nils-herrmann/clean_scirepeval huggingface dataset:
Gender , Language , and Influence

Mixedand same-sex dyads were observed to examine effects of gender composition on language and of language on gender differences in influence. Ss discussed a topic on which they disagreed. Women were more tentative than men, but only in mixed-sex dyads. Women who spoke tentatively were more influential with men and less influential with women. Language had no effect on how influential men were. In a second study, 120 Ss listened to an audiotape of identical persuasive messages presented either by a man or a woman, half of whom spoke tentatively. Female speakers who spoke tentatively were more influential with male Ss and less influential with female Ss than those who spoke assertively. Male speakers were equally influential in each condition.
Approximate Radix-8 Booth Multipliers for Low-Power and High-Performance Operation

The Booth multiplier has been widely used for high performance signed multiplication by encoding and thereby reducing the number of partial products. A multiplier using the radix-<inline-formula><tex-math notation="LaTeX">$4$ </tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="han-ieq1-2493547.gif"/></alternatives></inline-formula> (or modified Booth) algorithm is very efficient due to the ease of partial product generation, whereas the radix- <inline-formula><tex-math notation="LaTeX">$8$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="han-ieq2-2493547.gif"/></alternatives></inline-formula> Booth multiplier is slow due to the complexity of generating the odd multiples of the multiplicand. In this paper, this issue is alleviated by the application of approximate designs. An approximate <inline-formula><tex-math notation="LaTeX">$2$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="han-ieq3-2493547.gif"/></alternatives></inline-formula>-bit adder is deliberately designed for calculating the sum of <inline-formula><tex-math notation="LaTeX">$1\times$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="han-ieq4-2493547.gif"/></alternatives></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2\times$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="han-ieq5-2493547.gif"/> </alternatives></inline-formula> of a binary number. This adder requires a small area, a low power and a short critical path delay. Subsequently, the <inline-formula><tex-math notation="LaTeX">$2$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="han-ieq6-2493547.gif"/></alternatives></inline-formula>-bit adder is employed to implement the less significant section of a recoding adder for generating the triple multiplicand with no carry propagation. In the pursuit of a trade-off between accuracy and power consumption, two signed <inline-formula> <tex-math notation="LaTeX">$16\times 16$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="han-ieq7-2493547.gif"/> </alternatives></inline-formula> bit approximate radix-8 Booth multipliers are designed using the approximate recoding adder with and without the truncation of a number of less significant bits in the partial products. The proposed approximate multipliers are faster and more power efficient than the accurate Booth multiplier. The multiplier with 15-bit truncation achieves the best overall performance in terms of hardware and accuracy when compared to other approximate Booth multiplier designs. Finally, the approximate multipliers are applied to the design of a low-pass FIR filter and they show better performance than other approximate Booth multipliers.
Pokémon GO Forensics: An Android Application Analysis

As the geolocation capabilities of smartphones continue to improve, developers have continued to create more innovative applications that rely on this location information for their primary function. This can be seen with Niantic’s release of Pokémon GO, which is a massively multiplayer online role playing and augmented reality game. This game became immensely popular within just a few days of its release. However, it also had the propensity to be a distraction to drivers resulting in numerous accidents, and was used to as a tool by armed robbers to lure unsuspecting users into secluded areas. This facilitates a need for forensic investigators to be able to analyze the data within the application in order to determine if it may have been involved in these incidents. Because this application is new, limited research has been conducted regarding the artifacts that can be recovered from the application. In this paper, we aim to fill the gaps within the current research by assessing what forensically relevant information may be recovered from the application, and understanding the circumstances behind the creation of this information. Our research focuses primarily on the artifacts generated by the Upsight analytics platform, those contained within the bundles directory, and the Pokémon Go Plus accessory. Moreover, we present our new application specific analysis tool that is capable of extracting forensic artifacts from a backup of the Android application, and presenting them to an investigator in an easily readable format. This analysis tool exceeds the capabilities of UFED Physical Analyzer in processing Pokémon GO application data.
JPE 11-4-3 Control and Analysis of an Integrated Bidirectional DC / AC and DC / DC Converters for Plug-In Hybrid Electric Vehicle Applications

