title: Covariance matrix-based fire and flame detection method in video

abstract: This paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320 × 240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.


title: Simulation possibilities of vacuum electronic devices with CST PARTICLE STUDIO™

abstract: Due to high prototyping costs and the need of a time efficient development the importance of simulation programs has increased in almost all areas. The CST STUDIO SUITEtrade is widely used for the design and analysis of RF components using CST MICROWAVE STUDIOreg (MWS). For simulations including charged particles still MAFIA was used. With the recent developments CST PARTICLE STUDIOtrade has filled the gap between the established CST MWS and the charged particle simulations required for investigations of vacuum tubes. In this paper recent developments are presented.


title: Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs

abstract: Microblogs have become popular media for news propagation in recent years. Meanwhile, numerous rumors and fake news also bloom and spread wildly on the open social media platforms. Without verification, they could seriously jeopardize the credibility of microblogs. We observe that an increasing number of users are using images and videos to post news in addition to texts. Tweets or microblogs are commonly composed of text, image and social context. In this paper, we propose a novel Recurrent Neural Network with an attention mechanism (att-RNN) to fuse multimodal features for effective rumor detection. In this end-to-end network, image features are incorporated into the joint features of text and social context, which are obtained with an LSTM (Long-Short Term Memory) network, to produce a reliable fused classification. The neural attention from the outputs of the LSTM is utilized when fusing with the visual features. Extensive experiments are conducted on two multimedia rumor datasets collected from Weibo and Twitter. The results demonstrate the effectiveness of the proposed end-to-end att-RNN in detecting rumors with multimodal contents.


title: Semantically Annotated Lesson Observation Data in Learning Analytics Datasets: a Reference Model

abstract: Learning analytics (LA) and lesson observations are two approaches frequently used to study teaching and learning processes. In both cases, in order to extract meaningful data interpretations, there is a need for contextualization. Previous works propose to enrich LA datasets with observation data and to use the learning design as a framework to guide the data gathering and the later analysis. However, the majority of lesson observation tools collect data that is not compliant with LA datasets. Moreover, the connection between the learning design and the data gathered is not straightforward. This study reflects upon our research-based design towards an LA model for context-aware semantically annotated lesson observations that may be integrated in multimodal LA datasets. Six teachers (out of which 2 were also researchers) with previous experience in lesson observation were engaged in a focus group interview and participatory design session that helped us to evaluate the LA model through the conceptual design of Observata (a lesson observation tool that implements our model). The findings show the feasibility and usefulness of the proposal as well as the potential limitations in terms of adoption.


title: Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

abstract: Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this paper, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology: Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative. We evaluate our concept extraction approach using a benchmark movie review dataset provided by Cornell University and product review datasets on books, DVDs, and electronics. Comparative experimental results show that our proposed approach to sentiment analysis outperforms existing state-of-the-art methods.


title: Risk Factors for Post‐Traumatic Stress Disorder Symptoms in Surrogate Decision‐Makers of Patients with Chronic Critical Illness

abstract: RATIONALE ::: Chronically critically ill patients are often dependent on family members for surrogate decision-making, and these surrogates are at high risk for emotional distress. We hypothesized that patient- and surrogate-specific risk factors for surrogate post-traumatic stress disorder (PTSD) symptoms can be identified early in the course of chronic critical illness. ::: ::: ::: OBJECTIVES ::: To identify risk factors for PTSD symptoms in surrogate decision-makers of chronically critically ill patients. ::: ::: ::: METHODS ::: We performed a secondary analysis of the database from a multicenter randomized trial of a communication intervention for chronic critical illness patients and surrogates. Variables preselected for plausible mechanism for increasing PTSD symptoms and identifiable by Day 10 of mechanical ventilation were included in the analysis for association with surrogate PTSD symptoms at 90 days, as measured by the Impact of Events Score-Revised (IES-R). Patient factors included demographics, insurance status, baseline functional status, chronic comorbidities, illness severity, and presence of advance directive. Surrogate variables included demographics, education level and employment, religion, relationship to patient, and Hospital Anxiety and Depression Scale score measured at enrollment. Multivariable linear regression models were then constructed for 26 potential risk factors, including biologically or mechanistically plausible confounders for each, with IES-R score as the outcome. All models were adjusted for multiple respondents, using a mixed model, considering the patients as a random factor. ::: ::: ::: RESULTS ::: Our analysis included 306 surrogates for 224 patients. A total of 49% of patients were female, and mean age was 59 years (95% confidence interval [CI], 56.4-60.7). A total of 71% of surrogates were female, and mean age was 51 years (95% CI, 49.3-52.4). After examining each potential risk factor in a separate multivariable model, only Day-10 surrogate Hospital Anxiety and Depression Scale score (β coefficient = 1.02; 95% CI, 0.73-1.30) and patient unresponsiveness (β coefficient = 8.39; 95% CI, 0.83-15.95) were associated with higher IES-R scores. ::: ::: ::: CONCLUSIONS ::: Among surrogate decision-makers for chronically critically ill patients, high anxiety and depression scores and patient unresponsiveness on or near Day 10 of mechanical ventilation are risk factors for PTSD symptoms at 90 days.


title: A fast indoor tracking algorithm based on particle filter and improved fingerprinting

abstract: Wi-Fi based indoor tracking has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. Most existing Wi-Fi based indoor tracking systems suffer from the low accuracy, due to the complexity of indoor environment, and the high time-delay, caused by the time consumption of positioning algorithm. In this paper, we propose a new tracking scheme based on particle filter and an improved k-nearest neighbor (KNN) algorithm. The particle filter is used to add motion constrains to the tracking model and reduce the measurement error. The improved KNN algorithm is used to provide the position in a fast and precise way. A series of experiments were implemented on a mobile phone and the results show that our scheme achieves superior performance than other existing algorithms.


title: Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

abstract: This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ's high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent's capability of adapting to a changing environment is tested.


title: Road Detection and Segmentation from Aerial Images Using a CNN Based System

abstract: This paper proposes a system architecture based on deep convolutional neural network (CNN) for road detection and segmentation from aerial images. These images are acquired by an unmanned aerial vehicle implemented by the authors. The algorithm for image segmentation has two phases: the learning phase and the operating phase. The input aerial images are decomposed in their color components, preprocessed in Matlab on Hue channel and next partitioned in small boxes of dimension 33 × 33 pixels using a sliding box algorithm. These boxes are considered as inputs into a deep CNN. The CNN was designed using MatConvNet and has the following structure: four convolutional layers, four pooling layers, one ReLu layer, one full connected layer, and a Softmax layer. The whole network was trained using a number of 2,000 boxes. The CNN was implemented using programming in MATLAB on GPU and the results are promising. The proposed system has the advantage of processing speed and simplicity.


title: Exploiting the deep learning paradigm for recognizing human actions

abstract: In this paper we propose a novel method for recognizing human actions by exploiting a multi-layer representation based on a deep learning based architecture. A first level feature vector is extracted and then a high level representation is obtained by taking advantage of a Deep Belief Network trained using a Restricted Boltzmann Machine. The classification is finally performed by a feed-forward neural network. The main advantage behind the proposed approach lies in the fact that the high level representation is automatically built by the system exploiting the regularities in the dataset; given a suitably large dataset, it can be expected that such a representation can outperform a hand-design description scheme. The proposed approach has been tested on two standard datasets and the achieved results, compared with state of the art algorithms, confirm its effectiveness.


