and Curriculum in Higher Education

Higher education is constantly changing, both in relation to the characteristics of the different agents that constitute it (particularly teachers and students) and in relation to the particular experiences from this formative context. With the Covid-19 pandemic, new pedagogical challenges have emerged due to what were the emergency circumstances that we all experienced. Classrooms and teaching dynamics inevitably had to reinvent themselves, to get closer to the specific references and practices of distance teaching and learning. With this chapter, the authors present a plural set of experiences developed during their teaching experiences. Through reflection on these practices, five areas that were established as variables with special pedagogical value: i) availability of teachers and students; ii) programmatic content as a mean and not an end; iii) the centrality of interaction between peers; iv) the plurality of resources and perspectives and v) evaluation at the service of learning.
NURSING CARE FOR PATIENTS VICTIM OF ACUTE MYOCARDIAL INFARCTION

INTRODUCTION: Acute Myocardial Infarction (AMI) is characterized by reduced oxygen supply to the affected site, due to decreased blood flow in the coronary arteries, with consequent myocardial necrosis. OBJECTIVES: To analyze, through a literature review, the importance of nursing care for patients suffering from AMI and the relationship between the complexity of care provided and the duration of nursing care. MATERIALS AND METHODS: Refers to a literature review study focusing on Acute Myocardial Infarction, identifying the care and nursing care time required to care for clients affected by the disease. RESULTS AND DISCUSSION: For the planning and implementation of nursing care for patients diagnosed with AMI, one of the studies took into account the following complaints from female clients admitted to the ICU: Chest pain, cramps, headache with difficulties in falling asleep and incisional pain due to pericardial effusion drainage, tachycardia, nausea, cold and pale skin, hypotension, hypoactivity, bradycardia with irregular heart rate, slow response to verbal commands, in addition to anxiety. CONCLUSION: This integrative review study showed that AMI is a serious public health problem due to its high morbidity and mortality in Brazil.
Software Development Management

The software development and management (SDM) practice helps organizations to ensure that their software products are developed methodically and delivered in accordance with the stakeholders’ requirements. The main purpose of the SDM is to map out the management tasks and sequences them rationally. Software must be developed in a systematic manner to ensure that it is developed on time, within budget and with all functional and non-functional capabilities. SDM practice plays a significant role in ensuring that the software is developed in a systematic manner, traditionally using the models such as waterfall model, agile model, and V-model. There are many modern software development models such as DevSecOps that extends the capability of existing models and makes it more cloud friendly. Web-based development and delivery of software applications as well as Low-Code/No-Code (LCNC) are becoming increasingly popular and valuable due to availability of tools and technology for SDM. Selecting the right model for developing software ensures that all the disciplines of that model are implemented and monitored during the SDM process. Hence, SDM practices strategically manage how software is being developed, tested, and deployed and creates the business value for the stakeholders.
Hybrid Movie Recommendation System Using Machine Learning

This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, multiple cluster centers are obtained, the distance between each cluster center and the target user is calculated, and the target user is categorized into the closest cluster. Finally, a suggestion list is created using the collaborative filtering algorithm to forecast the scores for the target user's unrated items. The highdimensional rating matrix is transformed into a lowdimensional item category preference matrix, which further reduces the sparsity of the data. The items are then grouped based on item category preference. The recommendation algorithm suggested in this article addresses some of the limitations of a single algorithm model and enhances the suggestion effect, according to experiments using the MovieLens movie dataset.
CpsMark+: A scenario-oriented benchmark system for office desktop performance evaluation in centralized procurement via simulating user experience

Rapid business expansion of various companies has placed growing demand on office desktops recent decades. However, improper evaluation of system performance and inexplicit awareness of practical use conditions often hamper the efforts to make a consummate selection among multiple alternatives. From the perspective of end users, to optimize the evaluation process of desktop performance in centralized procurement, we present CpsMark+, a coherent benchmark system that evaluates office desktop performance based on simulated user experience. Specifically, CpsMark+ includes scenario-oriented workloads portraying representative user behaviors modeled from the cooperative workflow in modern office routines, and flexibly adapted metrics properly reflecting end-user experience according to different task types. The contrast experiment between state-of-the-art benchmarks demonstrates high sensitivity of CpsMark+ to various hardware components, e.g., CPU, and high repeatability with a Coefficient of Variation less than 3%. In a practical case study, we also demonstrate the effectiveness of CpsMark+ in simulating user experience of tested computer systems under modern office-oriented scenarios for improving the quality of office desktop performance evaluation in centralized procurement.
Searching for Researchers: an Ontology-based NoSQL Database System Approach and Practical Implementation

