nils-herrmann/clean_scirepeval huggingface dataset:
Morphological classification of brains via high-dimensional shape transformations and machine learning methods

A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.
MODEL-DRIVEN AND DATA-DRIVEN APPROACHES USING LIDAR DATA : ANALYSIS AND COMPARISON

Following the development of 3D data acquisition techniques like digital photogrammetry, remote sensing and airborne laser scanning, the automatic building modelling is still a challenging task. Indeed, it takes a growing place in many research fields like 3D city modelling, cartographic analysis, urban planning, visualization and Geographic Information Systems (GIS) database construction. Generally, two types of approaches characterize the automatic building reconstruction. The first one is a model-driven approach. This parametric modelling approach consists of searching the most appropriate model among basic building models contained in a models library. The second one is a data-driven approach, also called non-parametric modelling approach. This technique attempts to model a primitive or a complex building by using series of more or less complex operations. It allows the generation of a model without belonging to a specific library. After an extensive state of the art, this paper confronts methods belonging to each type of automatic building construction approach. Based on a concrete experiment, the essential points characterizing each approach, including their concept and the obtained model characteristics are analysed. It is of great interest to study these issues, since not many of such investigations have been made before and have delivered the useful formulas. Consequently, improvements are introduced and have led to develop a new method for each approach. After comparison of the accuracy of these two methods and the characteristics of the resulting models, some solutions and recommendations are proposed.
Explainable Black-Box Attacks Against Model-based Authentication

Establishing unique identities for both humans and end systems has been an active research problem in the security community, giving rise to innovative machine learning-based authentication techniques. Although such techniques offer an automated method to establish identity, they have not been vetted against sophisticated attacks that target their core machine learning technique. This paper demonstrates that mimicking the unique signatures generated by host fingerprinting and biometric authentication systems is possible. We expose the ineffectiveness of underlying machine learning classification models by constructing a blind attack based around the query synthesis framework and utilizing Explainable–AI (XAI) techniques. We launch an attack in under 130 queries on a state-of-the-art face authentication system, and under 100 queries on a host authentication system. We examine how these attacks can be defended against and explore their limitations. XAI provides an effective means for adversaries to infer decision boundaries and provides a new way forward in constructing attacks against systems using machine learning models for authentication.
Wissensmanagement: Einige Konzepte & Technologien

In das umfassende Gebiet Wissensmanagement (WM) wird durch eine allgemeine Einleitung Definitionen, und Grundlagen eingeführt. Die konzeptuelle Ebene im Wissensmanagement mit dem Schwerpunkt Geschäftsprozessorientierte WM-Ansätze wird anschließend näher erläutert. Zur Umsetzung der WM-Ansätze werden Technologien des WMs beschrieben und der Nutzen von Web-Technologien im WM aufgezeigt. Abschließend werden allgemeine Trends sowie der Forschungsschwerpunkt der Abteilung Knowledge Engineering im WM diskutiert.
Topic-based Social Influence Measurement for Social Networks

Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, user-generated content for topic-based influence measurement, and user’s interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.
Effect of garlic oil and four of its compounds on rumen microbial fermentation.

Different concentrations (3, 30, 300, and 3000 mg/L of culture fluid) of garlic oil (GAR), diallyl sulfide (DAS), diallyl disulfide (DAD), allicin (ALL), and allyl mercaptan (ALM) were incubated for 24 h in diluted ruminal fluid with a 50:50 forage:concentrate diet (17.7% crude protein; 30.7% neutral detergent fiber) to evaluate their effects on rumen microbial fermentation. Garlic oil (30 and 300 mg/L), DAD (30 and 300 mg/L), and ALM (300 mg/L) resulted in lower molar proportion of acetate and higher proportions of propionate and butyrate. In contrast, at 300 mg/L, DAS only increased the proportion of butyrate, and ALL had no effects on volatile fatty acid proportions. In a dual-flow continuous culture of rumen fluid fed the same 50:50 forage:concentrate diet, addition of GAR (312 mg/L), DAD (31.2 and 312 mg/L), and ALM (31.2 and 312 mg/L) resulted in similar changes to those observed in batch culture, with the exception of the lack of effect of DAD on the proportion of propionate. In a third in vitro study, the potential of GAR (300 mg/L), DAD (300 mg/L), and ALM (300 mg/L) to decrease methane production was evaluated. Treatments GAR, DAD, and ALM resulted in a decrease in methane production of 73.6, 68.5, and 19.5%, respectively, compared with the control. These results confirm the ability of GAR, DAD, and ALM to decrease methane production, which may help to improve the efficiency of energy use in the rumen.
Presumptions and actions affecting an e-learning adoption by the educational system Implementation using virtual private networks

