Reviewerly/exHarmony huggingface dataset:
Relationship between Poverty Intensity and Technical Efficiency among Cassava and Tomato Entrepreneurs in Benue State, Nigeria

The study engaged the Stochastic Frontier Production Function (SFPF) to analyze the technical efficiency of cassava processing and tomato production enterprises to ascertain poverty intensity among entrepreneurs while simple statistics, gross margin and Foster-Greer-Thorbecke poverty index were used to analyzed profitability of the enterprise and poverty status of the entrepreneurs.A multi-stage sampling technique was used to obtained data from 140 entrepreneurs, drawn from the 70 Cassava Enterprise and 70 from the tomato enterprise using a well-structured and pretest questionnaire.The findings revealed that the respondents' average age was 48.6 and was dominated by male (65%) with household size on the aggregate of eight people.Applying the Logistic regression model, the result showed that 58.3% of the respondents were below poverty margin.The severity of poverty was 63.7% and 54% for cassava and tomato enterprises.The study recommends that the capacities of entrepreneurs should be enhanced especially in the areas of the technical operations such as the use of improved seeds/seedlings and labor measured in terms of technical efficiency is capable of reducing the intensity of poverty among cassava and tomato enterprises.Also government at all levels should create an enabling environment where there will be credit facilities in cash and in kind (inputs) to enable potential and prospective entrepreneurs to thrive well in their chosen line of enterprise.
The Effects of Academic-Related Characteristics and ASE Model Factors on Learning Persistence of Dental Hygiene Students

Objectives: The purpose of this study was to determine the effect of academic-related characteristics and ASE Model factors on learning persistence of dental hygiene students.&#x0D; Methods: This study was conducted through an online questionnaire from April to May 2022 among 229 college students enrolled in the department of Dental Hygiene in Busan and Gyeongnam.&#x0D; Results: Learning persistence was higher for 4-year students compared to 3-year students. The higher the participation in the department study group, the more the department mentoring program, the more major club activities, and the higher the participation in the non-specialty program, the higher the learning persistence. The factors that had the greatest influence on learning persistence were attitude (β=0.519), social support (β=0.211), and academic-related characteristics (β=0.140) in that order.&#x0D; Conclusions: It can be seen that the better the student's learning attitude, the higher the social support, the more positive the academic-related characteristics, the higher the intention to continue studying.
A Generalized Notion of Consistency with Applications to Formal Argumentation

We propose a generic notion of consistency in an abstract labelling setting, based on two relations: one of intolerance between the labelled elements and one of incompatibility between the labels assigned to them, thus allowing a spectrum of consistency requirements depending on the actual choice of these relations. As a first application to formal argumentation, we show that traditional Dung’s semantics can be put in correspondence with different consistency requirements in this context. We consider then the issue of consistency preservation when a labelling is obtained as a synthesis of a set of labellings, as is the case for the traditional notion of argument justification. In this context we provide a general characterization of consistency-preserving synthesis functions and analyze the case of argument justification in this respect.
How Complex Is the Strong Admissibility Semantics for Abstract Dialectical Frameworks?

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments are called semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexity of the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument.
Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.
Correction to: A systematic approach to brain dynamics: cognitive evolution theory of consciousness

[This corrects the article DOI: 10.1007/s11571-022-09863-6.].
Digital High: The Art of Visual Seduction?

The paper focuses on the structure of an advertising image for a 2010s computer company in the neo-capitalist Moscow, Russia. The analysis looks back to the pioneering studies of advertising as a commercial “applied art” by Sergei Eisenstein, Leo Spitzer and Roland Barthes. The picture’s plot and composition are shown to be a consistent and sophisticated near-artistic design that uses textual puns, poetic topoi and visual stereotypes (in particular, sex appeal) for the promotion of the advertised merchandise (a smartphone). The psychological naturalization of the design is clarified with references to the insights of Sigmund Freud, Heinz Kohut and Gerard Genette into the dynamics of narcissism. In a widening circle, the contextualization of the design involves: the literary topos of using birds in love poetry (made famous by its treatment in the lyrics of the Roman poet Catullus) and in painterly variations on the theme; the narcissist discourse of a modern Russian poet (Eduard Limonov); and the grand pictorial tradition of portraying a nude (Venus) before the mirror (relevant classical canvases are considered briefly).
Analysis of communicative barriers in the educational process under lockdown

