nils-herrmann/clean_scirepeval huggingface dataset:
A multivariate empirical mode decomposition based filtering for subject independent BCI
Venue: 2016 27th Irish Signals and Systems Conference (ISSC)

A brain computer interface (BCI) provides a way to translate the motion intentions of human using brain signals such as electroencephalogram (EEG) into control commands. EEG signals are highly subject specific and non stationary. One of the most challenging tasks is to classify motion intentions since the recorded EEG signals have inherent non stationarities which are due to changes in the signal properties over time within as well as across sessions. Thus it becomes difficult to achieve a stable operation of BCI.  We present a novel filtering method based on the multivariate empirical mode decomposition (MEMD) using subject independent BCI (MEMD SI BCI) for classification of motor imagery (MI) based EEG signals to achieve enhanced BCI. A subject independent BCI can be used immediately by the new user without using the user's training data. The MEMD method helps to utilize the cross channel information and enhance localization properties. It decomposes multichannel EEG signals into a set of multivariate intrinsic mode functions (MIMFs) These MIMFs can be considered narrow band, amplitude and frequency modulated (AM FM) signals. The statistical property, namely, mean frequency measure of these MIMFs has been used to combine these MIMFs to compute the enhanced EEG signals which have major contributions due to m and b rhythms over the motor cortex region. The objective of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The common spatial pattern (CSP) feature has been computed from the enhanced EEG signals and has been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method.  We have achieved an improvement of >11% in the evaluation stage using the MEMD SI BCI method when compared with SI BCI.  This study helps to develop BCI systems with intuitive motor imaginations, thus facilitates broad use of noninvasive BCIs. We have evaluated our method on publicly available BCI competition IV dataset 2A and have obtained improved performance.
Customized expression recognition for performance driven cutout character animation
Venue: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

Performance driven character animation enables users to create expressive results by performing the desired motion of the character with their face and/or body. However, for cutout animations where continuous motion is combined with discrete artwork replacements, supporting a performance driven workflow has some unique requirements. To trigger the appropriate artwork replacements, the system must reliably detect a wide range of customized facial expressions that are challenging for existing recognition methods, which focus on a few canonical expressions (e.g. angry, disgusted, scared, happy, sad and surprised) Also, real usage scenarios require the system to work in realtime with minimal training. In this paper, we propose a novel customized expression recognition technique that meets all of these requirements. We first use a set of handcrafted features combining geometric features derived from facial landmarks and patch based appearance features through group sparsity based facial component learning. To improve discrimination and generalization, these handcrafted features are integrated into a custom designed Deep Convolutional Neural Network (CNN) structure trained from publicly available facial expression datasets. The combined features are fed to an online ensemble of SVMs designed for the few training sample problem and performs in realtime. To improve temporal coherence, we also apply a Hidden Markov Model (HMM) to smooth the recognition results. Our system achieves state of the art performance on canonical expression datasets and promising results on our collected dataset of customized expressions.
Implementing Community Engagement for Combination Prevention: Lessons Learnt From the First Year of the HPTN 071 (PopART) Community Randomized Study
Venue: Current HIV/AIDS Reports

Key to the success of a HIV combination prevention strategy, including galvanizing the current push to roll out universal test and treat (UTT) is the involvement and buy in of the populations that the strategy aims to reach. Drawing on the experiences of engaging with 21 communities in Zambia and South Africa in the design and implementation of a community randomized study of combination HIV prevention including UTT, this paper reflects on the commitment to, approaches for and benefits of involving communities. Key lessons learnt include that all communities require continuous community engagement (CE) and engagement needs to be adapted to diverse local contexts. Intrinsic goals of CE, such as building trusting relationships between study stakeholders, are necessary precursors to instrumental goals which strengthen the research quality. Engaging the community for combination prevention requires that CE successfully bridges science and real life, paying attention to influences in the wider social landscape.
AdaDelay: Delay Adaptive Distributed Stochastic Optimization
Venue: AISTATS

We develop distributed stochastic convex optimization algorithms under a delayed gradient model in which server nodes update parameters and worker nodes compute stochastic (sub)gradients. Our setup is motivated by the behavior of real world distributed computation systems; in particular, we analyze a setting wherein worker nodes can be differently slow at different times. In contrast to existing approaches, we do not impose a worst case bound on the delays experienced but rather allow the updates to be sensitive to the actual delays experienced. This sensitivity allows use of larger stepsizes, which can help speed up initial convergence without having to wait too long for slower machines; the global convergence rate is still preserved. We experiment with different delay patterns, and obtain noticeable improvements for large scale real datasets with billions of examples and features.
Reverse osmosis molecular differentiation of organic liquids using carbon molecular sieve membranes
Venue: Science

