Query: A Dual Prediction Network for Image Captioning

Relevant passages:
Long Short-Term Memory <=SEP=> Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning <=SEP=> Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism &#x2014; a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.

Deep Residual Learning for Image Recognition <=SEP=> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

CIDEr: Consensus-based image description evaluation <=SEP=> Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.

Mind's eye: A recurrent visual representation for image caption generation <=SEP=> In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

Enhanced Simulated Annealing for Globally Minimizing Functions of Many-Continuous Variables <=SEP=> A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space Rn, is replaced by another similar exploration, performed within a succession of Euclidean spaces Rp, with p <<n. This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.

Microbial Prevalence and Antimicrobial Resistance in Children and Adolescents with Chronic Rhinosinusitis in South Indian Population <=SEP=> Objective: Chronic rhinosinusitis (CRS) is a common multifactorial upper respiratory disease with a key role of microbes in worsening of disease and its associated co-morbidities. Further, significant region specific variation in patient demographics and antibiotic resistance of causative bacteria are reported to pose difficulty in diagnosis and treatment. In India, studies on the etiology and antibiotic resistance in chronic rhinosinusitis are very meager, especially in children. The present study aimed to determine the prevalence of common causative microbes and their antibiotic resistance in children and adolescents with chronic rhinosinusitis in South Indian population. Subjects and methods: The present study was conducted on 89 children and 99 adolescents with chronic rhinosinusitis who visited MAA ENT Institute, Hyderabad, South India. The study samples were collected under the nasal endoscopic guidance from the middle meatus at first visit and sinuses at surgery. Conventional and VITEK-2 methods were used for identification and antibiotic sensitivity of the microbes. Chi-square test and multinomial logistic regression was applied to determine statistical differences between the variable using PASW v. 18.0 software (SPSS Inc., Chicago, IL). Results: The male-female ratio was 2:1 with an average children age of 8.9 ± 3.65 years and 16.1 ± 1.23 years in adolescents. The risk for adenoids was seen in 49.2 % of children (OR; 2.6: 95% CI: 1.63-4.06) while allergic fungal sinusitis (18.1%, OR: 2.7; 95% CI: 1.12-6.57) and nasal polyps (26.6%, OR: 2.3; 95% CI: 1.07-4.86) was commonly seen in adolescents. About 26.6% of adolescents with fungal positivity also showed bacterial infection. Aspergillus flavus (68%) was the most common fungi identified. Bacterial culture rate was positive in 46.8% of the total subjects of which Streptococcus aureus was the most common bacteria (59.1%) followed by Streptococcus pnuemoniae (21.2%), Klebsiella sp. (11.4%), Pseudomonas aeruginosa (11.4%) and β hemolytic streptococci (1.1%). No Methicillin-resistant Staphylococcus aureus strains could be identified. Streptococcus pneumonia (63.2%) was commonly identified in younger children and Pseudomonas aeruginosa (80%) was mostly seen in adolescents. The frequency of bacterial positivity in adolescents with CRS when compared to CRS children was high and varied between different associated co-morbidities. High antibiotic resistance in Staphylococcus aureus was seen towards gentamicin (73%) and co-trimoxazole (64%), Streptococcus pnuemoniae to gentamicin (58%), cotrimoxazole (68%) and meropenem (32%), Pseudomonas aeruginosa to co-trimoxazole (100%), cefatoximine (60%) and cefatazidime (50%) while Klebsiella sp. to gentamicin (80%) and co-trimoxazole (60%). Streptococcus aureus showed high sensitivity to cefatoximine (95.8%) and Streptococcus pnuemoniae for ofloxacin (100%), ciprofloxacin (89.5%) and cefazolin (89.5%). Pseudomonas aeruginosa showed high sensitivity for amikacin (100%) and ciprofloxacin (80%) and Klebsiella sp. for amikacin (100%) Conclusion: Significant regional specific variation in bacterial etiology that differed with age, severity and comorbidities was observed in children and adolescents with chronic rhinosinusitis. High antimicrobial resistance in the cultures of chronic rhinosinusitis patients at their first visit and also at sinus surgery warrants urgent need for early initiation of personalized interventions for better management of the infectious disease.

