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The paper examines traditional knowledge about the role of financial and physical resources in entrepreneurship. Based on Bourdieu's theory, we search for an answer to the question of which forms of economic capital are used by Croatian entrepreneurs in the computer programming industry and what their value in the context of accessing other forms of capital is. The study is based on a qualitative methodological approach. In-depth interview technique accompanied by unstructured observations was used in data collection. In addition to primary data, research includes the use of qualitative and quantitative secondary data. Research suggests that in the computer programming industry, economic capital is just one tool in the conversion game of entrepreneurial capital. Moreover, the interviewed entrepreneurs attach the least importance to it in business, favoring the value of intangible resources such as specialized knowledge, business connections and personal acquaintances. The company's start-up phase is characterized by the entrepreneur's reliance on personal savings, which is replaced by financing based on retained earnings in the later stages of business. Other forms of growth financing, such as bank loans and recapitalization of external investors, are used by a minority of larger companies, with a good base of symbolic capital. The results on capital conversions indicate relatively easy conversion of economic capital into cultural capital and symbolic capital, and less frequent use of economic capital to create social capital. Fresh insights into the entrepreneurs' perceptions provided by the study expand existing knowledge about entrepreneurship within the computer programming industry, suggesting that it is an industry with huge potential for young talents without a personal financial base that is commonly considered a precondition for entering entrepreneurship. [ABSTRACT FROM AUTHOR]

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies. [ABSTRACT FROM AUTHOR]

Canada has just enacted legislation to give the broadcast regulator, the Canadian Radiotelevision and Telecommunications Commission (CRTC), authority to regulate online streaming platforms, both domestic and international. This legislation, known as Bill C-11 as it passed through an extensive parliamentary review process, was highly controversial. The bill empowers the CRTC to review and possibly amend the definition of “Canadian content.” It will also require that streaming services contribute financially to the production of Canadian content and will impose “discoverability” requirements on digital streaming platforms (like YouTube, Spotify, Netflix and others) to ensure that Canadian content is promoted. The CRTC will have broad leeway to issue and enforce regulations to achieve these ends. The U.S. high-tech and streaming industry does not like this legislation and has used various tactics to oppose it. Among these are arguments that C-11 violates commitments that Canada made to the United States in the Canada-U.S.-Mexico Agreement (CUSMA), the replacement for NAFTA, and that implementation of C-11 will result in U.S. trade retaliation. This paper analyzes the arguments to this effect put forth by the U.S. tech industry’s trade association, the Computer & Communications Industry Association (CCIA), and refutes them, particularly the argument that Canada will need to invoke CUSMA Article 32.6 (the cultural exception clause) to justify its actions. At the same time, the paper cautions that the CRTC needs to be careful with respect to imposing a requirement on foreign streamers to contribute to Canadian production if at the same time it denies them the ability to acquire and exploit the production they have helped finance when no such limitation is imposed on equivalent Canadian streaming services. [ABSTRACT FROM AUTHOR]

The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%). [ABSTRACT FROM AUTHOR]

The Argentine software and computer services sector (SSI) has been active for two decades, driven by public policies and international outsourcing. However, growth in the sector has currently stagnated. This stalling is the result of the extractivism of capacities that operate in three ways: price-salaries, training and financial. The first explanation is the growing competition for costs resulting in contractions of wages and benefits. The second is capacity building that is geared to the requirements of international demand in standardized skills. The third is the financial valuation of the undertakings. This article builds a set of statistical information that enables this phenomenon for the Argentine case to be described and documented. [ABSTRACT FROM AUTHOR]

