Hasil untuk "Business mathematics. Commercial arithmetic. Including tables, etc."

Dwi Siti Syarifah Usriani, Selmita Paranoan, Muhammad Din et al.

Purpose:  This study aims to assess the level of compliance of political parties in Palu in reporting their 2024 election campaign funds, with a focus on the transparency and accountability of reports in accordance with applicable regulations. Methodology/approach: The method used is quantitative descriptive research with a descriptive statistical approach. Data was obtained through documentation, which involved campaign finance reports from 18 political parties. The research sample used a saturated sampling technique. Findings: Most political parties were compliant in reporting campaign finances, with high compliance rates of 96,3% for RKDK, 95,8% for LADK, 100% for LPSDK, and 97,9% for LPPDK. Despite this progress, stricter supervision by the KPU and Bawaslu and more rigorous enforcement of sanctions are needed to ensure that campaign funds are clearly and transparently accounted for. Practical  and Theoretical Contribution/Originality: This study assists the KPU and Bawaslu of Palu City by demonstrating the effectiveness of existing regulations and increasing public political awareness of the importance of transparency in campaign finance reporting. Research   Limitation: This study is limited to the city of Palu and uses secondary data from audited reports, so it does not describe compliance dynamics throughout Indonesia.

Tassilo Klein, Clemens Biehl, Margarida Costa et al.

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.

Fabian Stiehle, Hans Weytjens, Ingo Weber

Large language models (LLMs) have changed the reality of how software is produced. Within the wider software engineering community, among many other purposes, they are explored for code generation use cases from different types of input. In this work, we present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions, an idea that has emerged in recent literature to overcome the limitations of traditional rule-based code generation approaches. However, current LLM-based work evaluates generated code on small samples, relying on manual inspection, or testing whether code compiles but ignoring correct execution. With this work, we introduce an automated evaluation framework and provide empirical data from larger data sets of process models. We test LLMs of different types and sizes in their capabilities of achieving important properties of process execution, including enforcing process flow, resource allocation, and data-based conditions. Our results show that LLM performance falls short of the perfect reliability required for smart contract development. We suggest future work to explore responsible LLM integrations in existing tools for code generation to ensure more reliable output. Our benchmarking framework can serve as a foundation for developing and evaluating such integrations.

Duncan McCoy, Connor Sell

We establish necessary and sufficient conditions for determining when a flat manifold can occur as a cusp cross-section within a given commensurability class of cusped arithmetic hyperbolic manifolds. This reduces the problem of identifying which commensurability classes of arithmetic hyperbolic manifolds can contain a specific flat manifold as a cusp cross-section to a question involving rational representations of the flat manifold's holonomy group. More generally we show that the holonomy representation provides an obstruction on the quasi-arithmetic manifolds containing a given flat manifold as a cusp cross-section. As applications, we prove that a flat manifold $M$ with a holonomy group of odd order appears as a cusp cross-section in every commensurability class of arithmetic hyperbolic manifolds if and only if $b_1(M)\geq 3$. We also provide examples of flat manifolds that arise as cusp cross-sections in a unique commensurability class of arithmetic hyperbolic manifolds and exhibit examples of pairs of flat manifolds that can never appear as cusp cross-sections in the same quasi-arithmetic hyperbolic manifold.

Márcio Rocha Ferreira, Tadeu Moreira de Classe, Sean Wolfgand Matsui Siqueira

Interactive narratives offer a novel approach to presenting business process models, making them more accessible and collaborative. These narratives create a hyper-textual environment that facilitates knowledge exchange and comprehension for ordinary individuals. However, designing such narratives is complex, as business process modelers must accurately identify and translate the graphic elements of a process model into dynamic narrative elements. This research paper introduces the Scripting Your Process (SYP) method, which provides a systematic approach to designing interactive narratives based on business process models. Following the principles of Design Science Research (DSR), a quasi-experimental study demonstrates and evaluates the SYP method. The results show that the SYP method successfully achieves its objective, contributing to the systematic design of interactive narratives derived from business process models. Consequently, individuals who are not experts in business process management can understand these processes in an engaging and gameful manner.

