nils-herrmann/clean_scirepeval huggingface dataset:
Player Modeling for Adaptive Games

w en we say that a game has "good AI," we typically mean that the characters in the game exhibit consistent and realistic behavior, reacting appropriately to the actions of the player and other characters. For certain genres of games-for example, first-person shooters and real-time strategy game~-~~good AI" also refers to the ability of the game to challenge the player on a tactical and strategic level. While these are certainly worthy and important goals to strive for in your game, they tend to overshadow a third, seldom-mentioned component of good game AI: the capacity to adapt over time to the quirks and habits of a particular player. An adaptive AI can drastically increase the replayability of your game, and make the game experience much more intense and personalized for the players. Nonetheless , most games today are limited to what might be called "manually adaptive AI"; in other words, the game provides difficulty sliders and configuration parameters that allow the player to directly control how game characters act at some very coarse level. While this isn't a great solution, it certainly seems much less daunting than introducing costly (and often finicky) machine-learning algorithms into your game. Although many learning algorithms are computationally expensive enough to make game programmers run screaming, there are some approaches that are relatively lightweight, simple, and flexible enough to be useful. One in particular is a technique we call player modeling, borrowed from the similar notion of "student modeling in intelligent tutoring system research. The basic idea is simple: the game maintains a profile of each player that captures the skills, weaknesses, preferences, and other characteristics of that player. This model is updated by the game as it interacts with the player. In turn, the game AI can query the player model to determine how best to adapt its behavior to that particular player, such as by asking which of several possible tactics will be most challenging to the player. Using player modeling, a game's A1 can adapt both during the course of a single play as well as over multiple sessions, resulting in a computer opponent that changes and evolves with time to suit the player.
Toward Deep Learning Software Repositories

Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models. Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts. We show how a particular deep learning model can remember its state to effectively model sequential data, e.g., streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix. Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects. We experiment with two of the models' hyperparameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization. Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models. Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts. Thus, our work serves as the first step toward deep learning software repositories.
ispc: A SPMD compiler for high-performance CPU programming

SIMD parallelism has become an increasingly important mechanism for delivering performance in modern CPUs, due its power efficiency and relatively low cost in die area compared to other forms of parallelism. Unfortunately, languages and compilers for CPUs have not kept up with the hardware's capabilities. Existing CPU parallel programming models focus primarily on multi-core parallelism, neglecting the substantial computational capabilities that are available in CPU SIMD vector units. GPU-oriented languages like OpenCL support SIMD but lack capabilities needed to achieve maximum efficiency on CPUs and suffer from GPU-driven constraints that impair ease of use on CPUs. We have developed a compiler, the Intel R® SPMD Program Compiler (ispc), that delivers very high performance on CPUs thanks to effective use of both multiple processor cores and SIMD vector units. ispc draws from GPU programming languages, which have shown that for many applications the easiest way to program SIMD units is to use a single-program, multiple-data (SPMD) model, with each instance of the program mapped to one SIMD lane. We discuss language features that make ispc easy to adopt and use productively with existing software systems and show that ispc delivers up to 35x speedups on a 4-core system and up to 240× speedups on a 40-core system for complex workloads (compared to serial C++ code).
The ATIS Spoken Language Systems Pilot Corpus

Speech research has made tremendous progress in the past using the following paradigm: de ne the research problem, collect a corpus to objectively measure progress, and solve the research problem. Natural language research, on the other hand, has typically progressed without the bene t of any corpus of data with which to test research hypotheses. We describe the Air Travel Information System (ATIS) pilot corpus, a corpus designed to measure progress in Spoken Language Systems that include both a speech and natural language component. This pilot marks the rst full-scale attempt to collect such a corpus and provides guidelines for future e orts.
An experimental investigation of malware attacks on SCADA systems

Modern critical infrastructures are continually exposed to new threats due to the vulnerabilities and architectural weaknesses introduced by the extensive use of information and communications technologies (ICT). Of particular significance are the vulnerabilities in the communication protocols used in supervisory control and data acquisition (SCADA) systems that are commonly employed to control industrial processes. This paper presents the results of our research on the impact of traditional ICT malware on SCADA systems. In addition, it discusses the potential damaging effects of computer malware created for SCADA
Linearization of NTC Thermistor Characteristic Using Op-Amp Based Inverting Amplifier

A low cost linearizing circuit is developed, placing the NTC thermistor in a widely used inverting amplifier circuit using operational amplifier. The performance of the system is verified experimentally. A linearity of approximately ± 1% is achieved over 30 °C -120 °C. When used for a narrower span, a much better linearity of ± 0.5% is obtained. The gain of the arrangement can be adjusted over a wide range by simply varying the feedback resistance. The simplicity of the configuration promises a greater reliability, and also curtails the deterioration in the stability of performance, by reducing the cumulation of drifts in the different circuit components and devices.
Dengue disease prediction using weka data mining tool

