Query: Modeling purposeful adaptive behavior with the principle of maximum causal entropy

Relevant passages:
Activity Recognition in the Home Using Simple and Ubiquitous Sensors <=SEP=> In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used .

TWO-WAY COMMUNICATION CHANNELS <=SEP=> input at terminal 2 and Y2 the corresponding output. Once each second, say, new inputs xi and x2 may be chosen from corresponding input alphabets and put into the channel; outputs yi and Y2 may then be observed. These outputs will be related statistically to the inputs and perhaps historically to previous inputs and outputs if the channel has memory. The problem is to communicate in both directions through the channel as effectively as possible. Particularly, we wish to determine what pairs of signalling rates R1 and R2 for the two directions can be approached with arbitrarily small error probabilities.

Maximum margin planning <=SEP=> Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.

Apprenticeship learning via inverse reinforcement learning <=SEP=> We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be difficult to write down an explicit reward function specifying exactly how different desiderata should be traded off. We think of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and give an algorithm for learning the task demonstrated by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output by the algorithm will attain performance close to that of the expert, where here performance is measured with respect to the expert's unknown reward function.

Algorithms for Inverse Reinforcement Learning <=SEP=> 

Design Of Ternary Logic Gates Using CNTFET <=SEP=> This paper presents a novel design of ternary logic gates like STI,PTI,NTI,NAND and NOR using carbon nanotube field effect transistors. Ternary logic is a promising alternative to the conventional binary logic design technique, since it is possible to accomplish simplicity and energy efficiency in modern digital design due to the reduced circuit overhead such as interconnects and chip area. In this paper novel design of basic logic gates for ternary logic based on CNTFET, is proposed. Keywords— Carbon nano-tube Field Effect Transistor(CNTFET),MVL(multi valued logic),Ternary logic,STI,NTI,PTI

A simple procedure for pruning back-propagation trained neural networks <=SEP=> The sensitivity of the global error (cost) function to the inclusion/exclusion of each synapse in the artificial neural network is estimated. Introduced are shadow arrays which keep track of the incremental changes to the synaptic weights during a single pass of back-propagating learning. The synapses are then ordered by decreasing sensitivity numbers so that the network can be efficiently pruned by discarding the last items of the sorted list. Unlike previous approaches, this simple procedure does not require a modification of the cost function, does not interfere with the learning process, and demands a negligible computational overhead.

A MAV that flies like an airplane and hovers like a helicopter <=SEP=> Near-earth environments, such as forests, caves, tunnels, and urban structures make reconnaissance, surveillance and search-and-rescue missions difficult and dangerous to accomplish. Micro-air-vehicles (MAVs), equipped with wireless cameras, can assist in such missions by providing real-time situational awareness. This paper describes an additional flight modality enabling fixed-wing MAVs to supplement existing endurance superiority with hovering capabilities. This secondary flight mode can also be used to avoid imminent collisions by quickly transitioning from cruise to hover flight. A sensor suite which allows for autonomous hovering by regulating the aircraft's yaw. pitch and roll angles is also described

Conversion of Natural Language Query to SQL Query <=SEP=> This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.

Secure Multiparty Computation and Secret Sharing <=SEP=> In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, whereas secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last thirty years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.

Improved Performance of Serially Connected Li-Ion Batteries With Active Cell Balancing in Electric Vehicles <=SEP=> This paper presents an active cell balancing method for lithium-ion battery stacks using a flyback dc/dc converter topology. The method is described in detail, and a simulation is performed to estimate the energy gain for ten serially connected cells during one discharging cycle. The simulation is validated with measurements on a balancing prototype with ten cells. It is then shown how the active balancing method with respect to the cell voltages can be improved using the capacity and the state of charge rather than the voltage as the balancing criterion. For both charging and discharging, an improvement in performance is gained when having the state of charge and the capacity of the cells as information. A battery stack with three single cells is modeled, and a realistic driving cycle is applied to compare the difference between both methods in terms of usable energy. Simulations are also validated with measurements.

Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming <=SEP=> Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.

