nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Check-worthy Factual Claims in Presidential Debates

Public figures such as politicians make claims about "facts" all the time. Journalists and citizens spend a good amount of time checking the veracity of such claims. Toward automatic fact checking, we developed tools to find check-worthy factual claims from natural language sentences. Specifically, we prepared a U.S. presidential debate dataset and built classification models to distinguish check-worthy factual claims from non-factual claims and unimportant factual claims. We also identified the most-effective features based on their impact on the classification models' accuracy.
Tools and techniques for Network Forensics

Network forensics deals with the capture, recording and analysis of network events in order to discover evidential information about the source of security attacks in a court of law. This paper discusses the different tools and techniques available to conduct network forensics. Some of the tools discussed include: eMailTrackerPro – to identify the physical location of an email sender; Web Historian – to find the duration of each visit and the files uploaded and downloaded from the visited website; packet sniffers like Ethereal – to capture and analyze the data exchanged among the different computers in the network. The second half of the paper presents a survey of different IP traceback techniques like packet marking that help a forensic investigator to identify the true sources of the attacking IP packets. We also discuss the use of Honeypots and Honeynets that gather intelligence about the enemy and the tools and tactics of network intruders.
Perspective: Concepts of printing technologies for oral film formulations.

Different types of printing methods have recently attracted interest as emerging technologies for fabrication of drug delivery systems. If printing is combined with different oral film manufacturing technologies such as solvent casting and other techniques, multifunctional structures can be created to enable further complexity and high level of sophistication. This review paper intends to provide profound understanding and future perspectives for the potential use of printing technologies in the preparation of oral film formulations as novel drug delivery systems. The described concepts include advanced multi-layer coatings, stacked systems, and integrated bioactive multi-compartments, which comprise of integrated combinations of diverse materials to form sophisticated bio-functional constructs. The advanced systems enable tailored dosing for individual drug therapy, easy and safe manufacturing of high-potent drugs, development and manufacturing of fixed-dose combinations and product tracking for anti-counterfeiting strategies.
Blockchain technology as infrastructure in public sector: an analytical framework

The blockchain technology has evolved beyond traditional payment solutions in the finance sector and offers a potential for transforming many sectors including the public sector. The novel integration of technology and economy that open public block-chains have brought represents both challenges to and opportunities for enhancing digital public services. So far, the public sector has lagged behind other sectors in both research and exploration of this technology, but pilot cases show that there is a great potential for reforming and even transforming public service delivery.
 We argue that the open blockchain technology is best understood as a possible information infrastructure, given its universal, evolving, open and transparent nature. A comparison with Internet is meaningful despite obvious differences between the two. Based on some case studies, we have developed an analytical framework for better understanding the potential benefits as well as the existing challenges when introducing blockchain technology in the public sector.
Adaptive Fuzzy Sliding-Mode Control of Dynamic Model Based Car-Like Mobile Robot

This paper presents an adaptive fuzzy sliding-mode dynamic controller (AFSMDC) of the car-like mobile robot (CLMR) for the trajectory tracking issue. First, a kinematics model of the nonholonomic CLMR is introduced. Then, according to the Lagrange formula, a dynamic model of the CLMR is created. For a real time trajectory tracking problem, an optimal controller capable of effectively driving the CLMR to track the desired trajectory is necessary. Therefore, an AFSMDC is proposed to accomplish the tracking task and to reduce the effect of the external disturbances and system uncertainties of the CLMR. The proposed controller could reduce the tracking errors between the output of the velocity controller and the real velocity of the CLMR. Therefore, the CLMR could track the desired trajectory without posture and orientation errors. Additionally, the stability of the proposed controller is proven by utilizing the Lyapunov stability theory. Finally, the simulation results validate the effectiveness of the proposed AFSMDC.
The Psychology of Life Stories

Recent years have witnessed an upsurge of interest among theorists and researchers in autobiographical recollections, life stories, and narrative approaches to understanding human behavior and experience. An important development in this context is D. P. McAdams's life story model of identity (1985, 1993, 1996), which asserts that people living in modern societies provide their lives with unity and purpose by constructing internalized and evolving narratives of the self. The idea that identity is a life story resonates with a number of important themes in developmental, cognitive, personality, and cultural psychology. This article reviews and integrates recent theory and research on life stories as manifested in investigations of self-understanding, autobiographical memory, personality structure and change, and the complex relations between individual lives and cultural modernity.
What Makes Reading Comprehension Questions Easier?

