nils-herrmann/clean_scirepeval huggingface dataset:
E-commerce Recommendation with Personalized Promotion

Most existing e-commerce recommender systems aim to recommend the right products to a consumer, assuming the properties of each product are fixed. However, some properties, including price discount, can be personalized to respond to each consumer's preference. This paper studies how to automatically set the price discount when recommending a product, in light of the fact that the price will often alter a consumer's purchase decision. The key to optimizing the discount is to predict consumer's willingness-to-pay (WTP), namely, the highest price a consumer is willing to pay for a product. Purchase data used by traditional e-commerce recommender systems provide points below or above the decision boundary. In this paper we collected training data to better predict the decision boundary. We implement a new e-commerce mechanism adapted from laboratory lottery and auction experiments that elicit a rational customer's exact WTP for a small subset of products, and use a machine learning algorithm to predict the customer's WTP for other products. The mechanism is implemented on our own e-commerce website that leverages Amazon's data and subjects recruited via Mechanical Turk. The experimental results suggest that this approach can help predict WTP, and boost consumer satisfaction as well as seller profit.
Expert Finding in a Social Network

This paper addresses the issue of expert finding in a social network. The task of expert finding, as one of the most important research issues in social networks, is aimed at identifying persons with relevant expertise or experience for a given topic. In this paper, we propose a propagation-based approach that takes into consideration of both person local information and network information (e.g. relationships between persons). Experimental results show that our approach can outperform the baseline approach.
Face Detection with a 3D Model

This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on Local Binary features relative to the 3D pose. A compatible subset of candidates is then obtained by non-maximal suppression. Experiments on two standard face detection datasets show that the proposed 3D model based approach obtains results comparable to state of the art.
BlueStreaming: towards power-efficient internet P2P streaming to mobile devices

P2P streaming applications are very popular on the Internet today. However, a mobile device in P2P streaming not only needs to continuously receive streaming data from other peers for its playback, but also needs to continuously exchange control information (e.g., buffermaps and file chunk requests) with neighboring peers and upload the downloaded streaming data to them. These lead to excessive battery power consumption on the mobile device.
 In this paper, we first conduct Internet experiments to study in-depth the impact of control traffic and uploading traffic on battery power consumption with several popular Internet P2P streaming applications. Motivated by measurement results, we design and implement a system called BlueStreaming that effectively utilizes the commonly existing Bluetooth interface on mobile devices. Instead of activating WiFi and Bluetooth interfaces alternatively, BlueStreaming keeps Bluetooth active all the time to transmit delay-sensitive control traffic while using WiFi for streaming data traffic. BlueStreaming trades Bluetooth's power consumption for much more significant energy saving from shaped WiFi traffic. To evaluate the performance of BlueStreaming, we have implemented prototypes on both Windows and Mac to access existing popular Internet P2P streaming services. The experimental results show that BlueStreaming can save up to 46% battery power compared to the commodity PSM scheme.
Constrained iterative LQR for on-road autonomous driving motion planning

There exist a lot of challenges in trajectory planning for autonomous driving: 1) Needs of both spatial and temporal planning for highly dynamic environments; 2) Nonlinear vehicle models and non-convex collision avoidance constraints. 3) High computational efficiency for real-time implementation. Iterative Linear Quadratic Regulator (ILQR) is an algorithm which solves predictive optimal control problem with nonlinear system very efficiently. However, it can not deal with constraints. In this paper, the Constrained Iterative LQR (CILQR) is proposed to handle the constraints in ILQR. Then an on road driving problem is formulated. Simulation case studies show the capability of the CILQR algorithm to solve the on road driving motion planning problem.
SECFUZZ: Fuzz-testing security protocols

We propose a light-weight, yet effective, technique for fuzz-testing security protocols. Our technique is modular, it exercises (stateful) protocol implementations in depth, and handles encrypted traffic. We use a concrete implementation of the protocol to generate valid inputs, and mutate the inputs using a set of fuzz operators. A dynamic memory analysis tool monitors the execution as an oracle to detect the vulnerabilities exposed by fuzz-testing. We provide the fuzzer with the necessary keys and cryptographic algorithms in order to properly mutate encrypted messages. We present a case study on two widely used, mature implementations of the Internet Key Exchange (IKE) protocol and report on two new vulnerabilities discovered by our fuzz-testing tool. We also compare the effectiveness of our technique to two existing model-based fuzz-testing tools for IKE.
Playing prosocial video games increases empathy and decreases schadenfreude.