The mass introduction of distance learning into the educational process during the COVID-19 pandemic, which was previously used only in the form of individual educational technologies, determined the relevance of the chosen research topic. Radical changes in the system of educational process organization have led to the emergence of new communication barriers that negatively affect the effectiveness of the learning process. The purpose of the study is to analyze these barriers. The objectives of this study are to identify new communication barriers in the educational process, as well as to determine the factors influencing their appearance. The study was conducted using the methods of classical and comparative analysis; a survey was also conducted in the form of a questionnaire using the Google Docs platform. The authors came to the conclusion that the distance education format required from the participants of the educational process a long period of adaptation, which is currently far from being completed. The main communicative barriers associated with the transition to the distance format of work are technical, organizational, communicative- psychological and substantive. Meanwhile, the number of communication barriers and the degree of their manifestation depends, among other things, on the educational model implemented in the university.
Trajectory Planning for Narrow Environments That Require Changes of Driving Directions

In the area of mobile robotics, trajectory planning is the task to find a sequence of primitive trajectories that connect two configurations, whereas non-holonomic constraints, obstacles and driving costs have to be considered. In this paper, we present an approach that is able to handle situations that require changes of driving directions. In such situations, optimal trajectory sequences contain costly turning maneuvers – sometimes not even on the direct path between start and target. These situations are difficult for most optimization approaches as the robot partly has to drive paths with higher cost values that seem to be disadvantageous. We discuss the problem in depth and provide a solution that is based on maneuvers, partial backdriving and free-place discovery. We applied the approach on top of our Viterbi-based trajectory planner.
Improving Medical Video Coding Using Multi Scale Quincunx Lattice: From Low Bitrate to High Quality

In recent years, the healthcare sector has seen an increase in the use of medical images and videos. However, storage and transmission of this huge volume of data remain a challenging task, requiring the use of compression techniques. In this paper, the authors propose an algorithm to improve the visual quality of compressed medical video for lower bitrate without modifying the content of information such as edges and textures, this is a unique way for doctors to store and share medical data over the internet. The algorithm has not yet been sufficiently explored in medical video coding. In this study, the performances of the quincunx wavelet transform (QWT) combined with the set partitioning in hierarchical trees (SPIHT) encoder are discussed. The QWTs were chosen due to their limited number of wavelets family and reduced dilatation factor. The high efficiency of the suggested algorithm is checked against the coding standard based on the discrete cosines transform (DCT) or discrete wavelet transform (DWT). The assessment of the quality of the decoded video is based on the use of the peak signal to noise ratio (PSNR), the mean structural similarity (MSSIM) and the visual information fidelity (VIF). The results prove that the QWT+SPIHT provide competing performance where the PSNR reached 33 dB value for lower bitrate (137.408 Kbps) against previous standards.
Subjective Study of the Impact of Compression, Framerate, and Navigation Trajectories on the Quality of Free-Viewpoint Video

This paper presents an exploratory study of the perceptual quality of different coding configurations of a real-time Free-viewpoint Video (FVV) system through a subjective experiment. In addition, different pre-defined camera trajectories were considered to analyze their impact on the visual quality and their relationship with the trajectories of the observers when freely exploring the content. For this experiment, a novel test methodology was used based on the participation of few observers who repeat the test in different moments. The results provide useful insights on options to reduce, if necessary, the amount of data to deliver FVV providing the highest possible quality to the end users. Also, they can help on defining trajectories that can be appealing for the users if they do not have the possibility to freely navigate through the content or that can be useful to perform valid subjective tests with pre-defined trajectories. Finally, the FVV dataset that has been created and used for this experiment will be made publicly available for the research community, once it is completed with more videos and results from future subjective tests.
Unleashing Adult Learners’ Numeracy Agency Through Self-Determined Online Professional Development

