Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Long-Term Erythropoietic Recovery After Allogeneic Stem Cell Transplants in Pediatric Patients

Long term erythropoietic reconstitution after allogeneic hematopoietic stem cell transplantation (alloHSCT) has not been extensively studied. We aimed to describe erythropoietic reconstitution as an indicator of long-term graft function by modeling hemoglobin levels during the first 3 years post HSCT in pediatric patients. We retrospectively included 414 patients and 11,957 measurements. The largest hemoglobin increase was at day 45 and levels reached a steady state at day 648 with a level of 7.48 mmol/L. In patients transplanted for hematological malignancies hemoglobin levels normalized faster (p < 0.0001). Increasing patient age correlated with faster recovery (p < 0.0001), while donor age had no influence. Conditioning, donor type and graft source did not influence recovery significantly. In the ABO mismatched group there was a transient negative effect on hemoglobin levels, and a delay in reticulocyte recovery (21 vs. 19 days; p = 0.012). In contrast, hemoglobin levels reached a higher plateau beyond 9 months in these patients (p < 0.0001). After alloHSCT, experiencing a CMV reactivation negatively affected reconstitution (p = 0.034), while EBV reactivations and acute graft vs. host disease did not. In summary, erythropoietic recovery was mainly influenced by patient factors and primary disease, and less influenced by donor factors.
Supporting Disconnected Operation in DOORS

In this paper we present a replicated object store used to support disconnected operation. The system uses an optimistic replication approach to provide high read and write availability. The development of new applications is supported by an object framework that decomposes objects in several components, each one managing a different aspect related with data sharing, such as concurrency control and adaptation to environmental changes. This way, objects can manage the “operational” aspects of data sharing in a flexible and type-specific way, using different approaches. To support disconnected operation the system also presents other features: a partial caching mechanism that relies on the definition of objects as a cluster of sub-objects; a “blind invocation” mechanism to overcome some cache misses; and integrated awareness support.
Comparison of performance and implementation complexity of soft-output sphere detectors for MIMO-OFDM systems

A list sphere detector (LSD) is a soft-output enhancement of a sphere detector (SD) that can be used to approximate the optimal MAP detector in the detection of the multiple-input multiple-output (MIMO) signals. The LSDs are based on tree search algorithms and they can be divided into three categories: the breadth-first (BF) search, the depth-first search (DF), and the metric-first (MF) search algorithms. A fair comparison of the feasibility of different search algorithms for MIMO detection requires efficient architecture designs and hardware implementations of the algorithms. In this paper, we compare and analyze the error rate performance, ASIC implementation complexity and detection rate of LSDs based on all three types of search strategies for 4 × 4 MIMO-OFDM systems with 16- and 64-QAM.
Homomorphism Encryption Algorithm for Elementary Operations over Real Number Domain

Based on the homomorphism of Lee et al, Xiang Guangli et al proposed similar module and implemented encrypting operations of addition, subtraction, multiplication and division over real number domain. There was additive and multiplicative homomorphism heretofore. But their cipher text leaks the information of decimal fraction, sign and relationship of big and small. We propose additive, subtractive, multiplicative and divisive homomorphism over real number domain based on representation of real number by positive integer and Fermat's Little Theorem. We prove the security and correctness of the proposed homomorphism encryption system. The results of instances show that there are no the problems mentioned above in our algorithm.
Tuning PI-controller using swarm algorithm for control of permanent magnet synchronous motor for electric vehicle

Electric Vehicle (EV) is a dream for the human being city traffic without exhausting gas and with low noise. Permanent Magnet Synchronous Motor (PMSM) became at the top of ac motors in high performance drive systems such as EV. This paper presents a modern approach of speed control for PMSM using Particle Swarm Optimization (PSO) algorithm to optimize the parameters of PI- Controller. The overall system will be simulated under various operating conditions and an experimental setup is prepared. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. The system is tested for a step change in load, the simulation results showing good dynamic response with fast recovery time.
Learning under Concept Drift: A Review

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.
Interactive Narrative Design beyond the Secret Art Status: A Method to Verify Design Conventions for Interactive Narrative

