nils-herrmann/clean_scirepeval huggingface dataset:
General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Facial expressions of a person representing similar emotion are not always unique. Naturally, the facial features of a subject taken from different instances of the same emotion have wide variations. In the presence of two or more facial features, the variation of the attributes together makes the emotion recognition problem more complicated. This variation is the main source of uncertainty in the emotion recognition problem, which has been addressed here in two steps using type-2 fuzzy sets. First a type-2 fuzzy face space is constructed with the background knowledge of facial features of different subjects for different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face space. Both interval and general type-2 fuzzy sets (GT2FS) have been used separately to model the fuzzy face space. The interval type-2 fuzzy set (IT2FS) involves primary membership functions for m facial features obtained from n-subjects, each having l-instances of facial expressions for a given emotion. The GT2FS in addition to employing the primary membership functions mentioned above also involves the secondary memberships for individual primary membership curve, which has been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership functions obtained from the n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary membership function with secondary memberships as unknown. The uncertainty management policy adopted using GT2FS has resulted in a classification accuracy of 98.333% in comparison to 91.667% obtained by its interval type-2 counterpart. A small improvement (approximately 2.5%) in classification accuracy by IT2FS has been attained by pre-processing measurements using the well-known interval approach.
External characteristics of the boost-converter with Cockroft-Walton voltage multiplier

This paper presents the method and results of calculating the output voltage of the boost-converter with Cockroft-Walton voltage multiplier according to the values of the capacitors and the load. The combination of boost-converter with voltage multiplier provides a very high voltage gain, which can be adjusted by changing the duty cycle of the boost converter. It is shown that the output voltage of the converter is a complex function of the switching frequency, duty cycle, number and value of the capacitors and load resistance. The proposed method for calculating the external characteristic is based on the analysis of the energy losses in the capacitors of the multiplier. The formulas and graphs for estimating the output voltage as a function of the converter and load parameters are obtained. Experimental results confirmed the theoretical expectations.
Mr. Emo: music retrieval in the emotion plane

This technical demo presents a novel emotion-based music retrieval platform, called Mr. Emo, for organizing and browsing music collections. Unlike conventional approaches which quantize emotions into classes, Mr. Emo defines emotions by two continuous variables arousal and valence and employs regression algorithms to predict them. Associated with arousal and valence values (AV values), each music sample becomes a point in the arousal-valence emotion plane, so a user can easily retrieve music samples of certain emotion(s) by specifying a point or a trajectory in the emotion plane. Being content centric and functionally powerful, such emotion-based retrieval complements traditional keyword- or artist-based retrieval. The demo shows the effectiveness and novelty of music retrieval in the emotion plane.
iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data

Long intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs). We have implemented iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software. A public available webserver for iSeeRNA is also provided for small size dataset. iSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.
A Planar Wobble Motor With a XY Compliant Mechanism Driven by Shape Memory Alloy

This paper describes the design concept, operating principle, analytical design, fabrication of a functional prototype, and experimental performance verification of a novel wobble motor with a XY compliant mechanism driven by shape memory alloy (SMA) wires. With the aim of realizing an SMA based motor which could generate bidirectional high-torque motion, the proposed motor is devised with wobble motor driving principle widely utilized for speed reducers. As a key mechanism which functions to guide wobbling motion, a planar XY compliant mechanism is designed and applied to the motor. Since the mechanism has monolithic flat structure with the planar mirror symmetric configuration, cyclic expansion and contraction of the SMA wires could be reliably converted into high-torque rotary motion. For systematic design of the motor, a characterization of electro-thermomechanical behavior of the SMA wire is experimentally carried out, and the design parametric analysis is conducted to determine parametric values of the XY compliant mechanism. The designed motor is fabricated as a functional prototype to experimentally investigate its operational feasibility and working performances. The observed experimental results obviously demonstrate the unique driving characteristics and practical applicability of the proposed motor.
Nonlocal Operators with Applications to Image Processing

We propose the use of nonlocal operators to define new types of flows and functionals for image processing and elsewhere. A main advantage over classical PDE-based algorithms is the ability to handle better textures and repetitive structures. This topic can be viewed as an extension of spectral graph theory and the diffusion geometry framework to functional analysis and PDE-like evolutions. Some possible application and numerical examples are given, as is a general framework for approximating Hamilton-Jacobi equations on arbitrary grids in high demensions, e.g., for control theory.
OLiA - Ontologies of Linguistic Annotation

This paper describes the Ontologies of Linguistic Annotation (OLiA) as one of the data sets currently available as part of Linguistic Linked Open Data (LLOD) cloud. Within the LLOD cloud, the OLiA ontologies serve as a reference hub for annotation terminology for linguistic phenomena on a great band-width of languages, they have been used to facilitate interoperability and information integration of linguistic annotations in corpora, NLP pipelines, and lexical-semantic resources and mediate their linking with multiple community-maintained terminology repositories.
Towards Understanding the Invertibility of Convolutional Neural Networks

