Hasil untuk "Cellular telephone services industry. Wireless telephone industry"

Arda Yüksel, Gabriel Thiem, Susanne Walter et al.

Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to the size of the company registers, manual annotation is costly, and fine-tuning models with every update in industry classification schemes requires significant data collection. We replicate the manual expert verification by using existing or easily retrievable multimodal resources for industry classification. We present MONETA, the first multimodal industry classification benchmark with text (Website, Wikipedia, Wikidata) and geospatial sources (OpenStreetMap and satellite imagery). Our dataset enlists 1,000 businesses in Europe with 20 economic activity labels according to EU guidelines (NACE). Our training-free baseline reaches 62.10% and 74.10% with open and closed-source Multimodal Large Language Models (MLLM). We observe an increase of up to 22.80% with the combination of multi-turn design, context enrichment, and classification explanations. We will release our dataset and the enhanced guidelines.

Yuheng Huang, Lei Ma, Keizaburo Nishikino et al.

The pre-training paradigm plays a key role in the success of Large Language Models (LLMs), which have been recognized as one of the most significant advancements of AI recently. Building on these breakthroughs, code LLMs with advanced coding capabilities bring huge impacts on software engineering, showing the tendency to become an essential part of developers' daily routines. However, the current code LLMs still face serious challenges related to trustworthiness, as they can generate incorrect, insecure, or unreliable code. Recent exploratory studies find that it can be promising to detect such risky outputs by analyzing LLMs' internal states, akin to how the human brain unconsciously recognizes its own mistakes. Yet, most of these approaches are limited to narrow sub-domains of LLM operations and fall short of achieving industry-level scalability and practicability. To address these challenges, in this paper, we propose PtTrust, a two-stage risk assessment framework for code LLM based on internal state pre-training, designed to integrate seamlessly with the existing infrastructure of software companies. The core idea is that the risk assessment framework could also undergo a pre-training process similar to LLMs. Specifically, PtTrust first performs unsupervised pre-training on large-scale unlabeled source code to learn general representations of LLM states. Then, it uses a small, labeled dataset to train a risk predictor. We demonstrate the effectiveness of PtTrust through fine-grained, code line-level risk assessment and demonstrate that it generalizes across tasks and different programming languages. Further experiments also reveal that PtTrust provides highly intuitive and interpretable features, fostering greater user trust. We believe PtTrust makes a promising step toward scalable and trustworthy assurance for code LLMs.

Wei-hsiang Yen, Lyn Chao-ling Chen

The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of semiconductor industry in Taiwan. For the rapid market changes and development of wafer technologies of semiconductor industry, traditional data analysis methods not perform well in the high variety and time series data. Textual data and time series data were collected from seasonal reports of TSMC including financial information. Textual data through sentiment analysis by considering the event intervention both from internal events of the company and the external global events. Using the sentiment-enhanced time series data, the LSTM model was adopted for predicting industry trend of TSMC. The prediction results reveal significant development of wafer technology of TSMC and the potential threatens in the global market, and matches the product released news of TSMC and the international news. The contribution of the work performed accurately in industry trend prediction of the semiconductor industry by considering both the internal and external event intervention, and the prediction results provide valuable information of semiconductor industry both in research and business aspects.

Tim Langer, Matthias Widra, Volkhard Beyer

In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.

Lorenz Brehme, Benedikt Dornauer, Thomas Ströhle et al.

Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant information retrieved from external knowledge sources. While industry adoption of RAG is now beginning, there is a significant lack of research on its practical application in industrial contexts. To address this gap, we conducted a semistructured interview study with 13 industry practitioners to explore the current state of RAG adoption in real-world settings. Our study investigates how companies apply RAG in practice, providing (1) an overview of industry use cases, (2) a consolidated list of system requirements, (3) key challenges and lessons learned from practical experiences, and (4) an analysis of current industry evaluation methods. Our main findings show that current RAG applications are mostly limited to domain-specific QA tasks, with systems still in prototype stages; industry requirements focus primarily on data protection, security, and quality, while issues such as ethics, bias, and scalability receive less attention; data preprocessing remains a key challenge, and system evaluation is predominantly conducted by humans rather than automated methods.

