nils-herrmann/clean_scirepeval huggingface dataset:
Planning, Executing, and Evaluating the Winograd Schema Challenge

50 AI MAGAZINE The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern, 2012) was proposed by Hector Levesque in 2011 as an alternative to the Turing test. Turing (1950) had first introduced the notion of testing a computer system’s intelligence by assessing whether it could fool a human judge into thinking that it was conversing with a human rather a computer. Although intuitively appealing and arbitrarily flexible — in theory, a human can ask the computer system that is being tested wide-ranging questions about any subject desired — in practice, the execution of the Turing test turns out to be highly susceptible to systems that few people would wish to call intelligent. The Loebner Prize Competition (Christian 2011) is in particular associated with the development of chatterbots that are best viewed as successors to ELIZA (Weizenbaum 1966), the program that fooled people into thinking that they were talking to a human psychotherapist by cleverly turning a person’s statements into questions of the sort a therapist would ask. The knowledge and inference that characterize conversations of substance — for example, discussing alternate metaphors in sonnets of Shakespeare — and which Turing presented as examples of the sorts of conversation that an intelligent system should be able to produce, are absent in these chatterbots. The focus is merely on engaging in surfacelevel conversation that can fool some humans who do not delve too deeply into a conversation, for at least a few minutes, into thinking that they are speaking to another person. The widely reported triumph of the chatterbot Eugene Goostman in fooling 10 out of 30 judges to judge, after a fiveminute conversation, that it was human (University of Read-
A Metamodel for GDPR-based Privacy Level Agreements

The adoption of the General Data Protection Regulation (GDPR) is a major concern for data controllers of the public and private sector, as they are obliged to conform to the new principles and requirements managing personal data. In this paper, we propose that the data controllers adopt the concept of the Privacy Level Agreement. We present a metamodel for PLAs to support privacy management, based on analysis of privacy threats, vulnerabilities and trust relationships in their Information Systems, whilst complying with laws and regulations, and we illustrate the relevance of the metamodel with the GDPR.
Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image

Recently, high dynamic range (HDR) imaging has attracted much attention as a technology to reflect human visual characteristics owing to the development of the display and camera technology. This paper proposes a novel deep neural network model that reconstructs an HDR image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively simple for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range but also has the advantage of restoring the light information of the actual physical world. The proposed method is an end-to-end reconstruction process, and it has the advantage of being able to easily combine a network to extend an additional range. In the experimental results, the proposed method shows quantitative and qualitative improvement in performance, compared with the conventional algorithms.
Learning to Program is Easy

The orthodox view that "programming is difficult to learn" leads to uncritical teaching practices and poor student outcomes. It may also impact negatively on diversity and equity within the Computer Science discipline. But learning to program is easy --- so easy that children can do it. We make our introductory courses difficult by establishing unrealistic expectations for novice programming students. By revisiting the expected norms for introductory programming we may be able to substantially improve outcomes for novice programmers, address negative impressions of disciplinary practices and create a more equitable environment.
Human Activity Detection from RGBD Images

Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person’s activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before).
Standardized Protocol Stack for the Internet of (Important) Things

We have witnessed the Fixed Internet emerging with virtually every computer being connected today; we are currently witnessing the emergence of the Mobile Internet with the exponential explosion of smart phones, tablets and net-books. However, both will be dwarfed by the anticipated emergence of the Internet of Things (IoT), in which everyday objects are able to connect to the Internet, tweet or be queried. Whilst the impact onto economies and societies around the world is undisputed, the technologies facilitating such a ubiquitous connectivity have struggled so far and only recently commenced to take shape. To this end, this paper introduces in a timely manner and for the first time the wireless communications stack the industry believes to meet the important criteria of power-efficiency, reliability and Internet connectivity. Industrial applications have been the early adopters of this stack, which has become the de-facto standard, thereby bootstrapping early IoT developments with already thousands of wireless nodes deployed. Corroborated throughout this paper and by emerging industry alliances, we believe that a standardized approach, using latest developments in the IEEE 802.15.4 and IETF working groups, is the only way forward. We introduce and relate key embodiments of the power-efficient IEEE 802.15.4-2006 PHY layer, the power-saving and reliable IEEE 802.15.4e MAC layer, the IETF 6LoWPAN adaptation layer enabling universal Internet connectivity, the IETF ROLL routing protocol enabling availability, and finally the IETF CoAP enabling seamless transport and support of Internet applications. The protocol stack proposed in the present work converges towards the standardized notations of the ISO/OSI and TCP/IP stacks. What thus seemed impossible some years back, i.e., building a clearly defined, standards-compliant and Internet-compliant stack given the extreme restrictions of IoT networks, is commencing to become reality.
Semantic interpretation of noun compounds using verbal and other paraphrases