Test-cost optimization in a scan-compression architecture using support-vector regression <=SEP=> Scan compression is widely used in high-volume testing of complex integrated circuits. With an increase in design complexity, the increased density of unknown (X) values from output responses reduces compression efficiency. In order to effectively block X values and maximize the effectiveness of test compression, a scan-compression architecture has recently been proposed, in which deterministic test patterns can be loaded into selected scan cells by controlling the initial state of the pseudo-random pattern generator (PRPG). A careful selection of the PRPG length is however essential to reduce test cost. We propose an optimization method based on support-vector regression to determine the PRPG length for test-cost reduction in a given scan-compression architecture. A correlation-based feature selection methodology is also proposed to reduce the amount of data needed for the accurate selection of the PRPG length. Experimental results on industrial designs highlight the effectiveness of the proposed method.

Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers professional development <=SEP=> This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers’ preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. 2010 Elsevier Ltd. All rights reserved.

3D Soft Body Simulation Using Mass-spring System with Internal Pressure Force and Simplified Implicit Integration <=SEP=> In this paper, we propose a method to simulate soft bodies by using gravitational force, spring and damping forces between surface points, and internal molecular pressure forces. We consider a 3D soft body model composed of mesh points that define the body’s surface such that the points are connected by springs and influenced by internal molecular pressure forces. These pressure forces have been modeled on gaseous molecular interactions. Simulation of soft body with internal pressure forces is known to become unstable when high constants are used and is averted using an implicit integration method. We propose an approximation to this implicit integration method that considerably reduces the number of computations in the algorithm. Our results show that the proposed method realistically simulates soft bodies and improves performance of the implicit integration method.

Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off! <=SEP=> We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2% when only 10 frames of an action are seen (2/3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7% when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3% when the prediction is made at every time step.

Grammatical Error Correction <=SEP=> Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this report, we investigate GEC as a translation task from incorrect to correct English and explore some machine translation approaches for developing end-to-end GEC systems for all error types. We apply Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches to GEC and show that they can correct multiple errors of different types in a sentence when compared to the earlier methods which focus on individual errors. We also discuss some of the weakness of machine translation approaches. Finally, we also experiment on a candidate re-ranking technique to re-rank the hypotheses generated by machine translation systems. With regression models, we try to predict a grammaticality score for each candidate hypothesis and re-rank them according to the score.

Stacked Convolutional Denoising Auto-Encoders for Feature Representation <=SEP=> Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.

Moving from syntactic to semantic organizations using JXML2OWL <=SEP=> Today’s enterprises face critical needs in integrating disparate information spread over several data sources inside and even outside the organization. Most organizations already rely on XML standard to define their data models. Unfortunately, even when using XML to represent data, problems arise when it is necessary to integrate different data sources. Emerging Semantic Web technologies, such as ontologies, RDF, RDFS, and OWL, can play an important role in the semantic definition and integration of data. The purpose of our study is to present a framework to assist organizations to move from a syntactic data infrastructure defined in XML to a semantic data infrastructure using OWL. The framework supports mappings and fully automated instance transformation from syntactic data sources in XML format to a common shared global model defined by an ontology using Semantic Web technologies. The presented framework, JXML2OWL, allows organizations to automatically convert their XML data sources to a semantic model defined in OWL.

Towards Frequent Subgraph Mining on Single Large Uncertain Graphs <=SEP=> Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain graph is #P-hard, we develop an approximation algorithm with accuracy guarantee for this purpose. To enhance the solution, we devise optimization techniques to achieve better mining performance. Experiment results on real-life data confirm the usability of the algorithm.

