nils-herrmann/clean_scirepeval huggingface dataset:
A comparison of neustonic plastic and zooplankton abundance in southern California's coastal waters.

The density of neustonic plastic particles was compared to that of zooplankton in the coastal ocean near Long Beach, California. Two trawl surveys were conducted, one after an extended dry period when there was little land-based runoff, the second shortly after a storm when runoff was extensive. On each survey, neuston samples were collected at five sites along a transect parallel to shore using a manta trawl lined with 333 micro mesh. Average plastic density during the study was 8 pieces per cubic meter, though density after the storm was seven times that prior to the storm. The mass of plastics was also higher after the storm, though the storm effect on mass was less than it was for density, reflecting a smaller average size of plastic particles after the storm. The average mass of plastic was two and a half times greater than that of plankton, and even greater after the storm. The spatial pattern of the ratio also differed before and after a storm. Before the storm, greatest plastic to plankton ratios were observed at two stations closest to shore, whereas after the storm these had the lowest ratios.
Learning a General-Purpose Confidence Measure Based on O(1) Features and a Smarter Aggregation Strategy for Semi Global Matching

Inferring dense depth from stereo is crucial for several computer vision applications and Semi Global Matching (SGM) is often the preferred choice due to its good tradeoff between accuracy and computation requirements. Nevertheless, it suffers of two major issues: streaking artifacts caused by the Scanline Optimization (SO) approach, at the core of this algorithm, may lead to inaccurate results and the high memory footprint that may become prohibitive with high resolution images or devices with constrained resources. In this paper, we propose a smart scanline aggregation approach for SGM aimed at dealing with both issues. In particular, the contribution of this paper is threefold: i) leveraging on machine learning, proposes a novel generalpurpose confidence measure suited for any for stereo algorithm, based on O(1) features, that outperforms state of-the-art ii) taking advantage of this confidence measure proposes a smart aggregation strategy for SGM enabling significant improvements with a very small overhead iii) the overall strategy drastically reduces the memory footprint of SGM and, at the same time, improves its effectiveness and execution time. We provide extensive experimental results, including a cross-validation with multiple datasets (KITTI 2012, KITTI 2015 and Middlebury 2014).
Integrated Manufacture of Exoskeletons and Sensing Structures for Folded Millirobots

Inspired by the exoskeletons of insects, we have developed a number of manufacturing methods for the fabrication of structures for attachment, protection, and sensing. This manufacturing paradigm is based on infrared laser machining of lamina and the bonding of layered structures. The structures have been integrated with an inexpensive palm-sized legged robot, the VelociRoACH [Haldane et al., 2013, “Animal-Inspired Design and Aerodynamic Stabilization of a Hexapedal Millirobot,”  May 6–10, pp. 3279–3286]. We also present a methodology to design and fabricate folded robotic mechanisms, and have released an open-source robot, the OpenRoACH, as an example implementation of these techniques. We present new composite materials which enable the fabrication of stronger, larger scale smart composite microstructures (SCM) robots. We demonstrate how thermoforming can be used to manufacture protective structures resistant to water and capable of withstanding terminal velocity falls. A simple way to manufacture traction enhancing claws is demonstrated. An electronics layer can be incorporated into the robot structure, enabling the integration of distributed sensing. We present fabrication methods for binary and analog force sensing arrays, as well as a carbon nanotube (CNT) based strain sensor which can be fabricated in place. The presented manufacturing methods take advantage of low-cost, high accuracy two-dimensional fabrication processes which will enable low-cost mass production of robots integrated with mechanical linkages, an exoskeleton, and body and limb sensing.
Unleashing Use-Before-Initialization Vulnerabilities in the Linux Kernel Using Targeted Stack Spraying

A common type of memory error in the Linux kernel is using uninitialized variables (uninitialized use). Uninitialized uses not only cause undefined behaviors but also impose a severe security risk if an attacker takes control of the uninitialized variables. However, reliably exploiting uninitialized uses on the kernel stack has been considered infeasible until now since the code executed prior to triggering the vulnerability must leave an attacker-controlled pattern on the stack. Therefore, uninitialized uses are largely overlooked and regarded as undefined behaviors, rather than security vulnerabilities. In particular, full memorysafety techniques (e.g., SoftBound+CETS) exclude uninitialized use as a prevention target, and widely used systems such as OpenSSL even use uninitialized memory as a randomness source. In this paper, we propose a fully automated targeted stackspraying approach for the Linux kernel that reliably facilitates the exploitation of uninitialized uses. Our targeted stack-spraying includes two techniques: (1) a deterministic stack spraying technique that suitably combines tailored symbolic execution and guided fuzzing to identify kernel inputs that user-mode programs can use to deterministically guide kernel code paths and thereby leave attacker-controlled data on the kernel stack, and (2) an exhaustive memory spraying technique that uses memory occupation and pollution to reliably control a large region of the kernel stack. We show that our targeted stackspraying approach allows attackers to reliably control more than 91% of the Linux kernel stack, which, in combination with uninitialized-use vulnerabilities, suffices for a privilege escalation attack. As a countermeasure, we propose a compiler-based mechanism that initializes potentially unsafe pointer-type fields with almost no performance overhead. Our results show that uninitialized use is a severe attack vector that can be readily exploited with targeted stack-spraying, so future memory-safety techniques should consider it a prevention target, and systems should not use uninitialized memory as a randomness source.
Perceptions of conflict and support in romantic relationships: the role of attachment anxiety.

