nils-herrmann/clean_scirepeval huggingface dataset:
A Model of Organisational Knowledge Management Maturity Based on People, Process, and Technology

A Model of Organizational Knowledge Management Maturity based on People, Process, and Technology L.G. Pee A. Kankanhalli Dept. of Information Systems School of Computing National University of Singapore (Forthcoming Journal of Information and Knowledge Management) Abstract Organizations are increasingly investing in knowledge management (KM) initiatives to promote the sharing, application, and creation of knowledge for competitive advantage. To guide and assess the progress of KM initiatives in organizations, various models have been proposed but a consistent approach that has been empirically tested is lacking. Based on the life cycle theory, this paper reviews, compares, and integrates existing models to propose a General KM Maturity Model (G-KMMM). G-KMMM encompasses the initial, aware, defined, managed, and optimizing stages, which are differentiated in terms of their characteristics related to the people, process, and technology aspects of KM. To facilitate empirical validation and application, an accompanying assessment tool is also explicated. As an initial validation of the proposed G-KMMM, a case study of a multi-unit information system organization of a large public university was conducted. Findings indicate that GKMMM is a useful diagnostic tool that can assess and direct KM implementation in organizations.
An improved calculation system for phase-functioned neural network and implementation in unreal engine

The problem attempting to be solved in this paper is optimizing phase-functioned neural network to support generated animation for game engine. The approach adopted is using CUDA and parallel programming to improve large prediction of matrices calculation. The results of this research included a 4-layer architecture of PFNN prediction framework, a CUDA calculation solution and a showcase binding in unreal engine. As for the effects of the results obtained, PFNN calculation has been sped up from 1.8 ms to 1.0, 1.1 ms. And according to the result of performance test of the utility of PFNN in real game development, its optimization has been proven.
What auto-encoders could learn from brains Generation as feedback in unsupervised deep learning and inference

This thesis explores fundamental improvements in unsupervised deep learning algorithms. Taking a theoretical perspective on the purpose of unsupervised learning, and choosing learnt approximate inference in a jointly learnt directed generative model as the approach, the main question is how existing implementations of this approach, in particular auto-encoders, could be improved by simultaneously rethinking the way they learn and the way they perform inference. In such network architectures, the availability of two opposing pathways, one for inference and one for generation, allows to exploit the symmetry between them and to let either provide feedback signals to the other. The signals can be used to determine helpful updates for the connection weights from only locally available information, removing the need for the conventional back-propagation path and mitigating the issues associated with it. Moreover, feedback loops can be added to the usual usual feed-forward network to improve inference itself. The reciprocal connectivity between regions in the brain’s neocortex provides inspiration for how the iterative revision and verification of proposed interpretations could result in a fair approximation to optimal Bayesian inference. While extracting and combining underlying ideas from research in deep learning and cortical functioning, this thesis walks through the concepts of generative models, approximate inference, local learning rules, target propagation, recirculation, lateral and biased competition, predictive coding, iterative and amortised inference, and other related topics, in an attempt to build up a complex of insights that could provide direction to future research in unsupervised deep learning methods.
A 24-pulse rectifier cascaded multilevel inverter with minimum number of transformer windings

This paper addresses the design of a medium voltage adjustable speed drive (ASD) utilizing the benefits of cascaded inverter technology while employing a 24-pulse rectifier inspired front end. Rectifier front ends of ASDs introduce a high level of harmonics into the utility grid, creating equipment overheating problems and distorting the voltage waveform near the ASD's connection point. To greatly reduce these harmonics while limiting cost, a cascaded multilevel inverter utilizing a 24-pulse transformer with a minimum number of windings is used. This design reduces the total harmonic current to less than 5% of the full load current over the entire frequency range. Simulation and experimental results are presented to show proof of concept.
Different Substrates Use in Microstrip Patch Antenna-A Survey

Here we discussed different dielectric substrate frequently used in microstrip patch antenna to enhance overall efficiency of antenna. Various substrates like foam, duroid, benzocyclobutane, roger 4350, epoxy, FR4, Duroid 6010 are in use to achieve better gain and bandwidth. A dielectric substrate is a insulator which is a main constituent of the microstrip structure, where a thicker substrate is considered because it has direct proportionality with bandwidth whereas dielectric constant is inversely proportional to bandwidth as lower the relative permittivity better the fringing is achieved. Another factor that impact directly is loss tangent it shows inverse relation with efficiency the dilemma is here is that substrate with lower loss tangent is costlier. A clear pros and cons are discussed here of different substrates for judicious selection. A substrate gives mechanical strength to the antenna.
Back-translation for discovering distant protein homologies in the presence of frameshift mutations