This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.
Power System State Fusion Estimation Based on Maximum Correntropy Criterion

By using the observations from multiple sensors, the fusion estimation of power system states, such as voltage, current, phase angle and other variables can be realized. When the observation information is affected by non-Gaussian noises, especially by some heavy tailed impulse noise, the performance of the fusion filter estimation based on Kalman filter will decline. Based on maximum correntropy criterion (MCC) and combining two fusion methods of parallel fusion and sequential fusion, this paper studies two power system state fusion estimation methods that can be used in non-Gaussian noise environment, named maximum correntropy parallel Kalman fusion filter (MC-PKFF) and maximum correntropy sequential Kalman fusion filter (MC-SKFF) respectively. The validity and the performance comparison of the proposed fusion algorithms are verified by the simulation example of three-phase voltage state estimation in power system.
Multimodal Wearable Technology Approaches to Human Falls

Recently, focusing research and innovation capacities on the main human fall elements and minimizing their complications is a necessity. For this purpose, using the multimodal method with wearable sensors, development systems, and algorithms or strategies that meet approaches of assessment, prevention, and detection. Besides searching for individuals' wellbeing and reducing the loss of lives and injuries, as well as decreasing the cost of healthcare systems. This paper provides a general outlook of wearable devices regarding falls with a multimodal focus, studying the different signals acquired, features extracted, and algorithms, also understanding their limitations in generating new methods. Likewise, this study uses the three approaches, to merge into one study, since the reviewed literature does not consider all three focuses simultaneously.
PROGRESSIVE STUDIES OVER SPARSENESS OF LEAST SQUARES SUPPORT VECTOR MACHINES BASED ON HYPOTHETICAL ASSUMPTION

No Reference (NR) Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is partially available which is our Hypothetical assumption due to issue raised by reviewers since we used DCT Coefcients in our past research work. Our research work explores the tradeoffs between quality prediction and Video compression. Therefore, we implemented least square support vector regression algorithm as NR-based Video Quality Metric (VQM) for quality estimation with simplied input features based on DCT coefcients (Hypothetical assumption). We concluded that our proposed model overcame sparseness due to hypothetical Assumption.
Neural Symbol Grounding with Multi-Layer Attention for Robot Task Planning

The high-level symbolic representation has proven to be an effective expression for planning problems and is widely used in robot task planning. However, the grounding of symbol based on multimodal raw data in complex environment still remains a significant challenge. In this paper, we put forward a Multi-layer Attention Network for Multimodal Symbol Grounding (maMSG Net) where we combine the high level symbolic representation and multimodal perception effectively, improving the capability and accuracy of understanding complex environment and increasing the diversity of the symbol definition. Meanwhile, we introduce both the cross-modality attention and intra-modality attention in our neural network, which is demonstrated to improve the accuracy of symbol grounding. The maMSG Net takes multimodal raw data as input and estimates values of state symbols defined in given planning domain. We designed computer simulated experiments to evaluate the effectiveness of our method and verify its robustness against external interference.
Cybersecurity of a Fact Certification Network

A system of protocols is defined which enables suitably equipped systems or devices to record validated “facts” in a network of servers in such a way that only facts which are genuinely true can be validated by the network. The servers or devices using this network make use of remote attestation to provide this secure and integrated network, which is called, hence-forth, Certnet (Certification Network). Certnet of this paper can be envisaged, for example, as providing rigorous fact-checking for a news service. Currently any individual trying to capture media evidence (photo or video) has no way to certify their evidence. Certnet will provide this missing certification of evidence, for suitable equipment.
printcase: A command for visualizing single observations

