nils-herrmann/clean_scirepeval huggingface dataset:
Philosophy of computing and information technology

Philosophy has been described as having taken a ‘computational turn’, referring to the ways in which computers and information technology throw new light upon traditional philosophical issues, provide new tools and concepts for philosophical reasoning, and pose theoretical and practical questions that cannot readily be approached within traditional philosophical frameworks. As such, computer technology is arguably the technology that has had the most profound impact on philosophy. Philosophers have studied computer technology and its philosophical implications extensively, and this chapter gives an overview of the field. We start with definitions and historical overviews of the field and its various subfields. We then consider studies of the fundamental nature and basic principles of computing and computational systems, before moving on to philosophy of computer science, which investigates the nature, scope and methods of computer science. Under this heading, we will also address such topics as data modeling, ontology in computer science, programming languages, software engineering as an engineering discipline, management of information systems, the use of computers for simulation, and humancomputer interaction. Subsequently, we will address the issue in computing that has received the most attention from philosophers, artificial intelligence (AI). The purpose of this section is to give an overview of the philosophical issues raised by the notion of creating intelligent machines. We consider philosophical critiques of different approaches within AI and pay special attention to philosophical studies of applications of AI. We then turn to a section on philosophical issues pertaining to new media and the Internet, including the convergence between media and digital computers. The theoretical and ethical issues raised by this relatively recent phenomenon are diverse. We will focus on philosophical theories of the ‘information society’, epistemological and ontological issues in relation to Internet information and virtuality, the philosophical study of social life online and cyberpolitics, and issues raised by the disappearing borders between body and artifact in cyborgs and virtual selves. The final section in this chapter is devoted to the many ethical questions raised by computers and information technology, as studied in computer ethics.
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.
On Machine Learning towards Predictive Sales Pipeline Analytics

Sales pipeline win-propensity prediction is fundamental to effective sales management. In contrast to using subjective human rating, we propose a modern machine learning paradigm to estimate the winpropensity of sales leads over time. A profile-specific two-dimensional Hawkes processes model is developed to capture the influence from seller’s activities on their leads to the win outcome, coupled with lead’s personalized profiles. It is motivated by two observations: i) sellers tend to frequently focus their selling activities and efforts on a few leads during a relatively short time. This is evidenced and reflected by their concentrated interactions with the pipeline, including login, browsing and updating the sales leads which are logged by the system; ii) the pending opportunity is prone to reach its win outcome shortly after such temporally concentrated interactions. Our model is deployed and in continual use to a large, global, B2B multinational technology enterprize (Fortune 500) with a case study. Due to the generality and flexibility of the model, it also enjoys the potential applicability to other real-world problems.
Entity Disambiguation by Knowledge and Text Jointly Embedding

For most entity disambiguation systems, the secret recipes are feature representations for mentions and entities, most of which are based on Bag-of-Words (BoW) representations. Commonly, BoW has several drawbacks: (1) It ignores the intrinsic meaning of words/entities; (2) It often results in high-dimension vector spaces and expensive computation; (3) For different applications, methods of designing handcrafted representations may be quite different, lacking of a general guideline. In this paper, we propose a different approach named EDKate. We first learn low-dimensional continuous vector representations for entities and words by jointly embedding knowledge base and text in the same vector space. Then we utilize these embeddings to design simple but effective features and build a two-layer disambiguation model. Extensive experiments on real-world data sets show that (1) The embedding-based features are very effective. Even a single one embedding-based feature can beat the combination of several BoW-based features. (2) The superiority is even more promising in a difficult set where the mention-entity prior cannot work well. (3) The proposed embedding method is much better than trivial implementations of some off-the-shelf embedding algorithms. (4) We compared our EDKate with existing methods/systems and the results are also positive.
An Intelligent Regenerative Braking Strategy for Electric Vehicles

Regenerative braking is an effective approach for electric vehicles (EVs) to extend their driving range. A fuzzy-logic-based regenerative braking strategy (RBS) integrated with series regenerative braking is developed in this paper to advance the level of energy-savings. From the viewpoint of securing car stability in braking operations, the braking force distribution between the front and rear wheels so as to accord with the ideal distribution curve are considered to prevent vehicles from experiencing wheel lock and slip phenomena during braking. Then, a fuzzy RBS using the driver’s braking force command, vehicle speed, battery SOC, battery temperature are designed to determine the distribution between friction braking force and regenerative braking force to improve the energy recuperation efficiency. The experimental results on an “LF620” prototype EV validated the feasibility and effectiveness of regenerative braking and showed that the proposed fuzzy RBS was endowed with good control performance. The maximum driving range of LF620 EV was improved by 25.7% compared with non-RBS conditions.
Stock time series pattern matching: Template-based vs. rule-based approaches

