nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-wideband, short-pulse ground-penetrating radar: simulation and measurement

Ultra-wideband (UWB), short-pulse (SP) radar is investigated theoretically and experimentally for the detection and identification of targets buried in and placed atop soil. The calculations are performed using a rigorous, three-dimensional (3-D) Method of Moments algorithm for perfectly conducting bodies of revolution. Particular targets investigated theoretically include anti-personnel mines, anti-tank mines, and a 55-gallon drum, for which we model the time-domain scattered fields and the buried-target late-time resonant frequencies. With regard to the latter, the computed resonant frequencies are utilized to assess the feasibility of resonance-based buried-target identification for this class of targets. The measurements are performed using a novel UWB, SP synthetic aperture radar (SAR) implemented on a mobile boom. Experimental and theoretical results are compared.
Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models

Yahoo’s native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is Offset a feature enhanced collaborative-filtering (CF) based event prediction algorithm. Offset is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, Offset includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal. In this work we present an online hyper-parameters tuning algorithm, which takes advantage of the system parallel map-reduce based architecture, and strives to adapt the hyper-parameters set to provide the best performance at a specific time interval. Online evaluation via bucket testing of the tuning algorithm showed a significant 4.3% revenue lift overall traffic, and a staggering 8.3% lift over Yahoo HomePage section traffic. Since then, the tuning algorithm was pushed into production, tuning both clickand conversionprediction models, and is generating a hefty estimated revenue lift of 5% yearly for Yahoo Gemini native. The proposed tuning mechanism can be easily generalized to fit any learning algorithm that continuously learns on incoming streaming data, in order to adapt its hyper-parameters to temporal changes.
Strengthening of Flat Slabs Against Punching Shear Using Post-Installed Shear Reinforcement

A significant number of existing flat slabs currently require strengthening against punching shear for safety reasons (the increase of applied loads and deficiencies during design or construction) or to comply with more stringent code requirements. Available strengthening methods are, however, not completely satisfactory, or they cannot be applied in many cases (depending on the possibilities to enlarge column sizes or to intervene on the upper face of slabs). In this paper, an innovative system overcoming most of the previous difficulties is presented. It consists of inclined shear reinforcement installed within existing slabs by drilling holes only from the soffit of the slab and by bonding it with high-performance epoxy adhesive. The results of a test program on 12 full-size slabs 3.0 x 3.0 x 0.25 m (118 x 118 x 9.8 in.) show that such reinforcement is an efficient way to increase both the strength and deformation capacity of flat slabs. Finally, the design of the reinforcement based on the critical shear-crack theory (CSCT) is presented.  The collapse of flat slabs without transverse reinforcement by punching shear has shown the danger of this failure mode. 1,2 Punching develops in the slab around the column in a very brittle way and is followed by a sudden drop in the load-carrying capacity of the slab. This behavior leads to limited warning signs, increases the sensitivity of these structures to imposed deformations, allows very limited redistribution of internal forces, and propagates through the slab, eventually leading to the total collapse of the structure.
Optimizing the Hadoop MapReduce Framework with high-performance storage devices

Solid-state drives (SSDs) are an attractive alternative to hard disk drives (HDDs) to accelerate the Hadoop MapReduce Framework. However, the SSD characteristics and today’s Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. This paper explores how to optimize a Hadoop MapReduce Framework with SSDs in terms of performance, cost, and energy consumption. It identifies extensible best practices that can exploit SSD benefits within Hadoop when combined with high network bandwidth and increased parallel storage access. Our Terasort benchmark results demonstrate that Hadoop currently does not sufficiently exploit SSD throughput. Hence, using faster SSDs in Hadoop does not enhance its performance. We show that SSDs presently deliver significant efficiency when storing intermediate Hadoop data, leaving HDDs for Hadoop Distributed File System (HDFS). The proposed configuration is optimized with the JVM reuse option and frequent heartbeat interval option. Moreover, we examined the performance of a state-of-the-art non-volatile memory express interface SSD within the Hadoop MapReduce Framework. While HDFS read and write throughput increases with high-performance SSDs, achieving complete system performance improvement requires carefully balancing CPU, network, and storage resource capabilities at a system level.
Multiple Handshakes Security of TLS 1.3 Candidates

The Transport Layer Security (TLS) protocol is by far the most widely deployed protocol for securing communications and the Internet Engineering Task Force (IETF) is currently developing TLS 1.3 as the next-generation TLS protocol. The TLS standard features multiple modes of handshake protocols and supports many combinational running of successive TLS handshakes over multiple connections. Although each handshake mode is now well-understood in isolation, their composition in TLS 1.2 remains problematic, and yet it is critical to obtain practical security guarantees for TLS. In this paper, we present the first formal treatment of multiple handshakes protocols of TLS 1.3 candidates. First, we introduce a multi-level&stage security model, an adaptation of the Bellare-Rogaway authenticated key exchange model, covering all kinds of compositional interactions between different TLS handshake modes and providing reasonably strong security guarantees. Next, we prove that candidate handshakes of TLS 1.3 draft meet our strong notion of multiple handshakes security. Our results confirm the soundness of TLS 1.3 security protection design. Such a multi-level&stage approach is convenient for analyzing the compositional design of the candidates with different session modes, as they establish dependencies of multiple sessions. We also identify the triple handshake attack of Bhargavan et al. on TLS 1.2 within our multiple handshakes security model. We show generically that the proposed fixes (RFC 7627) for TLS 1.2 offer good protection against multiple handshakes attacks.
An adaptive contour algorithm for irregular shape extended target

