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

Jiaqi Gao, Zijian Zhang, Yuqiang Sun et al.

Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike traditional bugs such as reentrancy or arithmetic errors, these vulnerabilities arise from missing or incorrectly enforced business invariants and are tightly coupled with protocol semantics. Existing static analysis techniques struggle to capture such high-level logic, while recent large language model based approaches often suffer from unstable outputs and low accuracy due to hallucination and limited verification. In this paper, we propose LogicScan, an automated contrastive auditing framework for detecting business logic vulnerabilities in smart contracts. The key insight behind LogicScan is that mature, widely deployed on-chain protocols implicitly encode well-tested and consensus-driven business invariants. LogicScan systematically mines these invariants from large-scale on-chain contracts and reuses them as reference constraints to audit target contracts. To achieve this, LogicScan introduces a Business Specification Language (BSL) to normalize diverse implementation patterns into structured, verifiable logic representations. It further combines noise-aware logic aggregation with contrastive auditing to identify missing or weakly enforced invariants while mitigating LLM-induced false positives. We evaluate LogicScan on three real-world datasets, including DeFiHacks, Web3Bugs, and a set of top-200 audited contracts. The results show that LogicScan achieves an F1 score of 85.2%, significantly outperforming state-of-the-art tools while maintaining a low false-positive rate on production-grade contracts. Additional experiments demonstrate that LogicScan maintains consistent performance across different LLMs and is cost-effective, and that its false-positive suppression mechanisms substantially improve robustness.

Lorenz Halbeisen, Regula Krapf

Huixin Gao, Tanya Evans, Anna Fergusson

This scoping review examines the use of student explanation strategies in postsecondary mathematics and statistics education. We analyzed 46 peer-reviewed articles published between 2014 and 2024, categorizing student explanations into three main types: self-explanation, peer explanation and explanation to fictitious others. The review synthesizes the theoretical underpinnings of these strategies, drawing on the retrieval practice hypothesis, generative learning hypothesis, and social presence hypothesis. Our findings indicate that while self-explanation and explaining to fictitious others foster individual cognitive processes enhancing generative thinking, peer explanation have the potential to combine these benefits with collaborative learning. However, explanation to fictitious others have the potential to mitigate some of the negative impacts that may occur in peer explanation, such as more knowledgeable students dominating peer discussions. The efficacy of the methods varies based on implementation, duration, and context. This scoping review contributes to the growing body of literature on generative learning strategies in postsecondary education and provides insights for optimizing the integration of student explanation techniques in mathematics and statistics.

Tammy Mackenzie, Branislav Radeljic, Leslie Salgado et al.

This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.

Tracy Tam, Asha Rao, Joanne Hall

Small businesses are increasingly adopting IT, and consequently becoming more vulnerable to cyber-incidents. Whilst small businesses are aware of the cyber-security risks, many struggle with implementing mitigations. Some of these can be traced to fundamental differences in the characteristics of small business versus large enterprises where modern cyber-security solutions are widely deployed. Small business specific cyber-security tools are needed. Currently available cyber-security tools and standards assume technical expertise and time resources often not practical for small businesses. Cyber-security competes with other roles that small business owners take on, e.g. cleaning, sales etc. A small business model, salient and implementable at-scale, with simplified non-specialist terminologies and presentation is needed to encourage sustained participation of all stakeholders, not just technical ones. We propose a new UML class (Small IT Data (SITD)) model to support the often chaotic information-gathering phase of a small business' first foray into cyber-security. The SITD model is designed in the UML format to help small business implement technical solutions. The SITD model structure stays relevant by using generic classes and structures that evolve with technology and environmental changes. The SITD model keeps security decisions proportionate to the business by highlighting relationships between business strategy tasks and IT infrastructure. We construct a set of design principles to address small business cyber-security needs. Model components are designed in response to these needs. The uses of the SITD model are then demonstrated and design principles validated by examining a case study of a real small business operational and IT information. The SITD model's ability to illustrate breach information is also demonstrated using the NotPetya incident.

Ioannis Zachos, Theodoros Damoulas, Mark Girolami

A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin-destination matrices that are sampled using coarse travel surveys. This is because fine-grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits non-identifiable due to the combinatorial space of data-consistent individual attributes. This problem is pertinent to any agent-based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad-hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear-time sampling of the ground truth origin-destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large-scale application to agent work trip reconstruction in Cambridge, UK.

Joseph M. Shunia, Lorenzo Sauras-Altuzarra

We construct arithmetic terms representing the partial sums of binomial coefficients, and we extend these results to obtain arithmetic terms representing the multisections of binomial coefficient sums. We also introduce an arithmetic term representing a certain type of multinomial coefficient sum and, as an application, we provide an arithmetic term representing the central trinomial coefficients. This solves one of the research problems of the celebrated book Concrete Mathematics, which remained open for nearly thirty years.

