Query: Hybrid music recommender using content-based and social information

Relevant passages:
Unified Collaborative and Content-Based Web Service Recommendation <=SEP=> The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users' interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: collaborative filtering and content-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

Deep content-based music recommendation <=SEP=> Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

The long tail of recommender systems and how to leverage it <=SEP=> The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

Theano: new features and speed improvements <=SEP=> Theano is a linear algebra compiler that optimizes a user’s symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano’s performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.

Unsupervised feature learning for audio classification using convolutional deep belief networks <=SEP=> In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks.

Challenges with applying vulnerability prediction models <=SEP=> Vulnerability prediction models (VPM) are believed to hold promise for providing software engineers guidance on where to prioritize precious verification resources to search for vulnerabilities. However, while Microsoft product teams have adopted defect prediction models, they have not adopted vulnerability prediction models (VPMs). The goal of this research is to measure whether vulnerability prediction models built using standard recommendations perform well enough to provide actionable results for engineering resource allocation. We define 'actionable' in terms of the inspection effort required to evaluate model results. We replicated a VPM for two releases of the Windows Operating System, varying model granularity and statistical learners. We reproduced binary-level prediction precision (~0.75) and recall (~0.2). However, binaries often exceed 1 million lines of code, too large to practically inspect, and engineers expressed preference for source file level predictions. Our source file level models yield precision below 0.5 and recall below 0.2. We suggest that VPMs must be refined to achieve actionable performance, possibly through security-specific metrics.

Distributed Event-Triggered Scheme for Economic Dispatch in Smart Grids <=SEP=> To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

Mixing Coins of Different Quality: A Game-Theoretic Approach <=SEP=> Cryptocoins based on public distributed ledgers can differ in their quality due to different subjective values users assign to coins depending on the unique transaction history of each coin. We apply game theory to study how qualitative differentiation between coins will affect the behavior of users interested in improving their anonymity through mixing services. We present two stylized models of mixing with perfect and imperfect information and analyze them for three distinct quality propagation policies: poison, haircut, and seniority. In the game of perfect information, mixing coins of high quality remains feasible under certain conditions, while imperfect information eventually leads to a mixing market where only coins of the lowest quality are mixed.

A conceptual framework to develop Green IT – going beyond the idea of environmental sustainability <=SEP=> This paper presents a conceptual framework aiming to better research, understand and develop Green IT within organizations. Based on a literature review on Green IT, regarding the concepts of responsibility and sustainability, we propose an initial framework with five dimensions: ethical, technological, economical, social and environmental. These dimensions compose Green IT strategies and practices. Additionally, the framework considers that environmental changing requirements, strategic requirements and dynamic capabilities are the forces which move organizations toward green practices and foster innovation. This framework is part of a five-year horizon project that began in 2009 and aims to contribute to the theory in the field, having as initial goal to identify the constructs associated with Green IT.

Door recognition and deep learning algorithm for visual based robot navigation <=SEP=> In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient scenes can be recognized and useful information such as the location of door can be identified. The extracted information can be used for robot navigation, so does the robot can approach the target accurately. In the field experiments, detecting doors and predicting the door poses such tasks are designed in the indoor environment to verify the proposed method. The experimental results demonstrate that the doors can be identified with good performance and the deep learning model is suitable for robot navigation.

Anomaly detection of malicious users' behaviors for web applications based on web logs <=SEP=> With more and more online services developed into web applications, security problems based on web applications become more serious now. Most intrusion detection systems are based on every single request to find the cyber-attack instead of users' behaviors, and these systems can only protect web application from known vulnerability rather than some zero-day attacks. In order to detect newly developed attacks, we analyze web logs from web servers and define users' behaviors to divide them into normal and malicious ones. The result shows that by using the feature of web resources to define users' behaviors, a higher accuracy rate and lower false alarm rate of intrusion detection can be obtained.

Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings <=SEP=> We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. To tune the parameters of this model, we generate self-induced spelling error corpora. We perform our experiments for two languages. For English, we greatly outperform off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of a noisy channel model, showing that neural embeddings can be successfully exploited to improve upon the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling correction tool on manually annotated clinical records from the Antwerp University Hospital, but can offer no empirical evidence that our method counters the frequency bias of a noisy channel model in this case as well. However, both our context-sensitive model and our implementation of the noisy channel model obtain high scores on the test set, establishing a state-of-the-art for Dutch clinical spelling correction with the noisy channel model.

The importance of drawing in the mechanical design process <=SEP=> -This paper is a study on the importance of drawing ( both formal drafting and informal sketching) during the process of mechanical design. Five hypotheses, focused on the t~es of drawings, their necessity in mechanical problem solving, and their relation to the external representation medium, are presented and supported. Support is through referenced studies in other domains and the results of protocol studies performed on five mechanical designers. Videotapes of all the marks-on-paper made by designers in representative sections of the design process were studied in detail for their type and purpose. The resulting data is supportive of the hypotheses. These results also give requirements for future computer aided design tools and graphics education, and goals for further studies. I. I N T R O D U C T I O N The goal of this paper is to study the importance of drawing (both formal drafting and informal sketching) during the process of mechanical design. This goal can be extended to state that we intend to show the necessio" of drawing during all the developmental stages of a mechanical design. Through the information presented here, the requirements for future computer aided design tools, graphics education, and further studies will be developed. All mechanical engineers are taught drafting. Thus, most engineers are skilled at making and interpreting these formal mechanical drawings. These drawings are representations of a final design (the end product of the design process) and they are intended to archive the completed design and communicate it to other designers and manufacturing personnel. Additionally, engineers are notorious for not being able to think without making "'back-of-the-envelope" sketches of rough ideas. Sometimes these informal sketches serve to communicate a concept to a colleague, but more often they just help the idea take shape on paper. It is in considering how these sketches help an idea take form that gives a hint that drawing's role in engineering is more than just to archive a concept or to communicate with others. Understanding the use of both drafting and sketching in design is important to help formulate the future development of Computer Aided Design or Drafting (CAD) systems. As CAD evolves and becomes more "'intelligent,'" the question of what attributes these systems must have becomes more important. In the past, CAD system attributes have primarily been driven from developments in the computer industry, it is only through understanding drawing's importance in the design process that these systems can be based on design needs. Additionally, the pressures of CAD tool development, faculty time demands, and course expenses cause academic institutions to reevaluate the content of their "graphics'" courses. Understanding drawing's importance in the design process helps establish what skills need to be taught to engineers during their training. This paper is organized by first, in Section 2, clarifying the types of drawings used in mechanical design. The hypotheses to be addressed in this paper are given in Section 3. A discussion of research on the understanding of visual imagery to be used as a basis for arguments in support of the hypotheses is in Section 4. In Section 5 is a discussion of the results of data taken on how mechanical engineers use drawings during design. Lastly, in Section 6, is a discussion of how well the hypotheses have been supported and the implications of our findings on CAD development, educational requirements, and future research directions. 2. TYPES OF D R A W I N G S USED IN DESIGN Engineers make many types of marks-on-paper. In research, to be described in Section 5, we have broken down these marks into two main groupings: support notation and graphic representations. Support notation includes textual notes, lists, dimensions (including leaders and arrows), and calculations. Graphic representations include drawings of objects and their functions, and plots and charts. Mechanical design graphic representations are often scale drawings made with mechanical instruments or CAD computer systems. These drawings, made in accordance with a set of widely accepted rules, are defined as having been drafted. Sketches, on the other hand, are defined as "free-hand" drawings. They are usually not to scale and may use shorthand notations to represent both objects and their function. A differentiation must be made between the act of graphic representation and the medium on which it occurs. The medium, whether it be paper and pencil, a computer stylus on a tablet, chalk on a blackboard, or other medium may put interface restrictions on the representation. The following discussions are concerned with what is being represented, not with how the representation is made. However, the discussions point to the medium's restriction on representation and the need for improved interfaces. Another aspect of drawings to be considered is the level of abstraction of the information to be represented. During the design process, the design is refined

A UWB based Localization System for Indoor Robot Navigation <=SEP=> For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.