Use of RFID Based Real Time Location Tracking System to Curb Diversion of Transit Goods in East Africa <=SEP=> In this paper, RFID based Real Time Location Tracking System (RTLS) for tracking transit goods is discussed. The system was designed to address the problem of diversions of transit wet cargo destined for hinterland countries into the local markets, so as to evade payment of duties and taxes. Transit goods are currently tracked manually and by use of manual physical escorts. Reliance on manual methods has rendered the system open to abuse by unscrupulous traders and clearing agents; at times in collusion with government officers. To be effective in tracking wet cargo, the required solution was established to be one that not only provides information in real time but also with features for intervention when cargo is perceived to be at risk. This paper discusses the design and implementation of RTLS for tracking wet cargo, and points out the strenghts, benefits, weaknesses and limitations of the solution. The effectiveness of the solution was demonstrated in addressing the weaknesses of the current manual tracking system in curbing diversion for wet cargo. In addition, the use of RTLS led to a reduction in turn around times by between thirty to forty percent compared to those which are manualy tracked. KeywordsElectronic Seals, RFID, Transit goods, Tanker Tracking, Real Time Location Tracking Systems, Cargo Tracking

Review of Face Recognition Techniques <=SEP=> Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition. Keywords— Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis

Experiences of System-Level Model-Based GUI Testing of an Android Application <=SEP=> This paper presents experiences in model-based graphical user interface testing of Android applications. We present how model-based testing and test automation was implemented with Android, including how applications were modeled, how tests were designed and executed, and what kind of problems were found in the tested application during the whole process. The main focus is on a case study that was performed with an Android application, the BBC News Widget. Our goal is to present actual data on the experiences and to discuss if advantages can be gained using model-based testing when compared with traditional graphical user interface testing. Another contribution of this paper is a description of a keyword-based test automation tool that was implemented for the Android emulator during the case study. All the models and the tools created or used in this case study are available as open source.

Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges <=SEP=> Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.

A 0.8V, 560fJ/bit, 14Gb/s injection-locked receiver with input duty-cycle distortion tolerable edge-rotating 5/4X sub-rate CDR in 65nm CMOS <=SEP=> A quarter-rate forwarded-clock receiver utilizes an edge-rotating 5/4X sub-rate CDR for improved jitter tolerance with low power overhead relative to conventional 2X oversampling CDR systems. Low-voltage operation is achieved with efficient quarter-rate clock generation from an injection-locked oscillator (ILO) and through automatic independent phase rotator control that optimizes timing margin of each input quantizer in the presence of receive-side clock static phase errors and transmitter duty-cycle distortion (DCD). Fabricated in GP 65nm CMOS, the receiver operates up to 16Gb/s with a BER<;10-12, achieves a 1MHz phase tracking bandwidth, tolerates ±50%UIpp DCD on input data, and has 14Gb/s energy efficiency of 560fJ/bit at VDD=0.8V.

