Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial emotional creature for human-robot interaction-a new direction for intelligent systems

Summary form only given. Recent advances in robotics have been applied to automation in industrial manufacturing, with the primary purpose of optimizing practical systems in terms of such objective measures as accuracy, speed, and cost. This paper introduces the Artificial Emotional Creature project that seeks to explore a different direction that is not so rigidly dependent on such objective measures. The goal of this project is to explore a new area in robotics, with an emphasis on human-robot interaction. There is a large body of evidence that shows the importance of the interaction between humans and animals such as pets. We have been building a pet robot, as an implementation of an artificial emotional creature, with the subjective appearance of "behaviors" that are dependent on internal states, or "emotions", as well as external stimuli from both the physical environment and human beings. Human-robot interaction plays a large role. The pet robot has visual, audio, and tactile sensors. Olfactory sensors will also be available. The paper will describe an algorithm implementing a focus of attention through the integration of those sensors. In particular, object localization has been developed through the integration of vision and audition, using the interaction of a human being with the robot as the training reference.
Backstepping control design of flexible joint manipulator using only position measurements

A tracking controller is presented for flexible joint robot manipulators with only position measurements and without any restrictions on the joint stiffness gains. The controller is developed based on the integrator backstepping design method and on the two observers: one for the filtered link velocity errors and the other for the actuator velocities. The proposed controller achieves exponential tracking of link positions and velocities while keeping all internal signals bounded. It also guarantees exponential convergence of the estimated signals to their actual ones.
Leadership Development as a Systematic and Multidisciplinary Enterprise

Abstract Leadership development is a fundamental responsibility of colleges and universities. In this article, the authors present the theoretical foundation of an innovative initiative, as well as criteria for assessing leadership development programs in higher education. They use the Student Leadership Development Institute at Rutgers University as a case study for demonstrating that leadership development initiatives should be systematic, multidisciplinary, and research oriented and have several experiential components.
Examination of image fusion using synthetic variable ratio (SVR) technique

The current study examines different approaches of computing synthetic variable ratio (SVR) technique of image fusion using Landsat ETM+ and IRS‐pan image. The study area is located on a tropical landscape of southeastern Bangladesh. The research work explores an optimal SVR fusion method. The technique functions well in image colour and radiometry if all necessary assumptions are followed. Complications arise due to violation of those assumptions which are also being discussed in the article.
Principles and Practice of Automatic Process Control

1. Introduction. 2. Mathematical Tools for Control Systems Analysis. 3. First-Order Dynamic Systems. 4. Higher-Order Dynamic Systems. 5. Basic Components of Control Systems. 6. Design of Single Loop Feedback Control Systems. 7. Tuning of Feedback Controllers. 8. Root Locus and Frequency Response Techniques. 9. Cascade Control. 10. Ratio, Override and Selective Control. 11. Feedforward Control. 12. Multivariable Process Control. 13. Dynamic Simulation of Control Systems. Appendix A: Instrumentation Symbols and Labels. Appendix B: Design Case Studies. Appendix C: Sensors, Transmitters, and Control Valves. Appendix D: Tuning Case Studies. Appendxi E: Operating Case Studies. Index.
New Life for Old Ideas: The "Second Wave" of Sequence Analysis Bringing the "Course" Back Into the Life Course

In this article the authors draw attention to the most recent and promising developments of sequence analysis. Taking methodological developments in life course sociology as the starting point, the authors detail the complementary strength in sequence analysis in this field. They argue that recent advantages of sequence analysis were developed in response to criticism of the original work, particularly optimal matching analysis. This debate arose over the past two decades and culminated in the 2000 exchange in Sociological Methods & Research. The debate triggered a "second wave" of sequence techniques that led to new technical implementations of old ideas in sequence analysis. The authors bring these new technical approaches together, demonstrate selected advances with synthetic example data, and show how they conceptually contribute to life course research. This article demonstrates that in less than a decade, the field has made much progress toward fulfilling the prediction that Andrew Abbott made in 2000, that "anybody who believes that pattern search techniques are not going to be basic to social sciences over the next 25 years is going to be very much surprised" (p. 75).
The non-uniform communication performance of adaptive routing for hierarchical interconnection network for 3D VLSI

The Tori-connected mESH (TESH) Network is a k-ary n-cube networks of multiple basic modules, in which the basic modules are 2D-mesh networks that are hierarchically interconnected for higher level k-ary n-cube networks. Many adaptive routing algorithms for k-ary n-cube networks have already been proposed. Thus, those algorithms can also be applied to TESH network. We have proposed three adaptive routing algorithms - channel-selection, link-selection, and dynamic dimension reversal - for the efficient use of network resources of a TESH network to improve dynamic communication performance. In this paper, we have evaluated the dynamic performance of a TESH network using different non uniform traffic patterns. In this paper, we have evaluated by local communication traffic pattern in addition to the hotspot, perfect shuffle, and complement traffic patterns. It was shown that the dynamic communication performance was improved when inter-BM communication appeared frequently such as perfect shuffle and local communication traffic patterns.
Efficient burst image compression using H.265/HEVC

