nils-herrmann/clean_scirepeval huggingface dataset:
Event-related EEG/MEG synchronization and desynchronization: basic principles

An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
A temperature-compensated low-noise digitally-controlled crystal oscillator for multi-standard applications

This paper presents an integrated 26-MHz digitally-controlled crystal oscillator (DCXO) with temperature compensation function for multi-standard cellular applications, that achieves a phase noise of -154 and -159 dBc/Hz at 10 kHz and 100 kHz offset, respectively. The frequency instability over temperature is compensated by built-in temperature sensor and compensating capacitor. The frequency instability from -10 to 55 degC is about +/- 1 ppm. The AFC frequency tuning is done by a digitally-controlled metal-oxide-metal capacitor array that is 13-bit thermometer decoded. The DCXO is implemented in a 0.13-mum CMOS technology.
A MATLAB based 3D modeling and inversion code for MT data

The development of a MATLAB based computer code, AP3DMT, for modeling and inversion of 3D Magnetotelluric (MT) data is presented. The code comprises two independent components: grid generator code and modeling/inversion code. The grid generator code performs model discretization and acts as an interface by generating various I/O files. The inversion code performs core computations in modular form – forward modeling, data functionals, sensitivity computations and regularization. These modules can be readily extended to other similar inverse problems like Controlled-Source EM (CSEM). The modular structure of the code provides a framework useful for implementation of new applications and inversion algorithms. The use of MATLAB and its libraries makes it more compact and user friendly. The code has been validated on several published models. To demonstrate its versatility and capabilities the results of inversion for two complex models are presented.
CSLIM: contextual SLIM recommendation algorithms

Context-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we introduce another matrix factorization approach for contextual recommendations, the contextual SLIM (CSLIM) recommendation approach. It is derived from the sparse linear method (SLIM) which was designed for Top-N recommendations in traditional recommender systems. Based on the experimental evaluations over several context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art CARS algorithms in the Top-N recommendation task.
The design and implementation of microdrivers

Device drivers commonly execute in the kernel to achieve high performance and easy access to kernel services. However, this comes at the price of decreased reliability and increased programming difficulty. Driver programmers are unable to use user-mode development tools and must instead use cumbersome kernel tools. Faults in kernel drivers can cause the entire operating system to crash. User-mode drivers have long been seen as a solution to this problem, but suffer from either poor performance or new interfaces that require a rewrite of existing drivers.
 This paper introduces the Microdrivers architecture that achieves high performance and compatibility by leaving critical path code in the kernel and moving the rest of the driver code to a user-mode process. This allows data-handling operations critical to I/O performance to run at full speed, while management operations such as initialization and configuration run at reduced speed in user-level. To achieve compatibility, we present DriverSlicer, a tool that splits existing kernel drivers into a kernel-level component and a user-level component using a small number of programmer annotations. Experiments show that as much as 65% of driver code can be removed from the kernel without affecting common-case performance, and that only 1-6 percent of the code requires annotations.
First-Order Mixed Integer Linear Programming

Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inference in FOL for corresponding problems.
Efficient Locking Techniques for Databases on Modern Hardware

Traditional database systems are driven by the assumption that disk I/O is the primary bottleneck, overshadowing all other costs. However, future database systems will be dominated by many-core processors, large main memory, and low-latency semiconductor mass storage. In the increasingly common case that the working data set fits in memory or low-latency storage, new bottlenecks emerge: locking, latching, logging, and critical sections in the buffer manager. Prior work has addressed two of these – latching and logging. This paper addresses locking and proposes new mechanisms optimized for modern hardware. We devised new algorithms and methods to improve all components of database locking, including key range locking, intent locks, detection and recovery from deadlocks, and early lock release. Most of the techniques are easily applicable to other database systems. Experiments with Shore-MT, the transaction processing engine we used as the implementation basis, show throughput improvement by factors of 5 to 50.
A cross-collection mixture model for comparative text mining

In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.
Differentially Private Online Learning for Cloud-Based Video Recommendation With Multimedia Big Data in Social Networks

