Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying probabilistic rules to relational worlds

Being able to represent and reason about the world as though it were composed of "objects" seems like a useful abstraction. The typical approach to representing a world composed of objects is to use a relational representation; however, other representations, such as deictic representations, have also been studied. I am interested not only in an agent that is able to represent objects, but in one that is also able to act in order to achieve some task. This requires the ability to learn a plan of action. While value-based approaches to learning plans have been studied in the past, both with relational and deictic representations, I believe the shortcomings uncovered by those studies can be overcome by the use of a world model. Knowledge about how the world works has the advantage of being re-usable across specific tasks. In general, however, it is difficult to obtain a completely specified model about the world. This work attempts to characterize an approach to planning in a relational domain when the world model is represented as a potentially incomplete and/or redundant set of uncertain rules. Thesis Supervisor: Leslie Pack Kaelbling Title: Professor of Electrical Engineering and Computer Science
EFFORT ESTIMATION USING A SOFT COMPUTING TECHNIQUE

Accurately estimating software effort is probably the biggest challenge facing software developers. Estimates done at the proposal stage has high degree of inaccuracy, where requirements for the scope are not defined to the lowest details, but as the project progresses and requirements are elaborated, accuracy and confidence on estimate increases. It is important to choose the right software effort estimation techniques for the prediction of software effort. In the present work Artificial Neural Network (ANN) model has been developed using Multi Layered Feed Forward Neural Network using Back Propagation learning algorithm by iteratively processing a set of training samples and comparing the network’s prediction with the actual effort. COCOMO dataset has been used to test and train the network using nine different types of training algorithms. It was observed that the proposed model improves the estimation accuracy of the model. The performance indices Mean-Square-Error (MSE), Magnitude of Relative-Error (MRE), and Regression analysis (R) have been used to compare the results obtained from this method. The preliminary results suggest that the proposed model can be replicated for accurately forecasting the software development effort.
GIS-Based Multicriteria Evaluation for Investment Environment

Traditional investment environment evaluation focuses on macrofactors, factors such as government stability, restrictions, and red tape. The introduction of GIS into this field provides investors and local government decisionmakers with more specific information on investment location. In this paper, a GIS-based multicriteria evaluation support system for investment evaluation is presented. The aim of the system is to develop an analysis environment to support various investment researchers and investors and to allow users with different experiences to find their own answers, which may vary. Criterion definition, management, evaluation scenario management, and user interface are four essential components of such a system. The support system is integrated with a GIS software package through a computer network facility. A case study on the Nanchang-Jiujiang industrial corridor is carried out to test the flexibility of the system.
A SURVEY ON VIDEO STREAMING OVER MULTIMEDIA NETWORKS USING TCP

Video Streaming has been used recently for various interactive services. Video Streaming is playing the content of the video in real time. Today's research is focused on video streaming through TCP. Regardless of the retransmission procedures, researchers focus on TCP Streaming due to its reliable service. This paper focuses more towards TCP Video Streaming and various congestion control algorithms used for video streaming.
Efficient Distributed Pipelines for Anomaly Detection on Massive Production Logs

The data volume of live corporate production logs is increasingly growing every day. On one hand, companies have to handle millions of data produced daily by their services which require high storage capacity. On the other hand, relevant information can be extracted from this massive amount of data and used for analysis according to different requirements, such as generating behavior patterns, detecting anomalies and making predictions. All of these can be achieved by machine learning and data mining techniques where the distributed platforms provide the computation ability and memory storage capacity for data intensive processing. Services such as payment monitoring in a company are very sensitive and require fast anomaly detection over streams of transactions. However, traditional anomaly detection techniques using distributed batch processing platforms such as Hadoop is very expensive to run and the anomalies cannot be detected in real time.In order to overcome this drawback, Distributed Stream Processing (DSP) platforms such as Storm have proven to be a more flexible and powerful tool for dealing with such streams. Furthermore, since the anomaly patterns in data streams are not predefined and may change over time, unsupervised learning algorithms such as clustering should be used first to output significant anomalies which contribute to forming and updating anomaly patterns. The real-time anomaly detection on new data streams can be established by such patterns. This thesis project is aiming at providing a distributed system on top of Storm combining both batch-based unsupervised learning and streaming rule-based methods to detect anomalies in Spotify payment transactions in real time.The anomaly detection system implements k-means and DBSCAN clustering algorithms as an unsupervised learning module to find out anomalous behaviors from payment transaction streams. Based on those anomalies, the frequent item set algorithm estDec is implemented to extract anomaly patterns. Stratified Complex Event Processing (CEP) engines based on Esper get reconfigured with such patterns to do rule-based anomaly detection in real time over absolute time sliding windows. Experimental results indicate that such a complex system over a unified data flow pipeline is feasible to detect anomalies in real time by rule-based anomaly detection with CEP engine. Unsupervised learning methods can providelight weighted batch (nearly real time) based anomaly detection but different factors heavily influence the performance. The rule-based method shows that it performs better in the heavy anomaly density scenario in terms of sensitivity and lower detection latency.
A methodology for designing semantic annotations

