nils-herrmann/clean_scirepeval huggingface dataset:
Benchmarking of Beyond-CMOS Exploratory Devices for Logic Integrated Circuits

A new benchmarking of beyond-CMOS exploratory devices for logic integrated circuits is presented. It includes new devices with ferroelectric, straintronic, and orbitronic computational state variables. Standby power treatment and memory circuits are included. The set of circuits is extended to sequential logic, including arithmetic logic units. The conclusion that tunneling field-effect transistors are the leading low-power option is reinforced. Ferroelectric transistors may present an attractive option with faster switching delay. Magnetoelectric effects are more energy efficient than spin transfer torque, but the switching speed of magnetization is a limitation. This article enables a better focus on promising beyond-CMOS exploratory devices.
Why Do Ethnic Groups rEbEl ? new Data and Analysis

D its fundamental role in legitimizing the modern state system, nationalism has rarely been linked to the outbreak of political violence in the recent literature on ethnic conflict and civil war. to a large extent, this is because the state is absent from many conventional theories of ethnic conflict. indeed, some studies analyze conflict between ethnic groups under conditions of state failure, thus making the absence of the state the very core of the causal argument. others assume that the state is ethnically neutral and try to relate ethnodemographic measures, such as fractionalization and polarization, to civil war. in contrast to these approaches, we analyze the state as an institution that is captured to different degrees by representatives of particular ethnic communities, and thus we conceive of ethnic wars as the result of competing ethnonationalist claims to state power. While our work relates to a rich research tradition that links the causes of such conflicts to the mobilization of ethnic minorities, it also goes beyond this tradition by introducing a new data set that addresses some of the shortcomings of this tradition. our analysis is based on the Ethnic power relations data set (epr), which covers all politically relevant ethnic groups and their access to power around the world from 1946 through 2005. this data set improves significantly on the widely used minorities at risk data set, which restricts its sample to mobilized
A Novel Hybrid Text Summarization System for Punjabi Text

Text summarization is the task of shortening text documents but retaining their overall meaning and information. A good summary should highlight the main concepts of any text document. Many statistical-based, location-based and linguistic-based techniques are available for text summarization. This paper has described a novel hybrid technique for automatic summarization of Punjabi text. Punjabi is an official language of Punjab State in India. There are very few linguistic resources available for Punjabi. The proposed summarization system is hybrid of conceptual-, statistical-, location- and linguistic-based features for Punjabi text. In this system, four new location-based features and two new statistical features (entropy measure and Z score) are used and results are very much encouraging. Support vector machine-based classifier is also used to classify Punjabi sentences into summary and non-summary sentences and to handle imbalanced data. Synthetic minority over-sampling technique is applied for over-sampling minority class data. Results of proposed system are compared with different baseline systems, and it is found that F score, Precision, Recall and ROUGE-2 score of our system are reasonably well as compared to other baseline systems. Moreover, summary quality of proposed system is comparable to the gold summary.
Split and Rephrase

We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labelers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning.1 Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.
ADAPTIVE PRIMAL-DUAL HYBRID GRADIENT METHODS FOR SADDLE-POINT PROBLEMS

The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps. Unfortunately, PDHG methods require the user to choose stepsize parameters, and the speed of convergence is highly sensitive to this choice. We introduce new adaptive PDHG schemes that automatically tune the stepsize parameters for fast convergence without user inputs. We prove rigorous convergence results for our methods, and identify the conditions required for convergence. We also develop practical implementations of adaptive schemes that formally satisfy the convergence requirements. Numerical experiments show that adaptive PDHG methods have advantages over non-adaptive implementations in terms of both efficiency and simplicity for the user.
Scripting Argumentative Knowledge Construction in Computer-Supported Learning Environments

