nils-herrmann/clean_scirepeval huggingface dataset:
Online Segment to Segment Neural Transduction

We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders that have to read and memorize the entire input sequence in their fixedlength hidden states before producing any output. It is different from previous attentive models in that, instead of treating the attention weights as output of a deterministic function, our model assigns attention weights to a sequential latent variable which can be marginalized out and permits online generation. Experiments on abstractive sentence summarization and morphological inflection show significant performance gains over the baseline encoder-decoders.
A Short-term Intervention for Long-term Fairness in the Labor Market

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of “individual fairness.” Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups’ divergent accesses to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms’ hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that statistically discriminate or employ a “group-blind” criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.  Lily Hu and Yiling Chen. 2018. A Short-term Intervention for Long-term Fairness in the Labor Market. In WWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages.
Contextual superpixel description for remote sensing image classification

The performance of pattern classifiers depends on the separability of the classes in the feature space - a property related to the quality of the descriptors - and the choice of informative training samples for user labeling - a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We introduce a new scheme for superpixel description based on Bag of visual Words, which includes information from adjacent superpixels, and validate it by using two remote sensing images and several region descriptors as baselines.
Search Techniques for the Web of Things: A Taxonomy and Survey

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.
Subcategory-Aware Object Classification

In this paper, we introduce a subcategory-aware object classification framework to boost category level object classification performance. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification datasets, we explicitly split data into subcategories by ambiguity guided subcategory mining. We then train an individual model for each subcategory rather than attempt to represent an object category with a monolithic model. More specifically, we build the instance affinity graph by combining both intra-class similarity and inter-class ambiguity. Visual subcategories, which correspond to the dense sub graphs, are detected by the graph shift algorithm and seamlessly integrated into the state-of-the-art detection assisted classification framework. Finally the responses from subcategory models are aggregated by subcategory-aware kernel regression. The extensive experiments over the PASCAL VOC 2007 and PASCAL VOC 2010 databases show the state-of-the-art performance from our framework.
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs

Natural graphs with skewed distributions raise unique challenges to distributed graph computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab) or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this article, we argue that skewed distributions in natural graphs also necessitate differentiated processing on high-degree and low-degree vertices. We then introduce PowerLyra, a new distributed graph processing system that embraces the best of both worlds of existing graph-parallel systems. Specifically, PowerLyra uses centralized computation for low-degree vertices to avoid frequent communications and distributes the computation for high-degree vertices to balance workloads. PowerLyra further provides an efficient hybrid graph partitioning algorithm (i.e., hybrid-cut) that combines edge-cut (for low-degree vertices) and vertex-cut (for high-degree vertices) with heuristics. To improve cache locality of inter-node graph accesses, PowerLyra further provides a locality-conscious data layout optimization. PowerLyra is implemented based on the latest GraphLab and can seamlessly support various graph algorithms running in both synchronous and asynchronous execution modes. A detailed evaluation on three clusters using various graph-analytics and MLDM (Machine Learning and Data Mining) applications shows that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs, respectively, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra.
Geometric Mean Metric Learning

We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive propertie s: (i) an innate geometric appeal through the Riemannian geometry of positive definite matrices; (ii) ease of interpretability; and (iii) computational speed several orders of magnitude faster tha n the widely used LMNN and ITML methods. Furthermore, on standard benchmark datasets, our closed-form solution consist ently attains higher classification accuracy.
The detection of epileptic seizure signals based on fuzzy entropy

Entropy is a nonlinear index that can reflect the degree of chaos within a system. It is often used to analyze epileptic electroencephalograms (EEG) to detect whether there is an epileptic attack. Much research into the state inspection of epileptic seizures has been conducted based on sample entropy (SampEn). However, the study of epileptic seizures based on fuzzy entropy (FuzzyEn) has lagged behind.



We propose a method of state inspection of epileptic seizures based on FuzzyEn. The method first calculates the FuzzyEn of EEG signals from different epileptic states, and then feature selection is conducted to obtain classification features. Finally, we use the acquired classification features and a grid optimization method to train support vector machines (SVM).



The results of two open-EEG datasets in epileptics show that there are major differences between seizure attacks and non-seizure attacks, such that FuzzyEn can be used to detect epilepsy, and our method obtains better classification performance (accuracy, sensitivity and specificity of classification of the CHB-MIT are 98.31%, 98.27% and 98.36%, and of the Bonn are 100%, 100%, 100%, respectively).