Opportunities for self-determined online professional development (OPD) are emerging, but their potential for increasing adult learners’ agency is not yet fully realised. Faced with the problem of successfully designing a self-determined comprehensive evidence-based online numeracy resource for educators who are often time poor and do not engage with online learning unless they are intrinsically motivated, we engaged in design research to conceptualise the Birth to Level 10 Numeracy Guide for educators and families. The Birth to Level 10 Numeracy Guide fosters educators’ and adult learners’ numeracy capability across numeracy focus areas from birth to level 10 (16-year-olds). This extensive OPD resource incorporates consistent design elements, double-looped learning, nonlinear learning, self-reflection, and metacognition activities to foster educators’ pedagogical content knowledge (PCK) through experiential learning. With a section dedicated to families, the resource provides suggestions and advice to parents and carers on everyday, authentic activities to develop children and young people’s numeracy understandings at home and in the local community. As education systems continue to grapple with the disruption brought about by the COVID-19 pandemic, the Birth to Level 10 Numeracy Guide is a timely, freely accessed, viable, and scalable option for providing low-cost OPD.
Masked Autoencoder for Pre-Training on 3D Point Cloud Object Detection

In autonomous driving, the 3D LiDAR (Light Detection and Ranging) point cloud data of the target are missing due to long distance and occlusion. It makes object detection more difficult. This paper proposes Point Cloud Masked Autoencoder (PCMAE), which can provide pre-training for most voxel-based point cloud object detection algorithms. PCMAE improves the feature representation ability of the 3D backbone for long-distance and occluded objects through self-supervised learning. First, a point cloud masking strategy for autonomous driving scenes named PC-Mask is proposed. It is used to simulate the problem of missing point cloud data information due to occlusion and distance in autonomous driving scenarios. Then, a symmetrical encoder–decoder architecture is designed for pre-training. The encoder is used to extract the high-level features of the point cloud after PC-Mask, and the decoder is used to reconstruct the complete point cloud. Finally, the pre-training method proposed in this paper is applied to SECOND (Sparsely Embedded Convolutional Detection) and Part-A2-Net (Part-aware and Aggregate Neural Network) object detection algorithms. The experimental results show that our method can speed up the model convergence speed and improve the detection accuracy, especially the detection effect of long-distance and occluded objects.
SENTIMENT ANALYSIS ON NEWS ARTICLES

Sentiment analysis or opinion mining is the computational study of people's opinion, sentiments, attitudes and emotions expressed in written language. It is one of the most active research areas in natural language processing and text mining in recent years. It is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinion appraisal and emotions of people in regards to entities, events and their attributes. In decision making, the opinion of others have a significant effect on customer ease, making choices with regards have a significant effect, product entity. The approach of text sentiment analysis typically works at a particular level like phrase, sentence or document level. This project aims at analyzing a solution for the sentiment identification at a fine-grained level,namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral. The data set is gathered from inshorts.com and the project is restricted to 3 news article domains namely- Sports, World and Politics. Lexicon based approach is used for Sentiment Analysis. VADER gives the polarity of negativity, neutrality, positivity and also the consolidated compound score for the given text. The Data Labeling, Data Processing and Finalization is done using Compound score from VADER.
FACTORS AFFECTING THE EFFECTIVENESS OF SCIENCE TEACHING DURING COVID – 19 PANDEMIC