Carbon sieving to separate the similar Separating organic molecules, particularly those with almost equal sizes and similar physical properties, can be challenging and may require energy intensive techniques such as freeze fractionation. Taking inspiration from reverse osmosis of aqueous fluids, Koh et al. describe the synthesis, characterization, and mass transport performance of carbon molecular sieve membranes for the separation of liquid phase organic molecules at room temperature. This technique is capable of separating very similar isomers, such as ortho and para xylene, on an industrial scale. Science, this issue p. 804 Carbon membranes efficiently separate similarly sized organic liquid molecules and isomers. Liquid phase separations of similarly sized organic molecules using membranes is a major challenge for energy intensive industrial separation processes. We created free standing carbon molecular sieve membranes that translate the advantages of reverse osmosis for aqueous separations to the separation of organic liquids. Polymer precursors were cross linked with a one pot technique that protected the porous morphology of the membranes from thermally induced structural rearrangement during carbonization. Permeation studies using benzene derivatives whose kinetic diameters differ by less than an angstrom show kinetically selective organic liquid reverse osmosis. Ratios of single component fluxes for para and ortho xylene exceeding 25 were observed and para and ortho liquid mixtures were efficiently separated, with an equimolar feed enriched to 81 mole para xylene, without phase change and at ambient temperature.
Integrating Axiomatic and Analogical Reasoning
Venue: AGI

We present a computational model of a developing system with bounded rationality that is surrounded by an arbitrary number of symbolic domains. The system is fully automatic and makes continuous observations of facts emanating from those domains. The system starts from scratch and gradually evolves a knowledge base consisting of three parts: (1) a set of beliefs for each domain, (2) a set of rules for each domain, and (3) an analogy for each pair of domains. The learning mechanism for updating the knowledge base uses rote learning, inductive learning, analogy discovery, and belief revision. The reasoning mechanism combines axiomatic reasoning for drawing conclusions inside the domains, with analogical reasoning for transferring knowledge from one domain to another. Thus the reasoning processes may use analogies to jump back and forth between domains.
Recent Developments in Design and Fabrication of Graphene Based Interdigital Micro Supercapacitors for Miniaturized Energy Storage Devices
Venue: IEEE Transactions on Components, Packaging and Manufacturing Technology

The rapid development of miniaturized electronic devices requires state of the art small form factor energy storage units for power supply. Micro supercapacitors (MSCs) as microscale supercapacitors, have great potential to replace microbatteries or electrolytic capacitors due to their high power density and long cycling stability. The planar design of interdigital microelectrodes not only shortens the ionic diffusion distance but also enables the direct integration of the device on chip or on a flexible substrate for various applications. Graphene, with a unique 2 D nanostructure, has been extensively studied as a supercapacitor material with superior charge storage capability. Recently, the progress in thin film fabrication technologies has facilitated the development of graphene based MSCs, which results in device miniaturization and improved areal capacitance. This paper reviews the recent advances on the design of graphene based MSCs and discusses the technical trend regarding large scale fabrication and stretchable/wearable energy storage devices.
Potentialities of micro supercapacitors as energy storage buffers in embedded micro systems
Venue: 2016 Symposium on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP)

Thanks to their unique properties and potential applications in various smart electronic devices, micro supercapacitors have been increasingly investigated for on chip energy storage. In this presentation, we describe our most recent attempts to increase the areal capacitance and energy of micro supercapacitors. We deposited hydrated ruthenium oxide onto a highly porous gold current collector to realize a 3D electrode with unprecedented performances, fully compatible with current micro fabrication and silicon based device technology. The resulting electrode exhibits a capacitance per surface area in excess of 3 F.cm 2, and for the first time an all solid state micro supercapacitor with a specific energy per surface area comparable to that of lithium ion micro batteries has been achieved, but with superior power and cycling stability.
A new fuzzy regression model based on least absolute deviation
Venue: Eng. Appl. Artif. Intell.