Yuan Zhang, Songtao Xi

In this paper, a low sidelobe feeding network has been developed utilizing the louver-shaped defected ground structure (DGS). By adjusting the louver-shaped DGS, the output amplitude and phase of the corresponding ports can be altered, minimizing deviations from theoretical values. This enables antenna arrays equipped with this feeding network to more easily achieve low sidelobe performance. The impact of the louver-shaped DGS on the amplitude and phase of each port in the power divider within the feeding network is analyzed, and a 16-channel feeding network incorporating the louver-shaped DGS has been designed, fabricated, and then measured. The test results indicate that the performance of the line-feeding network is effectively improved by designing and adjusting the louver-shaped DGS. Through the debugging procedure, the amplitude deviation of the feeding network has been reduced from ±0.45 dB to ±0.2 dB, while the phase deviation of the feeding network has been reduced from ±8° to ±2.5°, and the maximum value of the first sidelobe has been reduced from −24.2 dB to −28.1 dB.

S. P. Chen, Q. Zhou, J. Y. Zhang et al.

Aiming at the application requirements of underwater cross-domain communication, based on the equivalent relationship between the rotating permanent magnet and the orthogonal time-varying current loop, this paper establishes an air-seawater-seabed three-layer medium model and analyzes the magnetic field distribution and propagation characteristics of the rotating permanent magnet-based mechanical antenna (RMBMA). Based on the electromagnetic field simulation software FEKO, the influence of vertical rotation and horizontal rotation of RMBMA on the radiation magnetic field is analyzed. The magnetic field distribution and magnetic field attenuation characteristics of RMBMA at different depths are obtained by simulation. The influence of RMBMA operating frequency and magnetic moment on the propagation characteristics is studied. The research shows that the horizontal rotation of the magnetic source is better than the vertical rotation in the long-distance underwater communication. When the magnetic source and the receiving point are close to the interface of the medium, the magnetic field strength and the propagation distance can be relatively increased. With appropriate frequency and magnetic moment, the magnetic field strength and communication distance can be further increased.

Abhinav Garg, Naman Shukla, Maarten Wormer

Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.

Mingming Qiu, Elie Najm, Rémi Sharrock et al.

A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment states are complex, and the inhabitant does not know how to adjust related actuators to achieve the target values of the states monitored by services. Moreover, machine learning-based data-driven approaches that we are interested in are like black boxes and cannot show the inhabitant in which situations certain services proposed certain actuators' states. To solve these problems, we propose a hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system), where knowledge-based and machine learning-based data-driven services are profitably integrated. The principal advantage is that it inherits the explicability of knowledge-based services and the dynamism of data-driven services. We compare HKD-SHO with several systems for creating dynamic smart home services, and the results show the better performance of HKD-SHO.

Quentin Raillard-Cazanove, Thibaut Knibiehly, Robin Girard

The decarbonisation of the energy system is crucial for achieving climate goals and is inherently linked to the decarbonisation of industry. Despite this, few studies explore the simultaneous impacts of decarbonising both sectors. This paper aims to examine how industrial decarbonisation in Europe affects the energy system and vice versa. To address this, an industry model incorporating key heavy industry sectors across six European countries is combined with an energy system model for electricity and hydrogen covering fifteen European regions, refered to as the EU-15, divided into eleven zones. The study evaluates various policy scenarios under different conditions.The results demonstrate that industrial decarbonisation leads to a significant increase in electricity and hydrogen demand. This additional demand for electricity is largely met through renewable energy sources, while hydrogen supply is predominantly addressed by blue hydrogen production when fossil fuels are authorized and the system lacks renewable energy. This increased demand results in higher prices with considerable regional disparities. Furthermore, the findings reveal that, regardless of the scenario, the electricity mix in the EU-15 remains predominantly renewable, exceeding 85%.A reduction in carbon taxes lowers the prices of electricity and hydrogen, but does not increase consumption, as the lower carbon tax makes the continued use of fossil fuels more attractive to industry. In scenarios that enforce a phase-out of fossil fuels, electricity prices rise, leading to a greater reliance on imports of low-carbon hydrogen and methanol. Results also suggest that domestic hydrogen production benefits from synergies between electrolytic hydrogen and blue hydrogen, helping to maintain competitive prices.

