nils-herrmann/clean_scirepeval huggingface dataset:
Perfect Matching Quad Layouts for Manifold Meshes

This paper introduces a new approach to automatically generate pure quadrilateral patch layouts on manifold meshes. The algorithm is based on a careful construction of a singularity graph of a given input frame field or a given periodic global parameterization. A pure quadrilateral patch layout is then derived as a constrained minimum weight perfect matching of that graph. The resulting layout is optimal relative to a balance between coarseness and geometric feature alignment. We formulate the problem of finding pure quadrilateral patch layouts as a global optimization problem related to a well-known concept in graph theory. The main advantage of the new method is its simplicity and its computation speed. Patch layouts generated by the present algorithm are high quality and are very competitive compared to current state of the art.
Integrated Plasma Coal Gasification Power Plant

This paper presents the results of modeled investigations of a developed flow diagram for a small scale (3-4 MW) combined cycle power plant with integrated plasma coal gasification. This includes a highly efficient heat recovery module, a multistaged plasma gasifier, and a gas turbine engine. Total efficiency of the coal conversion into electric power using direct-flow gasification and plasma process initiation could be in the range of 25%-36%, and is primarily dependent on gas turbine performance and the gasification method selected.
Proximal Algorithms in Statistics and Machine Learning

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closedform solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford and Half-Quadratic envelopes. Envelope representations lead to novel proximal algorithms for statistical optimisation of composite objective functions which include both non-smooth and non-convex objectives. We illustrate our methodology with regularized Logistic and Poisson regression and non-convex bridge penalties with a fused lasso norm. We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions. Finally, we provide directions for future research.
Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.
Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)

Machine learning is often used to automatically solve human tasks. In this paper, we look for tasks where machine learning algorithms are not as good as humans with the hope of gaining insight into their current limitations. We studied various Human Interactive Proofs (HIPs) on the market, because they are systems designed to tell computers and humans apart by posing challenges presumably too hard for computers. We found that most HIPs are pure recognition tasks which can easily be broken using machine learning. The harder HIPs use a combination of segmentation and recognition tasks. From this observation, we found that building segmentation tasks is the most effective way to confuse machine learning algorithms. This has enabled us to build effective HIPs (which we deployed in MSN Passport), as well as design challenging segmentation tasks for machine learning algorithms.
Reducing Ambiguities in Requirements Specifications Via Automatically Created Object-Oriented Models

In industry, reviews and inspections are the primary methods to identify ambiguities, inconsistencies, and under specifications in natural language (NL) software requirements specifications (SRSs). However, humans have difficulties identifying ambiguities and tend to overlook inconsistencies in a large NL SRS. This paper presents a three-step, semi-automatic method, supported by a prototype tool, for identifying inconsistencies and ambiguities in NL SRSs. The method combines the strengths of automation and human reasoning to overcome difficulties with reviews and inspections. First, the tool parses a NL SRS according to a constraining grammar. Second, from relationships exposed in the parse, the tool creates the classes, methods, variables, and associations of an object-oriented analysis model of the specified system. Third, the model is diagrammed so that a human reviewer can use the model to detect ambiguities and inconsistencies. Since a human finds the problems, the tool has to have neither perfect recall nor perfect precision. The effectiveness of the approach is demonstrated by applying it and the tool to a widely published example NL SRS. A separate study evaluates the tool’s domain-specific term detection.
Making Machines Understand Us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition

Speech recognition technology has left the research laboratory and is increasingly coming into practical use, enabling a wide spectrum of innovative and exciting voice-driven applications that are radically changing our way of accessing digital services and information. Most of today's applications still require a microphone located near the talker. However, almost all of these applications would benefit from distant-talking speech capturing, where talkers are able to speak at some distance from the microphones without the encumbrance of handheld or body-worn equipment . For example, applications such as meeting speech recognition, automatic annotation of consumer-generated videos, speech-to-speech translation in teleconferencing, and hands-free interfaces for controlling consumer-products, like interactive TV, will greatly benefit from distant-talking operation. Furthermore, for a number of unexplored but important applications, distant microphones are a prerequisite. This means that distant talking speech recognition technology is essential for extending the availability of speech recognizers as well as enhancing the convenience of existing speech recognition applications.
The FoxMeyer Drugs ' Bankruptcy : Was it a Failure of ERP ?