Helen Zhou, Sercan O. Arik, Jingtao Wang

Time-series forecasts play a critical role in business planning. However, forecasters typically optimize objectives that are agnostic to downstream business goals and thus can produce forecasts misaligned with business preferences. In this work, we demonstrate that optimization of conventional forecasting metrics can often lead to sub-optimal downstream business performance. Focusing on the inventory management setting, we derive an efficient procedure for computing and optimizing proxies of common downstream business metrics in an end-to-end differentiable manner. We explore a wide range of plausible cost trade-off scenarios, and empirically demonstrate that end-to-end optimization often outperforms optimization of standard business-agnostic forecasting metrics (by up to 45.7% for a simple scaling model, and up to 54.0% for an LSTM encoder-decoder model). Finally, we discuss how our findings could benefit other business contexts.

Takao Komatsu

Let $a_1,a_2,\dots,a_k$ be positive integers with $\gcd(a_1,a_2,\dots,a_k)=1$. The concept of the weighted sum $\sum_{n\in{\rm NR}}λ^{n}$ is introduced in \cite{KZ0,KZ}, where ${\rm NR}={\rm NR}(a_1,a_2,\dots,a_k)$ denotes the set of positive integers nonrepresentable in terms of $a_1,a_2,\dots,a_k$. When $λ=1$, such a sum is often called Sylvester sum. The main purpose of this paper is to give explicit expressions of the Sylvester sum ($λ=1$) and the weighed sum ($λ\ne 1$), where $a_1,a_2,\dots,a_k$ forms arithmetic progressions. As applications, various other cases are also considered, including weighted sums, almost arithmetic sequences, arithmetic sequences with an additional term, and geometric-like sequences. Several examples illustrate and confirm our results.

Gyunam Park, Wil. M. P. van der Aalst

Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 hours after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are object-centric, i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves order, item, delivery, etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce Object-Centric Constraint Graphs (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.

Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak et al.

Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a non-trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.

Ralph ter Hoeven, Ymke Roosjen

This article analyses the impact of COVID-19 on the disclosures of 24 financial statements of passenger airline companies in Europe (including United Kingdom), North America, China (including Hong Kong), Middle East and South America for financial year 2020. This impact is significant in our research sample as evidenced by a total revenue decrease of 60% compared to previous year. We have examined for specific areas whether the airline companies contribute to transparent reporting and useful information to existing and potential investors, regulators, supportive government bodies and other stakeholders following the COVID-19 pandemic. The areas of our research focus on going concern, rent concessions, significant judgements and estimates, impairments, governmental support and the auditor’s report. Our study shows diversity in the extent of transparency in both financial statements and auditor’s opinions. Good financial practices are included and discussed in this study to further stimulate transparency in corporate reporting.

Takayuki Kihara

In his book, John Stillwell wrote "finding the exact strength of the Brouwer invariance theorems seems to me one of the most interesting open problems in reverse mathematics." In this article, we solve Stillwell's problem by showing that (some forms of) the Brouwer invariance theorems are equivalent to weak König's lemma over the base system ${\sf RCA}_0$. In particular, there exists an explicit algorithm which, whenever weak König's lemma is false, constructs a topological embedding of $\mathbb{R}^4$ into $\mathbb{R}^3$.

Miklós Koren, Rita Pető

Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12 percent wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.

Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas

Modern organizations need real-time awareness about the current business conditions and the various events that occur from multiple and heterogeneous environments and influence their business operations. Moreover, based on real-time awareness they need a mechanism that allows them to respond quickly to the changing business conditions, in order to either avoid problematic situations or exploit opportunities that may arise in their business environment. In this paper we present BEAM, an event-driven framework that enables awareness about the situations happening in business environments and increases organizations responsiveness to them. We illustrate how BEAM increases the awareness of managers about the running business processes, as well as their flexibility by presenting a practical application of the framework in the transportation and logistics domain.

Ayana Kawamura, Yuma Kinoshita, Takayuki Nakachi et al.

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.

Masoud Rabbani, Soroush Aghamohammadi Bosjin, Reza Yazdanparast et al.

This paper presents a new multi-objective model for a vehicle routing problem under a stochastic uncertainty. It considers traffic point as an inflection point to deal with the arrival time of vehicles. It aims to minimize the total transportation cost, traffic pollution, customer dissatisfaction and maximizes the reliability of vehicles. Moreover, resiliency factors are included in the model to increase the flexibility of the system and decrease the possible losses that may impose on the system. Due to the NP-hardness of the presented model, a meta-heuristic algorithm, namely Simulated Annealing (SA) is developed. Furthermore, a number of sensitivity analyses are provided to validate the effectiveness of the proposed model. Lastly, the foregoing meta-heuristic is compared with GAMS, in which the computational results demonstrate an acceptable performance of the proposed SA.

Lei Huang, Guangjie Ren, Shun Jiang et al.

Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on internal and external data. To solve this problem, this paper introduces the graph theory into BAs with aim of building extensible data-driven analytics and automatically generating business insights. We use IBM's Component Business Model (CBM) as an example to illustrate various ways in which graph theory can be leveraged for data-driven analytics, including what and how business insights can be obtained. Future directions for applying graph theory to business architecture analytics are discussed.

Roman Kossak