Due to the impact of COVID-19 pandemic, the world is facing the new setup of the education system, where blended learning will be used. The teachers' challenges are integrating the innovative programs in different subject areas effectively, how to motivate the students' learning process and ensure that learning continues for all the students. This descriptive survey assessed different factors affecting the effectiveness of Science teachers during COVID-19 pandemic. A total of 44 Science teachers from the schools of Subic National High School, Castillejos National High School and San Guillermo National High School responded to the researcher-crafted survey questionnaire (Î±=0.962). Results of the study showed that majority of the respondents strongly agree on factors affecting the effectiveness of Science teaching in which expertise in the subject matter is the primary factor followed by teaching performance, administrative support, studentâ€™s level of understanding, teacher communication, digital proficiency, instructional materials and professional development. Based on the descriptions of an effective science teaching given by the science teachers, it is said that teaching science concepts can be apply in real life situations. The strategies use to be effective in teaching Science during pandemic must be engage flexible learning. In other factors that may affect the effectiveness of Science teaching is teaching resources. There was no significant difference on the teacherâ€™s perspective of the eight factors affecting the effectiveness of science teaching when grouped according to profile variables. Furthermore, positive significant moderate to high correlations were observed among factors affecting the effectiveness of science teaching in the current pandemic. The study suggests that training and seminars that focuses on enhancing content knowledge and pedagogy in teaching science during pandemic should be given among Science teachers that highlights on techniques and strategies on science teaching during COVID-19 pandemic. Correspondingly, future studies that focus on determining other factors that can affect the effectiveness of science teaching in the current pandemic must be conducted.
Joint extraction of Chinese cybersecurity events based on bidirectional TCN and attention

Cybersecurity event extraction aims to extract threat intelligence information from unstructured text and provide a data basis for correlation analysis of cybersecurity events. However, in the field of cybersecurity, the complexity of terminology in event data as well as the problem of polysemy and crossover between Chinese and English in Chinese texts pose great challenges to event extraction. Existing methods usually perform event extraction in a pipelined manner, ignoring the dependencies between event elements and event triggers. Therefore, a joint event extraction model is proposed in this paper. The model captures the contextual features of sentences in the encoding layer by an improved temporal convolutional network (TCN) and then enhances the dependency features between triggers and arguments using a multihead attention mechanism to achieve sentence-level joint extraction of Chinese cybersecurity events. We conducted comparison experiments on real cybersecurity news data to evaluate the event extraction performance of the model. Compared with a baseline model LSTM, the method improves the F1 values by 13.2% and 33.2% in two subtasks of trigger extraction and argument extraction, respectively.
An Ontology-Based Knowledge Evolution Mechanism to Support the Adaptive Design of Autonomous Transportation System

Along with the transformation of transportation system toward autonomous transportation system (ATS), which impels its ability on intelligence and automation consistently and gradually, how to reasonably analyze the evolution across its development stages is essential to support an orderly development. Since the data explodes in the information era, contexts encoded in related knowledge bases become even more complex to be analyzed for the extraction of evolution details. To tackle such a challenge, an evolution analysis mechanism based on the knowledge graph (KG) is proposed to support knowledge modeling, evolution analysis, and information extraction. Moreover, through an evaluation based on a common set of KGs designed to store information about technologies, functions, and services to support autonomous driving in different levels, the merits of proposed mechanism are revealed in detecting more accurate evaluations with a reduced number of operations to rebuild the evolutions across KGs.
Analyses on characteristics of microelement and rare-earth element zone of hadamengou gold deposit, Inner Mongolia

The pre-mountain potash belt in Hadamengou Gold Deposit, Inner Mongolia, is located in the south of the ore belt and the north of the pre-mountain Baotou-Hohhot fault. The study of element geochemistry shows that the Piedmont potash belt belongs to the ultrapotassic basalt series. The rocks are characterized by enrichment of LILE (Rb, Ba, K, Sr) and light rare earth elements (LREE), and have obvious negative anomalies of Ti, Ga, and Be, the total rare earth (REE) has the opposite trend with the alteration intensity of potassium. The results show that the rocks of the Piedmont potash belt are structurally fractured and strongly altered, so it is considered that the Piedmont potash belt was originally a tectonically fractured zone, in the later period, the ore-bearing hydrothermal solution with high content of potassium entered the fracture zone and the rocks in the fracture zone underwent hydrothermal alteration.
Indoor Navigation System of Mobile Robot with Trajectory Optimization

This paper proposes a navigation system for mobile robot working in indoor environment, which includes mapping and trajectory generation for the motion tracking problem of the real mobile robot. The mapping process comprises a Gaussian model to improve the accuracy of the environment representation. With this new mapping method, the mobile robot knows the obstacle in the environment around robot itself. For the trajectory generation process, a multimodality method is used to explore all feasible paths to reach the target point by estimating the density of distribution of the objective function. Finally, the experiments show the effectiveness of this navigation framework.