Miracle-dz/UniFAR-FTUs huggingface dataset:
An Embedded Software Primer

From the Publisher: An Embedded Software Primer is a clearly written insightful manual for engineers interested in writing embedded-system software. The example-driven approach puts you on a fast track to understanding embedded-system programming and applying what you learn to your projects. This book will give you the necessary foundation to work confidently in this field. "After reading this book, you will be able to tackle the challenges of embedded system programming and quickly reap the benefits of your new skills.
Creating a readable language for checking XML

Today sharing data is done everywhere. Doctors might want to share patient journal information. Patient journals may contain sensitive information that doctors do not want to share. The journals needs to be checked before they are shared. In this thesis, data and journals are coded in XML and checking journals and data is the same as validating XML. Validating XML documents is usually done by following rules from a validator. A validator processes XML documents and checks that the XML documents follows the validation rules. The issue with most validators today is that they cannot compare arbitrary elements in the XML document with each other and there are no mathematical operations to supply these comparisons. Sometimes there is a need to verify the validation rules. This might be done by someone who has little programming skills. The validator has to be readable so that this someone can verify that the validator matches the requirements. This thesis attempts to solve the issue with existing solutions by creating a readable language for validating XML documents. The solution is done in three steps: investigating similar solutions, implementing a validator, and testing the readability of the validator with a usability test.
Inertial navigation system developed for MEMS applications

The purpose of this study is to validate a developed INS/GPS integration algorithm for embeddable positioning and navigation purpose using MEMS IMU (Inertial Measurement Unit) sensors. MEMS IMU sensors present unique noise characteristics and generally contain high-level noise inherent in the output data. In this paper, we investigate an advanced error modeling technique to evaluate these errors. With the proposed error models, the impacts of the MEMS noises can be characterized in terms of random noise, bias, quantization error, scale factor correction and miss-alignment errors. By employing this model in the INS calculation, the compensation of the deterministic noise terms from the raw MEMS measurements can be greatly improved. A loosely coupled Kalman filtering design for INS/GPS integration is also presented in this paper. Hence, the random noise compensation of MEMS sensors can be achieved for the proposed Kalman filter design.
Sign Language and Linguistic Universals: Entering the lexicon: lexicalization, backformation, and cross-modal borrowing

Sign languages are of great interest to linguists, because while they are the product of the same brain, their physical transmission differs greatly from that of spoken languages. In this 2006 study, Wendy Sandler and Diane Lillo-Martin compare sign languages with spoken languages, in order to seek the universal properties they share. Drawing on general linguistic theory, they describe and analyze sign language structure, showing linguistic universals in the phonology, morphology, and syntax of sign language, while also revealing non-universal aspects of its structure that must be attributed to its physical transmission system. No prior background in sign language linguistics is assumed, and numerous pictures are provided to make descriptions of signs and facial expressions accessible to readers. Engaging and informative, Sign Language and Linguistic Universals will be invaluable to linguists, psychologists, and all those interested in sign languages, linguistic theory and the universal properties of human languages.
Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines

Abstract This work presents the identification of a Wiener-Hammerstein system by a learning-from-examples approach, namely the Support Vector Machines for Regression, on the basis of a set of real-life benchmark data. A multi-objective optimization procedure based on genetic algorithms is employed in order to select the best model that describes the input-output relationship of the considered system. Training sets of reduced size are employed to analyze the effect on the accuracy performance.
Affective Communication Model with Multimodality for Humanoids