One of the major duties of financial analysts is technical analysis. It is necessary to locate the technical patterns in the stock price movement charts to analyze the market behavior. Indeed, there are two main problems: how to define those preferred patterns (technical patterns) for query and how to match the defined pattern templates in different resolutions. As we can see, defining the similarity between time series (or time series subsequences) is of fundamental importance. By identifying the perceptually important points (PIPs) directly from the time domain, time series and templates of different lengths can be compared. Three ways of distance measure, including Euclidean distance (PIP-ED), perpendicular distance (PIP-PD) and vertical distance (PIP-VD), for PIP identification are compared in this paper. After the PIP identification process, both templateand rule-based pattern-matching approaches are introduced. The proposed methods are distinctive in their intuitiveness, making them particularly user friendly to ordinary data analysts like stock market investors. As demonstrated by the experiments, the templateand the rule-based time series matching and subsequence searching approaches provide different directions to achieve the goal of pattern identification.
Botryococcus braunii: a renewable source of hydrocarbons and other chemicals.

Botryococcus braunii, a green colonial microalga, is an unusually rich renewable source of hydrocarbons and other chemicals. Hydrocarbons can constitute up to 75% of the dry mass of B. braunii. This review details the various facets of biotechnology of B. braunii, including its microbiology and physiology; production of hydrocarbons and other compounds by the alga; methods of culture; downstream recovery and processing of algal hydrocarbons; and cloning of the algal genes into other microorganisms. B. braunii converts simple inorganic compounds and sunlight to potential hydrocarbon fuels and feedstocks for the chemical industry. Microorganisms such as B. braunii can, in the long run, reduce our dependence on fossil fuels and because of this B. braunii continues to attract much attention.
Look around you: Saliency maps for omnidirectional images in VR applications

Understanding visual attention has always been a topic of great interest in the graphics, image/video processing, robotics and human-computer interaction communities. By understanding salient image regions, the compression, transmission and rendering algorithms can be optimized. This is particularly important in omnidirectional images (ODIs) viewed with a head-mounted display (HMD), where only a fraction of the captured scene is displayed at a time, namely viewport. In order to predict salient image regions, saliency maps are estimated either by using an eye tracker to collect eye fixations during subjective tests or by using computational models of visual attention. However, eye tracking developments for ODIs are still in the early stages and although a large list of saliency models are available, no particular attention has been dedicated to ODIs. Therefore, in this paper, we consider the problem of estimating saliency maps for ODIs viewed with HMDs, when the use of an eye tracker device is not possible. We collected viewport center trajectories (VCTs) of 32 participants for 21 ODIs and propose a method to transform the gathered data into saliency maps. The obtained saliency maps are compared in terms of image exposition time used to display each ODI in the subjective tests. Then, motivated by the equator bias tendency in ODIs, we propose a post-processing method, namely fused saliency maps (FSM), to adapt current saliency models to ODIs requirements. We show that the use of FSM on current models improves their performance by up to 20%. The developed database and testbed are publicly available with this paper.
An Area and Power-Efficient Analog Li-Ion Battery Charger Circuit

The demand for greater battery life in low-power consumer electronics and implantable medical devices presents a need for improved energy efficiency in the management of small rechargeable cells. This paper describes an ultra-compact analog lithium-ion (Li-ion) battery charger with high energy efficiency. The charger presented here utilizes the tanh basis function of a subthreshold operational transconductance amplifier to smoothly transition between constant-current and constant-voltage charging regimes without the need for additional area- and power-consuming control circuitry. Current-domain circuitry for end-of-charge detection negates the need for precision-sense resistors in either the charging path or control loop. We show theoretically and experimentally that the low-frequency pole-zero nature of most battery impedances leads to inherent stability of the analog control loop. The circuit was fabricated in an AMI 0.5-μm complementary metal-oxide semiconductor process, and achieves 89.7% average power efficiency and an end voltage accuracy of 99.9% relative to the desired target 4.2 V, while consuming 0.16 mm2 of chip area. To date and to the best of our knowledge, this design represents the most area-efficient and most energy-efficient battery charger circuit reported in the literature.
160-GHz Power Amplifier Design in Advanced SiGe HBT Technologies With ${P}_{\rm sat}$ in Excess of 10 dBm