Argumentative knowledge construction in computer-supported collaborative learning environments is often weak. This experimental study analyzes two collaboration scripts, which should facilitate argumentative knowledge construction. One script aims to support the construction of argumentation sequences and the other script aims to support the argument construction. These scripts should facilitate the argumentative knowledge construction on both the microand macrostructure of argumentation during process as well as with respect to the individual outcome. Both types of scripts were varied independently in a 2X2-factorial design. 120 students of Educational Science participated in the study in groups of three. Results show that discourse supported by the collaboration scripts was of higher quality and students in these conditions acquired more individual knowledge about argumentation than learners without cooperation scripts.
Multiway broadband substrate integrated waveguide (SIW) power divider

A planar integrated multi-way broadband SIW power divider is proposed. It can be combined by the fundamental modules of T-type or Y-type two-way power dividers and an SIW bend directly. A sixteen way SIW power divider prototype was designed, fabricated and measured. The whole structure is made by various metallic-vias on the same substrate. Hence, it can be easily fabricated and conveniently integrated into microwave and millimeter-wave integrated circuits for mass production with low cost and small size.
Prediction of business process durations using non-Markovian stochastic Petri nets

Companies need to efficiently manage their business processes to deliver products and services in time. Therefore, they monitor the progress of individual cases to be able to timely detect undesired deviations and to react accordingly. For example, companies can decide to speed up process execution by raising alerts or by using additional resources, which increases the chance that a certain deadline or service level agreement can be met. Central to such process control is accurate prediction of the remaining time of a case and the estimation of the risk of missing a deadline. To achieve this goal, we use a specific kind of stochastic Petri nets that can capture arbitrary duration distributions. Thereby, we are able to achieve higher prediction accuracy than related approaches. Further, we evaluate the approach in comparison to state of the art approaches and show the potential of exploiting a so far untapped source of information: the elapsed time since the last observed event. Real-world case studies in the financial and logistics domain serve to illustrate and evaluate the approach presented.
Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance

The process of identifying the actual meanings of words in a given text fragment has a long history in the field of computational linguistics. Due to its importance in understanding the semantics of natural language, it is considered one of the most challenging problems facing this field. In this article we propose a new unsupervised similarity-based word sense disambiguation (WSD) algorithm that operates by computing the semantic similarity between glosses of the target word and a context vector. The sense of the target word is determined as that for which the similarity between gloss and context vector is greatest. Thus, whereas conventional unsupervised WSD methods are based on measuring pairwise similarity between words, our approach is based on measuring semantic similarity between sentences. This enables it to utilize a higher degree of semantic information, and is more consistent with the way that human beings disambiguate; that is, by considering the greater context in which the word appears. We also show how performance can be further improved by incorporating a preliminary step in which the relative importance of words within the original text fragment is estimated, thereby providing an ordering that can be used to determine the sequence in which words should be disambiguated. We provide empirical results that show that our method performs favorably against the state-of-the-art unsupervised word sense disambiguation methods, as evaluated on several benchmark datasets through different models of evaluation.
Agreement and Disagreement: Comparison of Points of View in the Political Domain

The automated comparison of points of view between two politicians is a very challenging task, due not only to the lack of annotated resources, but also to the different dimensions participating to the definition of agreement and disagreement. In order to shed light on this complex task, we first carry out a pilot study to manually annotate the components involved in detecting agreement and disagreement. Then, based on these findings, we implement different features to capture them automatically via supervised classification. We do not focus on debates in dialogical form, but we rather consider sets of documents, in which politicians may express their position with respect to different topics in an implicit or explicit way, like during an electoral campaign. We create and make available three different datasets.
E-Banking in Emerging Economy : Empirical Evidence of Iran

Now a day’s due to emerging global economy, e-commerce and e-business have increasingly become a necessary component of business strategy and a strong catalyst for economic development. The integration of information and communications technology in business has revolutionized relationships within organizations and those between and among organizations and individuals. The new information technology is becoming an important factor in the future development of financial services industry, and especially banking industry. The results of this study shows that e-banking serves several advantages to Iranian banking sector, however, the study also shows that the Iranian customers have not enough knowledge regarding e-banking which is rendering by banking sector in Iran.
Beamlets and Multiscale Image Analysis