International Journal of Antennas and Propagation

Sicong Xie, Binbin Hu, Fengze Li et al.

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation

Hussain Sadiq Abuwala, Bohan Zhang, Mushi Wang

The goal of our research was to investigate the effects of collaboration between academia and industry on Natural Language Processing (NLP). To do this, we created a pipeline to extract affiliations and citations from NLP papers and divided them into three categories: academia, industry, and hybrid (collaborations between academia and industry). Our empirical analysis found that there is a trend towards an increase in industry and academia-industry collaboration publications and that these types of publications tend to have a higher impact compared to those produced solely within academia.

Andrés Felipe Posada-Moreno, Kai Müller, Florian Brillowski et al.

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their ``black box'' nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, ``Extracting Concepts with Local Aggregated Descriptors'' (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.

Daniel Abode, Ramoni Adeogun, Gilberto Berardinelli

6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficiency in dense deployments of such subnetworks. However, existing solutions for centralized power control may require full channel state information (CSI) of all the desired and interfering links, which may be cumbersome and time-consuming to obtain in dense deployments. This paper presents a novel solution for centralized power control for industrial subnetworks based on Graph Neural Networks (GNNs). The proposed method only requires the subnetwork positioning information, usually known at the central controller, and the knowledge of the desired link channel gain during the execution phase. Simulation results show that our solution achieves similar spectral efficiency as the benchmark schemes requiring full CSI in runtime operations. Also, robustness to changes in the deployment density and environment characteristics with respect to the training phase is verified.

Misq Archivist, P. Rahmati, Ali Tafti et al.

During the last four decades, digital technologies have disrupted many industries. Car control systems have gone from mechanical to digital. Telephones have changed from sound boxes to portable computers. But have the firms that digitized their products and services become more valuable than firms that didn’t? Here we introduce the construct of digital proximity, which considers the interdependent activities of firms linked in an economic network. We then explore how the digitization of products and services affects a company’s Tobin’s q—the ratio of market value over assets—a measure of the intangible value of a firm. Our panel regression methods and robustness tests suggest the positive influence of a firm’s digital proximity on its Tobin’s q. This implies that firms able to come closer to the digital sector have increased their intangible value compared to those that have failed to do so. These findings contribute a new way of measuring digitization and its impact on firm performance that is complementary to traditional measures of information technology (IT) intensity.

Kristiana Widiawati, Nurul Eka Santoso

Service is an activity that is carried out regularly and has an important role for an organization or company. Service is needed to carry out an activity to produce a service in a company activity be it services, construction, industry or manufacturing. Services are not only provided to external parties but internal parties in a company or agency. Therefore, services must be carried out as well as possible, to achieve a goal and reputation for the company. The purpose of this study was to determine the implementation of excellent reception service at PT Citramas Heavy Industries. The method is carried out by making observations and interviews directly with the HR & GA Department who is directly responsible with the Receptionist. The analysis used is descriptive qualitative. The results showed that the implementation of excellent service carried out by the receptionist at PT Citramas Heavy Industries, which is based on the duties of the receptionist including handling telephones, handling guests, receiving, and replying to e-mails, handling incoming mail, handling outgoing mail, distributing

Angela K. Hollman, Timothy R. Obermier, P. Burger et al.