To verify the performance of the proposed method, a comparison of the classification performance for epileptic seizures using FuzzyEn and SampEn is conducted. Our method obtains better classification performance, which is superior to the SampEn-based methods currently in use.



The results indicate that FuzzyEn is a better index for detecting epileptic seizures effectively. The FuzzyEn-based method is preferable, exhibiting potential desirable applications for medical treatment.
Subdivisions of auditory cortex and processing streams in primates.

The auditory system of monkeys includes a large number of interconnected subcortical nuclei and cortical areas. At subcortical levels, the structural components of the auditory system of monkeys resemble those of nonprimates, but the organization at cortical levels is different. In monkeys, the ventral nucleus of the medial geniculate complex projects in parallel to a core of three primary-like auditory areas, AI, R, and RT, constituting the first stage of cortical processing. These areas interconnect and project to the homotopic and other locations in the opposite cerebral hemisphere and to a surrounding array of eight proposed belt areas as a second stage of cortical processing. The belt areas in turn project in overlapping patterns to a lateral parabelt region with at least rostral and caudal subdivisions as a third stage of cortical processing. The divisions of the parabelt distribute to adjoining auditory and multimodal regions of the temporal lobe and to four functionally distinct regions of the frontal lobe. Histochemically, chimpanzees and humans have an auditory core that closely resembles that of monkeys. The challenge for future researchers is to understand how this complex system in monkeys analyzes and utilizes auditory information.
Inferring social ties between users with human location history

The location-based social networks have been becoming flourishing in recent years. In this paper, we aim to estimate the similarity between users according to their physical location histories (represented by GPS trajectories). This similarity can be regarded as a potential social tie between users, thereby enabling friend and location recommendations. Different from previous work using social structures or directly matching users’ physical locations, this approach model a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls ? restaurants ? cinemas. Then, we measure the similarity between different users’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. When matching SLHs, we consider the sequential property, the granularity and the popularity of semantic locations. We evaluate our method based on a realworld GPS dataset collected by 109 users in a period of 1 year. The results show that SLH outperforms a physicallocation-based approach and MTM is more effective than several widely used sequence matching approaches given this application scenario.
Faster Fully Homomorphic Encryption: Bootstrapping in less than 0.1 Seconds

In this paper, we revisit fully homomorphic encryption (FHE) based on GSW and its ring variants. We notice that the internal product of GSW can be replaced by a simpler external product between a GSW and an LWE ciphertext. We show that the bootstrapping scheme FHEW of Ducas and Micciancio [14] can be expressed only in terms of this external product. As a result, we obtain a speed up from less than 1 second to less than 0.1 seconds. We also reduce the 1GB bootstrapping key size to 24MB, preserving the same security levels, and we improve the noise propagation overhead by replacing exact decomposition algorithms with approximate ones. Moreover, our external product allows to explain the unique asymmetry in the noise propagation of GSW samples and makes it possible to evaluate deterministic automata homomorphically as in [16] in an efficient way with a noise overhead only linear in the length of the tested word. Finally, we provide an alternative practical analysis of LWE based scheme, which directly relates the security parameter to the error rate of LWE and the entropy of the LWE secret key.
Analysis of customer churn prediction in telecom industry using decision trees and logistic regression

Customer churn prediction in Telecom industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, the mobile telecommunication market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of telecommunication companies has therefore shifted from building a large customer base into keeping customers in house. For that reason, it is valuable to know which customers are likely to switch to a competitor in the near future. The data extracted from telecom industry can help analyze the reasons of customer churn and use that information to retain the customers. We have proposed to build a model for churn prediction for telecommunication companies using data mining and machine learning techniques namely logistic regression and decision trees. A comparison is made based on efficiency of these algorithms on the available dataset.
Seeing is believing: Trustworthiness as a dynamic belief

Recent efforts to understand the mechanisms underlying human cooperation have focused on the notion of trust, with research illustrating that both initial impressions and previous interactions impact the amount of trust people place in a partner. Less is known, however, about how these two types of information interact in iterated exchanges. The present study examined how implicit initial trustworthiness information interacts with experienced trustworthiness in a repeated Trust Game. Consistent with our hypotheses, these two factors reliably influence behavior both independently and synergistically, in terms of how much money players were willing to entrust to their partner and also in their post-game subjective ratings of trustworthiness. To further understand this interaction, we used Reinforcement Learning models to test several distinct processing hypotheses. These results suggest that trustworthiness is a belief about probability of reciprocation based initially on implicit judgments, and then dynamically updated based on experiences. This study provides a novel quantitative framework to conceptualize the notion of trustworthiness.
Nonlinear sliding mode control and feasible workspace analysis for a cable suspended robot with input constraints and disturbances