In this article, we introduce the printcase command, which outputs data from a specific observation into an easy-to-read Microsoft Word or PDF document. printcase allows analysts to focus on a single observation within a dataset and view that observation in its entirety. The output displays fields in table format, with all variables identified by their corresponding labels and all responses identified by their corresponding value labels. We explain how printcase works, give examples of circumstances under which this type of table-based quasiquestionnaire would be useful, and provide code for printing single observations.
Multi-Modal Manipulation Planning for an Upper-Torso Humanoid System

Motion planning problems for manipulators are often considered and planned on a single submanifold. To solve a more challenging manipulation task, the manipulator needs to use both prehensile and non-prehensile manipulation actions to solve the task. Non-Prehensile manipulation actions operate on a lower dimension compared to prehensile manipulation actions. In this work, we propose a multi-modal motion planner for tree kinematic structure robotic systems, which plans through different submanifolds to provide a continuous trajectory to fulfill the given task. As both manipulators share the same torso joints, a movement involving the torso and one of the manipulators often causes an undesired behavior in the second manipulator, such as being in a collision state or an object that cannot be received from the second manipulator. A multi-task propagation function is developed to find optimal joint angles for the tree kinematic structure while propagating on the submanifolds of the planning space. This planner is evaluated and tested on an upper-torso humanoid system in two different scenarios. The results show that our planner achieved a higher success rate and needs less planning time compared to a planner which uses dual-arm manipulators mounted on a rigid body.
One-Shot Open-Set Skeleton-Based Action Recognition

Action recognition is a fundamental capability for humanoid robots to interact and cooperate with humans. This application requires the action recognition system to be designed so that new actions can be easily added, while “unknown” actions are identified and ignored. In recent years, deep-learning approaches represented the principal solution to the Action Recognition problem. However, most models often require a large dataset of manually-labeled samples. In this work we target One-Shot deep-learning models, because they can deal with just a single instance for class. Unfortunately, One-Shot models assume that, at inference time, the action to recognize falls into the support set and they fail when the action lies outside the support set. Few-Shot Open-Set Recognition (FSOSR) solutions attempt to address that flaw, but current solutions consider only static images and not sequences of images. Static images remain insufficient to discriminate actions such as sitting-down and standing-up. In this paper we propose a novel model that addresses the FSOSR problem with a One-Shot model that is augmented with a discriminator that rejects “unknown” actions. This model is useful for applications in humanoid robotics, because it allows to easily add new classes and determine whether an input sequence is among the ones that are known to the system. We show how to train the whole model in an end-to-end fashion and we perform quantitative and qualitative analyses. Finally, we provide real-world examples.
From Offline to Online: A Perception-Based Local Planner for Dynamic Obstacle Avoidance

The deployment of robots within realistic environments necessitates robots to be capable of replanning their loco-manipulation trajectories on the fly to avoid unexpected interactions that may occur due to the uncertainty that is present in such dynamic and varying workspaces. This work introduces a novel method for the online local replanning of precomputed global trajectories for redoundant robots. The local nature of the problem leads to a sparse system that a hyper-graph can encode more intuitively. Using a graph, we can also store the entire global trajectory, preventing the local planner from getting stuck in local minima, and vertices and edges can be dynamically added or removed to ignore those constraints that do not interfere with the local problem, further increasing the computational efficiency. This process is accompanied by a control layer that iteratively takes the online refined solution and safely moves the robot. The method is validated both in simulation and experimentally on the wheeled-legged quadrupedal robot CENTAURO, demonstrating its effectiveness in replanning online the loco-manipulation trajectories of the robot under the occurrence of obstacles that intervene with the initially planned trajectories.
Classification and Analysis of Textual data using Naive Bayes with TF-IDF

