Miracle-dz/UniFAR-FTUs huggingface dataset:
A Structural Analysis of the Significance of A Set of the Original Tqm Measurement Items in Information Systems Function

Many researchers suggested that TQM concepts are useful/valuable and applicable to all functional areas. However, the evidence of proof to the direct usage of TQM concept from literature has not been visibly documented. This paper examines the significance of a set of the original measurement items of TQM factors in Information Systems Function (ISF). It has shown that the proposed set of original measurement items of TQM factors of top management support, product/service design and quality information reporting have direct and positive impacts to the ISF performance. Whereas, the proposed set of original measurement items of TQM factor of training has a direct and negative impact to the ISF performance. It is suggested that more research should be conducted to identify training items for ISF so that full potential and benefit of TQM implementation can be realized in this functional unit.
A semi-supervised convolutional neural network for hyperspectral image classification

ABSTRACT Convolutional neural network (CNN) for hyperspectral image classification can provide excellent performance when the number of labeled samples for training is sufficiently large. Unfortunately, a small number of labeled samples are available for training in hyperspectral images. In this letter, a novel semi-supervised convolutional neural network is proposed for the classification of hyperspectral image. The proposed network can automatically learn features from complex hyperspectral image data structures. Furthermore, skip connection parameters are added between the encoder layer and decoder layer in order to make the network suitable for semi-supervised learning. Semi-supervised method is adopted to solve the problem of limited labeled samples. Finally, the network is trained to simultaneously minimize the sum of supervised and unsupervised cost functions. The proposed network is conducted on a widely used hyperspectral image data. The experimental results demonstrate that the proposed approach provides competitive results to state-of-the-art methods.
GESTÃO EM SISTEMAS E TECNOLOGIAS DE INFORMAÇÃO EM HOSPITAIS DE MARINGÁ-PR

Este artigo apresenta uma análise dos resultados obtidos na aplicação do Questionário Prospectivo – QP do Projeto GESITI em três hospitais da cidade de Maringá-PR. As entrevistas foram conduzidas por professores de uma instituição de ensino superior em parceria com o Projeto GESITI/DGE-CTI - Centro de Tecnologia da Informação Renato Archer. A metodologia utilizada foi pesquisa quantitativo-qualitativa, por meio de estudo de casos múltiplos, utilizando questionário fornecido pelo Projeto GESITI. O estudo apontou que os hospitais conhecem os recursos de TI modernos, porém encontram dificuldades em implantá-los devido ao alto custo dos equipamentos e software e também à falta de recursos humanos preparados para utilizá-los.
Are Top Management Teams Compensated as Teams? A Structural Modeling Approach

I develop two empirical models to quantify agency friction caused by moral hazard in top management teams. They capture shareholders’ distinct perspectives on compensating top managers: the Individual Model, in which managers in a firm unilaterally shirk, and the Team Model, in which managers choose effort jointly. The Team Model rationalizes observed compensation better than the Individual Model, underlining the importance of managerial coordination and team-based incentives. This paper also offers novel estimates of agency friction in a team production setting. Risk premium explains a large portion of the pay differential across and within firms. Firm value would have dropped between 4% and 15% if managers shirked. Additional counterfactual estimation suggests that shareholders could reduce the total compensation by up to $17 million if they were to switch from the individual perspective to the team perspective. This paper was accepted by Shivaram Rajgopal, accounting. Funding: This work was supported by the Professional Staff Congress and the City University of New York (PSC-CUNY) [Grant 67780-00 45]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024. 4982 .
Natural Language Processing

TECHNOLOGICAL PROBLEMS IN NLP . 99 Input Speech Recognition – Can You Hear Me Now? 100 Acoustic Model – Bridging Sound and Words . 101 Language Model – God Save the Gerbil . 102 Context-Based Language Model – Know Thyself . 102 Legal Implications . 103
Project Management Effectiveness in relation to Post Merger Integration: The case of Ethiopian Construction Design and Supervision Works Corporation