I. Astakhov, N. Romanenko, Y. Borovikova

Relevance. The main challenge of the modern world is the birth of a new paradigm for the development of world civilization, due to «... the strengthening of economic and political interaction between countries, the development of established market relations and the creation of new economic systems associated with modern science and the digital market environment….» These changes reflect the state of economic education of students focused on high-tech production, creation of innovative products with a high share of added value. «The system of higher economic education must evolve taking into account new transnational challenges…», the answer to which should be a high level of economic readiness of graduates as a result of economic education. It is quite obvious that the metamorphoses in global market relations and business are directly related to the renewal of the structural content of professional training and the economic readiness of future specialists for professional activity. The relevance of the stated topic is explained by the need of the world community in a new quality of economic readiness of modern graduates, accompanied by an update of the component composition of economic readiness and ways of its formation.Aim. To test a set of proprietary methods and technology for the formation of students’ economic readiness for professional activity.Methodology. The research methodology included system-activity, competence-based and comparative approaches, which allowed to prove the effectiveness of the technologies used and a set of diagnostic tools, including authors’ questionnaires, tests, game technologies, peer review, as well as methods of mathematical statistics for processing the results obtained. The empirical base of the study included professional educational organizations of the city of Voronezh. In total, 50 students were involved in experimental work (25 students made up the experimental group and 25 students of the control group), methods of analysis and interpretation of the results were also applied in the study. Fischer's criterion was used to establish statistically significant differences between the groups.Scientific novelty / theoretical and/or practical significance. In the course of theoretical research, the concept of economic readiness of students was clarified, the original structural composition of the economic readiness of college graduates was determined, an algorithm and stages of effective formation of economic readiness were developed based on the analysis of world problems in the field of economics. The results obtained in the course of the study on the basis of diagnostic tools, including the original developments of studying the economic readiness of college students for professional activity, allow them to be used both in academic and extracurricular activities, including interactive forms of cooperation with students based on project activities.Results. The concept of «economic readiness of graduates of economic specialties» is analyzed at the fundamental level. The main result of the students’ economic readiness for professional activity formation process was an applied algorithm, which includes the development and the experimentally tested program for providing a model for the formation of college students’ economic readiness for professional activity, allowing to optimize the process of their preparation, significantly increase the level of economic readiness of students by means of original teaching materials (cases, projects, educational tasks, situations, etc.) in the process of training specialists in the commercial sphere.Conclusion. The developed program for providing a model for the formation of college students’ economic readiness for professional activity makes it possible to optimize the process of their preparation and significantly increases the level of their economic readiness. The results obtained during the experimental study also prove that the diagnostic complex developed and implemented by the authors to determine the effectiveness of the formation of students’ economic readiness for professional activity meets the modern challenges of the world economy and has high practical significance.

Phuc Nguyen, Nam Tuan Ly, Hideaki Takeda et al.

Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learning techniques 1) to extract table content and structural information from images and 2) to answer various questions related to numerical data, text-based information, and complex queries from structured tables. The evaluation results on VQAonBD 2023 dataset demonstrate the effectiveness of TabIQA in achieving promising performance in answering table-related questions. The TabIQA repository is available at https://github.com/phucty/itabqa.

