nils-herrmann/clean_scirepeval huggingface dataset:
28 GHz propagation measurements for outdoor cellular communications using steerable beam antennas in New York city

The millimeter wave frequency spectrum offers unprecedented bandwidths for future broadband cellular networks. This paper presents the world's first empirical measurements for 28 GHz outdoor cellular propagation in New York City. Measurements were made in Manhattan for three different base station locations and 75 receiver locations over distances up to 500 meters. A 400 megachip-per-second channel sounder and directional horn antennas were used to measure propagation characteristics for future mm-wave cellular systems in urban environments. This paper presents measured path loss as a function of the transmitter - receiver separation distance, the angular distribution of received power using directional 24.5 dBi antennas, and power delay profiles observed in New York City. The measured data show that a large number of resolvable multipath components exist in both non line of sight and line of sight environments, with observed multipath excess delay spreads (20 dB) as great as 1388.4 ns and 753.5 ns, respectively. The widely diverse spatial channels observed at any particular location suggest that millimeter wave mobile communication systems with electrically steerable antennas could exploit resolvable multipath components to create viable links for cell sizes on the order of 200 m.
Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography.

We give a new method for direct reconstruction of three-dimensional objects from a few electron micrographs taken at angles which need not exceed a range of 60 degrees. The method works for totally asymmetric objects, and requires little computer time or storage. It is also applicable to X-ray photography, and may greatly reduce the exposure compared to current methods of body-section radiography.
The stability of centrality measures when networks are sampled

The ability to measure centrality in social networks has been a particularly useful development in social network analysis. For researchers trying to decide which centrality measure is most meaningful and valid for their research purposes, various papers have explored the conceptual foundations of centrality measures. Less well documented is the empirical performance of centrality measures under different research scenarios or constraints. This study uses bootstrap sampling procedures to determine how sampling affects the stability of 11 different network centrality measures. Results indicate that some measures are more stable than others, and that stability is also a function of network and study properties.
On the burstiness of visual elements

Burstiness, a phenomenon initially observed in text retrieval, is the property that a given visual element appears more times in an image than a statistically independent model would predict. In the context of image search, burstiness corrupts the visual similarity measure, i.e., the scores used to rank the images. In this paper, we propose a strategy to handle visual bursts for bag-of-features based image search systems. Experimental results on three reference datasets show that our method significantly and consistently outperforms the state of the art.
A method of error correction for digital class D power amplifier

Because of the nonlinearity of digital PWM generator and the effect of power supply noise in power stage, the error is introduced into digital class D power amplifier. A method used to eliminate the error is presented in this paper, and it is easy to implement. Based on this method, a digital class D power amplifier is designed and simulated, the simulation results indicate this method can basically eliminate the error produced by digital PWM generator and power stage, and improve the performance of the system.
Bid landscape forecasting in online ad exchange marketplace

Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One important business model in online display advertising is Ad Exchange marketplace, also called non-guaranteed delivery (NGD), in which advertisers buy targeted page views and audiences on a spot market through real-time auction. In this paper, we describe a bid landscape forecasting system in NGD marketplace for any advertiser campaign specified by a variety of targeting attributes. In the system, the impressions that satisfy the campaign targeting attributes are partitioned into multiple mutually exclusive samples. Each sample is one unique combination of quantified attribute values. We develop a divide-and-conquer approach that breaks down the campaign-level forecasting problem. First, utilizing a novel star-tree data structure, we forecast the bid for each sample using non-linear regression by gradient boosting decision trees. Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions. The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.
Scalable sequential pattern mining for biological sequences

Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., "don't care") of arbitrary size. Mining frequent patterns in such sequences faces a different type of explosion than in transaction sequences primarily motivated in market-basket analysis. In this paper, we study how this explosion affects the classic sequential pattern mining, and present a scalable two-phase algorithm to deal with this new explosion. The <i>Segment Phase</i> first searches for short patterns containing no gaps, called <i>segments</i>. This phase is efficient. The <i>Pattern Phase</i> searches for long patterns containing multiple segments separated by variable length gaps. This phase is time consuming. The purpose of two phases is to exploit the information obtained from the first phase to speed up the pattern growth and matching and to prune the search space in the second phase. We evaluate this approach on synthetic and real life data sets.
Information-theoretic co-clustering

Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory---the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters. We present an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages. Using the practical example of simultaneous word-document clustering, we demonstrate that our algorithm works well in practice, especially in the presence of sparsity and high-dimensionality.
Surface street traffic estimation

In this paper, we propose a simple yet effective method of identifying traffic conditions on surface streets given location traces collected from on-road vehicles---this requires only GPS location data, plus infrequent low-bandwidth cellular updates. Unlike other systems, which simply display vehicle speeds on the road, our system characterizes unique traffic patterns on each road segment and identifies unusual traffic states on a segment-by-segment basis. We developed and evaluated the system by applying it to two sets of location traces. Evaluation results show that higher than 90% accuracy in characterization can be achieved after ten or more traversals are collected on a given road segment. We also show that traffic patterns on a road are very consistent over time, provided that the underlying road conditions do not change. This allows us to use a longer history in identifying traffic conditions with higher accuracy.
PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs

Given a graph with node attributes, how can we find meaningful patterns such as clusters, bridges, and outliers? Attributed graphs appear in real world in the form of social networks with user interests, gene interaction networks with gene expression information, phone call networks with customer demographics, and many others. In effect, we want to group the nodes into clusters with similar connectivity and homogeneous attributes. Most existing graph clustering algorithms either consider only the connectivity structure of the graph and ignore the node attributes, or require several user-defined parameters such as the number of clusters. We propose PICS, a novel, parameter-free method for mining attributed graphs. Two key advantages of our method are that (1) it requires no user-specified parameters such as the number of clusters and similarity functions, and (2) its running time scales linearly with total graph and attribute size. Our experiments show that PICS reveals meaningful and insightful patterns and outliers in both synthetic and real datasets, including call networks, political books, political blogs, and collections from Twitter and YouTube which have more than 70K nodes and 30K attributes.
Cybercrime: Understanding and addressing the concerns of stakeholders

Cybercrime and cybercriminal activities continue to impact communities as the steady growth of electronic information systems enables more online business. The collective views of sixty-six computer users and organizations, that have an exposure to cybercrime, were analyzed using concept analysis and mapping techniques in order to identify the major issues and areas of concern, and provide useful advice. The findings of the study show that a range of computing stakeholders have genuine concerns about the frequency of information security breaches and malware incursions (including the emergence of dangerous security and detection avoiding malware), the need for e-security awareness and education, the roles played by law and law enforcement, and the installation of current security software and systems. While not necessarily criminal in nature, some stakeholders also expressed deep concerns over the use of computers for cyberbullying, particularly where younger and school aged users are involved. The government’s future directions and recommendations for the technical and administrative management of cybercriminal activity were generally observed to be consistent with stakeholder concerns, with some users also taking practical steps to reduce cybercrime risks. a 2011
Leveraging Multi-Domain Prior Knowledge in Topic Models

Topic models have been widely used to identify topics in text corpora. It is also known that purely unsupervised models often result in topics that are not comprehensible in applications. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we go one step further to study how the prior knowledge from other domains can be exploited to help topic modeling in the new domain. This problem setting is important from both the application and the learning perspectives because knowledge is inherently accumulative. We human beings gain knowledge gradually and use the old knowledge to help solve new problems. To achieve this objective, existing models have some major difficulties. In this paper, we propose a novel knowledge-based model, called MDK-LDA, which is capable of using prior knowledge from multiple domains. Our evaluation results will demonstrate its effectiveness.
A Graph-theoretic perspective on centrality