Dengue is a life threatening disease prevalent in several developed as well as developing countries like India. This is a virus born disease caused by breeding of Aedes mosquito. Datasets that are available for dengue describe information about the patients suffering with dengue disease and without dengue disease along with their symptoms like: Fever Temperature, WBC, Platelets, Severe Headache, Vomiting, Metallic Taste,  Diarrhea, Hematocrit, Hemoglobin, and how many days suffer in different city. In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Naïve Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1 , had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results.
Deep neural network based instrument extraction from music

This paper deals with the extraction of an instrument from music by using a deep neural network. As prior information, we only assume to know the instrument types that are present in the mixture and, using this information, we generate the training data from a database with solo instrument performances. The neural network is built up from rectified linear units where each hidden layer has the same number of nodes as the output layer. This allows a least squares initialization of the layer weights and speeds up the training of the network considerably compared to a traditional random initialization. We give results for two mixtures, each consisting of three instruments, and evaluate the extraction performance using BSS Eval for a varying number of hidden layers.
Intrinsic shape context descriptors for deformable shapes

In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; `angle' is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geometric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analysis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks.
Learning Hash Functions for Cross-View Similarity Search

Many applications in Multilingual and Multimodal Information Access involve searching large databases of high dimensional data objects with multiple (conditionally independent) views. In this work we consider the problem of learning hash functions for similarity search across the views for such applications. We propose a principled method for learning a hash function for each view given a set of multiview training data objects. The hash functions map similar objects to similar codes across the views thus enabling cross-view similarity search. We present results from an extensive empirical study of the proposed approach which demonstrate its effectiveness on Japanese language People Search and Multilingual People Search problems.
The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services

Current debugging and optimization methods scale poorly to deal with the complexity of modern Internet services, in which a single request triggers parallel execution of numerous heterogeneous software components over a distributed set of computers. The Achilles’ heel of current methods is the need for a complete and accurate model of the system under observation: producing such a model is challenging because it requires either assimilating the collective knowledge of hundreds of programmers responsible for the individual components or restricting the ways in which components interact. Fortunately, the scale of modern Internet services offers a compensating benefit: the sheer volume of requests serviced means that, even at low sampling rates, one can gather a tremendous amount of empirical performance observations and apply “big data” techniques to analyze those observations. In this paper, we show how one can automatically construct a model of request execution from pre-existing component logs by generating a large number of potential hypotheses about program behavior and rejecting hypotheses contradicted by the empirical observations. We also show how one can validate potential performance improvements without costly implementation effort by leveraging the variation in component behavior that arises naturally over large numbers of requests to measure the impact of optimizing individual components or changing scheduling behavior. We validate our methodology by analyzing performance traces of over 1.3 million requests to Facebook servers. We present a detailed study of the factors that affect the end-to-end latency of such requests. We also use our methodology to suggest and validate a scheduling optimization for improving Facebook request latency.
Security and Usability: Analysis and Evaluation

The differences between the fields of Human-Computer Interaction and Security (HCISec) and Human-Computer Interaction (HCI) have not been investigated very closely. Many HCI methods and procedures have been adopted by HCISec researchers, however the extent to which these apply to the field of HCISec is arguable given the fine balance between improving the ease of use of a secure system and potentially weakening its security. That is to say that the techniques prevalent in HCI are aimed at improving users' effectiveness, efficiency or satisfaction, but they do not take into account the potential threats and vulnerabilities that they can introduce. To address this problem, we propose a security and usability threat model detailing the different factors that are pertinent to the security and usability of secure systems, together with a process for assessing these.
Secure Blocking + Secure Matching = Secure Record Linkage

Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.  Ubiquitous computing; Security and privacy
Classification performance of the frequency-related parameters derived from uterine EMG signals

Frequency-related parameters derived from the uterine electromyogram (EMG) signals are widely used in many pregnancy monitoring and preterm delivery prediction studies. Although they are classical parameters, they are well suited for quantifying uterine EMG signals and have many advantages over amplitude-related parameters. The present work aims to compare various frequency-related parameters according to their classification performances (pregnancy vs. labor) using the receiver operating characteristic (ROC) curve analysis. The comparison between the parameters indicates that median frequency is the best frequency-related parameter that can be used for distinguishing between pregnancy and labor contractions. We conclude that median frequency can be the representative frequency-related parameter for classification problems of uterine EMG.
Study and Mitigation of Origin Stripping Vulnerabilities in Hybrid-postMessage Enabled Mobile Applications

