Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic text summarization using pathfinder network scaling

This thesis describes an automatic text summarization system based on graph-theory. Summaries are useful indicators of the document content. Traditionally summaries are created by humans by reading the text and identifying the important points in the text. So called information-overload makes such manual work very difficult (if not impossible). Many attempts have been made to automate the summarization task. This task is very difficult and involves natural language understanding. We propose an extractive text summarization system. Extractive summarization works by selecting a subset of sentences from the original text. Thus the system needs to identify most important sentences in the text. We use graph theory to identify relative importance of a sentence. The proposed method, SumGraph, uses a fully connected network to model a text with nodes as sentences and the lexical dissimilarity between pairs sentences as the link weights. This fully connected network is then scaled using Pathfinder Network Scaling technique. The scaled network reveals the conceptual organization of the sentences. Sentences are then selected for inclusion in the summary depending upon their relative importance in the conceptual network. Performance of the method is illustrated on a collection of newspaper articles. The data comes from Document Understanding Conferences. ROUGE measure is used for evaluating goodness of a summary. Comparison with other methods indicate that SumGraph produces better summaries.
Does High Sentiment Cause Negative Excess Return

Most of the empirical studies on investor sentiment have shown that the excess return of stock is negatively related to sentiment. However, some financial experiments implied that there is positive expected return when investor sentiment is high. Moreover, all of the empirical researches which treated the time series values of sentiment as an aggregate are in average meanings by regression analysis. Maybe there are some particular characters by considering the sentiment in different situations. This paper investigates whether the high sentiment will cause negative excess return by theoretical model. We obtained a critical point indicated that the excess return is negatively related to a high sentiment bigger than the critical point, but positively related to it smaller than this point.
Multi-Model Parallel Programming

Multi-model parallel programming is the simultaneous use of different programming models, either within a single program, or in separate programs. A variety of programming models is best implemented in runtime libraries, yet current operating systems are not designed to support user-level implementations of processes and communication. Even if an efficient implementation of a programming model can be constructed in user space, interactions with other programming models is complicated by the lack of a common communication mechanism. The thesis of this dissertation is that in order to support multi-model parallel programming, an operating system must provide first-class user-level processes and model-independent communication. First-class user-level processes support the construction of multiple models by allowing the flexibility and efficiency of user-level implementations to be combined with the control over resource allocation traditionally enjoyed only by kernel-level implementations. Model-independent communication supports the integration of multiple models into a single application by eliminating model-dependencies from the data transfer and concurrency control mechanisms used for communication. Together, these concepts facilitate multi-model programming, making possible a wide variety of high-quality implementations that are easily combined within a single application. To support the thesis, we designed and implemented a prototype kernel, called Psyche, ported the implementations of different parallel programming models, and built several multi-model programs. Our experiences with the various programming models demonstrate the feasibility of according first-class status to processes implemented in user space. Our experiences with multi-model applications demonstrate how model-independent communication facilitates the construction of multi-model programs. Through these experiences, this dissertation demonstrates the feasibility and utility of multi-model parallel programming.
Real-time face tracking system for human-robot interaction

When a person instructs operations of a robot, or performs a cooperative task with a robot, it is necessary to inform the robot of the person's intention and attention. Since the motion of a person's face and the direction of the gaze is deeply related with person's intention and attention, detection of such motions can be utilized as a natural way of communication in human-robot interaction. We describe our real-time stereo face tracking system. The key of our system is the use of a stereo vision. Since the 3D coordinates of the features on the face can be directly measured in our system, we can drastically simplify the algorithm of the 3D model fitting to obtain the full 3D pose of the head compared with conventional system with a monocular camera. Consequently we achieved a non-contact, passive, real-time, robust, accurate and compact measurement system of a human's head pose and gaze direction.
Performance Of Parallel Combinatory Spread Spectrum Multiple Access Communication Systems

data 1 data 2 Abstract In this paper, several results of study for our newly proposed parallel combinatory spread spectrum communication system are presented. The proposed system can transmit 1.58N bits of information for a period of spreading sequence of length N in the maximum. For multiple access performance, more than 47% improvement of the spectral efficiency is achieved by the proposed system compared to conventional direct sequence spread spectrum system.
Semantic relations across syntactic levels

