nils-herrmann/clean_scirepeval huggingface dataset:
Usability of E-learning tools

The new challenge for designers and HCI researchers is to develop software tools for effective e-learning. Learner-Centered Design (LCD) provides guidelines to make new learning domains accessible in an educationally productive manner. A number of new issues have been raised because of the new "vehicle" for education. Effective e-learning systems should include sophisticated and advanced functions, yet their interface should hide their complexity, providing an easy and flexible interaction suited to catch students' interest. In particular, personalization and integration of learning paths and communication media should be provided.It is first necessary to dwell upon the difference between attributes for platforms (containers) and for educational modules provided by a platform (contents). In both cases, it is hard to go deeply into pedagogical issues of the provided knowledge content. This work is a first step towards identifying specific usability attributes for e-learning systems, capturing the peculiar features of this kind of applications. We report about a preliminary users study involving a group of e-students, observed during their interaction with an e-learning system in a real situation. We then propose to adapt to the e-learning domain the so called SUE (Systematic Usability Evaluation) inspection, providing evaluation patterns able to drive inspectors' activities in the evaluation of an e-learning tool.
Wide-band & high efficiency small cell PA characterized by dual band DPD for LTE carrier aggregation application

This paper reports on a 27.5 dBm wide band high efficiency InGaP/GaAs HBT two stage module operating at 1805 to 2170MHz for LTE small cell base station carrier aggregation applications. It uses a high linearity, low thermal resistance HBT flip chip process developed by Qorvo. The Psat of this device reaches 35dBm, with a gain of around 31dB, and a PAE of 55%. With a DPD (digital pre-distortion) system, this power amplifier can achieve an ACLR of -50dBc at 27.5dBm Pout with a power added efficiency of 30% for a 20MHz LTE modulation signal (PAR=7.5dBc) at 1.96GHz. Tested with dual band DPD system with 1.84GHz and 2.14GHz input signals and a total 40MHz bandwidth, this PA can deliver Pout 23.3dBm for each carrier with ACPR around -50dBc each, total efficiency of about 26%. To the best of authors knowledge, this is the first wide band small cell PA characterized by dual band DPD system.
Toward locality-aware scheduling for containerized cloud services

The state-of-the-art scheduler of containerized cloud services considers load-balance as the only criterion and neglects many others such as application performance. In the era of Big Data, however, applications have evolved to be highly data-intensive thus perform poorly in existing systems. This particularly holds for Platform-as-a-Service environments that encourage an application model of stateless application instances in containers reading and writing data to services storing states, e.g., key-value stores. To this end, this work strives to improve today's cloud services by incorporating sensitivity to both load-balance and application performance. We built and analyzed theoretical models that respect both dimensions, and unlike prior studies, our model abstracts the dilemma between load-balance and application performance into an optimization problem and employs a statistical method to meet the discrepant requirements. Using heuristic algorithms and approaches we try to solve the abstracted problems. We implemented the proposed approach in Diego (an open-source cloud service scheduler) and demonstrate that it can significantly boost the performance of containerized applications while preserving a relatively high load-balance.
Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks

Human gesture recognition is one of the central research fields of computer vision, and effective gesture recognition is still challenging up to now. In this paper, we present a pyramidal 3D convolutional network framework for large-scale isolated human gesture recognition. 3D convolutional networks are utilized to learn the spatiotemporal features from gesture video files. Pyramid input is proposed to preserve the multi-scale contextual information of gestures, and each pyramid segment is uniformly sampled with temporal jitter. Pyramid fusion layers are inserted into the 3D convolutional networks to fuse the features of pyramid input. This strategy makes the networks recognize human gestures from the entire video files, not just from segmented clips independently. We present the experiment results on the 2016 ChaLearn LAP Large-scale Isolated Gesture Recognition Challenge, in which we placed third.
A Versatile Power Electronics Test-Bench Architecture Applied to Domestic Induction Heating

