nils-herrmann/clean_scirepeval huggingface dataset:
3D Convolutional Neural Network Based on Face Anti-spoofing

Face anti-spoofing is very significant to the security of face recognition. Many existing literatures focus on the study of photo attack. For the video attack, however, the related research efforts are still insufficient. In this paper, instead of extracting features from a single image, features are learned from video frames. To realize face anti-spoofing, the spatiotemporal features of continuous video frames are extracted using 3D convolution neural network (CNN) from the short video frame level. Experimental results show that the two sets of face anti-spoofing public databases, Replay-Attack and CASIA, have achieved the HTER (Half Total Error Rate) of 0.04% and 10.65%, respectively, which is better than the state-of-the-art.
Controller Area Network for Vehicle Automation

Based on requirements of modern vehicle, invehicle Controller Area Network (CAN) architecture has been implemented. In order to reduce point to point wiring harness in vehicle automation, CAN is suggested as a means for data communication within the vehicle environment. The benefits of CAN bus based network over traditional point to point schemes will offer increased flexibility and expandability for future technology insertions. This paper describes the ARM7 based design and implementation of CAN Bus prototype for vehicle automation. It focus on hardware and software design of intelligent node. Hardware interface circuit mainly consists of MCP2515 stand alone CAN-Controller with SPI interface, LPC2148 microcontroller based on 32-bit ARM7 TDMI-S CPU and MCP2551 high speed CAN Transceiver. MCP2551 CAN Transceiver implements ISO-11898 standard physical layer requirements. The software design for CAN bus network are mainly the design of CAN bus data communication between nodes, and data processing for analog signals. The design of software communication module includes system initialization and CAN controller initialization unit, message sending unit, message receiving unit and the interrupt service unit. Vehicle Automation, Controller Area Network (CAN), Electronic Control Unit (ECU), CANopen, LIN, SAE J1939.
Information extraction from scientific paper using rhetorical classifier

Time constraints often lead a reader of scientific paper to read only the title and abstract of the paper, but reading these parts is often ineffective. This study aims to extract information automatically in order to help the readers get structured information from a scientific paper. The information extraction is done by rhetorical classification of each sentence in a scientific paper. Rhetoric information is the intention to be conveyed to the reader by the author of the paper. This research used corpus-based approach to build rhetorical classifier. Since there was a lack of rethorical corpus, we constructed our own corpus, which is a collection of sentences that have been labeled with rhetorical information. Each sentence represented as a vector of content, location, citation, and meta-discourses features. This collection of feature vectors is used to build rhetorical classifiers by using machine learning techniques. Experiments were conducted to select the best learning techniques for rhetorical classifier. Training set consists of 7239 labeled sentences, and the testing set consists of 3638 labeled sentences. We used WEKA (Waikato Environment for Knowledge Analysis) and LibSVM libraries. Learning techniques being considered were Naive Bayes, C4.5, Logistic, Multi-Layer Perceptron, PART, Instance-based Learning, and Support Vector Machines (SVM). The best performers are the SVM and Logistic classifier with accuracy of 0.51. By applying one-against-all strategy, the SVM accuracy can be improved to 0.60.
The Diabolo Classifier

We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each class. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or radial basis functions: it has a low computational complexity and uses a compact distributed representation of the models. The classifier is also well suited for the incorporation of a priori knowledge by means of a problem-specific distance measure. In particular, we will show that tangent distance (Simard, Le Cun, & Denker, 1993) can be used to achieve transformation invariance during learning and recognition. We demonstrate the application of this classifier to optical character recognition, where it has achieved state-of-the-art results on several reference databases. Relations to other models, in particular those based on principal component analysis, are also discussed.
Creating Capsule Wardrobes from Fashion Images

We propose to automatically create capsule wardrobes. Given an inventory of candidate garments and accessories, the algorithm must assemble a minimal set of items that provides maximal mix-and-match outfits. We pose the task as a subset selection problem. To permit efficient subset selection over the space of all outfit combinations, we develop submodular objective functions capturing the key ingredients of visual compatibility, versatility, and user-specific preference. Since adding garments to a capsule only expands its possible outfits, we devise an iterative approach to allow near-optimal submodular function maximization. Finally, we present an unsupervised approach to learn visual compatibility from "in the wild" full body outfit photos; the compatibility metric translates well to cleaner catalog photos and improves over existing methods. Our results on thousands of pieces from popular fashion websites show that automatic capsule creation has potential to mimic skilled fashionistas in assembling flexible wardrobes, while being significantly more scalable.
Logistic regression analysis on the determinants of stillbirth in Ethiopia

