nils-herrmann/clean_scirepeval huggingface dataset:
Sex similarities and differences in preferences for short-term mates: what, whether, and why.

Are there sex differences in criteria for sexual relationships? The answer depends on what question a researcher asks. Data suggest that, whereas the sexes differ in whether they will enter short-term sexual relationships, they are more similar in what they prioritize in partners for such relationships. However, additional data and context of other findings and theory suggest different underlying reasons. In Studies 1 and 2, men and women were given varying "mate budgets" to design short-term mates and were asked whether they would actually mate with constructed partners. Study 3 used a mate-screening paradigm. Whereas women have been found to prioritize status in long-term mates, they instead (like men) prioritize physical attractiveness much like an economic necessity in short-term mates. Both sexes also show evidence of favoring well-rounded long- and short-term mates when given the chance. In Studies 4 and 5, participants report reasons for having casual sex and what they find physically attractive. For women, results generally support a good genes account of short-term mating, as per strategic pluralism theory (S. W. Gangestad & J. A. Simpson, 2000). Discussion addresses broader theoretical implications for mate preference, and the link between method and theory in examining social decision processes.
Applying Epidemic Algorithm for Financial Service Based on Blockchain Technology

Our global market is emerging transformation strategy that can make the difference between success and failure. Smart contract systems through developments of technological innovations are increasingly seen as alternative technologies to impact transactional processes significantly. Blockchain is a smart contract protocol with trust offering the potential for creating new transaction platforms and thus shows a radical change of the current core value creation in third parties. These results in enormous cost and time savings and the reduced risk for the parties. This study proposed a method to improve the efficiency of distributed consensus in blockchains using epidemic algorithm. The results showed that epidemic protocols can distribute the information similar to blockchain.
Using depth in visual simultaneous localisation and mapping

We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the feely available software “Parallel Tracking and Mapping” by Georg Klein. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.
Health big data analytics: current perspectives, challenges and potential solutions

Modern health information systems can generate several exabytes of patient data, the so called ‘health big data’, per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of health big data as well as the challenges and solutions for health big data analytics (BDA) – the process of extracting knowledge from sets of health big data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.
Towards a model of the U.S. stock market: How important is the securities information processor?

Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor — the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its importance to efficient, price discovery. Via extensions to a previous market model, we experiment with four different coupling mechanisms which operate across the US stock market. Of the four, we find that the SIP contributes most to efficient price discovery.
User stories don't help users: introducing persona stories

User stories are one of the most popular alternatives to traditional user requirement specifications . But despite their promising name, user stories are not about – and don’t necessarily help – users at all. In most cases, user stories are written about roles that users adopt and take no account of the needs and behaviours of real users. Were that not indictment enough, user stories suffer from demonstrable flaws in structure and are often written by the wrong people at the wrong time.
A Fog-Based Internet of Energy Architecture for Transactive Energy Management Systems

Internet of Energy (IoE) is a subset of the Internet of Things which covers all aspects of electrical energy systems and provides secure connectivity and interoperability between power grid and Internet. In this paper, we present a fog-based IoE architecture for transactive energy (TE) management systems. The proposed design consists of three different layers. In the first tier, home gateways are employed which collect customers energy consumption data and provide necessary interface between customers and power grid. In the second layer, there are some local fog nodes located at the network edge and provide services with low latency. From the TE system point of view, the fog node act as retail energy market server which provides energy services to the end users. In the third layer, cloud servers are utilized to provide permanent and reliable data storage and high computing power. The proposed architecture supports different communication protocols such as hypertext transfer protocol, constrained application protocol, and OpenADR. We calculate the required bandwidth and delay performance of both fog- and cloud-based models. We present an optimal day ahead energy consumption schedule and an intercustomer energy trading mechanism for exchanging energy between end users. The performance of the proposed architecture is evaluated in terms of different power grid and communication network metrics. Results confirm the superiority of the proposed architecture.
A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets

This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest learning methods: i) transfer learning and low-dimensional space embedding allows to reduce the dimensionality of the representations, ii) visual temporal information handled by a simple score-per-frame selection process averaged across time, iii) simple frame selection mechanism for weighting images within sequences, iv) fusion of the different modalities at prediction level (late fusion). The paper also highlights the inherent challenges of the AFEW dataset and the difficulty of model selection with as few as 383 validation sequences. The proposed real-time emotion classifier achieved a state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at the Emotion in the Wild 2018 challenge.
Development of a capacitive-type 6-axis force-torque sensor

Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments; to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.
Efficient online spherical k-means clustering