The design of new power-converter solutions optimized for specific applications requires, at a certain step, the design and implementation of several prototypes in order to verify the converter operation. This is a time-consuming task which also involves a significant economical cost. The aim of this paper is to present a versatile power electronics architecture which provides a tool to make the implementation and evaluation of new power converters straightforward. The adopted platform includes a versatile control architecture and a modular power electronics hardware solution. The control architecture is a field-programmable-gate-array-based system-on-programmable-chip solution which combines the advantages of the processor-firmware versatility and the effectiveness of ad hoc paralleled digital hardware. Moreover, the modular power electronics hardware provides a fast method to reconfigure the power-converter topology. The architecture proposed in this paper has been applied to the development of power converters for domestic induction heating, although it can be extended to other applications with similar requirements. A complete development test bench has been carried out, and some experimental results are shown in order to verify the proper system operation.
Single Image Dehazing Using Color Attenuation Prior

In this paper, we propose a simple but powerful prior, color attenuation prior, for haze removal from a single input hazy image. By creating a linear model for modelling the scene depth of the hazy image under this novel prior and learning the parameters of the model by using a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily remove haze from a single image. Experimental results show that the proposed approach is highly efficient and it outperforms state-of-the-art haze removal algorithms in terms of the dehazing effect as well.
A Comparitive Survey of ANN and Hybrid HMM/ANN Architectures for Robust Speech Recognition

This paper proposes two hybrid connectionist structural acoustical models for robust context independent phone like and word like units for speaker-independent recognition system. Such structure combines strength of Hidden Markov Models (HMM) in modeling stochastic sequences and the non-linear classification capability of Artificial Neural Networks (ANN). Two kinds of Neural Networks (NN) are investigated: Multilayer Perceptron (MLP) and Elman Recurrent Neural Networks (RNN). The hybrid connectionist-HMM systems use discriminatively trained NN to estimate the a posteriori probability distribution among subword units given the acoustic observations. We efficiently tested the performance of the conceived systems using the TIMIT database in clean and noisy environments with two perceptually motivated features: MFCC and PLP. Finally, the robustness of the systems is evaluated by using a new preprocessing stage for denoising based on wavelet transform. A significant improvement in performance is obtained with the proposed method.
PTDF-based power system equivalents

The large size of power systems makes behavioral analysis of electricity markets computationally taxing. Reducing the system into a smaller equivalent, based on congestion zones, can substantially reduce the computational requirements. In this paper, we propose a scheme to determine the equivalent reactance of interfaces of a reduced system based upon the zonal power transfer distribution factors of the original system. The dc power flow model is used to formulate the problem. Test examples are provided using both an illustrative six-bus system and a more realistically sized 12 925-bus system.
3.3kW on board charger for electric vehicle

In this paper 3.3 kW on-board charger is proposed for electric vehicles or plug-in hybrid electric vehicles. Considering the life time of charger, new topology which is not using the high-voltage electrolytic capacitors is proposed. This charger is comprised of two stages that one is boost converter stage for PFC control and the other is uncontrolled LLC resonant converter for galvanic isolation and step-down of voltage. Proposed charger is implemented and it is 7.1 liters in volume and 6.8kg in weight. The experimental result indicates 92.5% of the efficiency at input voltage 220Vac and 88.3% at input voltage 110Vac under 3.3kW of output power. And the output voltage ranges is from 150V to 450V.
Design and Characterization of Soft Actuator for Hand Rehabilitation Application

In biomedical application, conventional hard robots have been widely used for a long time. However, when they come in contact with human body especially for rehabilitation purposes, the hard and stuff nature of the robots have received a lot of drawbacks as they interfere with movement. Recently, soft robots are drawing attention due to their high customizability and compliance, especially soft actuators. In this paper, we present a soft pneumatic bending actuator and characterize the performance of the actuator such as radius of curvature and force output during actuation. The characterization was done by a simple measurement system that we developed. This work serves as a guideline for designing soft bending actuators with application-specific requirements, for example, soft exoskeleton for rehabilitation.  Soft Robots, Actuators, Wearable, Hand Exoskeleton, Rehabilitation.
Automatic generation of ETL processes from conceptual models

