nils-herrmann/clean_scirepeval huggingface dataset:
Foundations of Control and Estimation Over Lossy Networks

This paper considers control and estimation problems where the sensor signals and the actuator signals are transmitted to various subsystems over a network. In contrast to traditional control and estimation problems, here the observation and control packets may be lost or delayed. The unreliability of the underlying communication network is modeled stochastically by assigning probabilities to the successful transmission of packets. This requires a novel theory which generalizes classical control/estimation paradigms. The paper offers the foundations of such a novel theory. The central contribution is to characterize the impact of the network reliability on the performance of the feedback loop. Specifically, it is shown that for network protocols where successful transmissions of packets is acknowledged at the receiver (e.g., TCP-like protocols), there exists a critical threshold of network reliability (i.e., critical probabilities for the successful delivery of packets), below which the optimal controller fails to stabilize the system. Further, for these protocols, the separation principle holds and the optimal LQG controller is a linear function of the estimated state. In stark contrast, it is shown that when there is no acknowledgement of successful delivery of control packets (e.g., UDP-like protocols), the LQG optimal controller is in general nonlinear. Consequently, the separation principle does not hold in this circumstance
Measures of semantic similarity and relatedness in the biomedical domain

Measures of semantic similarity between concepts are widely used in Natural Language Processing. In this article, we show how six existing domain-independent measures can be adapted to the biomedical domain. These measures were originally based on WordNet, an English lexical database of concepts and relations. In this research, we adapt these measures to the SNOMED-CT ontology of medical concepts. The measures include two path-based measures, and three measures that augment path-based measures with information content statistics from corpora. We also derive a context vector measure based on medical corpora that can be used as a measure of semantic relatedness. These six measures are evaluated against a newly created test bed of 30 medical concept pairs scored by three physicians and nine medical coders. We find that the medical coders and physicians differ in their ratings, and that the context vector measure correlates most closely with the physicians, while the path-based measures and one of the information content measures correlates most closely with the medical coders. We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures.
What do we perceive in a glance of a real-world scene?

What do we see when we glance at a natural scene and how does it change as the glance becomes longer? We asked naive subjects to report in a free-form format what they saw when looking at briefly presented real-life photographs. Our subjects received no specific information as to the content of each stimulus. Thus, our paradigm differs from previous studies where subjects were cued before a picture was presented and/or were probed with multiple-choice questions. In the first stage, 90 novel grayscale photographs were foveally shown to a group of 22 native-English-speaking subjects. The presentation time was chosen at random from a set of seven possible times (from 27 to 500 ms). A perceptual mask followed each photograph immediately. After each presentation, subjects reported what they had just seen as completely and truthfully as possible. In the second stage, another group of naive individuals was instructed to score each of the descriptions produced by the subjects in the first stage. Individual scores were assigned to more than a hundred different attributes. We show that within a single glance, much object- and scene-level information is perceived by human subjects. The richness of our perception, though, seems asymmetrical. Subjects tend to have a propensity toward perceiving natural scenes as being outdoor rather than indoor. The reporting of sensory- or feature-level information of a scene (such as shading and shape) consistently precedes the reporting of the semantic-level information. But once subjects recognize more semantic-level components of a scene, there is little evidence suggesting any bias toward either scene-level or object-level recognition.
Cover trees for nearest neighbor

We present a tree data structure for fast nearest neighbor operations in general <i>n</i>-point metric spaces (where the data set consists of <i>n</i> points). The data structure requires <i>O</i>(<i>n</i>) space <i>regardless</i> of the metric's structure yet maintains all performance properties of a navigating net (Krauthgamer & Lee, 2004b). If the point set has a bounded expansion constant <i>c</i>, which is a measure of the intrinsic dimensionality, as defined in (Karger & Ruhl, 2002), the cover tree data structure can be constructed in <i>O</i> (<i>c</i><sup>6</sup><i>n</i> log <i>n</i>) time. Furthermore, nearest neighbor queries require time only logarithmic in <i>n</i>, in particular <i>O</i> (<i>c</i><sup>12</sup> log <i>n</i>) time. Our experimental results show speedups over the brute force search varying between one and several orders of magnitude on natural machine learning datasets.
SNARKs for C: Verifying Program Executions Succinctly and in Zero Knowledge

