nils-herrmann/clean_scirepeval huggingface dataset:
Design of a Real-Time Micro-Winch Controller for Kite-Power Applications Master ’ s Thesis in Embedded

Airborne wind energy is a technology to extract energy from high altitude winds. This technology is under heavy development by several companies and universities. An actual problem with the commercialization of the technology is the reliability and safety of the system. In this thesis a real time environment suitable to perform research and further development of the prototype steering and depower control is proposed. Additionally, the overload prevention of the kite lines is researched. This thesis presents a method to estimate the tension on the kite control tapes using only one tension sensor. Thus, reducing the amount of hardware needed to protect the kite from overloads. The method relies on the characterization of the powertrain efficiency and can be used to estimate the tensions at high loads. An algorithm to limit the forces on the steering lines by depowering the kite is shown; it controls the depower state of the kite based on the desired depower state, the actual tension, and previous tensions on the KCU’s tapes. The tensions history is used to calculate a higher depower state to prevent future overloads, this reduces the amount of action needed by the motors and enable the system to use a brake to save energy. The limiter output is used as an input to a position controller, which allows the project to use off the shelf solutions to build the KCU prototype. The controller was implemented in a real time system and is able to run as fast as 20 Hz being the communication protocol the execution time bottleneck. The control algorithms were tested using a mathematical model of the kite, the environment, and trajectory control inputs from FreeKiteSim. Three scenarios were considered for the model test, normal operation, overload operation without tension limitation, and overload operation with tension limitation. The apparent wind speed during the reel out phase of the normal scenario is approximately 30 m/s and 35 m/s for the overload scenarios. During the overload scenario the limiter spent roughly 22% more energy than the normal operation scenario to counteract an increase of 5 m/s in the apparent wind during 3.5 hours of operation, but it spent 15% less energy than the overload scenario without tension limitation.
Leveraging social media networks for classification

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors’ latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.
A paradigmatic and methodological examination of knowledge management research: 2000 to 2004

Knowledge management (KM) research has been evolving for more than a decade, yet little is known about KM theoretical perspectives, research paradigms, and research methods. This paper explores KM research in influential journals for the period 2000–2004. A total of 160 KM articles in ten top-tier information systems and management journals are analyzed. Articles that may serve as useful exemplars of KM research from positivist, interpretivist, and critical pluralist paradigms are selected. We find that KM research in information systems journals differs from that in management journals, but neither makes a balanced use of positivist and non-positivist research approaches.
2-MHz GaN PWM isolated SEPIC converters

The eGaN HEMTs with ZVS technique have emerged in high frequency converters to reduce the switching loss. This paper investigates an eGaN HEMT ZVS isolated SEPIC converter running up to 2 MHz. Furthermore, an inductor optimization method is proposed to ensure ZVS and reduce reverse conduction loss of the SR. The efficiency curve is optimized based on the trade-off method between the switching and conduction losses. Moreover, the embedded inductor is proposed to reduce the conduction loss of the inductor. Finally, two 2 MHz eGaN HEMT prototypes with the commercial and embedded inductor with 18–36 V input and 5 V/2 A output are built respectively to verify the effectiveness of the proposed methods. The converters achieve a peak efficiency of over 88.2% and power density of 52.9 W/in3, outperforming the state-of-art power module products.
Classification of Five Ambulatory Activities Regarding Stair and Incline Walking Using Smart Shoes

In this paper, we propose a novel method to classify five ambulatory activities, i.e., level ground, incline descent, incline ascent, stair descent, and stair ascent walking using smart shoes which contain eight plantar-pressure sensors on each shoe. Pressure data are collected using an insole-based monitoring system regarding the walking activities conducted by participants at their self-imposed “normal” speed. We present three new features based on an analysis of step patterns to characterize the ambulatory activities and utilize a k-nearest neighbor algorithm to classify the activities from the created features. In experimental results, we obtain walking activity-recognition error rates of 2.16% at the sixth walking step. Furthermore, a proposed method outperforms two reference methods in terms of F1-score and overall accuracy rate.
Distribution-Aware Binarization of Neural Networks for Sketch Recognition

