nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Domain Adaptation for Duplicate Question Detection

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.
Empirical evaluation of interactive multimodal error correction

Commercial dictation systems for continues speech have recently become available. Although they generally received positive reviews, error correction continuous to be limited to choosing from a list of alternatives, speaking or typing. We developed a set of interactive methods to correct errors without using keyboard or mouse, allowing the user to switch between the modalities continuous speech, spelling, handwriting and pen gestures. These correction methods were integrated with our large vocabulary speech recognition system to build a prototypical multimodal listening typewriter. The efficiency of different error correction methods was evaluated in a user study. The experiment compares multimodal correction with other methods available in current speech recognition applications. Results confirm the hypothesis that switching between modalities can significantly expedite corrections. Thus, state-of-the-art speech recognition technology with multimodal error correction makes it possible to input text at a faster speed than unskilled typing, including the time necessary to correct errors. In applications where a keyboard is acceptable, however, typing still remains the fastest way to correct errors for users with good typing skills.
Intrusion Detection Techniques for Mobile Wireless Networks ∗

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective. We need to search for new architecture and mechanisms to protect the wireless networks and mobile computing application. In this paper, we examine the vulnerabilities of wireless networks and argue that we must include intrusion detection in the security architecture for mobile computing environment. We have developed such an architecture and evaluated a key mechanism in this architecture, anomaly detection for mobile ad-hoc network, through simulation experiments.
Integer and Combinatorial Optimization

Integer optimization problems are concerned with the efficient allocation of limited resources to meet a desired objective when some of the resources in question can only be divided into discrete parts. In such cases, the divisibility constraints on these resources, which may be people, machines, or other discrete inputs, may restrict the possible alternatives to a finite set. Nevertheless, there are usually too many alternatives to make complete enumeration a viable option for instances of realistic size. For example, an airline may need to determine crew schedules that minimize the total operating cost; an automotive manufacturer may want to determine the optimal mix of models to produce in order to maximize profit; or a flexible manufacturing facility may want to schedule production for a plant without knowing precisely what parts will be needed in future periods. In today’s changing and competitive industrial environment, the difference between ad hoc planning methods and those that use sophisticated mathematical models to determine an optimal course of action can determine whether or not a company survives.
Introducing Speech and Language Processing

In October 2003, a group of multidisciplinary researchers convened at the Symposium on Next Generation Automatic Speech Recognition (ASR) to consider new directions in building ASR systems (Lee 2003). Although the workshop's goal of " integrating multidisciplinary sources of knowledge, from acoustics, speech, linguistics, cognitive science, signal processing, human computer interaction, and computer science, into every stage of ASR component and system design " is an important goal, there remains a divide among these communities that can only be addressed through the educational process. The book Introducing Speech and Language Processing by John Coleman represents a bold effort to educate students in speech science about some of the important methods used in speech and natural language processing (NLP). This book represents an important first step for forging effective collaborations with the speech and language processing communities. Coleman states in chapter 1 of his book that " This is a first, basic, elementary and short textbook in speech and natural language processing for beginners with little or no previous experience of computer programming " (page 2). Coleman targets the book at students in a variety of disciplines, including arts, humanities, linguistics, psychology, and speech science, as well as early science and engineering students who want a glimpse into natural language and speech processing. However, since it assumes prior knowledge of basic linguistics, the text is likely to be less accessible to traditional science and engineering students. Coleman's motivation for writing this book is that the currently available textbooks in NLP and speech require knowledge that students from more of a humanities background would not have (e.g., programming, signal processing). The author also astutely points out that there tends to be a divide between the areas of signal processing and computational linguistics, although in recent years with ubiquity of statistical modeling and machine learning techniques in both areas, this divide is becoming much smaller. The author's motivation for this book is excellent: " a refusal to let the old sociological divide between arts and sciences stand in the way of a new wave of spoken language researchers with a foot in both camps " (page 4). The textbook covers a variety of techniques in speech and natural language processing , along with computer programs implementing many of them in either C or Prolog, and it capitalizes on Coleman's insights from courses offered to graduate linguistics students. It comes with a companion CD …
Anatomy of news consumption on Facebook.