With the rapid growth in multimedia services and the enormous offers of video content in online social networks, users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. However, they ignore that the accelerated proliferation of social media data has led to the big data era, which has greatly impeded the process of video recommendation. In addition, none of them has considered both the privacy of users' contexts (e.g., social status, ages, and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value. To handle these problems, we propose a cloud-assisted differentially private video recommendation system based on distributed online learning. In our framework, service vendors are modeled as distributed cooperative learners, recommending videos according to user's context, while simultaneously adapting the video-selection strategy based on user-click feedback to maximize total user clicks (reward). Considering the sparsity and heterogeneity of big social media data, we also propose a novel geometric differentially private model, which can greatly reduce the performance loss. Our simulation shows the proposed algorithms outperform other existing methods and keep a delicate balance between the total reward and privacy preserving level.
EvilSeed: A Guided Approach to Finding Malicious Web Pages

Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user's computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honey clients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the "toxicity" of the stream of URLs being analyzed is low. In this paper, we present EVILSEED, an approach to search the web more efficiently for pages that are likely malicious. EVILSEED starts from an initial seed of known, malicious web pages. Using this seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the initial seed. By doing so, EVILSEED leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSEED increases the "toxicity" of the input URL stream. Also, we envision that the features that EVILSEED presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSEED is able to identify malicious web pages more efficiently when compared to crawler-based approaches.
Diverse Sequential Subset Selection for Supervised Video Summarization

pressing need 100 hours of new Youtube video per min 422,000 CCTV cameras in London 24/7 Sequential DPP (seqDPP) 1. Partition video into T disjoint segments 2. Introduce subset selection (of frames) variable Yt for each segment 3. Condition Yt on Yt-1 = yt-1 by DPP User study on inter-annotator agreement Data: 100 videos from Open Video Project and Youtube  5 user summaries per video Observation: high inter-annotator agreement Generate target summaries by greedy search
A generalized hidden Markov model with discriminative training for query spelling correction

Query spelling correction is a crucial component of modern search engines. Existing methods in the literature for search query spelling correction have two major drawbacks. First, they are unable to handle certain important types of spelling errors, such as concatenation and splitting. Second, they cannot efficiently evaluate all the candidate corrections due to the complex form of their scoring functions, and a heuristic filtering step must be applied to select a working set of top-K most promising candidates for final scoring, leading to non-optimal predictions. In this paper we address both limitations and propose a novel generalized Hidden Markov Model with discriminative training that can not only handle all the major types of spelling errors, including splitting and concatenation errors, in a single unified framework, but also efficiently evaluate all the candidate corrections to ensure the finding of a globally optimal correction. Experiments on two query spelling correction datasets demonstrate that the proposed generalized HMM is effective for correcting multiple types of spelling errors. The results also show that it significantly outperforms the current approach for generating top-K candidate corrections, making it a better first-stage filter to enable any other complex spelling correction algorithm to have access to a better working set of candidate corrections as well as to cover splitting and concatenation errors, which no existing method in academic literature can correct.
Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs

In this paper we address reinforcement learning problems with continuous state-action spaces. We propose a new algorithm, tted natural actor-critic (FNAC), that extends the work in  to allow for general function approximation and data reuse. We combine the natural actor-critic architecture  with a variant of tted value iteration using importance sampling. The method thus obtained combines the appealing features of both approaches while overcoming their main weaknesses: the use of a gradient-based actor readily overcomes the di culties found in regression methods with policy optimization in continuous action-spaces; in turn, the use of a regression-based critic allows for e cient use of data and avoids convergence problems that TD-based critics often exhibit. We establish the convergence of our algorithm and illustrate its application in a simple continuous space, continuous action problem.
Generalized Barycentric Coordinates on Irregular Polygons

In this paper we present an easy computation of a generalized form of barycentric coordinates for irregular, convex n-sided polygons. Triangular barycentric coordinates have had many classical applications in computer graphics, from texture mapping to ray-tracing. Our new equations preserve many of the familiar properties of the triangular barycentric coordinates with an equally simple calculation, contrary to previous formulations. We illustrate the properties and behavior of these new generalized barycentric coordinates through several example applications.
Object Category Understanding via Eye Fixations on Freehand Sketches

The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch’s category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.
GramFuzz: Fuzzing testing of web browsers based on grammar analysis and structural mutation

Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).
Configuration Debugging as Search: Finding the Needle in the Haystack

This work addresses the problem of diagnosing configuration errors that cause a system to function incorrectly. For example, a change to the local firewall policy could cause a network-based application to malfunction. Our approach is based on searching across time for the instant the system transitioned into a failed state. Based on this information, a troubleshooter or administrator can deduce the cause of failure by comparing system state before and after the failure. We present the Chronus tool, which automates the task of searching for a failure-inducing state change. Chronus takes as input a user-provided software probe, which differentiates between working and non-working states. Chronus performs “time travel” by booting a virtual machine off the system’s disk state as it existed at some point in the past. By using binary search, Chronus can find the fault point with effort that grows logarithmically with log size. We demonstrate that Chronus can diagnose a range of common configuration errors for both client-side and server-side applications, and that the performance overhead of the tool is not prohibitive.
Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Accurate estimation of system frequency in real time is a prerequisite for the future smart grid, where the generation, loading, and topology will all be dynamically updated. In this article, we introduce a unified framework for the estimation of instantaneous frequency in both balanced and unbalanced conditions in a three-phase system, thus consolidating the existing approaches and providing next-generation solutions capable of joint adaptive frequency estimation and system fault identification. This is achieved by employing recent developments in the statistics of complex variables (augmented statistics) and the associated widely linear models, allowing us to benefit from a rigorous account of varying degrees of noncircularity corresponding to different sources of frequency variations. The advantages of such an approach are illustrated for both balanced and unbalanced conditions, including voltage sags, harmonics and supply-demand mismatch, all major obstacles for accurate frequency estimation in the smart grid.
QUASY: Quantitative Synthesis Tool

We present the tool Q UASY, a quantitative synthesis tool. Q UASY takes qualitative and quantitative specifications and automatic ally onstructs a system that satisfies the qualitative specification and optimizes the qu antitative specification, if such a system exists. The user can choose between a system that satisfies and optimi zes the specifications (a) under all possible environment behaviors or (b) under th most-likely environment behaviors given as a probability distribution on the po ssible input sequences. QUASY solves these two quantitative synthesis problems by reduct ion to instances of 2-player games and Markov Decision Processes (MDPs) with qua ntitative winning objectives. QUASY can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with meanpayoff objectives, and ergodic MDPs with mean-payoff parit y objectives.
Human movement analysis using stereophotogrammetry. Part 1: theoretical background.

This paper sets the stage for a series of reviews dealing with the problems associated with the reconstruction and analysis of in vivo skeletal system kinematics using optoelectronic stereophotogrammetric data. Instantaneous bone position and orientation and joint kinematic variable estimations are addressed in the framework of rigid body mechanics. The conceptual background to these exercises is discussed. Focus is placed on the experimental and analytical problem of merging the information relative to movement and that relative to the morphology of the anatomical body parts of interest. The various global and local frames that may be used in this context are defined. Common anatomical and mathematical conventions that can be used to describe joint kinematics are illustrated in a comparative fashion. The authors believe that an effort to systematize the different theoretical and experimental approaches to the problems involved and related nomenclatures, as currently reported in the literature, is needed to facilitate data and knowledge sharing, and to provide renewed momentum for the advancement of human movement analysis.
Automatic Dynamic Web Service Composition: A Survey and Problem Formalization

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web service composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their relation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas.
Mixed Initiative in Dialogue: An Investigation into Discourse Segmentation

Conversation between two people is usually of Mixed-Initiative, with Control over the conversation being transferred from one person to another. We apply a set of rules for the transfer of control to 4 sets of dialogues consisting of a total of 1862 turns. The application of the control rules lets us derive domain-independent discourse structures. The derived structures indicate that initiative plays a role in the structuring of discourse. In order to explore the relationship of control and initiative to discourse processes like centering, we analyze the distribution of four different classes of anaphora for two data sets. This distribution indicates that some control segments are hierarchically related to others. The analysis suggests that discourse participants often mutually agree to a change of topic. We also compared initiative in Task Oriented and Advice Giving dialogues and found that both allocation of control and the manner in which control is transferred is radically different for the two dialogue types. These differences can be explained in terms of collaborative planning principles.