Fuzzy set theory is a powerful tool to describe and process uncertainty information which exist in real world, and fuzzy regression is an important research topic which can be used to fulfill predicting by establishing the functional relationship between fuzzy variables. Trapezoidal fuzzy number is a common one which can represent other types of fuzzy numbers, and least absolute deviation is a robust method which is insensitive to outliers. So, in this paper, we propose a new fuzzy regression model based on trapezoidal fuzzy number and least absolute deviation method. Firstly, we introduce a new distance measure between trapezoidal fuzzy numbers which is the basis for applications, and merge least absolute deviation with the proposed distance measure to investigate fuzzy regression model whose parameters can be trapezoidal fuzzy numbers. Meanwhile, we investigate the model algorithms for three cases in detail, including different types of inputs, outputs and regression coefficients. Finally, we use four numerical examples to illustrate that our proposed model is reasonable, compare our proposed model with some existing fuzzy regression models, and do comprehensive analysis about the proposed model. The results show that our proposed model is robust, and has better fitting effect.
New identification method for Hammerstein models based on approximate least absolute deviation
Venue: Int. J. Syst. Sci.

Disorder and peak noises or large disturbances can deteriorate the identification effects of Hammerstein non linear models when using the least square (LS) method. The least absolute deviation technique can be used to resolve this problem; however, its absolute value cannot meet the need of differentiability required by most algorithms. To improve robustness and resolve the non differentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function that exhibits the characteristics of absolute value under certain situations. A new identification method for Hammerstein models based on ALAD is thus developed in this paper. The basic idea of this method is to apply the stochastic approximation theory in the process of deriving the recursive equations. After identifying the parameter matrix of the Hammerstein model via the new algorithm, the product terms in the matrix are separated by calculating the average values. Finally, algorithm convergence is proven by applying the ordinary differential equation method. The proposed algorithm has a better robustness as compared to other LS methods, particularly when abnormal points exist in the measured data. Furthermore, the proposed algorithm is easier to apply and converges faster. The simulation results demonstrate the efficacy of the proposed algorithm.
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4D printing smart biomedical scaffolds with novel soybean oil epoxidized acrylate
Venue: Scientific reports

Photocurable, biocompatible liquid resins are highly desired for 3D stereolithography based bioprinting. Here we solidified a novel renewable soybean oil epoxidized acrylate, using a 3D laser printing technique, into smart and highly biocompatible scaffolds capable of supporting growth of multipotent human bone marrow mesenchymal stem cells (hMSCs) Porous scaffolds were readily fabricated by simply adjusting the printer infill density; superficial structures of the polymerized soybean oil epoxidized acrylate were significantly affected by laser frequency and printing speed. Shape memory tests confirmed that the scaffold fixed a temporary shape at 18 degC and fully recovered its original shape at human body temperature (37 degC) which indicated the great potential for 4D printing applications. Cytotoxicity analysis proved that the printed scaffolds had significant higher hMSC adhesion and proliferation than traditional polyethylene glycol diacrylate (PEGDA) and had no statistical difference from poly lactic acid (PLA) and polycaprolactone (PCL) This research is believed to significantly advance the development of biomedical scaffolds with renewable plant oils and advanced 3D fabrication techniques.
The Nose Bleed Syndrome: A Study on the Attitude of the College Students towards English Language Learning and Their Linguistic Self Confidence in a Multi Cultural Context

The expression "nose bleed!" has become the most popular expression among Filipinos when asked to speak in English. In the Philippines, English is the most feared language. This fear is seen in classes where students feel so anxious that they may mispronounce a word or that they may commit grammatical inaccuracies. This also happens during job interviews when most of the applicants would sweat out while using English. The study was undertaken to determine the profile of the select students as respondents of the study, their attitude towards English language learning, and the situation/s that they feel linguistically confident in using the target language in a multi cultural context. The study was conducted at the Mindanao State University (MSU) Main Campus, in the Islamic City of Marawi. The study utilized both quantitative and qualitative data collection and analysis procedures. Survey questionnaire and Focus Group Discussion (FGD) were employed. The questionnaire was administered to 129 randomly selected Job Enabling English Proficiency (JEEP) Program students, and then the FGD was conducted to probe further certain points that require illumination. It was concluded that the respondents have a positive attitude toward learning the English language. It was also disclosed that the linguistic self confidence of the respondents leaned more on the situation specific self confidence.
What's in a university logo? Building commitment in higher education