Data warehouses (DW) integrate different data sources in order to give a multidimensional view of them to the decision-maker. To this aim, the ETL (Extraction, Transformation and Load) processes are responsible for extracting data from heterogeneous operational data sources, their transformation (conversion, cleaning, standardization, etc.), and its load in the DW. In recent years, several conceptual modeling approaches have been proposed for designing ETL processes. Although these approaches are very useful for documenting ETL processes and supporting the designer tasks, these proposals fail to give mechanisms to carry out an automatic code generation stage. Such a stage should be required to both avoid fails and save development time in the implementation of complex ETL process. Therefore, in this paper we define an approach for the automatic code generation of ETL processes. To this aim, we align the modeling of ETL processes in DW with MDA (Model Driven Architecture) by formally defining a set of QVT (Query, View, Transformation) transformations.
Unscented Kalman filter design for curvilinear motion models suitable for automotive safety applications

Research in automotive safety leads to the conclusion that modern vehicle should utilize active and passive sensors for the recognition of the environment surrounding them. Thus, the development of tracking systems utilizing efficient state estimators is very important. In this case, problems such as moving platform carrying the sensor and maneuvering targets could introduce large errors in the state estimation and in some cases can lead to the divergence of the filter. In order to avoid sub-optimal performance, the unscented Kalman filter is chosen, while a new curvilinear model is applied which takes into account both the turn rate of the detected object and its tangential acceleration, leading to a more accurate modeling of its movement. The performance of the unscented filter using the proposed model in the case of automotive applications is proven to be superior compared to the performance of the extended and linear Kalman filter.
A Wide-Band CMOS LC VCO With Linearized Coarse Tuning Characteristics

A pseudo-exponential capacitor bank structure is proposed to implement a wide-band CMOS LC voltage-controlled oscillator (VCO) with linearized coarse tuning characteristics. An octave bandwidth VCO employing the proposed 6-bit pseudo-exponential capacitor bank structure has been realized in 0.18-mum CMOS. Compared to a conventional VCO employing a binary weighted capacitor bank, the proposed VCO has considerably reduced the variations of the VCO gain (K VCO) and the frequency step per a capacitor bank code (f step/code) by 2.7 and 2.1 times, respectively, across the tuning range of 924-1850 MHz. Measurement results have also shown that the VCO provides the phase noise of - 127.1 dBc/Hz at 1-MHz offset for 1.752-GHz output frequency while dissipating 6 mA from a 1.8-V supply.
Understanding dynamic capabilities through knowledge management

This paper sets out to integrate research on knowledge management with the dynamic capabilities approach. This paper will add to the understanding of dynamic capabilities by demonstrating that dynamic capabilities can be seen as composed of concrete and well-known knowledge management activities.  This paper is based on a literature review focusing on key knowledge management processes and activities as well as the concept of dynamic capabilities, the paper connects these two approaches. The analysis is centered on knowledge management activities which then are compiled into dynamic capabilities.  In the paper eight knowledge management activities are identified; knowledge creation, acquisition, capture, assembly, sharing, integration, leverage, and exploitation. These activities are assembled into the three dynamic capabilities of knowledge development, knowledge (re)combination, and knowledge use. The dynamic capabilities and the associated knowledge management activities create flows to and from the firm’s stock of knowledge and they support the creation and use of organizational capabilities.  The findings in the paper demonstrate that the somewhat elusive concept of dynamic capabilities can be untangled through the use of knowledge management activities. Practicing managers struggling with the operationalization of dynamic capabilities should instead focus on the contributing knowledge management activities in order to operationalize and utilize the concept of dynamic capabilities.  The paper demonstrates that the existing research on knowledge management can be a key contributor to increasing our understanding of dynamic capabilities. This finding is valuable for both researchers and practitioners.
Biomechanical Design of a Powered Ankle-Foot Prosthesis