The plug-in hybrid electric vehicles (PHEVs) are specialized hybrid electric vehicles that have the potential to obtain enough energy for average daily commuting from batteries. The PHEV battery would be recharged from the power grid at home or at work and would thus allow for a reduction in the overall fuel consumption. This paper proposes an integrated power electronics interface for PHEVs, which consists of a novel Eight-Switch Inverter (ESI) and an interleaved DC/DC converter, in order to reduce the cost, the mass and the size of the power electronics unit (PEU) with high performance at any operating mode. In the proposed configuration, a novel Eight-Switch Inverter (ESI) is able to function as a bidirectional single-phase AC/DC battery charger/ vehicle to grid (V2G) and to transfer electrical energy between the DC-link (connected to the battery) and the electric traction system as DC/AC inverter. In addition, a bidirectional-interleaved DC/DC converter with dual-loop controller is proposed for interfacing the ESI to a low-voltage battery pack in order to minimize the ripple of the battery current and to improve the efficiency of the DC system with lower inductor size. To validate the performance of the proposed configuration, the indirect field-oriented control (IFOC) based on particle swarm optimization (PSO) is proposed to optimize the efficiency of the AC drive system in PHEVs. The maximum efficiency of the motor is obtained by the evaluation of optimal rotor flux at any operating point, where the PSO is applied to evaluate the optimal flux. Moreover, an improved AC/DC controller based Proportional-Resonant Control (PRC) is proposed in order to reduce the THD of the input current in charger/V2G modes. The proposed configuration is analyzed and its performance is validated using simulated results obtained in MATLAB/ SIMULINK. Furthermore, it is experimentally validated with results obtained from the prototypes that have been developed and built in the laboratory based on TMS320F2808 DSP.
Computational Aerodynamics of Low Reynolds Number Plunging , Pitching and Flexible Wings for MAV Applications

Micro Air Vehicles (MAVs) have the potential to revolutionize our sensing and information gathering capabilities in environmental monitoring and homeland security areas. Due to the MAVs‟ small size, flight regime, and modes of operation, significant scientific advancement will be needed to create this revolutionary capability. Aerodynamics, structural dynamics, and flight dynamics of natural flyers intersects with some of the richest problems in MAVs, including massively unsteady three-dimensional separation, transition in boundary layers and shear layers, vortical flows and bluff body flows, unsteady flight environment, aeroelasticity, and nonlinear and adaptive control are just a few examples. A challenge is that the scaling of both fluid dynamics and structural dynamics between smaller natural flyer and practical flying hardware/lab experiment (larger dimension) is fundamentally difficult. In this paper, we offer an overview of the challenges and issues, along with sample results illustrating some of the efforts made from a computational modeling angle.
Assessing Soft-and Hardware Bottlenecks in PC-based Packet Forwarding Systems

Due to grown capabilities of commodity hardware for packet processing and the high flexibility of software, the use of those systems as alternatives to expensive dedicated networking devices has gained momentum. However, the performance of such PC-based software systems is still low when compared to specialized hardware. In this paper, we analyze the performance of several packet forwarding systems and identify bottlenecks by using profiling techniques. We show that the packet IO in the operating system’s network stack is a significant bottleneck and that a six-fold performance increase can be achieved with user space networking frameworks like Intel DPDK. Linux Router; Intel DPDK; Performance Evaluation; Measurement.
Predicting wins and spread in the Premier League using a sentiment analysis of twitter