Byzantine Fault Tolerance, from Theory to Reality <=SEP=> Since its introduction nearly 20 years ago, the Byzantine Generals Problem has been the subject of many papers having the scrutiny of the fault tolerance community. Numerous Byzantine tolerant algorithms and architectures have been proposed. However, this problem is not yet sufficiently understood by those who design, build, and maintain systems with high dependability requirements. Today, there are still many misconceptions relating to Byzantine failure, what makes a system vulnerable, and indeed the very nature and reality of Byzantine faults. This paper revisits the Byzantine problem from a practitioner’s perspective. It has the intention to provide the reader with a working appreciation of the Byzantine failure from a practical as well as a theoretical perspective. A discussion of typical failure properties and the difficulties in preventing the associated failure propagation is presented. These are illustrated with real Byzantine failure observations. Finally, various architectural solutions to the Byzantine problem are presented. 1 What You Thought Could Never Happen In English, the phrase “one in a million” is popularly used to describe the highly improbable. The ratio itself is difficult to comprehend. The easiest way to give it reason is to equate it to real-world expectations. For example, the probability of winning the U.K. National Lottery is around one in fourteen million; the probability of getting struck by lightning in the U.S. is around one in six hundred thousand [1]. It is not safe to rely on intuition for reasoning about unfathomably small probabilities (for example, the 1-in-1,000,000,000 maximum failure probability for critical aerospace systems1). It is problematic in two ways: (1) real-world parallels are beyond typical human experience and comprehension; (2) faults that are not recognized, such as Byzantine faults, are incorrectly assumed to occur with zero or very low probability. The lack of recognition causes additional issues in that it allows the manifestation of such faults to pass unnoticed or be otherwise misclassified, reinforcing the misconception of low probability of occurrence. 1 Usually written as a failure rate of 10/hr 236 K. Driscoll et al. The lack of recognition leads to repeating the “Legionnaire’s Disease” phenomenon. After its “discovery” in 1976, a search of medical records found that the disease had seldom occurred for many decades. The lack of shared knowledge and the disease’s rarity made each occurrence appear to be unique. Only after 1976 was it realized that all these “unique” occurrences had a common cause. Similarly, an observation of a Byzantine failure will not be recognized as being an instance of a known class of failure by those who are not intimately familiar with Byzantine failures. The intent of this paper is to redress this situation. Drawing from the authors’ experiences with Byzantine failures in real-world systems, this paper shows that Byzantine problems are real, have nasty properties, and are likely to increase in frequency with emerging technology trends. Some of the myths with respect to the containment of Byzantine faults are dispelled and suitable mitigation strategies and architectures are discussed. 2 The Byzantine Army Is Growing The microprocessor revolution has seen electronic and software technologies proliferate into almost every domain of life, including an increasing responsibility in safety-critical domains. High assurance processes (e.g. DO-254 [2]) have been matured to manage the development of high integrity systems. The cost of using high assurance processes is relatively high in comparison to equivalent levels of functionality in commercial counterparts. In recent years, there has been a push to adopt commercial off-the-shelf (COTS) technology into high integrity systems. The timing of the COTS push is alarming, considering the decreasing reliability and dependability trends emerging within the COTS integrated circuit (IC) arena. With increasing clock frequencies, decreasing process geometries, and decreasing power supply voltages, studies [3] conclude that the dependability of modern ICs is decreasing. This development reverses the historical trend of increasing IC dependability. Bounding the failure behaviors of emerging COTS ICs will become increasingly more difficult. The expected lifetime of ICs is also decreasing, as modes of “silicon wear-out” (i.e. time-dependent dielectric breakdown, hot carrier aging, and electro-migration) become more significant. The anticipated working life of current ICs may be on the order of 5-10 years. Although viable in the commercial arena, this is a world away from the requirements for high integrity systems, which have traditionally had deployment lifetimes ranging over multiple decades. Strategies to mitigate the problems are in development [4]. It is safe to assume that the anticipated rate of IC failure will increase and the modes of failure will become