nils-herrmann/clean_scirepeval huggingface dataset:
Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior.
Estimating Software Project Effort Using Analogies

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.
Analysis and Synthesis of Double-Sided Parallel-Strip Transitions

Antenna feeders, mixers, and filters made in double-sided parallel-strip technology usually must be adapted to unbalanced lines like the microstrip structure, needing transitions from asymmetric to symmetric waveguides (baluns). In this paper, we propose a new method for the evaluation of a generic tapered balun based on a conformal-mapping technique and an integral equation. This method, along with the use of an optimization technique such as genetic algorithms, allows for quick evaluation of the return losses of any tapered balun and the synthesizing of specific shapes to achieve desired responses in terms of return losses or impedance values.
Neural networks for sentiment analysis on Twitter

The online medium has become a significant way that people express their opinions online. Sentiment analysis can be used to find out the polarity of an opinion, such as positive, negative, or neutral. Sentiment analysis has applications such as companies getting their customer's opinions on their products, political sentiment analysis, or opinions on movie reviews. Recent research has involved looking at text from online blogs, tweets, online movie reviews, etc. to try and classify the text as being positive, negative, or neutral. For this research, a feedforward neural network will be experimented with for sentiment analysis of tweets. The training set of tweets are collected using the Twitter API using positive and negative keywords. The testing set of tweets are collected using the same positive and negative keywords.
Single Active Switch Power Electronics for Kilowatt Scale Capacitive Power Transfer

The development of capacitive power transfer (CPT) as a competitive wireless/contactless power transfer solution over short distances is proving viable in both consumer and industrial electronic products/systems. The CPT is usually applied in low-power applications, due to small coupling capacitance. Recent research has increased the coupling capacitance from the pF to the nF scale, enabling extension of CPT to kilowatt power level applications. This paper addresses the need of efficient power electronics suitable for CPT at higher power levels, while remaining cost effective. Therefore, to reduce the cost and losses single-switch-single-diode topologies are investigated. Four single active switch CPT topologies based on the canonical Ćuk, SEPIC, Zeta, and Buck-boost converters are proposed and investigated. Performance tradeoffs within the context of a CPT system are presented and corroborated with experimental results. A prototype single active switch converter demonstrates 1-kW power transfer at a frequency of 200 kHz with >90% efficiency.
Cohesion , Coherence , and Expert Evaluations of Writing Proficiency

This study investigates the roles of cohesion and coherence in evaluations of essay quality. Cohesion generally has a facilitative effect on text comprehension and is assumed to be related to essay coherence. By contrast, recent studies of essay writing have demonstrated that computational indices of cohesion are not predictive of evaluations of writing quality. This study investigates expert ratings of individual text features, including coherence, in order to examine their relation to evaluations of holistic essay quality. The results suggest that coherence is an important attribute of overall essay quality, but that expert raters evaluate coherence based on the absence of cohesive cues in the essays rather than their presence. This finding has important implications for text understanding and the role of coherence in writing quality.
Cultural variations in emotions: a review.

The psychological and anthropological literature on cultural variations in emotions is reviewed. The literature has been interpreted within the framework of a cognitive-process model of emotions. Both cross-cultural differences and similarities were identified in each phase of the emotion process; similarities in 1 phase do not necessarily imply similarities in other phases. Whether cross-cultural differences or similarities are found depends to an important degree on the level of description of the emotional phenomena. Cultural differences in emotions appear to be due to differences in event types or schemas, in culture-specific appraisal propensities, in behavior repertoires, or in regulation processes. Differences in taxonomies of emotion words sometimes reflect true emotion differences like those just mentioned, but they may also just result from differences in which emotion-process phase serves as the basis for categorization.
Calvin: fast distributed transactions for partitioned database systems

Many distributed storage systems achieve high data access throughput via partitioning and replication, each system with its own advantages and tradeoffs. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. Calvin is a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. Unlike previous deterministic database system prototypes, Calvin supports disk-based storage, scales near-linearly on a cluster of commodity machines, and has no single point of failure. By replicating transaction inputs rather than effects, Calvin is also able to support multiple consistency levels---including Paxos-based strong consistency across geographically distant replicas---at no cost to transactional throughput.
Beware of BGP attacks

This note attempts to raise awareness within the network research community about the security of the interdomain routing infrastructure. We identify several attack objectives and mechanisms, assuming that one or more BGP routers have been compromised. Then, we review the existing and proposed countermeasures, showing that they are either generally ineffective (route filtering), or probably too heavyweight to deploy (S-BGP). We also review several recent proposals, and conclude by arguing that a significant research effort is urgently needed in the area of routing security.
Binary-code obfuscations in prevalent packer tools

