Miracle-dz/UniFAR-FTUs huggingface dataset:
Finance and Corporate Innovation: A Survey

Corporate innovation is an increasingly important topic that has attracted great attention from academic researchers in financial economics in recent years. Although the top three finance journals (i.e. the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies) together published a total of only five papers on corporate innovation from 2000 to 2008, the number of such papers published by these three journals skyrocketed to 56 from 2009 to the third quarter of 2017. The purpose of this survey is to provide a synthetic and evaluative monograph of academic papers that examine the drivers and financing sources of corporate innovation.
Measuring Competitive Balance in Formula One Racing

The Formula One Championship (F1) is one of the biggest sports businesses in the world. But, however, it seems to astonish that only very few scholarly articles analyze the F1 business. The aim of this study is to contribute to closing two gaps in the existing literature: it contributes (1) to the (sports) economic analysis of the F1 business and (2) to the literature on competitive balance in non-team sports. Like competitive balance in team sport leagues, also for F1 racing three dimensions can be distinguished: (a) race-specific competitive balance, (b) within-season competitive balance, and (c) between-season competitive balance. In addition to classical tools and data, some new and F1 specific indicators, like average lead changes or leading distance, are employed. Also, pitfalls induced especially by the used data source or calculation method are highlighted.
Theorizing Regulatory Intermediaries: The RIT Model

Regulation is typically conceived as a two-party relationship between a rule-maker or regulator (R) and a rule-taker or target (T). We set out an agenda for the study of regulation (and rules more broadly) as a three- (or more) party relationship – with intermediaries (I) at the center of the analysis. Intermediaries play major and varied roles in regulation, from providing expertise and feedback to facilitating implementation, monitoring the behavior of regulatory targets and building communities of assurance and trust. After developing the basic RIT model, we discuss important extensions and variations. We then discuss the varieties of regulatory capture that appear where intermediaries are involved. Empirical chapters in the volume apply the RIT framework and suggest extensions and modifications.
Towards Graph-based Text Representation

Text representation is the basis of text processing. Most text representation model didn’t consider the order of the terms in the text,which results in the losing of text semantics. We tried to introduce order in the text, using graphic structure to represent text. In this paper, we proposed a novel text representation model named Graph Space Model (GSM) and introduced a novel way to measure the representation ability of a text representation model. We compared the ability of the Vector Space Model [2] and our GSM. Our model didn’t excel the VSM now, but there are much more problems need to be investigated in the text representation to take back the losing semantics.
Design, Implementation, and Evaluation of Network Monitoring Tasks with the STREAM Data Stream Management System

The heavy load and rich variety of data on the Internet has resulted in the need to gain an understanding of the characteristics of the traffic to better plan, develop, and implement new network devices, applications, and protocols. In order to obtain such knowledge, network monitoring is becoming more and more important. However, tools available for network monitoring are restricted to either offline analysis in DBMSs or online analysis through hard-coded continuous queries. Many streaming applications would benefit from a system where network monitoring queries effectively can be inserted, deleted, modified, and processed online in a continuous and real time manner. Data Stream Management System (DSMS) is a promising technology with respect to the needs of network monitoring, because it is designed to meet the above requirements generated by many streaming applications. In the present paper, an experimental analysis of STREAM as a network monitoring tool is performed. STREAM is a general-purpose DSMS and its continuous query language is known as CQL (Continuous Query Language). We investigate whether the current implementation of CQL operators provides us the possibility of expressing a wideranged set of network monitoring queries. Furthermore, STREAM’s performance is measures by accomplishing several experiments that are processed online over real network traffic. Results reveal that STREAM can handle network loads up to 30 Mb/s for simple queries, and up to approximately 3 Mb/s complex queries. When queries are executed concurrently, STREAM can handle network loads up to approximately 2.5 Mb/s, strongly depending on the complexity and number of queries. STREAM provides a well-sized set of operators that provides us the possibility of expressing many types of queries. However, network monitoring queries are restricted by lack of specific network data types and operators. Consequently, these queries are expressed in cumbersome ways. STREAM manages to process network monitoring queries online in a continuous manner, nevertheless at a very limited network load. Thus, the applicability of STREAM as a network monitoring tool is restricted.
Threat of Platform-Owner Entry and Complementor Responses: Evidence from the Mobile App Market

