nils-herrmann/clean_scirepeval huggingface dataset:
CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies

The rich dependency structure found in the columns of real-world relational databases can be exploited to great advantage, but can also cause query optimizers---which usually assume that columns are statistically independent---to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce CORDS, an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies between columns. CORDS searches for column pairs that might have interesting and useful dependency relations by systematically enumerating candidate pairs and simultaneously pruning unpromising candidates using a flexible set of heuristics. A robust chi-squared analysis is applied to a sample of column values in order to identify correlations, and the number of distinct values in the sampled columns is analyzed to detect soft functional dependencies. CORDS can be used as a data mining tool, producing dependency graphs that are of intrinsic interest. We focus primarily on the use of CORDS in query optimization. Specifically, CORDS recommends groups of columns on which to maintain certain simple joint statistics. These "column-group" statistics are then used by the optimizer to avoid naive selectivity estimates based on inappropriate independence assumptions. This approach, because of its simplicity and judicious use of sampling, is relatively easy to implement in existing commercial systems, has very low overhead, and scales well to the large numbers of columns and large table sizes found in real-world databases. Experiments with a prototype implementation show that the use of CORDS in query optimization can speed up query execution times by an order of magnitude. CORDS can be used in tandem with query feedback systems such as the LEO learning optimizer, leveraging the infrastructure of such systems to correct bad selectivity estimates and ameliorating the poor performance of feedback systems during slow learning phases.
An optimal design methodology for inductive power link with class-E amplifier

This paper presents a design methodology of a highly efficient power link based on Class-E driven, inductively coupled coil pair. An optimal power link design for retinal prosthesis and/or other implants must take into consideration the allowable safety limits of magnetic fields, which in turn govern the inductances of the primary and secondary coils. In retinal prosthesis, the optimal coil inductances have to deal with the constraints of the coil sizes, the tradeoffs between the losses, H-field limitation and dc supply voltage required by the Class-E driver. Our design procedure starts with the formation of equivalent circuits, followed by the analysis of the loss of the rectifier and coils and the H-field for induced voltage and current. Both linear and nonlinear models for the analysis are presented. Based on the procedure, an experimental power link is implemented with an overall efficiency of 67% at the optimal distance of 7 mm between the coils. In addition to the coil design methodology, we are also presenting a closed-loop control of Class-E amplifier for any duty cycle and any value of the systemQ.
Data-driven physics for human soft tissue animation

Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.
Big data analytics on Apache Spark

Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.
Covariance Descriptors for 3D Shape Matching and Retrieval

Several descriptors have been proposed in the past for 3D shape analysis, yet none of them achieves best performance on all shape classes. In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves. Covariance matrices enable efficient fusion of different types of features and modalities. They capture, using the same representation, not only the geometric and the spatial properties of a shape region but also the correlation of these properties within the region. Covariance matrices, however, lie on the manifold of Symmetric Positive Definite (SPD) tensors, a special type of Riemannian manifolds, which makes comparison and clustering of such matrices challenging. In this paper we study covariance matrices in their native space and make use of geodesic distances on the manifold as a dissimilarity measure. We demonstrate the performance of this metric on 3D face matching and recognition tasks. We then generalize the Bag of Features paradigm, originally designed in Euclidean spaces, to the Riemannian manifold of SPD matrices. We propose a new clustering procedure that takes into account the geometry of the Riemannian manifold. We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.
Forecasting high-dimensional data

We propose a method for forecasting high-dimensional data (hundreds of attributes, trillions of attribute combinations) for a duration of several months. Our motivating application is guaranteed display advertising, a multi-billion dollar industry, whereby advertisers can buy targeted (high-dimensional) user visits from publishers many months or even years in advance. Forecasting high-dimensional data is challenging because of the many possible attribute combinations that need to be forecast. To address this issue, we propose a method whereby only a sub-set of attribute combinations are explicitly forecast and stored, while the other combinations are dynamically forecast on-the-fly using high-dimensional attribute correlation models. We evaluate various attribute correlation models, from simple models that assume the independence of attributes to more sophisticated sample-based models that fully capture the correlations in a high-dimensional space. Our evaluation using real-world display advertising data sets shows that fully capturing high-dimensional correlations leads to significant forecast accuracy gains. A variant of the proposed method has been implemented in the context of Yahoo!'s guaranteed display advertising system.
An Efficient Model to Improve Customer Acceptance of Mobile Banking