This paper presents a methodology for designing languages for semantic annotation. Central in this methodology is the specification of representation formats as renderings of conceptual structures defined by an abstract syntax as set-theoretic constructs. An ideal representation format is defined as one that is able to represent all the conceptual distinctions made in the abstract syntax, and of which each representation encodes one and only one structure defined by the abstract syntax. The semantics of an annotation language is defined for its abstract syntax and is shared by all its representation formats; every ideal representation format is therefore convertible through a meaning-preserving mapping to any other ideal representation format. The methodology is called CASCADES after its four stages: Conceptual analysis, Abstract syntax, Semantics and Concrete syntax for Annotation DES ign. The CASCADES model derives its usefulness from supporting a systematic design process for semantic annotations, giving due attention to the conceptual and semantic issues underlying choices in annotation formats, including support in the form of procedures for how to move from one stage of the design process to another, in particular for how to construct an abstract syntax given a conceptual analysis; how to define a semantics for a given abstract syntax; and how to map an abstract syntax to an XML-based concrete syntax. Three applications of the CASCADES methodology are discussed: (1) the design of an ISO standard for dialogue annotation starting from a conceptual analysis; (2) the analysis of existing annotation schemes such as those of the Penn Discourse Treebank and TimeML, as a basis for the development of ISO standards for semantic annotation; (3) the detection and repair of deficiencies in existing annotation schemes.
Propuesta de representación del Tesauro Eurovoc en SKOS para su integración en Sistemas de Información jurídica

Los tesauros se han adaptado progresivamente a la edición en entornos digitales y se enfrentan en la actualidad al reto de demostrar su utilidad e integración en la Web semántica, haciendo uso de estándares como RDF y SKOS. El estudio justifica, en primer lugar, la selección del Tesauro Eurovoc para su integración en un sistema de información legal. Se analizan, igualmente, las dificultades para la representación de Eurovoc en SKOS, así como las herramientas disponibles para la adecuada manipulación de tesauros. Finalmente, se reflexiona sobre el estado de la cuestión y se plantean líneas de trabajo futuras.
Classification based on Predictive Association Rules

Recentstudies indatamininghaveproposed a newclassification approach, called associative classification, which, according toseveral reports, suchas17,61,achieves higher classification accuracy thantraditional classification approaches suchasC4.5. However, theapproach alsosuffers fromtwomajordeficiencies: (1)itgenerates a verylarge numberofassociation rules, whichleads tohighprocessing overhead; and (2)itsconfidence-based ruleevaluation measure mayleadtooverfitting. Incomparison withassociative classification, traditional rule-based classifiers, suchasC4.5, FOILandRIPPER,are substantially faster buttheir accuracy, inmostcases, maynot beashigh. Inthispaper, we propose a newclassification approach, CLoPAR (Classification basedon Predictive Association Rules), whichcombines theadvantages ofboth associative classification and traditional rule-based classification. Instead ofgenerating a largenumberof candidate rulesas inassociative classification, CLoPAR adopts a greedy algorithm togenerate rulesdirectly from training data. Moreover, CLoPARgenerates andtests more rules thantraditional rule-based classifiers toavoidmissing important rules. Toavoid overfitting, CLoPARusesexpected accuracy toevaluate eachruleandusesthebestk rules in prediction. IndexTerms-association rule, rule-based classification
Rapid design and development of control applications for electrical engineering

This paper presents a control application programming concept based on attributive logic and context-based reasoning. General features and benefits. comparing with other contemporary approaches, are given. The proposed solution is targeted at electric machine tests and diagnostics. An illustrative example of programming an induction motor no-load test which compares the proposed solution with a Matlab-based application is also given.
Verification of memory access protocols in behavioral synthesis

In behavioral synthesis, a memory is viewed as an abstract construct which hides the detailed implementation of the memory. In this paper, we propose a systematic methodology of verifying the correctness of the memory wrapper. The automation of our solution shortens the verification time significantly in contrast to simulating memory accesses in the context of full design, which is quite complex and time-consuming.
Performance of multi-carrier CDMA systems with channel estimation in two dimensions