The first steps in analyzing defensive malware are understanding what obfuscations are present in real-world malware binaries, how these obfuscations hinder analysis, and how they can be overcome. While some obfuscations have been reported independently, this survey consolidates the discussion while adding substantial depth and breadth to it. This survey also quantifies the relative prevalence of these obfuscations by using the Dyninst binary analysis and instrumentation tool that was recently extended for defensive malware analysis. The goal of this survey is to encourage analysts to focus on resolving the obfuscations that are most prevalent in real-world malware.
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

3D scene understanding is important for robots to interact with the 3D world in a meaningful way. Most previous works on 3D scene understanding focus on recognizing geometrical or semantic properties of a scene independently. In this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a new recurrent neural network architecture for semantic labeling on RGB-D videos. The output of the network is integrated with mapping techniques such as KinectFusion in order to inject semantic information into the reconstructed 3D scene. Experiments conducted on real world and synthetic RGB-D videos demonstrate the superior performance of our method.
Parallax error in the monocular head-mounted eye trackers

This paper investigates the parallax error, which is a common problem of many video-based monocular mobile gaze trackers. The parallax error is defined and described using the epipolar geometry in a stereo camera setup. The main parameters that change the error are introduced and it is shown how each parameter affects the error. The optimum distribution of the error (magnitude and direction) in the field of view varies for different applications. However, the results can be used for finding the optimum parameters that are needed for designing a head-mounted gaze tracker. It has been shown that the difference between the visual and optical axes does not have a significant effect on the parallax error, and the epipolar geometry can be used for describing the parallax error in the HMGT.
A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.
Design of Ultra-Wideband Substrate Integrated Waveguide (SIW) Filters in Zigzag Topology

In this letter we propose a modified substrate integrated waveguide (SIW) zigzag filter topology, which allows the flexible introduction of extra cross-couplings between non adjacent resonators. This novel topology permits to control the location of the transmission zeros, thus improving the resulting filter selectivity. Therefore, it has been used to design filters in compliance with the restrictive ultra-wideband European mask. In order to validate the new proposed topology, two filter prototypes have been designed, manufactured and measured, showing very good agreement with simulation data.
The Knowledge Repository: Organizational Memory Information Systems

in the domain of computer technologies, our understanding of knowledge is somewhat more elusive. Data and information can be exactly the same thing, since their distinction lies in the context to which they are applied. One person's data could be another person's information. Although data often refers to the raw codification of facts, usually useful to few, it could be information for someone who could apply it to a decision or problem context. Typically, data is classified, summarized, transferred, or corrected to add value, and becomes information within a certain context. This conversion is relatively mechanical, and it has long been facilitated by storage, processing, and communication technologies. These technologies add place, time, and form utility to the data. In doing so, the information serves to "inform" or reduce uncertainty within the problem domain. Therefore, information is dyadic within the attendant condition, i.e., it has only utility within the context. Data, on the other hand, is not dyadic within the context. Independent of context, data and information could be identical.
Big Data Analytics: Security and privacy challenges

The digitalization of our day-to-day activities has resulted in a huge volume of data. This data, called Big Data, is used by many organizations to extract valuable information either to take marketing decisions, track specific behaviors or detect threat attacks. The processing of such data is made possible by using multiple techniques, called Big Data Analytics, which allow getting enormous benefits by dealing with any massive volume of unstructured, structured and semi-structured content that is fast changing and impossible to process using conventional database techniques. However, while Big Data represents an immense opportunity for many industries and decisions makers, it also represents a big risk for many users. This risk arises from the fact that these analytics tools consist of storing, managing and efficiently analyzing varied data gathered from all possible and available sources. The consequence is that people become widely vulnerable to exposure because of combining and exploring specific behavioral data. That is, it is possible to collect more data than it should have which leads to many security and privacy violations. Therefore, research community has to consider these issues by proposing strong protection techniques that enable getting benefits from big data without risking privacy. In this paper, we highlight the benefits of Big Data Analytics and then we review challenges of security and privacy in big data environments. Furthermore, we present some available protection techniques and propose some possible tracks that enable security and privacy in a malicious big data context.
Adaptive impedance control for upper limb assist exoskeleton

Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we present a new technique for the upper limb power exoskeleton robot in which load is gripped by the human subject and not by the robot while the robot assists. Main challenge is to find non-biological signal based human desired motion intention to assist as needed. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the force sensors is used to estimate the human interactive force from which desired human motion is extracted using adaptive Radial Basis Function Neural Network (RBFNN). Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five DOF are passive while two are active. Active joints include shoulder and elbow in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joints. To ensure high quality performance model reference based adaptive impedance controller is employed. Exoskeleton performance is evaluated experimentally by a neurologically intact subject which validates the effectiveness.
ENDA: embracing network inconsistency for dynamic application offloading in mobile cloud computing

