nils-herrmann/clean_scirepeval huggingface dataset:
Benchmark Requirements for Microservices Architecture Research

Microservices have recently emerged as a new architectural style in which distributed applications are broken up into small independently deployable services, each running in its own process and communicating via lightweight mechanisms. However, there is still a lack of repeatable empirical research on the design, development and evaluation of microservices applications. As a first step towards filling this gap, this paper proposes, discusses and illustrates the use of an initial set of requirements that may be useful in selecting a community-owned architecture benchmark to support repeatable microservices research.
BRIDE - A toolchain for framework-independent development of industrial service robot applications

Software integration is still a challenging and time consuming task and therefore a major part of the development of industrial and domestic service robot applications. The presented toolchain BRIDE is able to streamline this process by the separation of user roles and the separation of developer concerns of software components to ensure a frame-work independent implementation. The impact of the BRIDE toolchain in the development process is demonstrated in a case study on the SyncMM mobile manipulation control framework.
Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I.

In this experiment, we combined the measurement of observable facial behavior with simultaneous measures of brain electrical activity to assess patterns of hemispheric activation in different regions during the experience of happiness and disgust. Disgust was found to be associated with right-sided activation in the frontal and anterior temporal regions compared with the happy condition. Happiness was accompanied by left-sided activation in the anterior temporal region compared with disgust. No differences in asymmetry were found between emotions in the central and parietal regions. When data aggregated across positive films were compared to aggregate negative film data, no reliable differences in brain activity were found. These findings illustrate the utility of using facial behavior to verify the presence of emotion, are consistent with the notion of emotion-specific physiological patterning, and underscore the importance of anterior cerebral asymmetries for emotions associated with approach and withdrawal.
Foreign Capital and Economic Performance of Pakistan

This paper reviews the trends of two types of foreign capital inflows, namely foreign aid and foreign private investment, to Pakistan. Like other developing countries, the volume of foreign aid to Pakistan has been decreasing. Meanwhile foreign private investment to Pakistan has increased, though not as sharply as that to other developing Asian countries. The study finds that the impacts of foreign capital, aid and private investment on the economic performance of Pakistan have been insignificant. This paper suggests that these consequences are due to the inadequate development of domestic institutional structure, human capital, and indigenous entrepreneurship.
Speeding up distributed request-response workflows

We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.
CVAP: Validation for Cluster Analyses

Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.
Age effects on wayfinding and route learning skills

While existing evidence suggests that older adults have compromised spatial navigation abilities, the effects of age on specific aspects of navigational skill are less well specified. The current study examined age effects on spatial navigation abilities considering the multiple cognitive and neural factors that contribute to successful navigation. Young and older adults completed wayfinding and route learning tasks in a virtual environment and aspects of environmental knowledge were assessed. Prefrontal, caudate and hippocampal volumes were obtained in a subset of older adults. Age differences were observed in both wayfinding and route learning. For wayfinding, there were age effects in recalling landmarks, and recognizing environmental scenes. In the route learning condition, older adults evidenced difficulty with the location, temporal order and directional information of landmarks. In both conditions, there was evidence of age-related differences in the acquisition of configural knowledge. Wayfinding was associated with the hippocampus whereas route learning was associated with the caudate nucleus. These results provide indications of specific aspects of navigational learning that may contribute to age-related declines and potential neural substrates.
Emotional Integration and Advertising Effectiveness : Case of Perfumes Advertising

This paper examines emotions in advertising, its effects and functioning. Using an interview-based experiment on 256 participants, we found that emotions perceived during an advertising exposure could play an important role in eliciting responses towards the ad and the brand. However, this process is true provided that consumers associate the perceived emotion to the exposed brand or its consumption experience. Furthermore, we have identified efficiency differences between magazine ads, depending on how they visually describe emotions. In particular, we study emotional integration in advertising, i.e. salience of emotions expressed towards the ad, the presence of core brand information and clarity of the advertising message about the hedonic attributes of consumption. Interestingly, the impact of the staging process of emotions is moderated by respondents-specific variables, including their need for emotion, tolerance for ambiguity, their need for structure and need for cognition. KeywordsEmotional Reactions ;Emotional Integration; Need for Emotion; Tolerance for Ambiguity; Need for Structure; Need for Cognition;
Classification of VANET MAC, Routing and Approaches A Detailed Survey

