nils-herrmann/clean_scirepeval huggingface dataset:
Self-localization for indoor mobile robots based on optical mouse sensor values and simple global camera information

This paper describes a self-localization for indoor mobile robots based on integrating measurement values from multiple optical mouse sensors and a global camera. This paper consists of two parts. Firstly, we propose a dead-reckoning based on increments of the robot movements read directly from the floor using optical mouse sensors. Since the measurement values from multiple optical mouse sensors are compared to each other and only the reliable values are selected, accurate dead-reckoning can be realized compared with the conventional method based on increments of wheel rotations. Secondly, in order to realize robust localization, we propose a method of estimating position and orientation by integrating measured robot position (orientation information is not included) via global camera and dead-reckoning with the Kalman filter
How Useful Are Natural Language Interfaces to the Semantic Web for Casual End-Users?

Natural language interfaces offer end-users a familiar and convenient option for querying ontology-based knowledge bases. Several studies have shown that they can achieve high retrieval performance as well as domain independence. This paper focuses on usability and investigates if NLIs are useful from an end-user’s point of view. To that end, we introduce four interfaces each allowing a different query language and present a usability study benchmarking these interfaces. The results of the study reveal a clear preference for full sentences as query language and confirm that NLIs are useful for querying Semantic Web data.
45-nm CMOS SOI Technology Characterization for Millimeter-Wave Applications

This paper presents an in-depth study of a 45-nm CMOS silicon-on-insulator (SOI) technology. Several transistor test cells are characterized and the effect of finger width, gate contact, and gate poly pitch on transistor performance is analyzed. The measured peak ft is 264 GHz for a 30 × 1007 nm single-gate contact relaxed-pitch transistor and the best fmax of 283 GHz is achieved by a 58 × 513 nm single-gate contact regular pitch transistor. The measured transistor performance agrees well with the simulations including R/C extraction up to the top metal layer. Passive components are also characterized and their performance is predicted accurately with design kit models and electromagnetic simulations. Low-noise amplifiers from Q- to W-band are developed in this technology and they achieve state-of-the-art noise-figure values.
Learning from Limited Demonstrations

We propose a Learning from Demonstration (LfD) algorithm which leverages expert data, even if they are very few or inaccurate. We achieve this by using both expert data, as well as reinforcement signals gathered through trial-and-error interactions with the environment. The key idea of our approach, Approximate Policy Iteration with Demonstration (APID), is that expert’s suggestions are used to define linear constraints which guide the optimization performed by Approximate Policy Iteration. We prove an upper bound on the Bellman error of the estimate computed by APID at each iteration. Moreover, we show empirically that APID outperforms pure Approximate Policy Iteration, a state-of-the-art LfD algorithm, and supervised learning in a variety of scenarios, including when very few and/or suboptimal demonstrations are available. Our experiments include simulations as well as a real robot path-finding task.
Architecture evaluation for power-efficient FPGAs

This paper presents a flexible FPGA architecture evaluation framework, named fpgaEVA-LP, for power efficiency analysis of LUT-based FPGA architectures. Our work has several contributions: (i) We develop a mixed-level FPGA power model that combines switch-level models for interconnects and macromodels for LUTs; (ii) We develop a tool that automatically generates a back-annotated gate-level netlist with post-layout extracted capacitances and delays; (iii) We develop a cycle-accurate power simulator based on our power model. It carries out gate-level simulation under real delay model and is able to capture glitch power; (iv) Using the framework fpgaEVA-LP, we study the power efficiency of FPGAs, in 0.10um technology, under various settings of architecture parameters such as LUT sizes, cluster sizes and wire segmentation schemes and reach several important conclusions. We also present the detailed power consumption distribution among different FPGA components and shed light on the potential opportunities of power optimization for future FPGA designs (e.g., ≤: 0.10um technology).
Cachet: a decentralized architecture for privacy preserving social networking with caching

Online social networks (OSNs) such as Facebook and Google+ have transformed the way our society communicates. However, this success has come at the cost of user privacy; in today's OSNs, users are not in control of their own data, and depend on OSN operators to enforce access control policies. A multitude of privacy breaches has spurred research into privacy-preserving alternatives for social networking, exploring a number of techniques for storing, disseminating, and controlling access to data in a decentralized fashion. In this paper, we argue that a combination of techniques is necessary to efficiently support the complex functionality requirements of OSNs.
 We propose Cachet, an architecture that provides strong security and privacy guarantees while preserving the main functionality of online social networks. In particular, Cachet protects the confidentiality, integrity and availability of user content, as well as the privacy of user relationships. Cachet uses a distributed pool of nodes to store user data and ensure availability. Storage nodes in Cachet are untrusted; we leverage cryptographic techniques such as attribute based encryption to protect the confidentiality of data. For efficient dissemination and retrieval of data, Cachet uses a hybrid structured-unstructured overlay paradigm in which a conventional distributed hash table is augmented with social links between users. Social contacts in our system act as caches to store recent updates in the social network, and help reduce the cryptographic as well as the communication overhead in the network.
 We built a prototype implementation of Cachet in the FreePastry simulator. To demonstrate the functionality of existing OSNs we implemented the "newsfeed" application. Our evaluation demonstrates that (a) decentralized architectures for privacy preserving social networking are feasible, and (b) use of social contacts for object caching results in significant performance improvements.
Proofs of retrievability: theory and implementation