Frameshift mutations in protein-coding DNA sequences produce a drastic change in the resulting protein sequence, which prevents classic protein alignment methods from revealing the proteins' common origin. Moreover, when a large number of substitutions are additionally involved in the divergence, the homology detection becomes difficult even at the DNA level. We developed a novel method to infer distant homology relations of two proteins, that accounts for frameshift and point mutations that may have affected the coding sequences. We design a dynamic programming alignment algorithm over memory-efficient graph representations of the complete set of putative DNA sequences of each protein, with the goal of determining the two putative DNA sequences which have the best scoring alignment under a powerful scoring system designed to reflect the most probable evolutionary process. Our implementation is freely available at http://bioinfo.lifl.fr/path/ . Our approach allows to uncover evolutionary information that is not captured by traditional alignment methods, which is confirmed by biologically significant examples.
Digital Didactical Designs in iPad-Classrooms

Traditionally, Information and Communication Technology (ICT) “has been segregated from the normal teaching classroom” , e.g. in computer labs. This has been changed with the advent of smaller devices like iPads. There is a shift from separating ICT and education to co-located settings in which digital technology becomes part of the classroom. This paper presents the results from a study about exploring digital didactical designs using iPads applied by teachers in schools. Classroom observations and interviews in iPad-classrooms in Danish schools have been done with the aim to provide empirical evidence on the co-evolutionary design of both, didactical designs and iPads. The Danish community Odder has 7 schools where around 200 teachers and 2,000 students aged 6-16 use iPads in a 1:1 iPad-program. Three key aspects could be explored: The teachers’ digital didactical designs embrace a) new learning goals where more than one correct answer exists, b) focus on producing knowledge in informal-in-formal learning spaces, c) making learning visible in different products (text, comics, podcasts etc.). The results show the necessity of rethinking traditional Didaktik towards Digital Didactics.
A PID BASED ANFIS & FUZZY CONTROL OF INVERTED PENDULUM ON INCLINED PLANE ( IPIP )

The objective of this study is to present an offline control of highly non-linear inverted pendulum system moving on a plane inclined at an angle of 10° from horizontal. The stabilisation was achieved using three different soft-computing control techniques i.e. Proportional-integral-derivative (PID), Fuzzy logic and Adaptive neuro fuzzy inference system (ANFIS). A Matlab-Simulink model of the proposed system was initially developed which was further simulated using PID controllers based on trial and error method. The ANFIS controller were trained using data sets generated from simulation results of PID controller. The ANFIS controllers were designed using only three membership functions. A fuzzy logic control of the proposed system is also shown using nine membership functions. The study compares the three techniques in terms of settling time, maximum overshoot and steady state error. The simulation results are shown with the help of graphs and tables which validates the effectiveness of proposed techniques.
State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

We discuss state-of-the-art intelligent robotic aircraft with the special focus on evolutionary autopilots for small unmanned aerial vehicles (UAVs). Under the umbrella of adaptive autopilots, we highlight the pros and cons of the most widely implemented intelligent algorithms against the navigational and maneuvering capabilities of small UAVs. We present several cutting-edge applications of bioinspired flight control systems that have the capability of self-learning. We also highlight several research opportunities and challenges associated with each technique.Note to Practitioners—Soft computing methods have been widely implemented in numerous engineering applications. Recent advancements in computational technology have enabled the implementations of intelligent autopilots in real time. This paper aims to discuss many aspects of the developments and implementations of soft computing techniques in aerial robotics with the main focus on its flight control systems.
The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology.

In 1986, the European Organization for Research and Treatment of Cancer (EORTC) initiated a research program to develop an integrated, modular approach for evaluating the quality of life of patients participating in international clinical trials.