ever more difficult to characterize. Another significant trend is the move towards more distributed, safety-critical processing system topologies. These distributed architectures appear to be favored for the emerging automotive “by-wire” control systems [5], where a new breed of safety critical communications protocols and associated COTS are being developed [6]. Such technologies, if developed in compliance with the traditional high assurance processes, show promise to answer the cost challenges of high integrity applications. However, it is imperative that such technologies are developed with full knowledge of all possible failure modes. Distributed control systems, by their very nature, require Byzantine Fault Tolerance, from Theory to Reality 237 consensus among their constituent elements. The required consensus might be lowlevel (e.g. in the form of mutual synchronization) or at a higher level (e.g. in the form of some coordinated system action such as controlling a braking force). Addressing Byzantine faults that can disrupt consensus is thus a crucial system requirement. We expect Byzantine faults to be of increasing concern given the two major trends described in this section: (1) Byzantine faults are more likely to occur due to trends in device physics; (2) systems are becoming more vulnerable due to increasing emphasis on safety-critical distributed topologies. It is therefore imperative that Byzantine faults and failure mechanisms become widely understood and that the design of safety-critical systems includes mitigation strategies. 3 Parables from the Classical Byzantine Age The initial definition of Byzantine failure was given in a landmark paper by Lamport et al [7]. They present the scenario of a group of Byzantine Generals whose divisions surround an enemy camp. After observing the enemy, the Generals must communicate amongst themselves and come to consensus on a plan of action— whether to attack or retreat. If they all attack, they win; if none attack, they live to fight another day. If only some of the generals attack, then the generals will die. The generals communicate via messages. The problem is that one or more of the generals may be traitors who send inconsistent messages to disrupt the loyal generals from reaching consensus. The original paper discusses the problem in the context of oral messages and written signed messages that are attributed different properties. Oral messages are characterized as follows: A1. Every message that is sent is delivered correctly (messages are not lost). A2. The receiver of a message knows who sent it. A3. The absence of a message can be detected. For messages with these properties, it has been proven that consensus cannot be achieved with three generals, if one of the generals is assumed to be a traitor. A solution is presented in which each of four general exchanges information with his peers and a majority vote makes selections over all of the data exchanged. This solution is generalized to accommodate multiple traitors, concluding: to tolerate m traitorous generals, requires 3 m + 1 generals utilizing m + 1 rounds of information exchange. Written, signed messages assume all of the properties (A1-A3) of the oral messages, and are further characterized by the properties below: A4. A loyal general’s signature cannot be forged. A5. Anyone can verify the authenticity of a signature. Assuming the signed message properties above, it is shown that consensus is possible with just three generals, using a simple majority voting function. The solution is further generalized to address multiple fault scenarios, concluding: to tolerate m traitorous generals requires 2m + 1 loyal generals and m + 1 rounds of information exchange. The initial proofs presented in the paper assume that all generals communicate directly with one another. The assumptions are later relaxed to address topologies of 238 K. Driscoll et al. less connectivity. It is proven that for oral messages, consensus is possible if the generals are connected in a p regular graph, where p > 3m 1. For signed (authenticated) messages, it is proven that consensus is possible if the loyal generals are connected to each other. However, this solution requires additional rounds of information exchange Since its initial presentation, nearly two decades ago, the Byzantine Generals problem has been the subject of intense academic study, leading to the development and formal validation of numerous Byzantine-tolerant algorithms and architectures. As stated previously, industry’s recognition and treatment of the problem has been far less formal and rigorous. A reason for this might be the anthropomorphic tone and presentation of the problem definition. Although the authors warned against adopting too literal an interpretation, much of the related literature that has followed the original text has reinforced the “traitorous” anthropomorphic failure model. Such treatment has resulted in the work being ignored by a large segment of the commu