How do complementors respond to the threat of platform owner entry, and how do such responses differ from the responses to actual entry? Using the mobile platform Android as our research setting, we examine how app developers on Android adjust their rate and direction of innovation efforts and prices in response to Google’s entry threat and actual entry into to the app markets. Based on a difference-indifferences empirical framework, we find that app developers that are affected by Google’s entry reduce their innovation efforts on affected apps after entry threats increase; after Google’s actual entry, they reduce innovation efforts on affected apps further and also increase these apps’ prices. However, we find that affected app developers do not withdraw from the platform completely—once the threat occurs, they shift innovation efforts from affected apps to other unaffected apps, as indicated by an increase in updates on unaffected apps during both the entry-threat and actual-entry period.
Graph Based Word Sense Disambiguation Method Using Distance Between Words

Almost all existing knowledge-based word sense disambiguation(WSD) methods used exploit context information contain,in certain window size around ambiguous word,are ineffective because all words in the window size have the same impact on determining the sense of ambiguous word.In order to solve the problem,this paper proposes a novel WSD model based on distance between words,which is built on the basics of traditional graph WSD model and can make full use of distance information.Through model reconstruction,optimization,parameter estimation and evaluation of comparison,the study demonstrates the feature of the new model: The words nearby ambiguous word will have more impact to the final sense of ambiguous word while the words far away from it will have less.Experimental results show that the proposed model can improve Chinese WSD performance,compared with the best evaluation results of SemEval-2007: task #5,this model gets MacroAve(macro-average accuracy) increase 3.1%.
An Unsupervised Approach To Word Sense Disambiguation Based on Hownet

Word sense disambiguation is still as a considered one of the most challenging problems in natural language process-ing. Ever since the field's inception WSD has been perceived as one of the central problems in NLP. This paper presents an unsu-pervised approach which constructs context vector by means of second-order context, clustering by k-means and disambiguatesby calculating the similarity. Our experiments are based on the extraction of term and average accuracy is 82.62% and 84.55%for 7 ambiguous words in open test by this method.
Understanding and Reasoning with Text

Consider the assignment that teachers have been giving their students for years: “Write an expository essay on a scientific topic. Example topics may include global warming, human memory, or the spread of infectious diseases. You must have at least three references. ” The instructor makes it clear that the paper should have a thesis or claim that is supported by evidence. Claims might be that global warming will be disastrous only for some nations, why it is futile to teach mnemonics to young children, or that cell phone use causes cancer. From the perspective of the student (and cognitive psychologists), this assignment is challenging at any grade. The challenge is that the assignment entails a number of complicated and interconnected tasks. For example, reading a research paper requires the reader to make inferences that span sentences and paragraphs (in addition to a whole host of other processes), and to understand the logical and rhetorical structure of the text as a whole. If the paper describes an experiment, the student must additionally understand how to determine whether the data support the conclusion (i.e., the scientific method). In most cases, the student must also integrate the content of several papers (sources) into a coherent structure. This process involves evaluating the credibility of the sources, selecting relevant pieces of information from each, and putting them into a coherent argument structure. No wonder such assignments are met with groans.
Infrared dim moving target tracking via improved context learning

It is very critical that make full use of the local information for infrared dim and small target tracking. In this paper, an effective and fast algorithm based on the context learning is proposed to track infrared dim moving target. Firstly, the principle of the spatio-temporal context learning algorithm is described and the tracking deviation is analyzed. Then, a correlation filter is utilized to get a rational context prior for the dim moving target, the advantage is that the prior considers the image intensity information between a target and its surround pixels. Furthermore, a Gaussian high-pass filter is introduced to extract an accurate spatial context, which has little influence caused by the cluttered background. At last, the tracking problem is posed by computing a confidence map which takes into account sufficient information of a dim target and its surround background. Since the proposed algorithm is realized using fast Fourier transform, it is easy to be real-time. The experiments on various clutter background sequences have validated the tracking capability of the proposed method. The experimental results show that the proposed method can provide a higher accuracy and speed than several classical algorithms, including the improved Template Matching algorithm, the Temporal-Spatial Fusion Filtering algorithm and the Moving Pipeline Filtering algorithm.
Understanding the Impact of Diversity in Software Bugs on Bug Prediction Models

