Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiology of Trypanosomiasis in Wildlife—Implications for Humans at the Wildlife Interface in Africa

While both human and animal trypanosomiasis continue to present as major human and animal public health constraints globally, detailed analyses of trypanosome wildlife reservoir hosts remain sparse. African animal trypanosomiasis (AAT) affects both livestock and wildlife carrying a significant risk of spillover and cross-transmission of species and strains between populations. Increased human activity together with pressure on land resources is increasing wildlife–livestock–human infections. Increasing proximity between human settlements and grazing lands to wildlife reserves and game parks only serves to exacerbate zoonotic risk. Communities living and maintaining livestock on the fringes of wildlife-rich ecosystems require to have in place methods of vector control for prevention of AAT transmission and for the treatment of their livestock. Major Trypanosoma spp. include Trypanosoma brucei rhodesiense, Trypanosoma brucei gambiense, and Trypanosoma cruzi, pathogenic for humans, and Trypanosoma vivax, Trypanosoma congolense, Trypanosoma evansi, Trypanosoma brucei brucei, Trypanosoma dionisii, Trypanosoma thomasbancrofti, Trypanosma elephantis, Trypanosoma vegrandis, Trypanosoma copemani, Trypanosoma irwini, Trypanosoma copemani, Trypanosoma gilletti, Trypanosoma theileri, Trypanosoma godfreyi, Trypansoma simiae, and Trypanosoma (Megatrypanum) pestanai. Wildlife hosts for the trypansomatidae include subfamilies of Bovinae, Suidae, Pantherinae, Equidae, Alcephinae, Cercopithecinae, Crocodilinae, Pteropodidae, Peramelidae, Sigmodontidae, and Meliphagidae. Wildlife species are generally considered tolerant to trypanosome infection following centuries of coexistence of vectors and wildlife hosts. Tolerance is influenced by age, sex, species, and physiological condition and parasite challenge. Cyclic transmission through Glossina species occurs for T. congolense, T. simiae, T. vivax, T. brucei, and T. b. rhodesiense, T. b. gambiense, and within Reduviid bugs for T. cruzi. T. evansi is mechanically transmitted, and T. vixax is also commonly transmitted by biting flies including tsetse. Wildlife animal species serve as long-term reservoirs of infection, but the delicate acquired balance between trypanotolerance and trypanosome challenge can be disrupted by an increase in challenge and/or the introduction of new more virulent species into the ecosystem. There is a need to protect wildlife, animal, and human populations from the infectious consequences of encroachment to preserve and protect these populations. In this review, we explore the ecology and epidemiology of Trypanosoma spp. in wildlife.
Value Creation through Strategic Investments in Digital Technologies: the Role of Organizational Agility

The impact of traditional information technology resources on organizational agility and performance has been extensively studied. However, strategic information systems research still lacks a deeper understanding about the value creation mechanisms of strategic investments in emergent and innovative digital technologies like analytics, big data, cloud, social media and mobile platforms. Drawing on the digital options theory and dynamic capability theory, we propose a conceptual model in which the positive effect of digital business intensity on competitive advantage is mediated by sensing and responding agility, two underlying dimensions of organizational agility. Empirical analyses of survey-based data from 342 German firms suggest that digital business intensity is positively related to both sensing and responding agility. Moreover, sensing and responding agility mediate the relationship between digital business intensity and competitive advantage. Consequently, this study contributes to the literature by shedding light on the value creation mechanisms of strategic investments in digital technologies.
Partisan Responses to Fact-Checking in Online News Platforms: Evidence from a Political Rumor about the North Korean Leader

To correct misinformation and mitigate the social costs of political rumors and fake news, news providers, politicians, and researchers have exerted significant efforts on fact-checking and rumor debunking. This study examined how individuals will respond when a political rumor is debunked by large-scale fact-checking. To explore this question, we leveraged a quasi-experimental setting where the North Korean leader’s reappearance in the public event suddenly rebutted a political rumor about his death. Collecting 2.6 million comments from the largest online news portal in South Korea, we employed a difference-in-differences approach comparing differences in commenting behaviors between liberals and conservatives before and after this event. The results show that a political side empowered by the fact-checking coverage became more vocal and hostile. However, their explicit support level for the rumor did not change significantly compared to their partisan counterparts. Besides, we found that news outlets rebutted by fact-checking attracted more user comments than supported news outlets. Swearing comments of the supported political side mostly drove this difference, suggesting that partisans tend to utilize favorable fact-checking to empower their political side through blaming the other side. Our research stresses the importance of capturing the silence of partisans in considering the effectiveness of fact-checking and provides an alternative explanation on why fact-checking evokes hostile communication in online media.
A Framework for Digital Affordances