S. Kovalchuk, O. Khaietska, Larysa Feniak et al.

The agricultural sector was and remains a key component of social development. The current state of the agricultural sector of Ukraine shows the imbalance of its development, when priority is given to the economic component with secondary environmental and social determinants. Theoretical substantiation and practical development and implementation of determinants of sustainable development of agricultural enterprises of the national economy, which combines both internal contradictions and external challenges, become particularly relevant. An important direction of the progressive reproduction of the agrarian sector of the national economy is the practical implementation of the concept of sustainable development adopted in Ukraine as a model in the context of state policy and the program of its pragmatic implementation at the level of individual economic entities. The dynamics of agrarian processes within the limits of certain constants - financial and economic, organizational, technical and technological, commercial, etc., as the most optimal at the relevant market stage, collectively reflects the principles of sustainable development in the sense of permanence, not static. Such measures will be possible under the condition of balancing the interests of society, the agricultural environment, a separate agricultural enterprise, man and the environment. The monograph indicates that the process of improving the sectoral structure of agricultural enterprises involves the implementation of certain measures that precede the determination of the main directions and ways of developing and implementing a mechanism for ensuring the optimization of the production structure when using agricultural land. It is impossible and impractical to determine the priority of one of the branches of agriculture. Since animal husbandry is based on plant products, the fodder base for which is hay, straw, green fodder, grain fodder and some other types of agricultural crops. In turn, animal husbandry waste, namely manure, is used in crop production as organic fertilizers, which ensure the improvement of soil quality indicators and the yield of agricultural crops. At the same time, it should also be noted the undeniably important role of crop production in the social life of a person as a whole. This territory provides the population with food products and raw materials for the processing industry, including food, pharmaceutical, light, woodworking, etc. In today's realities, the problem of ensuring the financial security of the enterprise is urgent. This problem is especially acute in the conditions of the current global economic and financial crisis. Today, in the conditions of an unstable political situation, economic crisis, martial law in the country and a drop in the solvent demand of the population, domestic enterprises suffer from significant financial problems. The financial activity of the enterprise is associated with many risks, the degree of influence of which on the results of its activity increases significantly with the transition to a market economy. The risks accompanying this activity are allocated to a separate group of financial risks, which play a dominant role in the general "risk portfolio" of the enterprise. The increase in the degree of influence of financial risks on the results of the company's financial activity is associated with rapid changes in the economic situation in the country and on the financial market, the expansion of the sphere of financial relations, the emergence of new financial technologies and tools. Risks arise in the field of corporate relations with banks and other financial institutions and are associated with the probability of loss of funds or their non-receipt. It is emphasized that at the current stage of the development of the world economy, the integration of Ukraine into the European space, great attention is paid to the effective functioning of the enterprise, which in turn depends on the quality of products. Ignoring this factor, it is difficult to create optimal conditions for the development of any trade, sales and profitability of enterprises. Improving the quality system of enterprises' goods in modern conditions is a complex and urgent task that requires an immediate solution. The long-term course of sustainable development of the enterprise should be aimed at achieving not so much quantitative indicators as qualitative ones, therefore, the heads of enterprises should pay attention to the development of measures to increase competitiveness and reach the international level. The construction, implementation and certification of an integrated product quality management system will provide them with a number of competitive advantages and confidence in the level of production and service that meets international standards and is able to win in competition on the domestic and foreign markets. Scientific research was carried out within the framework of the research initiative topic "Organizational and economic aspects of the development of agroecosystems on the basis of ecologization of the economy" of the Vinnytsia National Agrarian University, state registration number: 0121U112882 for 2021-2024. Greening of production is possible under the conditions of development of business relations of business entities and use of rural areas. In the conditions of a competitive economy, the main factor in the assessment of economic activity is efficiency, which allows determining the need for material, labor and financial resources. Taking into account the instability of the global economy, its impact on the economies of the world's leading countries, the need to plan and manage the development processes of enterprise activities by preserving and increasing the potential of rural areas is of particular importance. Greening is an important influencing factor that determines the characteristics of the distribution of both material, labor, and financial resources. Thus, there is a need to create and gradually develop the environment for the functioning of enterprises in rural areas, which will allow optimizing their activities based on the principles of achieving efficiency: choosing the most important types of activities in agriculture; to increase the volume of production; cost regulation, including labor costs. The work uses general methods of modern rational and empirical systemology. The obtained results are substantiated by the fundamental principles of dialectics and systematic analysis of phenomena and processes. The work is formed on the basis of the methodology of research on the impact of greening on the development of enterprises and rural areas, in particular, taking into account the organizational and economic mechanism of the disposal of agricultural waste as a component of energy security. The basis of the study is the hypothesis of the formation of the environment for the functioning of enterprises engaged in activities in agriculture, forestry and fisheries, taking into account the characteristics of rural areas in the conditions of environmentalization, optimization of cause-and-effect relationships, adaptation and historical development. The study of resource management of agricultural enterprises and rural areas in the conditions of greening will be conducted on the basis of functional and process approaches. The main methods are methods of quantitative comparison, system analysis, methods of statistical evaluation, methods of economic-mathematical modeling, methods of decision-making theory. In the formation of separate theoretical propositions, in the process of fulfilling the assigned tasks, general scientific methods were used, such as: scientific abstraction, morphological analysis, generalization, decomposition and systematization, etc.