The concept of centrality is often invoked in social network analysis, and diverse indices have been proposed to measure it. This paper develops a unified framework for the measurement of centrality. All measures of centrality assess a node’s involvement in the walk structure of a network. Measures vary along four key dimensions: type of nodal involvement assessed, type of walk considered, property of walk assessed, and choice of summary measure. If we cross-classify measures by type of nodal involvement (radial versus medial) and property of walk assessed (volume versus length), we obtain a four-fold polychotomization with one cell empty which mirrors Freeman’s 1979 categorization. At a more substantive level, measures of centrality summarize a node’s involvement in or contribution to the cohesiveness of the network. Radial measures in particular are reductions of pair-wise proximities/cohesion to attributes of nodes or actors. The usefulness and interpretability of radial measures depend on the fit of the cohesion matrix to the onedimensional model. In network terms, a network that is fit by a one-dimensional model has a core-periphery structure in which all nodes revolve more or less closely around a single core. This in turn implies that the network does not contain distinct cohesive subgroups. Thus, centrality is shown to be intimately connected with the cohesive subgroup structure of a network.
A holistic lexicon-based approach to opinion mining

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
A practical FPGA-based framework for novel CMP research

Chip-multiprocessors are quickly gaining momentum in all segments of computing. However, the practical success of CMPs strongly depends on addressing the difficulty of multithreaded application development. To address this challenge, it is necessary to co-develop new CMP architecture with novel programming models. Currently, architecture research relies on software simulators which are too slow to facilitate interesting experiments with CMP software without using small datasets or significantly reducing the level of detail in the simulated models. An alternative to simulation is to exploit the rich capabilities of modern FPGAs to create FPGA-based platforms for novel CMP research. This paper presents ATLAS, the first prototype for CMPs with hardware support for Transactional Memory (TM), a technology aiming to simplify parallel programming. ATLAS uses the BEE2 multi-FPGA board to provide a system with 8 PowerPC cores that run at 100MHz and runs Linux. ATLAS provides significant benefits for CMP research such as 100x performance improvement over a software simulator and good visibility that helps with software tuning and architectural improvements. In addition to presenting and evaluating ATLAS, we share our observations about building a FPGA-based framework for CMP research. Specifically, we address issues such as overall performance, challenges of mapping ASIC-style CMP RTL on to FPGAs, software support, the selection criteria for the base processor, and the challenges of using pre-designed IP libraries.
Magnetic human body communication

This paper presents a new human body communication (HBC) technique that employs magnetic resonance for data transfer in wireless body-area networks (BANs). Unlike electric field HBC (eHBC) links, which do not necessarily travel well through many biological tissues, the proposed magnetic HBC (mHBC) link easily travels through tissue, offering significantly reduced path loss and, as a result, reduced transceiver power consumption. In this paper the proposed mHBC concept is validated via finite element method simulations and measurements. It is demonstrated that path loss across the body under various postures varies from 10-20 dB, which is significantly lower than alternative BAN techniques.
Cross Processor Cache Attacks

Multi-processor systems are becoming the de-facto standard across different computing domains, ranging from high-end multi-tenant cloud servers to low-power mobile platforms. The denser integration of CPUs creates an opportunity for great economic savings achieved by packing processes of multiple tenants or by bundling all kinds of tasks at various privilege levels to share the same platform. This level of sharing carries with it a serious risk of leaking sensitive information through the shared microarchitectural components. Microarchitectural attacks initially only exploited core-private resources, but were quickly generalized to resources shared within the CPU. We present the first fine grain side channel attack that works across processors. The attack does not require CPU co-location of the attacker and the victim. The novelty of the proposed work is that, for the first time the directory protocol of high efficiency CPU interconnects is targeted. The directory protocol is common to all modern multi-CPU systems. Examples include AMD's HyperTransport, Intel's Quickpath, and ARM's AMBA Coherent Interconnect. The proposed attack does not rely on any specific characteristic of the cache hierarchy, e.g. inclusiveness. Note that inclusiveness was assumed in all earlier works. Furthermore, the viability of the proposed covert channel is demonstrated with two new attacks: by recovering a full AES key in OpenSSL, and a full ElGamal key in libgcrypt within the range of seconds on a shared AMD Opteron server.
Hole-filling based on disparity map for DIBR