The advent of social media and microblogging platforms has radically changed the way we consume information and form opinions. In this paper, we explore the anatomy of the information space on Facebook by characterizing on a global scale the news consumption patterns of 376 million users over a time span of 6 y (January 2010 to December 2015). We find that users tend to focus on a limited set of pages, producing a sharp community structure among news outlets. We also find that the preferences of users and news providers differ. By tracking how Facebook pages "like" each other and examining their geolocation, we find that news providers are more geographically confined than users. We devise a simple model of selective exposure that reproduces the observed connectivity patterns.
A Systematic Approach for Attack Analysis and Mitigation in V2V Networks

The increasing popularity of V2V networks with the rise of driverless cars in the recent years have made them vulnerable to attacks in growing complexity. Accurate assessment of the safety and security needs of V2V networks based on context and the costs associated with attack mitigation mechanisms are significant for successful operation of these networks despite adversaries trying to disrupt their functions. In this paper we provide an analysis of the major security and reliability issues in V2V networks, and propose a systematic approach for the analysis and mitigation of attacks, based on the break-down of attacks into common feature sets. We also provide cost analyses of major classes of V2V attacks using the proposed model. The proposed analysis approach is promising to provide guidance for research in optimization models for tradeoffs between security, safety and performance requirements in V2V.
DYAN: A Dynamical Atoms-Based Network for Video Prediction

The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning. State-of-art video prediction is based on complex architectures that need to learn large numbers of parameters, are potentially hard to train, slow to run, and may produce blurry predictions. In this paper, we introduce DYAN, a novel network with very few parameters and easy to train, which produces accurate, high quality frame predictions, faster than previous approaches. DYAN owes its good qualities to its encoder and decoder, which are designed following concepts from systems identification theory and exploit the dynamics-based invariants of the data. Extensive experiments using several standard video datasets show that DYAN is superior generating frames and that it generalizes well across domains.
Inference in the age of big data: Future perspectives on neuroscience

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.
Sounding Board: A User-Centric and Content-Driven Social Chatbot

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.
Fundamental Issues in Affective Intelligent Social Machines

In the early 1990s, computer scientists became motivated by the idea of rendering human-computer interactions more humanlike and natural for their users in order to both address complaints that technologies impose a mechanical (sometimes even anti-social) aesthetic to their everyday environment, and also investigate innovative ways to manage system-environment complexity. With the recent development of the field of Social Robotics and particularly HumanRobot Interaction, the integration of intentional emotional mechanisms in a system’s control architecture becomes inevitable. Unfortunately, this presents significant issues that must be addressed for a successful emotional artificial system to be developed. This paper provides an additional dimension to documented arguments for and against the introduction of emotions into artificial systems by highlighting some fundamental paradoxes, mistakes, and proposes guidelines for how to develop successful affective intelligent social machines.
A Survey of Collaborative Filtering Algorithms for Social Recommender Systems

This paper introduces the status of social recommender system research in general and collaborative filtering in particular. For the collaborative filtering, the paper shows the basic principles and formulas of two basic approaches, the user-based collaborative filtering and the item-based collaborative filtering. For the user or item similarity calculation, the paper compares the differences between the cosine-based similarity, the revised cosine-based similarity and the Pearson-based similarity. The paper also analyzes the three main challenges of the collaborative filtering algorithm and shows the related works facing the challenges. To solve the Cold Start problem and reduce the cost of best neighborhood calculation, the paper provides several solutions. At last it discusses the future of the collaborative filtering algorithm in social recommender system.
SIEL: Aspect Based Sentiment Analysis in Reviews

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.
Algorithmic Crime Prediction Model Based on the Analysis of Crime Clusters A . Malathi α , Dr .

s Crime is a behavior disorder that is an integrated result of social, economical and environmental factors. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. In this paper we look at use of missing value and clustering algorithm for crime data using data mining. We will look at MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and identification of crime patterns. We applied these techniques to real crime data from a city police department. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy.
A unified input-output approach for networked control problems with decentralized and selfish optimality

In this paper, we take an input-output approach to enhance the study of cooperative multiagent optimization problems that admit decentralized and selfish solutions, hence eliminating the need for an interagent communication network. The framework under investigation is a set of $n$ independent agents coupled only through an overall cost that penalizes the divergence of each agent from the average collective behavior. In the case of identical agents, or more generally agents with identical essential input-output dynamics, we show that optimal decentralized and selfish solutions are possible in a variety of standard input-output cost criteria. These include the cases of $\ell_{1}, \ell_{2}, \ell_{\infty}$ induced, and $\mathcal{H}_{2}$ norms for any finite $n$. Moreover, if the cost includes non-deviation from average variables, the above results hold true as well for $\ell_{1}, \ell_{2}, \ell_{\infty}$ induced norms and any $n$, while they hold true for the normalized, per-agent square $\mathcal{H}_{2}$ norm, cost as $n\rightarrow\infty$. We also consider the case of nonidentical agent dynamics and prove that similar results hold asymptotically as $n\rightarrow\infty$ in the case of $\ell_{2}$ induced norms (i.e., $\mathcal{H}_{\infty}$) under a growth assumption on the $\mathcal{H}_{\infty}$ norm of the essential dynamics of the collective.
DEEP REINFORCEMENT LEARNING