title: Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning

abstract: We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning. Our proposal is simpler and more biologically-plausible. Unlike previous approaches, our technique can be applied out of the box to all learning scenarios (e.g., online learning, batch learning, fully-connected, convolutional, feedforward, recurrent and mixed --- recurrent and convolutional) and compare favorably with existing approaches. We also propose Lp Normalization for normalizing by different orders of statistical moments. In particular, L1 normalization is well-performing, simple to implement, fast to compute, more biologically-plausible and thus ideal for GPU or hardware implementations.


title: Deep Generative Filter for Motion Deblurring

abstract: Removing blur caused by camera shake in images has always been a challenging problem in computer vision literature due to its ill-posed nature. Motion blur caused due to the relative motion between the camera and the object in 3D space induces a spatially varying blurring effect over the entire image. In this paper, we propose a novel deep filter based on Generative Adversarial Network (GAN) architecture integrated with global skip connection and dense architecture in order to tackle this problem. Our model, while bypassing the process of blur kernel estimation, significantly reduces the test time which is necessary for practical applications. The experiments on the benchmark datasets prove the effectiveness of the proposed method which outperforms the state-of-the-art blind deblurring algorithms both quantitatively and qualitatively.


title: Diagnosis of suspected hypovitaminosis A using magnetic resonance imaging in African lions (Panthera leo)

abstract: Vitamin A deficiency is described in captive lions. Ante mortem diagnosis can either be made by serum analysis or liver biopsy, both of which may be problematic. This study utilised magnetic resonance imaging to identify vitamin A deficiency in lions with relatively mild clinical signs, which could otherwise be attributed to numerous other neurological conditions. Magnetic resonance imaging is a non-invasive, reliable diagnostic tool to demonstrate pathology typically associated with this condition. To accommodate varying lion ages and sizes, a number of cranium and brain measurements were compared with that of the maximum diameter of the occular vitreous humor. Occular ratios of the tentorium cerebelli osseum and occipital bone were most reliable in diagnosing the thickened osseous structures characteristic of hypovitaminosis A. The ratio of maximum : minimum dorsoventral diameter of the C1 spinal cord was also of value.


title: The atypical 16p11.2 deletion: A not so atypical microdeletion syndrome?

abstract: One of the recently recognized microdeletion syndromes is the 16p11.2 deletion syndrome (593 kb; similar to 29.5Mb to similar to 30.1 Mb), associated with developmental delay, autism spectrum disorder, epilepsy, and obesity. Less frequently reported is a smaller 220 kb deletion, adjacent and distal to this 16p11.2 deletion, which has been referred to as the atypical 16p11.2 deletion (220 kb; similar to 28.74Mb to similar to 28.95 Mb). We describe three patients with this deletion and update the manifestations in two sibs who have been described as possibly new entity in this Journal in 1997 [Bakker and Hennekam (1997); Am J Med Genet 70: 312-314] and were recently found to have the "atypical 16p11.2 deletion" as well. Patients show a developmental delay, behavioral problems, and unusual facial morphology (prominent forehead, downslanted, and narrow palpebral fissures), and some are obese. We suggest that this "atypical" deletion may turn out to become a microdeletion syndrome that will be recognizable in the future, or at least to show a phenotype that is recognizable in retrospect. As it may no longer be so "atypical," we suggest renaming the entity "distal 16p11.2 deletion," to distinguish it from the common proximal 16p11.2 deletion. (C) 2011 Wiley-Liss, Inc


title: Soft Errors in Static NMOS RAMs

abstract: Soft error rates in static RAMs have been measured, and shown to be comparable to those of DRAMs under typical operating conditions. Ways of reducing the alpha-sensitivity have been assessed both by analysis of a simplified model of the SRAM cell, and by SPICE simulations.


title: Pola Hubungan Etnik Cina dengan Masyarakat Pribumi di Bengkalis

abstract: Cohesion and national integration is a major challenge in the process of nation-state formation. The challenge was complicated because the Indonesian society is pluralistic, whether viewed horizontally or vertically. One of the unresolved issues in relation to the indigenous ethnic Chinese are confounding issues that led to the integration of the nation. The relationship between ethnic Chinese - native Bengkalis is quite good because of the cooperation mutualistis. However, this condition only occurs in sectors of the economy and not involve other parts are quite important in life. In these relationships also create stereotypes as a continuation of the colonial legacy that has placed the ethnic Chinese as the middle class. This led to jealousy of the indigenous groups mainly derived from economic inequality. This is a potential that can cause conflict if not handled seriously and carefully by various parties, especially in the era of reform, democracy and transparency.


title: A visualization tool for evaluating access control policies in facebook-style social network systems

abstract: Understanding the privacy implication of adopting a certain privacy setting is a complex task for the users of social network systems. Users need tool support to articulate potential access scenarios and perform policy analysis. Such a need is particularly acute for Facebook-style Social Network Systems (FSNSs), in which semantically rich topology-based policies are used for access control. In this work, we develop a prototypical tool for Reflective Policy Assessment (RPA) --- a process in which a user examines her profile from the viewpoint of another user in her extended neighbourhood in the social graph. We verify the utility and usability of our tool in a within-subject user study.


title: Association Rules with Graph Patterns

abstract: We propose graph-pattern association rules (GPARs) for social media marketing. Extending association rules for item-sets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering top-k diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy bound. We also study the problem of identifying potential customers with GPARs. While it is also NP-hard, we provide a parallel scalable algorithm that guarantees a polynomial speedup over sequential algorithms with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and effectiveness of the algorithms.


title: What's Going On in Neural Constituency Parsers? An Analysis

abstract: A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent to which information provided directly by the model structure in classical systems is still being captured by neural methods. To this end, we propose a high-performance neural model (92.08 F1 on PTB) that is representative of recent work and perform a series of investigative experiments. We find that our model implicitly learns to encode much of the same information that was explicitly provided by grammars and lexicons in the past, indicating that this scaffolding can largely be subsumed by powerful general-purpose neural machinery.


title: Bi-criteria Workflow Tasks Allocation and Scheduling in Cloud Computing Environments

abstract: Although there are few efficient algorithms in the literature for scientific workflow tasks allocation and scheduling for heterogeneous resources such as those proposed in grid computing context, they usually require a bounded number of computer resources that cannot be applied in Cloud computing environment. Indeed, unlike grid, elastic computing, such asAmazon's EC2, allows users to allocate and release compute resources on-demand and pay only for what they use. Therefore, it is reasonable to assume that the number of resources is infinite. This feature of Clouds has been called âillusion of infiniteresourcesâ. However, despite the proven benefits of using Cloud to run scientific workflows, users lack guidance for choosing between multiple offering while taking into account several objectives which are often conflicting. On the other side, the workflow tasks allocation and scheduling have been shown to be NP-complete problems. Thus, it is convenient to use heuristic rather than deterministic algorithm. The objective of this paper is to design an allocation strategy for Cloud computing platform. More precisely, we propose three complementary bi-criteria approaches for scheduling workflows on distributed Cloud resources, taking into account the overall execution time and the cost incurred by using a set of resources.