A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiplechoice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.
Wisecrackers: A theory-grounded investigation of phishing and pretext social engineering threats to information security

people by businesses and governments is ubiquitous. One of the main threats to people's privacy comes from human carelessness with this information, yet little empirical research has studied behaviors associated with information carelessness and the ways that people exploit this vulnerability. The studies that have investigated this important question have not been grounded in theory. In particular , the extant literature reveals little about social engineering threats and the reasons why people may or may not fall victim. Synthesizing theory from the marketing literature to explain consumer behavior, an empirical field study was conducted to see if factors that account for successful marketing campaigns may also account for successful social engineering attacks. People have become increasingly aware of the pervasive threats to information security and there are a variety of solutions now available for solving the problem of information insecurity such as improving technologies, including the application of advanced cryptography, or techniques, such as performing risk analyses and risk mitigation (Bresz, 2004; Sasse, Brostoff & Weirich, 2004). There has also been important suggestions from the information systems (IS) security literature that include augmenting security procedures as a solution (cf. Debar & Viinikka 2006), addressing situational factors such as reducing workload so that security professionals have time to implement the recommended procedures (Albrechtsen, 2007), improving the quality of policies (von Solms & von Solms, 2004), improving the alignment between an organization's security goals and its practices (Leach, 2003), and gaining improvements from software developers regarding the security implementations during the software development cycle (Jones & Rastogi, 2004). Yet despite all these recommendations, people often fail to take basic security precautions that result in billions of dollars annually in individual and corporate losses and even " Knowing better, but not doing better " is thus one of the key scholarly and practical issues that have not been fully addressed. One area of particular concern involves threats from social engineering. Social engineering consists of techniques used to manipulate people into performing actions or divulging confidential information (Mitnick & Simon, 2002). Social engineers often attempt to persuade potential victims with appeals to strong emotions such as excitement or fear, whereas others utilize ways to establish interpersonal relationships or create a feeling of trust and commitment (Gao & Kim, 2007). For example, they may promise that valuable prize or interest from a transfer bank deposit will be given if the victim complies with a request …
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic. We further find that the neural caching model (Grave et al., 2017b) especially helps the LSTM to copy words from within this distant context. Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models.
Robust Principal Component Analysis?

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the ℓ1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.
Legible compact calligrams

A calligram is an arrangement of words or letters that creates a visual image, and a compact calligram fits one word into a 2D shape. We introduce a fully automatic method for the generation of legible compact calligrams which provides a balance between conveying the input shape, legibility, and aesthetics. Our method has three key elements: a path generation step which computes a global layout path suitable for embedding the input word; an alignment step to place the letters so as to achieve feature alignment between letter and shape protrusions while maintaining word legibility; and a final deformation step which deforms the letters to fit the shape while balancing fit against letter legibility. As letter legibility is critical to the quality of compact calligrams, we conduct a large-scale crowd-sourced study on the impact of different letter deformations on legibility and use the results to train a letter legibility measure which guides the letter deformation. We show automatically generated calligrams on an extensive set of word-image combinations. The legibility and overall quality of the calligrams are evaluated and compared, via user studies, to those produced by human creators, including a professional artist, and existing works.
Virtually justifiable homicide: the effects of prosocial contexts on the link between violent video games, aggression, and prosocial and hostile cognition.

Previous work has shown that playing violent video games can stimulate aggression toward others. The current research has identified a potential exception. Participants who played a violent game in which the violence had an explicitly prosocial motive (i.e., protecting a friend and furthering his nonviolent goals) were found to show lower short-term aggression (Study 1) and show higher levels of prosocial cognition (Study 2) than individuals who played a violent game in which the violence was motivated by more morally ambiguous motives. Thus, violent video games that are framed in an explicitly prosocial context may evoke more prosocial sentiments and thereby mitigate some of the short-term effects on aggression observed in previous research. While these findings are promising regarding the potential aggression-reducing effects of prosocial context, caution is still warranted as a small effect size difference (d = .2-.3), although nonsignificant, was still observed between those who played the explicitly prosocial violent game and those who played a nonviolent game; indicating that aggressive behavior was not completely eliminated by the inclusion of a prosocial context for the violence.
A Survey of Decisional Requirements : Imprecision study

The success and the failure of a data warehouse (DW) project are mainly related to the design phase according to most researchers in this domain. When analyzing the decision-making system requirements, many recurring problems appear and requirements modeling difficulties are detected. Also, we encounter the problem associated with the requirements expression by non-IT professionals and non-experts makers on design models. The ambiguity of the term of decision-making requirements leads to a misinterpretation of the requirements resulting from data warehouse design failure and incorrect OLAP analysis. Therefore, many studies have focused on the inclusion of vague data in information systems in general, but few studies have examined this case in data warehouses. This article describes one of the shortcomings of current approaches to data warehouse design which is the study of in the requirements inaccuracy expression and how ontologies can help us to overcome it. We present a survey on this topic showing that few works that take into account the imprecision in the study of this crucial phase in the decision-making process for the presentation of challenges and problems that arise and requires more attention by researchers to improve DW design. According to our knowledge, no rigorous study of vagueness in this area were made.  Data warehouses Design, requirements analysis, imprecision, ontology
Heterosexuals ’ Attitudes Toward Transgender People : Findings from a National Probability Sample of U . S . Adults