In this paper we present a model of e-learning suitable for teacher training sessions. The main purpose of our work is to define the components of the educational system which influences the successful adoption of e-learning in the field of education. We also present the factors of the readiness of e-learning mentioned in the literature available and classifies them into the 3 major categories that constitute the components of every organization and consequently that of education. Finally, we present an implementation model of e-learning through the use of virtual private networks, which lends an added value to the realization of e-learning.
The Neurophysiology of Language Processing Shapes the Evolution of Grammar: Evidence from Case Marking

Do principles of language processing in the brain affect the way grammar evolves over time or is language change just a matter of socio-historical contingency? While the balance of evidence has been ambiguous and controversial, we identify here a neurophysiological constraint on the processing of language that has a systematic effect on the evolution of how noun phrases are marked by case (i.e. by such contrasts as between the English base form she and the object form her). In neurophysiological experiments across diverse languages we found that during processing, participants initially interpret the first base-form noun phrase they hear (e.g. she…) as an agent (which would fit a continuation like … greeted him), even when the sentence later requires the interpretation of a patient role (as in … was greeted). We show that this processing principle is also operative in Hindi, a language where initial base-form noun phrases most commonly denote patients because many agents receive a special case marker ("ergative") and are often left out in discourse. This finding suggests that the principle is species-wide and independent of the structural affordances of specific languages. As such, the principle favors the development and maintenance of case-marking systems that equate base-form cases with agents rather than with patients. We confirm this evolutionary bias by statistical analyses of phylogenetic signals in over 600 languages worldwide, controlling for confounding effects from language contact. Our findings suggest that at least one core property of grammar systematically adapts in its evolution to the neurophysiological conditions of the brain, independently of socio-historical factors. This opens up new avenues for understanding how specific properties of grammar have developed in tight interaction with the biological evolution of our species.
Software engineering tools classification based on SWEBOK taxonomy and software profile

In the field of software engineering, it is necessary for users to use various software engineering tools for developing quality software throughout the Software Development Life Cycle (SDLC). Currently, there are many software engineering tools served various aspects of software processes. User may need to select tool for a particular purpose. Selecting a suitable tool from software engineering point of view is a challenge issue. Therefore, we propose an approach for software engineering tool retrieval that is accurate and appropriate for the users' type of usage. We use the categories of software engineering tools specified by IEEE's Software Engineering Body of Knowledge as headings and software tool profile for classifying the tools, and use the K-Means on Batch mode algorithm for classification. This classification before the retrieval step allows the retrieval to have more precision than the classic information retrieval approach, and this method can be applied to searches in other fields with a given taxonomy.
Musical instrument identification using multiscale Mel-frequency cepstral coefficients

We investigate the benefits of evaluating Mel-frequency cepstral coefficients (MFCCs) over several time scales in the context of automatic musical instrument identification for signals that are monophonic but derived from real musical settings. We define several sets of features derived from MFCCs computed using multiple time resolutions, and compare their performance against other features that are computed using a single time resolution, such as MFCCs, and derivatives of MFCCs. We find that in each task - pair-wise discrimination, and one vs. all classification - the features involving multiscale decompositions perform significantly better than features computed using a single time-resolution.
Spotting Code Optimizations in Data-Parallel Pipelines through PeriSCOPE