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights. We give an exact connection to a particular model of model-based compressive sensing (and its recovery algorithms) and random-weight CNNs. We show empirically that several learned networks are consistent with our mathematical analysis and then demonstrate that with such a simple theoretical framework, we can obtain reasonable reconstruction results on real images. We also discuss gaps between our model assumptions and the CNN trained for classification in practical scenarios.
Simultaneous State Initialization and Gyroscope Bias Calibration in Visual Inertial Aided Navigation

State of the art approaches for visual-inertial sensor fusion use filter-based or optimization-based algorithms. Due to the nonlinearity of the system, a poor initialization can have a dramatic impact on the performance of these estimation methods. Recently, a closed-form solution providing such an initialization was derived in . That solution determines the velocity (angular and linear) of a monocular camera in metric units by only using inertial measurements and image features acquired in a short time interval. In this letter, we study the impact of noisy sensors on the performance of this closed-form solution. We show that the gyroscope bias, not accounted for in , significantly affects the performance of the method. Therefore, we introduce a new method to automatically estimate this bias. Compared to the original method, the new approach now models the gyroscope bias and is robust to it. The performance of the proposed approach is successfully demonstrated on real data from a quadrotor MAV.
HyperFlow: A Heterogeneous Dataflow Architecture

We propose a dataflow architecture, called HyperFlow, that offers a supporting infrastructure that creates an abstraction layer over computation resources and naturally exposes heterogeneous computation to dataflow processing. In order to show the efficiency of our system as well as testing it, we have included a set of synthetic and real-case applications. First, we designed a general suite of micro-benchmarks that captures main parallel pipeline structures and allows evaluation of HyperFlow under different stress conditions. Finally, we demonstrate the potential of our system with relevant applications in visualization. Implementations in HyperFlow are shown to have greater performance than actual hand-tuning codes, yet still providing high scalability on different platforms.
Online advertising: Analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.
FACE DETECTION BY HAAR CASCADE CLASSIFIER WITH SIMPLE AND COMPLEX BACKGROUNDS IMAGES USING OPENCV IMPLEMENTATION

This paper considers the problem of face detection in first attempt using haar cascade classifier from images containing simple and complex backgrounds. It is one of the best detector in terms of reliability and speed. Experiments were carried out on standard database i.e. Indian face database (IFD) and Caltech database. All images are frontal face images because side face views are harder to detect with this technique. Opencv 2.4.2 is used to implement the haar cascade classifier. We achieved 100% face detection rate on Indian database containing simple background and 93.24% detection rate on Caltech database containing complex background. Haar cascade classifier provides high accuracy even the images are highly affected by the illumination. The haar cascade classifier has shown superior performance with simple background images.
A survey on instance selection for active learning

Active learning aims to train an accurate prediction model with minimum cost by labeling most informative instances. In this paper, we survey existing works on active learning from an instance-selection perspective and classify them into two categories with a progressive relationship: (1) active learning merely based on uncertainty of independent and identically distributed (IID) instances, and (2) active learning by further taking into account instance correlations. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/weaknesses, followed by a simple runtime performance comparison, and discussion about emerging active learning applications and instance-selection challenges therein. This survey intends to provide a high-level summarization for active learning and motivates interested readers to consider instance-selection approaches for designing effective active learning solutions.
Data Mining Techniques for Credit Risk Assessment Task

This paper is review of current usage of data mining, machine learning and other algorithms for credit risk assessment. We are witnessing importance of credit risk assessment, especially after the global economic crisis on 2008.S o, it is very important to have a proper way to deal with the credit risk and provide powerful and accurate model for credit risk assessment. Many credit scoring techniques such as statistical techniques (logistic regression, discriminant analysis) or advanced techniques such as neural networks, decision trees, genetic algorithm, or support vector machines are used for credit risk assessment. Some of them are described in this article with theirs advantages/disadvantages. Even with many models and methods, it is still hard to say which model is the best or which classifier or which data mining technique is the best. Each model depends on particular data set or attributes set, so it is very important to develop flexible model which is adaptable to every dataset or attribute set.
Why do people play on-line games? An extended TAM with social influences and flow experience

On-line games have been a highly profitable e-commerce application in recent years. The market value of on-line games is increasing markedly and number of players is rapidly growing. The reasons that people play on-line games is an important area of research. This study views on-line games as entertainment technology. However, while most past studies have focused on task-oriented technology, predictors of entertainment-oriented technology adoption have seldom been addressed. This study applies the technology acceptance model (TAM) that incorporates social influences and flow experience as beliefrelated constructs to predict users’ acceptance of on-line games. The proposed model was empirically evaluated using survey data collected from 233 users about their perceptions of on-line games. Overall, the results reveal that social norms, attitude, and flow experience explain about 80% of game playing. The implications of this study are discussed.
Towards emotional control recognition through handwriting using fuzzy inference

Emotion control is one of personality characteristics that can be detected through handwriting or graphology. One of the advantages is it may help the counselor that has difficulties in identifying the emotion of their counselee. This study is to explore the fuzzy technique for feature extraction in handwriting and then identify the emotion of person. This study uses baseline or slope of the handwriting in determining the level of emotion control whether it is very low, low, medium, high or very high, through Mamdani inference.
An overview of localization techniques for Wireless Sensor Networks