Text classification has become an emerging topic in this modern era as it allow us to extract meaningful information from the data and improve the performance of business and organization. Often termed as Text Tagging or Categorization, these textual data can be Structured, Semi-Structured and Unstructured. This work has utilized unstructured data with the help of twitter API. These unstructured data are then structured using NLP cloud API as the process of manual sorting is time consuming and tedious. The structured textual data comprises of a set of categorical data that is labelled on the basis of the content of the comments. Text Classification has various use cases such as Sentiment analysis, Polarity Checking, Natural Language Inference and accessing grammatical correctness. Earlier experimental work has been carried out using Naive Bayes with a Bag of Words (BOW) feature extraction technique by previous researchers. The objective of this work is to analyze the transformed structured imbalanced data and study the impact it has on the accuracy of Naive Bayes model using Term Frequency-Inverse Document Frequency (TF-IDF) technique. Naive Bayes is a linear, probabilistic and supervised machine learning classifier based on Bayesian theorem. On training and testing the data using the proposed model, it is found that there is an improvement in the overall accuracy with 2-3%.
Advanced Image Processing Techniques for the Detection and Monitoring of TV Datacenter System

TV Datacenter is the primary storage facility where servers for data storage are located. TV Datacenters have exploded in popularity over the last decade, becoming the epicenter of the technical landscape. As the size and ability of the centers grow, the complexity in managing them grows as well. The information and functionality grid are in danger of imploding, with potentially catastrophic consequences. As a result, solutions that can keep pace with the growth of data delivery, as well as Datacenter storage sizes, are needed. The main objective behind this research study is to propose a complete and advanced system for accommodating the enlargement of Datacenters, for enhancing data delivery in Datacenters, and for detection of errors and problems in TV Datacenters. Moreover, the system will also perform advanced and robust monitoring of TV Datacenters. The significance of the study relies on the algorithms that are proposed since these algorithms are completely novel as well as they are highly performant. In addition, these algorithms incorporate new technologies of image processing. The results obtained using the algorithms showed a detection rate obtained of 83.33% which can be considered to be an indicator of the high performance of the proposed detection system. Moreover, according to the results, the minimum time of replacement is zero seconds meaning that the replacement was done automatically after the detection of the corruption in the video. The results also showed that for all videos, the detected number of frames were all successfully replaced. The frame replacement showed 100% efficiency for every video.
Application benchmark for quantum optimization on electromobility use case

Battery electric service vehicles are one step to reduce CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in the mobility sector. We present an use case for optimal charging scheduling in combination with local solar power generation to minimize power grid usage. The study compares results obtained with a classical optimizer and with a quantum computing algorithm on real quantum hardware. It is shown that for most benchmark experiments, the quantum computing method yields an optimal solution however the quantum approximate optimization algorithm is more sensitive to penalty factors than the classical optimization. Additionally, we present a comparison of the computing times and give a brief review of the current state of IBM’s gate-based quantum computing.
Intelligent Code Review Assignment for Large Scale Open Source Software Stacks

In the process of developing software, code review is crucial. By identifying problems before they arise in production, it enhances the quality of the code. Finding the best reviewer for a code change, however, is extremely challenging especially in large scale, especially open source software stacks with cross functioning designs and collaborations among multiple developers and teams. Additionally, a review by someone who lacks knowledge and understanding of the code can result in high resource consumption and technical errors. The reviewers who have the specialty in both functioning (domain knowledge) and non-functioning areas of a commit are considered as the most qualified reviewer to look over any changes to the code. Quality attributes serve as the connection among the user requirements, delivered function description, software architecture and implementation through put the entire software stack cycle. In this study, we target on auto reviewer assignment in large scale software stacks and aim to build a self-learning, and self-correct platform for intelligently matching between a commit based on its quality attributes and the skills sets of reviewers. To achieve this, quality attributes are classified and abstracted from the commit messages and based on which, the commits are assigned to the reviewers with the capability in reviewing the target commits. We first designed machine learning schemes for abstracting quality attributes based on historical data from the OpenStack repository. Two models are built and trained for automating the classification of the commits based on their quality attributes using the manual labeling of commits and multi-class classifiers. We then positioned the reviewers based on their historical data and the quality attributes characteristics. Finally we selected the recommended reviewer based on the distance between a commit and candidate reviewers. In this paper, we demonstrate how the models can choose the best quality attributes and assign the code review to the most qualified reviewers. With a comparatively small training dataset, the models are able to achieve F-1 scores of 77% and 85.31%, respectively.