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations. We also compare our learned experimentation policies to randomized baselines and show that the learned policies lead to better predictions.
Natural Language Processing Service Based on Stroke-Level Convolutional Networks for Chinese Text Classification

With the development of deep learning and artificial intelligence, more and more research apply neural networks to natural language processing tasks. However, while the majority of these research take English corpus as the dataset, few studies have been done using Chinese corpus. Meanwhile, Existing Chinese processing algorithms typically regard Chinese word or Chinese character as the basic unit but ignore the deeper information into the Chinese character. In Chinese linguistic, strokes are the basic unit of Chinese character who are similar to letters of the English word. Inspired by the recent success of deep learning at character-level, we delve deeper to Chinese stroke level for Chinese language processing and developed it into service for Chinese text classification. In this paper, we dig the basic feature of the strokes considering the similar Chinese character components and propose a new method to leverage Chinese stroke for learning the continuous representation of Chinese character and develop it into a service for Chinese text classification. We develop a dedicated neural architecture based on the convolutional neural network to effectively learn character embedding and apply it to Chinese word similarity judgment and Chinese text classification. Both experiments results show that the stroke level method is effective for Chinese language processing.
A Study of Employees' Self-Reported Cybersecurity Behaviors

The information security community has come to realize that the weakest link in a cybersecurity chain is human behavior. To develop effective cybersecurity training programs for employees in the workplace, it is necessary to identify factors that contribute to employees’ cybersecurity behaviors and then build a theoretical model to understand how these factors affect employees’ self-reported security behavior in the workplace. Supported by a grant from the National Science Foundation (NSF), we developed a model for studying employees’ self-reported cybersecurity behaviors, and conducted a survey study to investigate the cybersecurity behavior and beliefs of employees. Five-hundred-seventy-nine employees from various U.S. organizations and companies completed an online survey with 87 items carefully designed by six experts in cybersecurity, information technology, psychology, and decision science. The results from statistical analysis of the cybersecurity behavior survey questionnaire will be presented in this TREO Talk. Some of the key findings include:  Prior Experience was correlated with self-reported cyber security behavior. However, it was not identified as a unique predictor in our regression analysis. This suggests that the prior training may indirectly affect cybersecurity behavior through other variables.  Peer Behavior was not a unique predictor of self-reported cybersecurity behavior. Perceptions of peer behavior may reflect people’s own self-efficacy with cybersecurity and their perceptions of the benefits from cybersecurity behaviors.  The regression model revealed four unique predictors of self-reported cybersecurity behavior: Computer Skill, Perceived Benefits, Perceived Barriers, and Security Self-efficacy. These variables should be assessed to identify employees who are at risk of cyber attacks and could be the target of interventions.  There are statistically significant gender-wise differences in terms of computer skills, prior experience, cues-to-action, security self-efficacy and self-reported cybersecurity behaviors. Since women’s self-efficacy is significantly lower than men, women’s self-efficacy may be a target for intervention.
Transforming Singapore health care: public-private partnership.

Prudent health care policies that encourage public-private participation in health care financing and provisioning have conferred on Singapore the advantage of flexible response as it faces the potentially conflicting challenges of becoming a regional medical hub attracting foreign patients and ensuring domestic access to affordable health care. Both the external and internal health care markets are two sides of the same coin, the competition to be decided on price and quality. For effective regulation, a tripartite model, involving not just the government and providers but empowered consumers, is needed. Government should distance itself from the provider role while providers should compete - and cooperate - to create higher-value health care systems than what others can offer. Health care policies should be better informed by health policy research.
Under which conditions does ICT have a positive effect on teaching and learning? A Call to Action

Under which conditions does ICT have a positive effect on teaching and learning?’ This was the leading question of the International EDUsummIT in The Hague, the Netherlands. The bases for the discussion were the scholarly findings of the International Handbook of Information Technology in Primary and Secondary Education, a synthesis of research in the field of information and communication technology (ICT) in education. Seventy international policymakers, researchers, and practitioners developed a Call to Action, which summarizes the main action points where policy, research, and leadership need to join forces in order to successfully implement ICT in educational practice. These main action points include a view on the role of ICT in 21st century learning; conditions for realizing the potential of multiple technologies to address individual needs of students; better understanding of the relationship between formal and informal learning; the implications of technology for student assessment; the need for models for leadership and teacher learning to successfully implement technology; the potential of ICT for digital equity; and the development of a list of essential conditions to ensure benefit from ICT investments. In this contribution, we present the Call to Action and synthesize the research on which the Call is based.