We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis. The framework has five key components. The beamlet dictionary is a dyadicallyorganized collection of line segments, occupying a range of dyadic locations and scales, and occurring at a range of orientations. The beamlet transform of an image f(x, y) is the collection of integrals of f over each segment in the beamlet dictionary; the resulting information is stored in a beamlet pyramid. The beamlet graph is the graph structure with pixel corners as vertices and beamlets as edges; a path through this graph corresponds to a polygon in the original image. By exploiting the first four components of the beamlet framework, we can formulate beamlet-based algorithms which are able to identify and extract beamlets and chains of beamlets with special properties. In this paper we describe a four-level hierarchy of beamlet algorithms. The first level consists of simple procedures which ignore the structure of the beamlet pyramid and beamlet graph; the second level exploits only the parent-child dependence between scales; the third level incorporates collinearity and co-curvity relationships; and the fourth level allows global optimization over the full space of polygons in an image. These algorithms can be shown in practice to have suprisingly powerful and apparently unprecedented capabilities, for example in detection of very faint curves in very noisy data. We compare this framework with important antecedents in image processing (Brandt and Dym; Horn and collaborators; Götze and Druckenmiller) and in geometric measure theory (Jones; David and Semmes; and Lerman).
Parallel Skeletons for Structured Composition

In this paper, we propose a straightforward solution to the problems of compositional parallel programming by using skeletons as the uniform mechanism for structured composition. In our approach parallel programs are constructed by composing procedures in a conventional base language using a set of high-level, pre-defined, functional, parallel computational forms known as skeletons. The ability to compose skeletons provides us with the essential tools for building further and more complex application-oriented skeletons specifying important aspects of parallel computation. Compared with the process network based composition approach, such as PCN, the skeleton approach abstracts away the fine details of connecting communication ports to the higher level mechanism of making data distributions conform, thus avoiding the complexity of using lower level ports as the means of interaction. Thus, the framework provides a natural integration of the compositional programming approach with the data parallel programming paradigm.
Textual Paralanguage and Its Implications for Marketing Communications

Both face-to-face communication and communication in online environments convey information beyond the actual verbal message. In a traditional face-to-face conversation, paralanguage, or the ancillary meaningand emotion-laden aspects of speech that are not actual verbal prose, gives contextual information that allows interactors to more appropriately understand the message being conveyed. In this paper, we conceptualize textual paralanguage (TPL), which we define as written manifestations of nonverbal audible, tactile, and visual elements that supplement or replace written language and that can be expressed through words, symbols, images, punctuation, demarcations, or any combination of these elements. We develop a typology of textual paralanguage using data from Twitter, Facebook, and Instagram. We present a conceptual framework of antecedents and consequences of brands’ use of textual paralanguage. Implications for theory and practice are discussed. A customer of Whole Foods tweets that he received a bad cupcake from the grocer, to which Whole Foods replies, “A bad cupcake?!!?! Oh No!!! I’m so sorry. *sigh* Thank you for letting us know” (Whole Foods Market, 2013). How does communication on social media affect brand perceptions? Marketers are communicating with customers using a “shorthand, digital language” (Smith, 2015), yet the nature of these communications is under-investigated. In marketing, research on linguistics has focused primarily on the effects of word choice, such as the effect of explanatory words on consumption experiences (Moore, 2012), refusal words on choice (Patrick & Hagtvedt, 2012), and vowel sounds in brand names on brand preferences (Lowrey & Shrum, 2007). We also see evidence that imperative messages (e.g., “Buy Now!”) are more effective in uncommitted consumer-brand relationships (Moore, ZemackRugar, & Fitzsimons, working paper), and assertive statements are more effective at garnering consumer compliance for hedonic products (Kronrod, Grinstein, & Wathieu, 2012). In contrast, our work focuses not on the words said, but on the way nonverbal information is conveyed in writing. As computer-mediated communication (CMC) has become more prevalent, people have evolved new ways of communicating. Electronic messages are often imbued with nonverbal cues that signal individual characteristics, attitudes, and emotions. Indeed, various researchers recognize that people adapt to the limitations of CMC by creating surrogates for missing social cues (Byron & Baldridge, 2007; Ganster, Eimler, & Krämer, 2012; Walther, 1996). The primary goal of this paper is to provide a framework for the surrogates that people are using in digital communications. We define textual paralanguage (TPL) as written manifestations of nonverbal audible, tactile, and visual elements that supplement or replace written language and that can be
Wearable RGBD Indoor Navigation System for the Blind

