IT Affordances and Consumption Values Perspective for the Use of Social Commerce

Social commerce is growing throughout the world. This paper develops a framework based on affordance theory and theory of consumption values to understand which consumption values motivate feature-enabled affordances to satisfy buyers and sellers and how that impacts the overall transactional experience on the platform. Additionally, the paper also fills the gap in the literature regarding seller\u27s selling behavior which is relatively understudied. The study focuses on Facebook Marketplace as the online social commerce platform (OSC). This research will help system designers to design better social commerce platforms and identify features that need improvement or changes. Additionally, the findings from this research will create a better understanding of buyer and seller behavior and their motivations of using social commerce platforms

Healthcare 4.0: Trends, Challenges and Benefits

The Fourth Industry Revolution, known as Industry 4.0, refers to the forces that are transforming industry, including the healthcare industry, where it has been termed Healthcare 4.0. Though lagging other industries in the adoption of new innovative technologies, the healthcare industry is embracing the potential benefits that arise from new innovative technologies. New trends revealed both in the academic literature and by industry practice show that researchers and practitioners are becoming more aware of the benefits technology can bring to an industry as complex as the healthcare industry. The object of the study is to identify the challenges, trends and gaps in the existing body of research with regard to Healthcare 4.0. In this study, a systematic literature review on Healthcare 4.0 research papers was conducted to identify trends, challenges and the perceived benefits that may arise from it. This paper found that there is a need to conduct more empirical studies in this area. It, further, identified the need to implement practical procedures in the industry to get feedback from patients and healthcare participants in order to promote the adoption of new Healthcare 4.0 technologie

The Traditional Japanese Formula Keishibukuryogan Inhibits the Production of Inflammatory Cytokines by Dermal Endothelial Cells

Keishibukuryogan (KBG) is one of the traditional herbal formulations widely administered to patients with blood stagnation for improving blood circulation; currently, it is the most frequently prescribed medicine in Japan. KBG has been reported to improve conjunctional microcirculation. The aim of this study was to evaluate the role of KBG and paeoniflorin, a bioactive compound of KBG, in inhibiting the production of inflammatory cytokines using human dermal microvessel endothelial cells (HDMECs). The authors observed that lipopolysaccharide (LPS; 1 μg/mL) stimulated the secretion of proinflammatory cytokines in HDMECs. KBG treatment (10 mg/mL) significantly suppressed the mRNA levels of migration inhibitory factor (MIF), interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α in LPS-stimulated cultured HDMECs. Similarly, paeoniflorin significantly suppressed the mRNA levels of these cytokines in LPS-stimulated cultured HDMECs. ELISA showed that KBG and paeoniflorin suppressed the production of MIF, IL-6, IL-8, and TNF-α in LPS-stimulated HDMECs. Moreover, KBG and paeoniflorin decreased the expression of cyclooxygenase-2 and inducible nitric oxide synthase (iNOS) in these cells. These results suggest that KBG may be useful for improving microvascular inflammation in patients with skin diseases

Accurate and Scalable Detection and Investigation of Cyber Persistence Threats

In Advanced Persistent Threat (APT) attacks, achieving stealthy persistence within target systems is often crucial for an attacker\u27s success. This persistence allows adversaries to maintain prolonged access, often evading detection mechanisms. Recognizing its pivotal role in the APT lifecycle, this paper introduces Cyber Persistence Detector (CPD), a novel system dedicated to detecting cyber persistence through provenance analytics. CPD is founded on the insight that persistent operations typically manifest in two phases: the "persistence setup" and the subsequent "persistence execution". By causally relating these phases, we enhance our ability to detect persistent threats. First, CPD discerns setups signaling an impending persistent threat and then traces processes linked to remote connections to identify persistence execution activities. A key feature of our system is the introduction of pseudo-dependency edges (pseudo-edges), which effectively connect these disjoint phases using data provenance analysis, and expert-guided edges, which enable faster tracing and reduced log size. These edges empower us to detect persistence threats accurately and efficiently. Moreover, we propose a novel alert triage algorithm that further reduces false positives associated with persistence threats. Evaluations conducted on well-known datasets demonstrate that our system reduces the average false positive rate by 93% compared to state-of-the-art methods

An Investigation to reduce Overreliance on Explainable AI (XAI) in light of Two System Theory

As technology is evolving, there is a rise in the use of AI systems. The increased use of AI systems has revealed issues of gender and racial biases. To address these issues, explainable AI (XAI) is introduced, but the use of XAI has triggered various kinds of biases leading to issues such as overreliance. In this study, we seek to devise interventions to mitigate the issue of overreliance on AI by better understanding cognitive biases and acknowledging that different users have different cognitive abilities, and we need to be mindful of that when we design XAI systems. We will conduct multiple experiments using the recidivism dataset collected by ProPublica and to develop a better understanding of and solutions to mitigate the issue of overreliance. The findings from this research will allow us to design XAI systems better, improving user trust in AI and further improving AI adoption

Values of Social Commerce: Influence of Affordance

Social commerce is growing. This paper aims to understand how users interact on social commerce platforms. The design of the social commerce platform is unique, giving both buyers and sellers autonomy. The paper seeks to understand the consumer values of both buyers and sellers, how those have led to the social commerce platform design, and what feature enabled affordances help to satisfy the consumers\u27 values. The study will help system designers better design social commerce platforms and understand some of the consumer pain points. Additionally, in literature, the seller behavior is understudied, so the nature of the platform and the autonomy it provides to the seller allows us to study seller behavior. We aim to understand consumer behavior better, how sellers and buyers interact on a social commerce platform, and how these platforms enable those interactions to facilitate transactions

Correlational Analysis of Drivers Personality Traits and Styles in a Distributed Simulated Driving Environment

In this thesis report we conducted research study on driver's behavior in T-Intersections using simulated environment. This report describes and discusses correlation analysis of driver's personality traits and style while driving at T-Intersections. The experiments were performed on multi user driving simulator under controlled settings, at Linköping University. A total of forty-eight people participated in the study and were divided into groups of four, all driving in the same simulated world. During the experiments participants were asked to fill a series of well-known self-report questionnaires. We evaluated questionnaires to get the insight in driver's personality traits and driving style. The self-report questionnaires consist of Schwartz's configural model of 10 values types and NEO-five factor inventory. Also driver's behavior was studied with the help of questionnaires based on driver's behavior, style, conflict avoidance, time horizon and tolerance of uncertainty. Then these 10 Schwartz's values are correlated with the other questionnaires to give the detail insight of the driving habits and personality traits of the drivers

Correlational Analysis of Drivers Personality Traits and Styles in a Distributed Simulated Driving Environment

In this thesis report we conducted research study on driver's behavior in T-Intersections using simulated environment. This report describes and discusses correlation analysis of driver's personality traits and style while driving at T-Intersections. The experiments were performed on multi user driving simulator under controlled settings, at Linköping University. A total of forty-eight people participated in the study and were divided into groups of four, all driving in the same simulated world. During the experiments participants were asked to fill a series of well-known self-report questionnaires. We evaluated questionnaires to get the insight in driver's personality traits and driving style. The self-report questionnaires consist of Schwartz's configural model of 10 values types and NEO-five factor inventory. Also driver's behavior was studied with the help of questionnaires based on driver's behavior, style, conflict avoidance, time horizon and tolerance of uncertainty. Then these 10 Schwartz's values are correlated with the other questionnaires to give the detail insight of the driving habits and personality traits of the drivers