Rural areas continue to face digital inequality compared to urban areas. Urban areas have access to a myriad of next generation advanced information communications technology (ICT) whereas rural areas experience disparity of service type, price and reliability. Initially the urban-rural digital divide was one of quantity of subscribers or demand driven digital inclusion , it has now matured to become an issue of quality and capacity of connectivity. However, Silva, Badasyan & Busby found that the more rural a census tract is, the lower the broadband adoption rate. They found, in further testament to the need for increased capacity, that “broadband availability has the strongest impact on the adoption rate in non-metropolitan areas. If the availability were to increase to a 100%....it would increase the adoption rate by 6.12%”. Public policy and regulation has a direct impact upon availability or lack thereof for ICT services in rural areas. Hollifield, Donnermeyer, Wolford & Agunga found that when public policy, in the form of universal service funding in rural high cost areas, failed to support the early implementation of ICT services, communities began investing in self-development projects with limited success. This effort however, doesn’t address the circumstances of the rural household miles from an organized rural community. With the regulatory focus on increasing competition among ICT service providers since the passage of the Telecommunications Act of 1996, consumers in urban areas have benefited. In contrast, underserved or unserved rural consumers of ICT often pay a rural penalty in the form of a combination of one or more complicating factors, including; higher prices and lower bandwidth , and less reliability or no service at all. Whitacre & Mills reports several early studies pointed to the need for demand-oriented programs such as computer training, and demonstrations on internet usage, as more important than the development of access infrastructure to promote ICT usage in rural areas. However, as general societal demand for internet access grows from the diffusion of knowledge, infrastructure becomes the limiting factor in the development of rural areas. Lower population density areas are minimally (or un)profitable markets for ICT service providers and some areas may never experience for-profit investment in broadband provisioning due to the lack of a potential subscriber base. During the years of the traditional land-line telephone, high cost rural areas were supported by the federal universal service fund through subsidies paid by all users, rural and urban. This fund, reformulated as the Connect America Fund, was intended to take over this role in the new ICT economy, but has fell short of fulfilling its intended purpose. Additionally concerns exist with the measurement and reporting of the diffusion of broadband infrastructure. All facilities based service providers providing internet connection speeds exceeding 200 kbps must report bandwidth speeds to the Federal Communication Commission through form 477. Reporting of available bandwidth speeds initially occurred by zip code, and then by census tract, followed by a further adjustment to the level of census block, making longitudinal comparisons over multiple years difficult. Since 2014 broadband adoption rates are reported on the basis of a scale from 0 to 5 with 5 meaning over 800 broadband connections per 1,000 households. This is a problematic measurement methodology for low population density areas. Grubesic reports as of the first iteration of the national broadband map, the availability of broadband in the United States is overestimated. Both public policy and available technologies directly affect regional development. Salemink, Strijker & Bosworth has concluded that economic differences between “well-connected” and “poorly connected” areas will continue to grow. Mahasuweerachai, Whitacre, & Shideler concluded that rural counties with both digital subscriber line (DSL) and cable broadband technologies attracted a net positive number of in-migrations as compared to counties without broadband or with only one type of broadband. Salemink, Strijker & Bosworth further concludes that while not instrumental for economic growth in rural areas, digital connectivity is essential to support existing industries. Gallardo and Schmmahorn found that as the number of broadband providers increased, so did non-innovative entrepreneurs and as non-innovative entrepreneurs increased, income inequality decreased. A need exists for the accurate measurement and reporting of consumer available bandwidth to better understand this element of the urban-rural digital divide. The primary question for this research study is, can the rural-urban digital divide be accurately measured? To address this question and to better understand available ICT in a given region, two pilot studies have been performed in households to measure consumer available bandwidth and to ascertain multiple elements of consumer perceptions of their internet access. These pilot projects focused on two themes of digital connectivity issues as outlined by Salemink, Strijker & Bosworth including; policy and regulation, and technologies in rural areas.

Yang Ying, Wang Hongyan

Traditional online art teaching system has problems such as poor score improvement and low system throughput. Therefore, this paper designs an interactive online art teaching system based on BS mode and IoT. Design the overall structure of the art teaching system according to THE B/S structure, build the interactive art online teaching model according to the system role use cases, introduce the RFID technology in the Internet of Things to control the information transmission of the interactive art online teaching system, and complete the code development of interactive art online teaching function. Complete the interactive art online teaching system based on BS mode and the Internet of Things. The experimental results show that the designed system can improve the scores of students in art colleges and improve the throughput of the system.