title: Robust, Real-Time 3D Tracking of Multiple Objects with Similar Appearances

abstract: This paper proposes a novel method for tracking multiple moving objects and recovering their three-dimensional (3D) models separately using multiple calibrated cameras. For robustly tracking objects with similar appearances, the proposed method uses geometric information regarding 3D scene structure rather than appearance. A major limitation of previous techniques is foreground confusion, in which the shapes of objects and/or ghosting artifacts are ignored and are hence not appropriately specified in foreground regions. To overcome this limitation, our method classifies foreground voxels into targets (objects and artifacts) in each frame using a novel, probabilistic two-stage framework. This is accomplished by step-wise application of a track graph describing how targets interact and the maximum a posteriori expectation-maximization algorithm for the estimation of target parameters. We introduce mixture models with semiparametric component distributions regarding 3D target shapes. In order to not confuse artifacts with objects of interest, we automatically detect and track artifacts based on a closed-world assumption. Experimental results show that our method outperforms state-of-the-art trackers on seven public sequences while achieving real-time performance.


title: Instant Messaging: Effects of Relevance and Timing

abstract: Instant messaging (IM) has grown rapidly to involve millions of users spanning a variety of platforms. This paper outlines two preliminary studies that examined the effects of IM notifications on ongoing desktop computer productivity tasks. Results from the studies show that the disruptiveness of IM to productivity tasks is reduced if the incoming message is highly relevant to the current task, or if messages are queued until certain key computing operations have been completed. User interface design principles for the control of messaging are proposed based on the results.


title: Information as ontologization

abstract: The traditional view of data, information, and knowledge as a hierarchy fosters an understanding of information as an independent entity with objective meaning—that while information is tied to data and knowledge, its existence is not dependent upon them. While traditional conceptions assume a static nature of information, expressed by the equation information = data + meaning, we have argued that this understanding is based on an ontologization of an entwined process of sense making and meaning making. This process starts from the recognition of a pattern that is interpreted in a way that influences our behavior. At the same time, the process character of meaning making makes us aware of the fact that this ontologized hierarchy is in fact an interwoven process. We conclude that the phenomenological analysis of this ontologization that makes into being data, information, and knowledge has to go back to this process to reveal the essential underlying dependencies. © 2011 Wiley Periodicals, Inc.


title: Long-Term Visual Object Tracking Benchmark

abstract: In this paper, we propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for visual object tracking. The dataset consists of 50 videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and possibly train better deep learning architectures (avoiding/reducing augmentation, which may not reflect realistic real world behavior). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further categorize the test sequences with different attributes and present a thorough quantitative and qualitative evaluation. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of most trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long term tracking.


LaBB-CAT: an Annotation Store


title: Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus

abstract: A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of 'basic emotions', and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based on a theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC), evaluating the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct the language is used to express a certain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more 'basic'. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman's set, arguably the most frequently used for classifying emotions, is in fact the most semantically distinct overall. Next, taking all analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman's set (Angry, Disgusted, Joyful, Sad, Scared). We also demonstrate how using LSC data can help visualise emotions. We introduce the concept of an Emotion Profile and briefly analyse compound emotions both visually and mathematically.


title: Bacterial Nanocellulose Loaded with Bromelain: Assessment of Antimicrobial, Antioxidant and Physical-Chemical Properties

abstract: Bacterial nanocellulose (BNC) has desirable properties for wound healing such as high purity, good shape retention, and high water binding capacity. Bromelain is a protease found in pineapple tissues and has been applied in several fields, it has anti-inflammatory and anticancer properties, promotes cell apoptosis, amongst others. In this work, a BNC based device for the controlled release of bromelain was developed. BNC were submersed in sterilized bromelain solution and incubated at 25 °C under 100 rpm for 24 h. Physical-chemical properties, protein concentration, antioxidant and antimicrobial activities were measured. Results demonstrate that BNC could improve bromelain antimicrobial activity 9 times. Those findings allow concluding that bromelain is a promising molecule to be incorporated into BNC’s. The BNC’s characteristics seem to represent a new promising delivery system of the loaded biomolecule, and protected from external actions.


title: A 2.4GHz CMOS Gilbert Mixer in 180nm Technology

abstract: This paper presents the design of an analog CMOS Gilbert cell Mixer for RF Receiver front end application. Input RF frequency of the mixer is 2.4GHz and operated at 1.8V power supply. The schematic has been implemented using UMC-180nm CMOS technology and simulated on spectre-RF simulator of Cadence. The input RF and LO frequency of the proposed mixer is 2.4GHz and 2.25GHz respectively, resulting in an output IF frequency of 250MHz. It achieves a conversion Gain of around 13dB and considerable NF. IIP3 and 1-db Compression point are also measured. Layout design and extracted view of the mixer is also presented in this paper.


A S URVEY ON S ECURITY R ISK M ANAGEMENT F RAMEWORKS IN C LOUD C OMPUTING


title: Sleep and Alzheimer disease pathology—a bidirectional relationship

abstract: Factors other than age and genetics may increase the risk of developing Alzheimer disease (AD). Accumulation of the amyloid-β (Aβ) peptide in the brain seems to initiate a cascade of key events in the pathogenesis of AD. Moreover, evidence is emerging that the sleep-wake cycle directly influences levels of Aβ in the brain. In experimental models, sleep deprivation increases the concentration of soluble Aβ and results in chronic accumulation of Aβ, whereas sleep extension has the opposite effect. Furthermore, once Aβ accumulates, increased wakefulness and altered sleep patterns develop. Individuals with early Aβ deposition who still have normal cognitive function report sleep abnormalities, as do individuals with very mild dementia due to AD. Thus, sleep and neurodegenerative disease may influence each other in many ways that have important implications for the diagnosis and treatment of AD.


title: A wideband printed monopole antenna over dual-band AMC

abstract: A low profile antenna consisting of a wideband monopole over an artificial magnetic conductor (AMC) is presented in this paper. The AMC is a dual band structure, manufactured on 3.2mm thick FR-4 substrate with the reflection phase in the 2.0 GHz and 5.8 GHz ranges. A wideband printed circular disc monopole antenna uses this dual band AMC as a ground plane resulting in a low profile multiband antenna system with a height of 7.8mm. The frequency bands covered are at GPS (1.57542 GHz), DCS (1.710–1.880 GHz), PCS (1.850–1.990 GHz), UMTS (1.925–2.175 GHz), WLAN (2.4–2.485 GHz), WiFi (5.15–5.825 GHz) and V2X (5.76–5.84 GHz). It is demonstrated that the boresight gain increases when the AMC is used as a ground plane for the antenna in conjunction with a large metal plate.


title: A Unifying Semantics for Sequential Function Charts

abstract: Programmable Logic Controllers (PLC) are widely used as device controllers for assembly lines, chemical processes, or power plants. Sequential Function Charts (SFC) form one of the main programming languages for PLCs and, therefore, the correctness of the PLC software implemented as SFCs is crucial for a safe operation of the controlled process. A prerequisite for reasoning about program correctness is a clear understanding of the program semantics. As we show in this work, this is currently not the case for SFCs. Although syntactically specified in the IEC 61131-3 standard, SFCs lack an unambiguous, complete semantic description. We point out a number of problems and explain how these lead to different interpretations in commercial programming environments. To remedy this situation we introduce a parameterized formal semantics for SFCs including many high-level programming features such as parallelism, hierarchy, actions and activity manipulation. Moreover, we show how to extend the semantics to include time, clocks, and timed actions. The presented semantics is general enough to comprise different existing interpretations while at the same time being adjustable to precisely represent each of them.


