Miracle-dz/UniFAR-FTUs huggingface dataset:
Do corporate green investments improve environmental performance? Evidence from the perspective of efficiency

ABSTRACT By linking green investments to corporate environmental performance from the perspective of efficiency, this paper quantifies and evaluates firm-level green investment efficiency, with SBM-DEA approach and hand-collected emission data of Chinese listed companies in polluting industries. We find that corporate green investment efficiency is overall low, primarily due to excessive green investments, suggesting that managers only invest extensively in environmental dimensions without considering the efficient allocation and value-creating use of limited resources. Further, we conduct the Tobit regression and demonstrate that local environmental enforcement has an inverted U-shaped effect on green investment efficiency of polluting firms. Notably, this effect is only statistically significant in Non-SOEs and small-scale enterprises. Moreover, we document a U-shaped relationship between local environmental enforcement and excessive green investments, and this relationship is also only pronounced in Non-SOEs and small-scale firms. Our findings indicate that local governments should optimize corporate green investment efficiency through differentiated environmental regulation.
Security monitoring for network protocols and applications

Computer security, also known as cyber-security or IT security, is always an emerging topic in computer science research. Because cyber attacks are growing in both volume and sophistication, protecting information systems or networks becomes a difficult task. Therefore, researchers in research community give an ongoing attention in security including two main directions: (i)-designing secured infrastructures with secured communication protocols and (ii)-monitoring/supervising the systems or networks in order to find and re-mediate vulnerabilities. The former assists the later by forming some additional monitoring-supporting modules. Whilst, the later verifies whether everything designed in the former is correctly and securely functioning as well as detecting security violations. This is the main topic of this thesis.This dissertation presents a security monitoring framework that takes into consideration different types of audit dataset including network traffic and application logs. We propose also some novel approaches based on supervised machine learning to pre-process and analyze the data input. Our framework is validated in a wide range of case studies including traditional TCP/IPv4 network monitoring (LAN, WAN, Internet monitoring), IoT/WSN using 6LoWPAN technology (IPv6), and other applications' logs. Last but not least, we provide a study regarding intrusion tolerance by design and propose an emulation-based approach to simultaneously detect and tolerate intrusion.In each case study, we describe how we collect the audit dataset, extract the relevant attributes, handle received data and decode their security meaning. For these goals, the tool Montimage Monitoring Tool (MMT) is used as the core of our approach. We assess also the solution's performance and its possibility to work in "larger scale" systems with more voluminous dataset
The Application of Multi-step Ahead Prediction in Fault Detection

Compared with active robust approaches based on generating residuals, robustness and sensitivity of fault detection system can be enhanced by using adaptive thresholds too. A new adaptive threshold technique was proposed by using multi-step ahead prediction based on model for improving speed and robustness of fault detection system. Upper limiting filters and smoothing filters were acted on the data of adaptive thresholds and got better performance about detection. Finally, an example based on an industrial actuator will be used for testing the proposed approach.
Chosen Plaintext Attack for Hyper-chaotic System Image Encryption Algorithm

Hyper-chaotic system image encryption algorithm HYPER_HIE uses elementary transformation and exjunction to encrypt,so it is difficult to resist various attacks.Aiming at three steps of HYPER_HIE including key generation,pixel scrambling,image diffusion and confusion,this paper chooses three kinds of plaintext matrixes to do chosen plaintext attack without knowing encryption key.An instance shows that the attack method can decode ciphertext with low computing costs.
Research of fast image matching based on PCA

According to the characteristics of feature matching algorithm such as anti-jamming and high speed,an improved fusion matching algorithm is proposed to resolve the large calculating problem of traditional gray-scale image matching algorithm. Through experiments,the computing time reduced to 60%or less of the single gray level normalized cross-correlation matching which meets the requirements of fast and accurate image matching.
Research on covert techniques of kernel-mode Rootkits under Windows

