nils-herrmann/clean_scirepeval huggingface dataset:
Stochastic blockmodels and community structure in networks

Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly affect the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.
KUKA Control Toolbox

This article presents an open-source MATLAB toolbox for the motion control of KUKA robot manipulators. The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the Univer sity of Siena. The toolbox, which is compatible with all 6 degrees of freedom (DoF) small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via transmission control Protocol/Internet Protocol (TCP/IP). KCT includes more than 40 functions, spanning operations such as forward and inverse kine matics computation, point-to-point joint and Cartesian control, trajectory gen eration, graphical display, three-dimensional (3-D) animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.
Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique

The aim of study is to examine whether social influence affects medical professionals’ behavioral intention to use while introducing a new Clinical Decision Support System (CDSS). The series of Technology Acceptance Models (TAMs) have been widely applied to examine new technology acceptance by scholars; nevertheless, these models omit system diversity and the user’s profession. On the other hand, causal analysis greatly affects the efficiency of decision-making, and it is usually analyzed by Structural Equation Modeling (SEM); however, the method is often misapplied. This research applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to explore the causal relationship between the significant Unified Theory of Acceptance and Use of Technology (UTAUT) variables. Fuzzy concept is applied to illustrate human vague judgment. It is significant that, in contrary with UTAUT, this study found that social influence does not matter in the behavioral intention to use the CDSS for medical professionals.
Frugal ECC: efficient and versatile memory error protection through fine-grained compression

Because main memory is vulnerable to errors and failures, large-scale systems and critical servers utilize error checking and correcting (ECC) mechanisms to meet their reliability requirements. We propose a novel mechanism, Frugal ECC (FECC), that combines ECC with fine-grained compression to provide versatile protection that can be both stronger and lower overhead than current schemes, without sacrificing performance. FECC compresses main memory at cache-block granularity, using any left over space to store ECC information. Compressed data and its ECC information are then frequently read with a single access even without redundant memory chips; insufficiently compressed blocks require additional storage and accesses. As examples, we present chipkill-correct ECCs on a non-ECC DIMM with x4 chips and the first true chipkill-correct ECC for x8 devices using an ECC DIMM. FECC relies on a new Coverage-oriented-Compression that we developed specifically for the modest compression needs of ECC and for floating-point data.
Model-Based Wheel Slip Detection for Outdoor Mobile Robots

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.
Low-Voltage, Low-Power, and Wide-Tuning-Range Ring-VCO for Frequency ΔΣ Modulator

A low-voltage, low-power, and wide-tuning-range VCO which converts an analog input voltage to phase information for a frequency ΔΣ modulator is proposed in this paper. The VCO is based on a differential ring oscillator, which is improved with modified symmetric load and a positive feedback in the differential delay cells, a new bias circuit and a full-swing amplifier. The proposed VCO operating with two stages at a power supply voltage of 0.6 V can achieve wide tuning-range and low power consumption of 176.892 uW. The new VCO has a good linearity reducing harmonic distortion for frequency ΔΣ modulator. The circuit is designed using a 65 nm CMOS process.
Approaches to Measuring Inconsistent Information

Measures of quantity of information have been studied extensively for more than fifty years. The seminal work on information theory is by Shannon [67]. This work, based on probability theory, can be used in a logical setting when the worlds are the possible events. This work is also the basis of Lozinskii’s work  for defining the quantity of information of a formula (or knowledgebase) in propositional logic. But this definition is not suitable when the knowledgebase is inconsistent. In this case, it has no classical model, so we have no “event” to count. This is a shortcoming since in practical applications (e.g. databases) it often happens that the knowledgebase is not consistent. And it is definitely not true that all inconsistent knowledgebases contain the same (null) amount of information, as given by the “classical information theory”. As explored for several years in the paraconsistent logic community, two inconsistent knowledgebases can lead to very different conclusions, showing that they do not convey the same information. There has been some recent interest in this issue, with some interesting proposals. Though a general approach for information theory in (possibly inconsistent) logical knowledgebases is missing. Another related measure is the measure of contradiction. It is usual in classical logic to use a binary measure of contradiction: a knowledgebase is either consistent or inconsistent. This dichotomy is obvious when the only deductive tool is classical inference, since inconsistent knowledgebases are of no use. But there are now a number of logics developed to draw non-trivial conclusions from an inconsistent knowledgebase. So this dichotomy is not sufficient to describe the amount of contradiction of a knowledgebase, one needs more finegrained measures. Some interesting proposals have been made for this. The main aim of this paper is to review the measures of information and contradiction, and to study some potential practical applications. This has significant potential in developing intelligent systems that can be tolerant to inconsistencies when reasoning with real-world knowledge. 190 Anthony Hunter and Sébastien Konieczny
First- and second-order optimality conditions for piecewise smooth objective functions

