nils-herrmann/clean_scirepeval huggingface dataset:
Security Metrics for the Android Ecosystem

The security of Android depends on the timely delivery of updates to fix critical vulnerabilities. In this paper we map the complex network of players in the Android ecosystem who must collaborate to provide updates, and determine that inaction by some manufacturers and network operators means many handsets are vulnerable to critical vulnerabilities. We define the FUM security metric to rank the performance of device manufacturers and network operators, based on their provision of updates and exposure to critical vulnerabilities. Using a corpus of 20 400 devices we show that there is significant variability in the timely delivery of security updates across different device manufacturers and network operators. This provides a comparison point for purchasers and regulators to determine which device manufacturers and network operators provide security updates and which do not. We find that on average 87.7% of Android devices are exposed to at least one of 11 known critical vulnerabilities and, across the ecosystem as a whole, assign a FUM security score of 2.87 out of 10. In our data, Nexus devices do considerably better than average with a score of 5.17; and LG is the best manufacturer with a score of 3.97.
Linear network coding

Consider a communication network in which certain source nodes multicast information to other nodes on the network in the multihop fashion where every node can pass on any of its received data to others. We are interested in how fast eachnode can receive the complete information, or equivalently, what the information rate arriving at eachnode is. Allowing a node to encode its received data before passing it on, the question involves optimization of the multicast mechanisms at the nodes. Among the simplest coding schemes is linear coding, which regards a block of data as a vector over a certain base field and allows a node to apply a linear transformation to a vector before passing it on. We formulate this multicast problem and prove that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.
Recommendations on a Knowledge Graph

Most recommender system methods derive the user preferences from predefined information sources. For example, collaborative filtering is based on user rating values on items. Predefining information sources constrains the quality of the recommendation result by restricting the amount of information the recommendation method can operate on. In this paper we introduce an adaptive rating estimation method, which is capable to incorporate heterogeneous information sources and improves the recommendation quality. To represent heterogeneous information, a graph based knowledge base is introduced. Recommendations are calculated with our novel method, recommendation spreading. Comparing recommendation spreading to collaborative filtering on the MovieLens 1M dataset shows that our method is able to combine heterogeneous information sources to provide higher coverage and the same rating estimation error. Furthermore, recommendation spreading is a potential method to overcome the cold start problem.
Information Research in Leisure: Implications from an Empirical Study of Backpackers

In the modern, increasingly flat world, many individuals have seen an increase in the amount of leisure time they have available. This leisure time is used for different purposes, often including travel and tourism. Among the many types of travel and tourism, backpack or budget travelling is becoming more and more popular. This increasingly common leisure activity presumably involves intensive information search activities. This study places the backpackers’ search for travel information in an everyday life information seeking (ELIS) perspective. The search for information by backpackers can be seen as a three-stage information search process. In each stage, depending on the type of task, backpackers use various information resources for different purposes. Such sources may be used for more than one purpose and in more than one information search stage. However, their relative importance varies depending on the characteristics of the source of information and the information search stage in which the source is being used. In this article I suggest that studies of leisure information behaviors and leisure activities show theoretical and practical value for both the information seeking public and the information science community. We also suggest that library and information science scholars and practice communities direct attention and research resources to leisure research in general, and the concept of serious leisure and its structured information acquisition and sharing activities in particular.  Implications from an Empirical Study of Backpackers
A spatially varying PSF-based prior for alpha matting

In this paper we considerably improve on a state-of-the-art alpha matting approach by incorporating a new prior which is based on the image formation process. In particular, we model the prior probability of an alpha matte as the convolution of a high-resolution binary segmentation with the spatially varying point spread function (PSF) of the camera. Our main contribution is a new and efficient de-convolution approach that recovers the prior model, given an approximate alpha matte. By assuming that the PSF is a kernel with a single peak, we are able to recover the binary segmentation with an MRF-based approach, which exploits flux and a new way of enforcing connectivity. The spatially varying PSF is obtained via a partitioning of the image into regions of similar defocus. Incorporating our new prior model into a state-of-the-art matting technique produces results that outperform all competitors, which we confirm using a publicly available benchmark.
Even good bots fight: The case of Wikipedia

In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile "fights" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively "dumb" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases

Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of “canned” questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates, over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city’s population, we learn templates such as What’s the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.
Aware technologies for aging in place: understanding user needs and attitudes