Real-time underwater 3D scene reconstruction using commercial depth sensor <=SEP=> This paper presents preliminary work to utilize a commercial time of flight depth camera for real-time 3D scene reconstruction of underwater objects. Typical RGB stereo camera imaging for 3D capturing suffers from blur and haziness due to turbidity of water in addition to critical dependence on light, either from natural or artificial sources. We propose a method for repurposing the low-cost Microsoft Kinect™ Time of Flight camera for underwater environment enabling dense depth data acquisition that can be processed in real time. Our motivation is the ease of use and low cost of the device for high quality real-time scene reconstruction as compared to multi view stereo cameras, albeit at a smaller range. Preliminary results of depth data acquisition and surface reconstruction in underwater environment are also presented. The novelty of our work is the utilization of the Kinect depth camera for real-time 3D mesh reconstruction and our main objective is to develop an economical and compact solution for underwater 3D mapping.

1 Keyword extraction : a review of methods and approaches <=SEP=> Paper presents a survey of methods and approaches for keyword extraction task. In addition to the systematization of methods, the paper gathers a comprehensive review of existing research. Related work on keyword extraction is elaborated for supervised and unsupervised methods, with special emphasis on graphbased methods as well as Croatian keyword extraction. Selectivity-based keyword extraction method is proposed as a new unsupervised graph-based keyword extraction method which extracts nodes from a complex network as keyword candidates. The paper provides guidelines for future research and development of new graph-based approaches for keyword extraction.

Sentiment classification for Turkish Twitter feeds using LDA <=SEP=> Today, people are able to express their opinions and ideas on any subject using personal blogs or a variety of social media environments. Contents shared by users with each passing day increases the data size on the web but this situation is a complication for meaningful information extraction. Therefore, sentiment analysis studies aimed at automatically obtaining meaningful information from social media content showed a significant increase in recent years. In these studies, automatically analyzing the content of messages and identifying sentiment such as positive, neutral, negative etc. are intended. In this study, a system has been proposed for analyzing sentiment of Turkish Twitter feeds. In addition, sentiment classification which is a leading problem for sentiment analysis studies has been performed using topic modeling and the effect of modeling on results are examined. The topic model has been created using LDA (Latent Dirichlet Allocation) algorithm. Experimental results obtained with different feature extraction model and classifiers. The results show that topic modeling is more successful compared to the methods used in the previous studies. Also, sentiment classification based on topic model knowledge has been found to increase sentiment analysis success by %26 compared to our previous work.

A conceptual framework for serious games and its validation <=SEP=> FACULTY OF ENGINEERING, SCIENCES AND MATHEMATICS SCHOOL OF ELECTRONICS AND COMPUTER SCIENCE Doctor of Philosophy A CONCEPTUAL FRAMEWORK FOR SERIOUS GAMES AND ITS VALIDATION

Cytokine removal with high cut-off membrane: review of literature. <=SEP=> During the last decade, blood purification therapies have been proposed as an effective therapy to control the cytokines dysregulation in systemic inflammatory syndromes. Among them, the treatment with high cut-off membranes is characterized by larger pore size and more effective clearance for middle molecular weight molecules (cytokines). In this paper, we performed a thoughtful review of the literature on HCO being used for blood purification indications in all systemic inflammation syndromes. Clinical and experimental studies show that the use of high effluent flows in a pure diffusive treatment effectively removes serum cytokines with a safe profile in albumin clearance. In clinical studies, the removal of these inflammatory mediators is associated with a significant improvement in hemodynamic condition, oxygenation indices, and organ dysfunction.

Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation <ce:italic>in vitro</ce:italic> <=SEP=> BACKGROUND
The neocortex is the most common target of subdural electrotherapy and noninvasive brain stimulation modalities, including transcranial magnetic stimulation (TMS) and transcranial current simulation (TCS). Specific neuronal elements targeted by cortical stimulation are considered to underlie therapeutic effects, but the exact cell type(s) affected by these methods remains poorly understood.


OBJECTIVE
We determined whether neuronal morphology or cell type predicted responses to subthreshold and suprathreshold uniform electric fields.


METHODS
We characterized the effects of subthreshold and suprathreshold electrical stimulation on identified cortical neurons in vitro. Uniform electric fields were applied to rat motor cortex brain slices, while recording from interneurons and pyramidal cells across cortical layers, using a whole-cell patch clamp. Neuron morphology was reconstructed after intracellular dialysis of biocytin. Based solely on volume-weighted morphology, we developed a parsimonious model of neuronal soma polarization by subthreshold electric fields.


RESULTS
We found that neuronal morphology correlated with somatic subthreshold polarization. Based on neuronal morphology, we predict layer V pyramidal neuronal soma to be individually the most sensitive to polarization by optimally oriented subthreshold fields. Suprathreshold electric field action potential threshold was shown to reflect both direct cell polarization and synaptic (network) activation. Layer V/VI neuron absolute electric field action potential thresholds were lower than layer II/III pyramidal neurons and interneurons. Compared with somatic current injection, electric fields promoted burst firing and modulated action potential firing times.