Visual Denotations for Recognizing Textual


title: Certainty identification in texts: Categorization model and manual tagging results

abstract: This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions — level, perspective, focus, and time of certainty. The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. Our findings are that the editorial sample group had a significantly higher frequency of markers per sentence than did the sample group of the news stories. For editorials, high level of certainty, writer’s point of view, and future and present time were the most populated categories. For news stories, the most common categories were high and moderate levels, directly involved third party’s point of view, and past time. These patterns have positive practical implications for automation.


title: Just a Few Seeds More: Value of Network Information for Diffusion

abstract: Identifying the optimal set of individuals to first receive information ('seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.


title: Combining the AHP and TOPSIS to evaluate car selection

abstract: Car manufacturing is recently growing rapidly along with the development of technology and science. Consequently, car manufacturers have to improve their products, i.e., the cars, to meet customers' requirement. This study tried to evaluate car selection by utilizing nine criteria that are considered by the customers when they are willing to purchase the cars, namely, exterior, convenience, performance, safety, price, fuel efficiency, resale, spare part warranty, and dealer. The analytic hierarchy process (AHP) and technique for others reference by similarity to ideal solution (TOPSIS) were combined to accomplish the objective of this study. Firstly, the AHP was used to determine the weights of each criterion. The weights that have been identified by the AHP were then employed to select the best alternative using TOPSIS method. A case study to exhibit the applicability of the methods was conducted to evaluate two types of cars that have similar engine capacity and price range, i.e., All New Avanza 1.3 E and All New Xenia 1.3 R. Data were collected and compiled from three experts consist of a car rental owner, a car workshop owner, and a car dealer employee. The AHP result showed that the price was labelled as the most important criterion with weight of 0.182. The All New Avanza 1.3 E were regarded preferable than All New Xenia 1.3 R based on TOPSIS calculation. The finding of this study might offer the car manufacturers with valuable insight into the criteria that reflect customer's assessment in car selection.


title: Learning Heuristics for the TSP by Policy Gradient

abstract: The aim of the study is to provide interesting insights on how efficient machine learning algorithms could be adapted to solve combinatorial optimization problems in conjunction with existing heuristic procedures. More specifically, we extend the neural combinatorial optimization framework to solve the traveling salesman problem (TSP). In this framework, the city coordinates are used as inputs and the neural network is trained using reinforcement learning to predict a distribution over city permutations. Our proposed framework differs from the one in [1] since we do not make use of the Long Short-Term Memory (LSTM) architecture and we opted to design our own critic to compute a baseline for the tour length which results in more efficient learning. More importantly, we further enhance the solution approach with the well-known 2-opt heuristic. The results show that the performance of the proposed framework alone is generally as good as high performance heuristics (OR-Tools). When the framework is equipped with a simple 2-opt procedure, it could outperform such heuristics and achieve close to optimal results on 2D Euclidean graphs. This demonstrates that our approach based on machine learning techniques could learn good heuristics which, once being enhanced with a simple local search, yield promising results.


title: Mining Exoticism from Visual Content with Fusion-based Deep Neural Networks

abstract: Exoticism is the charm of the unfamiliar, it often means unusual, mystery, and it can evoke the atmosphere of remote lands. Although it has received interest in different arts, like painting and music, no study has been conducted on understanding exoticism from a computational perspective. To the best of our knowledge, this work is the first to explore the problem of exoticism-aware image classification, aiming at automatically measuring the amount of exoticism in images and investigating the significant aspects of the task. The estimation of image exoticism could be applied in fields like advertising and travel suggestion, as well as to increase serendipity and diversity of recommendations and search results. We propose a Fusion-based Deep Neural Network (FDNN) for this task, which combines image representations learned by Deep Neural Networks with visual and semantic hand-crafted features. Comparisons with other Machine Learning models show that our proposed architecture is the best performing one, reaching accuracy over 83% and 91% on two different datasets. Moreover, experiments with classifiers exploiting both visual and semantic features allow to analyze what are the most important aspects for identifying exotic content. Ground truth has been gathered by retrieving exotic and not exotic images through a web search engine by posing queries with exotic and not exotic semantics, and then assessing the exoticism of the retrieved images via a crowdsourcing evaluation. The dataset is publicly released to promote advances in this novel field.


title: Web-Based Educational Information System for Enhanced Learning, EISEL: Student Assessment

abstract: During the last decade, Information Technology (IT) has been the primary force driving the transformation of roles in the education industry. More specifically, the World Wide Web (WWW) and associated technologies provided a new playground with new rules and tools to conduct instruction and create novel approaches to learning. We have seen the application of IT in education in the form of CD-ROMs. With the evolution of the WWW we saw education marketed as long distance learning, web based learner centered environments, internet based learning environments, and self instructed learning. With all the different models used on the web, few have studied their acceptance and their effectiveness on learning. Many educational institutions today have embarked in the development of web based courses. However, they face enormous difficulty in achieving successful strategies including the delivery, effectiveness, and acceptance of the courses. This is mainly due to the fact that the problem of developing a successful web based course involves multiple inter-related dimensions ranging from technology related issues to pedagogical considerations. Davis (1989) proposed a Technology Assessment Model (TAM) to explain user acceptance of technology. The TAM identifies ‘perceived usefulness’ and ‘perceived ease-of-use’ as the antecedents to ‘behavioral intentions’ to use a technology. Extensive attention in previous TAM research dealt with business software in a business context. More effort is required to investi gate research results involving different technologies and user populations. In response, this paper reports on research work that investigated the applicability of a modified version of TAM in explaining students’ acceptance of web based technologies for their courses. The target IT presented herein is a web-based Educational Information System for Enhanced Learning (EISEL). The results of the modified version of TAM are discussed. The technology and user group are new to the IT acceptance and adoption research. The TAM constructs were operationalized in the context of the EISEL. This research is a first attempt (using the first version of EISEL) to understand students’ beliefs and perceptions in relation to using a web based learning tool. Our results suggest that TAM was able to provide a relatively reasonable view of students’ acceptance of EISEL. However, compared with prior TAM studies, it appears that the TAM has weaker utility for explaining students’ intentions in the context of education and learni ng. ‘Perceived usefulness’ was found to have a significant positive influence on intentions to use, but perceived ease of use did not. Furthermore, the influence of ‘perceived ease of use’ on ‘perceived usefulness’, was found to be strong. Overall, these findings suggest that students will not necessarily intend to use EISEL because it is easy to use, but rather perceive that it is easy to use. Then it will help them perform better in the course. This posits a strong positive relationship between ‘perceived ease of use’, ‘per


title: Multitemporal repeat pass SAR interferometry of boreal forests

abstract: Multitemporal interferometric European Remote Sensing 1 and 2 satellite tandem pairs from a forest test site in Finland are examined in order to determine the stem volume retrieval accuracy. A form of multitemporal filtering is introduced to investigate what forest stands show a multitemporal consistency in coherence. It is found that a large stand size is a major factor to obtain accurate retrievals. The effect of heterogeneity of forest stands is also discussed. Based on the stands showing highest multitemporal consistency different models for scattering and coherence are compared. The interferometric water cloud model is chosen for stem volume retrieval. The variation of the model parameters with meteorological parameters is investigated and the results illustrate that the best imaging conditions are obtained for subzero temperatures and windy conditions. It is shown that for the 20 stands showing highest multitemporal consistency the stem volume can be retrieved with a relative error of 21%, deteriorating when the number of testing stands is increased, e.g., for 80 stands the error is 48%. For 37 large forest stands representing 48% of the investigated area the relative stem volume error is 26%. With experience from another site in Sweden we may conclude that the error level for a multitemporal interferometric synthetic aperture radar evaluation of stem volume for large forest stands (>2 ha) in a well managed and homogeneous boreal forest may be expected to be in the 15% to 25% range, deteriorating for small and heterogeneous stands and for images acquired under nonwinter conditions.