Jan Postema

Het thema van dit artikel is het verklaren van de omzetgroei van MKB-ondernemingen uit (1) de gespreksonderwerpen van MKB-ondernemers met buitenstaanders, (2) het wel versus niet in gesprek zijn van MKB-ondernemers met buitenstaanders, (3) het in gesprek zijn van MKB-ondernemers met betaalde versus niet-betaalde buitenstaanders en (4) de combinatie van de gespreksonderwerpen met betaalde versus niet-betaalde buitenstaanders. Nieuw voor de literatuur over de betekenis van het in gesprek zijn van MKB-ondernemers met buitenstaanders om omzetgroei te realiseren is het verklaren van de omzetgroei van MKB-ondernemingen uit twee van de vier onderdelen van het thema, te weten: (1) de gespreksonderwerpen van MKB-ondernemers met buitenstaanders en (4) de combinatie van de gespreksonderwerpen met betaalde versus niet-betaalde buitenstaanders. Het verklaren van de omzetgroei van MKB-ondernemingen uit (2) het wel versus niet in gesprek zijn van MKB-ondernemers met buitenstaanders en uit (3) het in gesprek zijn van MKB-ondernemers met betaalde versus niet-betaalde buitenstaanders kan gezien worden als het testen van verbanden die eerder zijn onderzocht. Ad (2) en (3) vormen een aanvulling op het in dit artikel onderzoeken van de verbanden die als nieuw voor deze literatuur worden beschouwd. Uit het verrichte onderzoek komt naar voren dat het in gesprek zijn over de onderwerpen “prestaties van het personeel” en “fusie en overname” een positief effect hebben op de omzetgroei. Het wel versus niet in gesprek zijn van MKB-ondernemers met buitenstaanders heeft geen positief effect op de omzetgroei. Het in gesprek zijn van MKB-ondernemers met betaalde buitenstaanders versus niet-betaalde buitenstaanders heeft wel een positief effect op de omzetgroei. Het in gesprek zijn van MKB-ondernemers met betaalde versus niet-betaalde buitenstaanders heeft geen positief effect op de relatie tussen de gespreksonderwerpen en de omzetgroei.

K. Astahov, O. Yarmolyuk

Peter Pfeiffer, Johannes Lahann, Peter Fettke

Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or computer vision. Instead of training a model for one specific task, representation learning is about training a model to capture all useful information in the underlying data and make it accessible for a predictor. For predictive process analytics, it is essential to have all explanatory characteristics of a process instance available when making predictions about the future, as well as for clustering and anomaly detection. Due to the large variety of perspectives and types within business process data, generating a good representation is a challenging task. In this paper, we propose a novel approach for representation learning of business process instances which can process and combine most perspectives in an event log. In conjunction with a self-supervised pre-training method, we show the capabilities of the approach through a visualization of the representation space and case retrieval. Furthermore, the pre-trained model is fine-tuned to multiple process prediction tasks and demonstrates its effectiveness in comparison with existing approaches.

Ji-Hoon Kim, Minjae Lee, Jongsun Park et al.

