nils-herrmann/clean_scirepeval huggingface dataset:
Enclosed: a component-centric interface for designing prototype enclosures

This paper explores the problem of designing enclosures (or physical cases) that are needed for prototyping electronic devices. We present a novel interface that uses electronic components as handles for designing the 3D shape of the enclosure. We use the .NET Gadgeteer platform as a case study of this problem, and implemented a proof-of-concept system for designing enclosures for Gadgeteer components. We show examples of enclosures designed and fabricated with our system.
Performance Evaluation of Three-Phase Grid-Connected Photovoltaic Inverters Using Electrolytic or Polypropylene Film Capacitors

In grid-connected photovoltaic (PV) power stations, improving the life expectancy and long-term reliability of three-phase PV inverters is urgently needed to match the significantly higher lifetime of the PV modules. A key contribution toward such improvement is replacing the conventional electrolytic film capacitors by metallized polypropylene film ones. This paper presents a detailed evaluation of a conventional three-phase grid-connected PV inverter performance when replacing the electrolytic capacitor with a minimum value of metallized polypropylene film capacitor. Although the minimum dc bus capacitance leads to higher voltage ripples, such ripples were found to be within acceptable limits to operate the inverter satisfactorily. Simulation and experimental results are presented for a 5-kW grid-connected inverter prototype with a nominal dc voltage of 457 V to confirm the theoretical considerations.
The Effects of User Features on Twitter Hate Speech Detection

The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.
Wireless power transmission trends

Wireless power transmission (WPT) has been attracting a wide range of subjects in various fields and also become a highly active research area because of their potential in providing high technology to our daily lives. The wireless power transmission will be mandatory to use in the near future because this technology enables the transmission of electrical energy from a power source to an electrical load across an air gap without interconnecting wires. In this paper, we carry out a pilot study to present the existing technologies of wireless power transmission, their recent technology as well as its future trends. Furthermore, we also describe plenty of applications in wireless transmission.
Thirty Years Of Graph Matching In Pattern Recognition

A recent paper posed the question: “Graph Matching: What are we really talking about?”. Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.
Analyzing HTTP requests for web intrusion detection

Many web application security problems related to intrusion have resulted from the rapid development of web applications. To reduce the risk of web application problems, web application developers need to take measures to write secure applications to prevent known attacks. When such measures fail, it is important to detect such attacks and find the source of the attacks to reduce the estimated risks. Intrusion detection is one of the powerful techniques designed to identify and prevent harm to the system. Most defensive techniques in Web Intrusion Systems are not able to deal with the complexity of cyber-attacks in web applications. However, machine learning approaches could help to detect known and unknown web application attacks. In this paper, we present machine learning techniques to classify the HTTP requests in the well-known dataset CSIC 2010 HTTP (Giménez et al., 2012) as normal or abnormal traffic, and we compare our experimental results with the results reported by Pham et al. in 2016 and Nguyen et al. in 2011. These experiments produce results for overlapping sets of machine-learning techniques and different sets of features, allowing us to compare how good the various feature sets are for the various machine-learning techniques, at least on this dataset.
A Fast Computational Algorithm for the Discrete Cosine Transform

After his military service he joined N. V. Philips' Gloeilampenfabrieken, Eindhoven, where he worked in the fields of electronic design of broadcast radio sets, integrated circuits and optical character recognition. Since 1970 he has been at the Philips Research Laboratories , where he is now working on digital communication systems. Mr. Roza is a member of the Netherlands Electronics and Radio Society. he worked in the field of data transmission and on the synchronizationaspects of a videophone system. Since 1972 he has been engaged in system design and electronics of high speed digital transmission.  has been developed which provides a factor of six improvement in computational complexity when compared to conventional Discrete Cosine Transform algorithms using the Fast Fourier Transform. The algorithm is derived in the form of matrices and illustrated by a signal-flow graph, which may be readily translated to hardware or software implementations.
Hidden Community Detection in Social Networks

We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden community structure. We present a novel approach called HICODE (HIdden COmmunity DEtection) that identies the hidden community structure as well as the dominant community structure. By weakening the strength of the dominant structure, one can uncover the hidden structure beneath. Likewise, by reducing the strength of the hidden structure, one can more accurately identify the dominant structure. In this way, HICODE tackles both tasks simultaneously. Extensive experiments on real-world networks demonstrate that HICODE outperforms several state-of-the-art community detection methods in uncovering both the dominant and the hidden structure. For example in the Facebook university social networks, we nd multiple non-redundant sets of communities that are strongly associated with residential hall, year of registration or career position of the faculties or students, while the state-of-the-art algorithms mainly locate the dominant ground truth category. Due to the diculty of labeling all ground truth communities in real-world datasets, HICODE provides a promising approach to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Finding this unknown structure is an extremely important community detection problem.
Wireless Technologies for IoT in Smart Cities

As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed.
Re-thinking and retooling the social marketing mix