In recent years, game narrative has emerged as an area for novel game concepts and as a strategy to distinguish a particular title. However, innovation in this area comes primarily from indie companies and individual efforts by noted designers. There is a lack of trained specialists ready to produce interactive narrative experiences. Many existing practitioners are self-trained and often rely on intuition in their design practice. A key element missing from the effort towards a more sustained development and improved professional training is a set of design conventions that fulfill a role comparable to cinematic conventions like continuity editing or montage. Therefore, our research focuses on identifying, verifying and collecting such design strategies. We describe an empirical method to verify candidate design conventions through the evaluation of user reaction to A/B prototypes, which improves upon the trial-and-error process of old.
A Systematic Mapping Study of HCI Practice Research

ABSTRACT Human–computer interaction (HCI) practice has emerged as a research domain in the HCI field and is growing. The need to transfer HCI practices to the industry began significantly with the works of Nielsen on usability engineering. To date, methods and techniques for designing, evaluating, and implementing interactive systems for human use have continued to emerge. It is, therefore, justified to conduct a systematic mapping study to determine the landscape of HCI practice research. A Systematic Mapping Study method was used to map 142 studies according to research type, topic, and contribution. These were then analyzed to determine an overview of HCI practice research. The objective was to analyze studies on HCI practice and present prominent issues that characterize the HCI practice research landscape. Second, to identify pressing challenges regarding HCI practices in software/systems development companies. The results show that HCI practice research has steadily increased since 2012. The majority of the studies explored focused on evaluation research that largely contributed to the evaluation methods or processes. Most of the studies were on design tools and techniques, design methods and contexts, design work and organizational culture, and collaboration and team communication. Interviews, case studies, and survey methods have been prominently used as research methods. HCI techniques are mostly used during the initial phase of development and during evaluation. HCI practice challenges in companies are mostly process-related and on performance of usability and user experience activities. The major challenge seems to be to find a way to collect and incorporate user feedback in a timely manner, especially in agile processes. There are areas identified in this study as needing more research.
Questionify: Gamification in Education

In the education industry, lecturers are finding ways to improve students’ concentrations and grades by using smart devices to track students’ assignment or tutorial progress. One of the few possible and attractive solution is by using the gamification technique. This paper proposes an educational application called Questionify that implements the gamification elements and allow users to collect points, gain achievements, increase motivation and engagement towards students’ coursework in Software Engineering subject. Questionify is developed using C# and Java language has been evaluated using questionnaire among 24 respondents. The findings showed that the respondents believe that gamification can do better in education as compare to the traditional method of teaching the students. In the future, this gamification approach will be tested on more technical subjects such as programming and networking subjects to help students engage in a different learning approach.
Exploiting Spatial Statistics of Primary and Secondary Users towards Improved Cognitive Radio Networks

We show how locations of primary and secondary users of spectrum can be characterized quantitatively using modern spatial statistics techniques. We argue that several interesting practical engineering applications can be based on these methods. In order to demonstrate the developed techniques, we will apply those to real-world data in particularly TV-transmitter locations in the USA. The spatial statistic techniques have a natural use in the analysis of Dynamic Spectrum Access (DSA) opportunities. We expect that the presented methods have strong potential to be used not only for theoretical work, but also as foundations for concrete protocols and algorithms for improved cognitive radios.
Global motion guided adaptive temporal inter-/extrapolation for side information generation in Distributed Video Coding

The ongoing research on Distributed Video Coding (DVC) is focused on flexibility and increased rate distortion (RD) performance. Besides Slepian-Wolf (SW) coding and coder control, a good side information (SI) quality is essential for high RD performance. The SI is typically extracted by temporal inter-/extrapolation. Fast motion is still a challenge. Two global motion guided adaptive temporal inter-/extra-polation (GTIE/GpTIE) schemes are proposed. They incorporate fast camera motion by global motion estimation and subsequent refinement. The issue of occlusion and revelation at the frame border is solved by an adaptive temporal inter-/extrapolation method. Simulation results show an RD performance increase of up to 3.1 dB.
End-to-end learning for high-precision lane keeping via multi-state model

