Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach Customizable Distributed Network Service Discovery System

Computer systems and applications on the internet provide services to outsiders and, at the same time, the vulnerabilities may be exploited by attackers and leak some sensitive private information. To collect and monitor the service information provided by the network environment such as IoT (Internet of Things), vehicular networks, cloud computing, and cloud storage, it is particularly important that a system can provide faster service discovery for discovering and identifying specific network services. The current service discovery systems mainly use port scanning technology, including Nmap, Zmap, andMasscan. However, these technologies hard code the service features and only support common services so that cannot cope with real-time updates and changing network services. To solve the above problems, this paper proposed a customizable distributed network service discovery system based on stateless scanning technology of Masscan and proposed a customizable interactive pattern set syntax. The system used random destination address technologies to scan for Ipv4 address allocation and used a distributed deployment scheme. Experimental results show that the system has high scanning speed and has high adaptability to new services and special services.
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Social networking services (SNS) have become one of the most popular Internet services globally. Yet, there is also a dark side to SNS use that threatens users’ well-being. Recently, the constant growth in SNS users is diminishing and users are deciding to discontinue using SNS. Thus, research has started to explore the reasons behind discontinuance behavior from a technostress perspective. Nonetheless, there is a call for research and mechanisms explaining why some individuals continue their SNS use (compared to individuals who discontinue) in similar perceived technostress situations – especially regarding individual personality traits. As such, users’ resilience (one’s ability to bounce back and positively adapt) has been chosen as a promising area for our longitudinal research project. Through an initial two-wave study of 403 SNS users in two European countries, the preliminary results show mitigating effects of users’ resilience in SNS-induced stress processes. An outlook on the upcoming waves is provided.
A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

Traditional two-step passive localization methods need to extract the parameters like the direction of arrival (DOA), time of arrival (TOA), and time difference of arrival (TDOA) from the original data to determine the source position, which causes the poor positioning accuracy due to error accumulation. In this paper, a direct position determination (DPD) method is proposed to improve the positioning accuracy and robustness, which is based on a correlation algorithm. Firstly, the cost function directly related to the location of the source can be established by synthesizing the data received by multiantenna in the frequency domain. Then, the position of the source is estimated by the correlation DPD method to search the monitoring area. Compared to the improved TDOA algorithm and Least Squares DPD algorithm, the proposed method shows better localization accuracy of different SNRs. Finally, based on real measured data, it can be seen that the results of the proposed algorithm are better than the improved TDOA algorithm.
Algorithms in the Driver's Seat: Explaining Workers' Reactions to Algorithmic Control

Organizations increasingly rely on algorithmic control (AC), which leverages the capabilities of novel digital technologies and intelligent algorithms to automate control activities previously performed by human managers. Existing research points to a diverse set of worker reactions to AC. To explain these diverse reactions, our study focuses on gig-economy work, where AC is particularly prevalent, and draws on a recently proposed theoretical model, which highlights the importance of two antecedent dimensions (compatibility and coherence). On this theoretical basis, and through a qualitative content analysis of an online forum for ride-hailing drivers, our study contributes novel insights to prior literature by contextualizing and detailing ideal-typical worker reactions. In particular, we uncover a set of common, context-specific worker reactions to AC and discuss how AC features thwart workers’ autonomy and competence needs (compatibility) and why these features make controls only seemingly inescapable on the surface (coherence paradox). Theoretical implications are discussed.
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Individuals are increasingly confronted with transactions and decisions in digital environments. This shift in consumption behaviour and context has a high potential to influence operational business processes in various fields such as the grocery retail industry and contribute towards more sustainable operations across different levels. Accordingly, organizations need to identify opportunities to actively influence the purchase behaviour of consumers in line with social, economic and ecological objectives. Therefore, based on a threefold research approach, we develop and test several digital nudges to influence customer decision-making in the context of online grocery sustainability. First, digital nudge principles and instantiations are deduced from theory, before we develop several digital nudges and test their perception effectiveness by means of an eye-tracking experiment. Finally, the most effective nudge designs are employed in a sophisticated online-experiment, empirically indicating the significant value of hyperbolic discounting and anchoring for influencing e-grocery delivery preferences.
Consent Notices and the Willingness-to-Sell Observational Data: Evidence from User Reactions in the Field

Privacy regulations like the EU’s General Data Protection Regulation (GDPR) require e-commerce shops to request opt-in consent for usage data collection from EU website visitors. Despite the regulation’s intentional strengthening of consumer rights, consent notices are often regarded as burdensome by consumers and shops alike. Thus, ever more consumers turn to consent-automating browser extension tools, which decreases opt-in rates for e-commerce shops. To investigate potential for a win-win solution, we examined in cooperation with a fashion retailer in Germany how consumers react when they are presented a coupon for the website’s shop in the moment of cookie consent decision. Using the Becker-DeGroot-Marschak mechanism, we elicited consumers’ willingnesses-to-sell (WTS) for cookie consent. Our unique field dataset of 1274 participants allows us to derive the WTS of consumers for any desired consent rate. For instance, 65.05% of consumers are ready to trade their consent to cookies for a 10€-coupon value.
Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R-CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health & Medical” category. The experimental results confirm the conclusion that RR&R-CNN has a better effect compared to a traditional method.
Differences in the Activation Processes of Phosphine-Containing and Grubbs–Hoveyda-Type Alkene Metathesis Catalysts