To minimize the amount of data-shuffling I/O that occurs between the pipeline stages of a distributed data-parallel program, its procedural code must be optimized with full awareness of the pipeline that it executes in. Unfortunately, neither pipeline optimizers nor traditional compilers examine both the pipeline and procedural code of a data-parallel program so programmers must either hand-optimize their program across pipeline stages or live with poor performance. To resolve this tension between performance and programmability, this paper describes PeriSCOPE, which automatically optimizes a data-parallel program's procedural code in the context of data flow that is reconstructed from the program's pipeline topology. Such optimizations eliminate unnecessary code and data, perform early data filtering, and calculate small derived values (e.g., predicates) earlier in the pipeline, so that less data - sometimes much less data - is transferred between pipeline stages. PeriSCOPE further leverages symbolic execution to enlarge the scope of such optimizations by eliminating dead code. We describe how PeriSCOPE is implemented and evaluate its effectiveness on real production jobs.
Suicide with a homemade shotgun: case report and review of literature.

This is a 57-year-old white man with a medical history of depression and recent suicidal ideation that necessitated police response with confiscation of his shotgun. Approximately 3 weeks later he was found by his landlord lifeless on the floor surrounded by a pool of blood.Scene investigation revealed a homemade shotgun and a hammer lying near the decedent. Autopsy revealed a contact shotgun wound to the right side of the decedent's head. The shotgun blast caused injury to the skull with evisceration of the brain. Soot and shotgun filler were present surrounding and within the entrance wound.In this case report, we show that the preventive measures of taking away one's firearms and admission into a psychiatric hospital is in some cases not enough to prevent suicide. The desire to end one's existence may lead to a well thought out and ingenuous means to commit the act of suicide.
Tri-band miniature GPS Array with a single-fed CP antenna element

A single-fed CP stacked patch antenna is proposed to cover all the GPS bands, including E5a/E5b for the Galileo system. The small aperture size (lambda/8 at the L5 band) and the single feeding property make this antenna a promising element for small GPS arrays. The design procedures and antenna performances are presented, and issues related to coupling between array elements are discussed.
Compression without a common prior: an information-theoretic justification for ambiguity in language

Compression is a fundamental goal of both human language and digital communication, yet natural language is very different from compression schemes employed by modern computers. We partly explain this difference using the fact that information theory generally assumes a common prior probability distribution shared by the encoder and decoder, whereas human communication has to be robust to the fact that a speaker and listener may have different prior beliefs about what a speaker may say. We model this information-theoretically using the following question: what type of compression scheme would be effective when the encoder and decoder have (boundedly) different prior probability distributions. The resulting compression scheme resembles natural language to a far greater extent than existing digital communication protocols. We also use information theory to justify why ambiguity is necessary for the purpose of compression.
GlyphLens: View-Dependent Occlusion Management in the Interactive Glyph Visualization

Glyph as a powerful multivariate visualization technique is used to visualize data through its visual channels. To visualize 3D volumetric dataset, glyphs are usually placed on 2D surface, such as the slicing plane or the feature surface, to avoid occluding each other. However, the 3D spatial structure of some features may be missing. On the other hand, placing large number of glyphs over the entire 3D space results in occlusion and visual clutter that make the visualization ineffective. To avoid the occlusion, we propose a view-dependent interactive 3D lens that removes the occluding glyphs by pulling the glyphs aside through the animation. We provide two space deformation models and two lens shape models to displace the glyphs based on their spatial distributions. After the displacement, the glyphs around the user-interested region are still visible as the context information, and their spatial structures are preserved. Besides, we attenuate the brightness of the glyphs inside the lens based on their depths to provide more depth cue. Furthermore, we developed an interactive glyph visualization system to explore different glyph-based visualization applications. In the system, we provide a few lens utilities that allows users to pick a glyph or a feature and look at it from different view directions. We compare different display/interaction techniques to visualize/manipulate our lens and glyphs.
LOADED: link-based outlier and anomaly detection in evolving data sets

In this paper, we present LOADED, an algorithm for outlier detection in evolving data sets containing both continuous and categorical attributes. LOADED is a tunable algorithm, wherein one can trade off computation for accuracy so that domain-specific response times are achieved. Experimental results show that LOADED provides very good detection and false positive rates, which are several times better than those of existing distance-based schemes.
Diagnostic criteria and severity assessment of acute cholecystitis: Tokyo Guidelines

