nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing Control: Moving Beyond Multiple Choice

Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) continue to rise in popularity. Unfortunately, workers come with hugely varied skill sets and motivation levels. Ensuring high quality results is a serious challenge for all requesters. A key drawback of prior decision-theoretic approaches (Dai, Mausam, and Weld 2010; 2011) to quality control is the restriction to multiple choice questions, i.e., jobs where every alternative answer is known in advance. While many tasks can be formulated in a multiple-choice fashion (e.g. nary classification), there are a large number of tasks with an unbounded number of possible answers. A common example is completing a database with workers’ help, e.g., asking questions such as “Find the mobile phone number of Acme Corporation’s CEO.” Unfortunately, adapting multiple-choice models for these scenarios is not straightforward, because of the difficulty with reasoning about unknown answers. Requesters, therefore, must resort to using a majority-vote, a significant hindrance to achieving quality results (Dai, Mausam, and Weld 2010; 2011; Whitehill et al. 2009). Our paper tackles this challenging problem. We first create a probabilistic, generative model for tasks where workers are free to give any answer. We then present a decision-theoretic controller, LAZYSUSAN, that uses our model to dynamically infer answers to these tasks, and finally show that it obtains a better costquality tradeoff compared to an agent that uses majorityvoting.
PFC Cuk Converter-Fed BLDC Motor Drive

This paper deals with a power factor correction (PFC)-based Cuk converter-fed brushless dc motor (BLDC) drive as a cost-effective solution for low-power applications. The speed of the BLDC motor is controlled by varying the dc-bus voltage of a voltage source inverter (VSI) which uses a low frequency switching of VSI (electronic commutation of the BLDC motor) for low switching losses. A diode bridge rectifier followed by a Cuk converter working in a discontinuous conduction mode (DCM) is used for control of dc-link voltage with unity power factor at ac mains. Performance of the PFC Cuk converter is evaluated under four different operating conditions of discontinuous and continuous conduction modes (CCM) and a comparison is made to select a best suited mode of operation. The performance of the proposed system is simulated in a MATLAB/Simulink environment and a hardware prototype of the proposed drive is developed to validate its performance over a wide range of speed with unity power factor at ac mains.
The anatomy of the posterior aspect of the knee. An anatomic study.

The orthopaedic literature contains relatively little quantitative information regarding the anatomy of the posterior aspect of the knee. The purpose of the present study was to provide a detailed description of, and to propose a standard nomenclature for, the anatomy of the posterior aspect of the knee.



Detailed dissection of twenty nonpaired, fresh-frozen knees was performed. Posterior knee structures were measured according to length, width, and/or distance to reproducible osseous landmarks.



The semimembranosus tendon had eight attachments distal to the main common tendon. The main components were a lateral expansion to the oblique popliteal ligament; a direct arm, which attached to the tibia; and an anterior arm. The oblique popliteal ligament, the largest posterior knee structure, formed a broad fascial sheath over the posterior aspect of the knee and measured 48.0 mm in length and 9.5 mm wide at its medial origin and 16.4 mm wide at its lateral attachment. It had two lateral attachments, one to the meniscofemoral portion of the posterolateral joint capsule and one to the tibia, along the lateral border of the posterior cruciate ligament facet. The semimembranosus also had a distal tibial expansion, which formed a posterior fascial layer over the popliteus muscle. A thickening of the posterior joint capsule, the proximal popliteus capsular expansion, which in this study averaged 40.5 mm in length, connected the posteromedial knee capsule at its attachment at the intercondylar notch to the medial border of the popliteus musculotendinous junction. The plantaris muscle, popliteofibular ligament, fabellofibular ligament, and semimembranosus bursa were present in all specimens.