Long-term development in computer manufacturing is crucial to satisfying the demands for the software and hardware needed for artificial intelligence (AI) computation. However, the computer manufacturing sector is highly energy-intensive and resource-consuming, making company sustainability an issue. The framework developed in this study incorporates 15 sustainability criteria focusing predominantly on the economy, society, environment, and risk perspectives. A novel hybrid approach that integrates the ISVTNN (interval single-valued trapezoidal neutrosophic number), DEMATEL (decision-making trial and evaluation laboratory), and PGRA (probability-based grey relational analysis) techniques is proposed. This approach is applied to evaluate the interdependent weights of the criteria for a comprehensive calculation of the performance of the evaluated companies based on probabilities. The inclusion of ISVTNNs allows not only for the broader consideration of information uncertainty but also for more effective integration of experts' judgments. To verify the robustness of the proposed model, three sensitivity analyses were conducted. The results show no significant changes in the rankings of the evaluated companies, demonstrating the stability and reliability of the model. The proposed framework and methodology can serve as the foundation for assessing the sustainable performance of companies in the electronics manufacturing industry. This study indirectly strengthens the backbone of the AI system, by improving sustainability assessment in the computer manufacturing sector (which is integral to the foundation for AI computation). In the AI-driven era, it can be expected that hardware demands will skyrocket, making sustainable manufacturing the linchpin for long-term operational success, resilience, and industry growth. • Developed a novel group decision-making approach for assessing corporate sustainability performance. • Integrated ISVTNN to measure uncertainty and effectively amalgamate expert opinions. • The proposed ISVTNN PGRA retains fuzziness, calculating through probabilistic means. • Demonstrated approach efficacy across six electronics manufacturing enterprises. • Sensitivity analysis underscores the approach's robustness. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of human motion simulation technology and virtual simulation technology, natural human-computer interaction has become the main form of research in the computer industry. Due to the research capabilities and practical value of mobile video technology, it is widely used in advanced fields, such as video animation production, rehabilitation medicine, sports training, and game software, effectively realizing the connection between the three-dimensional world and the simulation world. However, the application of motion imaging technology in teaching activities is not perfect. This article is based on dance teaching, adopts a dance movement analysis method based on vector matching, and uses artificial intelligence technology to create a virtual panoramic scene. Experimental data show that the average recognition rate of gestures is 89.8%, which can meet the gesture switching interaction of working conditions in virtual simulation of equipment operation. [ABSTRACT FROM AUTHOR]

Maintaining the safe and efficient operation of network technology is an important development task of the computer industry. Topology constraint can optimize and combine the tracking results and select the target objects with better tracking performance to obtain the final tracking results and determine the target scale changes. Data mining technology can reduce the number of combinations to be detected, reduce the workload, and improve the timeliness and accuracy of the process of mining alarm association rules. Therefore, based on the summary and analysis of previous research results, this paper studied the network fault diagnosis of the embedded system method based on topology constraint and data mining. Firstly, a fault diagnosis topology model was established by constructing a topology search algorithm, which eliminated the filtering of association rules without topology relationship; the association rule-based data mining model was analyzed through the collection of network alarm data; the model algorithm was applied to the simulation experiment of network fault diagnosis of the embedded system and achieved good results. The results show that correcting rage of retrieval varies from 0.65 to 090 under different window sizes; the running time of the proposed method drops from 310 s to 35 s during 1–8 step/s of the sliding step, while the node degree ranges from 8 to 14 and diagnostic accuracy ranges from 0.97 to 0.94; the remaining alarm number increases from 0.5 to 3.5 threshold value, while the regular association number distributed in an interval of 40 to 140. The algorithm in this paper provides a reference for further research on network fault diagnosis of the embedded system. [ABSTRACT FROM AUTHOR]

Being a 16-year-old kid going to UC Berkeley right before the 1960s was not conducive to academic success. That was my situation: Having graduated from high school early with no strong academic motivation, being immature, and with the distractions of Cal, I dropped out after one year of classes and two more years of goofing off. In retrospect, this disaster (my parent's words) led to a 57-year (and still counting) successful career in the computer industry where I participated in and contributed to the evolution from a few large computers to billions of personal computing devices. [ABSTRACT FROM AUTHOR]

The computer industry was a vivid place in the 1980s. IT systems and technologies thrived, and the market offered ever better, smaller, and more useful machines. Innovative technical solutions or intelligent designs that satisfied customers' needs are often listed as computer hardware milestones. Consequently, they became a permanent part of the computerisation history and can be considered hardware heritage artefacts. The purpose of the paper is to analyse the usability of selected portable computer systems. The foundation of the work is a literature review that includes technical specifications, industry reviews, and research papers. Archival materials were obtained from the Internet Archive. Studies have revealed that the main problems design engineers of portable computers had to tackle in the 1980s were the reduction of mass and size of the computer system, portable power (self-power), and the quality of the displayed image. [ABSTRACT FROM AUTHOR]

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations. [ABSTRACT FROM AUTHOR]