Mobile radio systems with high power and bandwidth efficiency can be designed by the combination of code division multiple access (CDMA) and multi-carrier modulation. The problem of channel estimation in the downlink of a multi-carrier (MC)-CDMA system is investigated. We study pilot-symbol-aided channel estimation in two dimensions using two cascaded one-dimensional FIR filters, dimensioned as Wiener filters for the worst case channel conditions. The performance with real channel estimation is demonstrated for various mobile radio environments and velocities. Furthermore, we examine the accuracy of a simple approximation to evaluate the system performance with real channel estimation, where the analysis of the channel estimation is separated from the evaluation of the remaining mobile radio system.
Audification and Non-Standard Synthesis in Construction in Self

The author's Construction in Self (2009) belongs to the interdisciplinary context of auditory display/music. Its use of data at audio rate could be described as both audification and non-standard synthesis. The possibilities of audio-rate data use and the relation between the above descriptions are explored, and then used to develop a conceptual and theoretical basis of the work. Vickers and Hogg's term ‘indexicality’ is used to contrast audio with control rate. The conceptual implications of its use within the digital medium and the possibility for the formation of higher-order structures are discussed. Grond and Hermann's notion of ‘familiarity’ is used to illustrate the difference between audification and non-standard synthesis, and the contexts of auditory display and music respectively. Familiarity is given as being determined by Dombois and Eckel's categories of data. Kubisch's Electrical Walks, Xenakis's GENDYN and the audification of seismograms are used as examples. Bogost's concept of the alien is introduced, and its relevance to the New Aesthetic and Algorave are discussed. Sound examples from Construction in Self are used to demonstrate the varying levels of familiarity or noise possible and suggested as providing a way of bridging the divide between institutional and underground electronic music.
Review of Circuit Level Leakage Minimization Techniques in CMOS VLSI Circuits

ABSTRACT Ever increasing demand for portable and battery-operated systems has led to aggressive scaling. While technology scaling facilitates faster and high-performance devices, at the same time it causes excessive power dissipation especially the leakage. Leakage power dissipation is now a dominating component of total power consumption in today's high-performance chip. So there is a tremendous need to limit the power dissipation in high-density chips, which has initiated many innovative techniques to develop in the design of low power circuits and systems. Nano-scaled very large-scale integration (VLSI) chips have ultra-thin gate oxide, very low threshold voltage, and have short channels. Therefore, leakage power dissipation has emerged as the most challenging issue in VLSI circuit and systems. In this paper, we present a general review of the state-of-the-art circuit level leakage minimization techniques since 1995. It also conceptually classifies the different techniques for leakage minimization. Furthermore, a detailed analysis on the effect of technology nodes on leakage and speed has been carried out using a basic complementary metal-oxide-semiconductor (CMOS) gate with SPICE tool. It also verifies for the reliability issues under process, voltage, and temperature variations. This comprehensive study along with the experimental result can be used to choose the most effective technique for minimizing leakage.
Relay Selection in Cooperative Networks with Wireless Battery Charging

This paper studies the problem of relay selection in wireless powered cooperative networks, where spatially random relays are equipped with two-state batteries. In contrast to conventional techniques, the relay nodes can either harvest energy from the source signal (in case of uncharged battery) or attempt to decode and forward it (in case of charged battery). Three relay selection schemes that correspond to different state information requirements and implementation complexities are proposed. The charging/discharging behavior of the battery is modeled as a two-state Markov chain and analytical expressions for the steady-state distribution and the outage probability performance are derived for each relay selection scheme. We prove that energy storage significantly affects the performance of the system and results in a zeroth diversity gain at high signal-to-noise ratios.
Instruction set and simulation framework for transactional memory

Transactional Memory by Vinson Lee B.S. Electrical Engineering and Computer Science University of California at Berkeley, 2000 Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2003 @ Massachusetts Institute of Technology 2003. All rights reserved.
Modular proof development in acl2

The ACL2 theorem prover combines a first-order dialect of LISP with an automated proof engine for first-order logic. While ACL2 is logically quite powerful, it can be difficult to build and maintain large models due to its ad hoc systems for modularity, namespace management, logical encapsulation, and macro expansion. I propose a new language, Refined ACL2, extending ACL2 with expressive component and macro systems designed to accommodate large-scale proof development and flexible logical abstractions. The component system of Refined ACL2 adapts many features of ML's functors and signatures to ACL2. Components support nesting, parameterization, translucent specification, and refinement of abstract specifications with concrete definitions. Refined ACL2 inherits Racket's macro system; furthermore, macro definitions can be incorporated into component specifications.
Specification of a partial replication protocol with TLA