In an increasingly competitive market, public enterprises in developing countries adopt a number of strategies to deal with raising efficiency and productivity. One of such strategies is to enter into mergers or acquisitions (M&A). However, it has been reported that many companies fail to meet the expected synergy value and it takes longer time to achieve better performances. It is also known that integration processes during the post-merger integration period are critical to synergistic effects and performance of the merged companies over time. The Ethiopian Construction Design and Supervision Works Corporation (ECDSWCo) is the result of the amalgamation of three formerly independent public enterprises. As a project-driven professional service giving organization, its business is structured around the delivery of projects; and project management (PM) is the business itself. An extensive review of M&A and PM literatures conducted and the key factors for successful integration and project management effectiveness identified. Then the post-merger integration (PMI) and project management (PM) practices of the company examined for the possible correlation between the PMI success and its effect in the project management effectiveness of the company; with the hope to developing a usable framework that can be adopted. Concurrent mixed methods, which involve collecting analyzing and interpreting data using both quantitative and qualitative methods, are applied to draw empirical data from 36 project management staff of ECDSWCo. The results are statistically analyzed for reliability and correlation using SPSS, Cronbach's alpha reliability test and Bi-variate correlation tests. The results showed statistically significant positive correlations between PMI and a positive influence of PMI and its contributing variables on PME.
Threat detection in online discussion using convolutional neural networks

Unwanted behaviour in terms of threats of violence in online discussions are of common occurrence. We can find such behaviour in e.g. comment fields on Facebook, Youtube and in online news papers. With increasing use of these kind of discussion arenas, it generates a lot of work for moderators that have to, at worst manually go through comments, and remove the ones containing undesired content. In this project, we use a corpus with YouTube comments. The task will be to classify comments as containing violent threats or not. The comments in the corpus are manually annotated as "threat" or "non-threat". To attempt to solve this, we use deep learning techniques in combination with word embeddings. We have systematically explored the effects of a range of different choices regarding architecture and parameterization. In our result we find that threat detection using convolutional neural networks do not outperform earlier work on the same task.
Support Vector Machine and Naïve Bayes comparison of Sentiments on Terrorism

Text Analysis has become a major area of research. In order to be aware of what people think and how they feel after terrorism attacks, there needs to be some mechanism. We aim to propose a solution in this regard to learn about people's sentiments in detail on terrorism incidents in Pakistan using text analysis. In this research support vector machines and naïve Bayes algorithms are compared in finding out the sentiments from data set of opinions express on terrorism activities in Pakistan.
A regression analysis of elementary students’ ICT usage vis-à-vis access to technology in Singapore

Abstract This paper explores the relationship among ICT infrastructure (i.e., computing devices and Internet), one-to-one computing program and student ICT activities in school. It also looks into the differences of how ICT is being used in the teaching of English, mathematics and science at the elementary school level in relation to the availability of computing devices, Internet connection and one-to-one program. A total of 360 Grade 5 students from three schools (120 students from each school) in the western part of Singapore participated in a questionnaire adapted from the Program for International Student Assessment (PISA) ICT Familiarity Component for Student Questionnaire regarding students’ ICT use. One of the participating schools has implemented a school-based one-to-one computing program for all its students for the past nine years, with the school providing the necessary computing devices for all its Grade 1 to 3 students and a student computer ownership program to encourage all its Grade 4 students to procure their own computing devices to be used till completion of elementary education at Grade 6. The regression analyses suggest that how ICT is being used plays a more significant role in predicting the frequency of ICT use for the various subject areas than ICT infrastructure and one-to-one computing program. Further analysis also reveals that one-to-one computing program and availability of computing devices have a significant interaction effect on the frequency of ICT usage for English but not for mathematics and science—the presence of computing devices seem to have an impact for the usage of ICT for English but not the other two subject areas. The findings concur with earlier studies that there are differences in frequency and how ICT is being used in the teaching of English, mathematics and science.
International Conference on Business Sustainability and Innovation KNOWLEDGE ACCUMULATION ABSORPTION MEDIATION ON RELATIONSHIP BETWEEN E-BUSINESS ADOPTION TO BUSINESS PERFORMANCE

E-business opens up opportunities that lead to higher business performance among its adopters. However, e-business adoption is not the sole driver of higher business performance. E-business adopters who are successful in improving their business performance are those that possessed knowledge absorption capacity. Therefore, it is hypothesized that capacity to absorb new knowledge mediates the effect of e-business on performance. Based on theory of innovation diffusion by Rogers (1995) the causal relationships between adoption, absorption and business performance are explained. The empirical research on testing the mediation relationships collected 212 survey questionnaire data from Malaysian SMEs. The data was analyzed using PLS-SEM to test the mediating effect of knowledge absorption on the relationship between e-business adoptions to business performance. The research found significant mediating effect of knowledge absorption in the proposed model. The findings revealed the consequence and significance of this two important factors and suggestion for SMEs and authoritative training providers was put-forth.
Accidental Inversion During 3d Rotation With 2-dof Input Devices