A humanoid robot that can naturally interact with humans must be capable of high-level interaction skills; that is, it must be able to communicate with humans in the form of humanlike skills such as gestures, dialogue communication with mutual sympathy. In terms of a human symbiotic situation, it is necessary for a robot to be sociable in terms of thinking and feeling. In this aspect, social interaction and communication that share emotions are important issues in the field of a humanoid robot research. We propose an affective communication model for humanoid robots, so that these robots achieve high level communication through the framework of the affective communication model with multimodality. The proposed model comprises five subsystems: namely, perception, motivation, memory, behavior, and expression. In the perception system, we implemented a bimodal emotion recognizer. To ensure a humanoid robot can respond appropriately to the emotional status of users and itself, we designed subsystems that use their own drive, emotions, and memory. The major improvement in our framework is the construction of a memory system that stores explicit emotional memories of past events. The literature from cognitive science and neuroscience suggests that emotional memories are vital when the human brain synthesizes emotions (Ledoux, J., 1996). While previous research on sociable robots either ignores the emotional memory or maintains the emotional memory implicitly in high-level interaction, we need to establish explicit memories of emotions, events, and concepts. Therefore we have adopted the concept of emotional memory for our humanoid robot. Our memory system maintains explicit memories of previous emotional events. Thus, the emotional system can synthesize emotions on the basis of emotional memories. Since 1999, AIM lab directed by Hyun S. yang in KAIST has focused on building new humanoid robots with a self-contained physical body, perception to a degree which allows the robot to be autonomous, and social interaction capabilities of an actual human symbiotic robot. This study was built on previous researches about the social interactions and the developments of the first generation humanoid robots, AMI, AMIET coupled with a humansize biped humanoid robot, AMIO in the AIM Lab (Yong-Ho Seo, Hyun S. Yang et al., 2003, 2004, 2006). The proposed model was used to enhance the interaction between human and humanoid robot. Accordingly, we designed and implemented the methods which are mentioned above
Feedback in New Zealand Classrooms: Do Students Get the Message?

There is increasing evidence that feedback is a key factor in successful teaching and learning. It is also clear that there are effective and less effective forms of feedback. To be most effective, feedback should incorporate assessment for learning principles. Although there is a large body of literature on feedback, there is limited use of the student’s voice, and little from a New Zealand context. This thesis investigates students’ understandings of feedback in New Zealand classrooms. A feedback typology is used to categorise and analyse examples of teacher feedback given by the students. Individual student interviews were conducted with responses informing the kinds of questions used for the teacher interviews. Advisory work in schools that involves classroom observations on feedback was also used to substantiate the findings. The sample used for this research came from two schools, four classrooms and focussed on sixteen students. The classes ranged from Years 3-6, which meant that the students were aged between 7 and 10. The analysis concludes that while New Zealand students can describe a range of both evaluative and descriptive feedback their understandings do not always match what the teacher intended. The feedback typology was found to be a useful tool but was problematic in that there was no assurance that feedback was against shared expectations of the task.
Self-organization neural network for multiple texture image segmentation

Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape and depth perception. But no efficient methods captures all aspects of the very diverse texture family including natural scenes. We propose a novel approach for efficient texture image analysis that use unsupervised learning schemes for the texture recognition task. The self-organization neural network for texture image identification is based on features that is extracted at angle and magnitude in the orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, we have attempted to build various texture images. The experimental results show that the performance of the system is very successful.
A methodology for formal hardware verification, with application to microprocessors

Microprocessors are now ubiquitous, but their design is not without difficulties. Numerous microprocessors have been introduced only to find--sometimes years later--that they contain mistakes. The need for better ways of checking designs is clear. This research develops a methodology for formally verifying data-intensive circuits against abstract state machines defined by assertions, and applies it to show that a microprocessor circuit implements its intended instruction set. "Implementation" is a formal relation between input-output sequences: of an abstract state machine, and of a circuit. This simple abstract model of behavior is reconciled with concepts from digital-systems design including dynamic logic, pipelining, multi-phase clocks, and a separate memory system. The methodology structures proof of implementation using a series of decompositions. It exposes internal system state. It dissects sequences into their component transitions (using a new formalism called marked strings to reason about overlapped operation). For processor verification it also abstracts away most of the memory state. Making these simplifications from global I/O behavior once, in the methodology, allows the focus when verifying a circuit to be on the localized effects of single state transitions. Verifying a circuit then consists of a few steps. The user specifies intended behavior by giving high-level assertions denoting state transitions (much like a Hoare logic of an assembly language), plus an additional mapping of abstract state onto circuit state incorporating the temporal movement of state through pipeline registers. The verifier makes consistency checks on the state mapping, then simulates the circuit symbolically using patterns generated by mapping the state transitions onto the circuit. A circuit passing these tests correctly implements the abstract state machine. The verifier is based upon reduced, ordered, Binary-Decision Diagrams (BDDs), and operates at the switch level, but these details are not required by the methodology. The verifier has been used to check initialization, response to interrupt, and execution of several instructions of a pre-existing, 16-bit nMOS microprocessor called Hector, which is roughly similar to the DEC PDP-11. The specification was hand translated from a pre-existing instruction-set simulator, and the circuit was extracted from a description of the mask geometry used for actual chip fabrication.
MOTION ESTIMATION BY USING A NEUROMIMETIC APPROACH

