nils-herrmann/clean_scirepeval huggingface dataset:
A robust main-memory compression scheme

Lossless data compression techniques can potentially free up more than 50% of the memory resources. However, previously proposed schemes suffer from high access costs. The proposed main-memory compression scheme practically eliminates performance losses of previous schemes by exploiting a simple and yet effective compression scheme, a highly-efficient structure for locating a compressed block in memory, and a hierarchical memory layout that allows compressibility of blocks to vary with a low fragmentation overhead. We have evaluated an embodiment of the proposed scheme in detail using 14 integer and floating point applications from the SPEC2000 suite along with two server applications and we show that the scheme robustly frees up 30% of the memory resources, on average, with a negligible impact on the performance of only 0.2% on average.
Table Header Detection and Classification

In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of .that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeer to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.
Arabic Named Entity Recognition: A Feature-Driven Study

The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact of the different features in isolation and incrementally combine them in order to evaluate the robustness to noise of each approach. We achieve the highest performance using a combination of 15 features in conditional random fields using broadcast news data (Fbeta = 1=83.34).
Ray casting for modeling solids

This paper presents ray casting as the methodological basis for a CAD/CAM solid modeling system. Solid objects are modeled by combining primitive solids, such as blocks and cylinders, using the set operators union, intersection, and difference. To visualize and analyze the composite solids modeled, virtual light rays are cast as probes. By virtue of its simplicity, ray casting is reliable and extensible. The most difficult mathematical problem is finding linesurface intersection points. So surfaces such as planes, quad&, tori, and probably even parametric surface patches may bound the primitive solids. The adequacy and efficiency of ray casting are issues addressed here. A fast picture generation capability for interactive modeling is the biggest challenge. New methods are presented, accompanied by sample pictures and CPU times, to meet the challenge.
From Spin to Swindle: Identifying Falsification in Financial Text

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task. From the results produced, there is a clear indication that the language deployed by management engaged in wilful falsification of firm performance is discernibly different from truth-tellers. For the first time, this new interdisciplinary research extracts features for readability at a much deeper level, attempts to draw out collocations using n-grams and measures tone using appropriate financial dictionaries. This linguistic analysis with machine learning-driven data mining approach to fraud detection could be used by auditors in assessing financial reporting of firms and early detection of possible misdemeanours.
FRIDGE: A Fixed-Point Design and Simulation Environment

Digital systems, especially those for mobile applications are sensitive to power consumption, chip size and costs. Therefore they are realized using fixed-point architectures, either dedicated HW or programmable DSPs. On the other hand, system design starts from a floating-point description. These requirements have been the motivation for FRIDGE (Fixed-point pRogrammIng DesiGn Environment), a design environment for the specification, evaluation and implementation of fixed-point systems. FRIDGE offers a seamless design flow from a floating- point description to a fixed-point implementation. Within this paper we focus on two core capabilities of FRIDGE: (1) the concept of an interactive, automated transformation of floating-point programs written in ANSI-C into fixed-point specifications, based on an interpolative approach. The design time reductions that can be achieved make FRIDGE a key component for an efficient HW/SW-CoDesign. (2) a fast fixed-point simulation that performs comprehensive compile-time analyses, reducing simulation time by one order of magnitude compared to existing approaches.
XDoG: advanced image stylization with eXtended Difference-of-Gaussians

Recent extensions to the standard Difference-of-Gaussians (DoG) edge detection operator have rendered it less susceptible to noise and increased its aesthetic appeal for stylistic depiction applications. Despite these advances, the technical subtleties and stylistic potential of the DoG operator are often overlooked. This paper reviews the DoG operator, including recent improvements, and offers many new results spanning a variety of styles, including pencil-shading, pastel, hatching, and binary black-and-white images. Additionally, we demonstrate a range of subtle artistic effects, such as ghosting, speed-lines, negative edges, indication, and abstraction, and we explain how all of these are obtained without, or only with slight modifications to an extended DoG formulation. In all cases, the visual quality achieved by the extended DoG operator is comparable to or better than those of systems dedicated to a single style.
The dark side of social networking sites: Understanding phishing risks

LinkedIn, with over 1.5 million Groups, has become a popular place for business employees to create private groups to exchange information and communicate. Recent research on social networking sites (SNSs) has widely explored the phenomenon and its positive effects on firms. However, social networking’s negative effects on information security were not adequately addressed. Supported by the credibility, persuasion and motivation theories, we conducted 1) a field experiment, demonstrating how sensitive organizational data can be exploited, followed by 2) a qualitative study of employees engaged in SNSs activities; and 3) interviews with Chief Information Security Officers (CISOs). Our research has resulted in four main findings: 1) employees are easily deceived and susceptible to victimization on SNSs where contextual elements provide psychological triggers to attackers; 2) organizations lack mechanisms to control SNS online security threats, 3) companies need to strengthen their information security policies related to SNSs, where stronger employee identification and authentication is needed, and 4) SNSs have become important security holes where, with the use of social engineering techniques, malicious attacks are easily facilitated.
VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm

The sequential minimal optimization (SMO) algorithm has been extensively employed to train the support vector machine (SVM). This work presents an efficient application specific integrated circuit chip design for sequential minimal optimization. This chip is implemented as an intellectual property core, suitable for use in an SVM-based recognition system on a chip. The proposed SMO chip was tested and found to be fully functional, using a prototype system based on the Altera DE2 board with a Cyclone II 2C70 field-programmable gate array.
ThingSeek: A Crawler and Search Engine for the Internet of Things

The rapidly growing paradigm of the Internet of Things (IoT) requires new search engines, which can crawl heterogeneous data sources and search in highly dynamic contexts. Existing search engines cannot meet these requirements as they are designed for traditional Web and human users only. This is contrary to the fact that things are emerging as major producers and consumers of information. Currently, there is very little work on searching IoT and a number of works claim the unavailability of public IoT data. However, it is dismissed that a majority of real-time web-based maps are sharing data that is generated by things, directly. To shed light on this line of research, in this paper, we firstly create a set of tools to capture IoT data from a set of given data sources. We then create two types of interfaces to provide real-time searching services on dynamic IoT data for both human and machine users.
Feature Learning for Chord Recognition: The Deep Chroma Extractor

We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.
Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Although object tracking has been studied for decades, real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, realworld data. We present a tracker that combines 3D shape, color (when available), and motion cues to accurately track moving objects in real-time. Our tracker allocates computational effort based on the shape of the posterior distribution. Starting with a coarse approximation to the posterior, the tracker successively refines this distribution, increasing in tracking accuracy over time. The tracker can thus be run for any amount of time, after which the current approximation to the posterior is returned. Even at a minimum runtime of 0.7 milliseconds, our method outperforms all of the baseline methods of similar speed by at least 10%. If our tracker is allowed to run for longer, the accuracy continues to improve, and it continues to outperform all baseline methods. Our tracker is thus anytime, allowing the speed or accuracy to be optimized based on the needs of the application.
A Systematic Exploration of Diversity in Machine Translation

This paper addresses the problem of producing a diverse set of plausible translations. We present a simple procedure that can be used with any statistical machine translation (MT) system. We explore three ways of using diverse translations: (1) system combination, (2) discriminative reranking with rich features, and (3) a novel post-editing scenario in which multiple translations are presented to users. We find that diversity can improve performance on these tasks, especially for sentences that are difficult for MT.
Evaluating the Utility of Driving: Toward Automated Decision Making Under Uncertainty

The complexity of advanced driver-assistance systems (ADASs) is steadily increasing. While the first applications were based on mere warnings, current systems actively intervene in the driving process. Due to this development, such systems have to automatically choose between different action alternatives. From an algorithmic point of view, this requires automatic decision making on the basis of uncertain data. In this paper, the application of decision networks for this problem is proposed. It is demonstrated how this approach facilitates automatic maneuver decisions in a prototypical lane change assistance system. Furthermore, relevant research questions and unsolved problems related to this topic are identified.
A new methodology for security evaluation in cloud computing

Cloud service providers (CSPs) and cloud customers (CCs) are not only exposed to existing security risks but to new risks introduced by clouds, like multi-tenancy, virtualization and data outsourcing. Several international and industrial standards target information security and their conformity with cloud computing security challenges. We give an overview of these standards and evaluate their completeness. As a result we propose a new extension to the ISO 27001:2005 standard including a new control objective about virtualization applicable for cloud systems. We also define a new quantitative metric and evaluate the importance of existing ISO 27001:2005 control objectives if customer services are hosted on-premise or in cloud. Our conclusion is that obtaining the ISO 27001:2005 certificate is not enough for CSP and CC information security systems, especially in business continuity detriment that cloud computing produces and propose new solutions that mitigate the risks.
Capacitive power transfer for slip ring replacement in wound field synchronous machines

Permanent magnet synchronous machines are utilized in a wide variety of applications due to their many desirable characteristics, including high torque density capability and high efficiency. In the near future, however, the demand for the permanent magnet rare earth materials is projected to exceed world production. As a result, electric machines which do not rely on rare earth materials, such as wound field synchronous machines (WFSMs), are receiving renewed attention for use in traction and wind energy applications. However, WFSMs require a current delivery mechanism to the rotor such as mechanical slip rings whose components require periodic replacement and generate adverse debris within the machine enclosure. Rotary transformers may replace slip rings but also introduce rotor speed dependencies and magnetic coupling difficulties. This paper proposes a capacitive non-contact power transfer technique to eliminate the need for mechanical slip rings while also avoiding the pitfalls of rotating transformer technologies. The capacitive power transfer system is compared to traditional rotor power coupling techniques and its performance is validated with experimental results.
Towards natural language question generation for the validation of ontologies and mappings

The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging.