The anatomy of the posterior aspect of the knee is quite complex. This study provides information that can lead to further biomechanical, radiographic imaging, and clinical studies of the importance of these posterior knee structures.
Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks

We present a deep learning approach for speeding up constrained procedural modeling. Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks: these networks control how the model makes random choices based on what output it has generated thus far. We call such a model a neurally-guided procedural model. As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC. They are then used as efficient importance samplers for SMC, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems G.3 [Probability And Statistics]: Probabilistic algorithms (including Monte Carlo) I.2.6 [Artificial Intelligence]: Learning—Connectionism and neural nets
Agents of Things (AoT): An intelligent operational concept of the Internet of Things (IoT)

In this conceptual paper, we review the definitions, characteristics, and architecture of the Internet of Things (IoT) concept. We then identify the deficiencies of the IoT concept, analyze them, and discover the issue of lack of reasoning and intelligence in the IoT concept. We propose a solution to augment the IoT with intelligent software agents resulting in a new concept called the Agents of Things (AoT). The paper presents the AoT architecture and a scenario for one application of the proposed concept. Finally, it discusses the benefits of implementing AoT concept to solve real world issues and the future work.
Multi-part Representation Learning For Cross-domain Web Content Classification Using Neural Networks

Owing to the tremendous increase in the volume and variety of user generated content, train-once-apply-forever models are insufficient for supervised learning tasks. The need is to develop algorithms that can adapt across domains by leveraging labeled data from source domain(s) and efficiently perform the task in the unlabeled target domain. Towards this, we present a novel two-stage neural network learning algorithm for domain adaptation which learns a multi-part hidden layer where individual parts contribute differently to the tasks in source and target domains. The multiple parts of the representation (i.e. hidden layer) are learned while being cognizant of what characteristics to transfer across domains and what to preserve within domains for enhanced performance. The first stage embroils around learning a two-part representation i.e. source specific and common representations in a manner such that the former do not detract the ability of the later to represent the target domain. In the second stage, the generalized common representation is further iteratively extended with discriminating target specific characteristics to adapt to the target domain. We empirically demonstrate that the learned representations, in different arrangements, outperform existing domain adaptation algorithms in the source classification as well as the cross-domain classification tasks on the user generated content from different domains on the web.
Information Theoretic Learning with Infinitely Divisible Kernels

In this paper, we develop a framework for information theore tic l arning based on infinitely divisible matrices. We formulate an entropy-lik e functional on positive definite matrices based on Renyi’s axiomatic definition of en tropy and examine some key properties of this functional that lead to the conce pt of infinite divisibility. The proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel Hilbe rt spaces. As an application example, we derive a supervised metric learning algo rithm using a matrix based analogue to conditional entropy achieving results co mparable with the state of the art.
Ubiquitous Information Systems – Understanding privacy concerns

Ubiquitous information systems (UBIS) adapt current Information System thinking to explicitly differentiate technology between hardware devices and software components. An unfolding vision of vast numbers of computing devices becoming a pervasive part of our everyday lives in underway as more routine activities move into the realm of information and communication technology (ICT). Customer loyalty smart card tracking, mobile and smart phone application, wireless MP3 players, intelligent key cards, close circuit television cameras, motion sensors, electronic passports and RFID cards are some of the frequently used ubiquitous devices that handle personal information about their owners and of which a typical average consumer could own more than one of them. This research paper investigates personal privacy issues confronting ubiquitous system users with the aim of constructing a framework that can help designers of such systems to better protect the personal privacy of the users of these systems through the integration of certain design concepts suggested by the framework into their design processes. Ten selected users of ubiquitous devices were interviewed, focusing on issue around the misunderstanding of some personal privacy concepts relating to their ubiquitous devices and locations of use. Interview responses were transcribed into electronic format and analyzed using grounded theory analysis and microcoding techniques. The grounded theory analysis led to the identification of five concepts: Scope of potential disclosure of information, Scope of actual disclosure of information, Complexity of configuration, Top level control mechanism and integration of existing practices.
Design and Analysis of DC Solid-State Circuit Breakers Using SiC JFETs