Achieving commitment can be challenging for the service industry, particularly for universities. If these service organizations can align and convey that their identity or image is beneficial toward their stakeholders, commitment is achievable. The present article examines a specific form of brand identity and image, namely brand logo benefit, and establishes that self congruence is the driver of 'brand logo benefit' and that brand logo benefit positively influences commitment. Drawing on the self concept theory, the study develops and empirically tests a conceptual model using survey data collected from 478 students in Indonesia. The study demonstrates that self congruence (actual or ideal) affects the perceived brand logo benefit and brand logo benefit positively affects commitment. In addition, brand logo benefit partially mediates the link between self congruence and commitment. Results indicate that actual self congruence is a slightly better predictor of brand logo benefit compared with ideal self congruence. Interestingly, ideal self congruence is a slightly better predictor of commitment. Discussions and implications are provided.
Stimulating tensions in special education teachers' figured world: an approach toward inclusive education

I examine special educators' professional identity emergence and tensions within a researcher facilitated teacher learning community. I introduced tools to evoke and challenge inequities in educational systems and via which participants examined and planned general education instruction for students with dis/abilities. Initially, professional identity, or figured world, emerged as performance of pathologising and relatedly, remediating students. Over time, participants expressed tensions as they engaged tools to examine structural limitations and design more universally accessible instruction; figured worlds shifted to critical sense making about their positioning by general education colleagues and school structural barriers, and procedural identity performance tied to investigating student assets. Findings suggest potential for purposefully designed artefacts to mediate special educators' development as (more) inclusive educators.
A programmable metasurface with dynamic polarization, scattering and focusing control
Venue: Scientific reports

Diverse electromagnetic (EM) responses of a programmable metasurface with a relatively large scale have been investigated, where multiple functionalities are obtained on the same surface. The unit cell in the metasurface is integrated with one PIN diode, and thus a binary coded phase is realized for a single polarization. Exploiting this anisotropic characteristic, reconfigurable polarization conversion is presented first. Then the dynamic scattering performance for two kinds of sources, i.e. a plane wave and a point source, is carefully elaborated. To tailor the scattering properties, genetic algorithm, normally based on binary coding, is coupled with the scattering pattern analysis to optimize the coding matrix. Besides, inverse fast Fourier transform (IFFT) technique is also introduced to expedite the optimization process of a large metasurface. Since the coding control of each unit cell allows a local and direct modulation of EM wave, various EM phenomena including anomalous reflection, diffusion, beam steering and beam forming are successfully demonstrated by both simulations and experiments. It is worthwhile to point out that a real time switch among these functionalities is also achieved by using a field programmable gate array (FPGA) All the results suggest that the proposed programmable metasurface has great potentials for future applications.
Minimizing Denial of Service Attack for Multiple Base Stations in Wireless Sensor Network

Wireless sensor network (WSN) is a network communication technology that is characterized by its small size; low power consumption and limited memory capacity. It provides communication in a broadcast manner. These sensor nodes have no specific infrastructure and they are placed randomly throughout the network. The use of WSNs is to collect data from physical environment and in real time. Error in the collected data or malfunctioning of the sensors can create problems on large scale. There are few ways and methods through which WSNs can be attacked. One of them is the Denial of Service Attack (DoS) which captures the nodes and takes its important data, modifies it or halt it from responding to a bona fide user. Various techniques are used to tackle DoS attack on WSNS. By using the multiple base station approach we can reduce and minimize the drastic effect of DoS attack on WSNs and makes our network secure and functioning in its normal conditions
Intrusion Detection System to Overcome a Novel Form of Replay Attack (Data Replay) in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely and successfully employed in various application domains. They are easily deployed to collect valuable information and monitor potential environmental phenomena. However, the special nature of WSNs as well as their severe constraints and resource limitations make them vulnerable to various types of threats. Replay attack, is one example. According to this attack, the adversary intercepts and replays several times the same (old) message leading either to missed alerts or to false alerts. Many solutions have been proposed to mitigate message replay attack. However, all these solutions are of cryptographic natures and consider only external attacks exercising a trivial scenario of replay attack. In fact, the attacker could be a lot smarter, and in this case, it replays only the data field in the message while keeping the remaining fields updated. This novel form of replay attack is much more dangerous and difficult to be detected. We call this attack variant by data replay attack. As sensor nodes may be easily captured and compromised, the worst scenario occurs if data replay attack is performed by an internal intruder. In this paper we propose an efficient intrusion detection framework to overcome data replay attack in WSNs. The proposed intrusion detection system is named DR IDS (Data Replay Intrusion Detection System) The performance evaluations performed under NS2 simulator show that the proposed solution is sufficiently robust.