Miracle-dz/UniFAR-FTUs huggingface dataset:
SHOX at a glance: from gene to protein

Abstract The Short Stature Homeobox-containing Gene SHOX was identified as the genetic cause of the short stature phenotype in patients with Turner Syndrome and in certain patients with idiopathic short stature. Shortly after, SHOX mutations were also associated with the growth failure and skeletal deformities seen in patients with Léri – Weill dyschondrosteosis and Langer mesomelic dysplasia. Today it is estimated that SHOX mutations occur with an incidence of roughly 1:1000 in newborns, making mutations of this gene one of the most common genetic defects leading to growth failure in humans. This review summarises the involvement of SHOX in several short stature syndromes and describes recent advances in our understanding of SHOX functions and regulation. We also discuss the current evidence in the literature that points to a role of this protein in growth and bone development. These studies have improved our knowledge of the SHOX gene and protein functions, and have given insight into the etiopathogenesis of short stature. However, the exact role of SHOX in bone development still remains elusive and poses the next major challenge for researchers in this field.
Double phase transonic flow problems with variable growth: nonlinear patterns and stationary waves

In this paper we are concerned with a class of double phase energy functionals arising in the theory of transonic flows. Their main feature is that the associated Euler equation is driven by the Baouendi–Grushin operator with variable coefficient. This partial differential equation is of mixed type and possesses both elliptic and hyperbolic regions. After establishing a weighted inequality for the Baouendi–Grushin operator and a related compactness property, we establish the existence of stationary waves under arbitrary perturbations of the reaction.
A Generalized Notion of Time for Modeling Temporal Networks

Most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion of time. In this paper, we therefore introduce a generalized notion of time for temporal networks. Our approach also allows for considering non-deterministic time and incomplete data, two issues that are often found when analyzing data-sets extracted from online social networks, for example. In order to demonstrate the consequences of our generalized notion of time, we also discuss the implications for the computation of (shortest) temporal paths in temporal networks.
Towards Re-Conceptualizing DSS: a Text Mining based Approach

Decision support systems (DSS) have evolved significantly since the past 40 years. This evolution is fueled by its expansion from vantage directions. However, this flexibility in growth is disturbing with shifting trajectory. This dichotomy of expansion versus falling apart has a consequence of lack of coherence of the field. Thus, reconceptualization of this field in current juncture is wanting and this serves as the primal motivation behind this study. Consequently, the objective is to synthesize the conceptual elements which constitute the DSS literature. The use of text mining techniques to automate the review process is unique in this study. This approach addresses the subjective interpretation adopted by other alternate literature review methods. The word frequency distribution and topic mining lead to unveiling of three broad themes of discussion viz. foundational, methodological and application(s). The derived themes have been mapped with earlier propositions from various scholars of the filed to provide unanimity. Findings suggest that the propositions in the earlier decade of developments are not only relevant today but also dominate the literature. In continuation, we plan to extend our research to perform a decade-wise analysis of DSS publications for the last 40 years.
A Customized Educational Booster for Online Students in Cybersecurity Education

Real-world lab experiments have been an integral part of computer science and engineering curriculums. However, human and computing resources as well as financial support to courses may be limited at many universities ranging from small to large universities and from liberal arts colleges to top research universities as there is a dramatic increase of student enrollments in computer science and engineering for the past ten years. Nowadays, like many other universities nation wide, University of South Florida (USF) in collaboration with the Cyber Florida center offers online cyber security degrees as well as certificates. Throughout such programs, online students are expected to acquire knowledge via an interdisciplinary set of core courses prior to taking a deep dive into one of four following concentrations: cyber intelligence, digital forensics, information assurance and computer security fundamentals. However, when it comes to training student with real-world cyber security labs, both instructors and students face various challenges with regard to resources and virtual environment for exploring and running a broad range of security experiments and tests. In order to achieve the goal of our funded NSF project, in this paper we will discuss our teaching contributions to the development of a broad range of cyber security labs, facilitation of applied cryptography learning through experimental modules, and our customized virtual machine that fits the needs of various online computer networking and security courses. Specifically, we will first present our methodology for the design of our experimental modules and then present in details our pre-built Linux-based portable virtual machine. Those learning and experimental modules have been developed at different levels to meet the need of students with different academic and industrial backgrounds.
A Strategic View on intertwining Digital and Physical Materialities across Lifecycles of Products and Services

