nils-herrmann/clean_scirepeval huggingface dataset:
Sliding mode control of a photovoltaic-wind hybrid system

This paper proposes a hybrid energy system combing solar photovoltaic and wind turbine as a small-scale for stand-alone applications. The proposed system consists of a variable-speed wind generator, wind-side converter, solar array, DC-DC converter and isolating DC load. The power control strategy is to extract the maximum energy from the photovoltaic array and wind turbine under varying climatic conditions. The SMC proposed in this paper, to controlling the output voltage of the DC-DC converter and the out voltage of the AC-DC converter, in order to maintain and stabilize the constant DC-link voltage in climatic and load variations. Furthermore, classical controls PI compared with the SMC. The results show that the SMC technique is more suitable than those classical controls. It improves the transient response and reduces the steady state error of the DC-link voltage because offers a very good way to implement a control action which exploits the inherent variable structure nature of converters. This paper too proposes a control strategy of a maximum power point of a stand-alone PV system using the SMC technique. The efficiency of the proposed control approach is compared with the traditional P&O technique. The MPPT proposed has good performances of the PV system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink.
Characterizing Data Structures for Volatile Forensics

Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of latent program data in memory. We have developed Cafegrind, a tool that can systematically build an object map and track the use of data structures as a program is running. Statistics collected by our tool can show which data structures are the most numerous, which structures are the most frequently accessed and provide summary statistics to guide forensic analysts in the evidence gathering process. As programs grow increasingly complex and numerous, the ability to pinpoint specific evidence in memory dumps will be increasingly helpful. Cafegrind has been tested on a number of real-world applications and we have shown that it can successfully map up to 96% of heap accesses.
Power posing: brief nonverbal displays affect neuroendocrine levels and risk tolerance.

Humans and other animals express power through open, expansive postures, and they express powerlessness through closed, contractive postures. But can these postures actually cause power? The results of this study confirmed our prediction that posing in high-power nonverbal displays (as opposed to low-power nonverbal displays) would cause neuroendocrine and behavioral changes for both male and female participants: High-power posers experienced elevations in testosterone, decreases in cortisol, and increased feelings of power and tolerance for risk; low-power posers exhibited the opposite pattern. In short, posing in displays of power caused advantaged and adaptive psychological, physiological, and behavioral changes, and these findings suggest that embodiment extends beyond mere thinking and feeling, to physiology and subsequent behavioral choices. That a person can, by assuming two simple 1-min poses, embody power and instantly become more powerful has real-world, actionable implications.
Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation

The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p < 0.0001) outperforms text retrieval algorithms with text-based or PageRank-based PRF.
Copysets: Reducing the Frequency of Data Loss in Cloud Storage

Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. We present Copyset Replication, a novel generalpurpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node’s data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99% to 0.15%. For Facebook’s HDFS cluster, it reduces the probability from 22.8% to 0.78%.
Trap Design for Vibratory Bowl Feeders

The vibratory bowl feeder is the oldest and still most common approach to the automated feeding (orienting) of industrial parts. In this paper we consider a class of vibratory bowl filters that can be described by removing polygonal sections from the track; we refer to this class of filters as traps. For an n-sided polygonal part and an m-sided polygonal trap, we give an O(nm log n) algorithm to decide whether the part in a specific orientation will safely move across the trap or will fall through the trap and thus be filtered out. For an n-sided convex polygonal part and m-sided convex polygonal trap, this bound is improved to O((n+m) log n). Furthermore, we show how to design various trap shapes, ranging from simple traps to general polygons which will filter out all but one of the different stable orientations of a given part. Although the run times of our design algorithms are exponential in the number of trap parameters, many industrial part feeders use few-parameter traps (balconies, canyons, slots); in these cases the running times of our algorithms range from linear to low degree
Big data and cloud computing: current state and future opportunities

Scalable database management systems (DBMS)---both for update intensive application workloads as well as decision support systems for descriptive and deep analytics---are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification

Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. On the other hand, contextual information provided no significant gain for implicit discourse relations while it worked for explicit ones.
A 2-nW 1.1-V self-biased current reference in CMOS technology