Deep neural networks are highly effective at a range of computational tasks. However, they tend to be computationally expensive, especially in vision-related problems, and also have large memory requirements. One of the most effective methods to achieve significant improvements in computational/spatial efficiency is to binarize the weights and activations in a network. However, naive binarization results in accuracy drops when applied to networks for most tasks. In this work, we present a highly generalized, distribution-aware approach to binarizing deep networks that allows us to retain the advantages of a binarized network, while reducing accuracy drops. We also develop efficient implementations for our proposed approach across different architectures. We present a theoretical analysis of the technique to show the effective representational power of the resulting layers, and explore the forms of data they model best. Experiments on popular datasets show that our technique offers better accuracies than naive binarization, while retaining the same benefits that binarization provides - with respect to run-time compression, reduction of computational costs, and power consumption.
Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data

http://en.wikipedia.org/wiki/The_Dangerous_Days_of_Daniel_X http://en.wikipedia.org/wiki/The_Dangerous_Days_of_Daniel_X http://www.amazon.com/The-Dangerous-Days-Daniel-X/dp/0316119709 http://fanon.wikia.com/wiki/The_Dangerous_Days_of_Daniel_X http://www.goodreads.com/book/show/2235597.The_Dangerous_Days_of_Daniel_X http://www.amazon.com/The-Dangerous-Days-Daniel-X/dp/0316119709 http://www.daniel-x.co.uk/books/dangerous-days/ http://danielx.wikia.com/wiki/The_Dangerous_Days_of_Daniel_X_(novel) http://www.freebooknotes.com/summaries-analysis/the-dangerous-days-of-daniel-x/ http://danielx.wikia.com/wiki/Daniel_X http://www.jamespatterson.com/books_danielX.php#.VCR0vOfUe1A http://www.goodreads.com/series/49946-daniel-x http://jamespatterson.com/books_daniel_x.php#.VCR01efUe1A http://en.wikipedia.org/wiki/Daniel_X:_Watch_the_Skies http://books.google.com/books/about/The_Dangerous_Days_of_Daniel_X.html? id=2UBONTvr_BEC http://www.jamespatterson.com/books_danielX.php
Entropy based community detection in augmented social networks

Social network analysis has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the social network analysis is the community detection problem, which can be seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing and annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity and an entropy-based data clustering algorithm, which tries to find a partition with low entropy and keeping in mind the modularity.
The Evolution of the Peer-to-Peer File Sharing Industry and the Security Risks for Users

Peer-to-peer file sharing is a growing security risk for firms and individuals. Users who participate in these networks to share music, pictures, and video are subject to many security risks including inadvertent publishing of private information, exposure to viruses and worms, and the consequences of spyware. In this paper, we examine the peer-to-peer file sharing phenomena, including an overview of the industry, its business models, and evolution. We describe the information security risks users' face including personal identification disclosure and leakage of proprietary business information. We illustrate those risks through honey-pot experiments and discuss how peer-to-peer industry dynamics are contributing to the security problem.
Confabulation based sentence completion for machine reading

Sentence completion and prediction refers to the capability of filling missing words in any incomplete sentences. It is one of the keys to reading comprehension, thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the human information processing. The building of confabulation knowledge base uses an unsupervised machine learning algorithm that extracts the relations between objects at the symbolic level. In this work, we propose performance improved training and recall algorithms that apply the cogent confabulation model to solve the sentence completion problem. Our training algorithm adopts a two-level hash table, which significantly improves the training speed, so that a large knowledge base can be built at relatively low computation cost. The proposed recall function fills missing words based on the sentence context. Experimental results show that our software can complete trained sentences with 100% accuracy. It also gives semantically correct answers to more than two thirds of the testing sentences that have not been trained before.
Performance Analysis of RFID Protocols: CDMA Versus the Standard EPC Gen-2

Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique identification (ID) number. RFID tags can be categorized into passive and active tags. For passive tags, a standard communication protocol known as EPC-global Generation-2, or briefly EPC Gen-2, is currently in use. RFID systems are prone to transmission collisions due to the shared nature of the wireless channel used by tags. The EPC Gen-2 standard recommends using dynamic framed slotted ALOHA technique to solve the collision issue and to read the tag IDs successfully. Recently, some researchers have suggested to replace the dynamic framed slotted ALOHA technique used in the standard EPC Gen-2 protocol with the code division multiple access (CDMA) technique to reduce the number of collisions and to improve the tag identification procedure. In this paper, the standard EPC Gen-2 protocol and the CDMA-based tag identification schemes are modeled as absorbing Markov chain systems. Using the proposed Markov chain systems, the analytical formulae for the average number of queries and the total number of transmitted bits needed to identify all tags in an RFID system are derived for both the EPC Gen-2 protocol and the CDMA-based tag identification schemes. In the next step, the performance of the EPC Gen-2 protocol is compared with the CDMA-based tag identification schemes and it is shown that the standard EPC Gen-2 protocol outperforms the CDMA-based tag identification schemes in terms of the number of transmitted bits and the average time required to identify all tags in the system.
A compact association of particle filtering and kernel based object tracking