Stillbirth is often defined as fetal death after 24 weeks of gestation, but a fetus greater than any combination of 16, 20, 22, 24, or 28 weeks gestational age and 350 g, 400 g, 500 g, or 1000 g birth weight may be considered stillborn depending on local law. Once the fetus has died, the mother may or may not have contractions and undergo childbirth or in some cases, a Caesarean section. Most stillbirths occur in full-term pregnancies.



This study has intended to model determinants of experiencing stillbirth among women in child bearing age group of Ethiopia using the Ethiopian demographic and health Survey data (EDHS, 2011). First, the bivariate chi-square test of association was fitted to the data and significant variables were considered for further investigation binary logistic regression models were fitted.



This study revealed that the rate of experiencing stillbirth among women of child bearing age was about 25.5 per 1000 deliveries in Ethiopia. From binary logistic regression, region of residence, maternal age, place of residence, education level, parity, antenatal care utilization, place of delivery, body mass index (BMI) and anemia level were found to be significantly associated with experiencing stillbirth.



Researchers should use multilevel models than traditional regression methods when their data structure is hierarchical as like in Ethiopian Demographic and Health Survey data.
Improved Surface Quality in 3D Printing by Optimizing the Printing Direction

We present a pipeline of algorithms that decomposes a given polygon model into parts such that each part can be 3D printed with high (outer) surface quality. For this we exploit the fact that most 3D printing technologies have an anisotropic resolution and hence the surface smoothness varies significantly with the orientation of the surface. Our pipeline starts by segmenting the input surface into patches such that their normals can be aligned perpendicularly to the printing direction. A 3D Voronoi diagram is computed such that the intersections of the Voronoi cells with the surface approximate these surface patches. The intersections of the Voronoi cells with the input model’s volume then provide an initial decomposition. We further present an algorithm to compute an assembly order for the parts and generate connectors between them. A post processing step further optimizes the seams between segments to improve the visual quality. We run our pipeline on a wide range of 3D models and experimentally evaluate the obtained improvements in terms of numerical, visual, and haptic quality.
The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference

Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (vector autoregressive) modelling.


NEW METHOD
The MVGC Matlab© Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy.



In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference.


COMPARISON WITH EXISTING METHOD(S)
The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain.



The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference.
Fast Convergence Strategy for Multi-Image Superresolution via Adaptive Line Search

Multi-image superresolution (SR) techniques produce a high-resolution image from several low-resolution observations. Previous reconstruction-based SR approaches focus more on the optimization models but have not adequately emphasized the mathematic-solving techniques for this typically ill-conditioned and under-determined large scale problem. Since step size plays an important role in the iterative SR process, and there is a tradeoff between less computation cost and higher accuracy, conventional SR methods either adopt a fixed step size to obtain a higher running speed, or use a computationally expensive line search algorithm to pursue an improvement in accuracy. Taking both cues into consideration, in this paper, we propose an adaptive line search strategy to realize the fast convergence of reconstruction-based SR. The approximate analytical expression of step size is introduced to prevent us from setting it empirically or running iterations to test a proper one. We further modify the proposed strategy to be more adaptive under different SR conditions. Using our strategy, one can accelerate the SR process and obtain the optimal solution with less iteration. Experiments are conducted on both synthetic data sets and real-world scenes. The results have demonstrated the effectiveness and outperformance of our proposed strategy compared with other line search strategies.
Data mining applications for finding golden batch benchmarks and optimizing batch process control

This article discusses MPCA (Multi-way Principal Component Analysis) and MPLS (Multi-way Partial Least Squares) have been used to compress the information into low-dimensional spaces and pinpoint the root causes of batch-to-batch difference. From engineering perspective, this paper focuses on applying MPCA and MPLS to data analysis of batch process combining with operation experiences to find “golden batch benchmark” that describes the best operation of historical batches. This work includes data pre-treatment, batch process modelling and chemical reaction initiation status decision. Finally, optimizing control strategies and batch process improvement are also been discussed. Process and control engineers are be able to obtain the valuable data analyzing and control optimization methods for batch process from this study.
A higher-level phylogenetic classification of the Fungi.