We study the problem of semantic interpretation of noun compounds such as bee honey, malaria mosquito, apple cake, and stem cell. In particular, we explore the potential of using predicates that make explicit the hidden relation that holds between the nouns that form the noun compound. For example, mosquito that carries malaria is a paraphrase of the compound malaria mosquito in which the verb explicitly states the semantic relation between the two nouns. We study the utility of using such paraphrasing verbs, with associated weights, to build a representation of the semantics of a noun compound, for example, malaria mosquito can be represented as follows: carry (23), spread (16), cause (12), transmit (9), and so on. We also explore the potential of using multiple paraphrasing verbs as features for predicting abstract semantic relations such as CAUSE, and we demonstrate that using explicit paraphrases can help improve statistical machine translation.
A Deep Learning Approach for Network Intrusion Detection System

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organization. However, many challenges arise while developing a flexible and effective NIDS for unforeseen and unpredictable attacks. In this work, we propose a deep learning based approach to implement such an effective and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include the accuracy, precision, recall, and f-measure values.
Integración de optimización evolutiva para el reconocimiento de emociones en voz

Resumen. En este art́ıculo se presenta el desarrollo de un sistema de reconocimiento de emociones basado en la voz. Se consideraron las siguientes emociones básicas: Enojo, Felicidad, Neutro y Tristeza. Para este propósito una base de datos de voz emocional fue creada con ocho usuarios Mexicanos con 640 frases (8 usuarios × 4 emociones × 20 frases por emoción). Los Modelos Ocultos de Markov (Hidden Markov Models, HMMs) fueron usados para construir el sistema de reconocimiento. Basado en el concepto de modelado acústico de vocales espećıficas emotivas un total de 20 fonemas de vocales (5 vocales × 4 emociones) y 22 fonemas de consonantes fueron considerados para el entrenamiento de los HMMs. Un Algoritmo Genético (Genetic Algorithm, GA) fue integrado dentro del proceso de reconocimiento para encontrar la arquitectura más adecuada para el HMM para cada vocal espećıfica emotiva. Una tasa de reconocimiento total aproximada del 90.00 % fue conseguida con el reconocedor de voz construido con los HMMs optimizados.
Design of a Multi-agent System Architecture for the Scrum Methodology

The objective of this paper is to design a multi-agent system architecture for the Scrum methodology. Scrum is an iterative, incremental framework for software development which is flexible, adaptable and highly productive. An agent is a system situated within and a part of an environment that senses the environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future (Franklin and Graesser, 1996). To our knowledge, this is first attempt to include software agents in the Scrum framework. Furthermore, our design covers all the stages of software development. Alternative approaches were only restricted to the analysis and design phases. This Multi-Agent System (MAS) Architecture for Scrum acts as a design blueprint and a baseline architecture that can be realised into a physical implementation by using an appropriate agent development framework. The development of an experimental prototype for the proposed MAS Architecture is in progress. It is expected that this tool will provide support to the development team who will no longer be expected to report, update and manage non-core activities daily.
IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango .
Understanding the Digital Marketing Environment with KPIs and Web Analytics

In the practice of Digital Marketing (DM), Web Analytics (WA) and Key Performance Indicators (KPIs) can and should play an important role in marketing strategy formulation. It is the aim of this article to survey the various DM metrics to determine and address the following question: What are the most relevant metrics and KPIs that companies need to understand and manage in order to increase the effectiveness of their DM strategies? Therefore, to achieve these objectives, a Systematic Literature Review has been carried out based on two main themes (i) Digital Marketing and (ii) Web Analytics. The search terms consulted in the databases have been (i) DM and (ii) WA obtaining a result total of n = 378 investigations. The databases that have been consulted for the extraction of data were Scopus, PubMed, PsyINFO, ScienceDirect and Web of Science. In this study, we define and identify the main KPIs in measuring why, how and for what purpose users interact with web pages and ads. The main contribution of the study is to lay out and clarify quantitative and qualitative KPIs and indicators for DM performance in order to achieve a consensus on the use and measurement of these indicators.
Fast Instantiation of GGP Game Descriptions Using Prolog with Tabling

We present a method to instantiate game descriptions used in General Game Playing with a Prolog interpreter using tabling. Instantiation is a crucial step for speeding up the interpretation of game descriptions and increasing the playing strength of general players. Our method allows us to ground almost all of the game descriptions present on the GGP servers in a time that is compatible with the common time settings of the GGP competition. It instantiates more rapidly than previous published methods.
SymDrive: Testing Drivers without Devices

Device-driver development and testing is a complex and error-prone undertaking. For example, testing errorhandling code requires simulating faulty inputs from the device. A single driver may support dozens of devices, and a developer may not have access to any of them. Consequently, many Linux driver patches include the comment “compile tested only.” SymDrive is a system for testing Linux and FreeBSD drivers without their devices present. The system uses symbolic execution to remove the need for hardware, and extends past tools with three new features. First, SymDrive uses static-analysis and source-to-source transformation to greatly reduce the effort of testing a new driver. Second, SymDrive checkers are ordinary C code and execute in the kernel, where they have full access to kernel and driver state. Finally, SymDrive provides an executiontracing tool to identify how a patch changes I/O to the device and to compare device-driver implementations. In applying SymDrive to 21 Linux drivers and 5 FreeBSD drivers, we found 39 bugs.
Apache Flink: Stream Analytics at Scale

