Query: Lightweight Prevention of Architectural Erosion

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A Classification and Comparison Framework for Software Architecture Description Languages <=SEP=> ÐSoftware architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future. Index TermsÐSoftware architecture, architecture description language, component, connector, configuration, definition, classification, comparison.

Specifying Distributed Software Architectures <=SEP=> There is a real need for clear and sound design speci cations of distributed systems at the architectural level. This is the level of the design which deals with the high-level organisation of computational elements and the interactions between those elements. The paper presents the Darwin notation for specifying this high-level organisation. Darwin is in essence a declarative binding language which can be used to de ne hierarchic compositions of interconnected components. Distribution is dealt with orthogonally to system structuring. The language supports the speci cation of both static structures and dynamic structures which may evolve during execution. The central abstractions managed by Darwin are components and services. Services are the means by which components interact. In addition to its use in specifying the architecture of a distributed system, Darwin has an operational semantics for the elaboration of speci cations such that they may be used at runtime to direct the construction of the desired system. The paper describes the operational semantics of Darwin in terms of the -calculus, Milner's calculus of mobile processes. The correspondence between the treatment of names in the -calculus and the management of services in Darwin leads to an elegant and concise -calculus model of Darwin's operational semantics. The model is used to argue the correctness of the Darwin elaboration process. The overall objective is to provide a soundly based notation for specifying and constructing distributed software architectures. This paper will appear in the Fifth European Software Engineering Conference, ESEC '95 on 26 September 1995 in Barcelona.

Agile Software Development: The Business of Innovation <=SEP=> T he rise and fall of the dot-com-driven Internet economy shouldn't distract us from seeing that the business environment continues to change at a dramatically increasing pace. To thrive in this turbulent environment, we must confront the business need for relentless innovation and forge the future workforce culture. Agile software development approaches such as Extreme Programming , Crystal methods, Lean Development, Scrum, Adaptive Software Development (ASD), and others view change from a perspective that mirrors today's turbulent business and technology environment. In a recent study of more than 200 software development projects, QSM Associates' Michael Mah reported that the researchers couldn't find nearly half of the projects' original plans to measure against. Why? Conforming to plan was no longer the primary goal; instead, satisfying customers—at the time of delivery , not at project initiation—took precedence. In many projects we review, major changes in the requirements, scope, and technology that are outside the development team's control often occur within a project's life span. Accepting that Barry Boehm's life cycle cost differentials theory—the cost of change grows through the software's development life cycle—remains valid, the question today is not how to stop change early in a project but how to better handle inevitable changes throughout its life cycle. Traditional approaches assumed that if we just tried hard enough, we could anticipate the complete set of requirements early and reduce cost by eliminating change. Today, eliminating change early means being unresponsive to business con-ditions—in other words, business failure. Similarly, traditional process manage-ment—by continuous measurement, error identification, and process refine-ments—strove to drive variations out of processes. This approach assumes that variations are the result of errors. Today, while process problems certainly cause some errors, external environmental changes cause critical variations. Because we cannot eliminate these changes, driving down the cost of responding to them is the only viable strategy. Rather than eliminating rework, the new strategy is to reduce its cost. However, in not just accommodating change, but embracing it, we also must be careful to retain quality. Expectations have grown over the years. The market demands and expects innovative, high-quality software that meets its needs— and soon. Agile methods are a response to this expectation. Their strategy is to reduce the cost of change throughout a project. Extreme Programming (XP), for example, calls for the software development team to • produce the first delivery in weeks, to achieve an early win and rapid …

An Introduction to the Aesop System <=SEP=> As the design of software architectures emerges as a discipline within software engineering, it becomes increasingly important to support architectural description and analysis with tools and environments. This paper provides a brief introduction to Aesop, a set of tools for developing architectural design environments that exploit architectural styles to guide software architects in producing speci c systems.

ADLs and dynamic architecture changes <=SEP=> Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.’

A two-stage short-term load forecasting approach using temperature daily profiles estimation <=SEP=> Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

Performance analysis of DCO-OFDM in VLC system <=SEP=> The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.

When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers <=SEP=> This research adds to the rich discussion on whether increases in payment to crowdworkers lead to increases in performance quality by introducing the concept of perceived fairness in pay (PFP). PFP refers to the belief that one is fairly compensated for their work. We examine whether PFP mediates the impact of payment amount on the performance quality of crowdworkers. We conducted a field experiment with 152 crowdworkers performing a buttonclicking (BC) task and an instructional manipulation check (IMC) task. PFP mediated the impact of payment amount on performance quality in the BC task but not in the IMC task. PFP also mediated the impact of payment amount on satisfaction and task time. Results suggest that PFP can help us better understand the relationship between payment and performance quality in crowdsourcing.

Efficient frame-sequential label propagation for video object segmentation <=SEP=> In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.