New imaging use cases are emerging as more powerful camera hardware is entering consumer markets. One family of such use cases is based on capturing multiple pictures instead of just one when taking a photograph. That kind of a camera operation allows e.g. selecting the most successful shot from a sequence of images, showing what happened right before or after the shot was taken or combining the shots by computational means to improve either visible characteristics of the picture (such as dynamic range or focus) or the artistic aspects of the photo (e.g. by superimposing pictures on top of each other). Considering that photographic images are typically of high resolution and quality and the fact that these kind of image bursts can consist of at least tens of individual pictures, an efficient compression algorithm is desired. However, traditional video coding approaches fail to provide the random access properties these use cases require to achieve near-instantaneous access to the pictures in the coded sequence. That feature is critical to allow users to browse the pictures in an arbitrary order or imaging algorithms to extract desired pictures from the sequence quickly. This paper proposes coding structures that provide such random access properties while achieving coding efficiency superior to existing image coders. The results indicate that using HEVC video codec with a single reference picture fixed for the whole sequence can achieve nearly as good compression as traditional IPPP coding structures. It is also shown that the selection of the reference frame can further improve the coding efficiency.
Wearable computing and contextual awareness

Computer hardware continues to shrink in size and increase in capability. This trend has allowed the prevailing concept of a computer to evolve from the mainframe to the minicomputer to the desktop. Just as the physical hardware changes, so does the use of the technology, tending towards more interactive and personal systems. Currently, another physical change is underway, placing computational power on the user's body. These wearable machines encourage new applications that were formerly infeasible and, correspondingly, will result in new usage patterns. This thesis suggests that the fundamental improvement offered by wearable computing is an increased sense of user context. I hypothesize that on-body systems can sense the user's context with little or no assistance from environmental infrastructure. These body-centered systems that “see” as the user sees and “hear” as the user hears, provide a unique “first-person” viewpoint of the user's environment. By exploiting models recovered by these systems, interfaces are created which require minimal directed action or attention by the user. In addition, more traditional applications are augmented by the contextual information recovered by these systems. To investigate these issues, I provide perceptually sensible tools for recovering and modeling user context in a mobile, everyday environment. These tools include a downward-facing, camera-based system for establishing the location of the user; a tag-based object recognition system for augmented reality; and several on-body gesture recognition systems to identify various user tasks in constrained environments. To address the practicality of contextually-aware wearable computers, issues of power recovery, heat dissipation, and weight distribution are examined. In addition, I have encouraged a community of wearable computer users at the Media Lab through design, management, and support of hardware and software infrastructure. This unique community provides a heightened awareness of the use and social issues of wearable computing. As much as possible, the lessons from this experience will be conveyed in the thesis. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Design and simulation of very low insertion loss arrayed waveguide grating using full-vectorial beam propagation method with circular cylindrical coordinates

A radial finite-difference beam propagation method is proposed systematically with circular cylindrical coordinates, which is more accurate for simulation of some radiated waveguide structures. Theoretical design and simulation of low insertion loss arrayed waveguide gratings with couplers of tapered arrayed waveguides are presented using this full-vectorial beam propagation method.
Modeling human behavior at a large scale

Until recently, complex phenomena—such as human behavior and disease epidemics—have been modeled primarily at an aggregate level. Detailed studies have been limited to small domains encompassing only a few subjects, as scaling the methods involved poses considerable challenges in terms of cost, human effort required, computational bottlenecks, and data sources available. With the surge of online social media and sensor networks, the abundance of interesting and publicly accessible data is beginning to increase. However, we also need the ability to reason about it efficiently. The underlying theme of this thesis is the unification and data mining of diverse, noisy, and incomplete sensory data over large numbers of individuals. We show that the mined patterns can be leveraged in predictive models of human behavior and other phenomena at a large scale. We find that raw sensory data linked with the content of users' online communication, the explicit as well as the implicit online social interactions, and interpersonal relationships are rich information sources upon which strong machine learning models can be built. Example domains where such models apply include understanding human activities, predicting people's location and social ties from their online behavior, and predicting the emergence of global epidemics from day-to-day interpersonal interactions.
PRE-SERVICE MATHEMATICS TEACHERS’ SUBJECT MATTER KNOWLEDGE OF THE MODE AS A MEASURE OF CENTRAL TENDENCY

