nils-herrmann/clean_scirepeval huggingface dataset:
Detecting network-wide traffic anomalies based on robust multivariate probabilistic calibration model

Detecting network anomalies means a lot to network security. In respect of detection precision and range, network-wide anomaly detection approaches on the basis of traffic flows have distinctive advantages over the methods of the traditional host computer, single link and single path. However, these approaches face actual problems of performance reduction or being unavailable when noise interference or data loss take place. In order to solve these problems, anomaly detection algorithm based on robust multivariate probabilistic calibration model is proposed. This algorithm establishes a normal traffic model of traffic matrix based on the latent variable probability model of multivariate t-distribution, and implements network anomaly detection by judging if the sample's Mahalanobis distance exceeds the threshold. Both theoretical analysis and experimental results demonstrate its robustness and wider use. The algorithm is applicable when dealing with both data integrity and loss. It also has a stronger resistance over noise interference and lower sensitivity to the change of parameters, all of which indicate its performance stability.
Entrepreneurial Human Resource Strategy

[Excerpt] Entrepreneurship is the process by which "opportunities to create future goods and services are discovered, evaluated, and exploited" (Shane and Venkataraman, 2000: 218). In other words, it is the process by which organizations and individuals convert new knowledge into new opportunities in the form of new products and services. Strategic human resource management (SHRM) has been defined as the system of organizational practices and policies used to manage employees in a manner that leads to higher organizational performance (Wright and McMahan, 1992). Further, one perspective suggests that sets of HR practices do not themselves create competitive advantage; instead, they foster the development of organizational capabilities which in turn create such advantages (Lado and Wilson, 1994; Wright, Dunford, and Snell, 2001). Specifically, this body of literature suggests that HR practices lead to firm performance when they are aligned to work together to create and support the employee-based capabilities that lead to competitive advantage (Wright and Snell, 2000; Wright, Dunford, and Snell, 2001). Thus, entrepreneurial human resource strategy is best defined as the set or sets of human resources practices that will increase the likelihood that new knowledge will be converted to new products or services.
Design and Development of Floor Cleaner Robot (Automatic and Manual)

Manual work is taken over the robot technology and many of the related robot appliances are being used extensively also. Here represents the technology that proposed the working of robot for Floor cleaning. This floor cleaner robot can work in any of two modes i.e. “Automatic and Manual”. All hardware and software operations are controlled by AT89S52 microcontroller. This robot can perform sweeping and mopping task. RF modules have been used for wireless communication between remote (manual mode) and robot and having range 50m. This robot is incorporated with IR sensor for obstacle detection and automatic water sprayer pump. Four motors are used, two for cleaning, one for water pump and one for wheels. Dual relay circuit used to drive the motors one for water pump and another for cleaner. In previous work, there was no automatic water sprayer used and works only in automatic mode. In the automatic mode robot control all the operations itself and change the lane in case of hurdle detection and moves back. In the manual mode, the keypad is used to perform the expected task and to operate robot. In manual mode, RF module has been used to transmit and receive the information between remote and robot and display the information related to the hurdle detection on LCD. The whole circuitry is connected with 12V battery.
Virtual network security: threats, countermeasures, and challenges

Network virtualization has become increasingly prominent in recent years. It enables the creation of network infrastructures that are specifically tailored to the needs of distinct network applications and supports the instantiation of favorable environments for the development and evaluation of new architectures and protocols. Despite the wide applicability of network virtualization, the shared use of routing devices and communication channels leads to a series of security-related concerns. It is necessary to provide protection to virtual network infrastructures in order to enable their use in real, large scale environments. In this paper, we present an overview of the state of the art concerning virtual network security. We discuss the main challenges related to this kind of environment, some of the major threats, as well as solutions proposed in the literature that aim to deal with different security aspects.
Blockchain and Principles of Business Process Re-Engineering for Process Innovation

Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring implementation of use cases in hope of significant gains in efficiencies. However, to achieve the impact hoped for, it is not sufficient to merely replace existing technologies. The current business processes must also be redesigned and innovated to enable realization of hoped for benefits. This conceptual paper provides a theoretical contribution on how blockchain technology and smart contracts potentially can, within the framework of the seven principles of business process re-engineering (BPR), enable process innovations. In this paper, we analyze the BPR principles in light of their applicability to blockchain-based solutions. We find these principles to be applicable and helpful in understanding how blockchain technology could enable transformational redesign of current processes. However, the viewpoint taken, should be expanded from intrato inter-organizational processes operating within an ecosystem of separate organizational entities. In such a blockchain powered ecosystem, smart contracts take on a pivotal role, both as repositories of data and executioner of activities.
cGANs with Projection Discriminator

We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator. The code with Chainer (Tokui et al., 2015), generated images and pretrained models are available at https://github.com/pfnet-research/sngan_projection.
Analysing Twitter and web queries for flu trend prediction

Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p < 0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.
Entropy-Driven Adaptive

In the rst genetic programming (GP) book John Koza noticed that tness histograms give a highly informative global view of the evolutionary process (Koza, 1992). The idea is further developed in this paper by discussing GP evolution in analogy to a physical system. I focus on three interrelated major goals: (1) Study the the problem of search eeort allocation in GP; (2) Develop methods in the GA/GP framework that allow adap-tive control of diversity; (3) Study ways of adaptation for faster convergence to optimal solution. An entropy measure based on phenotype classes is introduced which abstracts tness histograms. In this context, entropy represents a measure of population diversity. An analysis of entropy plots and their correlation with other statistics from the population enables an intelligent adaptation of search control.
Backprop Evolution

The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific language to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization performance after a few epochs of training. We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition

Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data-and the CNNs outperformed the human test persons.
SfM-Net: Learning of Structure and Motion from Video

We propose SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frameto-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates. The model can be trained with various degrees of supervision: 1) self-supervised by the reprojection photometric error (completely unsupervised), 2) supervised by ego-motion (camera motion), or 3) supervised by depth (e.g., as provided by RGBD sensors). SfMNet extracts meaningful depth estimates and successfully estimates frame-to-frame camera rotations and translations. It often successfully segments the moving objects in the scene, even though such supervision is never provided.
Cyber-Physical Systems Security—A Survey

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. In this paper, we study and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: 1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; 2) from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and 3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS, and smart cars). The model can be both abstract to show general interactions of components in a CPS application, and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection. With this intensive literature review, we attempt to summarize the state-of-the-art on CPS security, provide researchers with a comprehensive list of references, and also encourage the audience to further explore this emerging field.
A Comprehensive survey on 5G: The prospect of future communications

Thanks to the enhancement of mobile devices, the multitude of information to be transferred and high data rate, a new generation of cellular networks referred to as 5G will be developed in near future. It is expected that 5G networks will provide fast and on-time accessibility of information at any time and place. For achieving these objectives, cellular networks have to be improved significantly. The purpose of the present study was to investigate the architecture of 5G networks which includes the followings: device-to-device communication (D2D), massive multiple input and multiple output (MIMO), millimeter wave communications, cognitive radio (CR), etc. Also, effective and efficient technologies such as Network Function Virtualization (NFV), Fast caching, Self-Interference Cancellation (SIC) and Downlink and Uplink Decoupling (DUD), etc. are discussed and investigated in this study so as to optimize the architectures which can meet the needs and expectations of users and operators in future. In this study, an attempt was made to examine different limitations which hinder the development of this network. Various challenges were identified and the possible solutions have been highlighted. At the end, possible and probable solutions have been explained. Moreover, methods and directions for further research on 5G networks have been suggested.
Misinformation spreading on Facebook