During the last ten years technological development has reshaped the banking industry, which has become one of the leading sectors in utilizing new technology on consumer markets. Today, mobile communication technologies offer vast additional value for consumers’ banking transactions due to their always-on functionality and the option to access banks anytime and anywhere. Various alternative approaches have used in analyzing customer’s acceptance of new technologies. In this paper, factors affect acceptance of Mobile Banking are explored and presented as a New Model.
Bootstrapping relation extraction from semantic seeds

Information Extraction (IE) is a technology for localizing and classifying pieces of relevant information in unstructured natural language texts and detecting relevant relations among them. This thesis deals with one of the central tasks of IE, i.e., relation extraction. The goal is to provide a general framework that automatically learns mappings between linguistic analyses and target semantic relations, with minimal human intervention. Furthermore, this framework is supposed to support the adaptation to new application domains and new relations with various complexities. The central result is a new approach to relation extraction which is based on a minimally supervised method for automatically learning extraction grammars from a large collection of parsed texts, initialized by some instances of the target relation, called semantic seed. Due to the semantic seed approach, the framework can accommodate new relation types and domains with minimal effort. It supports relations of different arity as well as their projections. Furthermore, this framework is general enough to employ any linguistic analysis tools that provide the required type and depth of analysis. The adaptability and the scalability of the framework is facilitated by the DARE rule representation model which is recursive and compositional. In comparison to other IE rule representation models, e.g., Stevenson and Greenwood (2006), the DARE rule representation model is expressive enough to achieve good coverage of linguistic constructions for finding mentions of the target relation. The powerful DARE rules are constructed via a bottom-up and compositional rule discovery strategy, driven by the semantic seed. The control of the quality of newly acquired knowledge during the bootstrapping process is realized through a ranking and filtering strategy, taking two aspects into account: the domain relevance and the trustworthiness of the origin.
Persuasion in Practical Argument Using Value-based Argumentation Frameworks

In many cases of disagreement, particularly in situations involving practical reasoning, it is impossible to demonstrate conclusively that either party is wrong. The role of argument in such cases is to persuade rather than to prove, demonstrate or refute. Following Perelman, we argue that persuasion in such cases relies on a recognition that the strength of an argument depends on the social values that it advances, and that whether the attack of one argument on another succeeds depends on the comparative strength of the values advanced by the arguments concerned. To model this we extend the standard notion of Argumentation Frameworks (AFs) to Value-based Argumentation Frameworks (VAFs). After defining VAFs we explore their properties, and show how they can provide a rational basis for the acceptance or rejection of arguments, even where this would appear to be a matter of choice in a standard AF. In particular we show that in a VAF certain arguments can be shown to be acceptable however the relative strengths of the values involved are assessed. This means that disputants can concur on the acceptance of arguments, even when they differ as to which values are more important, and hence that we can identify points for which persuasion should be possible. We illustrate the above using an example moral debate. We then show how factual considerations can be admitted to our framework and discuss the possibility of persuasion in the face of uncertainty and disagreement as to values.
Challenges of Ultra-Thin 5 Sides Molded WLCSP