Nowadays, data available and used by companies is growing very fast creating the need to use and manage this data in the most efficient way. To this end, data is replicated over multiple datacenters and use different replication protocols, according to their needs, like more availability or stronger consistency level. The costs associated with full data replication can be very high, and most of the times, full replication is not needed since information can be logically partitioned. Another problem, is that by using datacenters to store and process information clients become heavily dependent on them. We propose a partial replication protocol called ParTree, which replicates data to clients, and organizes clients in a hierarchy, using communication between them to propagate information. This solution addresses some of these problems, namely by supporting partial data replication and offline execution mode. Given the complexity of the protocol, the use of formal verification is crucial to ensure the protocol two correctness properties: causal consistency and preservation of data. The use of TLA+ language and tools to formally specificity and verify the proposed protocol are also described.
Autonomous suturing using minimally invasive surgical robots

This paper presents the design and implementation of a new robotic system for assisting surgeons in performing minimally invasive surgical procedures. This system is designed for collaborative operation between the surgeon and the robot. In addition, it contains the following attributes: quick interchangeable end tools, programmable collaboration with the surgeon, and coordinated motion of multiple robotic devices for performing complex procedures. Two such devices have been built with a variety of end tools. The system has demonstrated direct manual operation, remote teleoperation, and surgeon-supervised autonomous stitching and knot tying. In addition to the mechanical design, several autonomous knot tying algorithms, and the associated kinematic requirement, are discussed.
ETHNOMEDICINAL PLANTS USED AGAINST COMMON DIGESTIVE PROBLEMS

Background: Ethnomedicinal knowledge is highly significant for persistence of human health care. Different studies shown that medicinal plants are considered as curing agent for digestive disorders in Pakistan. The aim of present review is to explore this ethnomedicinal information all over the Pakistan and to investigate the ecological status of these species in the country. Methodology: Online litera
Comparison of routing and wavelength assignment algorithms for optical networks

The routing and wavelength assignment algorithm is indispensable to operate optical networks, which consist of the wavelength division multiplexing technique and optical cross-connect systems. This paper focuses on the routing and wavelength algorithm that minimizes the number of wavelengths for the static path demand situation. The paper shows that the routing problem and the wavelength assignment problem can be solved simultaneously by employing the multicommodity flow model, which has been comprehensively studied in the literature. On the basis of this notion, a heuristic routing and wavelength assignment algorithm is proposed. Through numerical examples, the proposed algorithm is compared with conventional algorithms that run under the same criteria.
Local DTW coefficients and pitch feature for back-propagation NN digits recognition

This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. This extracted feature coefficients represent as an input for neural network back-propagation. The coefficients are normalized with respect to the reference pattern according to the average number of frames over the samples recorded. This is due to neural network (NN) limitation where a fixed amount of input nodes are needed for every input class. The new feature processing used the famous frame matching technique, which is Dynamic Time Warping (DTW) to fix the input size to a fix number of input vectors. The LPC features vectors are aligned between the source frames to the template using our DTW frame fixing (DTW-FF) algorithm. By doing frame fixing, the source and template frames are adjusted so that they have the same number of frames. The speech recognition is performed using the back-propagation neural network (BPNN) algorithm to enhance the recognition performance. The results compare DTW using LPC coefficients to BPNN with DTW-FF coefficients. Added pitch feature investigate the improvement made to the previous experiment using different number of hidden neurons.
An integrated, ontology-based agricultural information system

This paper proposes an integrated, ontology-based agricultural information system (AIS) to provide all-round and precise information for efficiently guiding farmers and agri-professionals to conduct agricultural processing. Since the existing independent AIS platforms can only offer specific but incomplete agricultural information service, aiming at this issue, the newly proposed AIS system employs ontology techniques, including RDF-based representation and semantic reasoning, to integrate the index information provided by all involved independent agricultural information platforms. As a result, this newly proposed AIS system can provide users with integrated and accurate response information. Through a realistic case study and relevant experiments, it is clear that the accuracy ratio and integrity ratio of response information offered by this ontology-based integrated AIS can be enhanced to a great extent. In summary, not only agri-professionals, but also farmers, who might not understand information retrieval skills, can benefit from this newly proposed AIS to conduct activities in agricultural production life cycle.
Separation of estimation and control for discrete time systems

An attempt is made to coordinate the numerous results relating to separation of estimation and control in discrete time stochastic control theory. The results vary widely depending upon the assumptions about linearity, criteria, information pattern, constraints, and noise distributions. Some of the less well-known underlying concepts are discussed with the help of a fairly general model.