This article considers the role of the four Ps marketing mix model in social marketing, arguing that given reconfiguration of the marketing mix in the mainstream marketing discipline, and the characteristics of social marketing, a re-thought and re-tooled social marketing mix is required. A brief review of the four Ps marketing mix model in the mainstream marketing and social marketing fields is presented. Criticisms of the four Ps model are then examined. It is argued that the four Ps marketing mix model is outdated for application to social marketing, and an alternative approach to the social marketing mix is proposed. It is posited that an expanded approach recognizing strategies such as relational thinking, and upstream social marketing activities would offer a more suitable approach. Using a more open minded social marketing mix less reliant on the four Ps model can help guide social marketing research and practice.
National Cyber Range Overview

The National Cyber Range (NCR) is an innovative Department of Defense (DoD) resource originally established by the Defense Advanced Research Projects Agency (DARPA) and now under the purview of the Test Resource Management Center (TRMC). It provides a unique environment for cyber security testing throughout the program development life cycle using unique methods to assess resiliency to advanced cyberspace security threats. This paper describes what a cyber security range is, how it might be employed, and the advantages a program manager (PM) can gain in applying the results of range events. Creating realism in a test environment isolated from the operational environment is a special challenge in cyberspace. Representing the scale and diversity of the complex DoD communications networks at a fidelity detailed enough to realistically portray current and anticipated attack strategies (e.g., Malware, distributed denial of service attacks, cross-site scripting) is complex. The NCR addresses this challenge by representing an Internet-like environment by employing a multitude of virtual machines and physical hardware augmented with traffic emulation, port/protocol/service vulnerability scanning, and data capture tools. Coupled with a structured test methodology, the PM can efficiently and effectively engage with the Range to gain cyberspace resiliency insights. The NCR capability, when applied, allows the DoD to incorporate cyber security early to avoid high cost integration at the end of the development life cycle. This paper provides an overview of the resources of the NCR which may be especially helpful for DoD PMs to find the best approach for testing the cyberspace resiliency of their systems under development.
Detecting sex trafficking circuits in the U.S. through analysis of online escort advertisements

The use of social network analysis is used to identify possible victims of sex trafficking and the observation of domestic trafficking flows across the U.S. A novel approach to the analysis is presented with the understanding further development is needed. Online escort advertisements were analyzed for virtual indicators of sex trafficking and to identify patterns of activity that may be associated with criminal networks. The results suggest that 67% of the sample contained indicators of movement. Social network analysis methods were applied to identify movement trends across the US. The use of SNA methods allowed prominent hubs and circuits of this activity to be observed, by providing a tool to uncover covert network structures and activity, yielding a method to capture movement trends of potential trafficked persons. The integration of geospatial data allowed maps to be created in order to visualize movement patterns.
A service orchestration architecture for Fog-enabled infrastructures

The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to prof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture.
Is vaginal mucosal graft the excellent substitute material for urethral reconstruction in female-to-male transsexuals?

Construction of a neourethra is always considered to be a difficult part in phalloplasty, especially for the female-to-male (FTM) transsexual patients. We report our experience with prefabricated pars pendulans urethrae using vaginal mucosal graft for phalloplasty in FTM transsexuals. We retrospectively reviewed notes on the 22 FTM patients treated with pedicled-flap phalloplasty with prefabricated pars pendulans urethrae using vaginal mucosal graft between January 2008 and December 2012. Surgical outcome, urological function, and complications were recorded. Histological difference between normal mucosa and skin, and pathological changes of vaginal mucosal graft were also observed. All the reconstructive penis survived, and patients could void in a standing position finally at a median follow-up of 25.4 ± 6.0 months. Urethral fistula and urethral stricture rates were 31.8 % (7/22 patients) and 4.5 % (1/22 patients), respectively. The occurrence of the urethral stricture was remarkably low compared with previous reports. Our histological results also showed a pronounced similarity between vaginal and buccal mucosa. Morphologically, they resembled urethral epithelium more closely than the forearm skin. Following the free transfer, the vaginal mucosal graft also showed a good revascularization and the inflammatory reaction and the extent of fibrosis of the mucosa decreased to the normal level after a 6-month prefabrication. With prefabrication of vaginal mucosal graft, we reconstruct a competent phallic neourethra in these FTM transsexuals. According to its histological similarities and source character, the vaginal mucosa is the excellent substitute material for promising urethral reconstruction in FTM transsexuals.
A Survey of Biometric Gait Recognition : Approaches , Security and Challenges

Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. This paper presents biometric user recognition based on gait. Biometric gait recognition is categorized into three groups based on: machine vision, floor sensor and wearable sensor. An overview of each gait recognition category is presented. In addition, factors that may influence gait recognition are outlined. Furthermore, the security evaluations of biometric gait under various attack scenarios are also presented.
Fair Client Puzzles from the Bitcoin Blockchain