FoxMeyer Drugs was a $5 billion company and the nation's fourth largest distributor of pharmaceuticals before the fiasco. With the goal of using technology to increase efficiency, the Delta III project began in 1993. FoxMeyer conducted market research and product evaluation and purchased SAP R/3 in December of that year. FoxMeyer also purchased warehouse-automation from a vendor called Pinnacle, and chose Andersen Consulting to integrate and implement the two systems. Implementation of the Delta III project took place during 1994 and 1995.
Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning

For multi-view data representation learning, recently the traditional unsupervised CCA method has been converted to supervised ways by introducing label information from samples. However, such supervised CCA variants require large numbers of labeled samples which hampers its practical application. In this paper, in order to mine the most discriminant information only from a few labeled samples, inspired by sparse representation we propose a novel sparse regularized discriminative CCA method to make use of the label information as much as possible. Through constructing sparse weighted matrices in multiple views, we incorporate the structure information into the original CCA framework to extract fused multi-view features which not only are the most correlated but also carry the important discriminative structure information. Our approach is evaluated on both handwritten dataset and face dataset. The experimental results and the comparisons with other related algorithms demonstrate its effectiveness and superiority.
Multi-factor Authentication as a Service

An architecture for providing multi-factor authentication as a service is proposed, resting on the principle of a loose coupling and separation of duties between network entities and end user devices. The multi-factor authentication architecture leverages Identity Federation and Single-Sign-On technologies, such as the OpenID framework, in order to provide for a modular integration of various factors of authentication. The architecture is robust and scalable enabling service providers to define risk-based authentication policies by way of assurance level requirements, which map to concrete authentication factor capabilities on user devices.
Realtime Edge-Based Visual Odometry for a Monocular Camera

In this work we present a novel algorithm for realtime visual odometry for a monocular camera. The main idea is to develop an approach between classical feature-based visual odometry systems and modern direct dense/semi-dense methods, trying to benefit from the best attributes of both. Similar to feature-based systems, we extract information from the images, instead of working with raw image intensities as direct methods. In particular, the information extracted are the edges present in the image, while the rest of the algorithm is designed to take advantage of the structural information provided when pixels are treated as edges. Edge extraction is an efficient and higly parallelizable operation. The edge depth information extracted is dense enough to allow acceptable surface fitting, similar to modern semi-dense methods. This is a valuable attribute that feature-based odometry lacks. Experimental results show that the proposed method has similar drift than state of the art feature-based and direct methods, and is a simple algorithm that runs at realtime and can be parallelized. Finally, we have also developed an inertial aided version that successfully stabilizes an unmanned air vehicle in complex indoor environments using only a frontal camera, while running the complete solution in the embedded hardware on board the vehicle.
An Autoencoder Approach to Learning Bilingual Word Representations

Cross-language learning allows one to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are coherent between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments. We empirically investigate the success of our approach on the problem of cross-language text classification, where a classifier trained on a given language (e.g., English) must learn to generalize to a different language (e.g., German). In experiments on 3 language pairs, we show that our approach achieves state-of-the-art performance, outperforming a method exploiting word alignments and a strong machine translation baseline.
A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy-based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.
A Toolset for Navigation in Virtual Environments

Maintaining knowledge of current position and orientationis frequently a problem for people in virtual environments. In this paper we present a toolset of techniques based on principles of navigation derived from real world analogs, We include a discussion of human and avian navigation behaviors and show how knowledge about them were used to design our tools. We also summarize an informal study we performed to determine how our tools influenced the subjects’ navigation behavior. We conclude that principles extracted from real world navigation aids such as maps can be seen to apply in virtual environments.
An implementation of the earliest deadline first algorithm in Linux