Artificial Intelligence (AI) has evolved into a general technology for a wide range of purposes and has been applied in all aspects of economy and society. It has already been extensively used in various fields, including medical services, finance, security, education, transportation, and logistics, and had led to the emergence of new commercial activities, business models, and game-changing product applications. AI is a driving force to economic and social development at the forefront of the technological revolution and industrial transformation. Additionally, System-on-a-Chip (SoC) plays a vital role in post-PC era products like smartphones, tablets, and various wearable devices where form-factor, cost, and energy-efficiency, are critical drivers. It contains multiple processing parts such as the central processing unit (CPU), graphics processing unit (GPU), image processing unit (IPU), digital signal processor (DSP), video encoder/decoder, modems, and neural processing unit (NPU). Specifically, AI processors, another name for NPU, are specially optimized for mathematics and algorithms commonly used by neural networks. They can run neural networks and machine learning tasks faster and more efficiently than CPUs. In this special issue, we have selected papers that represent the current state-of-the-art in AI processors as well as in essential SoC blocks used in radar, RF/analog, hardware security, and design methodology. The first paper “40TFLOPS Artificial Intelligence Processor with Function-safe Programmable Many-Cores for ISO26262 ASIL-D” by Jinho Han et al. presents AI processor architecture that has high throughput for accelerating the neural network and reducing the required external memory bandwidth for processing the neural network. For high throughput, the proposed super thread core (STC) includes 128 × 128 nano cores operating at 1.2 GHz clock frequency and the general-purpose processor (GPP) core is integrated for the control of the STC and processing AI algorithm. For the functional safety that becomes very important in automotive systems, various microarchitectural techniques are adopted, including the self-recovering cache and dynamic lockstep (DLS) function, to achieve ASIL-D of ISO26262 standard fault tolerance levels. The entire AI processor fabricated in the 28-nm CMOS process yields peak performance up to 40TFLOPS at 1.2 GHz operating frequency and 1.1 V supply voltage, with a measured energy efficiency of 1.3TOPS/W and ISO26262 ASIL-D compliant, single-point fault tolerance rate equal to 99.64%. The next paper titled “An impulse radio (IR) radar SoC for through-the-wall human-detection applications” by Piljae Park et al. proposes through-the-wall radar (TTWR) SoC and its architecture with the test standards and methods, which can be used at disaster scenes in limited visibility conditions owing to smoke, walls, and collapse debris. Additive reception based on the coherent clocks and reconfigurability can fulfill the demands for the TTWR and a clock-based single-chip IR radar transceiver is implemented in 130-nm CMOS technology. By utilizing the repetitive coherent clock schemes, the proposed SoC can achieve signal-to-noise-ratio (SNR) enhancements. Furthermore, this paper shows the test results in various pseudo-disaster conditions of the hand-held prototype radar with the proposed TTWR SoC operating in real-time. The third paper “AB9: a Neural Processor for Inference Acceleration” by Yong Cheol Peter Cho et al. presents a neural processor for interference acceleration with the systolic tensor core (STC) by exploiting data-reuse and parallelism characteristics inherent in neural networks, while also providing fast access to large on-chip memory. AB9 shows a superior performance and power efficiency to those of a general-purpose GPU (GPGPU) for YOLOv2, and has been fabricated with a 28-nm CMOS process along with a 40 TFLOP STC that includes 32 k arithmetic units and over 36 MB of on-chip SRAM. To alleviate the high-computational and memory-intensive burdens in deep neural networks, the following paper “Automated Optimization for Memory-efficient High Performance Deep Neural Network Accelerators” by Hyun Mi Kim et al. investigates the efficient memory structure and operating scheme, which can provide an intuitive solution for high-performance accelerators along with dataflow control. The authors propose an efficient architecture with flexibility, while operating at high frequency despite the large memory size and PE array. They demonstrate an improvement in the efficiency and usability of the architecture by presenting

Mantas Mikaitis

We describe various issues caused by the lack of round-to-nearest mode in the \textit{gcc} compiler implementation of the fixed-point arithmetic data types and operations. We demonstrate that round-to-nearest is not performed in the conversion of constants, conversion from one numerical type to a less precise type and results of multiplications. Furthermore, we show that mixed-precision operations in fixed-point arithmetic lose precision on arguments, even before carrying out arithmetic operations. The ISO 18037:2008 standard was created to standardize C language extensions, including fixed-point arithmetic, for embedded systems. Embedded systems are usually based on ARM processors, of which approximately 100 billion have been manufactured by now. Therefore, the observations about numerical issues that we discuss in this paper can be rather dangerous and are important to address, given the wide ranging type of applications that these embedded systems are running.