Localization of sensor nodes is an important aspect in Wireless Sensor Networks (WSNs). This paper presents an overview of the major localization techniques for WSNs. These techniques are classified into centralized and distributed depending on where the computational effort is carried out. The paper concentrates on the factors that need to be considered when selecting a localization technique. The advantages and limitation of various techniques are also discussed. Finally, future research directions and challenges are highlighted.
WebSHArk 1.0: A Benchmark Collection for Malicious Web Shell Detection

Web shells are programs that are written for a specific purpose in Web scripting languages, such as PHP, ASP, ASP.NET, JSP, PERL-CGI, etc. Web shells provide a means to communicate with the server’s operating system via the interpreter of the web scripting languages. Hence, web shells can execute OS specific commands over HTTP. Usually, web attacks by malicious users are made by uploading one of these web shells to compromise the target web servers. Though there have been several approaches to detect such malicious web shells, no standard dataset has been built to compare various web shell detection techniques. In this paper, we present a collection of web shell files, WebSHArk 1.0, as a standard dataset for current and future studies in malicious web shell detection. To provide baseline results for future studies and for the improvement of current tools, we also present some benchmark results by scanning the WebSHArk dataset directory with three web shell scanning tools that are publicly available on the Internet. The WebSHArk 1.0 dataset is only available upon request via email to one of the authors, due to security and legal issues.
Web-based visualization of marine environment data

As the long-term marine survey and research, especially with the development of new marine environment monitoring technologies, prodigious amounts of complex marine environment data keep continued and rapid growth. This paper recommends an integrative visualization solution to those data, to enhance the visual display of data and data archives, and to develop a joint use of those distributed data from different organizations or communities. Following this strategy, after the analysis of web services technologies and the concept definition of marine information gird, this paper focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method. It also provides an original visualization architecture which is integrative and based on the explored technologies. Then it shows how the marine environment data are organized based on the spatiotemporal visualization method, and how the organized data are represented for use with web services and stored in a reusable fashion. Finally, the prototype system for the marine environment data of the South China Sea provides visualization of Argo floats, sea surface temperature fields, sea current fields, salinity, in-situ investigation data and ocean stations. The integration visualization architecture is illustrated on the prototype system which highlights the process-oriented temporal visualization method, which proves the good effect of the architecture and the method promoted by this work.
Wireless Fingerprint Attendance System using ZigBee Technology

In this paper provides the design method of wireless fingerprint attendance system based on ZigBee technology. The system includes terminal fingerprint acquisition module and attendance management module through computer. It can realize automatically such functions as information acquisition of fingerprint, processing, wireless transmission, fingerprint matching, and attendance management. Considering the fact and topology of ZigBee network, a fingerprint acquisition module and a wireless alarm module were designed by using the fingerprint sensor and module respectively. The whole system was implemented wireless alarm through messages and internet in the GSM web. In order to achieve the simple and high real-time system, it realized low-cost and high-performance wireless fingerprint attendance function, which provided a new wireless fingerprint attendance system for enterprises and institutions. Index Terms ZigBee technology, Fingerprint identification, Attendance System, Wireless communication, Wireless Alarm.
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.
Structural basis of long-term potentiation in single dendritic spines

Dendritic spines of pyramidal neurons in the cerebral cortex undergo activity-dependent structural remodelling that has been proposed to be a cellular basis of learning and memory. How structural remodelling supports synaptic plasticity, such as long-term potentiation, and whether such plasticity is input-specific at the level of the individual spine has remained unknown. We investigated the structural basis of long-term potentiation using two-photon photolysis of caged glutamate at single spines of hippocampal CA1 pyramidal neurons. Here we show that repetitive quantum-like photorelease (uncaging) of glutamate induces a rapid and selective enlargement of stimulated spines that is transient in large mushroom spines but persistent in small spines. Spine enlargement is associated with an increase in AMPA-receptor-mediated currents at the stimulated synapse and is dependent on NMDA receptors, calmodulin and actin polymerization. Long-lasting spine enlargement also requires Ca2+/calmodulin-dependent protein kinase II. Our results thus indicate that spines individually follow Hebb's postulate for learning. They further suggest that small spines are preferential sites for long-term potentiation induction, whereas large spines might represent physical traces of long-term memory.
A People Counting System Based on Face-Detection

This paper presents an automatic people counting system based on face detection, where the number of people passing through a gate or door is counted by setting a video camera. The basic idea is to first use the frame difference to detect the rough edges of moving people and then use the chromatic feature to locate the people face. Based on NCC (Normalized Color Coordinates) color space, the initial face candidate is obtained by detecting the skin color region and then the face feature of the candidate is analyzed to determine whether the candidate is real face or not. After face detection, a person will be tracked by following the detected face and then this person will counted if its face touches the counting line. Experimental results show that the proposed people counting algorithm can provide a high count accuracy of 80% on average for the crowded pedestrians.