nils-herrmann/clean_scirepeval huggingface dataset:
Security challenges in automotive hardware/software architecture design

State-of-the-art automotive architectures are highly heterogeneous and complex systems that rely on distributed functions based on electronics and software. As cars are getting more connected with their environment, the vulnerability to attacks is rapidly growing. Examples for such wireless communication are keyless entry systems, WiFi, or Bluetooth. Despite this increasing vulnerability, the design of automotive architectures is still mainly driven by safety and cost issues rather than security. In this paper, we present potential threats and vulnerabilities, and outline upcoming security challenges in automotive architectures. In particular, we discuss the challenges arising in electric vehicles, like the vulnerability to attacks involving tampering with the battery safety. Finally, we discuss future automotive architectures based on Ethernet/IP and how formal verification methods might be used to increase their security.
Mining multi-tag association for image tagging

Automatic media tagging plays a critical role in modern tag-based media retrieval systems. Existing tagging schemes mostly perform tag assignment based on community contributed media resources, where the tags are provided by users interactively. However, such social resources usually contain dirty and incomplete tags, which severely limit the performance of these tagging methods. In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images. Given an image dataset, all the near-duplicate clusters are discovered. For each near-duplicate cluster, all the tags occurring in the cluster form the cluster’s “document”. Given a test image, we firstly initialize the candidate tag set from its near-duplicate cluster’s document. The candidate tag set is then expanded by considering the implicit multi-tag associations mined from all the clusters’ documents, where each cluster’s document is regarded as a transaction. To further reduce noisy tags, a visual relevance score is also computed for each candidate tag to the test image based on a new tag model. Tags with very low scores can be removed from the final tag set. Extensive experiments conducted on a real-world web image dataset—NUS-WIDE, demonstrate the promising effectiveness of our approach.
Students' learning styles and their effects on the use of social media technology for learning

Students with varying different learning styles approach learning differentlyhave different approaches in learning activities,. and wWith the emergence rise of social media technologies, investigating the effect of these styles on their intentions to use social media for learning it ishas become all the more importantpertinent to investigate if their styles affect their intentions to use social media for learning. This study investigated explored the factors that affect affecting students’ intentions to use social media for learning based on their learning styles (i.e., participatory, collaborative, and independent), using the Ssocial Mmedia Aacceptance Mmodel. By Convenience convenience sampling, resulted in the recruitment of 300 Malaysian students were recruited via an online survey (Nparticipatory = 116; Nindependent = 97; and Ncollaborative = 87). The survey was prepared based on by drawing on the Ssocial Mmedia Aacceptance Mmodel. It was piloted before the final data collection exercise step was that was carried out conducted in August 2013. The students’ demographic details of the students were analyzed using Statistical Program for Social Sciences 21, whilst while path modeling and multivariate analyses were administered conducted using SmartPLS 2.0. The Results results revealed the significant effect of Self and Performance to significantly affect on students’ intentions to use social media regardless of their learning styles. A pair-wise comparison showed revealed that Self to was more significant be significantly more important toin participatory style students compared to than to in collaborative students. Effort is was found to be the least significant factor, indicating the popularity of social media among studentssuggesting existing widespread use and familiarity of social media amongst the students. Further insight into Understanding the different driving factors of that drive students with different learning styles to use social media for students with different learning styles will help educators use this technology to assist learning more effectivelybe beneficial for educators to effectively use social media to assist students in their academic endeavors.
Open domain question answering using Wikipedia-based knowledge model

This paper describes the use of Wikipedia as a rich knowledge source for a question answering (QA) system. We suggest multiple answer matching modules based on different types of semi-structured knowledge sources of Wikipedia, including article content, infoboxes, article structure, category structure, and definitions. These semi-structured knowledge sources each have their unique strengths in finding answers for specific question types, such as infoboxes for factoid questions, category structure for list questions, and definitions for descriptive questions. The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules. Through an experiment, our system showed promising results, with a precision of 87.1%, a recall of 52.7%, and an F-measure of 65.6%, all of which are much higher than the results of a simple text analysis based system.
To queue or not to queue : equilibrium behavior in queueing systems