We report here the results of an international field study of the practicality, reliability, and validity of the EORTC QLQ-C30, the current core questionnaire. The QLQ-C30 incorporates nine multi-item scales: five functional scales (physical, role, cognitive, emotional, and social); three symptom scales (fatigue, pain, and nausea and vomiting); and a global health and quality-of-life scale. Several single-item symptom measures are also included.



The questionnaire was administered before treatment and once during treatment to 305 patients with nonresectable lung cancer from centers in 13 countries. Clinical variables assessed included disease stage, weight loss, performance status, and treatment toxicity.



The average time required to complete the questionnaire was approximately 11 minutes, and most patients required no assistance. The data supported the hypothesized scale structure of the questionnaire with the exception of role functioning (work and household activities), which was also the only multi-item scale that failed to meet the minimal standards for reliability (Cronbach's alpha coefficient > or = .70) either before or during treatment. Validity was shown by three findings. First, while all interscale correlations were statistically significant, the correlation was moderate, indicating that the scales were assessing distinct components of the quality-of-life construct. Second, most of the functional and symptom measures discriminated clearly between patients differing in clinical status as defined by the Eastern Cooperative Oncology Group performance status scale, weight loss, and treatment toxicity. Third, there were statistically significant changes, in the expected direction, in physical and role functioning, global quality of life, fatigue, and nausea and vomiting, for patients whose performance status had improved or worsened during treatment. The reliability and validity of the questionnaire were highly consistent across the three language-cultural groups studied: patients from English-speaking countries, Northern Europe, and Southern Europe.



These results support the EORTC QLQ-C30 as a reliable and valid measure of the quality of life of cancer patients in multicultural clinical research settings. Work is ongoing to examine the performance of the questionnaire among more heterogenous patient samples and in phase II and phase III clinical trials.
Towards Model-Based Failure-Management for Automotive Software

Failure management is a particular challenge problem in the automotive domain. Today's cars host a network of 30 to 80 electronic control units (ECUs), distributed over up to five interconnected in-car networks supporting hundreds to thousands of softwaredefined functions. This high degree of distribution of hard- and software components is a key contributor to the difficulty of failure management in vehicle. This paper addresses comprehensive failure management, starting from domain models for logical and deployment models of automotive software. These models capture interaction patterns as a critical part of both logical and deployment architectures, introducing failure detection and mitigation as "wrapper" services to "unmanaged services", i.e. services without failure management. We show how these models can be embedded into an interaction-centric development process, which captures failure management information across development phases. Finally, we exploit the failure management models to verify that a particular architecture meets its requirements under the stated failure hypothesis.
Cloud Computing and Privacy Regulations: an Exploratory Study on Issues and Implications

Cloud computing is a new paradigm in the world of Information Technology Advancement. Considerable amount of cloud computing technology is already being used and developed in various flavors. Cloud Computing affects people, process and technology of the enterprise. In spite of having benefits with Cloud computing paradigm such as efficiency, flexibility, easy set up and overall reduction in IT cost [22], cloud computing paradigm could raise privacy and confidentiality risks. “Not all types of cloud computing raise the same privacy and confidentiality risks. Some believe that much of the computing activity occurring today entirely on computers owned and controlled locally by users will shift to the cloud in the future”[11]. In Cloud computing, users connect to the CLOUD, which appears as a single entity as opposed to the traditional way of connecting to multiple servers located on company premises. Public Private Partnership these days is a usually adopted pattern of governance to meet the diverse needs of their citizens with confidence and providing quality of these services. Cloud Computing Technology can also act as a facilitator between public and private partnership. In such cases there is a possibility that an external party can be involved in providing Cloud Services having partial control over the data storage, processing and transmission of data and privacy regulations become relevant [20]. Cloud computing has significant implications for the privacy of personal information as well as for the confidentiality of business and governmental information. A survey by EDUCAUSE involving 372 of its member institutions revealed that a great proportion of the respondents with use cases that involved cloud-based services reported that data privacy risks and data security risks were among their top barriers to overcome [22]. A principal goal of this paper is to identify privacy and confidentiality issue that may be of interest and concern to cloud computing participants and users [11]. Thus this paper explores to elicit possible issues and regulations in the area of privacy that affect the implementation of Cloud Computing Technologies.
Correction: A Rapid Lateral Flow Immunoassay for the Detection of Tyrosine Phosphatase-Like Protein IA-2 Autoantibodies in Human Serum