Social media are pervaded by unsubstantiated or untruthful rumors, that contribute to the alarming phenomenon of misinformation. The widespread presence of a heterogeneous mass of information sources may affect the mechanisms behind the formation of public opinion. Such a scenario is a florid environment for digital wildfires when combined with functional illiteracy, information overload, and confirmation bias. In this essay, we focus on a collection of works aiming at providing quantitative evidence about the cognitive determinants behind misinformation and rumor spreading. We account for users’ behavior with respect to two distinct narratives: a) conspiracy and b) scientific information sources. In particular, we analyze Facebook data on a time span of five years in both the Italian and the US context, and measure users’ response to i) information consistent with one’s narrative, ii) troll contents, and iii) dissenting information e.g., debunking attempts. Our findings suggest that users tend to a) join polarized communities sharing a common narrative (echo chambers), b) acquire information confirming their beliefs (confirmation bias) even if containing false claims, and c) ignore dissenting information.
The 10 million follower fallacy: audience size does not prove domain-influence on Twitter

With the advent of social networks and micro-blogging systems, the way of communicating with other people and spreading information has changed substantially. Persons with different backgrounds, age and education exchange information and opinions, spanning various domains and topics, and have now the possibility to directly interact with popular users and authoritative information sources usually unreachable before the advent of these environments. As a result, the mechanism of information propagation changed deeply, the study of which is indispensable for the sake of understanding the evolution of information networks. To cope up with this intention, in this paper, we propose a novel model which enables to delve into the spread of information over a social network along with the change in the user relationships with respect to the domain of discussion. For this, considering Twitter as a case study, we aim at analyzing the multiple paths the information follows over the network with the goal of understanding the dynamics of the information contagion with respect to the change of the topic of discussion. We then provide a method for estimating the influence among users by evaluating the nature of the relationship among them with respect to the topic of discussion they share. Using a vast sample of the Twitter network, we then present various experiments that illustrate our proposal and show the efficacy of the proposed approach in modeling this information spread.
Applying Machine Learning to Product Categorization

We present a method for classifying products into a set of known categories by using supervised learning. That is, given a product with accompanying informational details such as name and descriptions, we group the product into a particular category with similar products, e.g., ‘Electronics’ or ‘Automotive’. To do this, we analyze product catalog information from different distributors on Amazon.com to build features for a classifier. Our implementation results show significant improvement over baseline results. Taking into particular criteria, our implementation is potentially able to substantially increase automation of categorization of products. General Terms Supervised and Unsupervised Learning, E-Commerce
High Frequency Trading Acceleration Using FPGAs

This paper presents the design of an application specific hardware for accelerating High Frequency Trading applications. It is optimized to achieve the lowest possible latency for interpreting market data feeds and hence enable minimal round-trip times for executing electronic stock trades. The implementation described in this work enables hardware decoding of Ethernet, IP and UDP as well as of the FAST protocol which is a common protocol to transmit market feeds. For this purpose, we developed a microcode engine with a corresponding instruction set as well as a compiler which enables the flexibility to support a wide range of applied trading protocols. The complete system has been implemented in RTL code and evaluated on an FPGA. Our approach shows a 4x latency reduction in comparison to the conventional Software based approach.
Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency

We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset.
Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

Conversational interfaces recently gained a lot of attention. One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and socalled Natural Language Understanding (NLU) services. While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically. In this paper, we present a method to evaluate the classification performance of NLU services. Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers. Based on these corpora, we conduct an evaluation of some of the most popular NLU services. Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use.
Integrating information technology and marketing : An examination of the drivers and outcomes of e-Marketing capability

a Canisius College, Richard J. Wehle School of Business, Department of Management and Marketing, 2001 Main Street, Buffalo, NY 14208-1098, United States b Clemson University, College of Business and Behavioral Science, Department of Marketing, 245 Sirrine Hall, Clemson, SC 29634-1325, United States c University of Alabama at Birmingham, School of Business, Department of Marketing, Industrial Distribution and Economics, 1150 10th Avenue South, Birmingham, AL 35294, United States d Vlerick School of Management Reep 1, BE-9000 Ghent Belgium