Protection against short-circuit faults remains a major technical challenge in increasingly popular dc power networks. This paper describes a new concept of a self-powered dc solid-state circuit breaker (SSCB) with one or more normally on SiC JFETs as the main static switch and a fast-starting isolated dc/dc converter as the protection driver. The new SSCB detects short-circuit faults by sensing its terminal voltage rise and draws power from the fault condition itself to turn and hold off the SiC switch. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. A low-power isolated dc/dc converter is designed and optimized to provide a fast reaction to a short-circuit fault. Unidirectional and bidirectional SSCB prototypes based on this design concept have been built. Repeated interruption of fault currents up to 180 A at a dc bus voltage of 400 V within 0.8 μs was experimentally demonstrated. DC circuit protection applications provide a unique market opportunity for wide-bandgap semiconductors, which are outside the conventional focus on power electronic converters.
Analyses of Static and Dynamic Random Offset Voltages in Dynamic Comparators

When mismatches are present in a dynamic comparator, due to internal positive feedback and transient response, it is always challenging to analytically predict the input-referred random offset voltages since the operating points of transistors are time varying. In this paper, a novel balanced method is proposed to facilitate the evaluation of operating points of transistors in a dynamic comparator. Thus, it becomes possible to obtain an explicit expression for offset voltage in dynamic comparators. We include two types of mismatches in the model: 1) static offset voltages from the mismatch in muCox and threshold voltage Vth and 2) dynamic offset voltage due to the mismatch in the parasitic capacitances. From the analytical models, designers can obtain an intuition about the main contributors to offset and also fully explore the tradeoffs in dynamic comparator design, such as offset voltage, area and speed. To validate the balanced method, two topologies of dynamic comparator implemented in 0.25-mum and 40-nm CMOS technology are applied as examples. Input-referred offset voltages are first derived analytically based on SPICE Level 1 model, whose values are compared with more accurate Monte Carlo transient simulations using a sophisticated BSIM3 model. A good agreement between those two verifies the effectiveness of the balanced method. To illustrate its potential, the explicit expressions of offset voltage were applied to guide the optimization of ldquoLewis-Grayrdquo structure. Compared to the original design, the input offset voltage was easily reduced by 41% after the optimization while maintaining the same silicon area.
Order patterns recurrence plots in the analysis of ERP data

Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.
Neural Adaptive controller for Magnetic levitation system

In this study a Neural Adaptive method is used for position control and identification of a Magnetic levitation system. This controller consists of three parts: PID controller, radial basis function (RBF) network controller and radial basis function (RBF) network identifier. The combination of controllers produces a stable system which adapts to optimize performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.
Reducing Visual Confusion with Discriminative Attention

Recent developments in gradient-based attention modeling have led to improved model interpretability by means of class-specific attention maps. A key limitation, however, of these approaches is that the resulting attention maps, while being well localized, are not class discriminative. In this paper, we address this limitation with a new learning framework that makes class-discriminative attention and cross-layer attention consistency a principled and explicit part of the learning process. Furthermore, our framework provides attention guidance to the model in an end-to-end fashion, resulting in better discriminability and reduced visual confusion. We conduct extensive experiments on various image classification benchmarks with our proposed framework and demonstrate its efficacy by means of improved classification accuracy including CIFAR-100 (+3.46%), Caltech-256 (+1.64%), ImageNet (+0.92%), CUB-200-2011 (+4.8%) and PASCAL VOC2012 (+5.78%).
Collaborative Filtering on a Budget

Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring significant amounts of storage for each user or item to be added to the database. This is a problem whenever the collaborative filtering task is larger than the medium-sized Netflix Prize data. In this paper, we propose a new model for representing and compressing matrix factors via hashing. This allows for essentially unbounded storage (at a graceful storage / performance trade-off) for users and items to be represented in a pre-defined memory footprint. It allows us to scale recommender systems to very large numbers of users or conversely, obtain very good performance even for tiny models (e.g. 400kB of data suffice for a representation of the EachMovie problem). We provide both experimental results and approximation bounds for our compressed representation and we show how this approach can be extended to multipartite problems.
Using All Our Senses : the need for a Unified Theoretical Approach to Multi-sensory Information Visualization