The spherical k-means algorithm, i.e., the k-means algorithm with cosine similarity, is a popular method for clustering high-dimensional text data. In this algorithm, each document as well as each cluster mean is represented as a high-dimensional unit-length vector. However, it has been mainly used in hatch mode. Thus is, each cluster mean vector is updated, only after all document vectors being assigned, as the (normalized) average of all the document vectors assigned to that cluster. This paper investigates an online version of the spherical k-means algorithm based on the well-known winner-take-all competitive learning. In this online algorithm, each cluster centroid is incrementally updated given a document. We demonstrate that the online spherical k-means algorithm can achieve significantly better clustering results than the batch version, especially when an annealing-type learning rate schedule is used. We also present heuristics to improve the speed, yet almost without loss of clustering quality.
Two-factor user authentication in wireless sensor networks

Wireless sensor networks (WSN) are typically deployed in an unattended environment, where the legitimate users can login to the network and access data as and when demanded. Consequently, user authentication is a primary concern in this resource-constrained environment before accessing data from the sensor/gateway nodes. In this letter, we present a two-factor user authentication protocol for WSN, which provides strong authentication, session key establishment, and achieves efficiency.
Contraception and health

Increasing contraceptive use in developing countries has cut the number of maternal deaths by 40% over the past 20 years, merely by reducing the number of unintended pregnancies. By preventing high-risk pregnancies, especially in women of high parities, and those that would have ended in unsafe abortion, increased contraceptive use has reduced the maternal mortality ratio--the risk of maternal death per 100,000 livebirths--by about 26% in little more than a decade. A further 30% of maternal deaths could be avoided by fulfilment of unmet need for contraception. The benefits of modern contraceptives to women's health, including non-contraceptive benefits of specific methods, outweigh the risks. Contraception can also improve perinatal outcomes and child survival, mainly by lengthening interpregnancy intervals. In developing countries, the risk of prematurity and low birthweight doubles when conception occurs within 6 months of a previous birth, and children born within 2 years of an elder sibling are 60% more likely to die in infancy than are those born more than 2 years after their sibling.
Broadband SOI PA with tunable matching network for improved LTE performances under high VSWR

This paper describes the design of a broadband power amplifier (PA) for LTE PMR handheld applications using a 130nm SOI CMOS technology from ST Microelectronics with high efficiency LDMOS power transistors. The PA has two power modes (3GPP and PMR) and covers the 380–450MHz and 698–862MHz frequency bands thanks to the use of a low-loss SOI Tunable Output Matching Network (TOMN) presenting the optimal load impedance for each power mode and frequency band. The TOMN is used for load tuning purpose and is able to recover any impedance mismatch up to 4:1 VSWR with less than 2dB of insertion loss. The design procedure of the TOMN is detailed in this paper. In 3GPP/PMR modes, the proposed PA provides up to 28.5dBm/31dBm of linear output power in the different frequency bands while keeping an adjacent channel leakage power ratio (ACLR) less than −35dBc with a LTE signal. Corresponding efficiency (PAE) is higher than 35% in the different modes and bands.
Towards Principled Methods for Training Generative Adversarial Networks

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.
Multiple Kernel Learning Algorithms

In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources (different representations or different feature subsets). In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel methods. We perform experiments for better illustration. We see that though there may not be large differences in terms of accuracy, there is difference between them in complexity as given by the number of stored support vectors, the sparsity of the solution as given by the number of used kernels, and training time.
Investigating The Adoption of Knowledge Management in a Non-Govermental Organization in Malaysia

Non-governmental organizations use knowledge extensively in humanitarian efforts, engagements with organizations concerning the welfare of the people and other activities. The knowledge in nongovernmental organizations are mostly tacit. Nevertheless, little is known about knowledge management practices in non-governmental organizations. The aim of this study is to investigate the adoption of knowledge management in a nongovernmental organization. This study adopts a case study research design. Data was collected through a survey sent to a non-governmental organization. The questionnaire was based on the four pillars of knowledge management namely management and organization, people and culture, content and processes and infrastructure. A total of 31 respondents from one NGO participated in the survey. The data was analyzed using descriptive analyses. The findings indicate that knowledge is widely acknowledge as important in non-governmental organizations. To a certain extent, knowledge management principles have been practiced in the organization. However, non-governmental organizations have yet to establish an organizationwide knowledge management strategy. The study provides an understanding of how knowledge management is perceived by the non-governmental organizations and provide valuable insights to the practice of knowledge management in nongovernmental organizations.
Pedestrian Motion Tracking and Crowd Abnormal Behavior Detection Based on Intelligent Video Surveillance

Pedestrian tracking and detection of crowd abnormal activity under dynamic and complex background using Intelligent Video Surveillance (IVS) system are beneficial for security in public places. This paper presents a pedestrian tracking method combing Histogram of Oriented Gradients (HOG) detection and particle filter. This method regards the particle filter as the tracking framework, identifies the target area according to the result of HOG detection and modifies particle sampling constantly. Our method can track pedestrians in dynamic backgrounds more accurately compared with the traditional particle filter algorithms. Meanwhile, a method to detect crowd abnormal activity is also proposed based on a model of crowd features using Mixture of Gaussian (MOG). This method calculates features of crowd-interest points, then establishes the crowd features model using MOG, conducts self-adaptive updating and detects abnormal activity by matching the input feature with model distribution. Experiments show our algorithm can efficiently detect abnormal velocity and escape panic in crowds with a high detection rate and a relatively low false alarm rate.
Revised Estimates for the Number of Human and Bacteria Cells in the Body