Past research provided abundant evidence that exposure to violent video games increases aggressive tendencies and decreases prosocial tendencies. In contrast, research on the effects of exposure to prosocial video games has been relatively sparse. The present research found support for the hypothesis that exposure to prosocial video games is positively related to prosocial affect and negatively related to antisocial affect. More specifically, two studies revealed that playing a prosocial (relative to a neutral) video game increased interpersonal empathy and decreased reported pleasure at another's misfortune (i.e., schadenfreude). These results lend further credence to the predictive validity of the General Learning Model (Buckley & Anderson, 2006) for the effects of media exposure on social tendencies.
Brainstorm: A User-Friendly Application for MEG/EEG Analysis

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
Label Propagation via Teaching-to-Learn and Learning-to-Teach

How to propagate label information from labeled examples to unlabeled examples over a graph has been intensively studied for a long time. Existing graph-based propagation algorithms usually treat unlabeled examples equally, and transmit seed labels to the unlabeled examples that are connected to the labeled examples in a neighborhood graph. However, such a popular propagation scheme is very likely to yield inaccurate propagation, because it falls short of tackling ambiguous but critical data points (e.g., outliers). To this end, this paper treats the unlabeled examples in different levels of difficulties by assessing their reliability and discriminability, and explicitly optimizes the propagation quality by manipulating the propagation sequence to move from simple to difficult examples. In particular, we propose a novel iterative label propagation algorithm in which each propagation alternates between two paradigms, teaching-to-learn and learning-to-teach (TLLT). In the teaching-to-learn step, the learner conducts the propagation on the simplest unlabeled examples designated by the teacher. In the learning-to-teach step, the teacher incorporates the learner’s feedback to adjust the choice of the subsequent simplest examples. The proposed TLLT strategy critically improves the accuracy of label propagation, making our algorithm substantially robust to the values of tuning parameters, such as the Gaussian kernel width used in graph construction. The merits of our algorithm are theoretically justified and empirically demonstrated through experiments performed on both synthetic and real-world data sets.
Design of Sharp Roll-Off Lowpass Filter With Ultra Wide Stopband

A novel microstrip lowpass filter is proposed to achieve an ultra wide stopband with 12th harmonic suppression and extremely sharp skirt characteristics. The transition band is from 1.26 to 1.37 GHz with -3 and -20 dB, respectively. The operating mechanism of the filter is investigated based on proposed equivalent-circuit model, and the role of each section in creating null points is theoretically discussed. An overall good agreement between measured and simulated results is observed.
Predicting user activity level in social networks

The study of users' social behaviors has gained much research attention since the advent of various social media such as Facebook, Renren and Twitter. A major kind of applications is to predict a user's future activities based on his/her historical social behaviors. In this paper, we focus on a fundamental task: to predict a user's future activity levels in a social network, e.g. weekly activeness, active or inactive. This problem is closely related to Social Customer Relationship Management (Social CRM). Compared to traditional CRM, the three properties: user diversity, social influence, and dynamic nature of social networks, raise new challenges and opportunities to Social CRM. Firstly, the user diversity property implies that a global predictive model may not be precise for all users. On the other hand, historical data of individual users are too sparse to build precisely personalized models. Secondly, the social influence property suggests that relationships between users can be embedded to further boost prediction results on individual users. Finally, the dynamical nature of social networks means that users' behaviors may keep changing over time. To address these challenges, we develop a personalized and social regularized time-decay model for user activity level prediction. Experiments on the social media Renren validate the effectiveness of our proposed model compared with some baselines including traditional supervised learning methods and node classification methods in social networks.
Cognitive Cars: A New Frontier for ADAS Research

This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control. The main objective here is to clarify the goals and guidelines for future development in the area of advanced driver-assistance systems (ADASs). Two major research directions are investigated and discussed in detail: (1) stimuli-decisions-actions, which focuses on the driver side, and (2) perception enhancement-action-suggestion-function-delegation, which emphasizes the ADAS side. This paper addresses the important achievements and major difficulties of each direction and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving. Other related topics, including driver training and infrastructure design, are also studied.
OPTIMIS: A holistic approach to cloud service provisioning

We present the fundamentals for a toolkit for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of cloud services. The innovations behind the toolkit are aimed at optimizing the whole service life cycle, including service construction, deployment, and operation, on a basis of aspects such as trust, risk, ecoefficiency and cost. Notably, adaptive self-preservation is key to meet predicted and unforeseen changes in resource requirements. By addressing the whole service life cycle, taking into account the multitude of future cloud architectures, and a by taking a holistic approach to sustainable service provisioning, the toolkit is aimed to provide a foundation for a reliable, sustainable, and trustful cloud computing industry.
Privacy Regulation and Online Advertising