A combined transmembrane topology and signal peptide prediction method. <=SEP=> An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at

Design of a triangular fractal patch antenna with slit for IRNSS and GAGAN applications <=SEP=> In this paper a triangular fractal patch antenna with slit is designed for IRNSS and GAGAN applications using ADS software. India is intended to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user sector is indispensable. GPS Aided and Geo Augmented Navigation (GAGAN), a satellite based augmentation system for India, erect over the GPS system is anticipated to provide the flawless navigation support over the Asia-Pacific regions. The desired antenna has been deliberate on dielectric constant εr = 4.8 and substrate thickness h = 3.05 mm. The feed location of the antenna has been selected to produce the circular polarization. The self-similar property in antenna exhibits multi-band resonant frequencies. These specifications should be satisfied at the frequency L5 (1175 MHz), L1 (1575.42 MHz) and S (2492.08 MHz).

Flame detection in video using hidden Markov models <=SEP=> This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.

Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges <=SEP=> In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.

Visual inertial odometry for quadrotors on SE(3) <=SEP=> The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.

Sleep and autism spectrum disorders. <=SEP=> Sleep disorders are common in children with autism spectrum disorders and have a significant effect on daytime function and parental stress. The cornerstone of treatment is to establish the cause of the sleep concern, which is often multifactorial. Identifying and treating sleep disorders may result not only in more consolidated sleep, more rapid time to fall asleep, and avoidance of night waking but also favorably affect daytime behavior and parental stress. Targeting effective treatment strategies is dependent on understanding the underlying causes of sleep problems in children with Autism spectrum disorders, therefore further research is paramount.

Optimizing distributions over molecular space . An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry ( ORGANIC ) <=SEP=> Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design. 1

Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study <=SEP=> Selection of the proper electric traction drive is an important step in design and performance optimization of electrified powertrains. Due to the use of high energy magnets, permanent magnet synchronous machines (PMSM) have been the primary choice in the electric traction motor market. However, manufacturers are very interested to find a permanent magnet-free alternative as a fallback option due to unstable cost of rare-earth metals and fault tolerance issues related to the constant permanent magnet excitation. In this paper, a new comprehensive review of electric machines (EMs) that includes various new switched reluctance machine topologies in addition to conventional EMs such as PMSM, induction machine, synchronous reluctance machine (SynRel), and PM-assisted SynRel is presented. This paper is based on performances such as power density, efficiency, torque ripple, vibration and noise, and fault tolerance. These systematic examinations prove that recently proposed magnetic configurations such as double-stator switched reluctance machine can be a reasonable substitute for permanent magnet machines in electric traction applications.

Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search <=SEP=> This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semanticspreserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.

Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example <=SEP=> Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.

SHOP2: An HTN Planning System <=SEP=> The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Extending the Hierarchical Deep Reinforcement Learning framework <=SEP=> iv

Unsupervised Image-to-Image Translation Networks <=SEP=> Most of the existing image-to-image translation frameworks—mapping an image in one domain to a corresponding image in another—are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.

Process scheduling under uncertainty: Review and challenges <=SEP=> Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling nder uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. he purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production cheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different athematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, lternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process cheduling under uncertainty are identified and some new ideas are discussed. 2007 Elsevier Ltd. All rights reserved.

Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals <=SEP=> In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.

A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls. <=SEP=> In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.

Apposcopy: semantics-based detection of Android malware through static analysis <=SEP=> We present Apposcopy, a new semantics-based approach for identifying a prevalent class of Android malware that steals private user information. Apposcopy incorporates (i) a high-level language for specifying signatures that describe semantic characteristics of malware families and (ii) a static analysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain control- and data-flow properties. We have evaluated Apposcopy on a corpus of real-world Android applications and show that it can effectively and reliably pinpoint malicious applications that belong to certain malware families.

Towards 3D object maps for autonomous household robots <=SEP=> This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.

Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation <=SEP=> Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children's lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.

Designing an UHF RFID Reader Antenna <=SEP=> In this paper, a circularly polarized (CP) Octagonal shape microstrip patch antenna fed by coaxial feed is designed and analyzed for RFID (Radio Frequency Identification) reader applications. The physical parameters of the structure as well as its partial ground plane are analyzed and optimized using Zealand IE3D simulation software. Return loss (S11), voltage standing wave ratio (VSWR), directivity and gain are carried out. The results show that the proposed antenna has good impedance and radiation characteristics over the required bandwidth, 860-960 MHz (UHF RFID band). The return loss of the optimized Octagonal-shaped microstrip patch antenna is below 10dB over the UHF frequency band. The proposed antenna is very promising for various modern communication applications.

Emotion AI , Real-Time Emotion Detection using CNN <=SEP=> In this paper, we describe a Convolutional Neural Network (CNN) approach to realtime emotion detection. We utilize data from the Extended Cohn-Kanade dataset, Japanese Female Facial Expression data set, and our own custom images in order to train this model, and apply preprocessing steps to improve performance. We re-train a LeNet and AlexNet implementation, both of which perform with above 97% accuracy. Qualitative analysis of real-time images shows that the above models perform reasonably well at classifying facial expressions, but not as well as the quantitative results would indicate.