We present in this paper an approach for estimating the field of velocity vectors in order to evaluate motion in sequence of images. This method is based on application of a neural network to optical flow estimation. The network is discrete, deterministic and localiy connected. It contains redundant neurons for representing w(iJ2x and w(iJlr the components of the velocity vector at point (i,j). We formulate the motion estimation problem as one of minimizing an error function of two terms. The $rst term is a least-squares penalty expression which coerce the $ow field into satisBing the $ow equation; the second is a smoothness constraint used to obtain a smooth optical flow. In order to satisfL an homogeneous neural architecture, a quantity is inserted in the least-squares penalty expression. The minimization of this function is obtained by using a neural network. The model consists of 4*L*M Hopfield sub-networks for an image of size L*M. Each sub-network is total& interconnected. Results provide a good coherence between the homogeneous areas cartography of motion and textured regions present in the image.
Recognition of traffic signs by artificial neural network

An artificial neural network system for traffic sign recognition is proposed in the paper. The input image is first processed for extraction of color and geometric information. A morphological filter is applied to increase the saliency by eliminating smaller objects and by linking together objects broken in disjoint parts due to noise. The coordinates of the resulting objects are determined, and the objects are isolated from the original image according to these coordinates. After this, the objects are normalized and sent to the neural network which performs the recognition. The neural network consists of classification subnetwork, winner-takes-all subnetwork (Hopfield network), and validation subnetwork. By introducing the new concept of a validation sub-network, the network enhance the capability to correctly classify the different traffic signs and avoid misclassifying nontraffic signs into a traffic sign. The system is tested by simulation as a whole and in part on a large amount of data acquired by a video camera attached to a vehicle frame by frame. The performance is encouraging. It produced excellent results except for the images under very poor illumination such that the color threshold (preprocessing) fails to extract the color information.
The many prepositions of practice research : About, for, in, with and from

Practice research (PR) has emerged as a viable research approach in several professions oriented domains; for example in social work (Pain, 2011; Salisbury Forum Group, 2011), public administration (Huxham, 2003), strategizing (Whittington, 2006), planning (Watson, 2002), nursing (Reed, 2006), information systems (Goldkuhl, 2011). There are several threads coming together within a practice research approach (Goldkuhl & Julkunen, 2011; Goldkuhl, 2011): • Considering the empirical field in terms of practices • Research dealing with topics that are highly relevant to practice • An interest for what is actually going on in practices (not just what people think is going on) • Inquiries are conducted dependent on problematic situations in practices • An helping and guiding knowledge interest • Collaboration with and involvement of practitioners in the research process
Layered Crf A Model To Build More Accurate Intrusion Detection System

With the today’s changing and high-tech lifestyle the use of network becomes a part of each and everyone’s routine life. Most of us keep and access sensitive data online. So it becomes most important to secure your network from various network attacks. Intruder is one of the most publicized threats to network security. Network intrusion detection system (NIDS) has become a standard component in network security infrasture.But at the same time today’s intrusion detection system faces number of challenges. An intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper we address two concepts one layered architecture and second conditional random fields and integrate them to improve the overall accuracy and effiency of the system.
Instant Social Ride-Sharing

This paper explores the use of ride–sharing as a resource-efficient mode of personal transportation. While the perceived benefits of ride–sharing include reduced travel times, transportation costs, congestion, and carbon emissions, its wide–spread adoption is hindered by a number of barriers. These include the scheduling and coordination of routes, safety risks, social discomfort in sharing private spaces, and an imbalance of costs and benefits among parties. To address these barriers, the authors describe a system for ride–sharing that utilizing the concepts of social networks and social interest groups. The Social Ride/Sharing Service (SRSS) automatically groups ride offers and requests into ride–shares according to two objectives: to minimize ride–share detours, and to maximize the amount of social connections amongst participants of ride–shares. A mobile application places the SRSS at the fingertips of mobile users (anyplace and anytime), Minimizing detours and maximizing social connections in ride–shares is used to increase the social comfort level and trust among ride-share participants, ultimately leading to increased user acceptance and adoption of ride–sharing. As the number of users increases, the number of social connections between users increases, which allows more ride–share opportunities and more effective transport. The authors present realistic, city–wide simulations for Copenhagen that demonstrate that their proposed social ride–sharing system is viable and effective.
Users manual for Opt-MS : local methods for simplicial mesh smoothing and untangling.

