nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotationand scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.
Microphone array processing for distant speech recognition: Towards real-world deployment

Distant speech recognition (DSR) holds out the promise of providing a natural human computer interface in that it enables verbal interactions with computers without the necessity of donning intrusive body- or head-mounted devices. Recognizing distant speech robustly, however, remains a challenge. This paper provides a overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beamforming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a 64-channel linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Furthermore, we report the results of speech recognition experiments on data captured with a popular device, the Kinect . Even for speakers at a distance of four meters from the Kinect, our DSR system achieved acceptable recognition performance on a large vocabulary task, a WER of 24.1%, beginning from a WER of 42.5% with a single array channel.
Interface Theories for Component-Based Design

We classify component based models of computation into compo nent models and interface models A component model speci es for each com ponent how the component behaves in an arbitrary environment an interface model speci es for each component what the component expects from the en vironment Component models support compositional abstraction and there fore component based veri cation Interface models support compositional re nement and therefore component based design Many aspects of interface models such as compatibility and re nement checking between interfaces are properly viewed in a game theoretic setting where the input and output values of an interface are chosen by di erent players Interfaces vs Components Informally A generic way of depicting system structure is the block diagram A block dia gram consists of entities called blocks related by an interconnect which speci es a topology for communication between the blocks A block may represent a phys ical or logical component such as a piece of hardware or software but it more often represents either an abstract description of the component or a description of the component interface A component description answers the questionWhat does it do an interface description answers the question How can it be used A component description may be very close to the underlying component or it may specify as little as a single property of the underlying component Interface descriptions too can be more or less detailed but they must contain enough information for determining how the underlying components can be composed and connected and like any good abstraction they should not contain more information Component designers often make assumptions about the environ ment in which a component is to be deployed and such assumptions while not part of the component itself and therefore not part of any abstract component description can be part of an interface description This research was supported in part by the AFOSR MURI grant F the DARPA ITO grant F C the MARCO grant DT and the NSF ITR grant CCR An abbreviated version of this paper will appear in the Proceedings of the First International Workshop on Embedded Software EMSOFT Components do not constrain the environment interfaces do Consider for ex ample a division component with inputs x and y and output z Suppose that our description language is the predicate calculus The predicate
Data exfiltration: A review of external attack vectors and countermeasures

One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field.  This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research.  We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers.  We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest).  This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework.
Steganalysis via Deep Residual Network

Recent studies have demonstrated that a well designed deep convolutional neural network (CNN) model achieves competitive performances on detecting the presence of secret message in digital images, compared with the classical rich model based steganalysis. In this paper, we propose to investigate a category of very deep CNN model–the deep residual network (DRN), for steganalysis. DRN is suitable for steganalysis from two aspects. For the first, the DRN model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. For the second, DRN's residual learning (ResL) method actively strengthens the signal coming from secret messages, which is extremely beneficial for the discrimination between cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model achieves very low detection error rates for the state of arts steganographic algorithms. It also outperforms the classical rich model method and several recently proposed CNN based methods.
How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority

We present a polynomial-time algorithm that, given as a input the description of a game with incomplete information and any number of players, produces a protocol for playing the game that leaks no partial information, provided the majority of the players is honest.
Our algorithm automatically solves all the multi-party protocol problems addressed in complexity-based cryptography during the last 10 years. It actually is a completeness theorem for the class of distributed protocols with honest majority. Such completeness theorem is optimal in the sense that, if the majority of the players is not honest, some protocol problems have no efficient solution
Cost-Aware WWW Proxy Caching Algorithms

Difference between Web Caching & Conventional Paging Problems: Web caching is variable-size caching (web documents vary dramatically in size depending on the information they carry (text, image, video, etc.). Web pages take different amounts of time to download, even if they are of the same size (download latency). Access streams seen by the proxy cache are the union of web access streams from tens to thousands of users (instead of coming from a few programmed sources as in the case of virtual memory paging)
A novel 8T SRAM with minimized power and delay