We present a proof system that allows efficient verification of NP statements, given proofs produced by an untrusted yet computationally-bounded prover. Our system is publicly verifiable: after a trusted third-party has generated a proving key and a verification key, anyone can use the proving key to generate non-interactive proofs for adaptively-chosen NP statements, and the proofs can be verified by anyone using the verification key. Moreover, our system is statistically zero-knowledge and the generated public parameters are reusable. The NP-complete language we choose is the correct execution of programs on TinyRAM, a minimalistic (nondeterministic) random-access machine that we design. Together with TinyRAM port of gcc compiler this achieves the first practical realization of a zero-knowledge Succinct Non-interactive ARgument of Knowledge (zk-SNARK) for program executions, in the preprocessing model. This cryptographic primitive is a powerful solution for delegating NP computations, and enjoys many features not achieved by primitives implemented in prior works, most importantly, succinct verification and support for arbitrary computations. Our approach builds on recent theoretical work in the area of outsourced verified computation. We present efficiency improvements and implementations of the two main ingredients: 1. A transformation that, given as input a C program, outputs a circuit whose satisfiability encodes the correct execution of the program. We leverage nondeterminism to make the generated circuit’s size merely quasilinear in the size of the computation; in particular, we efficiently handle arbitrary loops, control flow, and random-memory accesses. This is in contrast with existing “circuit compilers”, which produce circuits of quadratic size. 2. A transformation that, given as input a linear PCP for verifying satisfiability of circuits, outputs a corresponding SNARK. Furthermore, by building on recent work about quadratic span programs, using suitable choices of finite field and FFT algorithms, we give a very efficient implementation of a zero-knowledge linear PCP: linear-time query generation and quasilinear-time prover.  Ronald L. Rivest Title: Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science Thesis Supervisor: Eli Ben-Sasson Title: Associate Professor of Computer Science, Technion – Israel Institute of Technology
An improved DNN-based approach to mispronunciation detection and diagnosis of L2 learners' speech

We extend the Goodness of Pronunciation (GOP) algorithm from the conventional GMM-HMM to DNN-HMM and further optimize the GOP measure toward L2 language learners’ accented speech. We evaluate the performance of the new proposed approach at phone-level mispronunciation detection and diagnosis on an L2 English learners’ corpus. Experimental results show that the Equal Error Rate (EER) is improved from 32.9% to 27.0% by extending GOP from GMM-HMM to DNN-HMM and the EER can be further improved by another 1.5% to 25.5% with our optimized GOP measure. For phone mispronunciation diagnosis, by applying our optimized DNN based GOP measure, the top-1 error rate is reduced from 61.0% to 51.4%, compared with the original DNN based one, and the top-5 error rate is reduced from 8.4% to 5.2%. On a continuously read, L2 Mandarin learners’ corpus, our approaches also achieve similar improvements.
Timekeeping in the Memory System: Predicting and Optimizing Memory Behavior

Techniques for analyzing and improving memory referencing behavior continue to be important for achieving good overall program performance due to the ever-increasing performance gap between processors and main memory. This paper offers a fresh perspective on the problem of predicting and optimizing memory behavior. Namely, we show quantitatively the extent to which detailed timing characteristics of past memory reference events are strongly predictive of future program reference behavior. We propose a family of time-keeping techniques that optimize behavior based on observations about particular cache time durations, such as the cache access interval or the cache dead time. Timekeeping techniques can be used to build small simple, and high-accuracy (often 90% or more) predictors for identifying conflict misses, for predicting dead blocks, and even for estimating the time at which the next reference to a cache frame will occur and the address that will be accessed. Based on these predictors, we demonstrate two new and complementary time-based hardware structures: (1) a time-based victim cache that improves performance by only storing conflict miss lines with likely reuse, and (2) a time-based prefetching technique that hones in on the right address to prefetch, and the right time to schedule the prefetch. Our victim cache technique improves performance over previous proposals by better selections of what to place in the victim cache. Our prefetching technique outperforms similar prior hardware prefetching proposals, despite being orders of magnitude smaller. Overall, these techniques improve performance by more than 11% across the SPEC2000 benchmark suite.
Excessive users of violent video games do not show emotional desensitization: an fMRI study