Creating meshes containing good-quality elements is a challenging, yet critical, problem facing computational scientists today. Several researchers have shown that the size of the mesh, the shape of the elements within that mesh, and their relationship to the physical application of interest can profoundly affect the efficiency and accuracy of many numerical approximation techniques. If the application contains anisotropic physics, the mesh can be improved by considering both local characteristics of the approximate application solution and the geometry of the computational domain. If the application is isotropic, regularly shaped elements in the mesh reduce the discretization error, and the mesh can be improved a priori by considering geometric criteria only. The Opt-MS package provides several local node point smoothing techniques that improve elements in the mesh by adjusting grid point location using geometric, criteria. The package is easy to use; only three subroutine calls are required for the user to begin using the software. The package is also flexible; the user may change the technique, function, or dimension of the problem at any time during the mesh smoothing process. Opt-MS is designed to interface with C and C++ codes, ad examples for both two-and three-dimensional meshes are provided.
Optimal Placement Strategy of DHT-Based Systems to Minimize Lookup Latency

This paper proposes an optimal placement strategy of physical nodes of DHT-based systems to minimize the lookup latency and improve the throughput of system. The main idea of our approach is to create a new hierarchy named link space between logical space and physical space of DHT overlay. We firstly give an assignment of link space and then present the optimal placement strategy of placing physical nodes on it based on the given lookup traffic matrix. At last, we use genetic algorithm to solve the optimization problem.
Processes in Acquiring Knowledge

Abstract : The objective of this paper is to develop a theory of Socratic tutoring in the form of pattern-action (or production) rules for a computer program. These pattern action rules are being programmed on a computer system for tutoring causal knowledge and reasoning. The production rules were derived from analysis of a variety of tutorial dialogues. The analysis accounts for the specific teaching strategies used by the tutors in the dialogues within a content-independent formalism. The paper includes twenty-three production rules derived from the data analyzed.
Neural networks for pattern recognition

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
A statistical parser for Chinese

This work describes how a statistical parser which was originally designed to parse English was adapted to parse Chinese with little human effort. The only modifications were a head table for Chinese and a set of automatically derived word features for dealing with unknown Chinese words. When trained on the Chinese Treebank, the resulting parser achieves state of art performance on parsing tokenized Chinese sentences.
Hierarchically clustered HMM for protein sequence motif extraction with variable length

Protein sequence motifs extraction is an important field of bioinformatics since its relevance to the structural analysis. Two major problems are related to this field:(1) searching the motifs within the same protein family; and(2) assuming a window size for the motifs search. This work proposes the Hierarchically Clustered Hidden Markov Model(HC-HMM) approach, which represents the behavior and structure of proteins in terms of a Hidden Markov Model chain and hierarchically clusters each chain by minimizing distance between two given chains’ structure and behavior. It is well known that HMM can be utilized for clustering, however, methods for clustering on Hidden Markov Models themselves are rarely studied. In this paper, we developed a hierarchical clustering based algorithm for HMMs to discover protein sequence motifs that transcend family boundaries with no assumption on the length of the motif. This paper carefully examines the effectiveness of this approach for motif extraction on 2593 proteins that share no more than 25% sequence identity. Many interesting motifs are generated.Three example motifs generated by the HC-HMM approach are analyzed and visualized with their tertiary structure.We believe the proposed method provides a unique protein sequence motif extraction strategy. The related data mining fields using Hidden Markova Model may also benefit from this clustering on HMM themselves approach.
Design optimization of a high-speed permanent magnet machine with the VEKOPT algorithm

A three phase four pole surface-mounted permanent magnet synchronous machine is optimized with respect to maximum power output and minimum magnet volume. The problem is treated as a vector optimization problem and is solved directly without any scalar transformation by a stochastic search algorithm. Mechanical strength of the rotor bandage and demagnetization limit of the rotor magnets in care of a short circuit of the stator winding are taken into account. The PARETO-set of optimum solutions for a magnetic machine design is derived.