nils-herrmann/clean_scirepeval huggingface dataset:
Highly-Available Applications on Unreliable Infrastructure: Microservice Architectures in Practice

In contrast to applications relying on specialized and expensive highly-available infrastructure, the basic approach of microservice architectures to achieve fault tolerance – and finally high availability – is to modularize the software system into small, self-contained services that are connected via implementation-independent interfaces. Microservices and all dependencies are deployed into self-contained environments called containers that are executed as multiple redundant instances. If a service fails, other instances will often still work and take over. Due to the possibility of failing infrastructure, these services have to be deployed on several physical systems. This horizontal scaling of redundant service instances can also be used for load-balancing. Decoupling the service communication using asynchronous message queues can increase fault tolerance, too. The Deutsche Bahn AG (German railway company) uses as system called EPA for seat reservations for inter-urban rail services. Despite its high availability, the EPA system in its current state has several disadvantages such as high operational cost, need for special hardware, technological dependencies, and expensive and time-consuming updates. With the help of a prototype, we evaluate the general properties of a microservice architecture and its dependability with reference to the legacy system. We focus on requirements for an equivalent microservice-based system and the migration process; services and data, containerization, communication via message queues; and achieving similar fault tolerance and high availability with the help of replication inside the resulting architecture.
Motion-based counter-measures to photo attacks in face recognition

Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our face images and videos are wide-spread on the internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy we lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we propose a new technique of countermeasure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this article.
CNN-Based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss

Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a CNN based patch matching approach for optical flow estimation. An important contribution of our approach is a novel thresholded loss for Siamese networks. We demonstrate that our loss performs clearly better than existing losses. It also allows to speed up training by a factor of 2 in our tests. Furthermore, we present a novel way for calculating CNN based features for different image scales, which performs better than existing methods. We also discuss new ways of evaluating the robustness of trained features for the application of patch matching for optical flow. An interesting discovery in our paper is that low-pass filtering of feature maps can increase the robustness of features created by CNNs. We proved the competitive performance of our approach by submitting it to the KITTI 2012, KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art results on all three datasets.
EmoSenticSpace: A novel framework for affective common-sense reasoning

Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the conceptual and affective information) associated with natural language is growing exponentially in the context of big social data analysis. To this end, this paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and supportvector-machine classification, and takes into account a number of similarity measures, including point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotionrelated natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state-of-the-art. In particular, the direct evaluation of EmoSenticSpace against psychological features provided in the benchmark ISEAR dataset shows a 92.15% agreement.
AppSec: A Safe Execution Environment for Security Sensitive Applications

Malicious OS kernel can easily access user's private data in main memory and pries human-machine interaction data, even one that employs privacy enforcement based on application level or OS level. This paper introduces AppSec, a hypervisor-based safe execution environment, to protect both the memory data and human-machine interaction data of security sensitive applications from the untrusted OS transparently.
 AppSec provides several security mechanisms on an untrusted OS. AppSec introduces a safe loader to check the code integrity of application and dynamic shared objects. During runtime, AppSec protects application and dynamic shared objects from being modified and verifies kernel memory accesses according to application's intention. AppSec provides a devices isolation mechanism to prevent the human-machine interaction devices being accessed by compromised kernel. On top of that, AppSec further provides a privileged-based window system to protect application's X resources. The major advantages of AppSec are threefold. First, AppSec verifies and protects all dynamic shared objects during runtime. Second, AppSec mediates kernel memory access according to application's intention but not encrypts all application's data roughly. Third, AppSec provides a trusted I/O path from end-user to application. A prototype of AppSec is implemented and shows that AppSec is efficient and practical.
Developing University Ontology using protégé OWL Tool: Process and Reasoning

The current web is based on html which can display information simply. Researchers are working towards the semantic web which is an intelligent and meaningful web proposed by Tim burner’s lee. Ontology and ontology based application are its basic ingredients. With the ontology we can focus on only main concepts and its relationship rather than information. Protégé is a most popular tool for ontology editing and for developing ontology . It has a GUI which enables ontology developers to concentrate on conceptual terms without thinking about syntax of an output language. Protégé has flexible knowledge model and extensible plug-in architecture. This paper explains the terms of university through university ontology. We will focus on creating an university ontology using protégé. Rajiv Gandhi Technical University Bhopal, India has been taken an example for the ontology development and various aspects like: super class and subclass hierarchy, creating a subclass instances for class illustration, query retrieval process visualization view and graph view have been demonstrated. Index Terms Semantic Web, Ontology, protégé tool, OWL, DL, Reasoning,Object. ——————————  ——————————
Patterns of amino acids near signal-sequence cleavage sites.