Can the sentiment contained in tweets serve as a meaningful proxy to predict match outcomes and if so, can the magnitude of outcomes be predicted based on a degree of sentiment? To answer these questions we constructed the CentralSport system to gather tweets related to the twenty clubs of the English Premier League and analyze their sentiment content, not only to predict match outcomes, but also to use as a wagering decision system. From our analysis, tweet sentiment outperformed wagering on odds-favorites, with higher payout returns (best $2,704.63 versus odds-only $1,887.88) but lower accuracy, a trade-off from non-favorite wagering. This result may suggest a performance degradation that arises from conservatism in the odds-setting process, especially when three match results are possible outcomes. We found that leveraging a positive tweet sentiment surge over club average could net a payout of $3,011.20. Lastly, we found that as the magnitude of positive sentiment between two clubs increased, so too did the point spread; 0.42 goal difference for clubs with a slight positive edge versus 0.90 goal difference for an overwhelming difference in positive sentiment. In both these
Unsupervised and semi-supervised learning via ℓ1-norm graph

In this paper, we propose a novel ℓ1-norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the ℓ2-norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the ℓ1-norm of spectral embedding with well motivation. The sparsity produced by the ℓ1-norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the ℓ1-norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.
Experimental evaluation of Snort against DDoS attacks under different hardware configurations

Network intrusion detection systems are considered as one of the basic entities widely utilized and studied in the field of network security that aim to detect any hostile intrusion within a given network. Among many network intrusion detection systems (NIDS), open source systems have gained substantial preference due to their flexibility, support and cost effectiveness. Snort, an open source system is considered as the de-facto standard for NIDS. In this paper, effort has been made to gauge Snort in terms of performance (packet handling) and detection accuracy against TCP Flooding Distributed Denial of Service attack. The evaluation has been done using a sophisticated test-bench under different hardware configurations. This paper has analyzed the major factors affecting the performance and detection capability of Snort and has recommended techniques to make Snort a better intrusion detection system (IDS). Experimental results have shown significant improvement in Snort packet handling capability by using better hardware. However; Snort detection capability is not improved by improving hardware and is dependent upon its internal architecture (signature database and rate filtration). Furthermore, the findings can be applied to other signature based intrusion detection systems for refining their performance and detection capability.
Analysis and Design of a New AC–DC Single-Stage Full-Bridge PWM Converter With Two Controllers

Single-phase power factor correction (PFC) ac-dc converters are widely used in the industry for ac-dc power conversion from single phase ac-mains to an isolated output dc voltage. Typically, for high-power applications, such converters use an ac-dc boost input converter followed by a dc-dc full-bridge converter. A new ac-dc single-stage high-power universal PFC ac input full-bridge, pulse-width modulated converter is proposed in this paper. The converter can operate with an excellent input power factor, continuous input and output currents, and a non-excessive intermediate dc bus voltage and has reduced number of semiconductor devices thus presenting a cost-effective novel solution for such applications. In this paper, the operation of the proposed converter is explained, a steady-state analysis of its operation is performed, and the results of the analysis are used to develop a procedure for its design. The operation of the proposed converter is confirmed with results obtained from an experimental prototype.
An Improved MPPT Controller for Photovoltaic System Under Partial Shading Condition

Maximum power point tracking (MPPT) is an integral part of a system of energy conversion using photovoltaic (PV) arrays. The power-voltage characteristic of PV arrays operating under partial shading conditions exhibits multiple local maximum power points (LMPPs). In this paper, a new method has been presented to track the global maximum power point (GMPP) of PV. Compared with the past proposed global MPPT techniques, the method proposed in this paper has the advantages of determining whether partial shading is present, calculating the number of peaks on P-V curves, and predicting the locations of GMPP and LMPP. The new method can quickly find GMPP, and avoid much energy loss due to blind scan. The experimental results verify that the proposed method guarantees convergence to the global MPP under partial shading conditions.
New Security Perspectives around BYOD

The dramatic growth of cloud computing services and mobility trends, in terms of 3/4G availability and smart devices, is creating a new phenomenon called "Consumerization" that affects the consumers habits in all facets of the their life. Also during the working time people prefer to stay in their consumer environment because that's their comfort zone. A collateral phenomenon is called BYOD (Bring Your Own Device), that means the employees use their own devices also during their working time. These changing of habits represent an opportunity and a challenge for the enterprises. The opportunity is related to two main aspects: the productivity increase and the costs reduction. In a BYOD scenario the end users would pay totally or partially the devices and would work independently from time and location. On the opposite side, the new scenario bring some risks that could be critical. The use of devices for both personal and working activities opens to new security threats to face for IT organization. Also, the direct comparison between public cloud services for personal use and company's IT services could be a frustrating user experience, that's because of the public cloud services are often almost more effective and usable than typical IT company's services. The aim of this work is presenting a brief survey about the emerging methods and models to approach the BYOD phenomenon from the security point of view.
Efficient Algorithms for Discovering Association Rules