In view of the extended target tracking with irregular shape, this paper proposes an adaptive contour algorithm based on star-convex Random Hypersurface Model. First, considering the unknown priori shape target with irregular extent, the adaptive contour method is proposed based on center contour distance. Then, on the basis of contour extraction method, we put forward a shape change detection method to cope with a sudden shape change in the process of target evolution. Simulations indicate that the adaptive algorithm can improve the estimation performance of target shape.
Research on the Matthews Correlation Coefficients Metrics of Personalized Recommendation Algorithm Evaluation

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm.
Design, kinematics and dynamics modeling of a lower-limb walking assistant robot

This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.
Security Analysis of Wearable Fitness Devices ( Fitbit )

This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per-minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end-to-end basis from the device to the Fitbit web service.
Approximation Algorithms for Some Postman Problems

Approxtmatton algorithms for several NP-complete edge-covermg routing problems are presented and analyzed m terms of the worst-case ratio of the cost of the obtained solutmn to the cost of the opt imum solutton A worst-case bound of 2 is proved for the mixed postman algortthm of Edmonds and Johnson, and a related algorithm for the mixed postman problem is shown also to have a worst-case bound of 2 A mixed strategy approach ts used to obtain a bound of ~ for the mixed postman problem A second mixed strategy algorithm, for the mtxed postman on a planar graph, ~s shown to have a worst-case bound of
Number : AUTCON-D16-00058 R 1 Title : BIM for Infrastructure : An Overall Review and Constructor Perspective

6 The subject of Building Information Modelling (BIM) has become a central topic to the improvement of the AECOO 7 (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the 8 concept is being expanded into domains it was not originally conceived to address. Transitioning BIM into the 9 domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. 10 Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its 11 use from the constructor perspective to review and analyse the current industry positioning and research state of 12 the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. From 13 the analysis, the related research gaps were identified regarding information integration, alignment of BIM 14 processes to constructor business processes & the effective governance and value of information. From this a 15 unique research strategy utilising a framework for information governance coupled with a graph based distributed 16 data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects.
A Wireless Sensor Network-Based Ubiquitous Paprika Growth Management System

Wireless Sensor Network (WSN) technology can facilitate advances in productivity, safety and human quality of life through its applications in various industries. In particular, the application of WSN technology to the agricultural area, which is labor-intensive compared to other industries, and in addition is typically lacking in IT technology applications, adds value and can increase the agricultural productivity. This study attempts to establish a ubiquitous agricultural environment and improve the productivity of farms that grow paprika by suggesting a 'Ubiquitous Paprika Greenhouse Management System' using WSN technology. The proposed system can collect and monitor information related to the growth environment of crops outside and inside paprika greenhouses by installing WSN sensors and monitoring images captured by CCTV cameras. In addition, the system provides a paprika greenhouse environment control facility for manual and automatic control from a distance, improves the convenience and productivity of users, and facilitates an optimized environment to grow paprika based on the growth environment data acquired by operating the system.
On the Theory of Weak Supervision for Information Retrieval

Neural network approaches have recently shown to be effective in several information retrieval (IR) tasks. However, neural approaches often require large volumes of training data to perform effectively, which is not always available. To mitigate the shortage of labeled data, training neural IR models with weak supervision has been recently proposed and received considerable attention in the literature. In weak supervision, an existing model automatically generates labels for a large set of unlabeled data, and a machine learning model is further trained on the generated "weak" data. Surprisingly, it has been shown in prior art that the trained neural model can outperform the weak labeler by a significant margin. Although these obtained improvements have been intuitively justified in previous work, the literature still lacks theoretical justification for the observed empirical findings. In this paper, we provide a theoretical insight into weak supervision for information retrieval, focusing on learning to rank. We model the weak supervision signal as a noisy channel that introduces noise to the correct ranking. Based on the risk minimization framework, we prove that given some sufficient constraints on the loss function, weak supervision is equivalent to supervised learning under uniform noise. We also find an upper bound for the empirical risk of weak supervision in case of non-uniform noise. Following the recent work on using multiple weak supervision signals to learn more accurate models, we find an information theoretic lower bound on the number of weak supervision signals required to guarantee an upper bound for the pairwise error probability. We empirically verify a set of presented theoretical findings, using synthetic and real weak supervision data.
Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task