A proof of retrievability (POR) is a compact proof by a file system (prover) to a client (verifier) that a target file F is intact, in the sense that the client can fully recover it. As PORs incur lower communication complexity than transmission of F itself, they are an attractive building block for high-assurance remote storage systems.
 In this paper, we propose a theoretical framework for the design of PORs. Our framework improves the previously proposed POR constructions of Juels-Kaliski and Shacham-Waters, and also sheds light on the conceptual limitations of previous theoretical models for PORs. It supports a fully Byzantine adversarial model, carrying only the restriction---fundamental to all PORs---that the adversary's error rate be bounded when the client seeks to extract F. We propose a new variant on the Juels-Kaliski protocol and describe a prototype implementation. We demonstrate practical encoding even for files F whose size exceeds that of client main memory.
Saliency Detection Based on Frequency and Spatial Domain Analyses

We propose a new saliency detection model by combining global information from frequency domain analysis and local information from spatial domain analysis. In the frequency domain analysis, instead of modeling salient regions, we model the nonsalient regions using global information; these so-called repeating patterns that are not distinctive in the scene are suppressed by using spectrum smoothing. In spatial domain analysis, we enhance those regions that are more informative by using a center-surround mechanism similar to that found in the visual cortex. Finally, the outputs from these two channels are combined to produce the saliency map. We demonstrate that the proposed model has the ability to highlight both small and large salient regions in cluttered scenes and to inhibit repeating objects. Experimental results also show that the proposed model outperforms existing algorithms in predicting objects regions where human pay more attention.
The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.
A Multi-transformational Model for Background Subtraction with Moving Cameras

We introduce a new approach to perform background subtraction in moving camera scenarios. Unlike previous treatments of the problem, we do not restrict the camera motion or the scene geometry. The proposed approach relies on Bayesian selection of the transformation that best describes the geometric relation between consecutive frames. Based on the selected transformation, we propagate a set of learned background and foreground appearance models using a single or a series of homography transforms. The propagated models are subjected to MAP-MRF optimization framework that combines motion, appearance, spatial, and temporal cues; the optimization process provides the final background/foreground labels. Extensive experimental evaluation with challenging videos shows that the proposed method outperforms the baseline and state-of-the-art methods in most cases.
Towards domain-independent information extraction from web tables

Traditionally, information extraction from web tables has focused on small, more or less homogeneous corpora, often based on assumptions about the use of <table> tags. A multitude of different HTML implementations of web tables make these approaches difficult to scale. In this paper, we approach the problem of domain-independent information extraction from web tables by shifting our attention from the tree-based representation of webpages to a variation of the two-dimensional visual box model used by web browsers to display the information on the screen. The there by obtained topological and style information allows us to fill the gap created by missing domain-specific knowledge about content and table templates. We believe that, in a future step, this approach can become the basis for a new way of large-scale knowledge acquisition from the current "Visual Web.
The extended SMAS facelift: identifying the lateral zygomaticus major muscle border using bony anatomic landmarks.

Extended superficial musculoaponeurotic system (SMAS) rhytidectomy has been advocated for improving nasolabial fold prominence. Extended subSMAS dissection requires release of the SMAS typically from the upper lateral border of the zygomaticus major muscle and continued dissection medial to this muscle. This maneuver releases the zygomatic retaining ligaments and achieves more effective mobilization and elevation of the ptotic malar soft tissues, resulting in more dramatic effacement of the nasolabial crease. Despite its presumed advantages, few reports have suggested greater risk of nerve injury with this technique compared with other limited sub-SMAS dissection techniques. Although the caudal extent of the zygomaticus muscle insertion to the modiolus of the mouth has been well delineated, the more cephalad origin has been vaguely defined. We attempted to define anatomic landmarks which could serve to more reliably identify the upper extent of the lateral zygomaticus major muscle border and more safely guide extended sub-SMAS dissections. Bilateral zygomaticus major muscles were identified in 13 cadaver heads with 4.0-power loupe magnification. Bony anatomic landmarks were identified that would predict the location of the lateral border of the zygomaticus major muscle. The upper extent of the lateral border of the zygomaticus major muscle was defined in relation to an oblique line extending from the mental protuberance to the notch defined at the most anterior-inferior aspect of the temporal fossa at the junction of the frontal process and temporal process of the zygomatic bone. The lateral border of the zygomaticus major muscle was observed 4.4 +/- 2.2 mm lateral and parallel to this line. More accurate prediction of the location of the upper extent of the lateral border of the zygomaticus major muscle using the above bony anatomic landmarks may limit nerve injury during SMAS dissections in extended SMAS rhytidectomy.
Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?

Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be improved by adding prosodic information. The study is based on more than 1000 conversations from the Switchboard corpus. DAs were hand-annotated, and prosodic features (duration, pause, F0, energy, and speaking rate) were automatically extracted for each DA. In training, decision trees based on these features were inferred; trees were then applied to unseen test data to evaluate performance. Performance was evaluated for prosody models alone, and after combining the prosody models with word information--either from true words or from the output of an automatic speech recognizer. For an overall classification task, as well as three subtasks, prosody made significant contributions to classification. Feature-specific analyses further revealed that although canonical features (such as F0 for questions) were important, less obvious features could compensate if canonical features were removed. Finally, in each task, integrating the prosodic model with a DA-specific statistical language model improved performance over that of the language model alone, especially for the case of recognized words. Results suggest that DAs are redundantly marked in natural conversation, and that a variety of automatically extractable prosodic features could aid dialog processing in speech applications.
Robust camera location estimation by convex programming

3D structure recovery from a collection of 2D images requires the estimation of the camera locations and orientations, i.e. the camera motion. For large, irregular collections of images, existing methods for the location estimation part, which can be formulated as the inverse problem of estimating n locations t1, t2, ..., tn in ℝ3 from noisy measurements of a subset of the pairwise directions ti-tj/∥ti-tj∥, are sensitive to outliers in direction measurements. In this paper, we firstly provide a complete characterization of well-posed instances of the location estimation problem, by presenting its relation to the existing theory of parallel rigidity. For robust estimation of camera locations, we introduce a two-step approach, comprised of a pairwise direction estimation method robust to outliers in point correspondences between image pairs, and a convex program to maintain robustness to outlier directions. In the presence of partially corrupted measurements, we empirically demonstrate that our convex formulation can even recover the locations exactly. Lastly, we demonstrate the utility of our formulations through experiments on Internet photo collections.
Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks

The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies.
Detecting Malicious Flux Service Networks through Passive Analysis of Recursive DNS Traces

In this paper we propose a novel, passive approach for detecting and tracking malicious flux service networks. Our detection system is based on passive analysis of recursive DNS (RDNS) traffic traces collected from multiple large networks. Contrary to previous work, our approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, our approach is able to detect malicious flux service networks in-the-wild, i.e., as they are accessed by users who fall victims of malicious content advertised through blog spam, instant messaging spam, social website spam, etc., beside email spam. We experiment with the RDNS traffic passively collected at two large ISP networks. Overall, our sensors monitored more than 2.5 billion DNS queries per day from millions of distinct source IPs for a period of 45 days. Our experimental results show that the proposed approach is able to accurately detect malicious flux service networks. Furthermore, we show how our passive detection and tracking of malicious flux service networks may benefit spam filtering applications.
An Advanced External Compensation System for Active Matrix Organic Light-Emitting Diode Displays With Poly-Si Thin-Film Transistor Backplane

An advanced method for externally compensating the nonuniform electrical characteristics of polycrystalline silicon thin-film transistors (TFTs) and the degradation of organic light-emitting diode (OLED) devices is proposed, and the method is verified using a 14.1-in active matrix OLED (AMOLED) panel. The proposed method provides an effective solution for high-image-quality AMOLED displays by removing IR-drop and temperature effects during the sensing and displaying operations of the external compensation method. Experimental results show that the electrical characteristics of TFTs and OLEDs are successfully sensed, and that the stained image pattern due to the nonuniform luminance error and the differential aging of the OLED is removed. The luminance error range without compensation is from -6.1% to 9.0%, but it is from -1.1% to 1.2% using the external compensation at the luminance level of 120 cd/m2 in a 14.1-inch AMOLED panel.
Internet Engineering Task Force (ietf) the Minimum Rank with Hysteresis Objective Function

The Routing Protocol for Low-Power and Lossy Networks (RPL) constructs routes by using Objective Functions that optimize or constrain the routes it selects and uses. This specification describes the Minimum Rank with Hysteresis Objective Function (MRHOF), an Objective Function that selects routes that minimize a metric, while using hysteresis to reduce churn in response to small metric changes. MRHOF works with additive metrics along a route, and the metrics it uses are determined by the metrics that the RPL Destination Information Object (DIO) messages advertise.
The Battle of the Giants - A Case Study of GPU vs FPGA Optimisation for Real-time Image Processing

This paper focuses on a thorough comparison of the two main hardware targets for real-time optimization of a computer vision algorithm: GPU and FPGA. Based on a complex case study algorithm for threaded isle detection, implementation on both hardware targets is compared in terms of resulting time performance, code translation effort, hardware cost, power efficiency and integrateability. A real-life case study as described in this paper is a very useful addition to discussions on a more theoretical level, going beyond artificial experiments. In our experiments, we show the speed-up gained by porting our algorithm to FPGA using manually written VHDL and to a heterogeneous GPU/CPU architecture with the OpenCL language. Also, issues and problems occurring during the code porting are detailed.
Mini-Batch Primal and Dual Methods for SVMs

We address the issue of using mini-batches in stochastic optimization of SVMs. We show that the same quantity, the spectral norm of the data, controls the parallelization speedup obtained for both primal stochastic subgradient descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it to derive novel variants of mini-batched SDCA. Our guarantees for both methods are expressed in terms of the original nonsmooth primal problem based on the hinge-loss.