A comprehensive phylogenetic classification of the kingdom Fungi is proposed, with reference to recent molecular phylogenetic analyses, and with input from diverse members of the fungal taxonomic community. The classification includes 195 taxa, down to the level of order, of which 16 are described or validated here: Dikarya subkingdom nov.; Chytridiomycota, Neocallimastigomycota phyla nov.; Monoblepharidomycetes, Neocallimastigomycetes class. nov.; Eurotiomycetidae, Lecanoromycetidae, Mycocaliciomycetidae subclass. nov.; Acarosporales, Corticiales, Baeomycetales, Candelariales, Gloeophyllales, Melanosporales, Trechisporales, Umbilicariales ords. nov. The clade containing Ascomycota and Basidiomycota is classified as subkingdom Dikarya, reflecting the putative synapomorphy of dikaryotic hyphae. The most dramatic shifts in the classification relative to previous works concern the groups that have traditionally been included in the Chytridiomycota and Zygomycota. The Chytridiomycota is retained in a restricted sense, with Blastocladiomycota and Neocallimastigomycota representing segregate phyla of flagellated Fungi. Taxa traditionally placed in Zygomycota are distributed among Glomeromycota and several subphyla incertae sedis, including Mucoromycotina, Entomophthoromycotina, Kickxellomycotina, and Zoopagomycotina. Microsporidia are included in the Fungi, but no further subdivision of the group is proposed. Several genera of 'basal' Fungi of uncertain position are not placed in any higher taxa, including Basidiobolus, Caulochytrium, Olpidium, and Rozella.
User Survey on Phone Security and Usage

Mobile phones are widely used nowadays and during the last years developed from simple phones to small computers with an increasing number of features. These result in a wide variety of data stored on the devices which could be a high security risk in case of unauthorized access. A comprehensive user survey was conducted to get information about what data is really stored on the mobile devices, how it is currently protected and if biometric authentication methods could improve the current state. This paper states the results from about 550 users of mobile devices. The analysis revealed a very low securtiy level of the devices. This is partly due to a low security awareness of their owners and partly due to the low acceptance of the offered authentication method based on PIN. Further results like the experiences with mobile thefts and the willingness to use biometric authentication methods as alternative to PIN authentication are also stated.
Wearable soft artificial skin for hand motion detection with embedded microfluidic strain sensing

This paper describes the design and manufacturing of soft artificial skin with an array of embedded soft strain sensors for detecting various hand gestures by measuring joint motions of five fingers. The proposed skin was made of a hyperelastic elastomer material with embedded microchannels filled with two different liquid conductors, an ionic liquid and a liquid metal. The ionic liquid microchannels were used to detect the mechanical strain changes of the sensing material, and the liquid metal microchannels were used as flexible and stretchable electrical wires for connecting the sensors to an external control circuit. The two heterogeneous liquid conductors were electrically interfaced through flexible conductive threads to prevent the two liquid from being intermixed. The skin device was connected to a computer through a microcontroller instrumentation circuit for reconstructing the 3-D hand motions graphically. The paper also presents preliminary calibration and experimental results.
Bridging the Gap between Software Development and Information Security

business units and thus not even practiced in a cohesive, coherent manner. In the worst cases, busy business unit executives trade roving bands of developers like Pokémon cards in a fifth-grade classroom (in an attempt to get ahead). Suffice it to say, none of this is good. The disconnect between security and development has ultimately produced software development efforts that lack any sort of contemporary understanding of technical security risks. Today's complex and highly connected computing environments trigger myriad security concerns, so by blowing off the idea of security entirely, software builders virtually guarantee that their creations will have way too many security weaknesses that could—and should—have been avoided. This article presents some recommendations for solving this problem. Our approach is born out of experience in two diverse fields: software security and information security. Central among our recommendations is the notion of using the knowledge inherent in information security organizations to enhance secure software development efforts. Don't stand so close to me Best practices in software security include a manageable number of simple activities that should be applied throughout any software development process . These lightweight activities should start at the earliest stages of software development and then continue throughout the development process and into deployment and operations. Although an increasing number of software shops and individual developers are adopting the software security touchpoints we describe here as their own, they often lack the requisite security domain knowledge required to do so. This critical knowledge arises from years of observing system intrusions, dealing with malicious hackers, suffering the consequences of software vulnera-bilities, and so on. Put in this position , even the best-intended development efforts can fail to take into account real-world attacks previously observed on similar application architectures. Although recent books 1,2 are starting to turn this knowledge gap around, the science of attack is a novel one. Information security staff—in particular, incident handlers and vulnerability/patch specialists— have spent years responding to attacks against real systems and thinking about the vulnerabilities that spawned them. In many cases, they've studied software vulnerabili-ties and their resulting attack profiles in minute detail. However, few information security professionals are software developers (at least, on a full-time basis), and their solution sets tend to be limited to reactive techniques such as installing software patches, shoring up firewalls, updating intrusion detection signature databases, and the like. It's very rare to find information security …
A 7-Layered E-Government Framework Consolidating Technical, Social and Managerial Aspects