Although computing technology has made inroads into home environments, it has yet to instigate a major shift in the design of homes or home activities. The convergence of television and the Internet is lagging behind expectations, and the combination of desktop computers, entertainment consoles, televisions, and cell phones has yet to form a cohesive whole. One possible reason for this lag in progress is that these technologies don't address a coherent need -they merely augment current entertainment and communication practices. We base our research on the premise that the next revolution of technology in the home will arise from devices that help older adults maintain their independence. A coherent suite of technologies will eventually let older adults be proactive about their own healthcare, will aid them in daily activities and help them learn new skills, will create new avenues for social communication, and will help ensure their safety and well being. The Aware Home Research initiative hopes to help older adults "age in place" by creating devices that can assist with daily tasks, offer memory support, and monitor daily activities. However, understanding user needs and attitudes is essential to deploying this technology.
Analysis and Verification of Inductor Coupling Effect in Interleaved Multiphase dc–dc Converters

Interleaved multiphase synchronous buck converters are often used to power computer CPU, GPU, and memory to meet the demands for increasing load current and fast current slew rate of the processors. This paper reports and explains undesired coupling between discrete inductors in typical commercial multiphase applications where space is limited. In this paper, equations of coupling coefficient are derived for single-turn and multiturn Ferrite core inductors commonly used in multiphase converters and are verified by Maxwell static simulation. The influence of the coupling effect on inductor current waveforms is demonstrated by Maxwell transient simulation and confirmed by experiments. The analysis provides a useful tool for mitigating the coupling effect in multiphase converters to avoid early inductor saturation and interference between phases. Design guidelines and recommendations are provided to minimize the unwanted coupling effect in multiphase converters.
Accelerated simulation of spiking neural networks using GPUs

Spiking neural network simulators provide environments in which to implement and experiment with models of biological brain structures. Simulating large-scale models is computationally expensive, however, due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present a platform (nemo) for such simulations which achieves high performance on parallel commodity hardware in the form of graphics processing units (GPUs). This work makes use of the Izhikevich neuron model which provides a range of realistic spiking dynamics while being computationally efficient. Learning is facilitated through spike-timing dependent synaptic plasticity. Our GPU kernel can deliver up to 550 million spikes per second using a single device. This corresponds to a real-time simulation of around 55 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.
Chapter 6 HR MetRics and WoRkfoRce analytics

The capacity to manage is limited by the accessible information in our possession. Research on goal setting confirms that being able to articulate the specific goal for a task and the level of the goal we want to achieve enhances performance of that task. Better information about the expectations of customers, the actions of competitors, and the state of the economy provides strong support for the strategic direction of organizations. Information about levels of output, for example, numbers of defects and efficiency of processes, positions line managers to produce high-quality products in the right amounts at the right time to meet customer needs. The same is true for the effective management of human capital in organizations. As discussed in this chapter, effective approaches to the measurement of human capital and the impact of people on organization processes, for example, HR programs such as recruiting, will enable both HRM professionals and line managers to utilize the human capital in organizations effectively. This measurement is accomplished by focusing on the development of systems of workforce analytics and supporting HR metrics that meet the needs of organization decision makers. This chapter offers a brief history of the efforts involved in the development of HR metrics and workforce analytics and of how these efforts have been enhanced by the advent of integrated human resource information systems. 1 From benchmarking to operational experiments, the HRIS field is rapidly evolving on many fronts. These advances are changing how HR metrics and analytics are used in organizations and their impact on organization effectiveness. The use of HR metrics and workforce analytics will help managers and organizations balance the costs and benefits consequences of decisions. These cost-benefit analyses are covered in Chapter 7. • BLOCKINDiscuss the factors that have led to increased organizational interest in HR metrics and workforce analytics • BLOCKINDiscuss why the information from numeric systems like HR metrics and workforce analytics 2 do not generate any return on investment (ROI) unless they lead to different and better decision making • BLOCKINDiscuss the difference between metrics and analytics • BLOCKINDescribe the limitations of the traditional HR metrics • BLOCKINDiscuss the historical role of benchmarking and its strengths and weakness today • BLOCKINDiscuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness • BLOCKINDiscuss the differences between metrics and analytics for HR efficiency, operational effectiveness, and organizational realignment, …
Introduction to Computational Advertising