Using data from a national probability sample of heterosexual U.S. adults (N02,281), the present study describes the distribution and correlates of men’s and women’s attitudes toward transgender people. Feeling thermometer ratings of transgender people were strongly correlated with attitudes toward gay men, lesbians, and bisexuals, but were significantly less favorable. Attitudes toward transgender people were more negative among heterosexual men than women. Negative attitudes were associated with endorsement of a binary conception of gender; higher levels of psychological authoritarianism, political conservatism, and anti-egalitarianism, and (for women) religiosity; and lack of personal contact with sexual minorities. In regression analysis, sexual prejudice accounted for much of the variance in transgender attitudes, but respondent gender, educational level, authoritarianism, anti-egalitarianism, and (for women) religiosity remained significant predictors with sexual prejudice statistically controlled. Implications and directions for future research on attitudes toward transgender people are discussed.
Indoor scene structure analysis for single image depth estimation

We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the scene, which models local depth jointly with mid-level and global scene structures. We formulate single image depth estimation as inference in a graphical model whose edges let us encode the interactions within and across the different layers of our hierarchy. Our method therefore still produces detailed depth estimates, but also leverages higher-level information about the scene. We demonstrate the benefits of our approach over local depth estimation methods on standard indoor datasets.
High capacity hydrogen storage materials: attributes for automotive applications and techniques for materials discovery.

Widespread adoption of hydrogen as a vehicular fuel depends critically upon the ability to store hydrogen on-board at high volumetric and gravimetric densities, as well as on the ability to extract/insert it at sufficiently rapid rates. As current storage methods based on physical means--high-pressure gas or (cryogenic) liquefaction--are unlikely to satisfy targets for performance and cost, a global research effort focusing on the development of chemical means for storing hydrogen in condensed phases has recently emerged. At present, no known material exhibits a combination of properties that would enable high-volume automotive applications. Thus new materials with improved performance, or new approaches to the synthesis and/or processing of existing materials, are highly desirable. In this critical review we provide a practical introduction to the field of hydrogen storage materials research, with an emphasis on (i) the properties necessary for a viable storage material, (ii) the computational and experimental techniques commonly employed in determining these attributes, and (iii) the classes of materials being pursued as candidate storage compounds. Starting from the general requirements of a fuel cell vehicle, we summarize how these requirements translate into desired characteristics for the hydrogen storage material. Key amongst these are: (a) high gravimetric and volumetric hydrogen density, (b) thermodynamics that allow for reversible hydrogen uptake/release under near-ambient conditions, and (c) fast reaction kinetics. To further illustrate these attributes, the four major classes of candidate storage materials--conventional metal hydrides, chemical hydrides, complex hydrides, and sorbent systems--are introduced and their respective performance and prospects for improvement in each of these areas is discussed. Finally, we review the most valuable experimental and computational techniques for determining these attributes, highlighting how an approach that couples computational modeling with experiments can significantly accelerate the discovery of novel storage materials (155 references).
Midterm Examination I: Solutions ECON 504 - Microeconomics II

Solution M and C are strictly dominated and hence cannot receive positive probability in any Nash equilibrium. Given that only L and R receive positive probability, T cannot receive positive probability either. So, in any Nash equilibrium player 1 must play B with probability one. Given that, any probability distribution over L and R is a best response for player 2. In other words, the set of Nash equilibria is given by
Accurate Object Pose Estimation Using Depth Only

Object recognition and pose estimation is an important task in computer vision. A pose estimation algorithm using only depth information is proposed in this paper. Foreground and background points are distinguished based on their relative positions with boundaries. Model templates are selected using synthetic scenes to make up for the point pair feature algorithm. An accurate and fast pose verification method is introduced to select result poses from thousands of poses. Our algorithm is evaluated against a large number of scenes and proved to be more accurate than algorithms using both color information and depth information.
Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders

We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy.
 Unlike prior privacy work concerned with cryptographically securing the computation of recommendations, differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output. Such algorithms necessarily introduce uncertainty--i.e., noise--to computations, trading accuracy for privacy.
 We find that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy. To adapt these algorithms, we explicitly factor them into two parts, an aggregation/learning phase that can be performed with differential privacy guarantees, and an individual recommendation phase that uses the learned correlations and an individual's data to provide personalized recommendations. The adaptations are non-trivial, and involve both careful analysis of the per-record sensitivity of the algorithms to calibrate noise, as well as new post-processing steps to mitigate the impact of this noise.
 We measure the empirical trade-off between accuracy and privacy in these adaptations, and find that we can provide non-trivial formal privacy guarantees while still outperforming the Cinematch baseline Netflix provides.
Semi-Supervised Event Extraction with Paraphrase Clusters

Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from multiple sources. If our system can make a highconfidence extraction of some mentions in such a cluster, it can then acquire diverse training examples by adding the other mentions as well. Our experiments show significant performance improvements on multiple event extractors over ACE 2005 and TAC-KBP 2015 datasets.