The current study examined the multifaceted effects of digital transformation on information disclosure and organisational efficiency in Chinese-listed companies. In this study technology adoption, digital strategy, process automation, transparency, and process innovation are used to understand the digital transformation to drive information disclosure towards organisational efficiency. A quantitative research design was utilised, employing a structured survey directed at senior executives, including directors, managers, chief information officers, and chief transformation officers. A nonprobability snowball sampling technique was employed to access a specialised population of experts. The research initially identified pertinent enterprises via the official information disclosure platform of the China Securities Regulatory Commission (CSRC). Five hundred questionnaires were disseminated through email and social media platforms from February 2023 to July 2023. Following the exclusion of incomplete and outlier responses, 300 completed questionnaires were analysed. The analysis was performed using a combination of PLS-SEM and artificial neural network (ANN) approaches. An ANN investigation was conducted to enhance the findings of PLS-SEM and improve predictive accuracy. The findings indicate a substantial positive correlation between digital transformation and increased information disclosure, along with enhanced organisational efficiency. Digital technologies enhance transparency and data-sharing systems, thereby improving decision-making processes. Moreover, research indicates that IT leadership within organisations is pivotal in facilitating successful digital transformation initiatives. These findings highlight the essential function of digital transformation in promoting corporate accountability and operational efficiency. Future research ought to investigate industry-specific impacts of digital transformation and incorporate longitudinal analyses to capture the evolving trends in digital adoption and corporate governance.
History of scholarship and learning. The humanities, Social Sciences
Abstract Burgeoning research in data sciences demonstrates that big data analytics capability (BDAC) transforms large amounts of data into valuable knowledge and information, enhancing decision processes and improving firm performance. Nevertheless, limited research has theoretically outlined and empirically established the frameworks and constructs through which BDAC impacts the performance of small and medium enterprises (SMEs). This study adds to existing research on the relationship between BDAC and SME performance. Drawing on the dynamic capability theory, it is essential to argue how BDAC influences marketing performance (MP) and financial performance (FP), which is dependent on the intervening role of knowledge management with big data analytics talent capability (BDATC). This study highlighted the mediating role of knowledge Management (KM) and the moderating effect of Big Data Analytics Talent Capabilities (BDATC) in relation to BDAC. Based on the Conceptual model, data was collected from 379 SMEs in China using a well-designed questionnaire. Structural Equation Modeling (SEM) was employed using AMOS and SPSS for data analysis. Findings show that BDAC positively influences the firm’s financial and marketing performance. Furthermore, results confirm that KM mediates the link between a firm’s BDAC and financial and marketing performance. The findings also confirm that BDATLC significantly moderates the relationship between BDAC and financial performance while negatively moderating the relationship between BDAC and marketing performance. This study contributes to understanding the important role of human talent capability in the era of technology and big data (BDATLC), particularly regarding the talent capability for Big Data Analytics (BDA). The findings highlight the strategic significance of nurturing and retaining BDA talent to enhance the performance of SMEs.
History of scholarship and learning. The humanities, Social Sciences
În studiul de față ne propunem să analizăm cum s-au răsfrânt diferite provocări asupra evoluției ramurii industriale
în Basarabia în perioada 1868-1914 și să apreciem realizările acesteia în comparație cu alte regiuni învecinate ale
Imperiului Rus. Materialele cercetate ne permite să afirmăm că întreprinderile industriale din Basarabia au cunoscut
în anii ʼ60-ʼ90, comparativ cu perioada anterioară, o anumită dezvoltare, care însă nu a fost suficientă pentru a face
față cererii existente pe piața internă și capacităților de producție agricolă a guberniei. Dezvoltarea industrială, cu
ritmurile modeste arătate, nu era totodată generată de politicile imperiale ruse din domeniu. Mai degrabă ele reflectau
gradul de integrare a regiunii în economia europeană și modernizarea instituțională generată de acest proces. În virtutea lipsei sprijinului din partea statului rus, decalajele față de ritmul și exigențele vest-europene ale procesului de
producție au continuat să se acumuleze, contribuind la aprofundarea caracterului periferic al economiei provinciei,
atât în cadrul Imperiului Rus, cât și la nivel european.
Cuvinte-cheie: Basarabia, bresle meșteșugărești, capital industrial, fabrici, muncitori, producție industrială, uzine.
DOI: https://doi.org/10.59295/sum10(180)2023_02
History of scholarship and learning. The humanities
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this challenge in a high-fidelity racing simulator, Gran Turismo. However, this agent relied on global features that require instrumentation external to the car. This paper introduces, to the best of our knowledge, the first super-human car racing agent whose sensor input is purely local to the car, namely pixels from an ego-centric camera view and quantities that can be sensed from on-board the car, such as the car's velocity. By leveraging global features only at training time, the learned agent is able to outperform the best human drivers in time trial (one car on the track at a time) races using only local input features. The resulting agent is evaluated in Gran Turismo 7 on multiple tracks and cars. Detailed ablation experiments demonstrate the agent's strong reliance on visual inputs, making it the first vision-based super-human car racing agent.
Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation.
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider human factors involved in providing feedback of different types. However, the systematic study of learning from diverse types of feedback is held back by limited standardized tooling available to researchers. To bridge this gap, we propose RLHF-Blender, a configurable, interactive interface for learning from human feedback. RLHF-Blender provides a modular experimentation framework and implementation that enables researchers to systematically investigate the properties and qualities of human feedback for reward learning. The system facilitates the exploration of various feedback types, including demonstrations, rankings, comparisons, and natural language instructions, as well as studies considering the impact of human factors on their effectiveness. We discuss a set of concrete research opportunities enabled by RLHF-Blender. More information is available at https://rlhfblender.info/.
Zindan Baynal Hubi, Ilham Aji Pangestu, Nursanda Rizki Adhari
et al.
The purpose of this study is to examine the role of the Bandung City Regional General Election Commission withits role in improving the political culture of community participants in the city of Bandung. This is mainly in facing the 2018 Bandung Mayor election. This research is a descriptive study thatwas analysed in several ways, namely data reduction, data presentation, conclusion drawing,and verification. The approach is done qualitatively, while the data collection technique is done by interview and observation. The participants selected in this study were the Regional General Election Commission of the City of Bandung (1 person) and the people of the City of Bandung (7 people). The results of this study indicate that the Bandung City Regional General Elections Commission in improving the political culture of participants with political education carried out various elements in the implementation of the Bandung City Regional Head election in 2018 it is known that the political participation of the community experienced significant growth and far exceeded the target set. The number of voter participants in the city of Bandung in the simultaneous regional head elections (Pilkada) in 2018 has increased compared to the last 5 (five) elections.The results also show that the increasing number of voter participants in the city of Bandung is inseparable from the cooperation of the Bandung City Regional Election Commission with various parties, both community organizations, policymakers, and other organizations in the city of Bandung. In the future, the political participation of the people of Bandung must always be maintained by involving various elements and innovations that adapt to changes and developments in society.
History of scholarship and learning. The humanities, Social Sciences
Edith Jacqueline Velázquez Haurón, María Gloria Cabrera Romero
El presente estudio tuvo como objetivo proponer lineamientos para políticas y planes de adecuación y protección ambiental de la cuenca Mboi Caé, ciudad de Encarnación, Paraguay, con base en las afecciones ambientales que han sido encontradas en la cuenca en cuestión. En el planteamiento metodológico de la investigación, fueron planteadas tres dimensiones generales de análisis: estado del agua y la cobertura boscosa, el estado del boque y el factor humano. Para la recolección de datos fueron utilizados los siguientes instrumentos: mapeo, observación y transecta, registro fotográfico y análisis documental. En este contexto, pudo verificarse la existencia de uso del suelo inapropiado y no controlado, en la referida cuenca, que está afectando la integridad de este y de la cuenca en general. Otro aspecto identificado fue el manejo forestal inadecuado y la gestión deficiente de los recursos hídricos, los cuales están siendo sobrexplotados e igualmente, ocasionando consecuencias negativas. Las políticas y planes de adecuación y protección ambiental de la cuenca Mboi Caé se enmarcaron en siete líneas de acción principales: monitoreo de la calidad del agua, registro de la información, balance hídrico, mejora del marco legal municipal, educación ambiental, manejo y calidad del suelo y preservación de la biodiversidad.
History of scholarship and learning. The humanities, Social sciences (General)
This study aims: to determine the effect of the application of the Gallery of Learning the learning achievement of students of Universitas Syiah Kuala. This study uses a quantitative approach and the type of experimental research. The population in this study are students of history education, amounting to 46 people. This research data collection techniques using tests and documentation. As for the stage of data analysis techniques in this study were; (1) calculate the average; (2) calculate the variance and standard deviation; (3) test for normality data; (4) test of homogeneity of variance; and t-test. Based on the data analysis stage it is obtained:Final test homogeneity of variance test experiment and control class was 1.53 and 0.49 t-test results. Results of research found is the implementation strategy of lear(gallery of learning) effect on student learning achievement history educationhistory of Universitas Syiah Kuala. Data Distribution final test scores of students in the experimental and control group with normal distribution based on a significant level ≤ X2countX2 tablenamely 7.81 and 4.51 ≤ 4.93 ≤ 7.81, to test the homogeneity of F ≤ 1.53 ≤ 2.03 Ftabel = then the data variance experimental class and control class homogeneous. The results of t-test to analyze the effect of learning outcomes by using strategies learned gallery acquired the t-test is t = 0.49, while table = 1.68. Means that t ≥ 1.68 ≥ table = 0.49.