Since its development in the field of ecological psychology, the concept ‘affordances’ has been prone to various adaptations. This is most evident in information systems (IS) research, where rapid developments and the generativity of today's digital technologies require a fast advancement of affordances theorization. Frequent but stepwise progress has been made to the concept over the past few years through IS researchers. While those advancements are necessary for understanding IT-associated organizational change, they carry the risk of theoretical fragmentation hindering knowledge accumulation. Applying a systematic five-staged literature review and reintegrating the results into existing conceptualizations of affordances, this paper strives toward a meta-view on affordances, constituted in a descriptive affordances framework and an affordances lifecycle model. The former provides researchers a tool for better identifying and understanding the meta-characteristics of affordances, while the latter offers a lens to interpret the circumstances of affordance emergence, actualization, and their outcomes.
Plasma parameters of the hollow cathode magnetron inside and downstream

This paper deals with the characterization of ionized physical vapour deposition (IPVD) by means of a hollow cathode magnetron (HCM). The pressure is 10 mTorr with 2.2 kW discharge power. A Langmuir probe was used to study the spatial distribution of plasma parameters such as the electron density and temperature, electron energy distribution function, plasma space and floating potentials inside and outside the HCM. It was found that the high density plasma inside the cathode occurs near the side surface in the ionization region. The plasma density on the axis is reduced. The EEDF shows the presence of electrons with energy up to 30 eV. The measurements showed that ionization occurs not only within the cathode but also outside. The magnetic trap placed around the anode ring increases the density and homogeneity of the plasma several times in the region of its location.
A Large Open Dataset from the Parler Social Network

Parler is as an ``alternative'' social network promoting itself as a service that allows to ``speak freely and express yourself openly, without fear of being deplatformed for your views. '' Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.
Syntactic annotation in the i3rab dependency treebank

Arabic dependency parsers have a poor performance compared to parsers of other languages. Recently the impact of annotation at lexical level of dependency treebank on the overall performance of the dependency parses has been extensively investigated. This paper focuses on the impact of coarse-grained and fine-grained dependency relations on the performance of Arabic dependency parsers. Moreover, this paper introduces the annotation rules for I3rab dependency treebank. Experimentally, the obtained results showed that having an appropriate set of dependency relations improves the performance of an Arabic dependency parser up to 27.55%.
A Novel Smart Depression Recognition Method Using Human-Computer Interaction System

In recent years, depression not only makes patients suffer from psychological pain such as self-blame but also has a high disability mortality rate. Early detection and diagnosis of depression and timely treatment of patients with different levels can improve the cure rate. Because there are quite a few potential depression patients who are not aware of their illness, some even suspect that they are sick but are unwilling to go to the hospital. In response to this situation, this research designed an intelligent depression recognition human-computer interaction system. The main contributions of this research are (1) the use of an audio depression regression model (DR AudioNet) based on a convolutional neural network (CNN) and a long-short-term memory network (LSTM) to identify the prevalence of depression patients. And it uses a multiscale audio differential normalization (MADN) feature extraction algorithm. The MADN feature describes the characteristics of nonpersonalized speech, and two network models are designed based on the MADN features of two adjacent segments of audio. Comparative experiments show that the method is effective in identifying depression. (2) Based on the research conclusion of the previous step, a human-computer interaction system is designed. After the user inputs his own voice, the final recognition result is output through the recognition of the network model used in this research. Visual operation is more convenient for users and has a practical application value.
Bridging the Gap between Industry Skill demand and University Skill Provision

Today’s working environments are subject to dynamic changes due to the proliferation of digital technologies and systems. This phenomenon poses a challenge for universities and other institutions of higher education, which are expected to adapt their course offerings to the rapidly changing demands in the labor market. The complexity of the task and the importance of speed pose an opportunity for automated data-driven methods with which the contents of study curricula can be compared, assessed, and, if necessary, adapted to the world of work. Owing to the lack of established solutions, this study presents a procedural methodology to create artifacts for analyzing, evaluating, and comparing curricula as well as job postings using topic modeling. In addition, we demonstrate the practical applicability of the methodology by the example of the IS discipline and present empirical results from the analysis of IS-related study programs in Germany.
The Ethics of Information Systems: The Present State of the Discussion and Avenues for Future Work

Despite occasional pleas to incorporate the ethics of information systems into the field’s research agenda, the analysis of the moral ramifications of information systems has remained unsystematic in nature. Work on a remarkable variety of ethical issues has appeared, but the field has yet to produce a coherent conception of how it construes and studies the ethics of information systems. In this research-in-progress paper, we report on work to redress the situation by addressing three goals. First, we provide an overview of contributions made to the study of ethics in information systems research in recent decades. Second, we present a classificatory scheme to organize past and future work on the subject. Third, we identify themes that have received little attention to date, proposing routes for future work. In particular, we find that our field has yet to fully explore the moral implications of the very artifacts of its own design. We conclude with a call to action for information systems researchers to work towards a structured account of ethics as part of the field’s research agenda.
News Information Platform Optimization Based on the Internet of Things