As mobile electronics are continuously driven for compact, slim and lightweight, miniaturization of IC packaging has been a must. There are increasing Wafer Level Chip Scale and fan-in Package (WLCSP) employed in electronics to achieve the miniaturization. Since WLCSP just is the Die with solder balls attached, WLCSP has the smallest footprint and the lightest weight compare to substrate base and lead-frame base package. However, WLCSP side wall with pure silicon is brittle, it is easily damaged and cracked during transportation and SMT processes. How to protect the WLCSP sidewall and to prevent the cracks is a rising issue for WLCSP applications. Therefore, as a molded WLCSP has aroused lots of attention in IC semiconductor industry. It not only provides side wall protection but also enhances package board level reliability by additional molding fixture around solder balls. By using a wafer form molding technology, it solves side wall crack issue and has no concern for high cost. Without rigid support and with unsymmetrical structure, the major challenges of this molded WLCSP come from the wafer warpage post backside grinding process. In order to diminish the warpage during backend processes, lots of experiments were conducted extensively to reduce the wafer warpage, including molding compound selection (which focus on its CTE and Tg adjustments), post-mold cure optimization, die thickness decision, and so on. In this paper, a test vehicle with ultra-low profile was evaluated. Stress simulation was conducted to determine the package construction and work out the bill of material. Screen and corner DOEs which includes molding compound selection and post-mold cure parameters were performed to come out the optimal material and process window. Functional test and Board reliability test have been passed as well. Additional collision test also shows good side wall protections. Hence five sides molded WLCSP has been proven to be a feasible and reliable way for the miniaturization in assembly industry.
Precise Distance and Velocity Measurement for Real Time Locating in Multipath Environments Using a Frequency-Modulated Continuous-Wave Secondary Radar Approach

We present a frequency-modulated continuous-wave secondary radar concept to estimate the offset in time and in frequency of two wireless units and to measure their distance relative to each other. By evaluating the Doppler frequency shift of the radar signals, the relative velocity of the mobile units is measured as well. The distance can be measured with a standard deviation as low as 1 cm. However, especially in indoor environments, the accuracy of the system can be degraded by multipath transmissions. Therefore, we show an extension of the algorithm to cope with multipath propagation. We also present the hardware setup of the measurement system. The system is tested in various environments. The results prove the excellent performance and outstanding reliability of the algorithms presented.
Spontaneous Speech Corpus of Japanese

Design issues of a spontaneous speech corpus is described. The corpus under compilation will contain 800-1000 hour spontaneously uttered Common Japanese speech and the morphologically annotated transcriptions. Also, segmental and intonation labeling will be provided for a subset of the corpus. The primary application domain of the corpus is speech recognition of spontaneous speech, but we plan to make it useful for natural language processing and phonetic/linguistic studies also.
A Proposed Lexicon-Based Sentiment Analysis Approach for the Vernacular Algerian Arabic

Nowadays, sentiment analysis research is widely applied in a variety of applications such as marketing and politics. Several studies on the Arabic sentiment analysis have been carried out in recent years. These studies mainly focus on Modern Standard Arabic among which few studies have investigated the case of Arab dialects, in this case, Egyptian, Jordanian, and Khaliji. In this paper, we propose a new lexicon-based sentiment analysis approach to address the specific aspects of the vernacular Algerian Arabic fully utilized in social networks. A manually annotated dataset and three Algerian Arabic lexicons have been created to explore the different phases of our approach.
A Prototype Electrical Actuator for Aircraft Flaps

This paper considers the electrical actuation of aircraft wing surfaces, with particular emphasis on flap systems. It discusses existing hydraulic and electrohydraulic systems and proposes an electrical alternative, examining the potential system benefits in terms of increased functionality, maintenance, and life-cycle costs. This paper then progresses to describe a full-scale actuation demonstrator of the flap system, including the high-speed electrical drive, step-down gearbox, and flaps. Detailed descriptions of the fault-tolerant motor, power electronics, control architecture, and position sensor systems are given, along with a range of test results, demonstrating the system in operation.
Recurrent deep neural networks for robust speech recognition