According to the signal hypothesis, a signal sequence, once having initiated export of a growing protein chain across the rough endoplasmic reticulum, is cleaved from the mature protein at a specific site. It has long been known that some part of the cleavage specificity resides in the last residue of the signal sequence, which invariably is one with a small, uncharged side-chain, but no further specific patterns of amino acids near the point of cleavage have been discovered so far. In this paper, some such patterns, based on a sample of 78 eukaryotic signal sequences, are presented and discussed, and a first attempt at formulating rules for the prediction of cleavage sites is made.
Fuzzy spatial relation ontology for image interpretation

The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.
Modal identification of simple structures with high-speed video using motion magnification

Video cameras offer the unique capability of collecting high density spatial data from a distant scene of interest. They can be employed as remote monitoring or inspection sensors for structures because of their commonplace availability, simplicity, and potentially low cost. An issue is that video data is difficult to interpret into a format familiar to developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements. In this paper, these methods are extended to modal identification in structures and the measurement of structural vibrations. Camera-based measurements of displacement are compared against laser vibrometer and accelerometer measurements for verification. The methodology is demonstrated on simple structures, a cantilever beam and a pipe, to identify and visualize the operational deflection shapes. Suggestions for applications of this methodology and challenges in real-world implementation are given. & 2015
Document summarization based on word associations

In the age of big data, automatic methods for creating summaries of documents become increasingly important. In this paper we propose a novel, unsupervised method for (multi-)document summarization. In an unsupervised and language-independent fashion, this approach relies on the strength of word associations in the set of documents to be summarized. The summaries are generated by picking sentences which cover the most specific word associations of the document(s). We measure the performance on the DUC 2007 dataset. Our experiments indicate that the proposed method is the best-performing unsupervised summarization method in the state-of-the-art that makes no use of human-curated knowledge bases.
Acceleration and balance in trotting dogs.

During quadrupedal trotting, diagonal pairs of limbs are set down in unison and exert forces on the ground simultaneously. Ground-reaction forces on individual limbs of trotting dogs were measured separately using a series of four force platforms. Vertical and fore-aft impulses were determined for each limb from the force/time recordings. When mean fore-aft acceleration of the body was zero in a given trotting step (steady state), the fraction of vertical impulse on the forelimb was equal to the fraction of body weight supported by the forelimbs during standing (approximately 60 %). When dogs accelerated or decelerated during a trotting step, the vertical impulse was redistributed to the hindlimb or forelimb, respectively. This redistribution of the vertical impulse is due to a moment exerted about the pitch axis of the body by fore-aft accelerating and decelerating forces. Vertical forces exerted by the forelimb and hindlimb resist this pitching moment, providing stability during fore-aft acceleration and deceleration.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling

Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multitask architectures or computationally expensive graphical models. We present a method that leverages a fully convolutional network (FCN) to predict semantic labels, depth and an instance-based encoding using each pixel’s direction towards its corresponding instance center. Subsequently, we apply low-level computer vision techniques to generate stateof-the-art instance segmentation on the street scene datasets KITTI and Cityscapes. Our approach outperforms existing works by a large margin and can additionally predict absolute distances of individual instances from a monocular image as well as a pixel-level semantic labeling.
A survey on face detection in the wild: Past, present and future

Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15 years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called ’in-thewild’) through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection.
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing

The ability to reliably identify sarcasm and irony in text can improve the perfo rmance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sar casm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impos sible to identify a sentence containing sarcasm without knowing the context. In this paper we describe a corpus generation experiment w h re e collect regular and sarcastic Amazon product reviews. We perform qualitative and quantitative analysis of the corpus. The resu lting corpus can be used for identifying sarcasm on two levels: a document and a text utterance (where a text utterance can be as short as a sentence and as long as a whole document).
Motion Control of a Wheeled Mobile Robot Using Model Predictive Control : A Survey

Model predictive control (MPC) has been one of the most promising control strategies in industrial processes for decades. Due to its remarkable advantages, it has been extended to many areas of robotic research, especially motion control. Therefore, the goal of this paper is to review motion control of wheeled mobile robots (WMRs) using MPC. Principles as well as key issues in real-time implementations are first addressed. We then update the current literature of MPC for motion control. We also classify publications by using three criteria, i.e., MPC models, robot kinematic models, and basic motion tasks. MPC models categorized here include nonlinear MPC, linear MPC, neural network MPC, and generalized predictive control (GPC), while robot kinematic models we focus on consist of unicycle-type vehicles, car-like vehicles, and omnidirectional vehicles. Basic motion tasks, in general, are classified into three groups, i.e., trajectory tracking, path following, and point stabilization. To show that MPC strategies are capable of real-time implementations, some experimental scenarios from our previous work are given. We also conclude by identifying some future research directions.
A Simple Control Law for UAV Formation Flying