This paper deals with the characterization of the feasible workspace of set-point control for a cable suspended robot. The motivation behind this work is to find admissible set points for the system under disturbances as well as input constraints. The main ideas are: (i) designing a sliding mode controller as a stabilizing controller for the given uncertain system, (ii) finding the range of system states in terms of set points by analyzing the reaching condition and sliding mode, and (iii) substituting states in inequalities of the input with either their upper values or lower values so that constraints are satisfied. This method results in 6 inequalities in terms of set point which can be drawn graphically in the 3-dimensional space.
A family of code coverage-based heuristics for effective fault localization

0164-1212/$ see front matter 2009  A doi:10.1016/j.jss.2009.09.037 q This research was supported by the MKE (Minis Korea, under the ITRC (Information Technology Resea supervised by the NIPA (National IT Industry Prom (C1090-0902-0032)).  Corresponding author.  fax E-mail addresses: ewong@utdallas.edu (W. Eric Wo (V. Debroy), bjchoi@ewha.ac.kr (B. Choi). 1 In this paper, we use ‘‘software” and ‘‘program” ‘‘bugs” and ‘‘faults” interchangeably. Locating faults in a program can be very time-consuming and arduous, and therefore, there is an increased demand for automated techniques that can assist in the fault localization process. In this paper a code coverage-based method with a family of heuristics is proposed in order to prioritize suspicious code according to its likelihood of containing program bugs. Highly suspicious code (i.e., code that is more likely to contain a bug) should be examined before code that is relatively less suspicious; and in this manner programmers can identify and repair faulty code more efficiently and effectively. We also address two important issues: first, how can each additional failed test case aid in locating program faults; and second, how can each additional successful test case help in locating program faults. We propose that with respect to a piece of code, the contribution of the first failed test case that executes it in computing its likelihood of containing a bug is larger than or equal to that of the second failed test case that executes it, which in turn is larger than or equal to that of the third failed test case that executes it, and so on. This principle is also applied to the contribution provided by successful test cases that execute the piece of code. A tool, vDebug, was implemented to automate the computation of the suspiciousness of the code and the subsequent prioritization of suspicious code for locating program faults. To validate our method case studies were performed on six sets of programs: Siemens suite, Unix suite, space, grep, gzip, and make. Data collected from the studies are supportive of the above claim and also suggest Heuristics III(a), (b) and (c) of our method can effectively reduce the effort spent on fault localization.
WAIS-IV and WISC-IV Structural Validity : Alternate Methods , Alternate Results . Commentary on Weiss et al . ( 2013 a ) and Weiss et al . ( 2013 b )

Weiss, Keith, Zhu, and Chen (2013a) and Weiss, Keith, Zhu, and Chen (2013b), this issue, report examinations of the factor structure of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) and Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV), respectively; comparing Wechsler Hierarchical Model (W-HM) and Cattell–Horn–Carroll Hierarchical Model (CHC-HM), subtest cross-loadings, and factorial invariance between clinical and normative groups from the respective standardizations. Both studies suffer from a number of theoretical, methodological, and practical problems that significantly limit conclusions and recommendations for practitioner interpretations of these instruments. Additional analyses and research are required to better inform practitioners for WAIS-IV and WISC-IV use.
BusyBody: creating and fielding personalized models of the cost of interruption

Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user's current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decision-theoretic models with the ability to predict the cost of interrupting the user. The models are used at run-time to compute the expected cost of interruptions, providing a mediator for incoming notifications, based on a consideration of a user's current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected.
Mining relationships among interval-based events for classification

Existing temporal pattern mining assumes that events do not have any duration. However, events in many real world applications have durations, and the relationships among these events are often complex. These relationships are modeled using a hierarchical representation that extends Allen's interval algebra. However, this representation is lossy as the exact relationships among the events cannot be fully recovered. In this paper, we augment the hierarchical representation with additional information to achieve a lossless representation. An efficient algorithm called IEMiner is designed to discover frequent temporal patterns from interval-based events. The algorithm employs two optimization techniques to reduce the search space and remove non-promising candidates. From the discovered temporal patterns, we build an interval-based classifier called IEClassifier to differentiate closely related classes. Experiments on both synthetic and real world datasets indicate the efficiency and scalability of the proposed approach, as well as the improved accuracy of IEClassifier.