This dissertation focuses on a human operator's ability to perform rotational control of a three-dimensional object using two-degrees of freedom (DOF) interface devices. Although input devices designed specifically for 3D interaction exist, devices traditionally used for two-dimensional user interaction, such as a mouse or joystick, have become ubiquitous to computer tasks. This research examines a particular human-computer interaction issue that arises from stimulus-response compatibility between three-dimensional stimuli spaces and 2-DOF response sets. The focal point of this research is a phenomenon referred to here as accidental inversion. Accidental inversions occur when an operator erroneously moves a three-dimensional object in a direction opposite than was intended. Thus, the effect of accidental inversion results from a mismatch between the operator's intended and actual input. A key assumption in diagnosing the causal factors involved in the accidental inversion effect is contribution from both internal (i.e., having to do with the individual) and external (i.e., having to do with the environment) influences. Three experiments were conducted to study accidental inversion. The first examined population stereotype, a measure of a target population's natural response tendencies to particular stimuli for a particular task. Results indicated a strong population stereotype for horizontal rotations (i.e., yaw) and weak stereotype for vertical rotations (i.e., pitch). This effect was mediated by whether the task was in the context of flight or ground-based movement. The second experiment analyzed the subjective preference for two opposite input-response (I-R) mappings (i.e., how the system responds to different input into the controlling device) for a task requiring control over vertical rotation. Results indicated that subjective preferences for I-R mappings were not heavily polarized. The third experiment also focused on vertical rotational control and examined how subjective preference for a particular I-R mapping affected performance. Furthermore, this experiment also examined performance when interference was introduced in the form of a temporary interruption where the participant had to conduct the task using an opposite I-R mapping. Results indicated that, upon being interrupted with the opposite I-R mapping, the group who used the mapping they subjectively preferred did worse than the group who used the mapping they did not prefer. This research has implications for the design of human-machine systems requiring human-in-the-loop three-dimensional rotational control. Some human-machine systems can have significant consequences from even a single mistake caused by a human-operator accidentally providing the wrong input. Findings from this research lead to two primary recommendations to the design of human-machine systems: a) an easily accessible and clearly indicated method to select input-response mapping which is provided before beginning the actual task, b) be informed of the current input-response mapping…
OUTREACHES CONTENT TRACING TECHNIQUE FOR SOCIAL NETWORKING SERVICES

Context. Today’s social networking services have turned into an efficient communication tool for the members of virtual communities termed actors. The high rate of content dissemination, transboundary interactions between the actors and the drives to group the latter up, could make the services efficient cyberwarfare against the person, society and country. Text content is the most frequently used means of communication between the actors of virtual communities. The actual theoretical and applied research, aimed at designing maintenance procedures for the national cybersecurity system, is to solve the problem of text content monitoring. Objective. The research objective is to suggest and design a method for detecting outreaches by their content in the collection of text to solve the problem of efficient monitoring the social media. Method. The method designed is based on the modern content analysis approaches, i. e. the latent semantic indexing and the semantic analysis based on ontology. Results. The latent semantic indexing selects the concept relevant context by its content ignoring key word density. The semantic analysis will specify the further techniques of detecting harmful semantic structures in the body of text selected. When any conceptual mismatches in the text content and ontology detected and professionally analyzed, the ontology knowledge bases get updates of the social networking service cyberthreat patterns. Thus, the combination of both produces cancelling effect on the defects of and uncovers latent relationships between linguistic units. Conclusions. The outreaches tracing method proactively identifies cyberwarfare in virtual communities waged against individual, social and national cybersecurity. The suggested method increases efficiency and effectiveness of the national cybersecurity system on social networking services.
Discovery of Predictive Representations With a Network of General Value Functions

The ability of an agent to {\em discover} its own learning objectives has long been considered a key ingredient for artificial general intelligence. Breakthroughs in autonomous decision making and reinforcement learning have primarily been in domains where the agent's goal is outlined and clear: such as playing a game to win, or driving safely. Several studies have demonstrated that learning extramural sub-tasks and auxiliary predictions can improve (1) single human-specified task learning, (2) transfer of learning, (3) and the agent's learned representation of the world. In all these examples, the agent was instructed what to learn about. We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent's representation of the world. Specifically, our system maintains a large collection of predictions, continually pruning and replacing predictions. We highlight the importance of considering stability rather than convergence for such a system, and develop an adaptive, regularized algorithm towards that aim. We provide several experiments in computational micro-worlds demonstrating that this simple approach can be effective for discovering useful predictions autonomously.
A Systematic Approach for Obtaining Performance on Matrix-Like Operations