A detailed exploration of the three proposed mechanisms (associative, dissociative, and interchange) for the activation of Grubbs–Hoveyda-type precatalysts is performed, using DFT (B3LYP) calculations. The effects induced by the nature of the reacting alkene, the bulk of the chelating alkoxy group, and the presence of substituents in the Hoveyda ligand are taken into account. Results show that, while the associative mechanism has always high energy barriers, neither the dissociative nor the interchange mechanism can be ruled out for the first step of the activation process. In fact, the preference for one or the other mechanism seems to be influenced, to a large extent, by the nature of the chelating alkoxy group in such a way that small OR groups tend to favor the interchange pathway. Moreover, for all considered Grubbs–Hoveyda-type precatalysts, the highest transition structure corresponds to the Hoveyda ligand decoordination at the end of the cross-metathesis process. It is worth noting that this is ob.
Chatbots often Fail! Can Anthropomorphic Design Mitigate Trust Loss in Conversational Agents for Customer Service?

Conversational agents (CAs) are frequently used in customer service. However, state-of-the-art CAs often fail to understand user input. Such failures represent trust violations and can thus stimulate negative word-of-mouth. Nevertheless, little attention has been paid to these trust violations or to strategies to mitigate associated trust loss. Initial evidence exists that anthropomorphic design may mitigate trust loss following a failure in human–computer interactions. From a provider’s perspective, protecting trust is important to keep users engaged and satisfied. From a user’s perspective, this creates ethical concerns, as anthropomorphic design may manipulate the assessment of the CA. Therefore, we investigated the role of anthropomorphic design as a trust shield. We developed a research model by integrating literature on trust in technology, trust repair, and anthropomorphic design. The results of our experimental study suggest that anthropomorphic design is an effective trust shield. We discuss these findings from theoretical, practical, and ethical perspectives.
Denitrification and dissimilatory nitrate reduction to ammonium in a lake receiving wastewater effluent

Denitrification and dissimilatory nitrate reduction to ammonium (DNRA) are anoxic microbial nitrate-reducing processes, which occur for example in lake sediment and hypolimnion. Denitrification produces N2 gas, which exits the water body, while DNRA produces ammonium, which is retained in the water. Wastewater effluent in a lake can be further purified by natural denitrification, but the role of DNRA is less known. Understanding these processes can help to improve wastewater treatment. In this thesis, rates of denitrification and DNRA and their correlations with water quality were studied in Lake Keurusselka, where a wastewater treatment plant releases its effluent waters. Incubation and reactor experiments were conducted by using 15 N tracers. The hypotheses were that the rates of denitrification are higher when wastewater effluent is present, and that the DNRA:denitrification ratio is also higher in the same conditions. The denitrification rates were highest near the effluent pipe, and in overall higher than those of DNRA. The ratio showed no such trend. Denitrification correlated positively with nitrate concentration and negatively with oxygen concentration and pH, while DNRA showed negative correlation with pH. The reactor experiment results showed initial positive correlation between ambient NO3 levels and denitrification, but not anymore after 24 hours of incubation.
GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation on historical data obtained from a large urban law enforcement agency.
SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions

Value functions are powerful abstractions broadly used across optimal control and robotics algorithms. Several lines of work have attempted to leverage trajectory optimization to learn value function approximations, usually by solving a large number of trajectory optimization problems as a means to generate training data. Even though these methods point to a promising direction, for sufﬁciently complex tasks, their sampling requirements can become computationally intractable. In this work, we leverage insights from adversarial learning in order to improve the sampling efﬁ-ciency of a simple value function learning algorithm. We demonstrate how generating adversarial samples for this task presents a unique challenge due to the loss function that does not admit a closed form expression of the samples, but that instead requires the solution to a nonlinear optimization problem. Our key insight is that by leveraging duality theory from optimization, it is still possible to compute adversarial samples for this learning problem with virtually no computational overhead, including without having to keep track of shifting distributions of approximation errors or having to train generative models. We apply our method, named SEAGuL, to a canonical control task (balancing the acrobot) and a more challenging and highly dynamic nonlinear control task (the perching of a small glider). We demonstrate that compared to random sampling, with the same number of samples, training value function approximations using SEAGuL leads to improved generalization errors that also translate to control performance improvement.
Designing Multimodal BI&A Systems for Co-Located Team Interactions

Teams are crucial for organizations in making data-driven decisions. However, current business intelligence & analytics (BI&A) systems are primarily designed to support individuals and, therefore, cannot be used effectively in co-located team interactions. To address this challenge, we conduct a design science research (DSR) project to design a multimodal BI&A system providing touch and speech interactions that can be used effectively by teams. Drawing on the theory of effective use and existing guidelines for multimodal user interfaces, we propose three design principles and instantiate them in a software artifact. The results of a focus group evaluation indicate that enhancing the BI&A system with multimodal capabilities increases transparent interaction and facilitates effective use of the system in co-located team interactions. Our DSR project contributes novel design knowledge for multimodal BI&A systems with touch and speech modalities that facilitate effective use in co-located team interactions.
Comparison of Topic modelling Techniques in Marketing - Results from an Analysis of distinctive Use Cases

