nils-herrmann/clean_scirepeval huggingface dataset:
Correctness By Construction: Better Can Also Be Cheaper

I n December 1999 CrossTalk , David Cook provided a well-reasoned historical analysis of programming language development and considered the role languages play in the software development process. The article was valuable because it showed that programming language developments are not sufficient to ensure success; however, it would be dangerous to conclude from this that they are not necessary for success. Cook rightly identifies other issues such as requirements capture, specifications, and verification and validation (V&V) that need to be addressed. Perhaps we need to look at programming languages not just in terms of their ability to code some particular design but in the influence the language has on some of these other vital aspects of the development process. The key notion is that of the benefit of a precise language or language subset. If the term subset has set anyone thinking " oh no, not another coding standard, " then read on, the topic is much more interesting and useful than that! Language Issues Programming languages have evolved in three main ways. First came improvements in structure; then attempts at improving compile-time error detection through such things as strong typing; and, most significantly , facilities to improve our ability to express abstractions. All of these have shaped the way we think about problem solving. However, programming languages have not evolved in their precision of expression. In fact, they may have actually gotten worse since the meaning of a sample of machine code is exact and unequivocal , whereas the meaning of the constructs of typical modern high-order languages are substantially less certain. The evolution of C into C++ certainly improved its ability to express design abstractions but, if anything, the predictability of the compiled code decreased. These ambiguities arise either from deficiencies in the original language definition or from implementation freedoms given to the compiler writer for ease of implementation or efficiency reasons. None of this may look like a very serious problem. We can still do code walk-throughs and reviews and, after all, we still have to do dynamic testing that should flush out any remaining ambiguities. In fact the evidence is quite strong that it does matter because it creates an environment where we are encouraged to make little attempt to reason about the software we are producing at each stage of its development. Since we typically do not have formal mathematical specifications and we use imprecise …
An integrated theory for improved skill acquisition and retention in the three stages of learning

We introduce an integrated theory of learning and forgetting that has implications for training theory and practice. We begin with a review of skill acquisition research that argues individuals employ different cognitive mechanisms when learning, which can be reliably associated with three stages of learning. This review leads to our proposed skill retention theory, which recommends a method to increase skill retention when designing systems, covering a range of system design issues, from interface design to training. We conclude with a discussion of how we might optimize skill retention based upon this approach. Specifically, we discuss how we might improve training by better spacing the iterations between training sessions to support proceduralizaion to improve skill retention.
Delayed Rejection in Reversible

In a Metropolis-Hastings algorithm, rejection of proposed moves is an intrinsic part of ensuring that the chain converges to the intended target distribution. However, persistent rejection, perhaps in particular parts of the state space, may indicate that locally the proposal distribution is badly calibrated to the target. As an alternative to careful oo-line tuning of state-dependent proposals, the basic algorithm can be modiied so that on rejection, a second attempt to move is made. A diierent proposal can be generated from a new distribution, that is allowed to depend on the previously rejected proposal. We generalise this idea of delaying the rejection and adapting the proposal distribution, due to Tierney and Mira (1999), to generate a more exible class of methods, that in particular applies to a variable dimension setting. The approach is illustrated by two pedagogical examples, and a more realistic application, to a change-point analysis for point processes.
Decision Tree Discovery

We describe the two most commonly used systems for induction of decision trees for classi cation: C4.5 and CART. We highlight the methods and di erent decisions made in each system with respect to splitting criteria, pruning, noise handling, and other di erentiating features. We describe how rules can be derived from decision trees and point to some di erence in the induction of regression trees. We conclude with some pointers to advanced techniques, including ensemble methods, oblique splits, grafting, and coping with large data.
Feature-Enhanced Probabilistic Models for Diffusion Network Inference

Cascading processes, such as disease contagion, viral marketing, and information diffusion, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. In this paper, we revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. We show that MAP inference is tractable and can therefore scale to very large real-world networks. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time.
CS principles goes to middle school: learning how to teach "Big Data"

Spurred by evidence that students' future studies are highly influenced during middle school, recent efforts have seen a growing emphasis on introducing computer science to middle school learners. This paper reports on the in-progress development of a new middle school curricular module for Big Data, situated as part of a new CS Principles-based middle school curriculum. Big Data is of widespread societal importance and holds increasing implications for the computer science workforce. It also has appeal as a focus for middle school computer science because of its rich interplay with other important computer science principles. This paper examines three key aspects of a Big Data unit for middle school: its alignment with emerging curricular standards; the perspectives of middle school classroom teachers in mathematics, science, and language arts; and student feedback as explored during a middle school pilot study with a small subset of the planned curriculum. The results indicate that a Big Data unit holds great promise as part of a middle school computer science curriculum.
Estimating the rumor source with anti-rumor in social networks

