nils-herrmann/clean_scirepeval huggingface dataset:
LINGUISTIC STEGANOGRAPHY: SURVEY, ANALYSIS, AND ROBUSTNESS CONCERNS FOR HIDING INFORMATION IN TEXT

Steganography is an ancient art. With the advent of computers, we have vast accessible bodies of data in which to hide information, and increasingly sophisticated techniques with which to analyze and recover that information. While much of the recent research in steganography has been centered on hiding data in images, many of the solutions that work for images are more complicated when applied to natural language text as a cover medium. Many approaches to steganalysis attempt to detect statistical anomalies in cover data which predict the presence of hidden information. Natural language cover texts must not only pass the statistical muster of automatic analysis, but also the minds of human readers. Linguistically naïve approaches to the problem use statistical frequency of letter combinations or random dictionary words to encode information. More sophisticated approaches use context-free grammars to generate syntactically correct cover text which mimics the syntax of natural text. None of these uses meaning as a basis for generation, and little attention is paid to the semantic cohesiveness of a whole text as a data point for statistical attack. This paper provides a basic introduction to steganography and steganalysis, with a particular focus on text steganography. Text-based information hiding techniques are discussed, providing motivation for moving toward linguistic steganography and steganalysis. We highlight some of the problems inherent in text steganography as well as issues with existing solutions, and describe linguistic problems with character-based, lexical, and syntactic approaches. Finally, the paper explores how a semantic and rhetorical generation approach suggests solutions for creating more believable cover texts, presenting some current and future issues in analysis and generation. The paper is intended to be both general enough that linguists without training in information security and computer science can understand the material, and specific enough that the linguistic and computational problems are described in adequate detail to justify the conclusions suggested.
Aesthetic Refinement of Secondary Cranioplasty Using Methyl Methacrylate Bone Cements

Cranioplasty using alternate alloplastic bone substitutes instead of autologous bone grafting is inevitable in the clinical field. The authors present their experiences with cranial reshaping using methyl methacrylate (MMA) and describe technical tips that are keys to a successful procedure. A retrospective chart review of patients who underwent cranioplasty with MMA between April 2007 and July 2010 was performed. For 20 patients, MMA was used for cranioplasty after craniofacial trauma (n = 16), tumor resection (n = 2), and a vascular procedure (n = 2). The patients were divided into two groups. In group 1, MMA was used in full-thickness inlay fashion (n = 3), and in group 2, MMA was applied in partial-thickness onlay fashion (n = 17). The locations of reconstruction included the frontotemporal region (n = 5), the frontoparietotemporal region (n = 5), the frontal region (n = 9), and the vertex region (n = 1). The size of cranioplasty varied from 30 to 144 cm2. The amount of MMA used ranged from 20 to 70 g. This biomaterial was applied without difficulty, and no intraoperative complications were linked to the applied material. The patients were followed for 6 months to 4 years (mean, 2 years) after MMA implantation. None of the patients showed any evidence of implant infection, exposure, or extrusion. Moreover, the construct appeared to be structurally stable over time in all the patients. Methyl methacrylate is a useful adjunct for treating deficiencies of the cranial skeleton. It provides rapid and reliable correction of bony defects and contour deformities. Although MMA is alloplastic, appropriate surgical procedures can avoid problems such as infection and extrusion. An acceptable overlying soft tissue envelope should be maintained together with minimal contamination of the operative site. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Blobworld: A System for Region-Based Image Indexing and Retrieval

Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions (“blobs”) with associated color and texture descriptors. Querying is based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both querying and indexing.
Unsupervised Wall Detector in Architectural Floor Plans

Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision.
Business Models in the Software Industry

Business models describe the strategic direction of a firm and have significant impact on the success of innovations. While the term business model is broadly used, a comprehensive definition in the context of the software industry has not yet been provided. In this paper, we conceptualize a software industry business model, supporting the description of software firms' business models in a standardized manner. Our model aggregates and unifies findings from a literature review in three disciplines, generic and software industry specific business model concepts as well as economic properties of the software industry. The resulting business model concept comprises 20 elements. A first empirical proof of concept among 10 software business models confirms the applicability of the presented concept. The business models are instantiated based on the proposed framework and the interviewees confirm the applicability, relevance, and importance of the proposed business model.
Towards a Model of Learning through Communication

Communication is an interactive, complex, structured process involving agents that are capable of drawing conclusions from the information they have available about some real-life situations. Such situations are generally characterized as being imperfect. In this paper, we aim to address learning from the perspective of the communication between agents. To learn a collection of propositions concerning some situation is to incorporate it within one's knowledge about that situation. That is, the key factor in this activity is for the goal agent, where agents may switch role if appropriate, to integrate the information offered with what it already knows. This may require a process of belief revision, which suggests that the process of incorporation of new information should be modeled nonmonotonically. We shall employ for reasoning a three-valued based nonmonotonic logic that formalizes some aspects of revisable reasoning and it is accessible to implementation. The logic is sound and complete. A theorem-prover of the logic has successfully been implemented.
Challenges in the manufacturing of hairpin windings and application opportunities of infrared lasers for the contacting process