title: Low complexity LLR metrics for polar coded QAM

abstract: This paper presents a low-complexity approach for calculating soft decision metrics of polar coded quadrature amplitude modulation (QAM) system. A simple and general bit log likelihood ratio (LLR) expression is provided for Gray coded QAM signals. The characteristics of Gray code mapping such as symmetries and repeated formats of the bit assignment in a symbol among bit groups, are exploited effectively for the simplification of the LLR expression. To reduce the complexity of the max-log-MAP algorithm for LLR calculation, these LLRs can be simplified even further by replacing the mathematical min function of the conventional LLR expression with simple arithmetic functions. We introduce supplemental approximation to the max-log-MAP algorithm by averaging the LLR values in different regions to get a single approximate expression. Over AWGN and Rayleigh fading channels, simulation results and complexity analysis demonstrate that the proposed demapper approaches traditional approximations in terms of BER performance, but with much lower low complexity.


title: Transformer less Intelligent Power Substation design with 15kV SiC IGBT for grid interconnection

abstract: Basic power topology for a Solid State Transformer (SST) with new 15kV SiC IGBT devices is discussed. It is difficult to build high efficient, light weight, magnetically isolated solid state transformer for high voltage (13.8 kV) grid connectivity with existing Si 6.5kV rated IGBTs and diodes. Existing state of the art high voltage (6.5kV), high speed power devices (IGBT) cause considerable amount of loss (switching and conduction loss). With the advent of SiC devices these limitations are largely mitigated and this provides the motivation for new power topologies. The targeted efficiency of the proposed SST is 98%.Simulation results for a 1 MVA proposed SST topology is presented.


title: Machine learning quantum phases of matter beyond the fermion sign problem

abstract: State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion systems such that they correctly identify and locate quantum phase transitions in such systems. Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green’s function holds sufficient information to allow for the distinction of different fermionic phases via a CNN. We demonstrate that this QMC + machine learning approach works even for systems exhibiting a severe fermion sign problem where conventional approaches to extract information from the Green’s function, e.g. in the form of equal-time correlation functions, fail.


Untangling emoji popularity through semantic embeddings


title: Max-Margin Multiple-Instance Dictionary Learning

abstract: Dictionary learning has became an increasingly important task in machine learning, as it is fundamental to the representation problem. A number of emerging techniques specifically include a codebook learning step, in which a critical knowledge abstraction process is carried out. Existing approaches in dictionary (codebook) learning are either generative (unsupervised e.g. k-means) or discriminative (supervised e.g. extremely randomized forests). In this paper, we propose a multiple instance learning (MIL) strategy (along the line of weakly supervised learning) for dictionary learning. Each code is represented by a classifier, such as a linear SVM, which naturally performs metric fusion for multi-channel features. We design a formulation to simultaneously learn mixtures of codes by maximizing classification margins in MIL. State-of-the-art results are observed in image classification benchmarks based on the learned codebooks, which observe both compactness and effectiveness.


title: A randomized, placebo-controlled phase III trial of masitinib plus gemcitabine in the treatment of advanced pancreatic cancer

abstract: Background ::: Masitinib is a selective oral tyrosine–kinase inhibitor. The efficacy and safety of masitinib combined with gemcitabine was compared against single-agent gemcitabine in patients with advanced pancreatic ductal adenocarcinoma (PDAC).


title: Stemming and lemmatization in the clustering of finnish text documents

abstract: Stemming and lemmatization were compared in the clustering of Finnish text documents. Since Finnish is a highly inflectional and agglutinative language, we hypothesized that lemmatization, involving splitting of the compound words, would be more appropriate normalization approach than the straightforward stemming. The relevance of the documents were evaluated with a four-point relevance assessment scale, which was collapsed into binary one by considering all the relevant and only the highly relevant documents relevant, respectively. Experiments with four hierarchical clustering methods supported the hypothesis. The stringent relevance scale showed that lemmatization allowed the single and complete linkage methods to recover especially the highly relevant documents better than stemming. In comparison with stemming, lemmatization together with the average linkage and Ward's methods produced higher precision. We conclude that lemmatization is a better word normalization method than stemming, when Finnish text documents are clustered for information retrieval.


title: Efficient Nearest-Neighbor Search in the Probability Simplex

abstract: Document similarity tasks arise in many areas of information retrieval and natural language processing. A fundamental question when comparing documents is which representation to use. Topic models, which have served as versatile tools for exploratory data analysis and visualization, represent documents as probability distributions over latent topics. Systems comparing topic distributions thus use measures of probability divergence such as Kullback-Leibler, Jensen-Shannon, or Hellinger. This paper presents novel analysis and applications of the reduction of Hellinger divergence to Euclidean distance computations. This reduction allows us to exploit fast approximate nearest-neighbor (NN) techniques, such as locality-sensitive hashing (LSH) and approximate search in k-d trees, for search in the probability simplex. We demonstrate the effectiveness and efficiency of this approach on two tasks using latent Dirichlet allocation (LDA) document representations: discovering relationships between National Institutes of Health (NIH) grants and prior-art retrieval for patents. Evaluation on these tasks and on synthetic data shows that both Euclidean LSH and approximate k-d tree search perform well when a single nearest neighbor must be found. When a larger set of similar documents is to be retrieved, the k-d tree approach is more effective and efficient.


title: Soft robot for gait rehabilitation of spinalized rodents

abstract: Soft actuators made of highly elastic polymers allow novel robotic system designs, yet application-specific soft robotic systems are rarely reported. Taking notice of the characteristics of soft pneumatic actuators (SPAs) such as high customizability and low inherent stiffness, we report in this work the use of soft pneumatic actuators for a biomedical use - the development of a soft robot for rodents, aimed to provide a physical assistance during gait rehabilitation of a spinalized animal. The design requirements to perform this unconventional task are introduced. Customized soft actuators, soft joints and soft couplings for the robot are presented. Live animal experiment was performed to evaluate and show the potential of SPAs for their use in the current and future biomedical applications.


title: Compressive Coded Aperture Spectral Imaging: An Introduction

abstract: Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral data cube. Push broom spectral imaging sensors, for instance, capture a spectral cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire data cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.


title: vDesign: a CAVE-based virtual design environment using hand interactions

abstract: The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.


title: Realistic smile expression recognition using biologically inspired features

abstract: A robust smile recognition system could be widely used for many real-world applications. In this paper, we introduce biologically inspired model (BIM) into the building of realistic smile classification system for solving challenging realistic tasks. To improve the performance of BIM, we develop a modified BIM (MBIM), which utilizes a more efficient pooling operation and boosting feature selection. Experiments demonstrate the effectiveness of themodifications and adjustments of BIM. By testing on the challenging realistic database, GENKI, our method is proved to be superior to some other state-of-the-art smile classification algorithms.


