nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Assortment Selection under the Nested Logit Models

We study a stylized dynamic assortment planning problem during a selling season of finite length T . At each time spot, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the seller is to maximize the expected revenue, or equivalently, to minimize the worst-case expected regret. One key challenge is that utilities of products are unknown to the seller, which need to be learned. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing work is based on the multinomial logit choice models (MNL). In this paper, we study the problem of dynamic assortment planning under a more general choice model— nested logit model, which models hierarchical choice behavior and is “the most widely used member of the GEV (generalized extreme value) family” (Train, 2009). By leveraging the revenue-ordered structure of the optimal assortment within each nest, we develop a novel upper confidence bound (UCB) policy with an aggregated estimation scheme. Our policy simultaneously learns customers’ choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the regret at the order of Õ( √ MNT + MN), where M is the number of nests and N is the number of products in each nest. We further provide a lower bound result of Ω( √ MT ), which shows the optimality of the upper bound when T > M and N is small. However, the N term in the upper bound is not ideal for applications where N is large as compared to T . To address this issue, we further generalize our first policy by introducing a discretization technique, which leads to a regret of Õ( √ MT 2/3 + MNT ) with a specific choice of discretization granularity. It improves the previous regret bound whenever N > T . We provide numerical results to demonstrate the empirical performance of both proposed policies.
Factors Affecting Adolescents' Involvement in Cyberbullying: What Divides the 20% from the 80%?

Despite some variations among cyberbullying studies, about 20% of the youth population surveyed appears to be involved in cyberbullying. Coincidentally, the current study found that exactly 20% of the students surveyed were involved in cyberbullying as bullies (7.8%), victims (7.5%), and bully/victims (4.7%). What divides those 20% from the 80% of noninvolved students? This study aimed to produce a parsimonious and accurate model that can predict the occurrence of involvement in cyberbullying among youth. Data were collected from a questionnaire survey administered to 1,036 students enrolled in secondary schools in South Korea. Stepwise logistic regression (SLR) was carried out to predict the dichotomous dependent variable (involved/noninvolved) with 10 independent variables grouped into three categories: (a) demographic, (b) media-related, and (c) school and psychology factors. The result of the SLR analysis yielded a four-step model including the variables of cyber-confidence, weekday game time, mobile activities, and age as being significant in explaining the 20/80 division (model χ(2)=34.306, df=4, p<0.001, Nagelkerke R(2)=0.071). This finding suggests that younger students who spend more time playing games on weekdays while being more confident in cyberspace and active in using mobile phones are more likely to be involved in cyberbullying than other students. In particular, the construct of cyber-confidence calls for further elaboration and research, given its controversial function with respect to adolescents' involvement in cyberbullying. Also, this study may bring about insights into practical considerations needed for concerned researchers, teachers, and parents to identify who is inside the group involved in cyberbullying so as to help the participating adolescents escape from the circle of cyberbullying.
A Generic Method for a Bottom-Up ASIL Decomposition

Automotive Safety Integrity Level (ASIL) decomposition is a technique presented in the ISO 26262: Road Vehicles Functional Safety standard. Its purpose is to satisfy safety-critical requirements by decomposing them into less critical ones. This procedure requires a system-level validation, and the elements of the architecture to which the decomposed requirements are allocated must be analyzed in terms of Common-Cause Faults (CCF). In this work, we present a generic method for a bottomup ASIL decomposition, which can be used during the development of a new product. The system architecture is described in a three-layer model, from which fault trees are generated, formed by the application, resource, and physical layers and their mappings. A CCF analysis is performed on the fault trees to verify the absence of possible common faults between the redundant elements and to validate the ASIL decomposition.
Network-Attack-Resilient Intrusion-Tolerant SCADA for the Power Grid

As key components of the power grid infrastructure, Supervisory Control and Data Acquisition (SCADA) systems are likely to be targeted by nation-state-level attackers willing to invest considerable resources to disrupt the power grid. We present Spire, the first intrusion-tolerant SCADA system that is resilient to both system-level compromises and sophisticated network-level attacks and compromises. We develop a novel architecture that distributes the SCADA system management across three or more active sites to ensure continuous availability in the presence of simultaneous intrusions and network attacks. A wide-area deployment of Spire, using two control centers and two data centers spanning 250 miles, delivered nearly 99.999% of all SCADA updates initiated over a 30-hour period within 100ms. This demonstrates that Spire can meet the latency requirements of SCADA for the power grid.
Impacts of Informal Caregiving on Caregiver Employment , Health , and Family

Impacts of Informal Caregiving on Caregiver Employment, Health, and Family As the aging population increases, the demand for informal caregiving is becoming an ever more important concern for researchers and policy-makers alike. To shed light on the implications of informal caregiving, this paper reviews current research on its impact on three areas of caregivers’ lives: employment, health, and family. Because the literature is inherently interdisciplinary, the research designs, sampling procedures, and statistical methods used are heterogeneous. Nevertheless, we are still able to draw several conclusions: first, despite the prevalence of informal caregiving and its primary association with lower levels of employment, the affected labor force is seemingly small. Second, such caregiving tends to lower the quality of the caregiver’s psychological health, which also has a negative impact on physical health outcomes. Third, the implications for family life remain under investigated. The research findings also differ strongly among subgroups, although they do suggest that female, spousal, and intense caregivers tend to be the most affected by caregiving.
RGSE: a regular property guided symbolic executor for Java