Recently, the problem of detecting the rumor source in a social network has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees. In this paper, we study the impact of an anti-rumor on the rumor source detection. We first show a negative result: the anti-rumor's diffusion does not increase the detection probability under Maximum-Likelihood-Estimator (MLE) when the number of infected nodes are sufficiently large by passive diffusion that the anti-rumor starts to be spread by a special node, called the protector, after is reached by the rumor. We next consider the case when the distance between the rumor source and the protector follows a certain type of distribution, but its parameter is hidden. Then, we propose the following learning algorithm: a) learn the distance distribution parameters under MLE, and b) detect the rumor source under Maximum-A-Posterior-Estimator (MAPE) based on the learnt parameters. We provide an analytic characterization of the rumor source detection probability for regular trees under the proposed algorithm, where MAPE outperforms MLE by up to 50% for 3-regular trees and by up to 63% when the degree of the regular tree becomes large. We demonstrate our theoretical findings through numerical results, and further present the simulation results for general topologies (e.g., Facebook and US power grid networks) even without knowledge of the distance distribution, showing that under a simple protector placement algorithm, MAPE produces the detection probability much larger than that by MLE.
ProgTest: An environment for the submission and evaluation of programming assignments based on testing activities

Programming foundations is not an easy subject to be taught — many students have difficulties understanding the abstract concepts of programming and have a wrong view about the programming activity. In order to address these problems, experiences have suggested the integrated teaching of programming concepts and software testing in introductory CS courses. Shortly, the idea is that testing can contribute to enhance the students' capabilities of understanding and analysis. However, such perspective requires tools to provide an adequate feedback to evaluate the students' performance concerning programming and testing activities. In this paper we describe ProgTest — a web-based tool for the submission and automatic evaluation of practical programming assignments based on testing activities. Results from a preliminary validation of ProgTest are also presented. Such results provide evidences on the practical use of ProgTest as a supporting mechanism for the integrated teaching of programming foundations and software testing.
A Review on Internet of Things Based Garbage Bins Detection Systems

With networking support the use of low power wireless sensors has increased over the past decade. Use of standardized Internet protocols, new hardware for communication, new generations of microcontrollers have resulted in increasing interoperability which allows information exchange between sensor nodes with large number of peers beneficial for developing flexible, advanced and reusable systems. The dustbins and garbage bins located at the public places of cities often results in bad environment with the stinking place and unhygienic situations for people. To get rid of all these problems an IoT based real time garbage and waste bin(s) detection system by integrating different sensing and communication technologies is proposed. The system in divided into three segments, the first segment consists of bins with sensor nodes installed in it that are moisture, ultrasonic and odour sensor interfaced with microcontroller. Second segment consists of Wi-Fi module for data transfer to the server and third segment is web page. The real time bin status and corresponding notifications are displayed on web page. Web page is developed using PHP and database is generated along with the priority algorithm embedded in it for more efficiency.
Compacting Neural Network Classifiers via Dropout Training

We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per unit dropout retention probability so that the optimizer can effectively prune hidden units during training. By changing the prior hyperparameters, we can control the size of the resulting network. We performed a systematic comparison of dropout compaction and competing methods on several realworld speech recognition tasks and found that dropout compaction achieved comparable accuracy with fewer than 50% of the hidden units, translating to a 2.5x speedup in run-time.
Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters

The enormous growth of data in a variety of applications has increased the need for high performance data mining based on distributed environments. However, standard data mining toolkits per se do not allow the usage of computing clusters. The success of MapReduce for analyzing large data has raised a general interest in applying this model to other, data intensive applications. Unfortunately current research has not lead to an integration of GUI based data mining toolkits with distributed file system based MapReduce systems. This paper defines novel principles for modeling and design of the user interface, the storage model and the computational model necessary for the integration of such systems. Additionally, it introduces a novel system architecture for interactive GUI based data mining of large data on clusters based on MapReduce that overcomes the limitations of data mining toolkits. As an empirical demonstration we show an implementation based on Weka and Hadoop.
Introduction to differential power analysis

The power consumed by a circuit varies according to the activity of its individual transistors and other components. As a result, measurements of the power used by actual computers or microchips contain information about the operations being performed and the data being processed. Cryptographic designs have traditionally assumed that secrets are manipulated in environments that expose no information beyond the specified inputs and outputs. This paper examines how information leaked through power consumption and other side channels can be analyzed to extract secret keys from a wide range of devices. The attacks are practical, non-invasive, and highly effective—even against complex and noisy systems where cryptographic computations account for only a small fraction of the overall power consumption. We also introduce approaches for preventing DPA attacks and for building cryptosystems that remain secure even when implemented in hardware that leaks.
An Analysis of Active Learning Strategies for Sequence Labeling Tasks

Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed light on the best active learning approaches for sequence labeling tasks such as information extraction and document segmentation. We survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. We also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed methods advance the state of the art.
Parallel simulation: parallel and distributed simulation systems

Originating from basic research conducted in the 1970's and 1980's, the parallel and distributed simulation field has matured over the last few decades. Today, operational systems have been fielded for applications such as military training, analysis of communication networks, and air traffic control systems, to mention a few. This tutorial gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems. Particular emphasis is placed on synchronization (also called time management) algorithms as well as data distribution techniques.
Wechsler Intelligence Scale for Children-IV Conceptual and Interpretive Guide