Power amplifier (PA) design for 160-GHz applications in an advanced SiGe heterojunction bipolar transistor (HBT) technology with saturated output power (Psat) in excess of 10 dBm is presented. The architecture is based on a three-stage pseudodifferential configuration that was implemented in SiGe HBT evaluation technology with fmax of 400 GHz. At saturation, a PA breakout circuit delivers 10 dBm with 20-dB gain. From 150 to 170 GHz, the small-signal gain is within 20-32 dB and the output referred 1-dB compression point (P1 dB) is 8.5 dBm at 160 GHz. High output power was possible due to optimum device sizing, efficient layout, and accurate EM modeling. To our best knowledge, this is the highest output power reported for silicon PAs operating beyond 120 GHz.
Text Summarization of Turkish Texts using Latent Semantic Analysis

Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores.
Performance Factors Analysis - A New Alternative to Knowledge Tracing

Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used to compute latencies of actions. On the other hand, existing models used for educational data mining (e.g. Learning Factors Analysis (LFA)[2]) and model search do not tend to allow the creation of a “model overlay” that traces predictions for individual students with individual skills so as to allow the adaptive instruction to automatically remediate performance. Because these limitations make the transition from model search to model application in adaptive instruction more difficult, this paper describes our work to modify an existing data mining model so that it can also be used to select practice adaptively. We compare this new adaptive data mining model (PFA, Performance Factors Analysis) with two versions of LFA and then compare PFA with standard KT.
Flash Memory Built-In Self-Test Using March-Like Algorithm

Flash memories are a type of non-volatile memory based on floating-gate transistors. The use of commodity and embedded flash memories are growing rapidly as we enter the system-on-chip (SOC) era. Conventional tests for flash memories are usually ad hoc—the test procedure is developed for a specific design. We propose improved March-like algorithms (i.e., March FT) for both bit-oriented and word-oriented flash memory, to cover the disturbance faults derived from the IEEE 1005 Standard, as well as conventional faults. A noval flash memory fault simulator is used to analyze and generate the test algorithms. In addition, we present BIST designs for two industrial flash memories. The area overhead is only about 3% for a medium-sized flash memory.
Direct and Fast Ray Tracing of NURBS Surfaces

Recently it has been shown that Bezier surfaces can be used as a geometric primitive for interactive ray tracing on a single commodity PC. However, the Bezier representation is restricted, as a large number of control points also imply a high polynomial degree, thus reducing the frame rate significantly. In this work we present a fast, efficient and robust algorithm to ray trace trimmed NURBS surfaces of arbitrary degree. Furthermore, our approach is largely independent of the number of control points of a surface with respect to the rendering performance. Additionally the degree and the number of control points of a surface do not influence the numerical stability of the intersection algorithm. The desired high performance is achieved by taking a novel approach of surface evaluation, which requires only minimal preprocessing. We present a method to transform the computationally expensive Cox-de Boor recursion into a SIMD suitable form that maximizes performance by avoiding the recursion and drastically reduces the number of executed commands
Gabor analysis of auditory midbrain receptive fields: spectro-temporal and binaural composition.

The spectro-temporal receptive field (STRF) is a model representation of the excitatory and inhibitory integration area of auditory neurons. Recently it has been used to study spectral and temporal aspects of monaural integration in auditory centers. Here we report the properties of monaural STRFs and the relationship between ipsi- and contralateral inputs to neurons of the central nucleus of cat inferior colliculus (ICC) of cats. First, we use an optimal singular-value decomposition method to approximate auditory STRFs as a sum of time-frequency separable Gabor functions. This procedure extracts nine physiologically meaningful parameters. The STRFs of approximately 60% of collicular neurons are well described by a time-frequency separable Gabor STRF model, whereas the remaining neurons exhibited obliquely oriented or multiple excitatory/inhibitory subfields that require a nonseparable Gabor fitting procedure. Parametric analysis reveals distinct spectro-temporal tradeoffs in receptive field size and modulation filtering resolution. Comparisons between an identical model used to study spatio-temporal integration areas of visual neurons further shows that auditory and visual STRFs share numerous structural properties. We then use the Gabor STRF model to compare quantitatively receptive field properties of contra- and ipsilateral inputs to the ICC. We show that most interaural STRF parameters are highly correlated bilaterally. However, the spectral and temporal phases of ipsi- and contralateral STRFs often differ significantly. This suggests that activity originating from each ear share various spectro-temporal response properties such as their temporal delay, bandwidth, and center frequency but have shifted or interleaved patterns of excitation and inhibition. These differences in converging monaural receptive fields expand binaural processing capacity beyond interaural time and intensity aspects and may enable colliculus neurons to detect disparities in the spectro-temporal composition of the binaural input.
Model Predictive Control—A Simple and Powerful Method to Control Power Converters