It is challenging to effectively check a regular property of a program. This paper presents RGSE, a regular property guided dynamic symbolic execution (DSE) engine, for finding a program path satisfying a regular property as soon as possible. The key idea is to evaluate the candidate branches based on the history and future information, and explore the branches along which the paths are more likely to satisfy the property in priority. We have applied RGSE to 16 real-world open source Java programs, totaling 270K lines of code. Compared with the state-of-the-art, RGSE achieves two orders of magnitude speedups for finding the first target path. RGSE can benefit many research topics of software testing and analysis, such as path-oriented test case generation, typestate bug finding, and performance tuning. The demo video is at: https://youtu.be/7zAhvRIdaUU, and RGSE can be accessed at: http://jrgse.github.io.
Deep Multi-task Learning for Large-Scale Image Classification

To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes or categories hierarchically and determine the inter-related learning tasks automatically. Our deep multi-task learning algorithm can integrate the deep CNNs to learn more discriminative high-level features for image representation, and it can also leverage multi-task learning and inter-level relationship constraint to train more discriminative tree classifier over the concept ontology and control the inter-level error propagation effectively. In our deep multi-task learning algorithm, we can use back propagation to simultaneously refine both the relevant node classifiers (at different levels of the concept ontology) and the deep CNNs according to a joint objective function. The experimental results have demonstrated that our deep multi-task learning algorithm can achieve very competitive results on both the accuracy and the cost of feature extraction for large-scale image classification.
Multilingual Question Answering over Linked Data (QALD-3): Lab Overview

The third instalment of the open challenge on Question Answering over Linked Data (QALD-3) has been conducted as a half-day lab at CLEF 2013. Differently from previous editions of the challenge, QALD-3 put a strong emphasis on multilinguality, offering two tasks: one on multilingual question answering and one on ontology lexicalization. While no submissions were received for the latter, the former attracted six teams who submitted their systems’ results on the provided datasets. This paper provides an overview of QALD-3, discussing the approaches experimented by the participating systems as well as the obtained results.
A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis

This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuroscience investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation.
Localization-Aware Active Learning for Object Detection

Active learning—a class of algorithms that iteratively searches for the most informative samples to include in a training dataset—has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis, which allow us to leverage active learning to reduce the amount of annotated data needed to achieve a target object detection performance. Our first metric measures “localization tightness” of an object hypothesis, which is based on the overlapping ratio between the region proposal and the final prediction. Our second metric measures “localization stability” of an object hypothesis, which is based on the variation of predicted object locations when input images are corrupted by noise. Our experimental results show that by augmenting a conventional active-learning algorithm designed for classification with the proposed metrics, the amount of labeled training data required can be reduced up to 25%. Moreover, on PASCAL 2007 and 2012 datasets our localization-stability method has an average relative improvement of 96.5% and 81.9% over the base-line method using classification only. Asian Conference on Computer Vision This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.  2018 201 Broadway, Cambridge, Massachusetts 02139 Localization-Aware Active Learning for Object
A Trusted Virtual Machine in an Untrusted Management Environment

Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to computing systems, including improved resource utilization, software portability, and reliability. Virtualization also has the potential to enhance security by providing isolated execution environments for different applications that require different levels of security. For security-critical applications, it is highly desirable to have a small trusted computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. For example, consider the "computing-as-a-service” scenario where remote users execute a guest OS and applications inside a VM on a remote computing platform. It would be preferable for many users to utilize such a computing service without being forced to trust the management OS on the remote platform. In this paper, we address the problem of providing a secure execution environment on a virtualized computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure runtime environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.
Migrating Multi-page Web Applications to Single-page AJAX Interfaces

Recently, a new Web development technique for creating interactive Web applications, dubbed Ajax, has emerged. In this new model, the single-page Web interface is composed of individual components which can be updated/replaced independently. If until a year ago, the concern revolved around migrating legacy systems to Web-based settings, today we have a new challenge of migrating Web applications to single-page Ajax applications. Gaining an understanding of the navigational model and user interface structure of the source application is the first step in the migration process. In this paper, we explore how reverse engineering techniques can help analyze classic Web applications for this purpose. Our approach, using a schema-based clustering technique, extracts a navigational model of Web applications, and identifies candidate user interface components to be migrated to a single-page Ajax interface. Additionally, results of a case study, conducted to evaluate our tool, are presented
On Lower Bounds on the Size of Designs in Compact Symmetric Spaces of Rank 1

The concept of t-designs in compact symmetric spaces of rank 1 is a generalization of the theory of classical t-designs. In this paper we obtain new lower bounds on the cardinality of designs in projective compact symmetric spaces of rank 1. With one exception our bounds are the first improvements of the classical bounds by more than one. We use the linear programming technique and follow the approach we have proposed for spherical codes and designs. Some examples are shown and compared with the classical bounds.
Overview of Wireless Mesh Networks