Opportunities to use virtual, augmented, and mixed reality (VAM-R) are emerging in various sectors of the economy. To seize them, managers need to develop comprehensive strategies that intertwine digital and physical forms of materiality. This paper proposes a way to assess and steer the transformations of the reality-virtuality continuum across lifecycles of products and services. Our approach identifies use cases for VAM-R according to the (1) strategic imperative, (2) physical materiality, (3) reality-virtuality assessment, (4) digital materiality, (5) information value, and (6) project portfolio. The findings result from three action research cycles in manufacturing and healthcare. For theory, we propose a framework to evaluate the reality-virtuality continuum and guide VAM-R transformations. For practice, we propose and test an artefact accessible to domain experts with different backgrounds, and a sequence of steps to assist managers in their digitalization strategies with VAM-R. Lifecycle approaches offer an alternative perspective to situational transformation, potentially improving the pervasiveness of organizational changes using information technologies.
Quantifying the Impact of Geospatial Recommendations: a field Experiment in High Street Retail

Once the mainstay of shopping and cultural exchange, many high streets increasingly struggle to compete for their customers’ wallets and leisure time against digital shopping. Besides superior convenience and broader assortments, data-driven recommendations of products that fit individual customers’ needs are particular assets possessed by online stores. While first recommender systems that leverage the properties of physical environments have been designed and evaluated in shopping malls, their applicability, accuracy, impact, and business value have neither been demonstrated nor evaluated in local high streets. We set out to identify and quantify the impact of geospatial recommendations on high street retail in a large German city center. Having equipped 66 local businesses with more than 120 Bluetooth Low Energy beacons and having distributed a mobile application to 400 customers, we collect geospatial data to trace customer journeys on the high street. In a field experiment, we plan to identify, analyze, and quantify the effects of recommendations in this setting. Our results will provide new data-driven insights into the accuracy, acceptance, impact, and business implications of geospatial recommendations in high street ecosystems.
Towards a Cloud Performance Research Repository

In this paper, we describe our goals and initial efforts to create a community wide infrastructure for empirical research on cloud systems performance modeling. Performance analysis and prediction has long been an active research area in software engineering. Recent work in this area has focused on cloud-deployed applications that are characteristic of an ever growing percentage of modern software systems. The drivers for cloud deployment are improved quality attributes such as performance and scalability, access to advanced cloud-based technologies, and reduced costs. In terms of research, efforts include optimizing resource utilization for cloud systems under various workloads, understanding the influence on performance of various cloud-based infrastructure components, and architecture design trade-offs. To support these efforts and energize the creation of new models and methods, our goal is to build a repository that stores both raw and analyzed performance results from a collection of benchmarks characterizing different types of cloud applications. This data can be used by researchers to explore, calibrate and validate higher fidelity performance models and identify new research directions. Results from benchmarks can also help architects understand the effects of their architectural decisions and cloud services selections and configurations on application quality attributes.
Communicating Learning Analytics: Stakeholder Participation and Early Stage Requirement Analysis

This paper reflects on a user-centered design methodology for requirements elicitation at early stages of a design process for Learning Analytics tools. This methodology may be used as a domain specific instrument to elicit user perspectives about the communicational aspects of learning analytics dashboards. The focus of this work is identifying ways to communicate the data analysis findings in a way that is easily perceptible and facilitates actionable decision making. We present the structure as well as the logic behind the design of this instrument. As a case study, the paper describes an implementation of this methodology in the context of school-wide analytics communicating to stakeholders quality indicators through summarising and visualising data collected through student and parent surveys. We provide high-level and transferable recommendations derived from the analysis of the workshop with key stakeholders and identify future improvements in our methodology.
Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map

Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this paper proposes an innovate navigation framework based on the GPS-level localization, which takes the raw perception data with publicly accessible navigation maps to produce an intermediate navigation cost map that allows subsequent flexible motion planning. A deterministic conditional adversarial network is adopted in our method to generate visual goal-directed paths under diverse navigation conditions. The adversarial loss avoids the pixel-level annotation and enables a weakly supervised training strategy to implicitly learn both of the traffic semantics in image perceptions and the planning intentions in navigation instructions. The navigation cost map is then rendered from the goal-directed path and the concurrently collected laser data, indicating the way towards the destination. Comprehensive experiments have been conducted with a real vehicle running in our campus and the results have verified the robustness to localization error of the proposed navigation system.
Designing an AI-based Advisory Platform for Design Techniques

The usage of design techniques in design processes is an important driver for the success of digital services. However, before using design techniques, suitable techniques need to be selected. With the continuous growth of the number of design techniques, the selection of appropriate ones becomes more difficult, especially for design novices with limited knowledge and expertise. In order to support the selection process, we propose design principles for the development of an advisory platform that interacts with design novices to suggest design techniques for different design situations using artificial intelligence (AI) techniques. Specifically, we leverage conversational agents, recommender techniques, and taxonomic background knowledge to conceptualize and implement an AI-based advisory platform. Following a design science research methodology, we contribute design knowledge for the class of advanced advisory platforms. Furthermore, from a practical point of view, we help design novices with our implemented advisory platform in the contextualized selection process of design techniques.
Economic Perspective on Algorithm Selection for predictive Maintenance

The increasing availability of data and computing capacity drives optimization potential. In the industrial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statistical measures such as absolute and relative prediction errors are considered. However, algorithm selection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Hotelling T2 Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambiguous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of 15% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithm selection and supports predictive maintenance investment decisions.
Real-time Operational Dashboards for Facilitating Transparency in Supply Chain Management: Some Considerations

The real-time sharing of data has created a unique opportunity to design software applications for the purpose of improving operations in a supply chain (SC) - both horizontally and vertically. As a result of these developments, dashboards have been designed to facilitate transparency - providing a better overview of a specific operation. In this paper, we outline our research to show that most of the operational dashboard designs are data driven but only a very few of them are designed from a user’s perspective. Further, not many in their design process tap into the benefits of building a dashboard based on the principles of cognition. We argue that building dashboards based on how our brain is wired will result in enhancing the decision-making processes for a Supply Chain.
Hamiltonian descent for composite objectives

In optimization the duality gap between the primal and the dual problems is a measure of the suboptimality of any primal-dual point. In classical mechanics the equations of motion of a system can be derived from the Hamiltonian function, which is a quantity that describes the total energy of the system. In the Hamiltonian formalism the energy is conserved, so we add a contractive term to the standard equations of motion so that this energy decreases linearly (i.e., geometrically) with time. This yields a continuous-time ordinary differential equation (ODE) in the primal and dual variables which converges to zero duality gap, i.e., optimality. This ODE has several useful properties: it induces a natural operator splitting; at convergence it yields both the primal and dual solutions; and it is invariant to affine transformation despite only using first order information. We provide several discretizations of this ODE, some of which are new algorithms and others correspond to known techniques, such as the alternating direction method of multipliers (ADMM). We conclude with some numerical examples that show the promise of our approach. Our framework provides new insights into previously known algorithms in the literature as well as providing a technique to generate new primal-dual algorithms.
FASTEN: An Open Extensible Framework to Experiment with Formal Specification Approaches