Web advertising is the primary driving force behind many Web activities, including Internet search as well as publishing of online content by third-party providers. Even though the notion of online advertising barely existed a decade ago, the topic is so complex that it attracts attention of a variety of established scientific disciplines, including computational linguistics, computer science, economics, psychology, and sociology, to name but a few. Consequently, a new discipline — Computational Advertising — has emerged, which studies the process of advertising on the Internet from a variety of angles. A successful advertising campaign should be relevant to the immediate user’s information need as well as more generally to user’s background and personalized interest profile, be economically worthwhile to the advertiser and the intermediaries (e.g., the search engine), as well as be aesthetically pleasant and not detrimental to user experience.
Real time data mining-based intrusion detection

In this paper, we present an overview of our research in real time data mining-based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions; we use artificial anomalies along with normal and/or intrusion data to produce more effective misuse and anomaly detection models. To improve efficiency, the computational costs of features are analyzed and a multiple-model costbased approach is used to produce detection models with low cost and high accuracy. We also present a distributed architecture for evaluating cost-sensitive models in realtime. To improve usability, adaptive learning algorithms are used to facilitate model construction and incremental updates; unsupervised anomaly detection algorithms are used to reduce the reliance on labeled data. We also present an architecture consisting of sensors, detectors, a data warehouse, and model generation components. This architecture facilitates the sharing and storage of audit data and the distribution of new or updated models. This architecture also improves the efficiency and scalability of the IDS.
A Study Using $n$-gram Features for Text Categorization

In this paper, we study the effect of using-grams (sequences of words of length n) for text categorization. We use an efficient algorithm for gener ating suchn-gram features in two benchmark domains, the 20 newsgroups data set and 21,578 REU TERS newswire articles. Our results with the rule learning algorithm R IPPER indicate that, after the removal of stop words, word sequences of length 2 or 3 are most useful. Using l o er sequences reduces classification performance.
FALL DETECTION AND PREVENTION FOR THE ELDERLY: A REVIEW OF TRENDS AND CHALLENGES

It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting falls and triggering notification alarms calling for help as soon as falls occur in order to diminish fall consequences. Furthermore, fall likelihood prediction systems have been emerged lately based on the manipulation of the medical and behavioral history of elderly patients in order to predict the possibility of falls occurrence. Accordingly, response from caregivers may be triggered prior to most fall occurrences and accordingly prevent falls from taking place. This paper presents an extensive review for the state-of-theart trends and technologies of fall detection and prevention systems assisting the elderly people and INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO. 3, JUNE 2013
Relative confidence sampling for efficient on-line ranker evaluation

A key challenge in information retrieval is that of on-line ranker evaluation: determining which one of a finite set of rankers performs the best in expectation on the basis of user clicks on presented document lists. When the presented lists are constructed using interleaved comparison methods, which interleave lists proposed by two different candidate rankers, then the problem of minimizing the total regret accumulated while evaluating the rankers can be formalized as a K-armed dueling bandits problem. In this paper, we propose a new method called relative confidence sampling (RCS) that aims to reduce cumulative regret by being less conservative than existing methods in eliminating rankers from contention. In addition, we present an empirical comparison between RCS and two state-of-the-art methods, relative upper confidence bound and SAVAGE. The results demonstrate that RCS can substantially outperform these alternatives on several large learning to rank datasets.
Depth from shading, defocus, and correspondence using light-field angular coherence

Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light-field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth. Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images.
Maxine: An approachable virtual machine for, and in, java

A highly productive platform accelerates the production of research results. The design of a Virtual Machine (VM) written in the Java#8482; programming language can be simplified through exploitation of interfaces, type and memory safety, automated memory management (garbage collection), exception handling, and reflection. Moreover, modern Java IDEs offer time-saving features such as refactoring, auto-completion, and code navigation. Finally, Java annotations enable compiler extensions for low-level “systems programming” while retaining IDE compatibility. These techniques collectively make complex system software more “approachable” than has been typical in the past.
 The Maxine VM, a metacircular Java VM implementation, has aggressively used these features since its inception. A co-designed companion tool, the Maxine Inspector, offers integrated debugging and visualization of all aspects of the VM's runtime state. The Inspector's implementation exploits advanced Java language features, embodies intimate knowledge of the VM's design, and even reuses a significant amount of VM code directly. These characteristics make Maxine a highly approachable VM research platform and a productive basis for research and teaching.