Playing violent video games have been linked to long-term emotional desensitization. We hypothesized that desensitization effects in excessive users of violent video games should lead to decreased brain activations to highly salient emotional pictures in emotional sensitivity brain regions. Twenty-eight male adult subjects showing excessive long-term use of violent video games and age and education matched control participants were examined in two experiments using standardized emotional pictures of positive, negative and neutral valence. No group differences were revealed even at reduced statistical thresholds which speaks against desensitization of emotion sensitive brain regions as a result of excessive use of violent video games.
Three-Dimensional Distance Field Metamorphosis

Given two or more objects of general topology, intermediate objects are constructed by a distance field metamorphosis. In the presented method the interpolation of the distance field is guided by a warp function controlled by a set of corresponding anchor points. Some rules for defining a smooth least-distorting warp function are given. To reduce the distortion of the intermediate shapes, the warp function is decomposed into a rigid rotational part and an elastic part. The distance field interpolation method is modified so that the interpolation is done in correlation with the warp function. The method provides the animator with a technique that can be used to create a set of models forming a smooth transition between pairs of a given sequence of keyframe models. The advantage of the new approach is that it is capable of morphing between objects having a different topological genus where no correspondence between the geometric primitives of the models needs to be established. The desired correspondence is defined by an animator in terms of a relatively small number of anchor points
Monte-Carlo Planning in Large POMDPs

This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs. The algorithm combines a Monte-Carlo update of the agent’s belief state with a Monte-Carlo tree search from the current belief state. The new algorithm, POMCP, has two important properties. First, MonteCarlo sampling is used to break the curse of dimensionality both during belief state updates and during planning. Second, only a black box simulator of the POMDP is required, rather than explicit probability distributions. These properties enable POMCP to plan effectively in significantly larger POMDPs than has previously been possible. We demonstrate its effectiveness in three large POMDPs. We scale up a well-known benchmark problem, rocksample, by several orders of magnitude. We also introduce two challenging new POMDPs: 10 × 10 battleship and partially observable PacMan, with approximately 10 and 10 states respectively. Our MonteCarlo planning algorithm achieved a high level of performance with no prior knowledge, and was also able to exploit simple domain knowledge to achieve better results with less search. POMCP is the first general purpose planner to achieve high performance in such large and unfactored POMDPs.
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks

The number of malicious programs has grown both in number and in sophistication. Analyzing the malicious intent of vast amounts of data requires huge resources and thus, effective categorization of malware is required. In this paper, the content of a malicious program is represented as an entropy stream, where each value describes the amount of entropy of a small chunk of code in a specific location of the file. Wavelet transforms are then applied to this entropy signal to describe the variation in the entropic energy. Motivated by the visual similarity between streams of entropy of malicious software belonging to the same family, we propose a file agnostic deep learning approach for categorization of malware. Our method exploits the fact that most variants are generated by using common obfuscation techniques and that compression and encryption algorithms retain some properties present in the original code. This allows us to find discriminative patterns that almost all variants in a family share. Our method has been evaluated using the data provided by Microsoft for the BigData Innovators Gathering Anti-Malware Prediction Challenge, and achieved promising results in comparison with the State of the Art.
Algorithms for Constraint-Satisfaction Problems: A Survey

A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This article overviews many of these approaches in a tutorial fashion.
Failure Mode, Mechanism and Effect Analysis for Single Board Computers

Failure Mode, Mechanism and Effect Analysis (FMMEA) is a reliability analysis method which is used to study possible failure modes, failure mechanisms of each component, and to identify the effects of various failure modes on the components and functions. This paper introduces how to implement FMMEA on the Single Board Computer in detail, including system definition, identification of potential failure modes, analysis of failure cause, failure mechanism, and failure effect analysis. Finite element analysis is carried out for the Single Board Computer, including thermal stress analysis and vibration stress analysis. Temperature distribution and vibration modes are obtained, which are the inputs of physics of failure models. Using a variety of Physics of Failure models, the quantitative calculation of single point failure for the Single Board Computer are carried out. Results showed that the time to failure (TTF) of random access memory chip which is SOP (small outline package) is the shortest and the failure is due to solder joint fatigue failure caused by the temperature cycle. It is the weak point of the entire circuit board. Thus solder joint thermal fatigue failure is the main failure mechanism of the Single Board Computer. In the implementation process of PHM for the Single Board Computer, the failure condition of this position should be monitored.
Fast High Voltage Switching Using Stacked MOSFETs