Visualisation and Instructional Design <=SEP=> Human cognitive architecture includes a working memory of limited capacity and duration with partially separate visual and auditory channels, and an effectively infinite long-term memory holding many schemas that can vary in their degree of automation. These cognitive structures have evolved to handle information that varies in the extent to which elements can be processed successively in working memory or, because they interact, must be processed simultaneously imposing a heavy load on working memory. Cognitive load theory uses this combination of information and cognitive structures to guide instructional design. Several designs that rely heavily on visual working memory and its characteristics are discussed in this paper. Knowledge of human cognitive architecture is essential for instructional design, and visual cognition is a central aspect of human cognition. Not surprisingly, there are several instructional design effects that rely heavily on the manner in which humans visually process information. This paper discusses some relevant information structures, cognitive structures and instructional designs that rely on our knowledge of visual information processing. A. Information structures While considerable work by many researchers over several decades has been devoted to the organization of human cognitive architecture, far less effort has gone into investigating the information structures that must have driven the evolution of that architecture. Some work has been carried out by Sweller (1994) and Halford, Wilson and Phillips (1998). Sweller (1994) suggested that all information can be placed on a continuum according to the extent to which the elements that constitute the information interact. At one extreme, there is no interaction between the elements that need to be learned. They are independent. Element interactivity is low, or indeed, non-existent, and that means each element can be considered and learned serially without reference to any other element. Because elements at the low element interactivity end of the continuum do not interact with each other, there is no loss of understanding despite each element being learned individually and in isolation. Understanding is defined as the ability to process all elements that necessarily interact, simultaneously in working memory. Learning such material

Learning Explanatory Rules from Noisy Data <=SEP=> Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data—which is not necessarily easily obtained—that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.

Falling for Fake News: Investigating the Consumption of News via Social Media <=SEP=> In the so called 'post-truth' era, characterized by a loss of public trust in various institutions, and the rise of 'fake news' disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news.

Mixed Type Audio Classification with Support Vector Machine <=SEP=> Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification of mixed type audio data, such as clips having speech with music as background, is still considered a difficult problem. In this paper, we present a mixed type audio classification system based on support vector machine (SVM). In order to capture characteristics of different types of audio data, besides selecting audio features, we also design four different representation formats for each feature. Our SVM-based audio classifier can classify audio data into five types: music, speech, environment sound, speech mixed with music, and music mixed with environment sound. The experimental results show that our system outperforms other classification systems using k nearest neighbor (k-NN), neural network (NN), and Naive Bayes (NB)

Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems <=SEP=> Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives it of long-term reward and learn to act to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique which prevents a reinforcement learning agent from learning to disable the big red button. Our technique interrupts the agent or robot by placing it in a virtual simulation where it continues to receive reward. We illustrate our technique in a simple grid world environment.

Efficient Mean-shift Clustering Using Gaussian KD-Tree <=SEP=> Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences

Meditation states and traits: EEG, ERP, and neuroimaging studies. <=SEP=> Neuroelectric and imaging studies of meditation are reviewed. Electroencephalographic measures indicate an overall slowing subsequent to meditation, with theta and alpha activation related to proficiency of practice. Sensory evoked potential assessment of concentrative meditation yields amplitude and latency changes for some components and practices. Cognitive event-related potential evaluation of meditation implies that practice changes attentional allocation. Neuroimaging studies indicate increased regional cerebral blood flow measures during meditation. Taken together, meditation appears to reflect changes in anterior cingulate cortex and dorsolateral prefrontal areas. Neurophysiological meditative state and trait effects are variable but are beginning to demonstrate consistent outcomes for research and clinical applications. Psychological and clinical effects of meditation are summarized, integrated, and discussed with respect to neuroimaging data.