To the memory of my parents, Sara and Avraham Haviv To my mother and late father, Fela and Shimon Hassin Contents Preface xi 1.  1 1.1 Basic concepts 2 1.1.1 Strategies, payoffs, and equilibrium 2 1.1.2 Steady-state 4 1.1.3 Subgame perfect equilibrium 5 1.1.4 Evolutionarily stable strategies 5 1.1.5 The Braess paradox 5 1.1.6 Avoid the crowd or follow it? 6 1.2 Threshold strategies 7 1.3 Costs and objectives 9 1.4 Queueing theory preliminaries 11 1.5 A shuttle example 14 1.5.1 The unobservable model 14 1.5.2 The observable model 17 1.5.3 Social optimality 18 1.6 Non-stochastic models 19
Cellular data network infrastructure characterization and implication on mobile content placement

Despite the tremendous growth in the cellular data network usage due to the popularity of smartphones, so far there is rather limited understanding of the network infrastructure of various cellular carriers. Understanding the infrastructure characteristics such as the network topology, routing design, address allocation, and DNS service configuration is essential for predicting, diagnosing, and improving cellular network services, as well as for delivering content to the growing population of mobile wireless users. In this work, we propose a novel approach for discovering cellular infrastructure by intelligently combining several data sources, i.e., server logs from a popular location search application, active measurements results collected from smartphone users, DNS request logs from a DNS authoritative server, and publicly available routing updates. We perform the first comprehensive analysis to characterize the cellular data network infrastructure of four major cellular carriers within the U.S. in our study.
 We conclude among other previously little known results that the current routing of cellular data traffic is quite restricted, as it must traverse a rather limited number (i.e., 4-6) of infrastructure locations (i.e., GGSNs), which is in sharp contrast to wireline Internet traffic. We demonstrate how such findings have direct implications on important decisions such as mobile content placement and content server selection. We observe that although the local DNS server is a coarse-grained approximation on the user's network location, for some carriers, choosing content servers based on the local DNS server is accurate enough due to the restricted routing in cellular networks. Placing content servers close to GGSNs can potentially reduce the end-to-end latency by more than 50% excluding the variability from air interface.
Record-Aware Two-Level Compression for Big Textual Data Analysis Acceleration

An increasing volume of data puts MapReduce data analytic platforms such as Hadoop under constant resource pressure. A new two-phase text compression scheme has been specially designed to accelerate data analysis and reduce cluster resource usage, and this has been implemented for Hadoop. The scheme consists of two levels of compression. The first level compression allows a Hadoop program to consume the compressed data directly, thus reducing the data transmission cost within a cluster during analysis. The second level packages data into fixed-size blocks that respect the logical data records. This further reduces the data size to a size similar to that achieved by a higher-order entropy encoder while also making the compressed data splittable for the HDFS. The use of the compression scheme is made transparent to Hadoop developers by the provided utility functions. The compression scheme is evaluated using a set of standard MapReduce jobs for a selection of real-world datasets. The experimental results show an improvement on analysis performance of up to 72% and compression ratios close to that achieved by a standard compressor such as Bzip.
SourceSight: Enabling Effective Source Selection

Recently there has been a rapid increase in the number of data sources and data services, such as cloud-based data markets and data portals, that facilitate the collection, publishing and trading of data. Data sources typically exhibit large heterogeneity in the type and quality of data they provide. Unfortunately, when the number of data sources is large, it is difficult for users to reason about the actual usefulness of sources for their applications and the trade-offs between the benefits and costs of acquiring and integrating sources. In this demonstration we present \textsc{SourceSight}, a system that allows users to interactively explore a large number of heterogeneous data sources, and discover valuable sets of sources for diverse integration tasks. \textsc{SourceSight}~uses a novel multi-level source quality index that enables effective source selection at different granularity levels, and introduces a collection of new techniques to discover and evaluate relevant sources for integration.
Knowledge sharing and yahoo answers: everyone knows something

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.
Customizing Project Management Methodology

Well-structured processes and methods are keys to successful construction and management of any project, however a formal methodology is detrimental when its costs outweigh the benefits, it provides. Moreover projects and teams differ, so one methodology won't fit all of them. Different set of processes are required to manage different projects. This selection of processes for each project is not simple because a lot of factors effect the decision. To address the long standing issue, we conducted a survey to make this part of the project management easier by facilitating the customization of the existing methodologies and knowledge areas
Why do teens spend real money in virtual worlds? A consumption values and developmental psychology perspective on virtual consumption