Perubahan terkini dalam pendidikan statistik menekankan kepada pengembangan literasi statistik kerana dunia secara keseluruhannya pada masa ini memerlukan maklumat statistik. Walau bagaimanapun, kajian sejak beberapa dekad yang lalu telah menunjukkan bahawa literasi statistik pelajar kita tidak begitu baik. Perkara ini telah menarik perhatian guru dan peranan mereka dalam pengembangan literasi statistik. Artikel ini melaporkan pengetahuan subjek enam guru matematik pra-perkhidmatan tentang mod. Data dikumpulkan melalui teknik temu bual klinikal berasaskan soalan jenis terbuka berkaitan dengan konsep mod. Hasil kajian menunjukkan bahawa kebanyakan guru matematik pra-perkhidmatan mengenal pasti mod berdasarkan nombor terbesar yang juga dipercayai sebagai menunjukkan kekerapan tertinggi. Mereka juga mempamerkan kekurangan dari segi pengetahuan bahawa mod boleh menjadi satu ukuran biasa dalam sesuatu set data. Ciri mod ini boleh digunakan sebagai satu kaedah pantas dalam pelaporan purata yang merupakan satu idea yang penting berkaitan dengan literasi statistik.
MOBILE PHONE ACOUSTICS : PERFORMANCE EVALUATION OF SPECTRAL SUBTRACTION AND ELKO’S ALGORITHM USING SPEECH QUALITY METRICS

In recent years a great deal of effort has been expended to develop methods that determine the quality of speech through the use of comparative algorithms. These methods are designed to calculate an index value of quality that correlates to a mean opinion score given by human subjects in evaluation sessions. In this work PESQ (ITU-T Recommendation P.862) which is the new ITU-T benchmarking for objective measurement of speech quality. In mobile phone acoustics, the presence of noise and room reverberation play a vital role in degrading the speech signal and therefore, spectral subtraction and Elko’s beamformer has been used for noise reduction. Weighted Overlap and Add method (WOLA) filter bank is used for frequency domain analysis of the speech signal. Elko’s algorithm is used for designing a differential microphone array, implemented by connecting two omni directional elements to form back-to-back cardioid directional microphones. The output from the Elko’s beamformer is then used as an input to spectral subtraction based on minimum statistics reducing further noise and to enhance the quality of the speech signal. The performance of this system is analysed by calculating the value of PESQ as a speech quality measure. The better the value the PESQ, the better is the output speech quality. Signal to Noise Ratio (SNR) is used to measure the amount of noise in the restored speech signal. Reverberation Index is also used to measure the amount of reverberation effect present in the restored speech signal.
An application of a recurrent network to an intrusion detection system

The authors present an application of recurrent neural networks for intrusion detection. A partially recurrent network has been chosen for this particular application. The neural network acts as a data filter that highlights anomalous or suspicious data according to previously learned patterns. It has proven adaptive, because the same results for several users have been obtained with varying activities. The network cosine function was tested, and a hetero-associative version of the network was used to analyze the flipflop problem. <<ETX>>
Run-time monitoring and on-line testing of middleware based communication services

With the present day's exponential growth of the (tele-)communications market, the explosion of the number of mobile communication customers, and the tremendous growth of the number of IP hosts, ensuring the reliability of communication services is one of the most challenging tasks in today's software engineering. This Thesis addresses this problem by providing a new method for increasing confidence in the implementation of communication services. We developed an approach for monitoring and testing communications services that are built on top of a middleware. Specifically, we show how to monitor a communication service and how to use the monitored information to test at runtime whether the behavior of the service conforms to constraints. The constraints are extracted from the requirements, expressed using a formal method. The approach advocated in this Thesis consists in combining the power of formal methods, which have reached a high degree of maturity in the academic domain, with a testing methodology, that fit the industrial needs, in order to test the implementation of a communication service. The developer can thus focus on the testing rather then on how the testing is performed. To solve this problem (1) we designed a monitoring mechanism that is able to observe at run-time, the events occuring in the system under scrutiny, (2) designed a testing mechanism that checks at runtime if properties expressed using Temporal Logic, are violated by the execution of the application, (3) implemented a prototype tool, MOTEL (MOnitoring and TEsting tooL). With our approach the tester need only to specify the properties. The other steps involved in the testing processes are performed automatically. According to the content of the properties the instrumentation is added in the system to be tested. This instrumentation reports a consistent order of the events occurring to a central Observer. Within this Observer, the properties, which are independent of the implementation language, are translated into deterministic automata. At run-time these automata are used, according to the events reported by the instrumentation to the Observer, to check if the properties are violated. Thus, our monitoring and testing mechanism fits well in the development process because it does not add noticeable new steps for the developer. In this thesis we put together many concepts and solutions from various domains (distributed systems, middleware, object-oriented development, CORBA, communication services, formal method, Temporal Logic, automata theory, monitoring, testing). By enhancing, developing and combining, and integrating them together, we reached a high level of integration that provides a new, powerful and useful functionality to test the implementation of communication services. We believe that there is not a more valuable argument for the application and practical relevance of our approach in the industry, than the introduction and use of our method and tool in and by the industry. Our partner, Alcatel, is currently integrating MOTEL in the distributed platform PERCO.
The Chimeria Platform: An Intelligent Narrative System for Modeling Social Identity-Related Experiences