Human safety considerations linked with rapidly growing auto mobile market has given special attention to the Intelligent Transportation System (ITS). ITS provides a set of standards for inter vehicular communication with emphasis on safety, traffic efficiency and infotainment related applications. In ITS, the vehicles acting as mobile nodes, form a specialized ad hoc network, known as Vehicular Ad-hoc Network (VANET). Although, VANET and ITS are under intense research since last decade, technology still lacks large scale deployment. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications are the main research goals of ITS. High relative node velocity and high active node density has presented peculiar challenges to connectivity within VANET. VANET connectivity and routing requirements range from the time critical safety applications, to the time and space hovering, delay tolerant and infotainment applications. This paper reviews connectivity issues in VANET with emphasis on routing, and offers comprehensive literature review on state of the art in VANET routing, with its detailed classifications. It also compares some standard architectures of VANET from MAC, routing and management perspective, i.e., WAVE by IEEE, CALM by ISO, C2CNet by C2C consortium / GeoNet.
Convolutional-Recursive Deep Learning for 3D Object Classification

Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images. The CNN layer learns low-level translationally invariant features which are then given as inputs to multiple, fixed-tree RNNs in order to compose higher order features. RNNs can be seen as combining convolution and pooling into one efficient, hierarchical operation. Our main result is that even RNNs with random weights compose powerful features. Our model obtains state of the art performance on a standard RGB-D object dataset while being more accurate and faster during training and testing than comparable architectures such as two-layer CNNs.
Predicting Movie Revenue from IMDb Data

Given the information known about a movie in the we ek of its release, can we predict the total gross revenue for that movie? Such information would be u s f l to marketers, theater operators, and others i n the movie industry, but it is a hard problem, even for human beings. We found that, given a set of numeric , textbased, and sentiment features from IMDb, linear reg ession outperforms class-based logistic regression at predicting gross revenue. However, neither gives su fficiently precise results to be used in practice.
Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF  and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT ). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG ) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.
Dancing with Giants: Wimpy Kernels for On-Demand Isolated I/O

To be trustworthy, security-sensitive applications must be formally verified and hence small and simple, i.e., wimpy. Thus, they cannot include a variety of basic services available only in large and untrustworthy commodity systems, i.e., in giants. Hence, wimps must securely compose with giants to survive on commodity systems, i.e., rely on giants' services but only after efficiently verifying their results. This paper presents a security architecture based on a wimpy kernel that provides on-demand isolated I/O channels for wimp applications, without bloating the underlying trusted computing base. The size and complexity of the wimpy kernel are minimized by safely outsourcing I/O subsystem functions to an untrusted commodity operating system and exporting driver and I/O subsystem code to wimp applications. Using the USB subsystem as a case study, this paper illustrates the dramatic reduction of wimpy-kernel size and complexity, e.g., over 99% of the USB code base is removed. Performance measurements indicate that the wimpy-kernel architecture exhibits the desired execution efficiency.
Lidar-histogram for fast road and obstacle detection

Detection of traversable road regions, positive and negative obstacles, and water hazards is a fundamental task for autonomous driving vehicles, especially in off-road environment. This paper proposes an efficient method, called Lidar-histogram. It can be used to integrate the detection of traversable road regions, obstacles and water hazards into one single framework. The weak assumption of the Lidar-histogram is that a decent-sized area in front of the vehicle is flat. The Lidar-histogram is derived from an efficient organized map of Lidar point cloud, called Lidar-imagery, to index, describe and store Lidar data. The organized point-cloud map can be easily obtained by indexing the original unordered 3D point cloud to a Lidar-specific 2D coordinate system. In the Lidar-histogram representation, the 3D traversable road plane in front of vehicle can be projected as a straight line segment, and the positive and negative obstacles are projected above and below the line segment, respectively. In this way, the problem of detecting traversable road and obstacles is converted into a simple linear classification task in 2D space. Experiments have been conducted in different kinds of off-road and urban scenes, and we have obtained very promising results.
Baby Talk : Understanding and Generating Image Descriptions

We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.
Empirical Bernstein Bounds and Sample-Variance Penalization