postMessage is popular in HTML5 based web apps to allow the communication between different origins. With the increasing popularity of the embedded browser (i.e., WebView) in mobile apps (i.e., hybrid apps), postMessage has found utility in these apps. However, different from web apps, hybrid apps have a unique requirement that their native code (e.g., Java for Android) also needs to exchange messages with web code loaded in WebView. To bridge the gap, developers typically extend postMessage by treating the native context as a new frame, and allowing the communication between the new frame and the web frames. We term such extended postMessage "hybrid postMessage" in this paper. We find that hybrid postMessage introduces new critical security flaws: all origin information of a message is not respected or even lost during the message delivery in hybrid postMessage. If adversaries inject malicious code into WebView, the malicious code may leverage the flaws to passively monitor messages that may contain sensitive information, or actively send messages to arbitrary message receivers and access their internal functionalities and data. We term the novel security issue caused by hybrid postMessage "Origin Stripping Vulnerability" (OSV). In this paper, our contributions are fourfold. First, we conduct the first systematic study on OSV. Second, we propose a lightweight detection tool against OSV, called OSV-Hunter. Third, we evaluate OSV-Hunter using a set of popular apps. We found that 74 apps implemented hybrid postMessage, and all these apps suffered from OSV, which might be exploited by adversaries to perform remote real-time microphone monitoring, data race, internal data manipulation, denial of service (DoS) attacks and so on. Several popular development frameworks, libraries (such as the Facebook React Native framework, and the Google cloud print library) and apps (such as Adobe Reader and WPS office) are impacted. Lastly, to mitigate OSV from the root, we design and implement three new postMessage APIs, called OSV-Free. Our evaluation shows that OSV-Free is secure and fast, and it is generic and resilient to the notorious Android fragmentation problem. We also demonstrate that OSV-Free is easy to use, by applying OSV-Free to harden the complex "Facebook React Native" framework. OSV-Free is open source, and its source code and more implementation and evaluation details are available online.
A New Way of Controlling Parallel-Connected Inverters by Using Synchronous-Reference-Frame Virtual Impedance Loop—Part I: Control Principle

A novel simple and effective autonomous current-sharing controller for parallel three-phase inverters is proposed in this paper. The proposed controller provides faster response and better accuracy in contrast to the conventional droop control, since this novel approach does not require any active or reactive power calculations. Instead, a synchronous-reference-frame (SRF) virtual impedance loop and an SRF-based phase-locked loop are used. Stationary analysis is provided in order to identify the inherent mechanism of the direct and quadrature output currents in relation to the voltage amplitude and frequency with different line impedances by means of the system transfer functions. Comparison experiments from two parallel inverters are presented to compare the control performance of the conventional droop control and the proposed control with different line impedances. In addition, experimental results from a setup with three parallel 2.2-kW inverters verify the effectiveness of the proposed control strategy in different scenarios.
Robust CNN-based speech recognition with Gabor filter kernels

As has been extensively shown, acoustic features for speech recognition can be learned from neural networks with multiple hidden layers. However, the learned transformations may not sufficiently generalize to test sets that have a significant mismatch to the training data. Gabor features, on the other hand, are generated from spectro-temporal filters designed to model human auditory processing. In previous work, these features are used as inputs to neural networks, which improved word accuracy for speech recognition in the presence of noise. Here we propose a neural network architecture called a Gabor Convolutional Neural Network (GCNN) that incorporates Gabor functions into convolutional filter kernels. In this architecture, a variety of Gabor features served as the multiple feature maps of the convolutional layer. The filter coefficients are further tuned by back-propagation training. Experiments used two noisy versions of the WSJ corpus: Aurora 4, and RATS re-noised WSJ. In both cases, the proposed architecture performs better than other noise-robust features that we have tried, namely, ETSI-AFE, PNCC, Gabor features without the CNN-based approach, and our best neural network features that don’t incorporate Gabor functions.
STAR: articulation training for young children

The Speech Training, Assessment, and Remediation (STAR) system is intended to assist Speech and Language Pathologists in treating children with articulation problems. The system is embedded in an interactive video game that is set in a spaceship and involves teaching aliens to “understand” selected words by spoken example. The sequence of events leads children through a series of successively more diff icult speech production tasks, beginning with CV syllables and progressing to words/phrases. Word selection is further tailored to emphasize the contrastive nature of phonemes by the use of minimal pairs (e.g., run/won) in production sets. To assess children’s speech, a discrete hidden Markov model recognition engine is used[1]. Phone models were trained on the CMU Kids database[2]. Performance of the HMM recognizer was compared to perceptual ratings of speech recorded from children who substitute /w/ for /r/. The difference in log likelihood between /r/ and /w/ models correlates well with perceptual ratings of utterances containing substitution errors, but very poorly for correctly articulated examples. The poor correlation between perceptual and machine ratings for correctly articulated utterances may be due to very restricted variance in the perceptual data for those utterances.