nils-herrmann/clean_scirepeval huggingface dataset:
Spoofing Face Recognition With 3D Masks

Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In this paper, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D, and 3D face recognition and to analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database.
Resource discovery in Internet of Things: Current trends and future standardization aspects

To realize the vision of Internet of Things, there must be mechanisms to discover resources and their capabilities. Thus resource discovery becomes a fundamental requirement of any IoT platform. This paper provides a comprehensive categorization of the current technology landscape for discovery while pointing out their advantages and limitations. Then, a novel search engine based resource discovery framework is proposed. The framework comprises of a proxy layer which includes drivers for various communication technologies and protocols. There is a central registry to store the configurations of the resources and index them based on the configuration parameters. A “lifetime” attribute is introduced which denotes the period through which a resource is discoverable. The search engine ranks the resulting resources of a discovery request and returns an URI to directly access each resource. The functionalities of the proposed framework are exposed using RESTful web services. The framework allows discovery of both smart and legacy devices. We have performed gap analysis of current IoT standards for discovery mechanisms and provide suggestions to improve them to maintain interoperability. The outcomes are communicated to Standard Development Organizations like W3C (in Web of Things Interest Group) and oneM2M.
Prediction of Indian election using sentiment analysis on Hindi Twitter

Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.
Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements

Recent advances in wireless sensor networks have led to renewed interests in the problem of source localization. Source localization has broad range of applications such as emergency rescue, asset inventory, and resource management. Among various measurement models, one important and practical source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for source localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing.
A 5-GHz fully integrated full PMOS low-phase-noise LC VCO

A 5-GHz fully integrated, full PMOS, low-phase-noise and low-power differential voltage-controlled oscillator (VCO) is presented. This circuit is implemented in a 0.35-/spl mu/m four-metal BiCMOS SiGe process. At 2.7-V power supply voltage and a total power dissipation of only 13.5 mW, the proposed VCO features a worst case phase noise of -97 dBc/Hz and -117 dBc/Hz at 100 kHz and 1 MHz frequency offset, respectively. The oscillator is tuned from 5.13 to 5.68 GHz with a tuning voltage varying from 0 to 2.7 V.
$LCL$ Filter Design and Performance Analysis for Grid-Interconnected Systems

The use of power converters is very important in maximizing the power transfer from renewable energy sources such as wind, solar, or even a hydrogen-based fuel cell to the utility grid. An LCL filter is often used to interconnect an inverter to the utility grid in order to filter the harmonics produced by the inverter. Although there is an extensive amount of literature available describing LCL filters, there has been a gap in providing a systematic design methodology. Furthermore, there has been a lack of a state-space mathematical modeling approach that considers practical cases of delta- and wye-connected capacitors showing their effects on possible grounding alternatives. This paper describes a design methodology of an LCL filter for grid-interconnected inverters along with a comprehensive study of how to mitigate harmonics. The procedures and techniques described in this paper may be used in small-scale renewable energy conversion systems and may be also retrofitted for medium- and large-scale grid-connected systems.
Presentation Attack Detection for Cadaver Iris

This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Post-mortem iris recognition, despite being a potentially useful method that could aid forensic identification, can also pose challenges when used inappropriately, i.e. utilizing a dead organ of a person in an unauthorized way. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the WarsawBioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER≈1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of anteand post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of anteand post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a postmortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.
Cracking the Bluetooth PIN

This paper describes the implementation of an attack on the Bluetooth security mechanism. Specifically, we describe a passive attack, in which an attacker can find the PIN used during the pairing process. We then describe the cracking speed we can achieve through three optimizations methods. Our fastest optimization employs an algebraic representation of a central cryptographic primitive (SAFER+) used in Bluetooth. Our results show that a 4-digit PIN can be cracked in less than 0.3 sec on an old Pentium III 450MHz computer, and in 0.06 sec on a Pentium IV 3Ghz HT computer.
A stretchable PIFA antenna

This paper presents a stretchable Planar Inverted-F Antenna (PIFA) based on a new metallization technique of depositing nanoscale thin (50/100nm) Au film on elastomer Polydimethylsiloxane (PDMS). The thin metal films can be reversibly stretched up to 20% without losing electrical conduction. The PIFA antenna made of new materials can work under a 10% strain and return to its original state after removal of an applied stress. Simulation and measurement results for the antenna performance are given.
Symbolic representations of action in the human cerebellum

Cerebellar cortical areas connected to the neocortical motor system process information important for the sensory guidance of action. Converging evidence also supports the view that cerebellar cortical areas connected with the prefrontal cortex process information similarly in the cognitive domain. Here, we test the hypothesis that the prefrontal-projecting zones in the human cerebellum process the abstract content of information embedded within sensory cues. Specifically, we use event-related fMRI to determine whether symbolic visual instructions activate the prefrontal-projecting zones of the cerebellum. On the basis of connectional anatomy, we predicted that such activity would be found in lobule HVIIA and adjacent vermal territories in the same lobule. Our experimental design enabled us to investigate activity time-locked specifically to instructions foraction that were either purely symbolic, or specified actions directly. Such activity was independent of action. Activity specifically time-locked to symbolic cues (compared with non-symbolic control cues) activated cerebellar cortical lobule HVIIA (Crus I and Crus II). Our results provide support for the view that prefrontal-projecting areas of the cerebellar cortex process information that is of a purely abstract nature.
Adversarial Networks for the Detection of Aggressive Prostate Cancer

Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the ‘structure’ of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.
Reducing Specific Energy to Shrink the Carbon Footprint in a Copper Electrowinning Facility

Various technologies and strategies to reduce specific energy consumption are examined. Results include 16 harvest cycles for 30 cells, totaling 62 400 cathodes with a weight of 2500 ton. The industrial site was Zaldivar, which is a Barrick mining operation located at 175 km southeast from Antofagasta, Chile. The production averages 140 000 ton/year of copper cathodes. With the technologies tested in this paper, 14 000 MWh and corresponding 14 000 ton of CO2 will be saved per year.
Human Identification From Freestyle Walks Using Posture-Based Gait Feature

With the increase of terrorist threats around the world, human identification research has become a sought after area of research. Unlike standard biometric recognition techniques, gait recognition is a non-intrusive technique. Both data collection and classification processes can be done without a subject’s cooperation. In this paper, we propose a new model-based gait recognition technique called postured-based gait recognition. It consists of two elements: posture-based features and posture-based classification. Posture-based features are composed of displacements of all joints between current and adjacent frames and center-of-body (CoB) relative coordinates of all joints, where the coordinates of each joint come from its relative position to four joints: hip-center, hip-left, hip-right, and spine joints, from the front forward. The CoB relative coordinate system is a critical part to handle the different observation angle issue. In posture-based classification, postured-based gait features of all frames are considered. The dominant subject becomes a classification result. The postured-based gait recognition technique outperforms the existing techniques in both fixed direction and freestyle walk scenarios, where turning around and changing directions are involved. This suggests that a set of postures and quick movements are sufficient to identify a person. The proposed technique also performs well under the gallery-size test and the cumulative match characteristic test, which implies that the postured-based gait recognition technique is not gallery-size sensitive and is a good potential tool for forensic and surveillance use.
Joint bilateral upsampling

Image analysis and enhancement tasks such as tone mapping, colorization, stereo depth, and photomontage, often require computing a solution (e.g., for exposure, chromaticity, disparity, labels) over the pixel grid. Computational and memory costs often require that a smaller solution be run over a downsampled image. Although general purpose upsampling methods can be used to interpolate the low resolution solution to the full resolution, these methods generally assume a smoothness prior for the interpolation.
 We demonstrate that in cases, such as those above, the available high resolution input image may be leveraged as a prior in the context of a joint bilateral upsampling procedure to produce a better high resolution solution. We show results for each of the applications above and compare them to traditional upsampling methods.
Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction

In this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint. We show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications.
Speaker Diarization with LSTM

For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of d-vector based speaker verification systems to develop a new d-vector based approach to speaker diarization. Specifically, we combine LSTM-based d-vector audio embeddings with recent work in nonparametric clustering to obtain a state-of-the-art speaker diarization system. Our system is evaluated on three standard public datasets, suggesting that d-vector based diarization systems offer significant advantages over traditional i-vector based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000 CALLHOME, while our model is trained with out-of-domain data from voice search logs.
A high efficient automotive roof-antenna concept for LTE, DAB-L, GNSS and SDARS with low mutual coupling

A combination of a new antenna for Long Term Evolution (LTE) technology and Digital Audio Broadcasting in L-band (DAB-L) together with compact antennas for Global Navigation Satellite System (GNSS) and Satellite Digital Audio Radio Services (SDARS) is presented. The antenna module is designed to be placed on the roof of a car underneath a shark-fin-formed housing. The LTE/DAB-L antenna operates at frequency bands - from 698 MHz to 960 MHz and from 1427.9 MHz to 2700 MHz - covering available LTE frequencies. This antenna has a high efficiency due to panel design and obtains monopole characteristics in all LTE frequencies. In combination with GNSS and SDARS ring antennas the structure yields a sufficient decoupling to these antennas. This is even true for the SDARS antenna sharing the same phase center with the LTE-antenna. The satellite antennas achieve the required characteristics at their center frequencies of 1575 MHz and 2332 MHz.
Factors Affecting Online Search Intention and Online Purchase Intention

This research focuses on various factors affecting online search intention which has been found to be a key predictor of online purchase intention. Data were collected from a sample consisting of mostly young adults with familiarity of computer use and online shopping experience. A structural equation model was employed to test hypotheses. According to the findings, utilitarian value of Internet information search, hedonic value of Internet information search, perceived benefits of Internet shopping, perceived risk of Internet shopping, and Internet purchase experience predicted online search intention well. The findings also showed that online search intention positively affects * Professor of Marketing, College of Business Administration, Seoul National University(jaekim@snu.ac.kr) ** Marketing, Telephone Service Team, Hanaro Telecom Co., Inc. (heechun@hanaro.com) *** M.B.A. candidate, College of Business Administration, Seoul National University(haejoo80@yahoo.com) This research was supported by the Institute of Management Research, Seoul National University. Seoul Journal of Business Volume 10, Number 2 (December 2004)
Hierarchical Deep Multiagent Reinforcement Learning

Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Sparse subspace clustering

We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points. In general, finding such a SR is NP hard. Our key contribution is to show that, under mild assumptions, the SR can be obtained `exactly' by using l1 optimization. The segmentation of the data is obtained by applying spectral clustering to a similarity matrix built from this SR. Our method can handle noise, outliers as well as missing data. We apply our subspace clustering algorithm to the problem of segmenting multiple motions in video. Experiments on 167 video sequences show that our approach significantly outperforms state-of-the-art methods.