This work presents the design of an ultra-low-power self-biased 400-pA current source. We propose the use of a very simple topology along with a design methodology based on the concept of inversion level. An efficient design methodology has resulted in a cell area around 0.045 mm/sup 2/ in the AMI 1.5-/spl mu/m CMOS technology and power consumption around 2 nW for 1.2-V supply. Simulated and experimental results validate the design and show that the current source can operate at supply voltages down to 1.1 V with a good regulation (<6% /V variation of the supply voltage) in a 1.5-/spl mu/m technology.
Graph Nodes Clustering Based on the Commute-Time Kernel

This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel (KCT), providing a similarity measure between any couple of nodes by taking the indirect links into account, is computed from the adjacency matrix of the graph. Then, the nodes of the graph are clustered by performing a kernel k-means or fuzzy k-means on this CT kernel matrix. For this purpose, a new, simple, version of the kernel k-means and the kernel fuzzy k-means is introduced. The joint use of the CT kernel matrix and kernel clustering appears to be quite successful. Indeed, this methodology provides good results, outperforming the spherical k-means, on a document clustering problem involving the newsgroups database.
Perspectives and Limitations of Self-Organizing Maps in Blind Separation of Source Signals

|The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.
On beamforming with finite rate feedback in multiple-antenna systems

In this paper, we study a multiple antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations. Assuming that the transmitter uses the quantized information for beamforming, we derive a universal lower bound on the outage probability for any finite set of beamformers. The universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel. Using the bound, it is shown that finite information systems approach the perfect information case as , where is the number of feedback bits and is the number of transmit antennas. The geometrical bounding technique, used in the proof of the lower bound, also leads to a design criterion for good beamformers, whose outage performance approaches the lower bound. The design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space time codes under certain conditions. Finally, we show that good beamformers are good packings of -dimensional subspaces in a -dimensional real Grassmannian manifold with chordal distance as the metric.  Permission to publish this abstract separately is granted.
VulPecker: an automated vulnerability detection system based on code similarity analysis

Software vulnerabilities are the fundamental cause of many attacks. Even with rapid vulnerability patching, the problem is more complicated than it looks. One reason is that instances of the same vulnerability may exist in multiple software copies that are difficult to track in real life (e.g., different versions of libraries and applications). This calls for tools that can automatically search for vulnerable software with respect to a given vulnerability. In this paper, we move a step forward in this direction by presenting Vulnerability Pecker (VulPecker), a system for automatically detecting whether a piece of software source code contains a given vulnerability or not. The key insight underlying VulPecker is to leverage (i) a set of features that we define to characterize patches, and (ii) code-similarity algorithms that have been proposed for various purposes, while noting that no single code-similarity algorithm is effective for all kinds of vulnerabilities. Experiments show that VulPecker detects 40 vulnerabilities that are not published in the National Vulnerability Database (NVD). Among these vulnerabilities, 18 are not known for their existence and have yet to be confirmed by vendors at the time of writing (these vulnerabilities are "anonymized" in the present paper for ethical reasons), and the other 22 vulnerabilities have been "silently" patched by the vendors in the later releases of the vulnerable products.
Simultaneous Mosaicing and Tracking with an Event Camera

An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.
Shape-Based Object Localization for Descriptive Classification

Discriminative tasks, including object categorization and detection, are central components of high-level computer vision. However, sometimes we are interested in a finer-grained characterization of the object’s properties, such as its pose or articulation. In this paper we develop a probabilistic method (LOOPS) that can learn a shape and appearance model for a particular object class, and be used to consistently localize constituent elements (landmarks) of the object’s outline in test images. This localization effectively projects the test image into an alternative representational space that makes it particularly easy to perform various descriptive tasks. We apply our method to a range of object classes in cluttered images and demonstrate its effectiveness in localizing objects and performing descriptive classification, descriptive ranking, and descriptive clustering.
SemEval-2016 Task 5: Aspect Based Sentiment Analysis

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.
Quantum coherence enabled determination of the energy landscape in light-harvesting complex II.