There is an opportunity to consider the theory of visualization for each of our senses together. We should have a united theory of visualization that covers all senses and allows users to integrate modalities. We believe there are several important facets to the theory that need to be addressed at all stages of the multi-sensory visualization process. (1) Data enhancement for multi-sensory visualization; (2) perceptual variables for each sense; (3) Transference of the visualization methods and designs from one domain to another. (4) Cross-modal device integration. How multi-sensory devices are built and are used together; (5) Sensory integration and cross-modal interference: how different sensations interfere or reinforce each other. We need a body of research that will help researchers tackle questions such as ‘what are the perceptual variables that are available?’, ‘what are their limitations?’, ‘Do ideas and concepts that work in one modality transfer to another?’ and ‘what are the best design strategies for depicting information in another modality?’.
A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia

Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.
Interpretation of Transformer FRA Responses— Part I: Influence of Winding Structure

Frequency-response analysis (FRA) has been accepted as one of the most sensitive tools to detect mechanical faults of power transformers. Correct interpretation of FRA responses is crucial when assessing the integrity of a transformer. Transformer FRA responses have distinctive frequency regions which are predominated by core, windings, and measuring setup. This paper addresses one of the major factors that affect the FRA responses: the winding structure itself. In terms of the structures of single winding, they can be categorized into windings with either high- or low-series capacitance in proportion to the shunt capacitance. Correspondingly, the FRA responses of windings of high series capacitance exhibit the increasing trend of magnitude, while windings of low series capacitance display a steady magnitude trend with the features of resonances and quasi-antiresonances or antiresonances. For a winding made of two concentric winding coils, the pattern of alternating resonances and antiresonances would be exhibited on its FRA response.
Benign Envy , Social Media , and Culture Completed Research Paper

Researchers have very limited understanding of how continuous usage intention of social media (SM) are associated with online benign envy and individual-level culture. This paper addresses this gap and provides a theoretical framework to illustrate how manifestations of national cultural values at the individual level of analysis may influence SM use behaviors through online envy. We argue that espoused individualism/collectivism and espoused uncertainty avoidance are important antecedents for eliciting benign envy, while the espoused power distance and espoused masculinity/femininity dimensions are salient moderators for the consequences resulting from online benign envy. Empirical results from a sample of 387 SM users supported the majority of the hypotheses, suggesting that espoused national culture interplaying with online benign envy are important factors for continuance use intention of SM. Finally, theoretical contributions and practical implications are provided.
An implementation of cloud-based platform with R packages for spatiotemporal analysis of air pollution

Recently, the R package has become a popular tool for big data analysis due to its several matured software packages for the data analysis and visualization, including the analysis of air pollution. The air pollution problem is of increasing global concern as it has greatly impacts on the environment and human health. With the rapid development of IoT and the increase in the accuracy of geographical information collected by sensors, a huge amount of air pollution data were generated. Thus, it is difficult to analyze the air pollution data in a single machine environment effectively and reliably due to its inherent characteristic of memory design. In this work, we construct a distributed computing environment based on both the softwares of RHadoop and SparkR for performing the analysis and visualization of air pollution with the R more reliably and effectively. In the work, we firstly use the sensors, called EdiGreen AirBox to collect the air pollution data in Taichung, Taiwan. Then, we adopt the Inverse Distance Weighting method to transform the sensors’ data into the density map. Finally, the experimental results show the accuracy of the short-term prediction results of PM2.5 by using the ARIMA model. In addition, the verification with respect to the prediction accuracy with the MAPE method is also presented in the experimental results.
Volatools : Integrating Volatile Memory Forensics into the Digital Investigation Process

In this work, we demonstrate the integral role of volatile memory analysis in the digital investigation process and how that analysis can be used to help address many of the challenges facing the digital forensics community. We also provide a look at some of the shortcomings of existing approaches to live response. Finally, we provide the technical details for extracting in-memory cryptographic keying material from a popular disk encryption application without knowledge of the password.