Monetizing the users base is a major business challenge in social media, online social networking and online entertainment. Many of these services employ the freemium business model i.e. offer a free access to the service and try to generate revenue from selling premium user accounts with exclusive features or selling virtual items. However, the value users create by virtual purchasing is not well understood. Furthermore, young people constitute an important user segment for online social networking services that has not received proportionate attention in the literature. This study draws on Theory of Consumption Values and developmental psychology to elucidate the value teenage users create by spending money in a social virtual world (SVW) that employs the freemium business model in parallel with selling virtual items. The benefits of the premium user account, decoration, status, and boosted enjoyment of the user experience were the most common reasons for teenagers’ purchasing within an SVW. From teenagers’ developmental standpoint, virtual purchasing facilitates identity experiments and membership of peer groups. The results also reveal social hierarchies and discrimination among the users.
InverseRenderNet: Learning single image inverse rendering

We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.
Space-Efficient Online Computation of Quantile Summaries

An ∈-approximate quantile summary of a sequence of <i>N</i> elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈<i>N</i>.
We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of <i>&Ogr;</i>(1÷∈ log(∈<i>N</i>)). This improves upon the previous best result of <i>&Ogr;</i>(1÷∈ log<sup>2</sup>(∈<i>N</i>)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence.
Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.
Internet of Things Business Models

Almost all businesses are aware of the potential gains that the Internet of Things (IoT) has to offer, they are unsure of how to approach it. This article proposes a business model that builds on Holler et al., (2014) . The model consists of three dimensions: “Who, Where, and Why”. “Who” describes collaborating partners, which builds the “Value Network”. “Where” describes sources of value co-creation rooted in the layer model of digitized objects, and “Why” describes how partners benefit from collaborating within the value network. With the intention of addressing “How”, the proposed framework has integrated the IoT strategy category, tactics, and value chain elements. The framework is then validated through the case studies of some successful players who are either the Awardees of the IoT Award 2014 or the ICT Award 2015 of Hong Kong.
Using the Output Embedding to Improve Language Models

We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding. Our methods lead to a significant reduction in perplexity, as we are able to show on a variety of neural network language models. Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance.
Decision-Theoretic User Interface Generation

For decades, researchers have debated the pros and cons of adaptive user interfaces with enthusiastic AI practitioners often confronting skeptical HCI experts (Shneiderman & Maes, 1997). This paper summarizes the SUPPLE project’s six years of work analyzing the characteristics of successful adaptive interfaces and developing efficient algorithms for their automatic generation (Gajos & Weld, 2004; Gajos et al., 2005; Gajos & Weld, 2005; Gajos et al., 2006; Gajos, Wobbrock, & Weld, 2007; Gajos et al., 2008; Gajos, Wobbrock, & Weld, 2008). We conclude that personalized user interfaces, which are adapted to a person’s devices, tasks, preferences and abilities, can improve user satisfaction and performance. Further, we demonstrate that automatic generation of these interfaces is computationally feasible. We developed two systems which autonomously construct a broad range of personalized adaptive interfaces: 1) SUPPLE/ARNAULD uses decision-theoretic optimization to automatically generate user interfaces adapted to target device, usage patterns, and a learned model of user preferences, and 2) SUPPLE++ first performs a one-time assessment of a person’s motor abilities and then automatically generates user interfaces adapted to that user’s abilities. Our experiments show that these automatically generated interfaces significantly improve speed, accuracy and satisfaction of users with motor impairments compared to manufacturers’ defaults. We also provide the first characterization of the design space of adaptive interfaces and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.
Partially overlapped channels not considered harmful

Many wireless channels in different technologies are known to have partial overlap. However, due to the interference effects among such partially overlapped channels, their simultaneous use has typically been avoided. In this paper, we present a first attempt to model partial overlap between channels in a systematic manner. Through the model, we illustrate that the use of partially overlapped channels is not always harmful. In fact, a careful use of some partially overlapped channels can often lead to significant improvements in spectrum utilization and application performance. We demonstrate this through analysis as well as through detailed application-level and MAC-level measurements. Additionally, we illustrate the benefits of our developed model by using it to directly enhance the performance of two previously proposed channel assignment algorithms --- one in the context of wireless LANs and the other in the context of multi-hop wireless mesh networks. Through detailed simulations, we show that use of partially overlapped channels in both these cases can improve end-to-end application throughput by factors between 1.6 and 2.7 in different scenarios, depending on wireless node density. We conclude by observing that the notion of partial overlap can be the right model of flexibility to design efficient channel access mechanisms in the emerging software radio platforms.
Evaluation of Features Detectors and Descriptors based on 3D Objects

We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25–30∘.
Generic decoding of seen and imagined objects using hierarchical visual features

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.
Learning analytics: envisioning a research discipline and a domain of practice

Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.