Advertisers use online customer data to target their marketing appeals. This has heightened consumers’ privacy concerns, leading governments to pass laws designed to protect consumer privacy by restricting the use of data and by restricting online tracking techniques used by websites. We use the responses of 3.3 million survey-takers who had been randomly exposed to 9,596 online display (banner) advertising campaigns to explore how strong privacy regulation in the European Union has influenced advertising effectiveness. We find that display advertising became far less effective at changing stated purchase intent after the laws were enacted relative to other countries. The loss in effectiveness was more pronounced for websites that had general content (such as news sites), where non-data-driven targeting is particularly hard to do. The loss of effectiveness was also more pronounced for ads with a smaller page presence and for ads that did not have additional interactive, video, or audio features. ∗Avi Goldfarb is Associate Professor of Marketing, Rotman School of Management, University of Toronto, 105 St George St., Toronto, ON. Tel. 416-946-8604.  agoldfarb@rotman.utoronto.ca. Catherine Tucker is Assistant Professor of Marketing, MIT Sloan School of Business, 1 Amherst St., E40-167, Cambridge, MA. Tel. 617-252-1499.  cetucker@mit.edu. We thank Glen Urban and participants at workshops at IDC, MIT, Michigan, Northwestern, UC Berkeley and Wharton for helpful comments.
Organizational Commitment and Psychological Attachment : The Effects of Compliance , Identification , and Internalization on Prosocial Behavior

Previous research on organizational commitment has typically not focused on the underlying dimensions of psychological attachment to the organization. Results of two studies using university employees (N = 82) and students (N = 162) suggest that psychological attachment may be predicated on compliance, identification, and internalization (e.g., Kelman, 19S8). Identification and internalization are positively related to prosocial behaviors and negatively related to turnover. Internalization is predictive of financial donations to a fund-raising campaign. Overall, the results suggest the importance of clearly specifying the underlying dimensions of commitment using notions of psychological attachment and the various forms such attachment can take.
Unstructured lumigraph rendering

We describe an image based rendering approach that generalizes many current image based rendering algorithms, including light field rendering and view-dependent texture mapping. In particular, it allows for lumigraph-style rendering from a set of input cameras in arbitrary configurations (i.e., not restricted to a plane or to any specific manifold). In the case of regular and planar input camera positions, our algorithm reduces to a typical lumigraph approach. When presented with fewer cameras and good approximate geometry, our algorithm behaves like view-dependent texture mapping. The algorithm achieves this flexibility because it is designed to meet a set of specific goals that we describe. We demonstrate this flexibility with a variety of examples.
Knowledge management in higher education institutions for the generation of organizational knowledge

Knowledge management (KM) has emerged as a tool that allows the creation, use, distribution and transfer of knowledge in organizations. There are different frameworks that propose KM in the scientific literature. The majority of these frameworks are structured based on a strong theoretical background. This study describes a guide for the implementation of KM in a higher education institution (HEI) based on a framework with a clear description on the practical implementation. This framework is based on a technological infrastructure that includes enterprise architecture, business intelligence and educational data mining. Furthermore, a case study which describes the experience of the implementation in a HEI is presented. As a conclusion, the pros and cons on the use of the framework are analyzed.
on Fault-Tolerant Control for Unmanned Aerial Vehicles ( UAVs )

The growing number of Unmanned Aerial Vehicles (UAVs) is considerable in the last decades. Many flight test scenarios, including single and multi-vehicle formation flights, are demonstrated using different control algorithms with different test platforms. In this paper, we present a brief literature review on the development and key issues of current researches in the field of Fault-Tolerant Control (FTC) applied to UAVs. It consists of various intelligent or hierarchical control architectures for a single vehicle or a group of UAVs in order to provide potential solutions for tolerance to the faults, failures or damages in relevant to UAV components during flight. Among various UAV test-bed structures, a sample of every class of UAVs, including single-rotor, quadrotor, and fixed-wing types, are selected and briefly illustrated. Also, a short description of terms, definitions, and classifications of fault-tolerant control systems (FTCS) is presented before the main contents of review.
Negative refractive index in left-handed materials.