The Internet of Things device online recommendation system has been applied in some Internet of Things operating companies and has achieved good results. In the process of designing and implementing the Internet of Things equipment online promotion system, this article uses the news information protocol transmission structure language to explain the use case analysis and activity diagram analysis of the Internet of Things equipment online promotion system and uses the Spring Hibernate (SH) integration framework in the field of the news information topology layer under the Internet of Things. Then, design and implement the technical architecture and main functional modules of the Internet of Things device online recommendation system, effectively improving the development efficiency and operating quality of the Internet of Things device online recommendation system. The system design concept and implementation ideas can be used as a reference for related industries when developing enterprise applications. This article uses the news information protocol transmission structure language to explain the demand analysis of the Internet of Things equipment online promotion system and mainly discusses the analysis of the use case of the Internet of Things equipment online promotion system and the analysis of core business activity diagrams. We completed the design of the Internet of Things device online promotion system based on the news information topology layer platform under the Internet of Things, mainly using the integration framework in the field of the news information topology layer under the Internet of Things to design the technical architecture of the Internet of Things device online promotion system, and design the Internet of Things of the function module structure and data table structure of the equipment online recommendation system. At the same time, they complete the realization of the main functions of the Internet of Things device online recommendation system; elaborate on the realization process of core modules such as Internet of Things device category management, Internet of Things device information management, announcement management, and device recommender management; and discuss the system testing process and application effects.
Towards Visualizing and simulating Business Models in Dynamic Platform Ecosystems

Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems.
Is It All About Transparency? The Effectiveness and Ethics of a Digital Salience Nudge

Digital nudges offer a promising avenue to promote individual welfare by supporting individuals in their daily decision-making. However, most existing applications merely adapt analog nudges to the digital world, rather than leveraging the unique possibilities that digital environments offer. At the same time, recent discussions raise ethical objections to nudges and suggest disclosing transparent information about nudges as a measure to overcome these. Based on salience theory, we develop a digital salience nudge to increase the healthiness of online food product choices by reducing the visual salience of unhealthy food products. In an online experiment with 399 participants, we evaluate the effectiveness of this nudge and investigate the effect of disclosing transparent information on the nudge. The results suggest that the digital salience nudge reduces the share of unhealthy food product choices by 44% (from 27 to 15%). Disclosing transparent information on the nudge, however, does not change its effectiveness
Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results, we found that the MLA could achieve a total accuracy of 96.92%, with a sensitivity of 95%, a specificity of 98.57%, precision of 98.33%, F1-score of 0.97, and Matthews correlation coefficient (MCC) of 0.94 using only 10 out of 1900 STE features, which implies that the STE matrix extracted from resting-state EEG data may be a promising tool for the clinical diagnosis of schizophrenia.
Conflict-Based Increasing Cost Search

Two popular optimal search-based solvers for the multi-agent pathfinding (MAPF) problem, Conflict-Based Search (CBS) and Increasing Cost Tree Search (ICTS), have been extended separately for continuous time domains and symmetry breaking. However, an approach to symmetry breaking in continuous time domains remained elusive. In this work, we introduce a new algorithm, Conflict-Based Increasing Cost Search (CBICS), which is capable of symmetry breaking in continuous time domains by combining the strengths of CBS and ICTS. Our experiments show that CBICS often finds solutions faster than CBS and ICTS in both unit time and continuous time domains.
Accounting for gambling design elements: A proposal to advance gamification taxonomies

Gamification has been touted as a powerful means to incite user engagement and behavioral changes. Recently, the rise of design elements characteristic of gambling design (e.g., chance-based outcomes) has received increased attention in gamification and started to spark a debate of what is gaming in contrast to gambling, and consequently what is gamification in contrast to gamblification. Therefore, to clearly delineate gamblification and associated design elements within the overarching gamification domain, we argue a comprehensive, common classification method is needed for game and gambling design elements applied to non-game and non-gambling contexts. Accordingly, we propose new dimensions and characteristics to extend gamification taxonomies and validate these along practical case examples. This advancement of classification systems can serve as basis for closer examination of design elements determining gamification’s and gamblification’s effectiveness in spurring user experience and engagement and for novel designs of gamified and gamblified systems.
Framing The Regulation Of Artificial Intelligence-Based Technologies

The discussion on the regulation of Artificial Intelligence (AI) is mainly absent in the field of information systems (IS). This research project develops a processual model of framing AI-based technology regulation building on data from a longitudinal systematic content analysis of media and policy documents. The analysis reveals that regulation discourse takes place in the four phases of discourse initiation, discourse anchoring, discourse legitimation, and reaction leading to regulatory changes. In particular, we found out that regulatory changes are enforced following critical incidents. The model unravels the complex process of AI regulation, stimulates further research and derives implications for policy and practice.
Classification of Tennis Video Types Based on Machine Learning Technology

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.