In this paper, we present a novel wearable RGBD camera based navigation system for the visually impaired. The system is composed of a smartphone user interface, a glass-mounted RGBD camera device, a real-time navigation algorithm, and haptic feedback system. A smartphone interface provides an effective way to communicate to the system using audio and haptic feedback. In order to extract orientational information of the blind users, the navigation algorithm performs real-time 6-DOF feature based visual odometry using a glass-mounted RGBD camera as an input device. The navigation algorithm also builds a 3D voxel map of the environment and analyzes 3D traversability. A path planner of the navigation algorithm integrates information from the egomotion estimation and mapping and generates a safe and an efficient path to a waypoint delivered to the haptic feedback system. The haptic feedback system consisting of four micro-vibration motors is designed to guide the visually impaired user along the computed path and to minimize cognitive loads. The proposed system achieves real-time performance faster than 30Hz in average on a laptop, and helps the visually impaired extends the range of their activities and improve the mobility performance in a cluttered environment. The experiment results show that navigation in indoor environments with the proposed system avoids collisions successfully and improves mobility performance of the user compared to conventional and state-of-the-art mobility aid devices.
Learning Cost-Effective and Interpretable Treatment Regimes

Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bail to defendants on a daily basis. Such decisions typically involve weighing the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning cost-effective, interpretable and actionable treatment regimes. We formulate this as a problem of learning a decision list – a sequence of if-then-else rules – that maps characteristics of subjects (eg., diagnostic test results of patients) to treatments. This yields an end-to-end individualized policy for tests and treatments. We propose a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs. Since we do not observe the outcomes corresponding to counterfactual scenarios, we use techniques from causal inference literature to infer them. We model the problem of learning the decision list as a Markov Decision Process (MDP) and employ a variant of the Upper Confidence Bound for Trees (UCT) strategy which leverages customized checks for pruning the search space effectively. Experimental results on real world observational data capturing judicial bail decisions and treatment recommendations for asthma patients demonstrate the effectiveness of our approach.
Hierarchical Text Classication using Methods from Machine Learning

Due to the permantently growing amount of textual data, automatic methods for organizing the data are needed. Automatic text classification is one of this methods. It automatically assigns documents to a set of classes based on the textual content of the document. Normally, the set of classes is hierarchically structured but today’s classification approaches ignore hierarchical structures, thereby loosing valuable human knowledge. This thesis exploits the hierarchical organization of classes to improve accuracy and reduce computational complexity. Classification methods from machine learning, namely BoosTexter and the newly introduced CentroidBoosting algorithm, are used for learning hierarchies. In doing so, error propagation from higher level nodes and comparing decisions between independently trained leaf nodes are two problems which are considered in this thesis. Experiments are performed on the Reuters 21578, the Reuters Corpus Volume 1 and the Ohsumed data set, which are well known in literature. Rocchio and Support Vector Machines, which are state of the art algorithms in the field of text classification, serve as base line classifiers. Comparing algorithms is done by applying statistical significance tests. Results show that, depending on the structure of a hierarchy, accuracy improves and computational complexity decreases due to hierarchical classification. Also, the introduced model for comparing leaf nodes yields an increase in performance.
Gambling in a rigged casino: The adversarial multi-armed bandit problem

In the multi-armed bandit problem, a gambler must decide which arm ofKnon-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected per-round payoff of our algorithm approaches that of the best arm at the rate