Client puzzles have been proposed as a mechanism for proving legitimate intentions by providing “proofs of work”, which can be applied to discourage malicious usage of resources. A typical problem of puzzle constructions is the difference in expected solving time on different computing platforms. We call puzzles which can be solved independently of client computing resources fair client puzzles. We propose a construction for client puzzles requiring widely distributed computational effort for their solution. These puzzles can be solved using the mining process of Bitcoin, or similar cryptocurrencies. Adapting existing definitions, we show that our puzzle construction satisfies formal requirements of client puzzles under reasonable assumptions. We describe a way of transforming our client puzzles for use in denial of service scenarios and demonstrate a practical construction.
Adult hippocampal neurogenesis is involved in anxiety-related behaviors

Adult hippocampal neurogenesis is a unique example of structural plasticity, the functional role of which has been a matter of intense debate. New transgenic models have recently shown that neurogenesis participates in hippocampus-mediated learning. Here, we show that transgenic animals, in which adult hippocampal neurogenesis has been specifically impaired, exhibit a striking increase in anxiety-related behaviors. Our results indicate that neurogenesis plays an important role in the regulation of affective states and could be the target of new treatments for anxiety disorders.
A resistively degenerated wide-band passive mixer with low noise figure and +60dBm IIP2 in 0.18μm CMOS

This paper presents a wide-band CMOS passive mixer with 8 dB double side band (DSB) noise figure (NF) and 24 dB voltage gain with +60 dBm of uncalibrated IIP2 and +9 dBm of IIP3 at 2 GHz. The linearity is maintained for a wide frequency offset range to ensure interferer performance for in-band jammers. A source degeneration method to improve NF and linearity is introduced and analyzed. Gm boosting methods, such as input cross-coupling, current reuse, complementary input, and back gate connection, are used. The Mixer consumes only 10 mW from a 2V power supply for I and Q both channels including trans-impedance amplifier stages (TIA).
Improved segmentation of abnormal cervical nuclei using a graph-search based approach

Reliable segmentation of abnormal nuclei in cervical cytology is of paramount importance in automation-assisted screening techniques. This paper presents a general method for improving the segmentation of abnormal nuclei using a graph-search based approach. More specifically, the proposed method focuses on the improvement of coarse (initial) segmentation. The improvement relies on a transform that maps round-like border in the Cartesian coordinate system into lines in the polar coordinate system. The costs consisting of nucleus-specific edge and region information are assigned to the nodes. The globally optimal path in the constructed graph is then identified by dynamic programming. We have tested the proposed method on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained liquid-based cytology (HELBC), and the comparative experiments with recent state-of-the-art approaches demonstrate the superior performance of the proposed method.
AQNet: Fine-grained 3D spatio-temporal air quality monitoring by aerial-ground WSN

This demo presents AQNet, an aerial-ground wireless sensor network (WSN) system, for fine-grained air quality monitoring and forecasting in urban three-dimensional (3D) area. AQNet contains 200 programmable on-ground PM2.5 sensors for 2D baseline monitoring, and an unmanned-aerial-vehicle (UAV) with the same sensor for air quality profiling at different heights. These low-cost sensors are programmed to wake up between adjustable time intervals, record and send real-time PM2.5 data back to the central server for data fusion. A learning model is proposed to utilize the data in both spatio-temporal perspectives to estimate PM2.5 at unmeasured locations and forecast the air quality distribution in the near future. Further, the collected data is also used to control and optimize the UAV's monitoring operation. For the convenience of user queries, we present the PM2.5 map by a website-based GUI for real-time visualization. AQNet has been realized and deployed on campus of Peking University, and is scalable and energy-efficient to be extended to larger and more dedicated areas.
An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games

In this paper, a new iterative adaptive dynamic programming (ADP)method is proposed to solve a class of continuous-time nonlinear two-person zero-sum differential games. The idea is to use the ADP technique to obtain the optimal control pair iteratively which makes the performance index function reach the saddle point of the zero-sum differential games. If the saddle point does not exist, the mixed optimal control pair is obtained to make the performance index function reach the mixed optimum. Stability analysis of the nonlinear systems is presented and the convergence property of the performance index function is also proved. Two simulation examples are given to illustrate the performance of the proposed method.
Exploring Game Space of Minimal Action Games via Parameter Tuning and Survival Analysis

Game designers can use computer-aided game design methods to model how players may experience the perceived difficulty of a game. We present methods to generate and analyze the difficulty of a wide variety of minimal action game variants throughout game space, where each point in this abstract design space represents a unique game variant. Focusing on a parameterized version of Flappy Bird, we predict hazard rates and difficulty curves using automatic playtesting, Monte Carlo simulation, a player model based on human motor skills (precision and actions per second), and survival analysis of score histograms. We demonstrate our techniques using simulated game play and actual game data from over 106 million play sessions of a popular online Flappy Bird variant, showing quantitative reasons why balancing a game for a wide range of player skill can be difficult. Some applications of our techniques include searching for a specific difficulty, game space visualization, computational creativity to find unique variants, and tuning game balance to adjust the difficulty curve even when game parameters are time varying, score dependent, or changing based on game progress.