Advanced Temporal Constraints for Business Processes Modelling and Execution

In several applications like healthcare, time in workflow execution is critical. Several control and data dependencies arise that must be specified, validated as conflict free, and maintained during workflow execution. The author models these kinds of dependencies as constraints that impose temporal restrictions on the relative order of execution of the activities. Hence, a finer granularity of activity execution with respect to time is introduced. The author incorporates a subset of interval algebra in the workflow specification model and the author proposes the T-WfMc specification model. The author examines the consistency issues that arise, and the author proposes different correctness criteria

THECOG 2022 - Transforms In Behavioral And Affective Computing (Revisited)

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results

Report on the 2nd International Workshop on Transforms in Behavioral and Affective Computing (THECOG 2022) at CIKM 2022

Human decision making is central in many functions across a broad spectrum of fields including marketing, investments and smart contracts, digital health, political campaigns, logistics, and strategic management to name only a few. Computational behavioral science, the focus of the second consecutive iteration of the international workshop on transforms in behavioral and affective computing (THECOG) which was held in conjunction with CIKM 2022, not only studies the various psychological, cultural, and social factors contributing to decision making besides reasoning, but it also seeks to construct robust, scalable, and efficient computational models imitating or extending decision making processes. This year the keynote speech focused on affective robotics and their expected advantages in substantially improving the quality of human life. Moreover, the accepted papers had a considerable topical variety covering among others smart cities, speech emotion recognition, deepfake discovery, and how smart coupons may influence online consumer behavior. THECOG 2022 for a second continuous year was a central meeting point where new results were presented. Date: 21 October, 2022. Website: https://www.cikm2022.org/workshops

THECOG - Transforms in Behavioral and Affective Computing

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results

Exploiting Time Series Analysis in Twitter to Measure a Campaign Process Performance

© 2017 IEEE. While there are several metrics to measure business process performance, recently there is an additional requirement from businesses to evaluate business processes based on their impact on users. In this work, we evaluate business process performance using social media analytics. We view a marketing campaign as a business process and we evaluate its performance based on its impact on the Twitter. We propose a new way to calculate the \u27follow\u27 relationship in Twitter based on the users\u27 reaction to the marketing campaign process activities and we use time series and sentiment analysis for defining and measuring performance. We re-build the Twitter graph based on users\u27 reactions to the marketing activities in time and we are using community detection algorithms to identify the size of the \u27follow\u27 community and thus we define metrics to calculate the impact of the marketing/campaign process. We evaluate our approach using a dataset for a given politician. We re-construct the campaign process as a set of activities on specific topics (promotions) in time using LDA. Our results show that social media analytics can be used as a valid metric for assessing business processes performance

Legal smart contracts in ethereum block chain: Linking the dots

© 2020 IEEE. Block chain technology provides a decentralized and secure platform for executing transactions. Smart contracts in Ethereum have been proposed as the mechanism to automate legal contracts securely without the involvement of third parties. Yet, there are still several issues to be resolved especially regarding the updating of smart contracts in blockchain as well as the use of blockchain as part of a legal smart contracts system. In this work we propose a methodology and an architecture for building and deploying legal contracts in the blockchain. As the blockchain is immutable, we cannot update the code of the smart legal contracts, but in real life applications updating of contracts is a requirement that cannot be ignored. In this paper we address the problem of contract update by introducing a new versioning system that keeps track of the changes and links the different versions using a linked list. Moreover, we propose a system architecture where the user interface, the application logic and the blockchain are smoothly integrated in a manner that each part of the system contributes for producing a flexible and transparent execution. We show the applicability of our approach by implementing a system for the case of a rental agreement

Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. The Opinions Sandbox is a running prototype that accesses comments collected from customers of a particular product or service, and calculates the overall sentiment toward that product or service. It performs topic extraction, displays the comments partitioned into topics, and presents a sentiment for each topic. This helps to quickly digest customers’ opinions, particularly negative ones, and sort them by the concerns expressed by the customers. These topics are now considered issues to be addressed. The Opinions Sandbox does two things with this list of issues. First, it simulates the social network of the future, after rectifying each issue. Comments with positive sentiment regarding this rectified issues are synthesized, they are injected into the comment corpus, and the effect on overall sentiment is produced. Second, it helps the user create a plan for addressing the issues identified in the comments. It uses the quantitative improvement of sentiment, calculated by the simulation in the first part, and it uses user-supplied cost estimates of the effort required to rectify each issue. Sets of possible actions are enumerated and analysed showing both the costs and the benefits. By balancing these benefits against these costs, it recommends actions that optimize the cost/benefit tradeoff

Proof systems in blockchains: A survey

© 2019 IEEE. Blockchain is a prime example of disruptive technology in multiple levels. With the advent of blockchains becomes obsolete the need for a mutually trusted third party acting as intermediary between agents which do not necessarily trust each other in transactions of any kind, including political or shareholder voting, crowdfunding, financial deals, logistics and supply chain management, and contract formulation. An integral part of the blockchain stack is the proof system, namely the mechanism efficiently verifying the claims of various blockchain stakeholders. Thus, trust is effectively established in a literally trustless environment with purely computational means. This is especially critical in the digital formulation of smart contracts where clauses are to be strictly upheld by intelligent agents. The most prominent proof systems recently proposed in the scientific literature are reviewed. Additionally, the applications of blockchain technology to smart contracts is discussed. The latter allows clause re-negotiation, increasing thus the flexibility factor in transactions. As a concrete example, a simple smart contract written in Solidity, a high level language for the Ethereum Virtual Machine, is presented

E-Healthcare Knowledge Creation Platform Using Action Research

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. There has been a long discussion on knowledge creation in the health care environment. Recently, the action research approach is attracting considerable attention. Action research supports a learning process where collaboratively the healthcare stakeholders are cooperating to produce knowledge that will influence their practice. Usually physicians are involved in case study research where information is produced but it is not used to offer insights back to the community. In this paper we propose a healthcare learning platform (HLP) that enables members of the health multidisciplinary communities to collaborate, share up-to-date information and harvest useful evidence. In this e-health platform knowledge is created based on patient feedback, the dynamic creation of communities that involve the participation of several stakeholders and the creation of an action learning environment where problem identification, investigation and planning, action and reflection is a cycle that enables knowledge and experience to contribute to healthcare knowledge creation

Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social Media

In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results