nils-herrmann/clean_scirepeval huggingface dataset:
Sign language recognition using Microsoft Kinect

In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.
Human Body as Antenna and Its Effect on Human Body Communications

Human body communication (HBC) is a promising wireless technology that uses the human body as part of the communication channel. HBC operates in the near-field of the high frequency (HF) band and in the lower frequencies of the very high frequency (VHF) band, where the electromagnetic field has the tendency to be confined inside the human body. Electromagnetic interference poses a serious reliability issue in HBC; consequently, it has been given increasing attention in regard to adapting techniques to curtail its degrading effect. Nevertheless, there is a gap in knowledge on the mechanism of HBC interference that is prompted when the human body is exposed to electromagnetic fields as well as the effect of the human body as an antenna on HBC. This paper narrows the gap by introducing the mechanisms of HBC interference caused by electromagnetic field exposure of human body. We derived analytic expressions for induced total axial current in the body and associated fields in the vicinity of the body when an imperfectly conducting cylindrical antenna model of the human body is illuminated by a vertically polarized plane wave within the 1–200 MHz frequency range. Also, fields in the vicinity of the human body model from an on-body HBC transmitter are calculated. Furthermore, conducted electromagnetic interference on externally embedded HBC receivers is also addressed. The results show that the maximum HBC gain near 50 MHz is due to whole-body resonance, and the maximum at 80 MHz is due to the resonance of the arm. Similarly, the results also suggest that the magnitude of induced axial current in the body due to electromagnetic field exposure of human body is higher near 50MHz.
The Cyber Security Modeling Language: A Tool for Assessing the Vulnerability of Enterprise System Architectures

The cyber security modeling language (CySeMoL) is a modeling language for enterprise-level system architectures coupled to a probabilistic inference engine. If the computer systems of an enterprise are modeled with CySeMoL, this inference engine can assess the probability that attacks on the systems will succeed. The theory used for the attack-probability calculations in CySeMoL is a compilation of research results on a number of security domains and covers a range of attacks and countermeasures. The theory has previously been validated on a component level. In this paper, the theory is also validated on a system level. A test indicates that the reasonableness and correctness of CySeMoL assessments compare with the reasonableness and correctness of the assessments of a security professional. CySeMoL's utility has been tested in case studies.
Information flow and cooperative control of vehicle formations

We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.
A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.
Chernoff-Hoeffding Inequality and Applications

When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making “many simple estimates” of the full data set, and then judging them as a whole. Perhaps magically, these “many simple estimates” can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 6]. This document provides a simple form of this bound, and two examples of its use.
Biocompatible PEGylated MoS2 nanosheets: controllable bottom-up synthesis and highly efficient photothermal regression of tumor.

Two-dimensional transition metal dichalcogenides, particularly MoS2 nanosheets, have been deemed as a novel category of NIR photothermal transducing agent. Herein, an efficient and versatile one-pot solvothermal synthesis based on "bottom-up" strategy has been, for the first time, proposed for the controlled synthesis of PEGylated MoS2 nanosheets by using a novel "integrated" precursor containing both Mo and S elements. This facile but unique PEG-mediated solvothermal procedure endowed MoS2 nanosheets with controlled size, increased crystallinity and excellent colloidal stability. The photothermal performance of nanosheets was optimized via modulating the particulate size and surface PEGylation. PEGylated MoS2 nanosheets with desired photothermal conversion performance and excellent colloidal and photothermal stability were further utilized for highly efficient photothermal therapy of cancer in a tumor-bearing mouse xenograft. Without showing observable in vitro and in vivo hemolysis, coagulation and toxicity, the optimized MoS2-PEG nanosheets showed promising in vitro and in vivo anti-cancer efficacy.
Beyond the 'east-west' dichotomy: Global variation in cultural models of selfhood.

Markus and Kitayama's (1991) theory of independent and interdependent self-construals had a major influence on social, personality, and developmental psychology by highlighting the role of culture in psychological processes. However, research has relied excessively on contrasts between North American and East Asian samples, and commonly used self-report measures of independence and interdependence frequently fail to show predicted cultural differences. We revisited the conceptualization and measurement of independent and interdependent self-construals in 2 large-scale multinational surveys, using improved methods for cross-cultural research. We developed (Study 1: N = 2924 students in 16 nations) and validated across cultures (Study 2: N = 7279 adults from 55 cultural groups in 33 nations) a new 7-dimensional model of self-reported ways of being independent or interdependent. Patterns of global variation support some of Markus and Kitayama's predictions, but a simple contrast between independence and interdependence does not adequately capture the diverse models of selfhood that prevail in different world regions. Cultural groups emphasize different ways of being both independent and interdependent, depending on individualism-collectivism, national socioeconomic development, and religious heritage. Our 7-dimensional model will allow future researchers to test more accurately the implications of cultural models of selfhood for psychological processes in diverse ecocultural contexts.
Design and simulation of 13.56 MHz RFID tag in ink-jet printing technology