Association rules are statements of the form \for 90 % of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B". Agrawal, Imielinski, and Swami introduced the problem of mining association rules from large collections of data, and gave a method based on successive passes over the database. We give an improved algorithm for the problem. The method is based on careful combinatorial analysis of the information obtained in previous passes; this makes it possible to eliminate unnecessary candidate rules. Experiments on a university course enrollment database indicate that the method outperforms the previous one by a factor of 5. We also show that sampling is in general a very eecient way of nding such rules.
Improving Distributed Gradient Descent Using Reed-Solomon Codes

Today's massively-sized datasets have made it necessary to often perform computations on them in a distributed manner. In principle, a computational task is divided into subtasks which are distributed over a cluster operated by a taskmaster. One issue faced in practice is the delay incurred due to the presence of slow machines, known as stragglers. Several schemes, including those based on replication, have been proposed in the literature to mitigate the effects of stragglers and more recently, those inspired by coding theory have begun to gain traction. In this work, we consider a distributed gradient descent setting suitable for a wide class of machine learning problems. We adopt the framework of Tandon et al.  and present a deterministic scheme that, for a prescribed per-machine computational effort, recovers the gradient from the least number of machines $f$ theoretically permissible, via an $O(f^{2})$ decoding algorithm. The idea is based on a suitably designed Reed-Solomon code that has a sparsest and balanced generator matrix. We also provide a theoretical delay model which can be used to minimize the expected waiting time per computation by optimally choosing the parameters of the scheme. Finally, we supplement our theoretical findings with numerical results that demonstrate the efficacy of the method and its advantages over competing schemes.
Gender identification using a general audio classifier

In the context of content-based multimedia indexing gender identification using speech signal is an important task. Existing techniques are dependent on the quality of the speech signal making them unsuitable for the video indexing problems. In this paper we introduce a novel gender identification approach based on a general audio classifier. The audio classifier models the audio signal by the first order spectrum’s statistics in 1s windows and uses a set of neural networks as classifiers. The presented technique shows robustness to adverse audio compression and it is language independent. We show how practical considerations about the speech in audio-visual data, such as the continuity of speech, can further improve the classification results which attain 92%.
WHO WILL LEAD AND WHO WILL FOLLOW ? A SOCIAL PROCESS OF LEADERSHIP IDENTITY CONSTRUCTION IN ORGANIZATIONS

We propose that a leadership identity is coconstructed in organizations when individuals claim and grant leader and follower identities in their social interactions. Through this claiming-granting process, individuals internalize an identity as leader or follower, and those identities become relationally recognized through reciprocal role adoption and collectively endorsed within the organizational context. We specify the dynamic nature of this process, antecedents to claiming and granting, and an agenda for research on leadership identity and development.
1.2Gbps LVDS interface

A low voltage differential signaling (LVDS) interface circuit for inter-chip communication in a DSL system has been designed, integrated and verified in 130 nm CMOS technology. Tailored for low supply voltage, the nominal transmitter differential output voltage is 330 mV with 640 mV common-mode (CM) so it is not fully compatible with the LVDS standard. To achieve high data rate performance, DC closed loop control was used in the transmitter together with wide CM input multi-stage receiver and source/load termination. 1.2 Gbps operation at 1.5 V supply was measured on fabricated test chips comprising LVDS transmitter, receiver, serial-to-parallel data framing and clocking. Power dissipation with one set of Receiver/Transmitter active and BIST has been measured to be 67.5 mW and the area of the interface is 0.45 mm2.
Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image.



We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment.



The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth.



We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.