In order to make sense of a message conveyed to us via a spoken or written utterance, we understand what things are talked about, and how they are connected. From this point of view, do these sentences convey different messages? (1) I will arrive at 11 am. and I will arrive when you arrive. (2) I will meet you in the office. and I will meet you where we met last time. (3) Sweets before dinner spoil your appetite. and (4) Eating sweets before dinner spoils your appetite. I will arrive at a certain point in time: at 11 am. , or when you arrive. I will meet you at a certain place: in the office, or where we met last time. We can talk about sweets and mean eating sweets. Literature review suggests that the relations exemplified by these pairs of sentences are different, because they connect different types of syntactic units. The first relation in each pair connects a verb and one of its arguments, the second—two clauses. Such distinctions are artificial. Semantic relations link concepts, and will surface on the syntactic level on which the concepts they connect surface. We aim to give an account of semantic relations that does not depend on syntactic levels. We will justify a unified view of semantic relations across syntactic levels. Such a view has a positive effect on text analysis. It will allow us to gather evidence for a particular semantic relation from all levels at which it appears. Having such information that is not separated according to syntactic levels will allow a text analysis and knowledge acquisition system to use at each processing step, all the evidence previously gathered. We will show that this translates into faster learning and better results. We can take semantic relation analysis onto another level. We can look for descriptions of concepts connected by a specific semantic relation to find what characteristics or features of the concepts connected make them interact in this way. (1) blue book, happy person, interesting study; (2) paper bag, wooden chair, iron gate; (3) oak tree, cumulus cloud, flounder fish. Blue, happy, interesting are properties, and paper, wood, iron are materials. Oak is a specific type of tree, cumulus is a type of cloud, and flounder is a type of fish. We will use ontologies to find similarities between concepts that explain or give us indications about the semantic relations in which they are involved. All these aspects we explore serve to improve text analysis. We propose a uniform processing of texts that allows us to extracts pairs of concepts that interact, and to describe this interaction through semantic relations.
Causal discounting and conditional reasoning in children

The conditional, if.then, is probably the most important term in natural language and forms the core of systems of logic and mental representation. It occurs in all human languages and allows people to express their knowledge of the causal or law-like structure of the world and of others' behaviour, e.g., if you turn the key the car starts, if John walks the dog he stops for a pint of beer; to make promises, e.g., if you cook tonight, I'll wash up all week; to regulate behaviour, e.g., if you are drinking beer, you must be over 18 years of age; to suggest what would have happened had things been different, e.g., if the match had been dry it would have lit, among many other possible uses. The way in which the conditional is modelled also determines the core of most logical systems. Unsurprisingly, it is also the most researched expression in the psychology of human reasoning. Cognition and Conditionals is the first volume for over 20 years (On Conditionals, 1986, CUP) that brings together recent developments in the cognitive science and psychology of conditional reasoning. Over the last 10 to 15 years, research on conditionals has come to dominate the psychology of reasoning providing a rich seam of results that have created new theoretical possibilities. This book shows how these developments have led researchers to view people's conditional reasoning behaviour more as succesful probabilistic reasoning rather than as errorful logical reasoning. It shows how the multifarious, and apparently competing, theoretical positions developed over the last 50 years in this area - mental logics, mental models, heuristic approaches, dual process theory, and probabilistic approaches-have responded to these insights. Its organisation reflects the view that an integrative approach is emerging that may need to exploit aspects of all these theoretical positions to explain the rich and complex phenomenon of reasoning with conditionals. It includes an introductory chapter relating the development of the psychology of reasoning to developments in the logic and semantics of the conditional. It also includes chapters by many of the leading figures in this field. Cognition and Conditionals will be a valuable resource for cognitive scientists, psychologists and philosophers interested how people actually reason with conditionals.
Association Mining in Database Machine

Association rule is wildly used in most of the data mining technologies. Apriori algorithm is the fundamental association rule mining algorithm. FP-growth tree algorithm improves the performance by reduce the generation of the frequent item sets. Simplex algorithm is a advanced FP-growth algorithm by using bitmap structure with the simplex concept in geometry. The bitmap structure implementation is particular designed for storing the data in database machines to support parallel computing the association rule mining.
Equalisation of analogue to digital converter nonlinearities