The Wechsler intelligence scales were developed by Dr. David Wechsler, a clinical psychologist with Bellevue Hospital. His initial test, the Wechsler-Bellevue Intelligence Scale, was published in 1939 and was designed to measure intellectual performance by adults. Wechsler constructed the WBIS based on his observation that, at the time, existing intelligence tests for adults were merely adaptations of tests for children and had little face validity for older age groups.
Kinect Gesture Recognition for Interactive System

Gaming systems like Kinect and XBox always have to tackle the problem of extracting features from video data sets and classifying the body movement. In this study, reasonable features like human joints positions, joints velocities, joint angles and joint angular velocities are extracted. We used several machine learning methods including Naive Bayes, Support Vector Machine and Random Forest to learn and classify the human gestures. Simulation results and the final confusion matrix show that by combining delicately preprocessed data sets and Random Forest methods, the F-scores of the correct predictions can be maximized. Such methods can also be applied in real-time scenarios.
mTOR Signaling in Growth Control and Disease

The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage ActorCritic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU’s computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at https://github.com/NVlabs/GA3C.
System Dynamics and Decision-Making in the Context of Dynamically Complex Multi-Dimensional Societal Issues

Dynamically complex multi-dimensional societal issues are societal issues characterised by complex time evolutionary behaviour on multiple dimensions, and very often also by many uncertainties, multiple views and parties concerned, and different ethical aspects. It will be argued here that stand-alone system dynamics often needs to be complemented with other methodologies or disciplines –for example multiple criteria decision analysis and/or ethics– in order to support the selection of appropriate strategies when dealing with such issues. This paper looks at the combination of system dynamics, multiple criteria decision analysis and ethics to support strategy selection in case of such dynamically complex multi-dimensional societal issues, with special attention paid to the capacity of the multi-methodology to deal with multiple dimensions, multiple time scales, multiple parties and views, uncertainty and
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports

For a large and evolving software system, the project team could receive a large number of bug reports. Locating the source code files that need to be changed in order to fix the bugs is a challenging task. Once a bug report is received, it is desirable to automatically point out to the files that developers should change in order to fix the bug. In this paper, we propose BugLocator, an information retrieval based method for locating the relevant files for fixing a bug. BugLocator ranks all files based on the textual similarity between the initial bug report and the source code using a revised Vector Space Model (rVSM), taking into consideration information about similar bugs that have been fixed before. We perform large-scale experiments on four open source projects to localize more than 3,000 bugs. The results show that BugLocator can effectively locate the files where the bugs should be fixed. For example, relevant buggy files for 62.60% Eclipse 3.1 bugs are ranked in the top ten among 12,863 files. Our experiments also show that BugLocator outperforms existing state-of-the-art bug localization methods.
A Multi-Task Framework for Weather Recognition

Weather recognition is important in practice, while this task has not been thoroughly explored so far. The current trend of dealing with this task is treating it as a single classification problem, i.e., determining whether a given image belongs to a certain weather category or not. However, weather recognition differs significantly from traditional image classification, since several weather features may appear simultaneously. In this case, a simple classification result is insufficient to describe the weather condition. To address this issue, we propose to provide auxiliary weather related information for comprehensive weather description. Specifically, semantic segmentation of weather-cues, such as blue sky and white clouds, is exploited as an auxiliary task in this paper. Moreover, a convolutional neural network (CNN) based multi-task framework is developed which aims to concurrently tackle weather category classification task and weather-cues segmentation task. Due to the intrinsic relationships between these two tasks, exploring auxiliary semantic segmentation of weather-cues can also help to learn discriminative features for the classification task, and thus obtain superior accuracy. To verify the effectiveness of the proposed approach, extra segmentation masks of weather-cues are generated manually on an existing weather image dataset. Experimental results have demonstrated the superior performance of our approach. The enhanced dataset, source codes and pre-trained models are available at https://github.com/wzgwzg/Multitask_Weather.
An automatic tool for the analysis of natural language requirements

Using automatic tools for the quality analysis of Natural Language (NL) requirements is recognized as a key factor for achieving software quality. Unfortunately few tools and techniques for the NL requirements analysis are currently available. This paper presents a methodology and a tool (called QuARS Quality Analyzer for Requirement Specifications) for analyzing NL requirements in a systematic and automatic way. QuARS allows requirements engineers to perform an initial parsing of the requirements in order to automatically detect potential linguistic defects that could cause interpretation problems at subsequent stages in developing the software. This tool is also able to partially support the consistency and completeness analysis by clustering the requirements according to specific topics.
Clap : Modeling Applause in Campaign Speeches

This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause. In prediction experiments, we find that lexical features carry the most information, but that a variety of features are predictive, including prosody, long-term contextual dependencies, and theoretically motivated features designed to capture rhetorical techniques.