Scientific Computation provides a critical role in the scientific process because it allows us ask complex queries and test predictions that would otherwise be unfeasible to perform experimentally. Because of its power, Scientific Computing has helped drive advances in many fields ranging from Engineering and Physics to Biology and Sociology to Economics and Drug Development and even to Machine Learning and Artificial Intelligence. Common among these domains is the desire for timely computational results, thus a considerable amount of human expert effort is spent towards obtaining performance for these scientific codes. However, this is no easy task because each of these domains present their own unique set of challenges to software developers, such as domain specific operations, structurally complex data and ever-growing datasets. Compounding these problems are the myriads of constantly changing, complex and unique hardware platforms that an expert must target. Unfortunately, an expert is typically forced to reproduce their effort across multiple problem domains and hardware platforms. In this thesis, we demonstrate the automatic generation of expert level high-performance scientific codes for Dense Linear Algebra (DLA), Structured Mesh (Stencil), Sparse Linear Algebra and Graph Analytic. In particular, this thesis seeks to address the issue of obtaining performance on many complex platforms for a certain class of matrix-like operations that span across many scientific, engineering and social fields. We do this by automating a method used for obtaining high performance in DLA and extending it to structured, sparse and scale-free domains. We argue that it is through the use of the underlying structure found in the data from these domains that enables this process. Thus, obtaining performance for most operations does not occur in isolation of the data being operated on, but instead depends significantly on the structure of the data.
Recreational angling demand in a mixed resource fishery

Several large Irish lake fisheries comprise both pike (Esox Lucius) and brown trout (Salmo trutta). Due to predation on trout, pike stocks are actively managed in several locations with the objective of enhancing the trout fishery, which is a policy strongly supported by some trout anglers but intensely opposed by pike anglers. In the context of scientific support for management decisions concerning these mixed resource fisheries there is a dearth of economics knowledge. This paper addresses some of that knowledge gap, investigating factors affecting angling recreational demand within these mixed resource fisheries and whether there are significant differences between pike and brown trout anglers. We estimate a travel cost model and test whether pike and trout anglers have different demand preferences. The most substantive policy-relevant finding from the research relates not to differences associated with target species but to angling club membership. Angling club members are more price inelastic in their demand and particularly so in the case of trout anglers. *Corresponding author: gianluca.grilli@esri.ie Acknowledgements: Funding from Inland Fisheries Ireland is gratefully acknowledged. a. Economic and Social Research Institute, Dublin b. Trinity College Dublin c. Socio-Economic Marine Research Unit, Whitaker Institute, National University of Ireland, Galway ESRI working papers represent un-refereed work-in-progress by researchers who are solely responsible for the content and any views expressed therein. Any comments on these papers will be welcome and should be sent to the author(s) by email. Papers may be downloaded for personal use only. Working Paper No. 622
Analisa Pengaruh Market Orientation Terhadap Purcahase Intention Dengan Brand Image Dan Brand Performance Sebagai Variabel Intervening Pada PT Vista International Corporation

Competition in the educational institutions, consultation and preparation abroad is increasing, especially in a city that has a high priority to education, Surabaya.This study aimed to analyze the effects of Market Orientation on Purchase Intention with see the effect of Brand Image and Brand Performance.This study will be conducted by distributing questionnaires to 100 respondents potential customers PT VIC (Vista International Corporation). Analysis technique used is quantitative analysis techniques.
PiPo, a Plugin Interface for Afferent Data Stream Processing Operators

We present PiPo, a plugin API for data stream processing with applications in interactive audio processing and music information retrieval as well as potentially other domains of signal processing. The development of the API has been motivated by our recurrent need to use a set of signal processing modules that extract low-level descriptors from audio and motion data streams in the context of different au-thoring environments and end-user applications. The API is designed to facilitate both, the development of modules and the integration of modules or module graphs into applications. It formalizes the processing of streams of multidimensional data frames which may represent regularly sampled signals as well as time-tagged events or numeric annotations. As we found it sufficient for the processing of incoming (i.e. afferent) data streams, PiPo modules have a single input and output and can be connected to sequential and parallel processing paths. After laying out the context and motivations, we present the concept and implementation of the PiPo API with a set of modules that allow for extracting low-level descriptors from audio streams. In addition, we describe the integration of the API into host environments such as Max, Juce, and OpenFrameworks.