Recently, many projects have been started to introduce some real-time mechanisms into general purpose operating systems (GPOS) in order to make them capable of providing the users with some temporal guarantees. Many of these projects focused especially on Linux for its capillary and widespread adoption throughout many different research and industrial environments.
 By tracking the kernel release cycle, we propose an efficient Earliest Deadline First implementation in the form of a patch-set against the 2.6.27 version, that is the latest released one, as of now. Our implementation provides the user with the possibility to choose SCHED_EDF as one of the possible scheduling policies for a task, with an enhanced version of the standard algorithm. In fact, we propose a new approach to shared resources' access which, differently from many other previous existing works, does not require the user to specify any parameters about the critical sections every task will enter during its execution.
Marked Temporal Dynamics Modeling based on Recurrent Neural Network

We are now witnessing the increasing availability of event s tream data, i.e., a sequence of events with each event typically be ing denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynam ics, i.e., when the next event will take place and what its mark will be. Exist ing methods either predict only the mark or the time of the next event, or pre dict both of them, yet separately. Indeed, in marked temporal dynamics, the ti m and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem , in this paper, we propose to model marked temporal dynamics by using a mark-specificintensity function to explicitly capture the dependency between the m ark and the time of the next event. Extensive experiments on two datasets demon strate that the proposed method outperforms state-of-the-art methods at pred icting marked temporal dynamics.
IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing

Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.
ROSRV: Runtime Verification for Robots

We present ROSRV, a runtime verification framework for robotic applications on top of the Robot Operating System (ROS ), a widely used open-source framework for robot software development. ROSRV aims to address the safety and security issues of robots by providing a transparent monitoring infrastructure that intercepts and monitors the commands and messages passing through the system. Safety and security properties can be defined in a formal specification language, and are ensured by automatically generated monitors. ROSRV integrates seamlessly with ROS—no change in ROS nor the application code is needed. ROSRV has been applied and evaluated on a commercial robot.
Support vector clustering

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.
An Intelligent Hybrid MAC With Traffic-Differentiation-Based QoS for Wireless Sensor Networks

In this paper, we present the Intelligent Hybrid MAC (IH-MAC), a novel low power with quality of service guaranteed medium access control protocol for wireless sensor networks (WSNs). The IH-MAC achieves high energy efficiency under wide range of traffic load. It ensures shorter latency to critical and delay-sensitive packets. The IH-MAC protocol achieves high channel utilization during high traffic load without compromising energy efficiency. The IH-MAC does it by using the strength of CSMA and TDMA approach with intelligence. The novel idea behind the IH-MAC is that it uses both the broadcast scheduling and link scheduling. Depending on the network loads, the IH-MAC protocol dynamically switches from broadcast scheduling to link scheduling and vice versa in order to achieve better efficiency. The scheduling is done in the IH-MAC with a novel decentralized approach where the nodes locally use the clock arithmetic to find the time slot, allocated for it. Furthermore, the IH-MAC uses Request-To-Send, Clear-To-send handshakes with methods for adapting the transmit power to the minimum level necessary to reach the intended neighbor. Thus, the IH-MAC reduces energy consumption by suitably varying the transmit power. The IH-MAC also uses the concept of parallel transmission that further reduces delay. The analytical and simulation results corroborate the theoretical idea, and show the efficiency of our proposed protocol.
Anger detection in call center dialogues

We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect.
Automatic Signature Verification: The State of the Art - 1989-1993

This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.
A Compact Parallel-Plane Perpendicular-Current Feed for a Modified Equiangular Spiral Antenna

The design and measurement is described of a compact (59.94 mm × 59.94 mm × 1.14 mm) bidirectional ultrawideband modified equiangular spiral antenna with an integrated feed internally matched to a 50-Ohm microstrip transmission line. An ultrawideband transition from microstrip to parallel-strip line soldered to a short (1.14 mm) twin-line transmission line feeds the spiral. The currents on the feed travel in a direction approximately perpendicular to the direction of the currents on the spiral at the points where the feed passes the spiral in close proximity (0.57 mm). Holes were etched from the metal arms of the spiral to reduce the impedance mismatch caused by coupling between the transmission line feed and the spiral. Measured and simulated radiation patterns and VSWR plots show good performance over the ultrawideband range (3.1-10.6 GHz) with a 2:1 VSWR everywhere in this range except for 3.33-3.95 GHz, which has a 3:1 VSWR. This spiral exhibits an elliptical polarization over the ultrawideband range.