Nowadays, software systems are essential for businesses, users and society. At the same time such systems are growing both in complexity and size. In this context, developing high-quality software is a challenging and expensive activity for the software industry. Since software organizations are always limited by their budget, personnel and time, it is not a trivial task to allocate testing and code-review resources to areas that require the most attention. To overcome the above problem, researchers have developed software bug prediction models that can help practitioners to predict the most bug-prone software entities. Although, software bug prediction is a very popular research area, yet its industrial adoption remains limited. In this thesis, we investigate three possible issues with the current state-of-the-art in software bug prediction that affect the practical usability of prediction models. First, we argue that current bug prediction models implicitly assume that all bugs are the same without taking into consideration their impact. We study the impact of bugs in terms of experience of the developers required to fix them. Second, only few studies investigate the impact of specific type of bugs. Therefore, we characterize a severe type of bug called Blocking bugs, and provide approaches to predict them early on. Third, false-negative files are buggy files that bug prediction models incorrectly as non-buggy files. We argue that a large number of false-negative files makes bug prediction models less attractive for developers. In our thesis, we quantify the extent of false-negative files, and manually inspect them in order to better understand their nature.
Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds

To meet the dynamic workload requirements in widespread task-batch based workflow applications, it is important to design algorithms for DAG-based platforms (such as Dryad, Spark and Pegasus) to rent virtual machines from public clouds dynamically. In terms of depths and functionalities, tasks of different task-batches are merged into task-units. A unit-aware deadline division method is investigated for properly dividing workflow deadlines to task deadlines so as to minimize the utilization of rented intervals. A rule-based task scheduling method is presented for allocating tasks to time slots of rented Virtual Machines (VMs) with a task right shifting operation and a weighted priority composite rule. A Unit-aware Rule-based Heuristic (URH) is proposed for elastically provisioning VMs to task-batch based workflows to minimize the rental cost in DAG-based cloud platforms. Effectiveness of the proposed URH methods is verified by comparing them against two adapted existing algorithms for similar problems on some realistic workflows.
New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm’s performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.
Network Topology in Command and Control: Organization, Operation, and Evolution

Over the past decade, the Command and Control (C2) field has been making a transformation from top-down, directive command to Network Centric Operations (NCO), peer-to-peer negation, self-synchronization, and agility. As the terms NCO and NEC suggest, C2 systems are regarded as networks, rather than a hierarchy. Accordingly, it is appropriate to view the C2 process and C2 systems through the lens of network theory.Network Topology in Command and Control: Organization, Operation, and Evolution aims to connect the fields of C2 and network science. Featuring timely research on topics pertaining to the C2 network evolution, security, and modeling, this publication is ideal for reference use by students, academicians, and security professionals in the fields of C2 and network science.
Analysis of stability for networked control systems with large (delays)

The stability of networked control systems with larger delays is studied. The analysis of networked (control) systems is carried out in theory by dispersing the systems. The stochastic networked-induced delay is dealt with by the idea of partition. Based on this idea, a method of combining controller design offline with switching law online is proposed and a gradient algorithm is developed for the system. Finally, a simulation example shows the (validity) of the algorithm.
MOTION-AE: an Intelligent Mobile Application for Aerobic Endurance Training

A significant part in the field of physical activity is represented by aerobic endurance training due to the fact that it depends primarily on aerobic energy generating processes. The stress of aerobic workout induces adaptations in the cardiovascular and respiratory systems which generate an optimal output of performance. Maintaining the heart rate and breathing at a set value of intensity, determined by the individual’s unique characteristics, is one way of enhancing the performance output of training. Determining these operating points that ensure desired results are often hard to calculate with classical methods, especially in the presence of nonlinearity of the heart rate variability. This paper presents a mobile application, MOTION-AE, designed for setting the optimal intensity of the aerobic endurance training, using an intelligent decision making method. The software application was tested and validated using data from various subjects. The designed mobile application takes into account the individual’s unique characteristics and ensures the optimal intensity of the training session, finding its usefulness in supporting the configuration process of training parameters by specialized professionals.
Segmentation and classification of offline hand drawn images for the BGT neuropsychological screening test

Shape drawing tests are widely used by practitioners to assess the neuropsychological conditions of patients. Most of these neuropsychological figure drawing tests comprise a set of figures drawn on a single sheet of paper which are inspected to analyze the presence or absence of certain properties and are scored accordingly. An automated scoring system for such a test requires the extraction and identification of a particular shape from the set of figures as a vital preprocessing step. This paper presents a system for effective segmentation and recognition of shapes for a well-known clinical test, the Bender Gestalt Test (BGT). The segmentation is based on connected component analysis, morphological processing and spatial clustering while the recognition is carried out using shape context matching. Experiments carried out on offline images of hand drawn samples contributed by different subjects realize promising segmentation and classification results validating the ideas put forward in this study.
Video-based Face Recognition Using Laplacianfaces and Hidden Markov Models

