nils-herrmann/clean_scirepeval huggingface dataset:
Diversity of meso-scale architecture in human and non-human connectomes

Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance. Meso-scale architecture of connectomes is usually modeled as segregated clusters and communities. Here the authors report that non-assortative communities are better able to capture the functional connectivity for some networks and offer measures of community diversity that predict cognitive performance.
Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs

We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions:popularity and productivity. Intuitively, a candidate ispopular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together, these two measures capture not only frequency of occurrence, but also cross-checking that the candidate occurs both near the class name and near other class members. We developed two algorithms that begin with just a class name and one seed instance and then automatically generate a ranked list of new class instances. We conducted experiments on four semantic classes and consistently achieved high accuracies.
Inducing Interpretability in Knowledge Graph Embeddings

We study the problem of inducing interpretability in KG embeddings. Specifically, we explore the Universal Schema (Riedel et al., 2013) and propose a method to induce interpretability. There have been many vector space models proposed for the problem, however, most of these methods don’t address the interpretability (semantics) of individual dimensions. In this work, we study this problem and propose a method for inducing interpretability in KG embeddings using entity co-occurrence statistics. The proposed method significantly improves the interpretability, while maintaining comparable performance in other KG tasks.
Antecedents of User Stickiness and Loyalty and Their Effects on Users' Group-Buying Repurchase Intention

Intense competition among a vast number of group-buying websites leads to higher product homogeneity, which allows customers to switch to alternative websites easily and reduce their website stickiness and loyalty. This study explores the antecedents of user stickiness and loyalty and their effects on consumers’ group-buying repurchase intention. Results indicate that systems quality, information quality, service quality, and alternative system quality each has a positive relationship with user loyalty through user stickiness. Meanwhile, information quality directly impacts user loyalty. Thereafter, user stickiness and loyalty each has a positive relationship with consumers’ repurchase intention. Theoretical and managerial implications are also discussed.
A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction

Distant-supervised relation extraction inevitably suffers from wrong labeling problems because it heuristically labels relational facts with knowledge bases. Previous sentence level denoise models don’t achieve satisfying performances because they use hard labels which are determined by distant supervision and immutable during training. To this end, we introduce an entity-pair level denoise method which exploits semantic information from correctly labeled entity pairs to correct wrong labels dynamically during training. We propose a joint score function which combines the relational scores based on the entity-pair representation and the confidence of the hard label to obtain a new label, namely a soft label, for certain entity pair. During training, soft labels instead of hard labels serve as gold labels. Experiments on the benchmark dataset show that our method dramatically reduces noisy instances and outperforms the state-of-the-art systems.
Real-time robust human tracking based on Lucas-Kanade optical flow and deep detection for embedded surveillance

Object tracking is one of the most important functions in surveillance systems, especially in the system with Pan/Tilt/Zoom Camera. In this paper, we propose a real-time robust human tracking method for embedded surveillance. The proposed human tracking method tracks human objects based on Lucas-Kanade (LK) optical flow algorithm , rectifies tracking error due to accumulation or object missing by readjusting tracked human object bounding boxes periodically. Human localization information is obtained from a reliable deep learning-based human. It also handles occlusion by combination of LK information and human detector information. In order to achieve fast and robust processing, computationally light but reliable human detector is developed based on YOLOv2 object detector  model. Through experiments in comparison with other state-of-the-art tracking methods, it is shown that the proposed human tracking method operates fast and reliably with occlusion handling, and that performs better than or comparable to others.
Student Portfolios and the College Admissions Problem

We develop a decentralized Bayesian model of college admissions with two ranked colleges, heterogeneous students and two realistic match frictions: students find it costly to apply to college, and college evaluations of their applications are uncertain. Students thus face a portfolio choice problem in their application decision, while colleges choose admissions standards that act like market-clearing prices. Enrollment at each college is affected by the standards at the other college through student portfolio reallocation. In equilibrium, student-college sorting may fail: weaker students sometimes apply more aggressively, and the weaker college might impose higher standards. Applying our framework, we analyze affirmative action, showing how it induces minority applicants to construct their application portfolios as if they were majority students of higher caliber. ∗Earlier versions were called “The College Admissions Problem with Uncertainty” and “A Supply and Demand Model of the College Admissions Problem”. We would like to thank Philipp Kircher (CoEditor) and three anonymous referees for their helpful comments and suggestions. Greg Lewis and Lones Smith are grateful for the financial support of the National Science Foundation. We have benefited from seminars at BU, UCLA, Georgetown, HBS, the 2006 Two-Sided Matching Conference (Bonn), 2006 SED (Vancouver), 2006 Latin American Econometric Society Meetings (Mexico City), and 2007 American Econometric Society Meetings (New Orleans), Iowa State, Harvard/MIT, the 2009 Atlanta NBER Conference, and Concordia. Parag Pathak and Philipp Kircher provided useful discussions of our paper. We are also grateful to John Bound and Brad Hershbein for providing us with student college applications data. †Arizona State University, Department of Economics, Tempe, AZ 85287. ‡Harvard University, Department of Economics, Cambridge, MA 02138. §University of Wisconsin, Department of Economics, Madison, WI 53706.
Modeling Restaurant Context for Food Recognition

Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks.
A CMOS Inverter-Based Self-biased Fully Differential Amplifier

A CMOS self-biased fully differential amplifier is presented. Due to the self-biasing structure of the amplifier and its associated negative feedback, the amplifier is compensated to achieve low sensitivity to process, supply voltage and temperature (PVT) variations. The output common-mode voltage of the amplifier is adjusted through the same biasing voltages provided by the common-mode feedback (CMFB) circuit. The amplifier core is based on a simple structure that uses two CMOS inverters to amplify the input differential signal. Despite its simple structure, the proposed amplifier is attractive to a wide range of applications, specially those requiring low power and small silicon area. As two examples, a sample-and-hold circuit and a second order multi-bit sigma-delta modulator either employing the proposed amplifier are presented. Besides these application examples, a set of amplifier performance parameters is given.
Corporate Governance and Accounting Scandals 1

This paper empirically examines whether certain corporate governance mechanisms are related to the probability of a company restating its earnings. We examine a sample of 159 U.S. public companies that restated earnings and an industrysize matched sample of control firms. We have assembled a novel, hand-collected dataset measuring corporate governance characteristics of these 318 firms. We find that several key governance characteristics are unrelated to the probability of a company restating earnings. These include the independence of boards and audit committees, and the provision of non-audit services by outside auditors. We find that the probability of restatement is lower in companies whose boards or audit committees have an independent director with a background in accounting or finance. This relation is statistically significant, large in magnitude, and robust to alternative specifications. Our findings are consistent with the idea that independent directors with financial expertise are valuable in providing oversight of a firm’s financial reporting practices.
Explaining collaborative filtering recommendations

Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.
Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance

Student video-watching behavior and quiz performance are studied in two Massive Open Online Courses (MOOCs). In doing so, two frameworks are presented by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, recurring subsequences of student behavior are extracted, which contain fundamental characteristics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). It is found that some of these behaviors are significantly correlated with changes in the likelihood that a student will be Correct on First Attempt (CFA) or not in answering quiz questions, and in ways that are not necessarily intuitive. Then, with the position-based framework, models of quiz performance are devised based on positions visited in a video. In evaluating these models through CFA prediction, it is found that three of them can substantially improve prediction quality, which underlines the ability to relate this type of behavior to quiz scores. Since this prediction considers videos individually, these benefits also suggest that these models are useful in situations where there is limited training data, e.g., for early detection or in short courses.
A Survey on Security and Privacy Issues of Bitcoin

Bitcoin is a popular cryptocurrency that records all transactions in a distributed append-only public ledger called blockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus protocol, which is run by the network nodes called miners. In exchange for the incentive, the miners are expected to maintain the blockchain honestly. Since its launch in 2009, Bitcoin economy has grown at an enormous rate, and it is now worth about 150 billions of dollars. This exponential growth in the market value of bitcoins motivate adversaries to exploit weaknesses for profit, and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends. In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin. We start by giving an overview of the Bitcoin system and its major components along with their functionality and interactions within the system. We review the existing vulnerabilities in Bitcoin and its major underlying technologies such as blockchain and PoW-based consensus protocol. These vulnerabilities lead to the execution of various security threats to the standard functionality of Bitcoin. We then investigate the feasibility and robustness of the state-of-the-art security solutions. Additionally, we discuss the current anonymity considerations in Bitcoin and the privacy-related threats to Bitcoin users along with the analysis of the existing privacy-preserving solutions. Finally, we summarize the critical open challenges, and we suggest directions for future research towards provisioning stringent security and privacy solutions for Bitcoin.
Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach

In this thesis, the multi-category dataset has been incorporated with the robust feature descriptor using the scale invariant feature transform (SIFT), SURF and FREAK along with the multi-category enabled support vector machine (mSVM). The multi-category support vector machine (mSVM) has been designed with the iterative phases to make it able to work with the multi-category dataset. The mSVM represents the training samples of main class as the primary class in every iterative phase and all other training samples are categorized as the secondary class for the support vector machine classification. The proposed model is made capable of working with the variations in the indoor scene image dataset, which are noticed in the form of the color, texture, light, image orientation, occlusion and color illuminations. Several experiments have been conducted over the proposed model for the performance evaluation of the indoor scene recognition system in the proposed model. The results of the proposed model have been obtained in the form of the various performance parameters of statistical errors, precision, recall, F1-measure and overall accuracy. The proposed model has clearly outperformed the existing models in the terms of the overall accuracy. The proposed model improvement has been recorded higher than ten percent for all of the evaluated parameters against the existing models based upon SURF, FREAK, etc.
IntI2 integron integrase in Tn7.

