nils-herrmann/clean_scirepeval huggingface dataset:
A Prototype Hand Geometry-based Verification System

Geometric measurements of the human hand have been used for identity authentication in a number of commercial systems. Yet, there is not much open public literature addressing research issues underlying hand geometry-based identity authentication. This work is our attempt to draw attention to this important biometric by designing a prototype hand geometrybased identity authentication system. We also present our preliminary verification results based on hand measurements of 50 individuals captured over a period of time. The results are encouraging and we plan to address issues to improve the system performance.
Multi-view Metric Learning for Multi-view Video Summarization

Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.
A Broad-Coverage Normalization System for Social Media Language

Social media language contains huge amount and wide variety of nonstandard tokens, created both intentionally and unintentionally by the users. It is of crucial importance to normalize the noisy nonstandard tokens before applying other NLP techniques. A major challenge facing this task is the system coverage, i.e., for any user-created nonstandard term, the system should be able to restore the correct word within its top n output candidates. In this paper, we propose a cognitivelydriven normalization system that integrates different human perspectives in normalizing the nonstandard tokens, including the enhanced letter transformation, visual priming, and string/phonetic similarity. The system was evaluated on both wordand messagelevel using four SMS and Twitter data sets. Results show that our system achieves over 90% word-coverage across all data sets (a 10% absolute increase compared to state-ofthe-art); the broad word-coverage can also successfully translate into message-level performance gain, yielding 6% absolute increase compared to the best prior approach.
Anonymizing Transaction Data by Integrating Suppression and Generalization

Privacy protection in publishing transaction data is an important problem. A key feature of transaction data is the extreme sparsity, which renders any single technique ineffective in anonymizing such data. Among recent works, some incur high information loss, some result in data hard to interpret, and some suffer from performance drawbacks. This paper proposes to integrate generalization and suppression to reduce information loss. However, the integration is non-trivial. We propose novel techniques to address the efficiency and scalability challenges. Extensive experiments on real world databases show that this approach outperforms the state-of-the-art methods, including global generalization, local generalization, and total suppression. In addition, transaction data anonymized by this approach can be analyzed by standard data mining tools, a property that local generalization fails to provide.
Foreground Segmentation Using Adaptive Mixture Models in Color and Depth

Segmentation of novel or dynamic objects in a scene, often referred to as “background subtraction” or “foreground segmentation”, is a critical early in step in most computer vision applications in domains such as surveillance and human-computer interaction. All previously described, real-time methods fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background, and non-static backgrounds (e.g. active video displays or trees waving in the wind). The recent advent of hardware and software for real-time computation of depth imagery makes better approaches possible. We propose a method for modeling the background that uses per-pixel, time-adaptive, Gaussian mixtures in the combined input space of depth and luminance-invariant color. This combination in itself is novel, but we further improve it by introducing the ideas of 1) modulating the background model learning rate based on scene activity, and 2) making colorbased segmentation criteria dependent on depth observations. Our experiments show that the method possesses much greater robustness to problematic phenomena than the prior state-of-the-art, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video event detection and recognition.
A Fast Backtracking Algorithm to Test Directed Graphs for Isomorphism Using Distance Matrices

A backtracking algorithm for testing a pair of digraphs for isomorphism is presented. The information contained in the distance matrix representation of a graph is used to establish an initial partition of the graph's vertices. This distance matrix information is then applied in a backtracking procedure to reduce the search tree of possible mappings. While the algorithm is not guaranteed to run in polynomial time, it performs efficiently for a large class of graphs.
Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

This paper presents prediction-based dynamic resource allocation algorithms to scale video transcoding service on a given Infrastructure as a Service cloud. The proposed algorithms provide mechanisms for allocation and deallocation of virtual machines (VMs) to a cluster of video transcoding servers in a horizontal fashion. We use a two-step load prediction method, which allows proactive resource allocation with high prediction accuracy under real-time constraints. For cost-efficiency, our work supports transcoding of multiple on-demand video streams concurrently on a single VM, resulting in a reduced number of required VMs. We use video segmentation at group of pictures level, which splits video streams into smaller segments that can be transcoded independently of one another. The approach is demonstrated in a discrete-event simulation and an experimental evaluation involving two different load patterns.
Fraud detection using self-organizing map visualizing the user profiles

We propose a fraud detection method based on the user accounts visualization and threshold-type detection. The visualization technique employed in our approach is the Self-Organizing Map (SOM). Since the SOM technique in its original form visualizes only the vectors, and the user accounts are represented in our work as the matrices storing a collection of records reflecting the user sequential activities, we propose a method of the matrices visualization on the SOM grid, which constitutes the main contribution of this paper. Furthermore, we propose a method of the detection threshold setting on the basis of the SOM U-matrix. The results of the conducted experimental study on real data in three different research fields confirm the advantages and effectiveness of the proposed approach.
TOTAL , DIRECT , AND INDIRECT EFFECTS IN STRUCTURAL EQUATION MODELS