Particle filtering (PF) and kernel based object tracking (KBOT) algorithms have shown their promises in a wide range of visual tracking contexts. This paper mainly addresses the association of PF and KBOT. Unlike other related association approaches which usually directly use KBOT to refine the position states of propagated particles for more accurate mode seeking, we elucidate the problem of what kind of particles is suitable for employing KBOT to refine their position states from a theoretical point of view. In accordance with the theoretical analysis, a two-stage solution is also proposed to resample propagated particles that are suitable for invoking KBOT from a computational perspective. The incremental Bhattacharyya dissimilarity (IBD) based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background, while the matrix condition number based stage is formulated to further eliminate the particles positioned at the ill-posed conditions for running KBOT. Once the appropriate particles are obtained, constrained gradient based mean shift optimization enables us to efficiently refine the particles’ position states. Besides, a state transition model embodying object-scale oriented information and prior motion cues is presented to adapt to fast movement scenarios. These ingredients lead to a new tracking algorithm. Experiments demonstrate that the proposed association approach is more robust to handle complex tracking conditions in comparison with related methods. Also, a limited number of particles are used in our association algorithm to maintain multiple hypotheses. & 2012 Elsevier Ltd. All rights reserved.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.
Nurse Managers’ Emotional Intelligence and Effective Leadership: A Review of the Current Evidence

Emotional Intelligence has made a significant contribution to effective leadership, becoming one of the key characteristics of leaders. The aim of the present study was to review qualitative and quantitative studies concerning Emotional Intelligence of nurse leaders and the evidence-based composition of their results.



A search was performed in the electronic databases (Pubmed, Scopus and CINAHL) for articles, which were published in the period 2000-2017 in English or Greek. Eleven studies met the inclusion criteria, of which 10 were quantitative and one was qualitative.



The results suggested that Emotional Intelligence is a useful tool for nurse leaders and contributes decisively to the achievement of effective management in healthcare.



It is necessary for nurses to improve their social and emotional skills because of the particular nature of the nursing profession, which places the healthy or weak person at its center.
Microcephaly and early-onset nephrotic syndrome —confusion in Galloway-Mowat syndrome

We report a 2-year-old girl with nephrotic syndrome, microcephaly, seizures and psychomotor retardation. Histological studies of a renal biopsy revealed focal glomerular sclerosis with mesangiolysis and capillary microaneurysms. Dysmorphic features were remarkable: abnormal-shaped skull, coarse hair, narrow forehead, large low-set ears, almond-shaped eyes, low nasal bridge, pinched nose, thin lips and micrognathia. Cases with this rare combination of microcephaly and early onset of nephrotic syndrome with various neurological abnormalities have been reported. However, clinical manifestations and histological findings showed a wide variation, and there is a lot of confusion in this syndrome. We therefore reviewed the previous reports and propose a new clasification of this syndrome.
Transformer Health Condition Monitoring Through GSM Technology

. Transformers are a vital part of the transmission and distribution system. Monitoring transformers for problems before they occur can prevent faults that are costly to repair and result in a loss of service. Current systems can provide information about the state of a transformer, but are either offline or very expensive to implement. Transformers being the essential part of power transmission system are expensive, as is the cost of power interruptions. Because of the cost of scheduled and unscheduled maintenance, especially at remote sites, the utility industry has begun investing in instrumentation and monitoring of transformer. On-line transformer diagnostics using conventional technologies like carrier power line communication, Radio frequency based control system, and Supervisory control and data acquiring systems, Distributed control systems and Internet based communications are having their own limitations. GSM is an open, digital cellular technology used for transmitting mobile voice and data services. This project objective is to develop low cost solution for monitoring health condition of remotely located distribution transformers using GSM technology to preent premature failure of distribution transformers and improving reliability of services to the customers. An Embedded based hardware design is developed to acquire data from electrical sensing system. It consists of a sensing system, signal conditioning electronic circuits, advanced embedded hardware for middle level computing, a powerful computer network for further transmission of data to various places. A powerful GSM networking is designed to send data from a network to other network for proper corrective action at the earliest. Any change in parameters of transmission is sensed to protect the entire transmission and distribution. The performance of prototype model developed is tested at laboratory for monitoring various parameters like transformer over load, voltage fluctuations, over temperature, oil quality and level etc.
Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets

The detection and identification of extreme weather events in large scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, there are many different types of spatially localized climate patterns of interest (including hurricanes, extra-tropical cyclones, weather fronts, blocking events, etc.) found in simulation data for which labeled data is not available at large scale for all simulations of interest. We present a multichannel spatiotemporal encoder-decoder CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. This architecture is designed to fully model multichannel simulation data, temporal dynamics and unlabelled data within a reconstruction and prediction framework so as to improve the detection of a wide range of extreme weather events. Our architecture can be viewed as a 3D convolutional autoencoder with an additional modified one-pass bounding box regression loss. We demonstrate that our approach is able to leverage temporal information and unlabelled data to improve localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data, and facilitate further work in understanding and mitigating the effects of climate change.
Finding surprising patterns in a time series database in linear time and space

The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of "surprise", and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these limitations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data.