Wireless Mesh Networks (WMNs) introduce a new paradigm ‎of wireless broadband Internet access by providing high data ‎rate service, scalability, and self-healing abilities at reduced ‎cost. ‎Obtaining high throughput for multi-cast applications (e.g. ‎video streaming broadcast) in WMNs is challenging due to the ‎interference and the change of channel quality. To overcome ‎this issue, cross-layer has been proposed to improve the ‎performance of WMNs. ‎Network coding is a powerful coding technique that has been ‎proven to be the very effective in achieving the maximum multi-cast ‎throughput. In addition to achieving the multi-cast ‎throughput, network coding offers other benefits such as load ‎balancing and saves bandwidth consumption. This ‎paper presents a review the fundamental concept types of medium access control ‎(MAC) layer, routing protocols, cross-layer and network ‎coding for wireless mesh networks. Finally, a list of directions for further research is considered.
Homing and cellular traffic in lymph nodes

Lymph nodes (LNs) are the organs where innate immune responses lead to acquired immunity, where some of our most devastating pathogens evade immunity, and where autoreactive lymphocytes first encounter tissue-specific self-antigens and are either tolerized or activated. The many roles of LNs depend on the coordinated migration of its cellular constituents. This article covers new insights into the organization and microvascular specialization of LNs, the guidance mechanisms that allow lymphocytes and antigen-presenting cells to find their correct place in the nodal parenchyma; and the role of afferent lymph flow in LN function.
Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges

Software engineering has been historically topdown. From a fully specified problem, a software engineer needs to detail each step of the resolution to get a solution. The resulting program will be functionally adequate as long as its execution environment complies with the original specifications. With their large amount of data, their ever changing multi-level dynamics, smart cities are too complex for a topdown approach. They prompt the need for a paradigm shift in computer science. Programs should be able to self-adapt on the fly, to handle unspecified events,, to efficiently deal with tremendous amount of data. To this end, bottom-up approach should become the norm. Machine learning is a first step,, distributed computing helps. Multi-Agent Systems (MAS) can combine machine learning, distributed computing, may be easily designed with a bottom-up approach. This paper explores how MASs can answer challenges at various levels of smart cities, from sensors networks to ambient intelligence.
Adaptive Information Extraction from Text by Rule Induction and Generalisation

(LP) 2 is a covering algorithm for adaptive Information Extraction from text (IE). It induces symbolic rules that insert SGML tags into texts by learning from examples found in a user-defined tagged corpus. Training is performed in two steps: initially a set of tagging rules is learned; then additional rules are induced to correct mistakes and imprecision in tagging. Induction is performed by bottom-up generalization of examples in the training corpus. Shallow knowledge about Natural Language Processing (NLP) is used in the generalization process. The algorithm has a considerable success story. From a scientific point of view, experiments report excellent results with respect to the current state of the art on two publicly available corpora. From an application point of view, a successful industrial IE tool has been based on (LP) 2. Real world applications have been developed and licenses have been released to external companies for building other applications. This paper presents (LP) 2 , experimental results and applications, and discusses the role of shallow NLP in rule induction.
ZVS isolated active clamp Sepic converter for high power LED applications

This paper analyses a ZVS isolated active clamp Sepic converter for high power LED applications. Due to the recent advancement in the light emitting diode technology, high brightness, high efficient LEDs becomes achievable in residential, industry and commercial applications to replace the incandescent bulbs, halogen bulbs, and even compact fluorescent light bulbs. Generally in these devices, the lumen is proportional to the current, so the converter has to control the LED string current, and for high power applications (greater than 100W), is preferable to have a galvanic isolation between the bus and the output; among different isolated topologies and taking into account the large input voltage variation in the application this paper is targeting, a ZVS active clamp Sepic converter has been adopted. Due to its circuit configuration, it can step up or down the input voltage, allowing a universal use, with lamps with different voltages and powers. A 300W, 5A, 48V input voltage prototype has been developed, and a peak efficiency of 91% has been reached without synchronous rectification.
Keshif : Out-ofthe-Box Visual and Interactive Data Exploration Environment

Keshif is an open-source, web-based data exploration environment that enables data analytics novices to create effective visual and interactive dashboards and explore relations with minimal learning time, and data analytics experts to explore tabular data in multiple perspectives rapidly with minimal setup time. In this paper, we present a high-level overview of the exploratory features and design characteristics of Keshif, as well as its API and a selection of its implementation specifics. We conclude with a discussion of its use as an open-source project.
Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game

Destiny is, to date, the most expensive digital game ever released with a total operating budget of over half a billion US dollars. It stands as one of the main examples of AAA titles, the term used for the largest and most heavily marketed game productions in the games industry. Destiny is a blend of a shooter game and massively multi-player online game, and has attracted dozens of millions of players. As a persistent game title, predicting retention and churn in Destiny is crucial to the running operations of the game, but prediction has not been attempted for this type of game in the past. In this paper, we present a discussion of the challenge of predicting churn in Destiny, evaluate the area under curve (ROC) of behavioral features, and use Hidden Markov Models to develop a churn prediction model for the game.