This paper presents a detailed description of finite control set model predictive control (FCS-MPC) applied to power converters. Several key aspects related to this methodology are, in depth, presented and compared with traditional power converter control techniques, such as linear controllers with pulsewidth-modulation-based methods. The basic concepts, operating principles, control diagrams, and results are used to provide a comparison between the different control strategies. The analysis is performed on a traditional three-phase voltage source inverter, used as a simple and comprehensive reference frame. However, additional topologies and power systems are addressed to highlight differences, potentialities, and challenges of FCS-MPC. Among the conclusions are the feasibility and great potential of FCS-MPC due to present-day signal-processing capabilities, particularly for power systems with a reduced number of switching states and more complex operating principles, such as matrix converters. In addition, the possibility to address different or additional control objectives easily in a single cost function enables a simple, flexible, and improved performance controller for power-conversion systems.
Virtual Metrology Technique for Semiconductor Manufacturing

IC metrology is a necessary means for measuring the fabrication performance in the semiconductor industry. It is significant for yield enhancement and process control. However, real-time monitoring of wafer production is required in recent years especially for the 300 mm semiconductor manufacturing. Therefore, virtual metrology (VM) is developed for the tide of demand. It is a novel technology to predict the process results based on the previous metrology measurements, instead of measuring practically. Consequently it can assist in achieving total quality management and enable run-to-run control. In this paper a systematic methodology for virtual metrology is proposed. This VM system which is mainly designed for the process subject to linear process drift consists of a piecewise linear neural network and a fuzzy neural network. Because many semiconductor processes exhibit inevitable steady drifts in nature, the design of piecewise linear neural network is to approximate the drift trend. In addition, the influence of process recipe on fabrication outcome is learned using the fuzzy neural network. The system has good generalization capability and performance. Thus, it provides an effective and economical solution for metrology prediction.
The 'Dresden Image Database' for benchmarking digital image forensics

This paper introduces and documents a novel image database specifically built for the purpose of development and bench-marking of camera-based digital forensic techniques. More than 14,000 images of various indoor and outdoor scenes have been acquired under controlled and thus widely comparable conditions from altogether 73 digital cameras. The cameras were drawn from only 25 different models to ensure that device-specific and model-specific characteristics can be disentangled and studied separately, as validated with results in this paper. In addition, auxiliary images for the estimation of device-specific sensor noise pattern were collected for each camera. Another subset of images to study model-specific JPEG compression algorithms has been compiled for each model. The 'Dresden Image Database' will be made freely available for scientific purposes when this accompanying paper is presented. The database is intended to become a useful resource for researchers and forensic investigators. Using a standard database as a benchmark not only makes results more comparable and reproducible, but it is also more economical and avoids potential copyright and privacy issues that go along with self-sampled benchmark sets from public photo communities on the Internet.
Energy-Balance Control of PV Cascaded Multilevel Grid-Connected Inverters Under Level-Shifted and Phase-Shifted PWMs

This paper presents an energy-balance control strategy for a cascaded single-phase grid-connected H-bridge multilevel inverter linking n independent photovoltaic (PV) arrays to the grid. The control scheme is based on an energy-sampled data model of the PV system and enables the design of a voltage loop linear discrete controller for each array, ensuring the stability of the system for the whole range of PV array operating conditions. The control design is adapted to phase-shifted and level-shifted carrier pulsewidth modulations to share the control action among the cascade-connected bridges in order to concurrently synthesize a multilevel waveform and to keep each of the PV arrays at its maximum power operating point. Experimental results carried out on a seven-level inverter are included to validate the proposed approach.