We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions.



This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9.



The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.
Is it culture or is it language? Examination of language effects in cross-cultural research on categorization.

Differences in reasoning styles between Chinese and European Americans held even when controlling for the language of testing. Bilingual Chinese organized objects in a more relational and less categorical way than European Americans, whether tested in English or in Chinese. Thus, culture affects categorization independent of the testing language. Nevertheless, language affected some Chinese bilinguals' categorization. The responses of Chinese from the Mainland and Taiwan were more relational when tested in Chinese than when tested in English. Responses of Chinese from Hong Kong and Singapore were equally relational when tested in Chinese and in English. Age and context of learning English are discussed to explain the differential language effects among different Chinese groups. Theoretical and methodological implications are discussed.
A Steganography Technique for Hiding Image in an Image using LSB Method for 24 Bit Color Image

In this paper, author have propose a steganographic technique by using improved LSB (least significant bit) replacement method for 24 bit color image capable of producing a secret-embedded image that is totally indistinguishable from the original image by the human eye. In addition this paper shows that how improved LSB method for 24 bit color image is better than LSB technique for 8 bit color image. Firstly LSB method for both 8 bit and 24 bit color image are described and then improved LSB method for 24 bit color image, compare their result by calculating PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error) and finally by histogram analysis. LSB Algorithm embedded MSB of secret image into LSB of cover image. In the case of 24 bit color image two methods are described. In first method, last 2 LSB of each plane (red, green and blue) of cover image, is replaced by 2 MSB of secret image. In the second method, last LSB of each red plane is replaced by first MSB of secret image, last 2 LSB of each green plane by next 2 MSB of secret image and then last 3 LSB of blue plane is replaced by next 3 MSB of secret image. This means that total 6 bits of secret image can be hide in 24 bit color image. Experimental results show that the stego-image is visually indistinguishable from the original cover-image in the case of 24 bit.
Generating photoacoustic signals unsing high-peak power pulsed laser diodes

Photoacoustic signals are usually generated using bulky and expensive Q-switched Nd:YAG lasers, with limited scope for varying the pulse repetition frequency, wavelength and pulse width. An alternative would be to use laser diodes as excitation sources; these devices are compact, relatively inexpensive, and available in a wide variety of NIR wavelengths. Their pulse duration and repetition rates can also be varied arbitrarily enabling a wide range of time and frequency domain excitation methods to be employed. The main difficulty to overcome when using laser diodes for pulsed photoacoustic excitation is their low peak power compared to Q-switched lasers. However, the much higher repetition rate of laser diodes (∼ kHz) compared to many Q-switched laser systems (∼ tens of Hz) enables a correspondingly greater number of events to be acquired and signal averaged over a fixed time period. This offers the prospect of significantly increasing the signal-to-noise ratio (SNR) of the detected photoacoustic signal. Choosing the wavelength of the laser diode to be lower than that of the water absorption peak at 940nm, may also provide a significant advantage over a system lasing at 1064nm for measurements in tissue. If the output of a number of laser diodes is combined it then becomes possible, in principle, to obtain a SNR approaching that achievable with a Q-switched laser. It is also suggested that optimising the pulse duration of the laser diode may reduce the effects of frequency-dependent acoustic attenuation in tissue on the photoacoustic signal. To investigate this, a numerical model based on the Poisson solution to the wave equation was developed. To validate the model, a high peak power pulsed laser diode system was built. It was composed of a 905nm stacked array laser diode coupled to an optical fibre and driven by a high current laser diode driver. Measurements of the SNR of photoacoustic signals generated in a purely absorbing medium (ink) were made as a function of pulse duration. This preliminary study shows the potential for using laser diodes as excitation sources for photoacoustic applications in the biomedical field.
AI-based Game Design Patterns

This paper proposes a model for designing games around Artificial Intelligence (AI). AI-based games put AI in the foreground of the player experience rather than in a supporting role as is often the case in many commercial games. We analyze the use of AI in a number of existing games and identify design patterns for AI in games. We propose a generative ideation technique to combine a design pattern with an AI technique or capacity to make new AI-based games. Finally, we demonstrate this technique through two examples of AI-based game prototypes created using these patterns.