High-precision lane keeping is essential for the future autonomous driving. However, due to the imbalanced and inaccurate datasets collected by human drivers, current end-to-end driving models have poor lane keeping the effect. To improve the precision of lane keeping, this study presents a novel multi-state model-based end-to-end lane keeping method. First, three driving states will be defined: going straight, turning right and turning left. Second, the finite-state machine (FSM) table as well as three kinds of training datasets will be generated based on the three driving states. Instead of collecting the dataset by human drivers, the accurate dataset will be collected by the high-performance path following controller. Third, three sets of parameters based on 3DCNN-LSTM model will be trained for going straight, turning left and turning right, which will be combined with FSM table to form a multi-state model. This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error. The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.
An Effective Method for Chinese Related Queries Recommendation

With the explosive growth of the Internet resources, Web search engines have become the most popular Web service to help people find relevant information on a given topic on the Web. However, many novice users have difficulty in formulating effective queries for their information needs, in the worst case, users are even not sure what exactly their specific information is. Under this condition, query suggestion technology is proposed to recommend a list of related queries for a given initial query submitted to a search engine, which can help users specify their information needs better. In this paper, we present an effective approach for query suggestion. For a given Chinese Web query, the approach can not only identify related queries already existed in the log of past submitted queries to search engines but also use synonyms extracted from Web-based corpuses to construct new related queries. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness, effectiveness and freshness. The achieved performance shows that the proposed approach is very effective in recommending related queries for high-frequency queries, moreover, it also performs reasonably well on less frequent queries.
Design and Implementation of the TrustedBSD MAC Framework

Developing access control extensions for operating systems is an expensive and time-consuming task. Mechanisms available for access control extension lag behind industry standard extension solutions for file systems, process schedulers, and device drivers, and suffer from a number of serious flaws in modern multi-processor, multi-threaded kernels. In this paper, we explore the limitations of current technologies for security extension. We describe the TrustedBSD MAC Framework, a flexible and modular environment for operating system access control extensions on the open source FreeBSD platform. The TrustedBSD MAC Framework permits extensions to be introduced at compiletime, boot-time, or at run-time, and provides a number of services to support dynamically introduced policies, including policy-agnostic object labeling services and application interfaces. We discuss the design and implementation of the framework, as well as the an implementation of a fixed-label Biba integrity policy based on the framework.
Highly scalable data processing framework for pervasive computing applications

One of the key problems in pervasive computing is enabling the collective processing of sensor data obtained from mobile devices such as smartphones. In this demonstration we present a highly scalable storage and processing framework for pervasive computing applications, enabling various estimation problems to be solved from massive data sets, consisting of measurements from millions of nodes or more. The key to achieving such scalability is the use of linear or sublinear time processing algorithms emerging from statistical and machine learning communities. We focus specifically on spatial and spatio-temporal estimation problems in the demonstration, such as prediction of sensor readings, user densities, or wireless network usage in regions for which direct measurements are not available.
Around the Lab in 40 days ...

In previous papers (11 we have argued that the LAAS architecture’ is one of the most suited for mobile robot control. This statement may seem over-optimistic, not to say pretentious and unverifiable. After all, can we compare architectures? can we set up benchmarks? or can we measure how good an architecture is compared to another? An architecture defines organization principles, integration methods and supporting tools. Comparing those tools, methods and principles may sometime end up in sterile controversies. However, we think there are means to measure the overall quality (or interest) of an architecture. Development time is for example one relevant criterion [5]. Basically, using a specific architecture, how long does it take to integrate a complete demonstration, including non trivial decisional capabilities, from the low level functional modules up to the supervisory level? This may seem a rather weak measure of an architecture quality, however, it encompasses properties such as: genericity and adaptability, ease of design and programming, extensibility and robustness. In this paper we describe our recent experience in integrating a complete demonstration from scratch in 40 days using the LAAS architecture.
Are "Disks in the Air"