In this work, we propose recurrent deep neural networks (DNNs) for robust automatic speech recognition (ASR). Full recurrent connections are added to certain hidden layer of a conventional feedforward DNN and allow the model to capture the temporal dependency in deep representations. A new backpropagation through time (BPTT) algorithm is introduced to make the minibatch stochastic gradient descent (SGD) on the proposed recurrent DNNs more efficient and effective. We evaluate the proposed recurrent DNN architecture under the hybrid setup on both the 2nd CHiME challenge (track 2) and Aurora-4 tasks. Experimental results on the CHiME challenge data show that the proposed system can obtain consistent 7% relative WER improvements over the DNN systems, achieving state-of-the-art performance without front-end preprocessing, speaker adaptive training or multiple decoding passes. For the experiments on Aurora-4, the proposed system achieves 4% relative WER improvement over a strong DNN baseline system.
Reusable Tagset Conversion Using Tagset Drivers

Part-of-speech or morphological tags are important means of annotation in a vast number of corpora. However, different sets of tags are used in different corpora, even for the same language. Tagset conversion is difficult, and solutions tend to be tailored to a particular pair of tagsets. We propose a universal approach that makes the conversion tools reusable. We also provide an indirect evaluation in the context of a parsing task.
Eye Movement Analysis for Activity Recognition Using Electrooculography

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
An undergraduate fuzzy logic control lab using a line following robot

Fuzzy logic controllers have gained popularity in the past few decades with highly successful implementation in many fields. Fuzzy logic enables designers to control complex systems more effectively than traditional methods. Teaching students fuzzy logic in a laboratory can be a time-consuming and an expensive task. This paper presents a low-cost educational microcontroller-based tool for fuzzy logic controlled line following mobile robot. The robot is used in the second year of undergraduate teaching in an elective course in the department of computer engineering of the Near East University. Hardware details of the robot and the software implementing the fuzzy logic control algorithm are given in the paper. 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ; ; DOI 10.1002/cae.20347
Exogenous testosterone decreases men's personal distance in a social threat context

Testosterone can motivate human approach and avoidance behavior. Specifically, the conscious recognition of and implicit reaction to angry facial expressions is influenced by testosterone. The study tested whether exogenous testosterone modulates the personal distance (PD) humans prefer in a social threat context.



82 healthy male participants underwent either transdermal testosterone (testosterone group) or placebo application (placebo group). Each participant performed a computerized stop-distance task before (T1) and 3.5h after (T2) treatment, during which they indicated how closely they would approach a human, animal or virtual character with varying emotional expression.



Men's PD towards humans and animals varied as a function of their emotional expression. In the testosterone group, a pre-post comparison indicated that the administration of 50mg testosterone was associated with a small but significant reduction of men's PD towards aggressive individuals. Men in the placebo group did not change the initially chosen PD after placebo application independent of the condition. However comparing the testosterone and placebo group after testosterone administration did not reveal significant differences. While the behavioral effect was small and only observed as within-group effect it was repeatedly and selectively shown for men's PD choices towards an angry woman, angry man and angry dog in the testosterone group. In line with the literature, our findings in young men support the influential role of exogenous testosterone on male's approach behavior during social confrontations.
Quantum Public-Key Cryptosystems

This paper presents a new paradigm of cryptography, quantum public-key cryptosystems. In quantum public-key cryptosystems, all parties including senders, receivers and adversaries are modeled as quantum (probabilistic) poly-time Turing (QPT) machines and only classical channels (i.e., no quantum channels) are employed. A quantum trapdoor one-way function, f , plays an essential role in our system, in which a QPT machine can compute f with high probability, any QPT machine can invert f with negligible probability, and a QPT machine with trapdoor data can invert f . This paper proposes a concrete scheme for quantum public-key cryptosystems: a quantum public-key encryption scheme or quantum trapdoor one-way function. The security of our schemes is based on the computational assumption (over QPT machines) that a class of subset-sum problems is intractable against any QPT machine. Our scheme is very efficient and practical if Shor’s discrete logarithm algorithm is efficiently realized on a quantum machine.