Accelerated by electric mobility and new requirements regarding quantities, quality and innovative motor designs, production technologies for windings of electrical drives gain in importance. Especially the demand for increasing slot fill ratios and a product design allowing manufacturers of electric drives to produce big quantities in a good quality impels innovations in the design of windings. The hairpin winding is a result of this development combining high slot fill ratios with new potentials for an economical production of the winding for electric drives. This is achieved by a new method for the production of the winding: The winding is assembled by mounting preformed elements of insulated copper wire to the stator simplifying the elaborate winding process on the one hand. On the other hand it becomes necessary to join these elements mechanically and electrically to manufacture the winding. Due to this, contacting technologies gain in importance regarding the production of hairpin windings. The new challenge consists of the high number of contact points that have to be produced in a small amount of space. On account of its process stability, high process velocities and the possibility of realizing a remote joining process, the laser welding shows big potentials for the realization of a contacting process for hairpin windings that is capable of series production. This paper describes challenges and possibilities for the application of infrared lasers in the field of hairpin winding production.
Ranking Measures and Loss Functions in Learning to Rank

Learning to rank has become an important research topic in machine learning. While most learning-to-rank methods learn the ranking functions by minimizing loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking functions. In this work, we reveal the relationship between ranking measures and loss functions in learningto-rank methods, such as Ranking SVM, RankBoost, RankNet, and ListMLE. We show that the loss functions of these methods are upper bounds of the measurebased ranking errors. As a result, the minimization of these loss functions will lead to the maximization of the ranking measures. The key to obtaining this result is to model ranking as a sequence of classification tasks, and define a so-called essential loss for ranking as the weighted sum of the classification errors of individual tasks in the sequence. We have proved that the essential loss is both an upper bound of the measure-based ranking errors, and a lower bound of the loss functions in the aforementioned methods. Our proof technique also suggests a way to modify existing loss functions to make them tighter bounds of the measure-based ranking errors. Experimental results on benchmark datasets show that the modifications can lead to better ranking performances, demonstrating the correctness of our theoretical analysis.
3D texture analysis on MRI images of Alzheimer’s disease

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimer’s disease (AD). T1-weighted magnetic resonance (MR) images were obtained from 17 AD patients and 17 age and gender-matched healthy controls. 3D texture features were extracted from the circular 3D ROIs placed using a semi-automated technique in the hippocampus and entorhinal cortex. We found that classification accuracies based on texture analysis of the ROIs varied from 64.3% to 96.4% due to different ROI selection, feature extraction and selection options, and that most 3D texture features selected were correlated with the mini-mental state examination (MMSE) scores. The results indicated that 3D texture could detect the subtle texture differences between tissues in AD patients and normal controls, and texture features of MR images in the hippocampus and entorhinal cortex might be related to the severity of AD cognitive impairment. These results suggest that 3D texture might be a useful aid in AD diagnosis.
One-class classification: taxonomy of study and review of techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/ novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.
Design of an algorithmic Wallace multiplier using high speed counters

Wallace tree multipliers provide a power-efficient strategy for high speed multiplication. The use of high speed 7:3 counters in the Wallace tree reduction can further improve the multiplier speed. This paper presents an algorithmic approach to construct the counter based Wallace tree multipliers. The proposed algorithm can be used to implement the efficient counter based Wallace multiplier of any size suitable for FPGA or ASIC synthesis tools. The designs are synthesized in Synopsys Design Compiler using 90 nm CMOS technology. The detailed comparison of traditional and counter based Wallace multipliers is performed which shows that the counter based Wallace multiplier is up to 22% faster as compared to the traditional Wallace multiplier.
How fast is fast enough ? Choosing between Xenomai and Linux for real-time applications

We needed data to help ourselves and our clients to decide when to expend the extra effort to use a real-time extension such as Xenomai; when it is sufficient to use mainline Linux with the PREEMPT RT patches applied; and when unpatched mainline Linux is sufficient. To gather this data, we set out to compare the performance of three kernels: a baseline Linux kernel; the same kernel with the PREEMPT RT patches; and the same kernel with the Xenomai patches. Xenomai is a set of patches to Linux that integrates real-time capabilities from the hardware interrupt level on up. The PREEMPT RT patches make sections of the Linux kernel preemptible that are ordinarily blocking. We measure the timing for performing two tasks. The first task is to toggle a General Purpose IO (GPIO) output at a fixed period. The second task is to respond to a changing input GPIO pin by causing an output GPIO pin’s value to follow it. For this task, rather than polling, we rely on an interrupt to notify us when the GPIO input changes. For each task, we have four distinct experiments: a Linux user-space process with real-time priority; a Linux kernel module; a Xenomai user-space process; and a Xenomai kernel module. The Linux experiments are run on both a stock Linux kernel and a PREEMPT RT-patched Linux kernel. The Xenomai experiments are run on a Xenomai-patched Linux kernel. To provide an objective metric, all timing measurements are taken with an external piece of hardware, running a small C program on bare metal. This paper documents our results. In particular, we begin with a detailed description of the set of tools we developed to test the kernel configurations. We then present details of a a specific hardware test platform, the BeagleBoard C4, an OMAP3 (Arm architecture) system, and the specific kernel configurations we built to test on that platform. We provide extensive numerical results from testing the BeagleBoard. For instance, the approximate highest external-stimulus frequency for which at least 95% of the time the latency does not exceed 1/2 the period is 31kHz. This frequency is achieved with a kernel module on stock Linux; the best that can be achieved with a userspace module is 8.4kHz, using a Xenomai userspace process. If the latency must not exceed 1/2 the frequency 100% of the time, then Xenomai is the best option for both kernelspace and userspace; a Xenomai kernel module can run at 13.5kHz, while a userspace process can hit 5.9kHz. In addition to the numerical results, we discuss the qualitative difficulties we experienced in trying to test these configurations on the BeagleBoard. Finally, we offer our recommendations for deciding when to use stock Linux vs. PREEMPT RTpatched Linux vs. Xenomai for real-time applications.
Simulated Attack on DNP 3 Protocol in SCADA System