Evolution of the mid range theory of comfort for outcomes research. <=SEP=> constructs. VOLUME 49  NUMBER 2 NURSING OUTLOOK 88 Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Figure 2. MR theory of comfort. Reprinted with permission from Aspen Publishers, Inc. Copyright © 1992 Aspen Publishers, Inc. Adv Nurs Sci 1992;15:1. The work of psychologist Murray in 1938 met these criteria. Because his theory was about human needs, it was applicable to patients who experience multiple stimuli in stressful health care situations. This was the deductive stage of theory development: beginning with an abstract, general theoretic construction and substructing downward to more specific levels that included concepts for nursing practice. Each nursing concept then could be operationalized relative to a specific research setting. Murray's intent was to synthesize a grand theory for psychology from existing lesser psychologic theories of his time. His concepts are found in Figure 2, lines 1, 2, and 3. Because comfort was perceived by patients, it was logically substructed under Murray's concept of "perception." "Obstructing forces" were substructed for nursing as health care needs; "facilitating forces" were nursing interventions, and "interacting forces" were intervening variables (line 4). This was the first and altruistic part of the theory, which stated that nurses identified unmet comfort needs of their patients, designed interventions to address those needs, and sought to enhance their patients' comfort, the immediate desired outcome. The second and practical part of the theory addressed the question? "Why comfort?" For nursing, unitary trend was substructed to health thema, which was further substructed to HSBs. HSB was Schlotfeldt's concept and represented the broad category of subsequent desired outcomes. She stated that HSBs could be internal, external, or a peaceful death. Some examples of HSBs are decreased length of stay, improved functional status, better response (or effort) to therapy, faster healing, or increased patient satisfaction. PATIENT COMFORT IN OUTCOMES RESEARCH: THE RETRODUCTIVE STAGE Retroduction is a form of reasoning that originates ideas. It is useful for the selection of phenomena that can be developed further and tested. This type of reasoning is applied in fields in which there are few available theories. Such is the case with outcomes research that, to date, is centered on collecting large databases for selected outcomes and relating those outcomes to types of nursing, medical, or institutional protocols. Adding a nursing theoretic framework to outcomes research would enhance this area of nursing investigation because theory-based practice enables nurses to design interventions that are congruent with desired outcomes, thus increasing the likelihood of finding significant results. Significant results on desired outcomes would provide data to respective institutions about nursing's "productivity" and the importance of nursing in the present competitive market. Murray's 20th century framework could not account for 21st century emphasis on institutional outcomes. However, with the use of retroduction, the concept of institutional integrity was added to the MR theory of comfort (Figure 3). Institutional integrity is conceptualized as the quality or state of health care corporations being complete, whole, sound, upright, honest, and sincere. The term has normative and descriptive components. Adding the term to the theory of comfort extends the theory to a consideration of the relationships between HSBs and institutional integrity. NURSING OUTLOOK MARCH/APRIL 2001 89 Figure 3. Comfort theory adapted for outcomes research. Box 1. Propositions in Theory of Comfort 1. Nurses identify patients' comfort needs that have not been met by existing support systems. 2. Nurses design interventions to address those needs. 3. Intervening variables are taken into account in designing interventions and mutually agreeing on reasonable immediate (enhanced comfort) and/or subsequent (HSBs) outcomes. 4. If enhanced comfort is achieved, patients are strengthened to engage in health-seeking behaviors. 5. When patients engage in health-seeking behaviors as a result of being strengthened by comforting actions, nurses and patients are more satisfied with their health care. 6. When patiencs are satisfied with their health care in a specific institution, that institution retains its integrity; institutional integrity has a normative and descriptive component. Box 2. Assumptions underpinning the Theory of Comfort 1. Human beings have holistic responses to complex stimuli. 2. Comfort is a desirable holistic outcome that is germane to the discipline of nursing. 3. Human beings strive to meet, or to have met, their basic comfort needs; it is an active endeavor. 4. Institutional integrity has a normative and descriptive component that is based on a patient-oriented value system. The theory now predicts that when patients engage fully in essential (comfort care). The concepts are (1) health care needs HSBs, such as their rehabilitation program or medical that include physical, psychospiritual, social, and environregimen, institutional integrity is enhanced also. Institutional mental needs that arise for patients in stressful health care situintegrity can be operationalized as patient satisfaction, ations; (2) nursing interventions, an umbrella term for successful discharges, cost-benefit ratios, or other outcomes commitment by nurses and institutions to promote comfort that are essential to institutional integrity. All of these concepts care (intentional care by nurses directed to meeting comfort are indicators of the integrity of the institution. The definineeds of patients); (3) intervening variables that will affect tions of underlined concepts (Figure 2) will be given; proposioutcomes (for example, institutions that are committed to tions that link the concepts are in Box 1, and assumptions that achieving improved outcomes through comfort care must underpin this theory are in Box 2. provide adequate staffing of registered nurses to meet comfort This theory describes traditional nursing practice as needs associated with existing patient acuity on any given unit); humanistic, needs-related, and holistic. Further, it relates prac(4) patient comfort, defined as the immediate state of being tice to institutional outcomes that make those nursing actions strengthened by having needs met in 4 contexts of human that promote soundness of health care institutions visible and experience (physical, psychospiritual, social, and environ90 VOLUME 49  NUMBER 2 NURSING OUTLOOK Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Box 3. Evaluation of the Teory of Comfort 1. Concepts are specific for nursing. 1 Comfort has been called the essence of nursing. 2. Concepts and propositions are readily operationalized. The theory has been tested in many settings. The outcome of comfort is opera-tionaiized easily using the taxonomic structure of comfort as a guide for item generation. 11 3. The theory can be applied to many situations. Through The Comfort Line, students and researchers are working with the author to adapt the theory to micro-level situations. 4. Propositions range from causal to associative. Propositions generated from the theory are shown in Box 1 and have the desired range. 5. Assumptions fit the theory. The theory is holistic and needs based and theoretically empowers patients to engage in health-seeking behaviors. Institutional integrity is an important theoretic link to outcomes research. 6. The theory is relevant for potential users of the theory. Nursing students learn this theory easily and apply it to practice and research. 7. The theory is oriented to an outcome chat is important to patients. Qualitative research indicates chat patients want, and often need, their nurses to assist them in meeting their comfort needs. 8. The theory entails a nursing-sensitive outcome. A traditional goal of nursing has been to attend to patient comfort. Patients expect this from nurses and give them credit when comfort is delivered. Through deliberate actions of nurses, patients receive what they need and want from their nurses. This theory explicates how and why to do so. mental) that can be operationalized by the general comfort questionnaire; (5) HSBs, defined as patient actions of which they may or may not be aware and which may or may not be observed that are predictors or indicators of improved health (categorized as internal [eg, healing, immune function], as external [eg, functional status, perception of health], or as a peaceful death; HSBs are more accurate indicator of nurse productivity than the number of patients cared for; and (6) institutional integrity (previously defined). The definition of comfort has grown from its early definition9 to one that incorporates the strengthening component of comfort, the immediate desired outcome of nursing care. It is this strengthening component that facilitates patients' increased engagement in HSBs, the subsequent outcome. These subsequent HSBs, as indicators of nurse productivity, are of great interest to health care administrators because they facilitate decreased lengths of stay, successful discharges, and improved public relations when patients and families are happy with their health care. The 3 parts of the theory can be tested separately, or all concepts can be tested in one study. Path analysis can indicate which variables have direct or indirect influences on desired outcomes. Now, by linking HSBs to institutional integrity in an explicit way, outcomes research is theoretically based in nursing. CRITERIA FOR ADEQUACY OF MR THEORIES The expanded theory of comfort meets the following criteria for MR theory: (1) its concepts and propositions are specific to nursing, (2) it is readily operationalized, (3) it can be applied to many situati