Fast switching of high voltage using stacked MOSFETs has been studied. It is shown that the effective drain-source capacitance has a negative influence in the turn-off process, especially when the load impedance is relatively high. In order to solve this problem, an alternative circuit configuration is tested where additional switching modules are used to deal with the drain-source capacitance. The experimental results have demonstrated fast switching performance, even for a high impedance load.
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer

Anton 2 is a second-generation special-purpose supercomputer for molecular dynamics simulations that achieves significant gains in performance, programmability, and capacity compared to its predecessor, Anton 1. The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations. A 512-node Anton 2 machine, currently in operation, is up to ten times faster than Anton 1 with the same number of nodes, greatly expanding the reach of all-atom biomolecular simulations. Anton 2 is the first platform to achieve simulation rates of multiple microseconds of physical time per day for systems with millions of atoms. Demonstrating strong scaling, the machine simulates a standard 23,558-atom benchmark system at a rate of 85 μs/day---180 times faster than any commodity hardware platform or general-purpose supercomputer.
StakeRare: Using Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation

Requirements elicitation is the software engineering activity in which stakeholder needs are understood. It involves identifying and prioritizing requirements-a process difficult to scale to large software projects with many stakeholders. This paper proposes StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritize requirements in large software projects. StakeRare identifies stakeholders and asks them to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritizes stakeholders using a variety of social network measures to determine their project influence. It then asks the stakeholders to rate an initial list of requirements, recommends other relevant requirements to them using collaborative filtering, and prioritizes their requirements using their ratings weighted by their project influence. StakeRare was evaluated by applying it to a software project for a 30,000-user system, and a substantial empirical study of requirements elicitation was conducted. Using the data collected from surveying and interviewing 87 stakeholders, the study demonstrated that StakeRare predicts stakeholder needs accurately and arrives at a more complete and accurately prioritized list of requirements compared to the existing method used in the project, taking only a fraction of the time.
Correlating events with time series for incident diagnosis

As online services have more and more popular, incident diagnosis has emerged as a critical task in minimizing the service downtime and ensuring high quality of the services provided. For most online services, incident diagnosis is mainly conducted by analyzing a large amount of telemetry data collected from the services at runtime. Time series data and event sequence data are two major types of telemetry data. Techniques of correlation analysis are important tools that are widely used by engineers for data-driven incident diagnosis. Despite their importance, there has been little previous work addressing the correlation between two types of heterogeneous data for incident diagnosis: continuous time series data and temporal event data. In this paper, we propose an approach to evaluate the correlation between time series data and event data. Our approach is capable of discovering three important aspects of event-timeseries correlation in the context of incident diagnosis: existence of correlation, temporal order, and monotonic effect. Our experimental results on simulation data sets and two real data sets demonstrate the effectiveness of the algorithm.
Scaling up Dynamic Time Warping to Massive Dataset

There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a piecewise linear representation. We demonstrate that our approach allows us to outperform DTW by one to three orders of magnitude. We experimentally evaluate our approach on medical, astronomical and sign language data.
Multiagent Smart Grid Automation Architecture Based on IEC 61850/61499 Intelligent Logical Nodes

Universal, intelligent, and multifunctional devices controlling power distribution and measurement will become the enabling technology of the Smart Grid ICT. In this paper, we report on a novel automation architecture which supports distributed multiagent intelligence, interoperability, and configurability and enables efficient simulation of distributed automation systems. The solution is based on the combination of IEC 61850 object-based modeling and interoperable communication with IEC 61499 function block executable specification. Using the developed simulation environment, we demonstrate the possibility of multiagent control to achieve self-healing grid through collaborative fault location and power restoration.