Decomposing the total effects of one variable on another into direct and indirect effects has long been of interest to researchers who use path analysis. In this paper, I review the decomposition of effects in general structural equation models with latent and observed variables. I present the two approaches to defining total effects. One is based on sums of powers of coefficient matrices. The other defines total effects as reducedform coefficients. I show the conditions under which these two definitions are equivalent. I also compare the different types of specific indirect effects. These are the influences that are transmitted through particular
Face pose discrimination using support vector machines (SVM)

This paper describes an approach for the problem of face pose discrimination using Support Vector Machines (SVM). Face pose discrimination means that one can label the face image as one of several known poses. Face images are drawn from the standard FERET data base. The training set consists of 150 images equally distributed among frontal, approximately 33.75 rotated left and right poses, respectively, and the test set consists of 450 images again equally distributed among the three different types of poses. SVM achieved perfect accuracy 100% discriminating between the three possible face poses on unseen test data, using either polynomials of degree 3 or Radial Basis Functions (RBFs) as kernel approximation functions.
Toward Air Traffic Complexity Assessment in New Generation Air Traffic Management Systems

The characterization of complex air traffic situations is an important issue in air traffic management (ATM). Within the current ground-based ATM system, complexity metrics have been introduced with the goal of evaluating the difficulty experienced by air traffic controllers in guaranteeing the appropriate aircraft separation in a sector. The rapid increase in air travel demand calls for new generation ATM systems that can safely and efficiently handle higher levels of traffic. To this purpose, part of the responsibility for separation maintenance will be delegated to the aircraft, and trajectory management functions will be further automated and distributed. The evolution toward an autonomous aircraft framework envisages new tasks where assessing complexity may be valuable and requires a whole new perspective in the definition of suitable complexity metrics. This paper presents a critical analysis of the existing approaches for modeling and predicting air traffic complexity, examining their portability to autonomous ATM systems. Possible applications and related requirements will be discussed.
Code Generation Using Tree Matching and Dynamic Programming

Compiler-component generators, such as lexical analyzer generators and parser generators, have long been used to facilitate the construction of compilers. A tree-manipulation language called twig has been developed to help construct efficient code generators. Twig transforms a tree-translation scheme into a code generator that combines a fast top-down tree-pattern matching algorithm with dynamic programming. Twig has been used to specify and construct code generators for several experimental compilers targeted for different machines.
Evolving fuzzy rule based controllers using genetic algorithms

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS l are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.
Deep Learning for Laser Based Odometry Estimation

In this paper we take advantage of recent advances in deep learning techniques focused on image classification to estimate transforms between consecutive point clouds. A standard technique for feature learning is to use convolutional neural networks. Leveraging this technique can help with one of the biggest challenges in robotic motion planning, real time odometry. Sensors have advanced in recent years to provide vast amounts of precise environmental data, but localization methods can have a difficult time efficiently parsing these large quantities. In order to address this hurdle we utilize convolution neural networks for reducing the state space of the laser scan. We implement our network in the Theano framework with the Keras wrapper. Input data is collected from a VLP-16 in both small office and large open environments. We present the results of our experiments on varying network configurations. Our approach shows promising results, achieving (per direction) accuracy within 10 cm and an average network prediction time of 4.58 ms.
Power Efficiency of Hypervisor-based Virtualization versus Container-based Virtualization February 11 , 2016

Up until recently, hypervisor-based virtualization platforms dominated the virtualization industry. However, container-based virtualization, an alternative to hypervisorbased virtualization, simplifies and fastens the deployment of virtual entities. Relevant research has already shown that container-based virtualization either performs equally or better than hypervisor-based virtualization in terms of performance in almost all cases. This research project investigates whether the power efficiency significantly differs on Xen, which is based on hypervisor virtualization, and Docker, which is based on container-based virtualization. The power efficiency is obtained by running synthetic applications and measuring the power usage on different hardware components. Rather than measuring the overall power of the system, or looking at empirical studies, hardware components such as CPU, memory and HDD will be measured internally by placing power sensors between the motherboard and circuits of each measured hardware component. This newly refined approach shows that both virtualization platforms behave roughly similar in IDLE state, when loading the memory and when performing sequential writes for the HDD. Contrarily, the results of CPU and sequential HDD reads show differences between the two virtualization platforms, where the performance of Xen is significantly weaker in terms of power efficiency.
Daily Load Profiles for Residential , Commercial and Industrial Low Voltage Consumers

This paper presents the results of the study carried out for the determination of the residential, commercial and industrial consumers daily load curves based on field measurements performed by the Utilities of Electric Energy of São Paulo State, Brazil. A methodology for the aggregation of these loads to determine the expected loading in equipment or in a preset part of the distribution network by using the representative daily curves of each consumer’s activity and the monthly energy consumption of the connected consumers is also presented.
Learning to Share Latent Tasks for Action Recognition