title: Research Article Centroid Localization of Uncooperative Nodes in Wireless Networks Using a Relative Span Weighting Method

abstract: Increasingly ubiquitous wireless technologies require novel localization techniques to pinpoint the position of an uncooperative node, whether the target is a malicious device engaging in a security exploit or a low-battery handset in the middle of a critical emergency. Such scenarios necessitate that a radio signal source be localized by other network nodes efficiently, using minimal information. We propose two new algorithms for estimating the position of an uncooperative transmitter, based on the received signal strength (RSS) of a single target message at a set of receivers whose coordinates are known. As an extension to the concept of centroid localization, our mechanisms weigh each receiver's coordinates based on the message's relative RSS at that receiver, with respect to the span of RSS values over all receivers. The weights may decrease from the highest RSS receiver either linearly or exponentially. Our simulation results demonstrate that for all but the most sparsely populated wireless networks, our exponentially weighted mechanism localizes a target node within the regulations stipulated for emergency services location accuracy.


STRATEGY AS PRACTICE: RECURSIVENESS, ADAPTATION AND STRATEGIC PRACTICES-IN-USE


title: Increasing RSSI Localization Accuracy with Distance Reference Anchor in Wireless Sensor Networks

abstract: Localization is a prominent application and research area in Wireless Sensor Networks. Various research studies have been carried out on localization techniques and algorithms in order to improve localization accuracy. Received signal strength indicator is a parameter, which has been widely used in localization algorithms in many research studies. There are several environmental and other factors that affect the localization accuracy and reliability. This study introduces a new technique to increase the localization accuracy by employing a dynamic distance reference anchor method. In order to investigate the performance improvement obtained with the proposed technique, simulation models have been developed, and results have been analyzed. The simulation results show that considerable improvement in localization accuracy can be achieved with the proposed model.


title: Two wheel self-balanced mobile robot identification based on experimental data

abstract: Many researches have been accomplished about modeling of two wheel robots which stabilize themselves autonomously and various models have been proposed to explain the dynamic model of them. This paper considers two phases of system identification process to achieve dynamic equilibrium of the robot. In the first phase, it investigates a part of system as multi input multi output (MIMO) system. Considering the current of motors as inputs, and the angular velocity of wheels as outputs. In the second phase, this study investigates another part of the system as multi input single output (MISO) system which its inputs are the angular velocity of the wheels (the output of the first phase) and output is the vertical angle of the robot axis. This study compares ARX, ARMAX, BJ and OE as candid as linear models and Wiener and Hammerstein as nonlinear models. The results shows that the Weiner nonlinear model with saturated output has the best performance for the first phase and the Wiener nonlinear model with Wavelet network is suggested for the second phase of identification process.


title: Advances in uncooled LWIR thermal sensors

abstract: Night vision technology is present in most military systems used today. The sensors range from night vision goggles to high performance infrared ()targetacquisition and tracking systems. These sensors have provided a distinct battlefield advantage and will continue to be important in future confrontations and peace-keeping missions. With the importance placed upon night vision, lower cost and wider availability of night vision technology is an important consideration. Sensors can be employed in greater numbers as the cost is reduced and as the sensor package becomes lighter and consumes lower power. Sensor packaging, conformal to the system configuration, helps the sensor readily adapt to new system configurations. Infrared sensors that do not require cryogenic cooling meet many of these characteristics. Recently, the excellent imaging performance of uncooled IR focal plane arrays have captured the attention of many system users and initiated the emergence of new applications. This paper reviews these application areas, explores new possibilities, and assesses the technology underway to further expand the realm of uncooled IR imaging. Technology advances, both in the sensor technology and the interface with the imaging system, will expand uncooled JR technology into additional military and commercial systems.


title: Classification of dengue dataset using J48 algorithm and ant colony based AJ48 algorithm

abstract: This research work manages productive data mining methodology for predicting the dengue from medicinal records of patients. Dengue is an extremely regular disease nowadays in all populations and in all age gatherings. Dengue to coronary disease and builds the dangers of creating kidney ailment, nerve harm, vein harm and visual deficiency. So mining the dengue data in a productive way is a basic issue. The Dengue Dataset collected on Krishnagiri district Government Hospital is utilized as a part of this paper; which gathers the data of patients with and without having dengue. The modified J48 classifier is utilized to build the precision rate of the data mining system. The data mining tool WEKA has been utilized for creating the modified J48 classifiers. Exploratory outcomes demonstrated a noteworthy change over the current J48 algorithm [1].


title: FIBERNET 2.0: An automatic neural network based tool for clustering white matter fibers in the brain

abstract: The brain's white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimer's disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brain's neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.


title: dRMT: Disaggregated Programmable Switching

abstract: We present dRMT (disaggregated Reconfigurable Match-Action Table), a new architecture for programmable switches. dRMT overcomes two important restrictions of RMT, the predominant pipeline-based architecture for programmable switches: (1) table memory is local to an RMT pipeline stage, implying that memory not used by one stage cannot be reclaimed by another, and (2) RMT is hardwired to always sequentially execute matches followed by actions as packets traverse pipeline stages. We show that these restrictions make it difficult to execute programs efficiently on RMT. dRMT resolves both issues by disaggregating the memory and compute resources of a programmable switch. Specifically, dRMT moves table memories out of pipeline stages and into a centralized pool that is accessible through a crossbar. In addition, dRMT replaces RMT's pipeline stages with a cluster of processors that can execute match and action operations in any order. We show how to schedule a P4 program on dRMT at compile time to guarantee deterministic throughput and latency. We also present a hardware design for dRMT and analyze its feasibility and chip area. Our results show that dRMT can run programs at line rate with fewer processors compared to RMT, and avoids performance cliffs when there are not enough processors to run a program at line rate. dRMT's hardware design incurs a modest increase in chip area relative to RMT, mainly due to the crossbar.


title: Correlation between seismocardiogram and systolic blood pressure

abstract: Modern health care system requires certain critically ill patients to monitor their blood pressure continuously through non-invasive techniques. The pulse transit time (PTT) and other parameter such as RS2 have been used previously for continuous BP monitoring. In this study, a system is designed using trial axis accelerometer as a sensor to collect seismocardiogram (SCG) data. The SCG waveforms have been used to find a relationship between Systolic Blood Pressure (SBP) and different axis of Seismocardiogram on 10 subjects aged 24+ years. The results reflect that the SBP is correlated with the starting point of the SCG wave in the x-axis to the mid-point of the z-axis. Their mean correlation and equations have been found which can state the systolic blood pressure of the subjects. The relative errors between the calculated SBP and the SBP measured from clinical equipment are up to 123 mmHg in normal range and 456 mmHg in high range. In summary, SCG can be used to determine the SBP as it has a moderate correlation.


title: JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

abstract: Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home services often employ either simple local models on the mobile or complex remote models on the cloud. However, recent studies have shown that partitioning the DNN computations between the mobile and cloud can increase the latency and energy efficiencies. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward- and backward-propagations in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18 and 32 times reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.


title: Price and Profit Awareness in Recommender Systems

abstract: Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.