Any piecewise smooth function that is specified by an evaluation procedure involving smooth elemental functions and piecewise linear functions like min and max can be represented in the so-called abs-normal form. By an extension of algorithmic, or automatic, differentiation, one can then compute certain first and second order derivative vectors and matrices that represent a local piecewise linearization and provide additional curvature information. On the basis of these quantities we characterize local optimality by first and second order necessary and sufficient conditions, which generalize the corresponding KKT theory for smooth problems. The key assumption is the Linear Independence Kink Qualification (LIKQ), a generalization of LICQ familiar from nonlinear optimization. It implies that the objective has locally a so-called VU decomposition and renders everything tractable in terms of matrix factorizations and other simple linear algebra operations. By yielding descent directions whenever they are violated the new optimality conditions point the way to a superlinearly convergent generalized QP solver, which is currently under development. We exemplify the theory on two nonsmooth examples of Nesterov.
Aspect Based Sentiment Analysis-A Survey

Analyzing user opinions has always been an integral part of information processing and thus Sentiment Analysis has been a very active research area since the last decade. Sentiment Analysis is done at different levels of granularity ranging from coarse grained Document level to fine grained Aspect Level. In this paper we describe the most granular form of analyzing opinions i.e Aspect Based Sentiment Analysis. In particular we focus on shared task for ABSA at International Workshop on Semantic Evaluation (SemEval2016) (Pontiki et al., 2016).
Searchable Encryption over Feature-Rich Data

Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.
Three Architectures and Control of Networked Robotic Systems

Networked robot systems are ensembles of robots that enhance their individual capabilities by sharing perception, computation and actuation capabilities with each other to solve problems that an individual robot could not solve alone. In this chapter, we focus on architectures and control of autonomous networked robot systems that communicate wirelessly. We will first provide an overview over current networked robot platforms spanning 3 orders of magnitude in size (from millimeter to meters in size), operation on the ground, in the air and under water, and that are networked using light, sound and radio. We will then describe classes of algorithms and their analysis used for coordination of teams of robots focusing on reactive and deliberative algorithms for sharing perception, computation, and actuation. The chapter is concluded with a summary of current challenges and promising directions.
Dynamic Factor Graphs for Time Series Modeling

This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden variables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Because the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated inference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also successfully reconstruct missing motion capture data.
Online Streaming Feature Selection Based on Conditional Information Entropy

Online streaming feature selection (OSFS) algorithms, producing an approximately optimal subset from so-far-seen features in real time, are capable of addressing feature selection issues in extreme large or even infinite dimensional space. There are several algorithms proposed carrying out in OSFS way. However, some of these algorithms need prior knowledge about the entire feature space which is inaccessible in real OSFS scenario. Besides, results of them are sensitive to the permutations of features. In this paper, we first propose an OSFS framework based on the uncertainty measures in rough sets theory. The framework needs no additional information, except for the given data. Moreover, a sorting mechanism is adopted in the framework, as creates its stability to varying the order of features. Then, specifying the uncertainty measure with conditional information entropy (CIE), we design an algorithm named CIE-OSFS based on the framework. Comprehensive experiments are conducted to verify the effectiveness of our method on several high dimensional benchmark data sets. The experimental results indicate that CIE-OSFS achieves more compactness with the prerequisite of guaranteeing the predictive accuracy and performs more stably to the changing of features' order than other algorithms in most cases.
16 AE-0421 APEX : Autonomous Vehicle Plan Verification and Execution

Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. In contrast, consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces state estimation errors and disturbances. Third, verification is restricted to a discretized state space with fixed-size cells; this assumption can artificially limit the set of available trajectories if the discretization is too coarse. Conversely, too fine of a discretization renders the problem intractable for automated analysis. This work presents a new verification tool, APEX, which investigates the combined action of a behavioral planner and state lattice-based motion planner to guarantee a safe vehicle trajectory is chosen. In APEX, decisions made at the behavioral layer can be traced through to the spatio-temporal evolution of the AV and verified. Thus, there is no need to create abstractions of the AV's controllers, and aggressive trajectories required for evasive maneuvers can be accurately investigated.
Toward Automatic Sound Source Recognition : Identifying Musical Instruments

average pitch average spectral centroid avg. normalized spect. cent. maximum onset slope onset duration vibrato frequency vibrato strength tremolo frequency tremolo strength slope of onset harmonic skew variance of ons. harm. skew amplitude decay odd/even harmonic ratio Features violin (bowed, muted, pizzicato) viola (bowed, muted, pizzicato) cello (bowed, muted, pizzicato) double bass (bowed, muted, pizz.) trumpet (C, Bach, C with Harmon mute) horn (normal, muted) tenor trombone (normal, muted) tuba flute piccolo oboe english horn bassoon Bb clarinet Instruments
Low-Cost Phased-Array Antenna Using Compact Tunable Phase Shifters Based on Ferroelectric Ceramics