The aim of this article is to propose new criteria for the diagnosis and severity assessment of acute cholecystitis, based on a systematic review of the literature and a consensus of experts. A working group reviewed articles with regard to the diagnosis and treatment of acute cholecystitis and extracted the best current available evidence. In addition to the evidence and face-to-face discussions, domestic consensus meetings were held by the experts in order to assess the results. A provisional outcome statement regarding the diagnostic criteria and criteria for severity assessment was discussed and finalized during an International Consensus Meeting held in Tokyo 2006. Patients exhibiting one of the local signs of inflammation, such as Murphy's sign, or a mass, pain or tenderness in the right upper quadrant, as well as one of the systemic signs of inflammation, such as fever, elevated white blood cell count, and elevated C-reactive protein level, are diagnosed as having acute cholecystitis. Patients in whom suspected clinical findings are confirmed by diagnostic imaging are also diagnosed with acute cholecystitis. The severity of acute cholecystitis is classified into three grades, mild (grade I), moderate (grade II), and severe (grade III). Grade I (mild acute cholecystitis) is defined as acute cholecystitis in a patient with no organ dysfunction and limited disease in the gallbladder, making cholecystectomy a low-risk procedure. Grade II (moderate acute cholecystitis) is associated with no organ dysfunction but there is extensive disease in the gallbladder, resulting in difficulty in safely performing a cholecystectomy. Grade II disease is usually characterized by an elevated white blood cell count; a palpable, tender mass in the right upper abdominal quadrant; disease duration of more than 72 h; and imaging studies indicating significant inflammatory changes in the gallbladder. Grade III (severe acute cholecystitis) is defined as acute cholecystitis with organ dysfunction.
A systematically conducted literature review: quality attribute variability in software product lines

Typically, products in a software product line differ by their functionality, and quality attributes are not intentionally varied. Why, how, and which quality attributes to vary has remained an open issue. A systematically conducted literature review on quality attribute variability is presented, where primary studies are selected by reading all content of full studies in Software Product Line Conference. The results indicate that the success of feature modeling influences the proposed approaches, different approaches suit specific quality attributes differently, and empirical evidence on industrial quality variability is lacking.
Portfolios of Subgraph Isomorphism Algorithms

Subgraph isomorphism is a computationally challenging problem with important practical applications, for example in computer vision, biochemistry, and model checking. There are a number of state-of-the-art algorithms for solving the problem, each of which has its own performance characteristics. As with many other hard problems, the single best choice of algorithm overall is rarely the best algorithm on an instance-by-instance. We develop an algorithm selection approach which leverages novel features to characterise subgraph isomorphism problems and dynamically decides which algorithm to use on a per-instance basis. We demonstrate substantial performance improvements on a large set of hard benchmark problems. In addition, we show how algorithm selection models can be leveraged to gain new insights into what affects the performance of an algorithm.
Amortized Context Vector Inference for Sequence-to-Sequence Networks

Neural attention (NA) is an effective mechanism for inferring complex structural data dependencies that span long temporal horizons. As a consequence, it has become a key component of sequence-to-sequence models that yield state-of-theart performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. However, recent work in the field of amortized variational inference (AVI) has shown that it is often useful to treat the representations generated by deep networks as latent random variables. This allows for the models to learn to infer representations that offer much stronger generalization capacity. Based on this motivation, in this work we introduce a novel regard towards a popular NA mechanism, namely softattention (SA). Our approach treats the context vectors generated by SA models as latent variables, the finite mixture model posteriors of which are inferred by employing AVI. Both the component means and the covariance matrices of the inferred posteriors are parameterized via deep network mechanisms similar to those employed in the context of standard SA. To illustrate our method, we implement it in the context of popular sequence-to-sequence model variants with SA. We conduct an extensive experimental evaluation using challenging ADS, VC, and MT benchmarks, and show how our approach compares to the baselines.