Rootkits is mainly used to execute covert control on the host computer under an administrator's competence.The kernel system of Windows being more thoroughly exposed by peoples the kernel-level Rootkits techniques have been being laid on greater emphasis.It is very important to maintaining the security of computers to conduct an in-depth research on the operational mechanism by using kernel-level Rootkits in hiding its traces,and the involved core techniques that are beginning to catch on nowadays.It is very useful to expound the mechanism of the covert operation of mainstream kernel-level Rookit,to analyze some new key techniques employed for application of Rootkits to discuss the specific methods and to put them into practice.
Machine Learning Approaches for Sentiment Analysis

Opinion Mining or Sentiment Analysis is the study that analyzes people’s opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs’, forums’, and social networking sites’ necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.
Meta-simulation design and analysis for large scale networks

Performance analysis techniques are fundamental to the process of network protocol design and operations. A variety of techniques have been used by researchers in different contexts: analytic models (e.g. TCP models, web models, self-similar models, topology models), simulation platforms (e.g.: ns-2, SSFnet, GloMoSim, Genesis), prototyping platforms (e.g. MIT Click Router toolkit [9], XORP [10]), tools for systematic design-of-experiments and exploring parameter state spaces (e.g. Recursive Random Search, STRESS [13]), experimental emulation platforms (e.g. Emulab), real-world overlay deployment platforms (e.g. Planetlab), and real-world measurement and data-sets (e.g. CAIDA [26], Rocketfuel [27]). The high-level motivation behind the use of these tools is simple: to gain varying degrees of qualitative and quantitative understanding of the behavior of the system-under-test. This high-level purpose translates into a number of specific lower level objectives, such as: validation of protocol design and performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions between protocols. Broadly, we may summarize the objective as a quest for general invariant relationships between network parameters and protocol dynamics. To address these needs, we developed an experiment design platform that will allow us to empirically model and heuristically search for optimizing protocol response. In general the protocol response is a function of a large vector of parameters, i.e., is a response surface in a large-dimensional parameter space (perhaps tens of thousands or more dimensions). We build off recent work at Rensselaer on an efficient search algorithm (called Recursive Random Search) for large-dimensional parameter optimization, and empirical modeling of protocol performance characteristics especially in "interesting" regions of the parameter state space. The result of this work includes a unified search, empirical modeling and optimization framework with demonstrated ability to pose meaningful large-scale network design questions and provide "good" models rapidly.
Linear Neural Networks and Its Application in System Identification

In this paper,the author introduces the basic intelligent control technology:artificial neural network (NN). Theories of system identification and modeling using neural networks are discussed.Based on an analysis on the modeling principles of linear system,a method for identification based on neural networks for linear systems is proposed.The method displays better ability,due to its advantages such as without the need of system order number,a smaller neural network structure and faster convergence.Simulation results testified the feasibility and validity of the proposed method.The paper is divided into four parts altogether.The first part provides the features about system identification based on neural network.The second part focuses on the basic principles of linear neural network,including the model of linear neural network,transfer function,studying formulae and the process of training.The third part describes the simulation example of system identification based on linear network.The last part is a brief summary.
Multi-Object QoS Routing Algorithm Based on Dijkstra Strategy

Formalized analyzing the topology of network and the target of QoS routing, this paper proposes the math model of QoS routing. After giving the integrated estimated merit of QoS routing, we present a Dijkstra+ QoS routing algorithm (DQRA) based on Dijkstra strategy. Simulation results demonstrate that the DQRA algorithm can meet QoS requirement of service connection, and the distribution of network load is balanced, so the throughout and the performance of network are improved.
Room Transfer Function Estimation Using Cepstral Smoothing

Many state-of-the-art applications nowadays make use of a distant microphone to receive the speech signal of a user, e.g. hands-free communication systems, automatic speech recognition or acoustic speaker localization. In an enclosed space this signal is corrupted by reverberation caused by the influence of the room impulse response (RIR) which depends on the characteristics of the room and on the positions of the user and the microphone. As reverberation can decrease speech intelligibility as well as quality, several dereverberation algorithms have been developed in the past. Some dereverberation algorithms, e.g. channel equalization, need an estimate of the time domain RIR typically obtained using blind system identification (BSI) while others, e.g. reverberation suppression, can be fed only with parameters of the RIR such as the reverberation time (T60) or the direct to reverberant ratio. An estimate of the amplitude of the room transfer function (ARTF) could be used to improve the performance of BSI algorithms, as a parameter within reverberation suppression methods or source localization algorithms. An adaptive, blind, single-channel estimator of the amplitude of the room transfer function based on cepstral smoothing is proposed in this contribution.
Optical Shop Testing