Mobile Cloud Computing (MCC) enables smartphones to offload compute-intensive codes and data to clouds or cloudlets for energy conservation. Thus, MCC liberates smartphones from battery shortage and embraces more versatile mobile applications. Most pioneering MCC research work requires a consistent network performance for offloading. However, such consistency is challenged by frequent mobile user movements and unstable network quality, thereby resulting in a suboptimal offloading decision. To embrace network inconsistency, we propose ENDA, a three-tier architecture that leverages user track prediction, realtime network performance and server loads to optimize offloading decisions. On cloud tier, we first design a greedy searching algorithm to predict user track using historical user traces stored in database servers. We then design a cloud-enabled Wi-Fi access point (AP) selection scheme to find the most energy efficient AP for smartphone offloading. We evaluate the performance of ENDA through simulations under a real-world scenario. The results demonstrate that ENDA can generate offloading decisions with optimized energy efficiency, desirable response time, and potential adaptability to a variety of scenarios. ENDA outperforms existing offloading techniques that do not consider user mobility and server workload balance management.
Hiding Messages using Musical Notes : A Fuzzy Logic Approach

“Music can be used as a communicable language”. Musical symbols and musical notes have been used as codes and ciphers from early days. The art of encrypting messages using music is termed as Musical cryptography and it uses predefined set of notes and set of rules for composing musical pieces which in turn are musical cryptograms. Traditional algorithms applied to musical cryptography used simple substitution cipher which merely produced good musical sequences. To overcome this problem a fuzzy logic based algorithm for musical cryptography is proposed in this paper. The paper proposes a symmetric key substitution cipher which uses one of the n candidate notes to encrypt a particular character. The application of fuzzy logic in musical cryptography produces acceptable musical sequences which are hard to be suspected as cipher.
Challenges in the diagnosis of fetal non-chromosomal abnormalities at 11-13 weeks.

To examine the performance of the 11-13 weeks scan in detecting non-chromosomal abnormalities.



Prospective first-trimester screening study for aneuploidies, including basic examination of the fetal anatomy, in 45 191 pregnancies. Findings were compared to those at 20-23 weeks and postnatal examination.



Aneuploidies (n = 332) were excluded from the analysis. Fetal abnormalities were observed in 488 (1.1%) of the remaining 44 859 cases; 213 (43.6%) of these were detected at 11-13 weeks. The early scan detected all cases of acrania, alobar holoprosencephaly, exomphalos, gastroschisis, megacystis and body stalk anomaly, 77% of absent hand or foot, 50% of diaphragmatic hernia, 50% of lethal skeletal dysplasias, 60% of polydactyly, 34% of major cardiac defects, 5% of facial clefts and 14% of open spina bifida, but none of agenesis of the corpus callosum, cerebellar or vermian hypoplasia, echogenic lung lesions, bowel obstruction, most renal defects or talipes. Nuchal translucency (NT) was above the 95th percentile in 34% of fetuses with major cardiac defects.



At 11-13 weeks some abnormalities are always detectable, some can never be and others are potentially detectable depending on their association with increased NT, the phenotypic expression of the abnormality with gestation and the objectives set for such a scan.
A nanosecond pulse generator for intracellular electromanipulation

Intracellular electromanipulation (ICEM), the manipulation of substructures of biological cells by means of externally applied electric fields requires electrical pulses of nanosecond to tens of nanosecond duration and amplitudes of tens of kilovolt. The load in these bioelectric experiments, a cuvette containing biological cells, immersed in a solution of high conductivity, is generally on the order of 10 /spl Omega/. Strip line pulsers satisfy the condition of low impedance. A Blumlein strip line pulser with a pressurized spark gap as switch has been designed. The electrical characteristics of a particular pulse generator, which produces an 8 ns pulse with voltages between 2 kV and 30 kV across a 10 /spl Omega/ load are described.
An UHF Band RFID Vehicle Management System

A vehicle management system based on UHF band RFID technology is proposed. This system is applied for vehicle entering/leaving at road gates. The system consists of tag-on-car, reader antenna, reader controller, and the monitoring and commanding software. It could effective control the vehicles passing through road gate and record the vehicles' data. The entering time, leaving time, and tag number of each vehicle are all recorded and saved for further processing. By the benefit of UHF band long distance sensing ability, within nine meter the distance between vehicle and reader antenna, the signal can be accurately detected even the vehicle's speed at nearly 30 km/hr. The monitoring and commanding software can not only identify car owners' identities but also determine the gate to open or not. The accessories: video recording and pressure sensing components are flexible to add for enhancing the system's performance. This system has been tested in many field tests and the results shown that it is suitable for vehicle management and the related applications.