Integrons can insert and excise antibiotic resistance genes on plasmids in bacteria by site-specific recombination. Class 1 integrons code for an integrase, IntI1 (337 amino acids in length), and are generally borne on elements derived from Tn5090, such as that found in the central part of Tn21. A second class of integron is found on transposon Tn7 and its relatives. We have completed the sequence of the Tn7 integrase gene, intI2, which contains an internal stop codon. This codon was found to be conserved among intI2 genes on three other Tn7-like transposons harboring different cassettes. The predicted peptide sequence (IntI2*) is 325 amino acids long and is 46% identical to IntI1. In order to detect recombination activity, the internal stop codon at position 179 in the parental allele was changed to a triplet coding for glutamic acid. The sequences flanking the cassette arrays in the class 1 and 2 integrons are not closely related, but a common pool of mobile cassettes is used by the different integron classes; two of the three antibiotic resistance cassettes on Tn7 and its close relatives are also found in various class 1 integrons. We also observed a fourth excisable cassette downstream of those described previously in Tn7. The fourth cassette encodes a 165-amino-acid protein of unknown function with 6.5 contiguous repeats of a sequence coding for 7 amino acids. IntI2*179E promoted site-specific excision of each of the cassettes in Tn7 at different frequencies. The integrases from Tn21 and Tn7 showed limited cross-specificity in that IntI1 could excise all cassettes from both Tn21 and Tn7. However, we did not observe a corresponding excision of the aadA1 cassette from Tn21 by IntI2*179E.
Distributed QoS Architectures for Multimedia Streaming Over Software Defined Networks

This paper presents novel QoS extensions to distributed control plane architectures for multimedia delivery over large-scale, multi-operator Software Defined Networks (SDNs). We foresee that large-scale SDNs shall be managed by a distributed control plane consisting of multiple controllers, where each controller performs optimal QoS routing within its domain and shares summarized (aggregated) QoS routing information with other domain controllers to enable inter-domain QoS routing with reduced problem dimensionality. To this effect, this paper proposes (i) topology aggregation and link summarization methods to efficiently acquire network topology and state information, (ii) a general optimization framework for flow-based end-to-end QoS provision over multi-domain networks, and (iii) two distributed control plane designs by addressing the messaging between controllers for scalable and secure inter-domain QoS routing. We apply these extensions to streaming of layered videos and compare the performance of different control planes in terms of received video quality, communication cost and memory overhead. Our experimental results show that the proposed distributed solution closely approaches the global optimum (with full network state information) and nicely scales to large networks.
Gated Bayesian networks for algorithmic trading

This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent processes that include several distinct phases. In essence, a GBN is a model that combines several Bayesian networks (BNs) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. In this paper we also present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how the learnt GBNs can substantially lower risk towards invested capital, while they at the same time generate similar or better rewards, compared to the benchmark investment strategy buy-and-hold. We also explore some differences and similarities between GBNs and other related formalisms.
An extended dependency graph for relation extraction in biomedical texts

Kernel-based methods are widely used for relation extraction task and obtain good results by leveraging lexical and syntactic information. However, in biomedical domain these methods are limited by the size of dataset and have difficulty in coping with variations in text. To address this problem, we propose Extended Dependency Graph (EDG) by incorporating a few simple linguistic ideas and include information beyond syntax. We believe the use of EDG will enable machine learning methods to generalize more easily. Experiments confirm that EDG provides up to 10% f-value improvement over dependency graph using mainstream kernel methods over five corpora. We conducted additional experiments to provide a more detailed analysis of the contributions of individual modules in EDG construction.
Inside the Right-Leaning Echo Chambers: Characterizing Gab, an Unmoderated Social System

The moderation of content in many social media systems, such as Twitter and Facebook, motivated the emergence of a new social network system that promotes free speech, named Gab. Soon after that, Gab has been removed from Google Play Store for violating the company's hate speech policy and it has been rejected by Apple for similar reasons. In this paper we characterize Gab, aiming at understanding who are the users who joined it and what kind of content they share in this system. Our findings show that Gab is a very politically oriented system that hosts banned users from other social networks, some of them due to possible cases of hate speech and association with extremism. We provide the first measurement of news dissemination inside a right-leaning echo chamber, investigating a social media where readers are rarely exposed to content that cuts across ideological lines, but rather are fed with content that reinforces their current political or social views.