A low-cost phased-array antenna at 10 GHz is presented for a scan angle of ±50°. The array employs continuously tunable phase shifters based on a screen printed barium-strontium-titanate thick-film ceramic. Due to the use of artificial transmission line topology, the proposed phase-shifter design has a very compact size (3 mm × 2.8 mm) for 342° total phase shift. In the frequency range from 8 to 10 GHz, it exhibits a figure of merit >;52°/dB, which is among the best of phase shifters based on ferroelectric thick films. In a prototyped phased array, the RF circuit consists of a feeding network, phase shifters, and antenna elements, which are integrated into one planar metallization layer. Furthermore, a simple way for routing bias lines for phase shifters is demonstrated using high resistive electrodes. Using screen printed thick films and applying a simplified fabrication process for the RF and bias circuitry can reduce the total expense of phased arrays considerably.
Section classification in clinical notes using supervised hidden markov model

As more and more information is available in the Electronic Health Record in the form of free-text narrative, there is a need for automated tools, which can process and understand such texts. One first step towards the automated processing of clinical texts is to determine the document-level structure of a patient note, i.e., identifying the different sections and mapping them to known section types automatically. This paper considers section mapping as a sequence-labeling problem to 15 possible known section types. Our method relies on a Hidden Markov Model (HMM) trained on a corpus of 9,679 clinical notes from NewYork-Presbyterian Hospital. We compare our method to a state-of-the-art baseline, which ignores the sequential aspect of the sections and considers each section independently of the others in a note. Experiments show that our method outperforms the baseline significantly, yielding 93% accuracy in identifying sections individually and 70% accuracy in identifying all the sections in a note, compared to 70% and 19% for the baseline method respectively.
Multi-agent Path Planning and Network Flow

This paper connects multi-agent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the latter, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multi-agent path planning problems on graphs. Exploiting this connecti on, we show that when the goals are permutation invariant, the problem always has a feasible solution path set with a longes t finish time of no more than n+V − 1 steps, in which n is the number of agents andV is the number of vertices of the underlying graph. We then give a complete algorithm that finds such a solution in O(nVE) time, with E being the number of edges of the graph. Taking a further step, we study time and distance optimality of the feasible solutions, show that th ey have a pairwise Pareto optimal structure, and again provide efficient algorithms for optimizing two of these practical objectives.
Algorithmic motion planning in robotics

A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning.<<ETX>>
Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance

Automotive applications based on stereo vision require robust and fast matching algorithms, which makes Semi-global matching (SGM) a popular method in this field. Typically the Census transform is used as a cost function, since it is advantageous for outdoor scenes. We propose an extension based on center-symmetric local binary patterns, which allows better efficiency and higher matching quality. Our second contribution exploits knowledge about the three-dimensional structure of the scene to selectively enforce the smoothness constraints of SGM. It is shown that information about surface normals can be easily integrated by weighing the paths according to the gradient of the disparity. The different approaches are evaluated on the KITTI benchmark, which provides real imagery with LIDAR ground truth. The results indicate improved performance compared to state-of-the-art SGM based algorithms.
Approximation Algorithms for Computing Maximin Share Allocations

We study the problem of computing maximin share allocations, a recently introduced fairness notion. Given a set of n agents and a set of goods, the maximin share of an agent is the best she can guarantee to herself, if she is allowed to partition the goods in any way she prefers, into n bundles, and then receive her least desirable bundle. The objective then is to find a partition, where each agent is guaranteed her maximin share. Such allocations do not always exist, hence we resort to approximation algorithms. Our main result is a 2/3-approximation that runs in polynomial time for any number of agents and goods. This improves upon the algorithm of Procaccia and Wang (2014), which is also a 2/3-approximation but runs in polynomial time only for a constant number of agents. To achieve this, we redesign certain parts of the algorithm in Procaccia and Wang (2014), exploiting the construction of carefully selected matchings in a bipartite graph representation of the problem. Furthermore, motivated by the apparent difficulty in establishing lower bounds, we undertake a probabilistic analysis. We prove that in randomly generated instances, maximin share allocations exist with high probability. This can be seen as a justification of previously reported experimental evidence. Finally, we provide further positive results for two special cases arising from previous works. The first is the intriguing case of three agents, where we provide an improved 7/8-approximation. The second case is when all item values belong to {0, 1, 2}, where we obtain an exact algorithm.