Mobile computers and wireless networks are emerging technologies which will soon make ubiquitous computing a reality. In the wireless environment, mobile clients may often be disconnected from stationary server machines or inay have only a low-bandwidth channel for sending messages to servers. This environment raises new challenges for the support of daiabase applications for three reasons: 1) the limited storage capacities of mobile machines, 2) the inability to accurately predict the future data needs of many data-intensive applications, and 3) ihe need to provide clients with new or updated data values in order to ensure consistent data access. One way (perhaps the only way) to address these challenges is to provide the stationary server machines with a relatively highbandswidth channel over which to broadcast portions of the database. Wireless networks are but one cornpelling example of the inore general class of asymmetric communication environments, which also includes CATV networks and information distribution services. We have proposed a mechanism called Broadcast Disks to provide database access in asymmetric cornmunication environments. By appropriate organization of the broadcast program, an arbitrarily fine-grained memory hierarchy can be created. Such a hierarchy raises fundamental new issues for pre-fetching and client cache management. In this short paper we present a brief overview of asymmetric environments, our novel approach to organizing broadcast disks and some initial simulation results.
Efficient end system multicast for mobile ad hoc networks

End system multicast protocols have the advantage that nonmember nodes are not burdened with multicast routing and storing state information. Unfortunately, most existing end system multicast protocols are developed for the traditional Internet. We show how these protocols can also be utilized in mobile ad-hoc networks (MANET). To achieve this the protocols have to be adapted to fit the highly dynamic network topology, link failures and other particularities of MANETs. We show how the existing NICE protocol can be improved to operate more efficient in such an environment. This is achieved by exploiting the broadcast capabilities of the medium, permanent adaptation to the dynamic network topology and a distance metric that is based on hop count instead of latency measurements. Simulations show that the proposed improvements greatly increase the efficiency of the protocol in MANETs. Moreover, the proposed protocol introduces the local broadcast cluster concept that exploits the broadcast medium better than any other existing overlay approach.
Development of a Pneumatically Driven 6 DOF Driving Simulator

A pneumatically-driven driving simulator that provides a realistic representation of the driving environment was developed. The motion platform for the driving simulator is a mechatronic device that gives a driver the realistic feeling of an actual vehicle. The cost of the motion platform comprises the largest part of the expenses in developing a driving simulator. In this project, to develop a low-cost motion platform, the self-built motion platform based on the Stewart platform configuration that is constructed by six pneumatic cylinders was used as its actuator. The Stewart platform that moves in response to the operating signals of the joystick showed satisfactory tracking performance. We confirmed the possibility of the driving simulator using rFactor that is a commercially available racing game software.
Performing Group-By before Join

Assume that we have an SqL query containing joins and a group-by. The standard way of evaluating this type of query is to first perform all the joins and then the group-by operation. However, it may be possible to perform the group-by early, that is, to push the groupby operation past one or more joins. Early grouping may reduce the query processing cost by reducing the amount of data participating in joins. We formally define the problem, adhering strictly to the semantics of NULL and duplicate elimination in SqLQ and prove necessary and suficient conditions for deciding when this transfownation is valid. In practice, it may be ezpensive OT even impossible to test whether the conditions are satisfied. Therefore, we also present a more practical algorithm that tests a simpler, suficient condition. This algorithm is fast and detects a large subclass of transformable queries.
MySRB & SRB: Components of a Data Grid

Data Grids are becoming increasingly important in scientific communities for sharing large data collections and for archiving and disseminating them in a digital library framework. The Storage Resource Broker provides transparent virtualized middleware for sharing data across distributed, heterogeneous data resources separated by different administrative and securitydomains. The MySRB is a web-based interface to the SRB that provides a user-friendly interface to distributed collections brokered by the SRB. In this paper we briefly describe the use of the SRB infrastructure as tools in the Data Grid Architecture for building distributed datacollections, digital libraries, and persistent archives. We also provide details about the mySRB and its functionalities.
Tracking a moving object with mobile robot based on vision

The paper proposes a real-time tracking algorithm for a moving object with mobile robot based on vision using adaptive color matching and Kalman filter. The adaptive color matching can limit the region containing moving object on vision image plane. It can adjust color matching threshold to reduce the influence of lighting variations in the scene. Kalman filter is used as our prediction module to calculate motion vectors of moving object in the robot coordinate system. A view window containing the position of moving object estimated by Kalman filter is determined on image plane to reduce the image processing area. Color matching threshold can adjust itself adaptively in view window, which is used as an updating module. Experimental results show that the algorithm can adapt to lighting variations and has good tracking precision. It can also be implemented in real time.