Supervisory Control and Data Acquisition (SCADA) system monitors and controls industrial process in physical critical Infrastructures. It is thus of vital importance that any vulnerabilities of SCADA system must be identified and mitigated. DNP3 is and open SCADA network protocol that is mainly used in electrical utilities. However, the security mechanisms of DNP3 were neglected at its design stage. For example, the coverage of DNP3 Secure Authentication is limited to itself only. In our experiments, we have successfully performed a number of attacks to DNP3 on a small-scale testbed. Hence, this paper will not only discuss our experimental results but also propose a novel hybrid method that can enhance the security of existing DNP3 protocol by combining both encryption and authentication techniques.
Introducing the GASP scale: a new measure of guilt and shame proneness.

Although scholars agree that moral emotions are critical for deterring unethical and antisocial behavior, there is disagreement about how 2 prototypical moral emotions--guilt and shame--should be defined, differentiated, and measured. We addressed these issues by developing a new assessment--the Guilt and Shame Proneness scale (GASP)--that measures individual differences in the propensity to experience guilt and shame across a range of personal transgressions. The GASP contains 2 guilt subscales that assess negative behavior-evaluations and repair action tendencies following private transgressions and 2 shame subscales that assess negative self-evaluations (NSEs) and withdrawal action tendencies following publically exposed transgressions. Both guilt subscales were highly correlated with one another and negatively correlated with unethical decision making. Although both shame subscales were associated with relatively poor psychological functioning (e.g., neuroticism, personal distress, low self-esteem), they were only weakly correlated with one another, and their relationships with unethical decision making diverged. Whereas shame-NSE constrained unethical decision making, shame-withdraw did not. Our findings suggest that differentiating the tendency to make NSEs following publically exposed transgressions from the tendency to hide or withdraw from public view is critically important for understanding and measuring dispositional shame proneness. The GASP's ability to distinguish these 2 classes of responses represents an important advantage of the scale over existing assessments. Although further validation research is required, the present studies are promising in that they suggest the GASP has the potential to be an important measurement tool for detecting individuals susceptible to corruption and unethical behavior.
A Comprehensive Survey of Brain Interface Technology Designs

In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.
A sliced spherical Luneburg lens

The Luneburg lens (LL) represents a very attractive candidate for many applications such as multibeam antennas, multifrequency scanning, and spatial scanning, due to its focusing properties. Indeed, it is a dielectric sphere on which each surface point is a frequency-independent perfect focusing point. This is produced by its index governing law n, which follows the radial distribution n/sup 2/=2-r/sup 2/, where r is the normalized radial position. Practically, an LL is manufactured as a finite number of concentric homogeneous dielectric shells - this is called a discrete LL. The inaccuracies in the curved shell manufacturing process produce intershell air gaps, which degrade the performance of the lens. Furthermore, this requires different materials whose relative dielectric constant covers the range 1-2. The paper proposes a new LL manufacturing process to avoid these drawbacks. The paper describe the theoretical background and the performance of the obtained lens.
Adaptive sampling of lion accelerometer data

The ability to track and monitor animals in the wild presents a challenge in both hardware and software development. This is a challenge facing all sensor networks, where there is a requirement for more computation to take place at the nodes whilst expending minimal battery life. In this paper we look at applying reinforcement learning on accelerometer data collected from a lion tag to learn a sampling control policy that adapts according to a lion’s behaviour. The results show that it is possible to reduce battery consumption by 73 % with a reconstruction accuracy of 51 %.
Evaluating the Online Capabilities of Onset Detection Methods

In this paper, we evaluate various onset detection algorithms in terms of their online capabilities. Most methods use some kind of normalization over time, which renders them unusable for online tasks. We modified existing methods to enable online application and evaluated their performance on a large dataset consisting of 27,774 annotated onsets. We focus particularly on the incorporated preprocessing and peak detection methods. We show that, with the right choice of parameters, the maximum achievable performance is in the same range as that of offline algorithms, and that preprocessing can improve the results considerably. Furthermore, we propose a new onset detection method based on the common spectral flux and a new peak-picking method which outperforms traditional methods both online and offline and works with audio signals of various volume levels.