title: Effect of NPK on growth and nitrogen fixation of Sesbania rostrata as a green manure for lowland rice (Oryza sativa L.)

abstract: The stem-nodulating tropical legume Sesbania rostrata is a promising green manure species for low input rice-farming systems in lowland areas. However, its success as biofertilizer depends on its biomass production and N2 fixation. Nutrient imbalances and soils low in available nutrients can considerably affect biofertilizer production. Use of mineral N, P, and K fertilizers in growing S. rostrata as biofertilizer for lowland rice was therefore evaluated in pot experiments, and in the fields in Central Luzon, Philippines. Two soils low in Olsen P (3–7.3 mg kg−1) and exchangeable K (0.05–0.08 meq 100g-1) were used. Increasing amounts of N (0, 10, 20, 30, and 40 mg kg-1), P (0, 50, and 100 mg kg-1), and K (0, 100, 200, and 300 mg kg-1) were applied to S. rostrata grown in the greenhouse, whereas small amounts of N, P, and K fertilizers (30, 15, and 33 kg ha-1, respectively) were applied in the field.


title: “i” — A novel algorithm for optical character recognition (OCR)

abstract: Computer vision, artificial intelligence and pattern recognition have been important areas of research for a while in the history of electronics and image processing. Optical character recognition (OCR) is one of the main aspects of computer vision and has evolved greatly since its inception. OCR is a method in which readable characters are recognized from optical data obtained digitally. Many methodologies and algorithms have been developed for this purpose using different approaches. Here we present one such approach for OCR named “ i ”. Amongst all other OCR systems available, “ i ” aims at a high speed, simple, font independent and size independent OCR system based on a unique segment extraction technique. This algorithm can be used as a kernel for single alphabet detection within a complete OCR solution system without the need for any complex mathematical operations. The highlight of this methodology is that, it does not use any libraries or databases of image matrices to recognize alphabets, but it has a unique algorithm to recognize alphabets instead. This algorithm has been implemented in MATLAB 7.14.0.739 build R2012a on a test set of 500 images of text and an accuracy of 100% for three font families namely Arial, Times New Roman and cchas been obtained.


title: A simultaneous X/Ka feed system for reflectors with a F/D ratio of 0.8

abstract: A simultaneous X/Ka feed system has been designed to cater for reflector antennas with a F/D ratio of 0.8. This work is an extension of the successful design of the initial X/Ka feed system that was designed for reflectors with a F/D ratio of 0.65. Although simple in concept, this move from F/D=0.65 to F/D=0.8 is not an easy task from a design point of view.


title: Examining neural plasticity and cognitive benefit through the unique lens of musical training

abstract: Abstract Training programs aimed to alleviate or improve auditory-cognitive abilities have either experienced mixed success or remain to be fully validated. The limited benefits of such regimens are largely attributable to our weak understanding of (i) how (and which) interventions provide the most robust and long lasting improvements to cognitive and perceptual abilities and (ii) how the neural mechanisms which underlie such abilities are positively modified by certain activities and experience. Recent studies indicate that music training provides robust, long-lasting biological benefits to auditory function. Importantly, the behavioral advantages conferred by musical experience extend beyond simple enhancements to perceptual abilities and even impact non-auditory functions necessary for higher-order aspects of cognition (e.g., working memory, intelligence). Collectively, preliminary findings indicate that alternative forms of arts engagement (e.g., visual arts training) may not yield such widespread enhancements, suggesting that music expertise uniquely taps and refines a hierarchy of brain networks subserving a variety of auditory as well as domain-general cognitive mechanisms. We infer that transfer from specific music experience to broad cognitive benefit might be mediated by the degree to which a listener's musical training tunes lower- (e.g., perceptual) and higher-order executive functions, and the coordination between these processes. Ultimately, understanding the broad impact of music on the brain will not only provide a more holistic picture of auditory processing and plasticity, but may help inform and tailor remediation and training programs designed to improve perceptual and cognitive benefits in human listeners. This article is part of a Special Issue entitled .


title: Distributed proportional-fairness control in microgrids via blockchain smart contracts

abstract: Residential microgrids (MGs) may host a large number of Distributed Energy Resources (DERs). The strategy that maximizes the revenue for each individual DER is the one in which the DER operates at capacity, injecting all available power into the grid. However, when the DER penetration is high and the consumption low, this strategy may lead to power surplus that causes voltage increase over recommended limits. In order to create incentives for the DER to operate below capacity, we propose a proportional-fairness control strategy in which (i) a subset of DERs decrease their own power output, sacrificing the individual revenue, and (ii) the DERs in the subset are dynamically selected based on the record of their control history. The trustworthy implementation of the scheme is carried out through a custom-designed blockchain mechanism that maintains a distributed database trusted by all DERs. In particular, the blockchain is used to stipulate and store a smart contract that enforces proportional fairness. The simulation results verify the potential of the proposed framework.


title: Opportunities and limitations for image-based remote sensing in precision crop management

abstract: Abstract This review addresses the potential of image-based remote sensing to provide spatially and temporally distributed information for precision crop management (PCM). PCM is an agricultural management system designed to target crop and soil inputs according to within, field requirements to optimize profitability and protect the environment. Progress in. PCM has been hampered by a lack of timely, distributed information on crop and soil conditions. Based on a review of the information requirements of PCM, eight areas were identified in which image-based remote sensing technology could provide information that is currently lacking or inadequate. Recommendations were made for applications with potential for near-term implementation with available remote sensing technology and instrumentation. We found that both aircraft- and satellite-based re-trote sensing could provide valuable information for PCM applications. Images from aircraft-based sensors have a unique role for monitoring seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have limited, but important, applications; and upcoming commercial Earth observation satellites may provide the resolution, timeliness, and high quality required for many PCM operations. The current limitations for image-based remote sensing applications are mainly due to sensor attributes, such as restricted spectral range, coarse spatial resolution, slow turnaround time, and inadequate repeat coverage. According to experts in PCM, the potential market for remote sensing products in PCM is good. Future work should be focused on assimilating remotely sensed infonna- tion into existing decision support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot projects.


title: Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex

abstract: Abstract Background: Integrating information from the different senses markedly enhances the detection and identification of external stimuli. Compared with unimodal inputs, semantically and/or spatially congruent multisensory cues speed discrimination and improve reaction times. Discordant inputs have the opposite effect, reducing performance and slowing responses. These behavioural features of crossmodal processing appear to have parallels in the response properties of multisensory cells in the superior colliculi and cerebral cortex of non-human mammals. Although spatially concordant multisensory inputs can produce a dramatic, often multiplicative, increase in cellular activity, spatially disparate cues tend to induce a profound response depression. Results: Using functional magnetic resonance imaging (fMRI), we investigated whether similar indices of crossmodal integration are detectable in human cerebral cortex, and for the synthesis of complex inputs relating to stimulus identity. Ten human subjects were exposed to varying epochs of semantically congruent and incongruent audio-visual speech and to each modality in isolation. Brain activations to matched and mismatched audio-visual inputs were contrasted with the combined response to both unimodal conditions. This strategy identified an area of heteromodal cortex in the left superior temporal sulcus that exhibited significant supra-additive response enhancement to matched audio-visual inputs and a corresponding sub-additive response to mismatched inputs. Conclusions: The data provide fMRI evidence of crossmodal binding by convergence in the human heteromodal cortex. They further suggest that response enhancement and depression may be a general property of multisensory integration operating at different levels of the neuroaxis and irrespective of the purpose for which sensory inputs are combined.