Guided by attachment theory, a 2-part study was conducted to test how perceptions of relationship-based conflict and support are associated with relationship satisfaction/closeness and future quality. Dating partners completed diaries for 14 days (Part 1) and then were videotaped while discussing a major problem that occurred during the diary study (Part 2). Part 1 reveals that more anxiously attached individuals perceived more conflict with their dating partners and reported a tendency for conflicts to escalate in severity. Perceptions of daily relationship-based conflicts negatively impacted the perceived satisfaction/closeness and relationship futures of highly anxious individuals, whereas perceptions of greater daily support had positive effects. Part 2 reveals that highly anxious individuals appeared more distressed and escalated the severity of conflicts (rated by observers) and reported feeling more distressed. The authors discuss the unique features of attachment anxiety and how changing perceptions of relationship satisfaction/closeness and stability could erode commitment over time.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.
Inertial Odometry on Handheld Smartphones

Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in realtime and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.
Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems

In this paper, we propose a feasible beamforming (BF) scheme realized in media access control (MAC) layer following the guidelines of the IEEE 802.15.3c criteria for millimeterwave 60GHz wireless personal area networks (60GHz WPANs). The proposed BF targets to minimize the BF set-up time and mitigates the high path loss of 60GHz WPAN systems. It is based on designed multi-resolution codebooks, which generate three kinds of patterns of different half power beam widths (HPBWs): quasi-omni pattern, sector and beam. These three kinds of patterns are employed in the three stages of the BF protocol, namely device-to-device (DEV-to-DEV) linking, sectorlevel searching and beam-level searching. All the three stages can be completed within one superframe, which minimizes the potential interference to other systems during BF set-up period. In this paper, we show some example codebooks and provide the details of BF procedure. Simulation results show that the setup time of the proposed BF protocol is as small as 2% when compared to the exhaustive searching protocol. The proposed BF is a complete design, it re-uses commands specified in IEEE 802.15.3c, completely compliant to the standard; It has thus been adopted by IEEE 802.15.3c as an optional functionality to realize Giga-bit-per-second (Gbps) communication in WPAN Systems.
Integrated Circuit Testing for Quality Assurance in Manufacturing : History , Current Status , and Future Trends

Integrated circuit (IC) testing for quality assurance is approaching 50% of the manufacturing costs for some complex mixed-signal IC’s. For many years the market growth and technology advancements in digital IC’s were driving the developments in testing. The increasing trend to integrate information acquisition and digital processing on the same chip has spawned increasing attention to the test needs of mixed-signal IC’s. The recent advances in wireless communications indicate a trend toward the integration of the RF and baseband mixed signal technologies. In this paper we examine the developments in IC testing form the historic, current status and future view points. In separate sections we address the testing developments for digital, mixed signal and RF IC’s. With these reviews as context, we relate new test paradigms that have the potential to fundamentally alter the methods used to test mixed-signal and RF parts.
Software product lines: practices and patterns [Book Review]

F or consumers, product lines mean presenting them with a managed range of products. All the products are variants tailored to meet specific market requirements but share certain parts and characteristics. For example, some products seem diverse: a veggie cheeseburger sandwich and a bacon and egg bap. They might be dressed differently, but they derive from the burger chain’s product line. They share components, cooking methods, and assembly processes. Software product lines attempt to use this model to develop a group of software products. Barry Boehm’s foreword reminds us that this is code reuse. Reuse theory has been published in various ways, but this book shows how real business options have resulted from reuse in different development domains. Good case studies are rare because the companies involved are normally too busy trying to stay ahead of the competition. However, the SEI team worked with or drew experience from major names in many technology markets.
Challenges of Connecting Edge and Cloud Computing: A Security and Forensic Perspective

A key benefit of connecting edge and cloud computing is the capability to achieve high-throughput under high concurrent accesses, mobility support, real-time processing guarantees, and data persistency. For example, the elastic provisioning and storage capabilities provided by cloud computing allow us to cope with scalability, persistency and reliability requirements and to adapt the infrastructure capacity to the exacting needs based on the amount of generated data.
Discriminative Corpus Weight Estimation for Machine Translation

Current statistical machine translation (SMT) systems are trained on sentencealigned and word-aligned parallel text collected from various sources. Translation model parameters are estimated from the word alignments, and the quality of the translations on a given test set depends on the parameter estimates. There are at least two factors affecting the parameter estimation: domain match and training data quality. This paper describes a novel approach for automatically detecting and down-weighing certain parts of the training corpus by assigning a weight to each sentence in the training bitext so as to optimize a discriminative objective function on a designated tuning set. This way, the proposed method can limit the negative effects of low quality training data, and can adapt the translation model to the domain of interest. It is shown that such discriminative corpus weights can provide significant improvements in Arabic-English translation on various conditions, using a state-of-the-art SMT system.
GEOMETRIC RANGE SEARCH ON ENCRYPTED SPATIAL DATA