Making no claim of being exhaustive, a review of the most popular MFCC (Mel Frequency Cepstral Coefficients) implementations is made. These differ mainly in the particular approximation of the nonlinear pitch perception of human, the filter bank design, and the compression of the filter bank output. Then, a comparative evaluation of the presented implementations is performed on the task of text-independent speaker verification, by means of the well-known 2001 NIST SRE (speaker recognition evaluation) one-speaker detection database.
The influence of geometrical deviations of electrical machine systems on the signal quality of the variable reluctance resolver

Variable reluctance resolvers are needed to regulate electrical machines with inverter control. Due to several issues, e.g. discrete windings in the stator and geometric tolerances in the assembly of the electrical machine, the rotors angular position cannot be acquired properly. The subject of this paper is a variable reluctance resolver model to investigate manufacturing induced geometrical tolerances of the electrical machine in means of rotor position. For this purpose, a block diagram of a stator segment is built, connecting 24 segments to one stator core using PLECS. The rotor can be set freely in shape and position. By varying a permeance block the rotation, i.e. the position dependent airgap, is mirrored. The proposed model allows a sensitivity analysis of the sensor signal according to geometrical changes and the absolute position in the electrical machine system. To evaluate the investigations, the results are verified by measurements on a sensor test bench. The simulation results show a similar behavior as the measurement. Although the values differ by a factor of five, this absolute difference is discussed by means of neglected boundary conditions.
LEARNING FROM DELAYED REWARDS USING INFLUENCE VALUES APPLIED TO COORDINATION IN MULTI-AGENT SYSTEMS

In this work we propose a new paradigm for learning coordination in multi-agent systems. This approach is based on social interaction of people, specially in the fact that people communicate to each other what they think about their actions and this opinion has some influence in the behavior of each other. We propose a model in which multi-agents learn to coordinate their actions giving opinions about the actions of other agents and also being influenced with opinions of other agents about their actions. We use the proposed paradigm to develop a modified version of the Q-learning algorithm. The new algorithm is tested and compared with independent learning (IL) and joint action learning (JAL) in a grid problem with two agents learning to coordinate. Our approach shows to have more probability to converge to an optimal equilibrium than IL and JAL Q-learning algorithms, specially when exploration increases. Also, a nice property of our algorithm is that it does not need to make an entire model of all joint actions like JAL algorithms.  Influence Value, Reinforcement Learning, Multi-agent coordination.
Social media interventions for diet and exercise behaviours: a systematic review and meta-analysis of randomised controlled trials

To conduct a systematic review of randomised controlled trials (RCTs) examining the use of social media to promote healthy diet and exercise in the general population.



MEDLINE, CENTRAL, ERIC, PubMed, CINAHL, Academic Search Complete, Alt Health Watch, Health Source, Communication and Mass Media Complete, Web of Knowledge and ProQuest Dissertation and Thesis (2000-2013).



RCTs of social media interventions promoting healthy diet and exercise behaviours in the general population were eligible. Interventions using social media, alone or as part of a complex intervention, were included.



Study quality was assessed using the Cochrane Risk of Bias Tool. We describe the studies according to the target populations, objectives and nature of interventions, outcomes examined, and results and conclusions. We extracted data on the primary and secondary outcomes examined in each study. Where the same outcome was assessed in at least three studies, we combined data in a meta-analysis.



22 studies were included. Participants were typically middle-aged Caucasian women of mid-to-high socioeconomic status. There were a variety of interventions, comparison groups and outcomes. All studies showed a decrease in programme usage throughout the intervention period. Overall, no significant differences were found for primary outcomes which varied across studies. Meta-analysis showed no significant differences in changes in physical activity (standardised mean difference (SMD) 0.13 (95% CI -0.04 to 0.30), 12 studies) and weight (SMD -0.00 (95% CI -0.19 to 0.19), 10 studies); however, pooled results from five studies showed a significant decrease in dietary fat consumption with social media (SMD -0.35 (95% CI -0.68 to -0.02)).



Social media may provide certain advantages for public health interventions; however, studies of social media interventions to date relating to healthy lifestyles tend to show low levels of participation and do not show significant differences between groups in key outcomes.
An Expressive (Zero-Knowledge) Set Accumulator

We present a new construction of an expressive set accumulator. Unlike existing cryptographic accumulators, ours provides succinct proofs for a large collection of operations over accumulated sets, including intersection, union, set difference, SUM, COUNT, MIN, MAX, and RANGE, as well as arbitrary nestings of the above. We also show how to extend our accumulator to be zero-knowledge. The security of our accumulator is based on extractability assumptions and other assumptions that hold in the generic group model. Our construction has asymptotically optimal verification complexity and proof size, constant update complexity, and public verifiability/updatability—namely, any client who knows the public key and the last accumulator value can verify the supported operations and update the accumulator. The expressiveness of our accumulator comes at the cost of quadratic prover time. However, we show that the cryptographic operations involved are cheap compared to those incurred by generic approaches (e.g., SNARKs) that are equally expressive: our prover runs faster for sets of up to 5 million items. Our accumulator serves as a powerful cryptographic tool with many applications. For example, it can be applied to efficiently support verification of a rich collection of SQL queries when used as a drop-in replacement in existing verifiable database systems (e.g., IntegriDB, CCS 2015).