Reported values in the literature on the number of cells in the body differ by orders of magnitude and are very seldom supported by any measurements or calculations. Here, we integrate the most up-to-date information on the number of human and bacterial cells in the body. We estimate the total number of bacteria in the 70 kg "reference man" to be 3.8·1013. For human cells, we identify the dominant role of the hematopoietic lineage to the total count (≈90%) and revise past estimates to 3.0·1013 human cells. Our analysis also updates the widely-cited 10:1 ratio, showing that the number of bacteria in the body is actually of the same order as the number of human cells, and their total mass is about 0.2 kg.
Digital Comics Image Indexing Based on Deep Learning

The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.
How to Train Your Browser: Preventing XSS Attacks Using Contextual Script Fingerprints

Cross-Site Scripting (XSS) is one of the most common web application vulnerabilities. It is therefore sometimes referred to as the “buffer overflow of the web.” Drawing a parallel from the current state of practice in preventing unauthorized native code execution (the typical goal in a code injection), we propose a script whitelisting approach to tame JavaScript-driven XSS attacks. Our scheme involves a transparent script interception layer placed in the browser’s JavaScript engine. This layer is designed to detect every script that reaches the browser, from every possible route, and compare it to a list of valid scripts for the site or page being accessed; scripts not on the list are prevented from executing. To avoid the false positives caused by minor syntactic changes (e.g., due to dynamic code generation), our layer uses the concept of contextual fingerprints when comparing scripts.
 Contextual fingerprints are identifiers that represent specific elements of a script and its execution context. Fingerprints can be easily enriched with new elements, if needed, to enhance the proposed method’s robustness. The list can be populated by the website’s administrators or a trusted third party. To verify our approach, we have developed a prototype and tested it successfully against an extensive array of attacks that were performed on more than 50 real-world vulnerable web applications. We measured the browsing performance overhead of the proposed solution on eight websites that make heavy use of JavaScript. Our mechanism imposed an average overhead of 11.1% on the execution time of the JavaScript engine. When measured as part of a full browsing session, and for all tested websites, the overhead introduced by our layer was less than 0.05%. When script elements are altered or new scripts are added on the server side, a new fingerprint generation phase is required. To examine the temporal aspect of contextual fingerprints, we performed a short-term and a long-term experiment based on the same websites. The former, showed that in a short period of time (10 days), for seven of eight websites, the majority of valid fingerprints stay the same (more than 92% on average). The latter, though, indicated that, in the long run, the number of fingerprints that do not change is reduced. Both experiments can be seen as one of the first attempts to study the feasibility of a whitelisting approach for the web.
A 140 dB-CMRR Current-Feedback Instrumentation Amplifier Employing Ping-Pong Auto-Zeroing and Chopping

This paper presents a precision general-purpose current-feedback instrumentation amplifier (CFIA) that employs a combination of ping-pong auto-zeroing and chopping to cancel its offset and 1/f noise. A comparison of offset-cancellation techniques shows that neither chopping nor auto-zeroing is an ideal solution for general-purpose CFIAs, since chopping results in output ripple, and auto-zeroing is associated with increased low-frequency noise. The presented CFIA mitigates these unintended side effects through a combination of these techniques. A ping-pong auto-zeroed input stage with slow-settling offset-nulling loops is applied to limit the bandwidth of the increased noise to less than half of the auto-zeroing frequency. This noise is then modulated away from DC by chopping the input stage at half the auto-zeroing frequency, reducing the low-frequency noise to the 27 nV/ white-noise level, without introducing extra output ripple. The auto-zeroing is augmented with settling phases to further reduce output transients. The CFIA was realized in a 0.5 μm analog CMOS process and achieves a typical offset of 2.8 μV and a CMRR of 140 dB in a common-mode voltage range that includes the negative supply.
SDN and NFV benchmarking for performance and reliability

With dynamic networking environment demands from Internet applications and services, network operators are adopting software-based infrastructure and functions. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) promise increased flexibility and agility for their networks. Accordingly, the SDN and NFV performance benchmarking methodologies are under discussion in standardization bodies. SDN and NFV combine. Information Technology (IT) with Communication Technology (CT). As the benchmarking metrics are different between IT and CT, new benchmarking metrics are highly necessary. This paper introduces benchmarking test metrics presented in standardization bodies and proposes SDN and NFV benchmarking metrics for performance and reliability from the perspective of operators. Moreover, benchmarking influence elements are also considered. In conclusion, the paper discusses benchmarking issues to put SDN and NFV into practical uses.