Although the potential benefits of a powered ankle-foot prosthesis have been well documented, no one has successfully developed and verified that such a prosthesis can improve amputee gait compared to a conventional passive-elastic prosthesis. One of the main hurdles that hinder such a development is the challenge of building an ankle-foot prosthesis that matches the size and weight of the intact ankle, but still provides a sufficiently large instantaneous power output and torque to propel an amputee. In this paper, we present a novel, powered ankle-foot prosthesis that overcomes these design challenges. The prosthesis comprises an unidirectional spring, configured in parallel with a force-controllable actuator with series elasticity. With this architecture, the ankle-foot prosthesis matches the size and weight of the human ankle, and is shown to be satisfying the restrictive design specifications dictated by normal human ankle walking biomechanics.
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment

Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.
Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations

Much attention is currently being paid in both the academic and practitioner literatures to the value that organisations could create through the use of big data and business analytics (Gillon et al, 2012; Mithas et al, 2013). For instance, Chen et al (2012, p. 1166–1168) suggest that business analytics and related technologies can help organisations to ‘better understand its business and markets’ and ‘leverage opportunities presented by abundant data and domain-specific analytics’. Similarly, LaValle et al (2011, p. 22) report that topperforming organisations ‘make decisions based on rigorous analysis at more than double the rate of lower performing organisations’ and that in such organisations analytic insight is being used to ‘guide both future strategies and day-to-day operations’. We argue here that while there is some evidence that investments in business analytics can create value, the thesis that ‘business analytics leads to value’ needs deeper analysis. In particular, we argue here that the roles of organisational decision-making processes, including resource allocation processes and resource orchestration processes (Helfat et al, 2007;  2009), need to be better understood in order to understand how organisations can create value from the use of business analytics. Specifically, we propose that the firstorder effects of business analytics are likely to be on decision-making processes and that improvements in organisational performance are likely to be an outcome of superior decision-making processes enabled by business analytics. This paper is set out as follows. Below, we identify prior research traditions in the Information Systems (IS) literature that discuss the potential of data and analytics to create value. This is to put into perspective the current excitement around ‘analytics’ and ‘big data’, and to position those topics within prior research traditions. We then draw on a number of existing literatures to develop a research agenda to understand the relationship between business analytics, decision-making processes and organisational performance. Finally, we discuss how the three papers in this Special Issue advance the research agenda. Disciplines Engineering | Science and Technology Studies Publication Details Sharma, R., Mithas, S. and Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations.
A Bottom Fed Deployable Conical Log Spiral Antenna Design for CubeSat

A conical log spiral antenna is presented with a new feeding scheme. This antenna is proposed for deployment on a CubeSat platform where the new feeding technique allows for easier antenna deployment. The antenna is composed of two arms that are wrapped around each other in a log-periodic manner. It is designed on top of a ground plane allowing its bottom feeding property. The feeding network is composed of quarter-wavelengths transmission lines connected to a planar balun. The feeding network along with the balun provides the antenna with the appropriate impedance matching and phase shift between the two arms. The antenna is fabricated and tested. The measurement and simulation results show good agreement.
Multiple-Instance Video Segmentation with Sequence-Specific Object Proposals

We present a novel approach to video segmentation which won the 4th place in DAVIS challenge 2017. The method has two main components: in the first part we extract video object proposals from each frame. We develop a new algorithm based on one-shot video segmentation (OSVOS) algorithm to generate sequence-specific proposals that match to the human-annotated proposals in the first frame. This set is populated by the proposals from fully convolutional instance-aware image segmentation algorithm (FCIS). Then, we use the segment proposal tracking (SPT) algorithm to track object proposals in time and generate the spatio-temporal video object proposals. This approach learns video segments by bootstrapping them from temporally consistent object proposals, which can start from any frame. We extend this approach with a semi-Markov motion model to provide appearance motion multi-target inference, backtracking a segment started from frame T to the 1st frame, and a ”re-tracking” capability that learns a better object appearance model after inference has been done. With a dense CRF refinement method, this model achieved 61.5% overall accuracy in DAVIS challenge 2017.
FLIC: Fast linear iterative clustering with active search

In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called “active search” which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering (SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence, and also provides better boundaries in the over-segmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest, achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available.
Interpolation of Spatial Data: Some Theory for Kriging

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