A signal-processing technique called threshold tracking is presented which can improve the linearity of any analog-to-digital conversion circuit (ADC). The basic idea is to use a mapping of the original output codes and their associated nonideal quantization levels to a set of codes whose corresponding quantization levels more closely match those of an ideal ADC. When the mapped codes are then assumed to be associated with ideal quantization levels by the rest of the signal processing system, the error between the actual and the ideal levels is reduced. The equalization system is described and the convergence and equalization performance in the presence of training signal noise is shown. <<ETX>>
Elimination of imbalance vibrations in magnetic bearing systems using discrete-time gain-scheduled Q-parametrization controllers

We propose a method to eliminate the imbalance vibrations in magnetic bearing systems using discrete-time gain-scheduled Q-parametrization controllers. Imbalance in rotating machines generates variable frequencies sinusoidal disturbance forces that cause the vibrations. Since the frequency of vibrations equals the rotational speed, the free parameter Q of the Q-parametrization controllers is scheduled as a function of the rotational speed to achieve rejection of the imbalance sinusoidal disturbance forces at all operating speeds. First, we present a mathematical model for the magnetic bearing in state space from which includes the effect of imbalance. Next, we explain the discrete-time Q-parametrization controller design for the magnetic bearing to achieve robust stability mid rejection of the variable frequencies sinusoidal disturbance forces. Finally, several simulation results are presented. The results showed that elimination of the imbalance vibrations are achieved at all operating speeds, and moreover robust stability is also achieved.
Simple shape space for 3D face registration

Three dimensional (3D) face recognition is a topic getting increasing interest in biometric applications. In our research framework we developed a laser scanner that provides 3D cloud information and texture data. In a user scenario with cooperative subjects with indoor light conditions, we address three problems of 3D face biometrics: the face registration, the formulation of a shape space together with a special designed gradient algorithm and the impact of initial approximation to the convergence of a registration algorithm. By defining the face registration as a problem of aligning a 3D data cloud with a predefined reference template, we solve the registration problem with a second order gradient algorithm working on a shape space designed for reducing the computational complexity of the method.
Identification and Assessment of Social Subgroups or Community Structure on Social Networking Websites

Social Networking Analysis is gaining more popularity day by day due to its major contribution towards business marketing analytics and future decision policies for any large organization. These kinds of assessments have their potential advantages towards designing future business strategies for a particular product/object available in the market. The social media is now open for all kind of blogs, independent views and open communities. People are free to express their ideas or opinion for any specific product. Now social media has become a widely accepted platform to establish an opinion set for a particular product. The author have described various strategies to gather data from these social communities and try to find the specific interest group/subgroup for any product or person or any object. In this paper, the authors have used an open source tool NodeXL for analysis of twitter tweets and find out most effective group or community individually. This analysis is targeted on the factor of centrality and clustering coefficient analysis.
A Circularly Polarized Omnidirectional Antenna

A circularly polarized omnidirectional antenna consisting of a vertical sleeve dipole and three pairs of tilted parasitic elements set around it is proposed. The antenna has a simple structure and no feeding network for radiating circular polarization. The authors describe the idea of the proposed antenna, some experimental approaches and the evaluation of the antenna by using the moment method.
Cross-contextual use of integrated information systems

Many organizations now purchase and customize software rather than build information systems. In this light, some argue that high-level data modeling no longer has a role. In this paper, we examine the contemporary relevance of high-level data modeling. We addressed this issue by asking 21 experienced data-modeling practitioners to reflect on their work and to give their opinions on trends and future directions in high-level data modeling. We analyzed transcripts of our interviews with them using Klein and Myers’s (1999) framework for qualitative research. We found considerable variation in the practice of high-level data modeling. We also found that high-level data modeling is still considered important, even though organizations ultimately may purchase off-the-shelf software. The reason is that high-level data modeling assists organizations to obtain clarity about IT project scope and requirements, thereby reducing the risk that costly implementation mistakes will be made.
A Data Driven Machine Learning Approach to Discovering Rules of Price Behavior in a Financial Market Simulation

The field of agent-based simulation of financial markets has grownconsiderably in the last decade. However, the interpretation ofsimulation results has received far less attention. Typically, theresults of a large number of simulations are reduced to one or twosummary statistics, such as sample moments. While suchsummarization is useful, it overlooks a vast amount of additionalinformation that might be gleaned by examining patterns ofbehavior that emerge at lower levels. In this paper we propose anapproach to interpreting simulation results that involves the use ofso-called data mining techniques to identify the rules of behaviorthat govern an underlying system. We demonstrate the approachby using data from a single run of an order market simulation toderive rules about the behavior of prices in that simulation.
RK+MOSIX: A Real-time Kernel with Automatic Task Migration Support