H. Sakai, M. Nakata, Weizhi Wu et al.

A rough set, first described by Polish computer scientist Zdzisław Pawlak, is a formal approximation of a crisp set, and it is now known as a new mathematical tool to process vague concepts. They are used for machine learning, knowledge discovery, feature selection, etc., and are applied to artificial intelligence, medical informatics, civil engineering, Kansei engineering, decision science, business administration, and so on. Especially, research on data mining using rough sets is widely spreading, and the obtained association rules are applied to the characterisation of data and decision support. Rough set research started with the use of equivalence classes defined in the table data, but has recently expanded to granularity calculation, which is a broader concept. Rough sets and granular computing provide a framework to adjust the granularity of information according to purpose. The combination of rough sets and fuzzy sets also needs to be considered. Both sets compensate for the weak points of frameworks mutually, thereby defining more robust frameworks. On the other hand, the progress of artificial intelligence technology seen in deep learning in recent years is remarkable. In this Special Issue entitled “Rough Sets and Data Mining from IET CAAI Transactions on Intelligence Technology” the latest research trends of rough sets, partly fuzzy sets and clustering, granularity calculation and data mining are summarised, and fundamental and applied research towards strengthening their association with artificial intelligence are introduced. Following comprehensive peer review seven papers were accepted. The first paper in this Special Issue, “Fuzzy decision implications: Interpretation within fuzzy decision context”, by Jing Zhang, Yanhui Zhai, and Deyu Li, considers fuzzy decision implications A ⇒ B under fuzzy decision tables. The authors define the interpretation of fuzzy decision implication in fuzzy decision context, and show that ‘a closed fuzzy set of fuzzy decision implications can be obtained from fuzzy decision contexts’ and its converse. A correspondence between closed fuzzy sets of fuzzy decision implications and fuzzy decision contexts is shown. Since a rule is often defined as an implication satisfying proper constraint, it is important to clarify the characteristics of such implications and decision tables. The second paper in this Special Issue, “Rough set-based rule generation and Apriori-based rule generation from table data sets: A survey and a combination”, by Hiroshi Sakai and Michinori Nakata, surveys rough set-based rule generation and Apriori-based rule generation. The authors combine multiple methodologies, including: Pawlak’s rough sets, Lipski’s incomplete information databases, Orłowska’s nondeterministic information systems (NIS) and Agrawal’s Apriori algorithm. They then propose the framework termed Rough sets Non-deterministic Information Analysis (RNIA). In this paper, the details of the investigated NIS-Apriori algorithm, which is an adjusted Apriori algorithm to NIS, are described. The NIS-Apriori algorithm realised rule generation from NIS. The third paper in this Special Issue, “Rough set-based rule generation and Apriori-based rule generation from table data sets II: SQL-based environment for rule generation and decision support”, by Hiroshi Sakai and Zhiwen Jian, is twinned with the second paper, and focuses on the application of the obtained rules to decision support. In order to realise the convenient decision support system, sub-programs in SQL are implemented. An example is presented to show the sequence of rule generation and decision support. The fourth paper in this Special Issue, “Survey on cloud model based similarity measure of uncertain concepts”, by Shuai Li, Guoyin Wang, and Jie Yang, gives similarity measure methods of a cloud model, which are applied to image retrieval, collaborative filtering, public opinion guidance, as well as, many fields of artificial intelligence. In this paper, especially Gaussian Cloud Model (GCM) is picked up, and related algorithms are described. The authors also consider the problems of current similarity measures from four aspects: discriminability, efficiency, stability and interpretability. Throughout this paper, cloud model and its application are surveyed. Finally, the authors give future perspectives on similarity measure of cloud model based on axiomatization. The fifth paper in this Special Issue, “Rule induction based on rough sets from information tables having continuous domains”, by Michinori Nakata, Hiroshi Sakai, and Keitarou Hara, discusses rough sets and rule induced from tables with continuous values. The authors start from neighbourhood rough sets handling complete information, and extend it to the case of handling incomplete information. They propose four types of rules: certain consistent, certain inconsistent, possible consistent and possible inconsistent combined rules, and present the methods to induce the four types of rules. The validity of each method is ensured by using possible world semantics in logic. These combined rules express more applicable regulation from tables with incomplete information and continuous domains. The sixth paper in this Special Issue, “Neighborhood systems based attribute reduction in formal decision contexts”, by Xiaohe Zhang, Jusheng Mi, Meishe Liang, and Meizheng Li, presents attribute reduction and rule acquisition in formal decision contexts. In formal decision context, it is necessary to remark the relationship between concept lattices and redundant condition attributes. The authors consider consistent formal decision contexts at first and extend it to inconsistent formal decision contexts. Weak neighbourhood granular decision rules and strong neighbourhood granular decision rules are proposed, and they are obtained by using the discernibility matrix methods. Theoretical results supporting the validity of the authors’ framework are given. The seventh paper in this Special Issue, “Influence of kernel clustering on a radial basis function network”, by Changming Zhu and Duoqian Miao, investigates classical radial basis function network (RBFN) and the influence of kernel clustering. In order to strengthen RBFN, the authors propose kernel clustering algorithm and dynamic kernel clustering. Using experiments the authors examine the capabilities of kernel clustering algorithms and state that ‘the performance of RBFN with a new kernel clustering method becomes better than the original algorithm’. Finally, the authors describe the necessity of kernel clustering for achieving a better performance for classification. In closing, the Guest Editors would like to acknowledge the efforts of all of the authors for their generous and insightful contributions. We also thank the reviewers for their decisive, on-time reviews. We are grateful to Professors Cesare Alippi and Hong Liu, Chief Editors-in-Chief of CAAI Transactions on Intelligence Technology for inviting us to serve as Guest Editors CAAI Transactions on Intelligence Technology