We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functions whose growth function is polynomial in the sample sizen. The bounds lead us to consider sample variance penalization , a novel learning method which takes into account the empirical variance of the loss function. We give conditions under which sample variance penalization is effective. In particular, we present a bound on the excess risk incurred by the method. Using this, we argue that there are situations in which the excess risk of our method is of order1/n, while the excess risk of empirical risk minimization is of order 1/ √ n. We show some experimental results, which confirm the theory. Finally, we discuss the potential application of our results to sample compression schemes.
Emotion recognition with multimodal features and temporal models

This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.
Learning Semantic Correspondences with Less Supervision

A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.
A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

Research in neuroevolutionthat is, evolving artificial neural networks (ANNs) through evolutionary algorithmsis inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.
Telecommunication System Based on Fuzzy Graphs

Telecommunication is one of the unavoidable utilities of daily life. Telecommunication service providers have data records of its users. These records are represented by crisp graphs. Certain parameters like center persons, churn prediction, etc. are more perfectly calculated if the data are represented by fuzzy graphs. In this paper, we have introduced a fuzzy telecommunication network (FTN) using fuzzy graph theory.Star persons and related terms are defined in this fuzzy telecommunication network.A small example is described to illustrate the network. Churn prediction is a big issue for telecom service providers. This study introduces churn prediction in the fuzzy telecommunication network. New idea on measurement of churn prediction is presented here.
Compact Yagi antenna for handheld UHF RFID reader

This paper describes a compact linearly polarized high gain antenna for a handheld UHF RFID reader. The antenna is a Yagi type structure, with three elements (driver, director, and reflector) which fit into compact footprint (100 mm × 100 mm). The antenna has maximum gain of 6 dBi and VSWR better than 1.3 in approximately 50 MHz band around either European or US RFID bands (865–870 MHz or 902–928 MHz). We describe the antenna structure and present the comparison of simulation results with experimental data.
Kinect For Windows V 2 . 0 Sensor And Sdk 2

The emotional facial expression is an important element in human communication, along with other forms of non-verbal communication, such as gestures and postures. The human facial expression, with its cunning and moment movements, carries an amazing amount of information that can reflect emotional feelings. Observing people’s facial expressions can help a person understand their emotions. The new technology provided today for capturing the facial expressions, with rapid, high resolution image acquisition, helps us to analyze and recognize in real time the true facial emotions. This can be useful in many real time applications, like airport security, trading (the customer's feeling about a product), patient monitoring, and others.In this paper, we focus on the emotion recognition from facial expressions by using Microsoft Kinect for Windows sensor V2 and the face tracking SDK to recognize eight expressions. The implementation of our emotion recognition application was made with Visual Studio 2013 (C++) and Matlab 2014.
Event driven analog modeling for the verification of PLL frequency synthesizers

The focus of this work is to provide a efficient modeling approach for the functional verification of complex analog frequency synthesizers. The event driven analog modeling approach uses the double precision data type wreal (supported by VerilogAMS), that enables analog accuracy in the digital simulation domain. It is therefore possible to separate high frequency signal paths in the frequency synthesizers from the analog domain, in order to archive higher simulation efficiency for fast verification purposes. The modeling approach and an investigation of the necessary accuracy requirements for the verification including phase noise performances of the analog frequency synthesizers is presented. The proposed approach is demonstrated on baseof a sub micron CMOS Fractional-N frequency synthesizer and compared with different traditional modeling approaches, like phase model and pure digital model. The paper concludes with a proposal of a verification approach for RF mixed signal systems.
Three Models for the Description of Language

The grammar of a language is a device that describes the structure of that language. The grammar is comprised of a set of rules whose goal is twofold: first these rules can be used to create sentences of the associated language and only these, and second they can be used to classify whether a given sentence is an element of the language or not. The goal of a linguist is to discover grammars that are simple and yet are able to fully span the language. In  Chomsky describes three possible options of increasing complexity for English grammars: Finite-state, Phrase Structure and Transformational. This paper briefly present these three grammars and summarizes Chomsky’s analysis and results which state that finite-state grammars are inadequate because they fail to span all possible sentences of the English language, and phrase structure grammar is overly complex.
Architecture for an Artificial Immune System

An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.