Impact of Customer Relationship Management on Customer Loyalty , Customer Retention and Customer Profitability for Hotelier Sector <=SEP=> Since the entrance of strategies oriented to marketing relational in Hotelier Sector, the traditional way of travel agents and other representatives arranging hospitality services for hotel and travel reservations has changed. The strategies oriented to customer relationship management are a relatively new area of specialty loyalty marketing in the hotel and hotelier sector, with advancements being made constantly. The use of this type of strategy can allow hoteliers or companies to tailor special guest programs, services and promotions based on hotel guest preferences. The hotel can use the data collected in a program to identify the needs of particular customers across hotel chains to be able to use marketing that can be targeted at specific groups of people. It also gives hoteliers the opportunity to evaluate frequent guest programs, personalize their services and perform trend analysis. A program based in marketing relational is typically run by hotels and companies to collect guest information and transaction data for use and examining to allow hoteliers to see target groups that should be marketed too. Based on these transactions hotels are able to create and manage guest loyalty programs and reward schemes. This research approach is to appraise the impact of customer relationship management on customer profitability as mediated by customer loyalty and customer retention within the hotelier sector of Mexico, and specifically for those hoteliers classified like of three stars. A sample of 100 hotels three stars was interviewed as respondents in this study. The objective of the study was to find the impact relationship between effective customer relationship implementation, customer loyalty, and customer retention and customer profitability. The findings of the study add value to hotels three stars in Mexico, and provide some invaluable statistical results essential for hotel managers and owners to successfully enhance customer loyalty, customer retention and customer profitability. By applying a questionnaire that count with seven blocks different one each other, the results of our study identified and provide path analysis of the relevant systems; and enumerated among the relevant system, those critical inter-component relationships within this highly competitive industry. This study’s findings add to the body of knowledge and enable the managers of this sector to implement customer relationship management in the best shape possible, to match it with Mexican market-needs thereby creating more loyal and repeat clientele.

Detection of camera artifacts from camera images <=SEP=> Cameras are frequently used in state-of-the-art systems in order to get detailed information of the environment. However, when cameras are used outdoors they easily get dirty or scratches on the lens which leads to image artifacts that can deteriorate a system's performance. Little work has yet been done on how to detect and cope with such artifacts. Most of the previous work has concentrated on detecting a specific artifact like rain drops on the windshield of a car. In this paper, we show that on moving systems most artifacts can be detected by analyzing the frames in a stream of images from a camera for static image parts. Based on the observation that most artifacts are temporally stable in their position in the image we compute pixel-wise correlations between images. Since the system is moving the static artifacts will lead to a high correlation value while the pixels showing only scene elements will have a low correlation value. For testing this novel algorithm, we recorded some outdoor data with the three different artifacts: raindrops, dirt and scratches. The results of our new algorithm on this data show, that it reliably detects all three artifacts. Moreover, we show that our algorithm can be implemented efficiently by means of box-filters which allows it to be used as a self-checking routine running in background on low-power systems such as autonomous field robots or advanced driver assistant systems on a vehicle.

FLAP: An End-to-End Event Log Analysis Platform for System Management <=SEP=> Many systems, such as distributed operating systems, complex networks, and high throughput web-based applications, are continuously generating large volume of event logs. These logs contain useful information to help system administrators to understand the system running status and to pinpoint the system failures. Generally, due to the scale and complexity of modern systems, the generated logs are beyond the analytic power of human beings. Therefore, it is imperative to develop a comprehensive log analysis system to support effective system management. Although a number of log mining techniques have been proposed to address specific log analysis use cases, few research and industrial efforts have been paid on providing integrated systems with an end-to-end solution to facilitate the log analysis routines.
 In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs. FLAP provides an end-to-end solution that utilizes advanced data mining techniques to assist log analysts to conveniently, timely, and accurately conduct event log knowledge discovery, system status investigation, and system failure diagnosis. Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly. Since 2016, FLAP has been used by Huawei Technologies Co. Ltd for internal event log analysis, and has provided effective support in its system operation and workflow optimization.

Communication in reactive multiagent robotic systems <=SEP=> Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and e ectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three di erent types of communication for three di erent tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can signi cantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as e ective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.