This paper presents a Lie group setting for the problem of control of formations, as a natural outcome of the analysis of a planar two-vehicle formation control law. The vehicle trajectories are described using planar Frenet-Serret equations of motion, which capture the evolution of both the vehicle position and orientation for unit-speed motion subject to curvature (steering) control. The set of all possible (relative) equilibria for arbitrary G-invariant curvature controls is described (where G = SE(2) is a symmetry group for the control law). A generalization of the control law for n vehicles is presented, and the corresponding (relative) equilibria are characterized. Work is on-going to discover stability and convergence results for the n-vehicle problem. The practical motivation for this work is the problem of formation control for meter-scale UAVs; therefore, an implementation approach consistent with UAV payload constraints is also discussed.
Layout Dependent Effect: Impact on device performance and reliability in recent CMOS nodes

In this paper, we analyze the impact of Layout Dependent Effect (LDE) observed on MOSFETs. It is shown that changing the Layout have an impact on MOSFET device parameters and reliability. Here, we studied the Well Proximity Effect (WPE), Length of diffusion (LOD) and Oxide Spacing Effect (OSE) impacts on device MOSFET parameters and reliability. We also analyzed SiGe impacts on LDE, since it is commonly used to boost device performance.
Efficient Pattern Recognition Using a New Transformation Distance

Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product ... ), which are not particularly meaningful on pattern vectors. More complex, better suited distance measures are often expensive and rather ad-hoc (elastic matching, deformable templates). We propose a new distance measure which (a) can be made locally invariant to any set of transformations of the input and (b) can be computed efficiently. We tested the method on large handwritten character databases provided by the Post Office and the NIST. Using invariances with respect to translation, rotation, scaling, shearing and line thickness, the method consistently outperformed all other systems tested on the same databases.
A review of routing protocols for mobile ad hoc networks

The 1990s have seen a rapid growth of research interests in mobile ad hoc networking. The infrastructureless and the dynamic nature of these networks demands new set of networking strategies to be implemented in order to provide efficient end-to-end communication. This, along with the diverse application of these networks in many different scenarios such as battlefield and disaster recovery, have seen MANETs being researched by many different organisations and institutes. MANETs employ the traditional TCP/IP structure to provide end-to-end communication between nodes. However, due to their mobility and the limited resource in wireless networks, each layer in the TCP/IP model require redefinition or modifications to function efficiently in MANETs. One interesting research area in MANET is routing. Routing in the MANETs is a challenging task and has received a tremendous amount of attention from researches. This has led to development of many different routing protocols for MANETs, and each author of each proposed protocol argues that the strategy proposed provides an improvement over a number of different strategies considered in the literature for a given network scenario. Therefore, it is quite difficult to determine which protocols may perform best under a number of different network scenarios, such as increasing node density and traffic. In this paper, we provide an overview of a wide range of routing protocols proposed in the literature. We also provide a performance comparison of all routing protocols and suggest which protocols may perform best in large networks.
Affect regulation, brain development, and behavioral/emotional health in adolescence.

This paper addresses the importance of affect regulation (AR) in relation to a broad range of behavioral and emotional health problems that emerge during adolescence. AR is defined as the adaptive modulation of emotional experience to serve a goal or purpose. This conceptualization of AR emphasizes the use of cognitive skills to guide, inhibit, or modify emotion and behavior, including the expression of emotional responses, in learned, strategic ways-skills that ultimately underpin adult levels of social maturity and the ability to show "responsible" behavior across a range of emotional situations. Neurobehavioral systems that subserve these AR skills include areas of the inferior and orbital prefrontal cortex (PFC), with rich interconnections to several limbic structures and other cortical areas, including the dorsolateral PFC. Adolescence represents an important developmental period in the functional maturation of adult AR skills; it is also a critical time in the development of clinical disorders of AR (eg, rates of depression increase dramatically and gender differences in depression emerge). Maturational changes in AR that occur during adolescence-particularly with respect to the role of emotions influencing responsible decision making-are also relevant to understanding key aspects of the developmental pathways of some behavioral health problems, such as alcohol use and nicotine dependence. A strong case is made for developmental research in affective neuroscience aimed at this important maturational period, particularly the kind of transdisciplinary research leading toward mechanistic understanding of the development of adolescent-onset disorders. Improving understanding in these areas could ultimately lead to the development of early interventions in targeted high-risk populations, and has enormous clinical and social policy relevance.