A building’s façade is its main interface with the external environment. Adaptive façade, one recent invention in the façade industry, has the capability to change its behaviour in real-time to respond to internal and/or external parameters, by means of materials, components, and systems. Among these, the adaptive shading and the façade glazing are two components that must fit together. This paper focuses on the spatial relationship between these components. It presents the results of the morphological analysis of façades with adaptive shading systems and the spatial relation between the adaptive shading system and the building’s glass envelope. To characterise this relation, we formulated two measures: depth and distance. The results revealed four types of such relations: (i) the shading elements are located outside the building’s glass envelope, (ii) they are covered by the glass envelope, (iii) they are located between the layers of the glazing façade, and (iv) they represent thin coatings that are flush with the surface of the glass. These results provide important insights into the emergence of new aesthetical trends in architecture, especially given the most recent technologies adopted in façades. In conclusion, we bring empirical evidence that the location of the shading system in relation to the glass envelope of a building is the key morphological feature that determines the extent of spatial transformation of the architectural structure on which such a system is installed.
A. C. Umuhire, J. Uwamahoro, K. Sasikumar Raja
et al.
Solar radio type II bursts serve as early indicators of incoming geo-effective space weather events such as coronal mass ejections (CMEs). In order to investigate the origin of high-frequency type II bursts (HF type II bursts), we have identified 51 of them (among 180 type II bursts from SWPC reports) that are observed by ground-based Compound Astronomical Low-cost Low-frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) spectrometers and whose upper-frequency cutoff (of either fundamental or harmonic emission) lies in between 150 MHz-450 MHz during 2010-2019. We found that 60% of HF type II bursts, whose upper-frequency cutoff $\geq$ 300 MHz originate from the western longitudes. Further, our study finds a good correlation $\sim $ 0.73 between the average shock speed derived from the radio dynamic spectra and the corresponding speed from CME data. Also, we found that analyzed HF type II bursts are associated with wide and fast CMEs located near the solar disk. In addition, we have analyzed the spatio-temporal characteristics of two of these high-frequency type II bursts and compared the derived from radio observations with those derived from multi-spacecraft CME observations from SOHO/LASCO and STEREO coronagraphs.
Recent event of ousting Rohingyas from Rakhine State by the Tatmadaw provoked worldwide public-and-academic interest in history and social evolution of the Rohingyas, and this is to what the article is devoted. As the existing literature presents a debate over Who are the Rohingyas?, and How legitimate is their claim over Rakhine State?, the paper reinvestigates the issues using a qualitative research method. Compiling a detailed history, the paper finds that Rohingya community developed through historically complicated processes marked by invasions and counter-invasions. The paper argues many people entered Bengal from Arakan before British brought people into Rakhine state. The Rohingyas believe Rakhine State is their ancestral homeland and they developed a sense of Ethnic Nationalism. Their right over Rakhine State is as significant as other groups. The paper concludes that the UN must pursue solution to the crisis and the government should accept the Rohingyas as it did the land or territory.
Michail Tsiaousis, Gertjan Burghouts, Fieke Hillerström
et al.
The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by their context, such as relevant objects and actors present in the video. To this end, we introduce an architecture based on self-attention and Graph Convolutional Networks in order to model contextual cues, such as actor-actor and actor-object interactions, to improve human action detection in video. We are interested in achieving this in a weakly-supervised setting, i.e. using as less annotations as possible in terms of action bounding boxes. Our model aids explainability by visualizing the learned context as an attention map, even for actions and objects unseen during training. We evaluate how well our model highlights the relevant context by introducing a quantitative metric based on recall of objects retrieved by attention maps. Our model relies on a 3D convolutional RGB stream, and does not require expensive optical flow computation. We evaluate our models on the DALY dataset, which consists of human-object interaction actions. Experimental results show that our contextualized approach outperforms a baseline action detection approach by more than 2 points in Video-mAP. Code is available at \url{https://github.com/micts/acgcn}
This article examines how literature is a networked social space of political repression and resistance, refracting broader contestations over national sovereignty, self-determination, and identity. Politicizing the traditionally apolitical “world of letters” in Habermas’s Structural Transformation of the Public Sphere , this article employs a novel analysis of the influence that the literary public sphere wields over political consciousness. Using the historical case of Taiwan’s literary networks from the 1970s to the 1990s, this article asserts that the literary public sphere produces a rational-critical generalization of knowledge and exposure to dissonant perspectives that invigorates civil society by creating intelligentsia. Through intelligentsia, ideas within the Taiwanese literary public sphere birthed powerful Dangwai parties that instituted democracy, informed their platforms, and ushered in a new wave of political elites. The Taiwanese case demonstrates how civic tasks can predict political tasks with enough force to stimulate a unique postcolonial political consciousness and spark a revolution.
History of scholarship and learning. The humanities, Social Sciences
Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios.
Robert Dadashi, Léonard Hussenot, Matthieu Geist
et al.
Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. We present a reward function which is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and which requires little fine-tuning. We show that we can recover expert behavior on a variety of continuous control tasks of the MuJoCo domain in a sample efficient manner in terms of agent interactions and of expert interactions with the environment. Finally, we show that the behavior of the agent we train matches the behavior of the expert with the Wasserstein distance, rather than the commonly used proxy of performance.
Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.