The recent emergence of Big Data in healthcare (including large linked data from electronic patient records (EPR) as well as streams of real-time geolocated health data collected by personal wearable devices, etc.) and the open data movement enabling sharing datasets are creating new challenges around ownership of personal data whilst at the same time opening new research opportunities and drives for commercial exploitation. A balance must be struck between an individual’s desire for privacy and their desire for good evidence to drive healthcare, which may sometimes be in conflict. With the increasing use of mobile and wearable devices, new opportunities have been created for personalized health (tailored care to the needs of an individual), crowdsourcing, participatory surveillance, and movement of individuals pledging to become “data donors” and the “quantified self” initiative (where citizens share data through mobile device-connected technologies). These initiatives created large volumes of data with considerable potential for research through open data initiatives. In this workshop we will hear from a panel of international speakers working across the digital health, Big Data ethics, computer science, public health divide on how they have addressed the challenges presented by increased use of Big Data and AI systems in healthcare with insights drawn from their own experience to illustrate the new opportunities that development of these movements has opened up. The potential of open access to healthcare data, sharing Big Data sets and rapid development of AI technology, is enormous - so as are the challenges and barriers to achieve this goal. Policymakers, scientific and business communities should work together to find novel approaches for underlying challenges of a political and legal nature associated with use of big data for health.

S. A. Rahim

M. Keita

Rasha Abbas

Diego Calvanese, Marlon Dumas, Ülari Laurson et al.

The Decision Model and Notation (DMN) is a standard notation to capture decision logic in business applications in general and business processes in particular. A central construct in DMN is that of a decision table. The increasing use of DMN decision tables to capture critical business knowledge raises the need to support analysis tasks on these tables such as correctness and completeness checking. This paper provides a formal semantics for DMN tables, a formal definition of key analysis tasks and scalable algorithms to tackle two such tasks, i.e., detection of overlapping rules and of missing rules. The algorithms are based on a geometric interpretation of decision tables that can be used to support other analysis tasks by tapping into geometric algorithms. The algorithms have been implemented in an open-source DMN editor and tested on large decision tables derived from a credit lending dataset.