The real part of the refractive index n(omega) of a nearly transparent and passive medium is usually taken to have only positive values. Through an analysis of a current source radiating into a 1D "left-handed" material (LHM)-where the permittivity and permeability are simultaneously less than zero-we determine the analytic structure of n(omega), demonstrating frequency regions where the sign of Re[n(omega)] must, in fact, be negative. The regime of negative index, made relevant by a recent demonstration of an effective LHM, leads to unusual electromagnetic wave propagation and merits further exploration.
Friction Compensation in Robotics: an Overview

Friction effects are particularly critical for industrial robots, since they can induce large positioning errors, stick-slip motions, and limit cycles. This paper offers a reasoned overview of the main friction compensation techniques that have been developed in the last years, regrouping them according to the adopted kind of control strategy. Some experimental results are reported, to show how the control performances can be affected not only by the chosen method, but also by the characteristics of the available robotic architecture and of the executed task.
Scripts, Plans and Knowledge

"Of what a strange nature is knowledge! It clings to the mind, when it has once seized on it, like a lichen on the rock,"  We describe a theoretical system intended to facilitate the use of knowledge In an understand­ ing system. The notion of script is introduced to account for knowledge about mundane situations. A program, SAM, is capable of using scripts to under­ stand. The notion of plans is introduced to ac­ count for general knowledge about novel situa­ tions. I. Preface In an attempt to provide theory where there have been mostly unrelated systems, Minsky (1974) recently described the as fitting into the notion of "frames." Minsky at­ tempted to relate this work, in what is essentially language processing, to areas of vision research that conform to the same notion. Mlnsky's frames paper has created quite a stir in AI and some immediate spinoff research along the lines of developing frames manipulators (e.g. Bobrow, 1975; Winograd, 1975). We find that we agree with much of what Minsky said about frames and with his characterization of our own work. The frames idea is so general, however, that It does not lend itself to applications without further specialization. This paper is an attempt to devel­ op further the lines of thought set out in Schank (1975a) and Abelson (1973; 1975a). The ideas pre­ sented here can be viewed as a specialization of the frame idea. We shall refer to our central constructs as "scripts." II. The Problem Researchers in natural language understanding have felt for some time that the eventual limit on the solution of our problem will be our ability to characterize world knowledge. Various researchers have approached world knowledge in various ways. Winograd (1972) dealt with the problem by severely restricting the world. This approach had the po­ sitive effect of producing a working system and the negative effect of producing one that was only minimally extendable. Charniak (1972) approached the problem from the other end entirely and has made some interesting first steps, but because his work is not grounded in any representational sys­ tem or any working computational system the res­ triction of world knowledge need not critically concern him. Our feeling is that an effective characteri­ zation of knowledge can result in a real under­ standing system in the not too distant future. We expect that programs based on the theory we out­ …
RDFPeers: a scalable distributed RDF repository based on a structured peer-to-peer network

Centralized Resource Description Framework (RDF) repositories have limitations both in their failure tolerance and in their scalability. Existing Peer-to-Peer (P2P) RDF repositories either cannot guarantee to find query results, even if these results exist in the network, or require up-front definition of RDF schemas and designation of super peers. We present a scalable distributed RDF repository (RDFPeers) that stores each triple at three places in a multi-attribute addressable network by applying globally known hash functions to its subject predicate and object. Thus all nodes know which node is responsible for storing triple values they are looking for and both exact-match and range queries can be efficiently routed to those nodes. RDFPeers has no single point of failure nor elevated peers and does not require the prior definition of RDF schemas. Queries are guaranteed to find matched triples in the network if the triples exist. In RDFPeers both the number of neighbors per node and the number of routing hops for inserting RDF triples and for resolving most queries are logarithmic to the number of nodes in the network. We further performed experiments that show that the triple-storing load in RDFPeers differs by less than an order of magnitude between the most and the least loaded nodes for real-world RDF data.
Investigating hidden Markov models capabilities in anomaly detection

Hidden Markov Model (HMM) based applications are common in various areas, but the incorporation of HMM's for anomaly detection is still in its infancy. This paper aims at classifying the TCP network traffic as an attack or normal using HMM. The paper's main objective is to build an anomaly detection system, a predictive model capable of discriminating between normal and abnormal behavior of network traffic. In the training phase, special attention is given to the initialization and model selection issues, which makes the training phase particularly effective. For training HMM, 12.195% features out of the total features (5 features out of 41 features) present in the KDD Cup 1999 data set are used. Result of tests on the KDD Cup 1999 data set shows that the proposed system is able to classify network traffic in proportion to the number of features used for training HMM. We are extending our work on a larger data set for building an anomaly detection system.