We demonstrate the Chimeria Platform that computationally models aspects of social identity dynamics for use in digital media such as in videogames and social networks. The Engine models users’ degrees of membership across multiple categories as gradient values, enabling more representational nuance than binary statuses of member/nonmember. The Application Interface handles user interaction and visuals for experiencing the narratives. Domain Epistemologies specify domain-specific ontologies that describe cultural knowledge and beliefs for each narrative. Our Visual Narrative Editor GUI is being developed to make authoring more accessible to a wider audience.
Popular song and lyrics synchronization and its application to music information retrieval

An automatic synchronization system of the popular song and its lyrics is presented in the paper. The system includes two main components: a) automatically detecting vocal/non-vocal in the audio signal and b) automatically aligning the acoustic signal of the song with its lyric using speech recognition techniques and positioning the boundaries of the lyrics in its acoustic realization at the multiple levels simultaneously (e.g. the word / syllable level and phrase level). The GMM models and a set of HMM-based acoustic model units are carefully designed and trained for the detection and alignment. To eliminate the severe mismatch due to the diversity of musical signal and sparse training data available, the unsupervised adaptation technique such as maximum likelihood linear regression (MLLR) is exploited for tailoring the models to the real environment, which improves robustness of the synchronization system. To further reduce the effect of the missed non-vocal music on alignment, a novel grammar net is build to direct the alignment. As we know, this is the first automatic synchronization system only based on the low-level acoustic feature such as MFCC. We evaluate the system on a Chinese song dataset collecting from 3 popular singers. We obtain 76.1% for the boundary accuracy at the syllable level (BAS) and 81.5% for the boundary accuracy at the phrase level (BAP) using fully automatic vocal/non-vocal detection and alignment. The synchronization system has many applications such as multi-modality (audio and textual) content-based popular song browsing and retrieval. Through the study, we would like to open up the discussion of some challenging problems when developing a robust synchronization system for largescale database.
Optimization of features for robust speaker recognition

Currently, the old feature extraction method, which was used early for speech recognition, is used in speaker recognition in our speaker recognition group. Standard Mell Frequency Cepstral Coefficients (MFCC) features are used. They can be extended by delta and acceleration coefficients eventually. Whereas features for speech recognition has been evolved and optimized until now, features for speaker recognition remains same. These outdated features suffer from various deficiencies, regarding low robustness in particular. It can be said that these features are unsuitable in practical application. This study is aimed to examine possibilities of improving of the features. In conclusion then came up with suggestion of appropriate features extraction technique, which have been combined from examined method on the basis of the before explored methods. Main emphasis is placed on the robustness, i.e. noisy test data and/or channel disturbances (e.g. microphone mismatch). The study can be divided into several parts. At first, standard MFCC and Perceptual Linear Prediction (PLP) feature sets were optimized, i.e. the optimal numbers of the band filters and of the cepstral coefficients were examined. Next, the influence of delta and acceleration coefficients was discussed. Then, the channel normalization techniques were employed. Next, the possibilities of the linear transformations Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were investigated. Then, the smoothing of spectrum or cepstrum in time was examined. Finally, several proposed combinations of above described approaches were tested. The new proposed features allow us to decrease the recognition error rate by 35-50%.
Traffic flow theory and control

For current subscribers of the web edition of the Materials Book, be advised and LRT running in mixed traffic and transit lanes, and within medians along based on a review of relevant AASHTO, TRB, and ITE documents, as well as guide has been updated from the previous guide published in 1999. 5th Edition. Multi-modal scientific and practical transportation organization for traffic engineers, planners, and others involved with surface transport. of Engineering, 1974. • M.S. Civil Engineering, New Jersey Institute of Technology, Traffic analysis and forecasting. Dan Tierny. Qualifications: Orono, 1999 U.S. Army Corps of Engineers Geometric Design of Highways and Streets, 5th. Edition. ” November 2004. – “House Management Handbook. ” November.