CONCLUSIONS
We present experimental data indicating that cortical neuron morphology relative to electric fields and cortical cell type are factors in determining sensitivity to sub- and supra-threshold brain stimulation.

Predicting Price Changes in Ethereum <=SEP=> The market capitalization of publicly traded cryptocurrencies is currently above $230 billion (Bovaird, 2017). Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value (Van Alstyne, 2014), and its price predictability has been well-studied (Hegazy and Mumford, 2016). However, Ethereum has the second-highest market capitalization and supports much more functionality than Bitcoin. While its price predictability is sparsely covered in published literature, the technology’s additional functionality may cause Ether’s price predictability to differ significantly from that of Bitcoin. These characteristics are outlined in the following subsection; the underlying details of Bitcoin (Nakamoto, 2008) and Ethereum (Buterin, 2013) are elided, as they are described in depth in the cited papers.

Binary malware image classification using machine learning with local binary pattern <=SEP=> Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.

The Bluetooth radio system <=SEP=> A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.

The return of AdaBoost.MH: multi-class Hamming trees <=SEP=> Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem toK binary one-againstall classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH.

Formalizing agility, part 2: how an agile organization embraced the CMMI <=SEP=> Most large IT organizations need the best of both worlds - working software that supports ever-changing business needs, and a process for delivering software that is predictable, trainable, and auditable. Organizations with agile software teams often receive timely, cost-effective solutions of sufficient quality. Organizations with formal processes often benefit from industry-recognized certifications and robust process improvement mechanisms. Rarely does a single, large IT organization achieve both. DTE Energy has such a combination: its lightweight yet CMMI-compatible methodology is now used within its 600+ person IT organization to deliver and support working software. Its small teams embrace core agile principles as they provide "just enough" solutions that satisfy maturing business needs. Yet they passed two SCAMPI appraisals toward formal CMMI Level III accreditation, scheduled for mid-2006. This report extends the Agile 2005 experience report on DTE Energy's agile IT organization's journey toward CMMI staged maturity accreditation. This report briefly recaps their seven years of agile experience, presents governance mechanisms and change management techniques, and highlights their four-release, one-year plan for CMMI Level III accreditation. Finally, this report offers suggestions on embracing a formal process framework that are applicable to any organization

Applications of Trajectory Data in Transportation: Literature Review and Maryland Case Study <=SEP=> This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor <=SEP=> With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.

GUSS: Solving Collections of Data Related Models Within GAMS <=SEP=> In many applications, optimization of a collection of problems is required where each problem is structurally the same, but in which some or all of the data defining the instance is updated. Such models are easily specified within modern modeling systems, but have often been slow to solve due to the time needed to regenerate the instance, and the inability to use advance solution information (such as basis factorizations) from previous solves as the collection is processed. We describe a new language extension, GUSS, that gathers data from different sources/symbols to define the collection of models (called scenarios), updates a base model instance with this scenario data and solves the updated model instance and scatters the scenario results to symbols in the GAMS database. We demonstrate the utility of this approach in three applications, namely data envelopment analysis, cross validation and stochastic dual dynamic programming. The language extensions are available for general use in all versions of GAMS starting with release 23.7.

Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model <=SEP=> The ABSTRACT is to be in fully-justified italicized text, at the top of the left-hand column, below the author and affiliation information. Use the word “Abstract” as the title, in 12-point Times, boldface type, centered relative to the column, initially capitalized. The abstract is to be in 10point, single-spaced type. Leave two blank lines after the Abstract, then begin the main text. Look at previous CVPR abstracts to get a feel for style and length.

Signal Characteristics on Sensor Data Compression in IoT -An Investigation <=SEP=> In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.

Resolving constraint conflicts <=SEP=> In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.

Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning <=SEP=> Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1.

A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance <=SEP=> This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Co-operative downloading in vehicular ad-hoc wireless networks <=SEP=> Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.

Internet Traffic Measurement <=SEP=> This tutorial article discusses the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications. The article begins with some background information on Internet traffic measurement, and then proceeds to discuss the “tools of the trade”, including examples of both hardwarebased and software-based approaches to network traffic measurement. The article concludes with a summary of the main observations from the past fifteen years of network measurement research, along with pointers to the relevant literature for more information.

Runtime Verification of Ethereum Smart Contracts <=SEP=> The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.