This paper presents design, modeling and simulation cost-effective RFID (Radio Frequency Identification) tag using ink-jet printing technology. Passive 13.56 MHz RFID tag is based on an inductive-capacitive resonant circuit. Designed RFID tag is part of the ID (Identification) card, which has standard ISO ID1 size (85.60 mm × 53.98 mm). It consists of rectangular spiral planar inductor (antenna) and interdigital capacitor. The modeling and optimization of RFID tag are discussed. At the first step, inductor and capacitor were designed using in-house developed simulation tools AntInd and INDICON, respectively, in order to provide proper electrical characteristics of tag. After that, the designed geometrical structures are modeled and simulated using CST Microwave Studio software. A radiation pattern at operational frequency of 13.56 MHz simulated in CST of the RFID tag insulated in free spaces is presented.
ASSET STOCK ACCUMULATION AND SUSTAINABILITY OF COMPETITIVE ADVANTAGE

Given incomplete factor markets, appropriate time paths of flow variables must be chosen to build required stocks of assets. That is, critical resources are accumulated rather than acquired in "strategic factor markets" (Barney 1986). Sustainability of a firm's asset position hinges on how easily assets can be substituted or imitated. Imitability is linked to the characteristics of the asset accumulation process: time compression diseconomies, asset mass efficiencies, inter-connectedness, asset erosion and causal ambiguity.
The Detection of Fraudulent Financial Statements: an Integrated Language Model

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements.
Dual-Polarized, Differential Fed Microstrip Patch Antennas With Very High Interport Isolation for Full-Duplex Communication

This communication presents two dual-polarized microstrip patch antenna systems based on a single radiating element with 180° ring hybrid coupler for differential feeding to achieve high interport RF isolation. One of the implemented single-layer patch antennas provides more than 67 dB isolation between transmit (Tx) and receive (Rx) ports at 2.4 GHz while the second fabricated antenna with slot coupled Tx port along with differential feeding for Rx operation provides more than 90 dB RF isolation at 2.41 GHz between dc isolated Tx–Rx ports. Moreover, the single-layer antenna provides better than 62 dB interport isolation for 10 dB-return loss impedance bandwidth (BW) of 50 MHz. Measured interport isolation for implemented antenna with slot coupled Tx port achieves around 70 and 79 dB port to port RF isolation for 50 and 20 MHz BWs, respectively, and these are the highest amounts of isolation for a single antenna to best of our knowledge. The antenna with slot coupled Tx port provides more interport RF isolation as compared to single-layer antenna due to low coupling between slot coupled and co-planar quarter-wave microstrip fed ports. Both antenna structures have the capability to remove in-band nonlinear self-interference (SI) components without complex SI cancellation circuits.
Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images

This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.
Joint Learning of CNN and LSTM for Image Captioning

In this paper, we describe the details of our methods for the participation in the subtask of the ImageCLEF 2016 Scalable Image Annotation task: Natural Language Caption Generation. The model we used is the combination of a procedure of encoding and a procedure of decoding, which includes a Convolutional neural network(CNN) and a Long Short-Term Memory(LSTM) based Recurrent Neural Network. We first train a model on the MSCOCO dataset and then fine tune the model on different target datasets collected by us to get a more suitable model for the natural language caption generation task. Both of the parameters of CNN and LSTM are learned together.
The Recognition of Human Movement Using Temporal Templates

ÐA new view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal templateÐa static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms. Index TermsÐMotion recognition, computer vision.
Design of High-Performing Microstrip Receiving GPS Antennas With Multiple Feeds

In this letter, we present the design of two new high-performing microstrip receiving antennas for the Global Positioning System (GPS). Both layouts proposed here are based on the employment of multiple feeds in order to improve the polarization purity and the pattern symmetry, thus leading to an improved accuracy of the system. The first antenna configuration consists of a truncated-corner squared patch with concentric rings of miniaturized magnetic resonators in order to suppress the surface wave contribution and reduce the back-radiation of the antenna. The second radiator consists of a circular patch antenna with four feeds and loaded with a number of vias in order to improve the front-to-back ratio. Full-wave numerical simulations are presented to show the effectiveness of the proposed design in terms of impedance matching, polarization purity, antenna gain, front-to-back ratio, and shape of the radiation pattern.
On the effectiveness of local binary patterns in face anti-spoofing

Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ~15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.
Zebra: An East-West Control Framework for SDN Controllers

Traditional networks are surprisingly fragile and difficult to manage. Software Defined Networking (SDN) gained significant attention from both academia and industry, as if simplify network management through centralized configuration. Existing work primarily focuses on networks of limited scope such as data-centers and enterprises, which makes the development of SDN hindered when it comes to large-scale network environments. One way of enabling communication between data-centers, enterprises and ISPs in a large-scale network is to establish a standard communication mechanism between these entities. In this paper, we propose Zebra, a framework for enabling communication between different SDN domains. Zebra has two modules: Heterogeneous Controller Management (HCM) module and Domain Relationships Management (DRM) module. HCM collects network information from a group of controllers with no interconnection and generate a domain-wide network view. DRM collects network information from other domains to generate a global-wide network view. Moreover, HCM supports different SDN controllers, such as floodlight, maestro and so on. To test this framework, we develop a prototype system, and give some experimental results.
A glasses-type wearable device for monitoring the patterns of food intake and facial activity

Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F1 score of the classification was about 94.0% and the accuracy above 89%. We believe this approach will be helpful for designing a non-intrusive and un-obtrusive eyewear-based ingestive behavior monitoring system.
Threat awareness for critical infrastructures resilience

Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.