Formal specification approaches have been successfully used to specify and verify complex systems. Verification engineers so far either directly use formal specification languages which can be consumed by verification tools (e.g. SMV, Promela) or main stream modeling languages which are then translated into formal languages and verified (e.g. SysML, AADL). The first approach is expressive and effective but difficult to use by non-experts. The second approach lowers the entry barrier for novices but users are limited to the constructs of the chosen modeling languages and thereby end up abusing the language to encode behaviors of interest. In this paper, we introduce a third approach that we call FASTEN, in which modular and extensible Domain Specific Languages (DSLs) are used to raise the abstraction level of specification languages towards the domain of interest. The approach aims to help novice users to use formal specification, enable experts to use multi-paradigm modeling, and provide tools for the developers of verification technologies to easily experiment with various types of specification approaches. To show the feasibility of the approach, we release an open-source tool based on Jetbrains' MPS language workbench that provides an extensible stack of more than ten DSLs, situated at different levels of abstraction, built on top of the SMV language. We use the NuSMV model checker to perform verification, to simulate the models and lift the traces at the abstraction level of the DSLs. We detail on the experience with designing and developing the DSLs stack and briefly report on using the DSLs in practice for the study of a communication protocol of a safety critical system.
Detecting Role Inconsistencies in Process Models

Business process models capture crucial information about business operations. To overcome the challenge of maintaining process definitions in large process repositories, researchers have suggested methods to discover errors in the functional and the behavioral perspectives of process models. However, there is a gap in the literature on the detection of problems on the organizational perspective of process models, which is critical to manage the resources and the responsibilities within organizations. In this paper, we introduce an approach to automatically detect inconsistencies between activities and roles in process models. Our approach implements natural language processing techniques and enterprise semantics to identify ambiguous, redundant, and missing roles in textual descriptions. We applied our approach on the process model repository of a major telecommunication company. A quantitative evaluation of our approach with 282 real-life activities displayed that this approach can accurately discover role inconsistencies. Practitioners can achieve significant quality improvements in their process model repositories by applying the approach on process models complemented with textual descriptions.
Towards Automated Testing of Blockchain-Based Decentralized Applications

Blockchain-based decentralized applications (DApp) have been widely adopted in different areas and trusted by more and more users due to the fact that the back end code of a DApp is publicly run on the blockchain and cannot be modified implicitly. However, there are few effective methods and tools for testing DApps and bugs can be easily introduced by inexperienced developers. The existing testing techniques either focus on testing front-end programs or back-end code but ignore the interaction between them, which makes it difficult to apply the techniques directly on DApp. In this paper, we present an automated testing technique for DApps which works in a two-phase manner. First, we employ random events to infer an abstract relation between browser-side events and blockchain-side contracts. Second, our technique generates a set of test cases under the guidance of inferred relations and orders the test cases based on a read-write graph. We also use taint analysis to track data flow of the smart contract and feed it to the generation procedure for following test cases. We have developed a tool called Sungari to implement our approach, and evaluated it on representative real-world DApps. The preliminary evaluation results demonstrated the potential of Sungari in achieving a significant optimization compared to random testing approaches.
OULAD MOOC Dropout and Result Prediction using Ensemble, Deep Learning and Regression Techniques

Massive Open Online Courses (MOOCs) have become increasingly popular since their start in the year 2008. Universities known worldwide for their traditional confined classroom education are also changing their practices by hosting MOOCs. These are Internet-based courses where students can learn at their own pace and follow their own schedule. Study materials and videos are provided that can be used in a blended learning program. Despite its many advantages, it suffers from problems such as high dropout and failure rates. Previous studies have mostly focused on predicting student dropout. This paper contributes to the body of research by investigating both student dropout and result prediction performance of machine learning models built based on different types of attributes such as demographic info, assessment info and interaction with the VLE. An analysis on the OULAD dataset showed that models based on student’s interaction with the VLE achieved the high performance in terms of AUC, of up to 0.91 for dropout prediction and 0.93 for result prediction in case of Gradient Boosting Machine.
Phased Classroom Instruction: A Case Study on Teaching Programming Languages

This article describes a novel educational format called “phased classroom instruction” and its enabling technology specially tuned to the effective learning of formal languages in tertiary STEM education. Like flipped classroom, phased classroom instruction aims at promoting active learning. In contrast to flipped classroom, phased classroom instruction scales to large classes thanks to its associated enabling technology. The article reports on a real-life evaluation of the proposed format and of its enabling technology pointing to their