Due to sharp depth transition, big holes may be found in the novel view that is synthesized by depth-image-based rendering (DIBR). A hole-filling method based on disparity map is proposed. One important aspect of the method is that the disparity map of destination image is used for hole-filling, instead of the depth image of reference image. Firstly, the big hole detection based on disparity map is conducted, and the start point and the end point of the hole are recorded. Then foreground pixels and background pixels are distinguished for hole-dilating according to disparity map, so that areas with matching errors can be determined and eliminated. In addition, parallaxes of pixels in the area with holes and matching errors are changed to new values. Finally, holes are filled with background pixels from reference image according to these new parallaxes. Experimental results show that the quality of the new view after hole-filling is quite well; and geometric distortions are avoided in destination image, in contrast to the virtual view generated by depth-smoothing methods and image inpainting methods. Moreover, this method is easy for hardware implementation.
MASCOT: Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams

How can we estimate local triangle counts accurately in a graph stream without storing the whole graph? The local triangle counting which counts triangles for each node in a graph is a very important problem with wide applications in social network analysis, anomaly detection, web mining, etc.
 In this paper, we propose MASCOT, a memory-efficient and accurate method for local triangle estimation in a graph stream based on edge sampling. To develop MASCOT, we first present two naive local triangle counting algorithms in a graph stream: MASCOT-C and MASCOT-A. MASCOT-C is based on constant edge sampling, and MASCOT-A improves its accuracy by utilizing more memory spaces. MASCOT achieves both accuracy and memory-efficiency of the two algorithms by an unconditional triangle counting for a new edge, regardless of whether it is sampled or not. In contrast to the existing algorithm which requires prior knowledge on the target graph and appropriately set parameters, MASCOT requires only one simple parameter, the edge sampling probability. Through extensive experiments, we show that for the same number of edges sampled, MASCOT provides the best accuracy compared to the existing algorithm as well as MASCOT-C and MASCOT-A. Thanks to MASCOT, we also discover interesting anomalous patterns in real graphs, like core-peripheries in the web and ambiguous author names in DBLP.
SmartAuth: dynamic context fingerprinting for continuous user authentication

As recent incidents have shown, weak passwords are a severe security risk for authenticating users and granting access to protected resources. Additionally, strong passwords score low on usability, especially on mobile devices. In this work, we present SmartAuth, a scalable context-aware authentication framework built on top of OpenAM, a state-of-practice identity and access management suite. It uses adaptive and dynamic context fingerprinting based on Hoeffding trees to continuously ascertain whether a user's identity is authentic or not, and it respects privacy preferences by adopting consent-driven use of context information. We assess our approach from both an offensive and defensive security perspective. Our results show that dynamic context fingerprinting has good potential for a zero-interaction authentication scheme, with a minimal performance overhead compared to traditional authentication schemes.
Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods

Ranker evaluation is central to the research into search engines, be it to compare rankers or to provide feedback for learning to rank. Traditional evaluation approaches do not scale well because they require explicit relevance judgments of document-query pairs, which are expensive to obtain. A promising alternative is the use of interleaved comparison methods, which compare rankers using click data obtained when interleaving their rankings.
 In this article, we propose a framework for analyzing interleaved comparison methods. An interleaved comparison method has fidelity if the expected outcome of ranker comparisons properly corresponds to the true relevance of the ranked documents. It is sound if its estimates of that expected outcome are unbiased and consistent. It is efficient if those estimates are accurate with only little data.
 We analyze existing interleaved comparison methods and find that, while sound, none meet our criteria for fidelity. We propose a probabilistic interleave method, which is sound and has fidelity. We show empirically that, by marginalizing out variables that are known, it is more efficient than existing interleaved comparison methods. Using importance sampling we derive a sound extension that is able to reuse historical data collected in previous comparisons of other ranker pairs.