Apache Flink is an open source system for expressive, declarative, fast, and efficient data analysis on both historical (batch) and real-time (streaming) data. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in parallel databases. At its core, Flink builds on a distributed dataflow runtime that unifies batch and incremental computations over a true-streaming pipelined execution. Its programming model allows for stateful, fault tolerant computations, flexible user-defined windowing semantics for streaming and unique support for iterations. Flink is converging into a use-case complete system for parallel data processing with a wide range of top level libraries ranging from machine learning through to graph processing. Apache Flink originates from the Stratosphere project led by TU Berlin and has led to various scientific papers (e.g., in VLDBJ, SIGMOD, (P)VLDB, ICDE, and HPDC). In this half-day tutorial we will introduce Apache Flink, and give a tutorial on its streaming capabilities using concrete examples of application scenarios, focusing on concepts such as stream windowing, and stateful operators.
Genetic Algorithms: Theory and Applications

FLLL 2 Preface This is a printed collection of the contents of the lecture " Genetic Algorithms: Theory and Applications " which I gave first in the winter semester 1999/2000 at the Johannes Kepler University in Linz. The reader should be aware that this manuscript is subject to further reconsideration and improvement. Corrections, complaints, and suggestions are cordially welcome. The sources were manifold: Chapters 1 and 2 were written originally for these lecture notes. All examples were implemented from scratch. The third chapter is a distillation of the books of Goldberg [13] and Hoffmann  and a handwritten manuscript of the preceding lecture on genetic algorithms which was given by Andreas Stöckl in 1993 at the Johannes Kepler University. Chapters 4, 5, and 7 contain recent adaptations of previously published material from my own master thesis and a series of lectures which was given by Francisco Herrera and myself at the Second Summer School on Advanced Control at the Slovak Technical University, Bratislava, in summer 1997 . Chapter 6 was written originally, however, strongly influenced by A. Geyer-Schulz's works and H. Hörner's paper on his C++ GP kernel [18]. I would like to thank all the students attending the first GA lecture in Winter 1999/2000, for remaining loyal throughout the whole term and for contributing much to these lecture notes with their vivid, interesting, and stimulating questions, objections, and discussions. Last but not least, I want to express my sincere gratitude to Sabine Lumpi and Susanne Saminger for support in organizational matters, and Pe-ter Bauer for proofreading .
Determining Uses and Gratifications for Indian Internet Users

Uses and gratification theory aids in the assessment of how audiences use a particular medium and the gratifications they derive from that use. In this paper this theory has been applied to derive Internet uses and gratifications for Indian Internet users. This study proceeds in four stages. First, six first-order gratifications namely self development, wide exposure, user friendliness, relaxation, career opportunities, and global exchange were identified using an exploratory factor analysis. Then the first order gratifications were subjected to firstorder confirmatory factor analysis. Third, using second-order confirmatory factor analysis three types of secondorder gratifications were obtained, namely process gratifications, content gratifications and social gratifications. Finally, with the use of t-tests the study has shown that males and females differ significantly on the gratification factors “self development”, “user friendliness”, “wide exposure” and “relaxation.” The intended audience consists of masters’ level students and doctoral students who want to learn exploratory factor analysis and confirmatory factor analysis. This case study can also be used to teach the basics of structural equation modeling using the software AMOS.
Collaborative exploratory search for information filtering and large-scale information triage

Modern information seekers face dynamic streams of large-scale heterogeneous data that are both intimidating and overwhelming. They need a strategy to filter this barrage of massive data sets, and to find all of the information responding to their information needs, despite the pressures imposed by schedules and budgets. In this applied research, we present an exploratory search strategy that allows professional information seekers to efficiently and effectively triage all of the data. We demonstrate that exploratory search is particularly useful for information filtering and large-scale information triage, regardless of the language of the data, and regardless of the particular industry, whether finance, medical, business, government, information technology, news, or legal. Our strategy reduces a dauntingly large volume of information into a manageable, high-precision data set, suitable for focused reading. This strategy is interdisciplinary, integrating concepts from information filtering, information triage, and exploratory search. Key aspects include advanced search software, interdisciplinary paired search, asynchronous collaborative search, attention to linguistic phenomena, and aggregated search results in the form of a search matrix or search grid. We present the positive results of a task-oriented evaluation in a real-world setting, discuss these results from a qualitative perspective, and share future research areas.
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection

We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, twostream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding box around the person, which provides a frame of reference for appearance and motion and also suppresses background noise that is not within the bounding box. We train two additional streams on motion and appearance cropped to the tracked bounding box, along with full-frame streams. Our motion streams use pixel trajectories of a frame as raw features, in which the displacement values corresponding to a moving scene point are at the same spatial position across several frames. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bi-directional Long Short-Term Memory (LSTM) layer. We show that our bi-directional LSTM network utilizes about 8 seconds of the video sequence to predict an action label. We test on two action detection datasets: the MPII Cooking 2 Dataset, and a new MERL Shopping Dataset that we introduce and make available to the community with this paper. The results demonstrate that our method significantly outperforms state-of-the-art action detection methods on both datasets.