Thank you for downloading optical shop testing. As you may know, people have search numerous times for their chosen readings like this optical shop testing, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop. optical shop testing is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the optical shop testing is universally compatible with any devices to read.
Design and field programmable gate array implementation of cascade neural network based flux estimator for speed estimation in induction motor drives

This study presents design and hardware implementation of cascade neural network (NN) based flux estimator using field programmable gate array (FPGA) for speed estimation in induction motor drives. The main focus of this study is the FPGA implementation of cascade NN based flux estimator. The major issues in FPGA implementation are optimisation of cost (resource) and execution time. A simple non-linear activation function called as Elliott function is used to reduce the execution time. To reduce the cost, and effectively utilise resource, the concept of layer multiplexing is adopted. The lowest bit precision needed for good performance of the estimator is identified and implemented. The proposed NN based flux estimator using simple excitation function and minimum bit precision is implemented using layer multiplexing technique. The designed estimator is tested on Spartan FPGA kit (3sd1800afg676-4) and the results obtained are presented.
Distributed Graph Storage And Querying System

Graph databases offer an efficient way to store and access inter-connected data. However, to query large graphs that no longer fit in memory, it becomes necessary to make multiple trips to the storage device to filter and gather data based on the query. But I/O accesses are expensive operations and immensely slow down query response time and prevent us from fully exploiting the graph specific benefits that graph databases offer. The storage models of most existing graph database systems view graphs as indivisible structures and hence do not allow a hierarchical layering of the graph. This adversely affects query performance for large graphs as there is no way to filter the graph on a higher level without actually accessing the entire information from the disk. Distributing the storage and processing is one way to extract better performance. But current distributed solutions to this problem are not entirely effective, again due to the indivisible representation of graphs adopted in the storage format. This causes unnecessary latency due to increased inter-processor communication. In this dissertation, we propose an optimized distributed graph storage system for scalable and faster querying of big graph data. We start with our unique physical storage model, in which the graph is decomposed into three different levels of abstraction, each with a different storage hierarchy. We use a hybrid storage model to store the most critical component and restrict the I/O trips to only when absolutely necessary. This lets us actively make use of multi-level filters while querying, without the need of comprehensive indexes. Our results show that our system outperforms established graph databases for several class of queries. We show that this separation also eases the difficulties in distributing graph data and go on propose a more efficient distributed model for querying general purpose graph data using the Spark framework. INDEX WORDS: Graph Databases, Distributed Graph Databases, Distributed Graph Query Processing, Spark DISTRIBUTED GRAPH STORAGE AND QUERYING SYSTEM
Spatial routines for sketches: a framework for modeling spatial problem-solving

Spatial problem-solving tasks are often used to evaluate people's cognitive abilities. For example, Raven's Progressive Matrices is a popular intelligence test. In it, an individual is shown an array of two-dimensional images, with one image missing. The individual must compare the images and identify a pattern of differences between them, in order to solve for the missing image. Performance on tasks such as Raven's and geometric analogy (“A is to B as C is to.? ”) correlates strongly with performance on many other ability tasks, in the spatial, verbal, and mathematical domains. Thus, these tasks appear to depend on core, general-purpose representations and processes. However, it is as yet unclear what those representations and processes are. To better understand these tasks, we developed Spatial Routines for Sketches (SRS), a general framework for modeling spatial problem-solving. SRS is based on a set of psychological claims about how people perform spatial problem-solving: 1) When possible, people use qualitative representations describing features such as relative position or orientation, rather than exact numerical values. 2) Spatial representations are hierarchical. A given image might be represented as object groups, individual objects, or the parts within each object. 3) Qualitative spatial representations can be compared via structure-mapping. Structure-mapping involves aligning the relational structure in two representations to find the corresponding elements. Three task models were built within the SRS framework: geometric analogy, Raven's Progressive Matrices, and the oddity task, in which one sees a set of images and picks the one that is different. The three task models use identical representations and similar processes. Thus, they allow us to test the generality of the psychological claims, as well as the representations and processes that implement these claims. Each task model was compared against human performance. All three models perform at least as well as human adults, and problems that are difficult for the models are also difficult for people. This supports the claims on which the models are based. Furthermore, by ablating a model's ability to perform certain operations and examining the error pattern this produces, we are able to produce new hypotheses about human reasoning.
Queuing analysis and optimization techniques for energy efficiency in packet networks