Geometric range search is a fundamental primitive for spatial data analysis in SQL and NoSQL databases.It has extensive applications in location-based services, computer aided design, and computational geometry. Due to the dramatic increase in data size, it is necessary for companies and organizations to outsource their spatial data sets to third-party cloud services (e.g., Amazon) in order to reduce storage and query processing costs, but, meanwhile, with the promise of no privacy leakage to the third party. Searchable encryption is a technique to perform meaningful queries on encrypted data without revealing privacy. However, geometric range search on spatial data has not been fully investigated nor supported by existing searchable encryption schemes. In this paper, we design a symmetric-key searchable encryption scheme that can support geometric range queries on encrypted spatial data. One of our major contributions is that our design is a general approach, which can support different types of geometric range queries. In other words, our design on encrypted data is independent from the shapes of geometric range queries. Moreover, we further extend our scheme with the additional use of tree structures to achieve search complexity that is faster than linear. We formally define and prove the security of our scheme with indistinguishability under selective chosen-plaintext attacks, and demonstrate the performance of our scheme with experiments in a real cloud platform (Amazon EC2).
M-SIFT: A new method for Vehicle Logo Recognition

In this paper, a new algorithm for Vehicle Logo Recognition is proposed, on the basis of an enhanced Scale Invariant Feature Transform (Merge-SIFT or M-SIFT). The algorithm is assessed on a set of 1500 logo images that belong to 10 distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1500 images to a training set (database) and to a testing set (query). It is shown that the MSIFT approach, which is proposed in this paper, boosts the recognition accuracy compared to the standard SIFT method. The reported results indicate an average of 94.6% true recognition rate in vehicle logos, while the processing time remains low (~0.8sec).
Detecting phishing e-mails using text and data mining

This paper presents text and data mining in tandem to detect the phishing email. The study employs Multilayer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), Group Method of Data Handling (GMDH), Probabilistic Neural Net (PNN), Genetic Programming (GP) and Logistic Regression (LR) for classification. A dataset of 2500 phishing and non phishing emails is analyzed after extracting 23 keywords from the email bodies using text mining from the original dataset. Further, we selected 12 most important features using t-statistic based feature selection. Here, we did not find statistically significant difference in sensitivity as indicated by t-test at 1% level of significance, both with and without feature selection across all techniques except PNN. Since, the GP and DT are not statistically significantly different either with or without feature selection at 1% level of significance, DT should be preferred because it yields ‘if-then’ rules, thereby increasing the comprehensibility of the system.
Algorithms for packet classification

The process of categorizing packets into “flows” in an Internet router is called packet classification. All packets belonging to the same flow obey a pre-defined rule and are processed in a similar manner by the router. For example, all packets with the same source and destination IP addresses may be defined to form a flow. Packet classification is needed for non “best-effort” services, such as firewalls and quality of service; services that require the capability to distinguish and isolate traffic in different flows for suitable processing. In general, packet classification on multiple fields is a difficult problem. Hence, researchers have proposed a variety of algorithms which, broadly speaking, can be categorized as “basic search algorithms,” geometric algorithms, heuristic algorithms, or hardware-specific search algorithms. In this tutorial we describe algorithms that are representative of each category, and discuss which type of algorithm might be suitable for different applications.
Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints

This paper studies the use of received signal strength indicators (RSSI) applied to fingerprinting method in a Bluetooth network for indoor positioning. A Bayesian fusion (BF) method is proposed to combine the statistical information from the RSSI measurements and the prior information from a motion model. Indoor field tests are carried out to verify the effectiveness of the method. Test results show that the proposed BF algorithm achieves a horizontal positioning accuracy of about 4.7 m on the average, which is about 6 and 7 % improvement when compared with Bayesian static estimation and a point Kalman filter method, respectively.
Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies

For years, ontologies have been known in computer science as consensual models of domains of discourse, usually implemented as formal definitions of the relevant conceptual entities. Researchers have written much about the potential benefits of using them, and most of us regard ontologies as central building blocks of the semantic Web and other semantic systems. Unfortunately, the number and quality of actual, "non-toy" ontologies available on the Web today is remarkably low. This implies that the semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the semantic Web a reality. Theoretically minded advocates often assume that the lack of ontologies is because the "stupid business people haven't realized ontologies' enormous benefits." As a liberal market economist, the author assumes that humans can generally figure out what's best for their well-being, at least in the long run, and that they act accordingly. In other words, the fact that people haven't yet created as many useful ontologies as the ontology research community would like might indicate either unresolved technical limitations or the existence of sound rationales for why individuals refrain from building them - or both. Indeed, several social and technical difficulties exist that put a brake on developing and eventually constrain the space of possible ontologies
Beyond crowd judgments: Data-driven estimation of market value in association football

Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent, crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd’s practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics facilitates precise, objective, and reliable estimates of market value that can be updated at any time.