The near-unity efficiency of energy transfer in photosynthesis makes photosynthetic light-harvesting complexes a promising avenue for developing new renewable energy technologies. Knowledge of the energy landscape of these complexes is essential in understanding their function, but its experimental determination has proven elusive. Here, the observation of quantum coherence using two-dimensional electronic spectroscopy is employed to directly measure the 14 lowest electronic energy levels in light-harvesting complex II (LHCII), the most abundant antenna complex in plants containing approximately 50% of the world's chlorophyll. We observe that the electronically excited states are relatively evenly distributed, highlighting an important design principle of photosynthetic complexes that explains the observed ultrafast intracomplex energy transfer in LHCII.
A Direct Least-Squares (DLS) method for PnP

In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).
Swarm: Hyper Awareness, Micro Coordination, and Smart Convergence through Mobile Group Text Messaging

Text messaging has become an integral part of mobile communication, with a significant impact on cultural and social norms in many countries. Our goal is to explore how group-based text messaging enables continuous social awareness, group coordination and smart convergence on social events. We implemented a group text messaging system, Swarm, and deployed it to several communication groups for ten months. Through usage logs, questionnaires, text analysis, and direct observation we examined how people integrate group text messaging into their day to day social lives. Swarm was used primarily for lightweight distribution of context information (availability, location, and event status) in order to facilitate social convergence. We discuss the lessons learned from our deployment, and the implications for the design of mobile, group communication systems.
Mining Very Large Databases with Parallel Processing

ion e.g. from "day_oCpurchase" to "month_oCpurchase" is called roll up. The inverse operation is called drill down. The operation of "rotating" the data cube to show a given face (e.g. the face with dimensions customer and product) is called pivoting. The operation of selecting a subset of the values of a dimension (e.g. showing only data referring to the most important customers) is called slicing-dicing. [Gray et al. 96]. [Gray et al. 97) have proposed a data cube operator generalizing the SQL group by operator. In essence this operator computes group bys for all possible combinations of a set of attributes. Other operators designed to improve the flexibility of multidimensional databases and also designed to be translated into SQL are proposed by [Agrawal et al. 97). One of the goals of the OLAP paradigm is to provide the user with facilities for interactive analysis. Hence, the response time of a query must be kept within a few seconds. Recently there has been significant research on techniques to reduce the response time of OLAP queries see e.g. [Ho et al. 96], [Agarwal et al. 96), [Ullman 96). In general these techniques are based on the ideas of pre-computing aggregate values along relevant dimensions, materializing views, etc., trading storage space for query-processing time. A list of some commercial OLAP systems can be found in [Watterson 95). However, note that a pure OLAP approach for KDD relies on a human user to perform some kind of knowledge discovery. The aim of the system is only to facilitate this discovery by providing the user with a simple and efficient way of submitting analytical queries to the database. Hence, if OLAP is to be regarded as a knowledge discovery paradigm, it is a paradigm where the system has very little autonomy, in comparison with the machine learning-based paradigms discussed above. 2.6 FOCUS ON RULE INDUCTION. This book focuses mainly on the Rule Induction (RI) paradigm. (However, the paradigms of instance-based learning, neural networks and genetic algorithms will also be further discussed in other parts of this book, particularly in the Chapters on parallel data mining). We make no claim that the rule induction paradigm is "better" than the others concerning the effectiveness of knowledge discovery (e.g. classification accuracy). Within each KDD paradigm there is a large number of algorithms, and the effectiveness of a KDD algorithm of any paradigm depends strongly on the type and size of the data, the nature of the application domain, etc. (see Section 4.3). However, it should be noted that RI is by far the most used paradigm in the KDD literature. This is probably due to two main reasons. The first reason is the comprehensibility of the discovered knowledge. This comprehensibility can be achieved at different degrees and with different amounts of effort in any knowledge discovery paradigm. Overall, however, the RI paradigm is considered to be the one which most naturally mirrors the human decision-making process and produces highlevel, comprehensible knowledge.