A method on video-based face recognition using Laplacianfaces and Hidden Markov Models(HMM) is proposed.During the training process,all the training images in each subject are projected into the obtained Laplacianspace and corresponding feature vectors,which will be used as observation vectors in the HMM training are generated.The statistics of training video sequences of each subject,and the temporal dynamics are learned by an HMM.During the recognition process,the temporal characteristics of test video sequence are analyzed over time by the HMM,and the highest score among the likelihood scores provided by HMM estimates the identity of the test video sequence.Experimental results show that the proposed method can get satisfied performance in video-based face recognition.
Correction des défauts de généralisation dans les diagrammes de cas d'utilisation UML

Dans le contexte d'un processus de developpement base sur la notation UML, les diagrammes de cas d'utilisation et les modeles ou documents qui les accompagnent pour traduire les besoins utilisateurs contribuent a formaliser la definition des besoins. Ils guident egalement toutes les etapes de construction du systeme depuis l'etablissement du cahier des charges jusqu'a la realisation des dossiers de test. Nous etudions la faisabilite d'une approche permettant la correction de certains defauts des diagrammes de cas d'utilisation UML (lies a une mauvaise utilisation des concepteurs) par introduction d'acteurs et de cas plus generaux, ainsi que de relations de specialisation/generalisation appropriees. Les differents cas de refactorisation par introduction de generalisations sont consideres de maniere systematique, en utilisant la structure du diagramme et une segmentation des noms des cas. Cette etude debouche sur une utilisation de l'Analyse Formelle de Concepts pour traiter la refactorisation de maniere globale. Une chaine d'outils permet de partir d'un diagramme de cas d'utilisation UML et d'analyser differentes generalisations possibles qui aboutissent le plus souvent a une simplification du diagramme.
Adaptive control of nonlinear servo system: comparison of standard and dual approaches

The general task of optimal adaptive control with recursive identification (self-tuning control) is very complicated problem. This problem is solved usually by the separation of identification and control -- the Certainty Equivalency (CE) Principle. The aim of this paper is to present the solution of this problem using the Dual Adaptive Control (Bicriterial Approach). The main idea of this approach involves two cost functions: (1) the system output should track cautiously the desired reference signal; (2) the control signal should excite the controlled process sufficiently for accelerating the parameter estimates. This approach was verified and compared with some other adaptive control approaches by a real-time control of highly nonlinear laboratory model - DR300 Speed Control with Variable Load.
Анализ и моделирование развития зернового хозяйства региона (на примере Кабардино-Балкарской Республики)

В статье дается экономическое обоснование стратегических направлений развития зернового хозяйства Кабардино-Балкарской Республики, основанное на анализе и моделировании развития зернового хозяйства региона. Научно-информационный журнал «Экономические науки»
Molecular Mechanisms Regulating Developmental Axon Pruning

The formation of neural connections in the mammalian nervous system is a complex process. During development, axons are initially overproduced and compete for limited quantities of target-derived growth factors. Axons which participate in functional circuits and secure appropriate amounts of growth factors are stabilized, while those axons that are either inappropriately connected or do not obtain sufficient concentrations of growth factors are eliminated in a process termed ‘axon pruning’. In this thesis, I examined the mechanisms that regulate pruning of peripheral, NGF-dependent sympathetic neurons that project to the eye. I determined that pruning of these projections in vivo requires the p75 neurotrophin receptor (p75NTR) and synthesis of brain-derived neurotrophic factor (BDNF) from the activity-dependent exon IV promoter. Furthermore, analysis of an in vitro model of axon competition, which is regulated by the interplay between nerve growth factor (NGF) and neuronal activity, revealed that p75NTR and BDNF are also essential for axon competition in culture. In this model, in the presence of NGF, neural activity confers a competitive growth advantage to stimulated, active axons by enhancing downstream TrkA (NGF receptor) signaling locally in axons. More interestingly, the unstimulated, inactive axons deriving from the same and neighboring neurons acquire a "growth disadvantage" due to secreted BDNF acting through p75NTR, which induces axon degeneration by suppressing TrkA signaling that is essential for axonal integrity. These data support a model where, during developmental axon