We combine two well-known technologies, the resource kernel for real-time task guarantees and MOSIX for dynamic task migrations, and describe how to alleviate the problems that result from their interactions. We describe the ideas, motivations, and design and implementation of middleware based on these two separate systems. By combining a resource kernel (Linux/RK) and a dynamic load-balancing system (OpenMosix), we increase the chance of completing a real-time task, even if there aren’t enough resources in a local machine to meet the deadline of the task. Since the original Linux/RK was implemented for a single machine, not supporting task migrations, several issues relating to the resource kernel system calls had to be resolved before migration of real-time tasks was possible. We also resolved some compatibility issues in combining OpenMosix and Linux/RK. We discuss detailed implementation issues in combining these three systems and lessons learned. We also discuss network overhead issues relating to the admission control of real-time tasks.
Performance optimization and system clock determination for synthesis of DSP cores targeting FPGAs

In this paper, we present details of an integer linear programming (ILP) formulation for synthesis of high performance digital signal processing architectures targeting FPGA implementation. The formulation allows general multi-level chaining of operations in conjunction with multicycle and deeply pipelined function units. This novel ILP formulation simultaneously performs scheduling and binding while minimizing the architecture structural complexity, total execution time (rather than total cycle count), and determining the system clock duration. We demonstrate using high level synthesis benchmarks, that our approach explores larger solution space and obtains more efficient architectures as well as higher performance than previously possible with other synthesis approaches.
Reward system design for incorporating control performance

Reinforcement learning (RL) is a machine learning technique whereby the controller learns the control law by optimizing the received cumulative amount of reward. A reward is an instantaneous evaluation of the applied action at the current state, given by reward function. However in theory the reward function is assumed to be given, in practice it is an effort consuming work to design a good reward function. Reward is the only information about the learning task given to the controller and therefore optimizing the cumulative amount of reward corresponds to fulfilling a particular control performance. Designing the reward function to achieve the desired control specification is thus a crucial task to use RL as a controller synthesizing algorithm. The goal of this thesis is to synthesize a method to design proper reward function to achieve the desired control performance. This thesis focuses on two types of control performance. In the first part, reward function is designed to learn control law fulfilling classical control performance. Hereby an automaton is created to evaluate classical control criteria by mode-dependent reward functions. By modeling the process with an automaton, the control problem is divided into smaller subproblems such that the reward functions are kept simple. In the second part, temporal logic specification is converted into a reward system whereby a petri net is used to model the process suitable for rewarding. To the given temporal formula, reward function is assigned which is a function from the state, i.e. the marking, of the petri net. By putting information about the task into the petri net, reward function becomes simple and structured. Simulation experiments are done for several temporal logic specification.
Proxy signature schemes with time limitation

Abstract In this paper, we propose a key-revolution concept for proxy signature schemes to obtain a property of time limitation on delegation duration of proxy signatures. Proxy signature scheme is a kind of signature schemes in which an original signer delegates its signing capability to a designated signer called a proxy signer. Then, the proxy signer creates a digital signature on behalf of the original signer. The problems of time limitation on delegation duration are essential for proxy signature schemes. We propose a proxy signature scheme with time limitation in which a proxy key is renewed in each period until the end of period. Then, the proxy key is revoked automatically.
Advances in Human Performance and Cognitive Engineering Research

From the Book: Focusing on human performance in organizational systems as cognitive engineering principles are applied, these eight chapters describe cognitive research at RISO from 1962-1979, the development of adaptability, the use of pathfinder networks, cognitive task analysis, human modeling technology, critical thinking in novel situations, the use of simulations for team research, and research paradigms. The authors are affiliated with various American universities, private companies, and the military. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Approach to clustering large visual databases using wavelet transform

Many applications demand the capability of retrieval based on image content. A classification mechanism is needed to categorize images based on feature similarity. An effective classification of the images can support efficient retrieval of images. In this paper, we investigate a feature-based approach to image clustering and retrieval. Four different texture-based feature sets of images are extracted using Haar and Daubechies wavelet transforms. Using multi- resolution property of wavelets, we extract the features at different levels. The experimental results of our clustering approach on air photo images are reported.