Currently, topic modelling is an effective analytical tool for the automated investigation of text data. However, identifying the underlying topics is still a challenging task that is dependent on the selection of the proper technique. Moreover, due to the considerable number of topic modelling techniques re-ported in the literature, uncertainty about the application of the techniques arises for both researchers and practitioners. Therefore, we conducted a comparison of three different topic modelling techniques (LDA, PAM, DMR) to give recommendations for three use cases identified in the literature: content extraction, trend analysis and content structuring. For each of them, we identified several requirements and by conducting the method ‘Goal Question Metric’, we derived several comparison metrics. We applied these metrics to a real-world Facebook data set (4,155,992 posts) to highlight the differences between the three topic modelling techniques and to give recommendations for our defined use cases.
Effective Leadership Approaches in Socio-Technical Change

This paper investigates 15 leaders’ effective leadership approaches in five different companies in Denmark, during eight months of Covid-19 restrictions. Initially, a massive scaling of digital technology adoption took place, with leaders and members forced to work virtually from home (WFH). Categorized as a major and abrupt socio-technical change, we inquired into the experiences before, during and after lock-down, and discovered that member well-being and team productivity had prevailed. Drawing on critical realism, we took a grounded theory approach to explore the phenomenon of effective leadership in socio-technical change. After extensive coding, we abstracted an underlying pattern that explains how effective leaders apply situational leadership by continuously activating synergies between mechanisms of socialization, institutionalization, and individualization in their socio-technical work-systems. These findings can serve as an inspiration to researchers and managers that seek to find new ways of harmonizing social and technical structures during rapid digital transformation.
Designing a collaboration process for big data analytics projects

Although there is broad agreement that big data analytics (BDA) creates business value, knowledge about how organizations should proceed to extract value remains limited. Scholars have found that utilizing BDA insights in business greatly contributes to BDA value, but existing BDA processes tend to focus more on data science activities and do not adequately outline collaboration activities with business users. However, this relationship seems troubled and may even impede BDA success. To alleviate this discrepancy, we conduct an action research project with a tool manufacturer. The goal is to design a collaboration process for BDA. We apply social capital theory to evaluate the process, as social capital can improve collaboration. This paper gives the first glimpse of the collaboration process which intends to contribute to literature and practice by outlining critical collaboration activities that improve the relationship between business and data science and increase BDA use in business.
Designing Anthropomorphic Therapeutic Conversational Agents: An Uncanny Valley Perspective

Recent technological advancements promise new potentials for the use of therapeutic conversational agents (CAs) to deliver mental health interventions. However, both researchers and practitioners face challenges in anthropomorphizing therapeutic CAs for more human-like interactions while avoiding the uncanny feeling from users. Drawing upon the literature of CA anthropomorphism and the uncanny valley theory, the current short paper offers a novel insight into users’ receptivity to therapeutic CAs. It differentiates CAs’ anthropomorphic design features as visual and verbal anthropomorphism and examines their distinct and contrast effects on user perception and advice adherence. We developed a research design to test our model by deploying a 3x2 experiment. We believe this study will provide important contributions that are crucial for the anthropomorphic design of CAs and understanding of the uncanny valley effect in the therapeutic healthcare service setting.
How Organizational Transparency Strengthens Digital Innovation Capabilities in Startups

Organizational transparency has been identified as an essential principle for organizations to adopt since it enables organizational agility and digital innovation capability, which have become crucial for companies to handle today's volatile digital market. Startups seem to embrace transparency, but there is little empirical evidence in the literature describing how startups use transparency to develop their organizational agility and increase their digital innovation capability. We conducted a qualitative study based on 23 semi-structured interviews on nine globally active automotive startups to understand how organizational transparency strengthens digital innovation capability. The result shows that in the cases where transparency enables trusting relationships and commitment between employees and across company borders, transparency can strengthen the startups' digital innovation capability and making more use of people's creativity. We conclude that startups that apply an external transparency strategy to open innovation can enhance their digital innovation pace and the likelihood of market success.
Conceptualizing Digital Resilience for AI-based Information Systems

The increasing volume of external shocks, such as the ﬁnancial crisis or the COVID-19 pandemic, emphasizes the need for businesses to increase their resilience, i.e., a system’s ability to reduce the impact or recover from such shocks quickly. In the past, information systems were often viewed as a source of vulnerability and complexity. In contrast, we conceptualize digital resilience—a form of resilience that is enabled through information systems by taking an integrated approach. In this work, we develop a framework for the design of digital resilience in AI-based information systems. Based on this framework, we conduct and evaluate an illustrative study addressing demand shocks in the airline industry. We, thereby, show the technical feasibility of concept drift detection and discuss why digital resilience must be designed holistically.
A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance

Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.