E-Government has been hype for the last 2 decades and still several implementations do not reach the intended success. Different definitions and consequently different models of operations and assessment were developed. This required the formulation of various frameworks describing the different perceptions and understandings of e-Government. The different frameworks proposed tend to agree on a set of elements, but each framework seems to have one or few different elements, depending on the perception of the framework founder. Also, entire categories (or dimensions) of elements seem to be left out. Through a literature review and field survey, the authors identified challenges of an e-Government initiative, categorized in five dimensions: technical, adoption, organizational, strategy and cultural. Not all categories were covered in any of the existing government frameworks. This would prove to be awkward in the formulation of new government initiatives or in the assessment of existing ones and evolution plan. In an effort to represent the majority of the factors and elements involved in most e-Government initiatives, the authors present a proposed seven-layer-framework for e-government. The layers included are: 1) end user access layer, 2) e-government layer, 3) organization layer, 4) national infrastructure layer, 5) strategic layer, 6) social cultural layer, and 7) national execution layer. The proposed model is compared with existing models and demonstrates that it covers all the aforementioned dimensions. E-government; framework; e-government; challenges, decision support system (DSS)
Approaches to the safety assessment of engineered nanomaterials (ENM) in food.

A systematic, tiered approach to assess the safety of engineered nanomaterials (ENMs) in foods is presented. The ENM is first compared to its non-nano form counterpart to determine if ENM-specific assessment is required. Of highest concern from a toxicological perspective are ENMs which have potential for systemic translocation, are insoluble or only partially soluble over time or are particulate and bio-persistent. Where ENM-specific assessment is triggered, Tier 1 screening considers the potential for translocation across biological barriers, cytotoxicity, generation of reactive oxygen species, inflammatory response, genotoxicity and general toxicity. In silico and in vitro studies, together with a sub-acute repeat-dose rodent study, could be considered for this phase. Tier 2 hazard characterisation is based on a sentinel 90-day rodent study with an extended range of endpoints, additional parameters being investigated case-by-case. Physicochemical characterisation should be performed in a range of food and biological matrices. A default assumption of 100% bioavailability of the ENM provides a 'worst case' exposure scenario, which could be refined as additional data become available. The safety testing strategy is considered applicable to variations in ENM size within the nanoscale and to new generations of ENM.
Siamese Network for Underwater Multiple Object Tracking

For underwater videos, the performance of object tracking is greatly affected by illumination changes, background disturbances and occlusion. Hence, there is a need to have a robust function that computes image similarity, to accurately track the moving object. In this work, a hybrid model that incorporates the Kalman Filter, a Siamese neural network and a miniature neural network has been developed for object tracking. It was observed that the usage of the Siamese network to compute image similarity significantly improved the robustness of the tracker. Although the model was developed for underwater videos, it was found that it performs well for both underwater and human surveillance videos. A metric has been defined for analyzing detections-to-tracks mapping accuracy. Tracking results have been analyzed using Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP)metrics.
Trials of cybernetic human HRP-4C toward humanoid business

We have developed a humanoid robot (a cybernetic human called “HRP-4C”) which has the appearance and shape of a human being, can walk and move like one, and interacts with humans using speech recognition. Standing 158 cm tall and weighing 43 kg (including the battery), with the joints and dimensions set to average values for young Japanese females, HRP-4C looks very human-like. In this paper, we present ongoing challenges to create a new bussiness in the contents industry with HRP-4C.