Sentiment Analysis of Citations Using Word2vec <=SEP=> Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.

Planning algorithms for s-curve trajectories <=SEP=> Although numerous researches on s-curve motion profiles have been carried out, up to date, no systematic investigation on the general model of polynomial s-curve motion profiles is considered. In this paper, the model of polynomial s-curve motion profiles is generalized in a recursive form. Based on that, a general algorithm to design s-curve trajectory with jerk bounded and time-optimal consideration is proposed. In addition, a special strategy for planning s-curve motion profiles using a trigonometric model is also presented. The algorithms are implemented on a linear motor system. Experimental results show the effectiveness and promising application ability of the algorithms in s-curve motion profiling.

Design, simulation and implementation of a self-oscillating control circuit to drive series resonant inverter feeding a brazing induction furnace <=SEP=> This research deals with the design and simulation of induction furnace power source (inverter) using MATLAB package. This source designed to lock on the resonant frequency of the load by using self-oscillating technique, also it has the capability to control the power supplied to the load using phase shift pulse width modulation (PSPWM) technique. These characteristics used to overcome the load nonlinear behavior during the brazing process and to achieve soft switching of the inverter elements. Also, the inverter has the capability to operate with or without load (workpiece). The implemented prototype operates at a frequency range (50-100)kHz and 10kW was successfully used for brazing two copper workpieces.

A complete solution to the inverse kinematics problem for 4-DOF manipulator robot <=SEP=> A complete closed-form solution to the inverse kinematics problem for a 4-DOF manipulator robot is proposed. With the discussion of the existence of the offset and the distance between the axis of 1st joint and 2nd joint, all the possible solutions and singular configurations are presented. “Task attitude” is defined to describe the orientation of the end effector, which is convenient to solve the inverse kinematics problems in engineering. Finally, simulation of path-planning is performed, and the efficiency of the proposed method is verified through Mathematica.