Type 1 diabetes (T1D) results from the destruction of pancreatic insulin-producing beta cells and is strongly associated with the presence of islet autoantibodies. Autoantibodies to tyrosine phosphatase-like protein IA-2 (IA-2As) are considered to be highly predictive markers of T1D. We developed a novel lateral flow immunoassay (LFIA) based on a bridging format for the rapid detection of IA-2As in human serum samples. In this assay, one site of the IA-2As is bound to HA-tagged-IA-2, which is subsequently captured on the anti-HA-Tag antibody-coated test line on the strip. The other site of the IA-2As is bound to biotinylated IA-2, allowing the complex to be visualized using colloidal gold nanoparticle-conjugated streptavidin. For this study, 35 serum samples from T1D patients and 44 control sera from non-diabetic individuals were analyzed with our novel assay and the results were correlated with two IA-2A ELISAs. Among the 35 serum samples from T1D patients, the IA-2A LFIA, the in-house IA-2A ELISA and the commercial IA-2A ELISA identified as positive 21, 29 and 30 IA-2A-positive sera, respectively. The major advantages of the IA-2A LFIA are its rapidity and simplicity.
Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery

Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud (<1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ∼50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored. Remote Sens. 2012, 4 1574
Single Particle Tracking: From Theory to Biophysical Applications.

After three decades of developments, single particle tracking (SPT) has become a powerful tool to interrogate dynamics in a range of materials including live cells and novel catalytic supports because of its ability to reveal dynamics in the structure-function relationships underlying the heterogeneous nature of such systems. In this review, we summarize the algorithms behind, and practical applications of, SPT. We first cover the theoretical background including particle identification, localization, and trajectory reconstruction. General instrumentation and recent developments to achieve two- and three-dimensional subdiffraction localization and SPT are discussed. We then highlight some applications of SPT to study various biological and synthetic materials systems. Finally, we provide our perspective regarding several directions for future advancements in the theory and application of SPT.
Origin, Diversity, Expansion History, and Functional Evolution of the Plant Receptor-Like Kinase/ Pelle Family

The RLK/Pelle gene family is one of the largest gene families in plants with several hundred to more than a thousand members, but only a few family members exist in animals. This unbalanced distribution indicates a rather dramatic expansion of this gene family in land plants. In this chapter we review what is known about the RLK/Pelle family’s origin in eukaryotes, its domain content evolution, expansion patterns across plant and animal species, and the duplication mechanisms that contribute to its expansion. We conclude by summarizing current knowledge of plant RLK/Pelle functions for a discussion on the relative importance of neutral evolution and natural selection as the driving forces behind continuous expansion and innovation in this gene family.
Building a 3-D Line-Based Map Using Stereo SLAM

This paper presents a graph-based visual simultaneous localization and mapping (SLAM) system using straight lines as features. Compared with point features, lines provide far richer information about the structure of the environment and make it possible to infer spatial semantics from the map. Using a stereo rig as the sole sensor, our proposed system utilizes many advanced techniques, such as motion estimation, pose optimization, and bundle adjustment. We use two different representations to parameterize 3-D lines in this paper: Plücker line coordinates for efficient initialization of newly observed line features and projection of 3-D lines, and orthonormal representation for graph optimization. The proposed system is tested with indoor and outdoor sequences, and it exhibits better reconstruction performance against a point-based SLAM system in line-rich environments.
Searching Personnel Relationship from Myanmar census data using Graph database and Deductive Reasoning prolog rules

Searching relationship among persons is more and more growing importance to acquire related information, genealogical relationship and personnel history. In this paper, the framework with three portions is proposed for exploring peoples' relationship from their personnel information. The first portion focuses on storage structure to store data in Graph database by representing persons as nodes and their attributes as properties. There are no predefined relationships or edges between person nodes. The second part focuses on searching relationships among persons. Graph database searching algorithm called Personnel Relationship Searching Algorithm is proposed. The last portion proposes Deductive Reasoning Algorithm to define two persons' relationship based on search results of match domain (or) attributes using predefined prolog rules. For example, if the match domain is relatives, the relation may be consanguine, such as GrandFather, Eldest_Son, Youngest_Son_In_Law, Khame_Khamet, etc. If the match domain is organization (or) hobby (or) religion, the relation may work in same organization, etc.