A Compact Highly Reconfigurable CMOS MMIC Directional Coupler <=SEP=> This paper presents a tunable CMOS directional coupler that utilizes lumped-element L-C sections. The lumped-element approach used to build the directional coupler makes it possible to integrate the coupler onto a single monolithic microwave integrated circuit (MMIC), as it occupies a small area compared to printed designs. The directional coupler uses varactors and tunable active inductors (TAIs) to synthesize the series and shunt reactances, respectively, which allows extensive electronic control over the coupling coefficient, while insuring a low return loss and a very high isolation. Furthermore, using varactors and TAIs allows the directional coupler to be reconfigured for operation over a wide range of frequencies. Moreover, the symmetric configuration of the coupler allows it to switch from forward to backward operation by simply exchanging the bias voltages applied across the series varactors. The MMIC coupler was fabricated in a standard 0.13-mum CMOS process and operates from a 1.5-V supply. The circuit occupies 730 mum times 600 mum, and is capable of achieving tunable coupling coefficients from 1.4 to 7.1 dB, while maintaining an isolation higher than 41 dB. The MMIC coupler is also capable of operating at any center frequency over the 2.1-3.1-GHz frequency range with higher than 40-dB isolation. The coupler achieves a -4.1-dBm 1-dB compression point while operating from a 1.5-V supply.

Reconstructing the feedback polynomial of a linear scrambler with the method of hypothesis testing <=SEP=> Blind identification of the feedback polynomial in a linear scrambler is an important component in the wireless communication systems which have the function of automatic standard recognition and parameter estimation. The following research contains three parts. In the first part, promotion model is deduced based on Cluzeau’s model. Comparative analysis shows the advantage of computational complexity by use of the promotion model. In the second part, the improved method of estimation for the normalised standard deviation is proposed, which plays an important role for the computation time and precision. In the final part, simulation results explain the better practical application of my algorithm compared with Cluzeau’s algorithm and XiaoBei-Liu’s algorithm.

An analysis of developer metrics for fault prediction <=SEP=> Background: Software product metrics have been widely used as independent variables for constructing a fault prediction model. However, fault injection depends not only on characteristics of the products themselves, but also on characteristics of developers involved in the project. Aims: The goal of this paper is to study the effects of developer features on software reliability. Method: This paper proposes developer metrics such as the number of code churns made by each developer, the number of commitments made by each developer and the number of developers for each module. By using the eclipse project dataset, we experimentally analyzed the relationship between the number of faults and developer metrics. Second, the effective of developer metrics for performance improvements of fault prediction models were evaluated. Results: The result revealed that the modules touched by more developer contained more faults. Compared with conventional fault prediction models, developer metrics improved the prediction performance. Conclusions: We conclude that developer metrics are good predictor of faults and we must consider the human factors for improving the software reliability.

Happiness Economics , Eudaimonia and Positive Psychology : From Happiness Economics to Flourishing Economics <=SEP=> A remarkable current development, happiness economics focuses on the relevance of people’s happiness in economic analyses. As this theory has been criticised for relying on an incomplete notion of happiness, this paper intends to support it with richer philosophical and psychological foundations. Specifically, it suggests that happiness economics should be based on Aristotle’s philosophical eudaimonia concept and on a modified version of ‘positive psychology’ that stresses human beings’ relational nature. First, this analysis describes happiness economics and its shortcomings. Next, it introduces Aristotle’s eudaimonia and takes a look at positive psychology with this lens, elaborating on the need to develop a new approach that goes beyond the economics of happiness: the economics of flourishing. Finally, the paper specifies some possible socio-economic objectives of a eudaimonic economics of happiness.

Compact dual-band microstrip bandpass filter without external feeds <=SEP=> A compact dual-band microstrip bandpass filter is proposed and designed to operate at 2.4 and 5.2GHz without needing any external impedance-matching block. The modified half-wavelength stepped-impedance resonator with sinuous configuration is constructed to simultaneously excite the dual resonances at these two specified frequencies with miniaturized overall size. The parallel-coupled microstrip line is properly characterized to minimize the return losses within both dual passbands. The optimized results exhibit the good dual-band filtering performances with return losses higher than 20dB as well being confirmed by experimentation with a fabricated filter circuit.