Energy efficiency in all aspects of human life has become a major concern, due to its significant environmental impact as well as its economic importance. Information and Communication Technology (ICT) plays a dual role in this; not only does it constitute a major consumer itself (estimated 2-10% of the global consumption), but is also expected to enable global energy efficiency through new technologies tightly dependent on networks (smart grid, smart homes, cloud computing etc.). To this purpose, this work studies the problem of energy efficiency in wired networks. As this subject has recently become very active in the research community, there is parallel research towards several research directions. In this work, the problem is being examined from its foundations and a solid analytical approach is presented. Specifically, a network model based on G-network queuing theory is built, which can incorporate all the important parameters of power consumption together with traditional performance metrics and routing control capability. This generalized model can be applied for any network case to build optimization algorithms and estimate the performance of different policies and network designs. Composite optimization goals functions are proposed, comprising both power consumption and performance metrics. A gradient descent optimization algorithm that can run in O(N3) time complexity is built thereof. Using power consumption characteristics of current and future equipment, several case studies are presented and the optimization results are evaluated. Moreover, a faster gradient-descent based heuristic and a decentralized algorithm are proposed. Apart from the routing control analysis, the case of a harsher energy saving solution, namely turning off the networking equipment, is also experimentally explored. Applying a tradeoff study on a laboratory testbed, implementation challenges are identified and conclusions significant for future work are drawn. Finally, a novel admission control mechanism is proposed and experimentally evaluated, which can monitor and manage the power consumption and performance of a network.
Interactive Visual Summarization for Visualizing Large, Multidimensional Datasets

KOCHERLAKOTA, SARAT MOHAN. Interactive Visual Summarization for Visualizing Large Multidimensional Datasets. (Under the direction of Christopher G. Healey.) Because of its ability to help users analyze and explore data from a diverse set of domains, visualization is becoming integral to the knowledge discovery process. However, existing visualization techniques for displaying large, multidimensional datasets often produce detailed, cluttered images that overwhelm the user’s ability to effectively absorb the underlying data. To visualize such datasets effectively we have developed a visual summarization framework that intelligently summarizes datasets by extracting its important and relevant characteristics prior to visualization. The summaries are then visualized both in place of the original data, or along with the original data. Our approach performs this summarization in three broad steps. First, size and dimensionality of the data are reduced meaningfully. Next, patterns and dependencies in the form of association rules, along with outliers are extracted from the reduced data. Finally, these summary characteristics are visualized using techniques that are aimed at enhancing the comprehension of the data. Summary characteristics, as well as summarization steps are also recorded. Our framework is designed to harness the benefits of both visual and non-visual methods to intuitively guide users to produce relevant data summaries. Initial results in applying our approach to practical datasets suggest that our approach could be used to generate effective visual summaries of large, multidimensional datasets from a wide variety of domains and applications. INTERACTIVE VISUAL SUMMARIZATION FOR VISUALIZING LARGE MULTIDIMENSIONAL DATASETS by SARAT M. KOCHERLAKOTA A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy
Maintain cache consistency of mobile database using dynamical periodical broadcast strategy

In mobile database, in order to reduce the contention of limited network bandwidth by mobile computers and improve availability of local data, frequently, and used data are cached locally. How to effectively maintain cache consistency is a hot research topic. In this paper, we propose strategies to maintain cache data consistency, which are, based on updating frequency of data items, to broadcast dynamically using variant period, to broadcast updating messages instead of invalidation messages, to use substitution strategy based on semantically cached data units, to maintain cache consistent based on historical report after mobile computer reconnects. Finally we analyze performance of the strategies.