Sharing knowledge for multiple related machine learning tasks is an effective strategy to improve the generalization performance. In this paper, we investigate knowledge sharing across categories for action recognition in videos. The motivation is that many action categories are related, where common motion pattern are shared among them (e.g. diving and high jump share the jump motion). We propose a new multi-task learning method to learn latent tasks shared across categories, and reconstruct a classifier for each category from these latent tasks. Compared to previous methods, our approach has two advantages: (1) The learned latent tasks correspond to basic motion patterns instead of full actions, thus enhancing discrimination power of the classifiers. (2) Categories are selected to share information with a sparsity regularizer, avoiding falsely forcing all categories to share knowledge. Experimental results on multiple public data sets show that the proposed approach can effectively transfer knowledge between different action categories to improve the performance of conventional single task learning methods.
Ehlers-Danlos Syndrome, Hypermobility Type: An Underdiagnosed Hereditary Connective Tissue Disorder with Mucocutaneous, Articular, and Systemic Manifestations

Ehlers-Danlos syndrome, hypermobility type, constituting a phenotypic continuum with or, perhaps, corresponding to the joint hypermobility syndrome (JHS/EDS-HT), is likely the most common, though the least recognized, heritable connective tissue disorder. Known for decades as a hereditary condition with predominant rheumatologic manifestations, it is now emerging as a multisystemic disorder with widespread manifestations. Nevertheless, the practitioners' awareness of this condition is generally poor and most patients await years or, perhaps, decades before reaching the correct diagnosis. Among the various sites of disease manifestations, skin and mucosae represent a neglected organ where the dermatologist can easily spot diagnostic clues, which consistently integrate joint hypermobility and other orthopedic/neurologic manifestations at physical examination. In this paper, actual knowledge on JHS/EDS-HT is summarized in various sections. Particular attention has been posed on overlooked manifestations, including cutaneous, mucosal, and oropharyngeal features, and early diagnosis techniques, as a major point of interest for the practicing dermatologist. Actual research progresses on JH/EDS-HT envisage an unexpected link between heritable dysfunctions of the connective tissue and a wide range of functional somatic syndromes, most of them commonly diagnosed in the office of various specialists, comprising dermatologists.
SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)

This paper describes the first shared task on Taxonomy Extraction Evaluation organised as part of SemEval-2015. Participants were asked to find hypernym-hyponym relations between given terms. For each of the four selected target domains the participants were provided with two lists of domainspecific terms: a WordNet collection of terms and a well-known terminology extracted from an online publicly available taxonomy. A total of 45 taxonomies submitted by 6 participating teams were evaluated using standard structural measures, the structural similarity with a gold standard taxonomy, and through manual quality assessment of sampled novel relations.
Parameter space exploration with Gaussian process trees

Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are unnecessary in regions where the response is easy predicted; well-chosen designs could allow a mapping of the response with far fewer simulation runs. Thus, there is a need for computationally inexpensive surrogate models and an accompanying method for selecting small designs. We explore a general methodology for addressing this need that uses non-stationary Gaussian processes. Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling. Our methods are illustrated on several examples, including a motivating example involving computational fluid dynamics simulation of a NASA reentry vehicle.
Classification of research papers using citation links and citation types: Towards automatic review article generation.

We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author’s viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. Further, if these citing areas are properly classified and organized, they can act as a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method.
Bayesian Factorization and Learning for Monaural Source Separation

This paper presents a new Bayesian nonnegative matrix factorization (NMF) for monaural source separation. Using this approach, the reconstruction error based on NMF is represented by a Poisson distribution, and the NMF parameters, consisting of the basis and weight matrices, are characterized by the exponential priors. A variational Bayesian inference procedure is developed to learn variational parameters and model parameters. The randomness in separation process is faithfully represented so that the system robustness to model variations in heterogeneous environments could be achieved. Importantly, the exponential prior parameters are used to impose sparseness in basis representation. The variational lower bound of log marginal likelihood is adopted as the objective to control model complexity. The dependencies of variational objective on model parameters are fully characterized in the derived closed-form solution. A clustering algorithm is performed to find the groups of bases for unsupervised source separation. The experiments on speech/music separation and singing voice separation show that the proposed Bayesian NMF (BNMF) with adaptive basis representation outperforms the NMF with fixed number of bases and the other BNMFs in terms of signal-to-distortion ratio and the global normalized source to distortion ratio.
Gated three-terminal device architecture to eliminate persistent photoconductivity in oxide semiconductor photosensor arrays.

The composition of amorphous oxide semiconductors, which are well known for their optical transparency, can be tailored to enhance their absorption and induce photoconductivity for irradiation with green, and shorter wavelength light. In principle, amorphous oxide semiconductor-based thin-film photoconductors could hence be applied as photosensors. However, their photoconductivity persists for hours after illumination has been removed, which severely degrades the response time and the frame rate of oxide-based sensor arrays. We have solved the problem of persistent photoconductivity (PPC) by developing a gated amorphous oxide semiconductor photo thin-film transistor (photo-TFT) that can provide direct control over the position of the Fermi level in the active layer. Applying a short-duration (10 ns) voltage pulse to these devices induces electron accumulation and accelerates their recombination with ionized oxygen vacancy sites, which are thought to cause PPC. We have integrated these photo-TFTs in a transparent active-matrix photosensor array that can be operated at high frame rates and that has potential applications in contact-free interactive displays.