In this paper, a novel 8T SRAM cell is proposed which aims at decreasing the delay and lowering the total power consumption of the cell. The threshold voltage variations in the transistor affect the read and write stability of the cell. Also, power dissipation increases with the number of transistors which in turn affects the read and write stability. The proposed 8T SRAM bitcell is designed using 180 nm CMOS, n-well technology with a supply voltage of 1.8 V. The results show that the average delay has been improved by 80 % compared to the conventional 6T cell. The total power is improved by 14.5 % as compared to conventional 6T SRAM cell.
SEQUENCE BASED HIERARCHICAL CONFLICT-FREE ROUTING STRATEGY OF BI-DIRECTIONAL AUTOMATED GUIDED VEHICLES

Conflict-free routing is an important problem to be addressed in AGV Systems, in particular when they are bi-directional. This paper considers the problem of routing AGVs in the presence of contingencies. The importance of this work stems from the realization that path-planning approaches are ill suited to AGVSs subject to contingencies. In this paper, a two stage closed-loop control strategy is proposed. In the first control stage, a pre-planning method proposed by Kim and Tanchoco (1991) is used to establish the fastest conflict-free routes for AGVs. The objective of the second stage is the avoidance of deadlocks in the presence of interruptions while maintaining the planned routes.
Sharing graphs using differentially private graph models

Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techniques to improve privacy on graphs only target specific attacks, and have been proven to be vulnerable against powerful de-anonymization attacks.
 Our work seeks a solution to share meaningful graph datasets while preserving privacy. We observe a clear tension between strength of privacy protection and maintaining structural similarity to the original graph. To navigate the tradeoff, we develop a differentially-private graph model we call Pygmalion. Given a graph G and a desired level of e-differential privacy guarantee, Pygmalion extracts a graph's detailed structure into degree correlation statistics, introduces noise into the resulting dataset, and generates a synthetic graph G'. G' maintains as much structural similarity to G as possible, while introducing enough differences to provide the desired privacy guarantee. We show that simply applying differential privacy to graphs results in the addition of significant noise that may disrupt graph structure, making it unsuitable for experimental study. Instead, we introduce a partitioning approach that provides identical privacy guarantees using much less noise. Applied to real graphs, this technique requires an order of magnitude less noise for the same privacy guarantees. Finally, we apply our graph model to Internet, web, and Facebook social graphs, and show that it produces synthetic graphs that closely match the originals in both graph structure metrics and behavior in application-level tests.
Characterizing the influence of domain expertise on web search behavior

Domain experts search differently than people with little or no domain knowledge. Previous research suggests that domain experts employ different search strategies and are more successful in finding what they are looking for than non-experts. In this paper we present a large-scale, longitudinal, log-based analysis of the effect of domain expertise on web search behavior in four different domains (medicine, finance, law, and computer science). We characterize the nature of the queries, search sessions, web sites visited, and search success for users identified as experts and non-experts within these domains. Large-scale analysis of real-world interactions allows us to understand how expertise relates to vocabulary, resource use, and search task under more realistic search conditions than has been possible in previous small-scale studies. Building upon our analysis we develop a model to predict expertise based on search behavior, and describe how knowledge about domain expertise can be used to present better results and query suggestions to users and to help non-experts gain expertise.
Looking at, looking up or keeping up with people?: motives and use of facebook

This paper investigates the uses of social networking site Facebook, and the gratifications users derive from those uses. In the first study, 137 users generated words or phrases to describe how they used Facebook, and what they enjoyed about their use. These phrases were coded into 46 items which were completed by 241 Facebook users in Study 2. Factor analysis identified seven unique uses and gratifications: social connection, shared identities, content, social investigation, social network surfing and status updating. User demographics, site visit patterns and the use of privacy settings were associated with different uses and gratifications.
Direct Multisearch for Multiobjective Optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.
Vehicle Guidance for an Autonomous Vehicle

This paper describes the vehicle guidance system of an autonomous vehicle. This system is part of the control structure of the vehicle and consists of a path generator, a motion planning algorithm and a sensor fusion module. A stereo vision sensor and a DGPS sensor are used as position sensors. The trajectory for the vehicle motion is generated in a rst step by using only information from a digital map. Object-detecting sensors such as the stereo vision sensor, three laserscanner and a radar sensor observe the vehicle environment and report detected objects to the sensor fusion module. This information is used to dynamically update the planned vehicle trajectory to the nal vehicle motion.
Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph

Bitcoin exchanges operate between digital and fiat currency networks, thus providing an opportunity to connect real-world identities to pseudonymous addresses, an important task for anti-money laundering efforts. We seek to characterize, understand, and identify patterns centered around exchanges in the context of a directed hypergraph model for Bitcoin transactions. We introduce the idea of motifs in directed hypergraphs, considering a particular 2-motif as a potential laundering pattern. We identify distinct statistical properties of exchange addresses related to the acquisition and spending of bitcoin. We then leverage this to build classification models to learn a set of discriminating features, and are able to predict if an address is owned by an exchange with > 80% accuracy using purely structural features of the graph. Applying this classifier to the 2-motif patterns reveals a preponderance of interexchange activity, while not necessarily significant laundering patterns.
A trade-off between space and efficiency for routing tables

Two conflicting goals play a crucial role in the design of routing schemes for communication networks. A routing scheme should use paths that are as short as possible for routing messages in the network, while keeping the routing information stored in the processors' local memory as succinct as possible. The efficiency of a routing scheme is measured in terms of its <italic>stretch factor</italic>-the maximum ratio between the length of a route computed by the scheme and that of a shortest path connecting the same pair of vertices.
 Most previous work has concentrated on finding good routing schemes (with a small fixed stretch factor) for special classes of network topologies. In this paper the problem for general networks is studied, and the entire range of possible stretch factors is examined. The results exhibit a trade-off between the efficiency of a routing scheme and its space requirements. Almost tight upper and lower bounds for this trade-off are presented. Specifically, it is proved that any routing scheme for general <italic>n</italic>-vertex networks that achieves a stretch factor <italic>k</italic> ≥ 1 must use a total of &OHgr;(<italic>n</italic><supscrpt>1+1/(2<italic>k</italic>+4)</supscrpt>) bits of routing information in the networks. This lower bound is complemented by a family <italic>K</italic>(<italic>k</italic>) of <italic>hierarchical</italic> routing schemes (for every k ≥ l) for unit-cost general networks, which guarantee a stretch factor of <italic>O</italic>(<italic>k</italic>), require storing a total of <italic>O</italic>(<italic>k</italic><supscrpt>3</supscrpt><italic>n</italic><supscrpt>1+(1/h)</supscrpt>log<italic>n</italic>)- bits of routing information in the network, name the vertices with <italic>O</italic>(log<supscrpt>2</supscrpt><italic>n</italic>)-bit names and use <italic>O</italic>(log<italic>n</italic>)-bit headers.
Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement

High-level spinal cord injury (SCI) in humans causes paralysis below the neck. Functional electrical stimulation (FES) technology applies electrical current to nerves and muscles to restore movement, and controllers for upper extremity FES neuroprostheses calculate stimulation patterns to produce desired arm movement. However, currently available FES controllers have yet to restore natural movements. Reinforcement learning (RL) is a reward-driven control technique; it can employ user-generated rewards, and human preferences can be used in training. To test this concept with FES, we conducted simulation experiments using computer-generated “pseudo-human” rewards. Rewards with varying properties were used with an actor-critic RL controller for a planar two-degree-of-freedom biomechanical human arm model performing reaching movements. Results demonstrate that sparse, delayed pseudo-human rewards permit stable and effective RL controller learning. The frequency of reward is proportional to learning success, and human-scale sparse rewards permit greater learning than exclusively automated rewards. Diversity of training task sets did not affect learning. Long-term stability of trained controllers was observed. Using human-generated rewards to train RL controllers for upper-extremity FES systems may be useful. Our findings represent progress toward achieving human-machine teaming in control of upper-extremity FES systems for more natural arm movements based on human user preferences and RL algorithm learning capabilities.
Cyclic redundancy code (CRC) polynomial selection for embedded networks

Cyclic redundancy codes (CRCs) provide a first line of defense against data corruption in many networks. Unfortunately, many commonly used CRC polynomials provide significantly less error detection capability than they might. An exhaustive exploration reveals that most previously published CRC polynomials are either inferior to alternatives or are only good choices for particular message lengths. Unfortunately these shortcomings and limitations often seem to be overlooked. This paper describes a polynomial selection process for embedded network applications and proposes a set of good general-purpose polynomials. A set of 35 new polynomials in addition to 13 previously published polynomials provides good performance for 3- to 16-bit CRCs for data word lengths up to 2048 bits.