title: Ouroboros: early identification of at-risk students without models based on legacy data

abstract: This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models. There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk students using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the challenge of imbalanced data, which appears both in training and testing data. The results show the comparison with the traditional approach of learning the models from the legacy course data, validating the proposed concept.


title: High-level motion planning for CPG-driven modular robots

abstract: Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments. A novel method for motion planning of modular robots is presented.The robots are equipped with a vocabulary of motion primitives.The primitives are realized using Central Pattern Generators.The motion planner combines the primitives to achieve a goal.The system is experimentally verified in simulated and real environments.


title: The Damage Equivalence of Electrons, Protons, and Gamma Rays in MOS Devices

abstract: In most cases of device testing or characterization in the laboratory, Cobalt-60 sources are commonly used, since they are an inexpensive and convenient source of ionizing radiation. However, some scientists and engineers had suspected for some time that the damage effects from gamma rays in MOS devices are not equivalent to those produced by electrons and protons. The issue of the validity of device test data obtained with Cobalt-60 sources arises when the devices are intended for use on satellites, because the effective space radiation environment consists predominantly of charged particles. This paper reports on an experiment which was designed to establish the relation among the damage effects obtained with Cobalt-60, electron, and proton radiation in order to derive relative damage factors. These factors would then greatly increase the validity of assessments of the vulnerability of MOS LSI devices in space.


title: Minimum Description Length Shape and Appearance Models

abstract: The Minimum Description Length (MDL) approach to shape modelling is reviewed. It solves the point correspondence problem of selecting points on shapes defined as curves so that the points correspond across a data set. An efficient numerical implementation is presented and made available as open source Matlab code. The problems with the early MDL approaches are discussed. Finally the MDL approach is extended to an MDL Appearance Model, which is proposed as a means to perform unsupervised image segmentation.


title: A Survey of Model Compression and Acceleration for Deep Neural Networks

abstract: Deep convolutional neural networks (CNNs) have recently achieved great success in many visual recognition tasks. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. During the past few years, tremendous progress has been made in this area. In this paper, we survey the recent advanced techniques for compacting and accelerating CNNs model developed. These techniques are roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. Methods of parameter pruning and sharing will be described at the beginning, after that the other techniques will be introduced. For each scheme, we provide insightful analysis regarding the performance, related applications, advantages, and drawbacks etc. Then we will go through a few very recent additional successful methods, for example, dynamic capacity networks and stochastic depths networks. After that, we survey the evaluation matrix, the main datasets used for evaluating the model performance and recent benchmarking efforts. Finally, we conclude this paper, discuss remaining challenges and possible directions on this topic.


title: FastCap: a multipole accelerated 3-D capacitance extraction program

abstract: A fast algorithm for computing the capacitance of a complicated three-dimensional geometry of ideal conductors in a uniform dielectric is described and its performance in the capacitance extractor FastCap is examined. The algorithm is an acceleration of the boundary-element technique for solving the integral equation associated with the multiconductor capacitance extraction problem. The authors present a generalized conjugate residual iterative algorithm with a multipole approximation to compute the iterates. This combination reduces the complexity so that accurate multiconductor capacitance calculations grow nearly as nm, where m is the number of conductors. Performance comparisons on integrated circuit bus crossing problems show that for problems with as few as 12 conductors the multipole accelerated boundary element method can be nearly 500 times faster than Gaussian-elimination-based algorithms, and five to ten times faster than the iterative method alone, depending on required accuracy. >


title: Spinocerebellar Ataxia: A Rational Approach to Aetiological Diagnosis

abstract: The objective of this study was to determine the main causal diagnosis for spinocerebellar ataxia (SCA) in a geographically defined population of ataxia patients and to suggest a rational basis for choosing appropriate clinical and paraclinical assessments. Given the many aetiologies responsible for SCA, the diagnosis requires the performance of a wide range of paraclinical analyses. At present, there is no consensus on the diagnostic value of these examinations. Furthermore, most of the currently available data gathered by reference centres suffer from selection bias. We performed a prospective study of consecutive cerebellar ataxia patients referred by their family doctors to a university hospital in northern France. Multiple system atrophy and obvious secondary causes (e.g. alcoholism) were excluded by our screening process. The patient’s family members were also assessed. Of the 204 patients examined, 47% presented autosomal dominant ataxia and 33% presented sporadic ataxia. Autosomal recessive ataxia was rare (8%) and age at onset was significantly earlier for this condition than for other forms. An aetiological diagnosis was established in 44% of patients, a plausible hypothesis could be formed in 13% of cases, and no diagnosis was made in the remaining 44%. Established diagnoses included SCA1, SCA2, SCA3 and SCA6 mutations, Friedreich’s ataxia, and one rare case of ataxia associated with anti-glutamic acid decarboxylase antibodies. Two families presented ataxia associated with autosomal, dominant, optic atrophy with an OPA1 mutation. Mitochondrial diseases were suspected in about 10% of patients. In SCA, reliable determination of the transmission mode always requires the assessment of family members. Mitochondrial disease may be an emerging cause of ataxia. Metabolite assays appeared to be of little value when systematically performed and so should be prescribed only by metabolic disorder specialists in selected cases of sporadic and recessive ataxia. Ophthalmological examination was the most helpful physiological assessment.


title: Generalizing Latent Semantic Analysis

abstract: An apparatus for the heating-up and degassing of make-up water by means of steam in a power station plant. The apparatus includes a steam condenser having a casing wall and a second casing wall mounted on the steam condenser. The casing walls are spaced apart and form a mixing chamber and a separation column, with the mixing chamber being located above the separation column. The mixing chamber includes a means for spraying oxygen-rich make-up water. A receiver is positioned to collect water from the separation column and includes a water outlet opening in fluid communication with an interior of the steam condenser. The steam condenser casing wall has a steam-inlet opening located between the receiver and the separation column through which steam flows into and through the separation column and into the mixing chamber due to a lower pressure prevailing in the first stage mixing chamber relative to a pressure in an interior of the condenser. In operation, when the oxygen-rich make-up water is sprayed into the mixing chamber of the apparatus and is brought into contact with the steam introduced through the steam-inlet opening in the steam condenser casing wall, the oxygen-rich make-up water is essentially degassed of most gases by a drop in pressure due to the spraying and an increase in temperature by contact with the steam. Subsequently, the oxygen-rich make-up water enters the separation column located below the mixing chamber where the oxygen-rich make-up water is further degassed and heated up before it is collected in the receiver and fed to the steam condenser.


title: PSpice simulation of ultrasonic systems

abstract: The usage of electrical analogies for the simulation of wave generation and propagation in ultrasound transducers is well established. In this paper a PSpice approach that includes the temperature and frequency dependency of the transducer performance is proposed. The analogy between acoustic wave propagation and wave propagation in an electric transmission line is given. Further ways to deduce temperature and frequency dependencies are discussed. The simulation approach is applied to a pulse-echo setup for the determination of speed of sound and attenuation in liquids and solids. Experiments and simulations are made for three temperatures and in the frequency range 1-12 MHz using water, glycerine, and polymers (PMMA and PEEK) as test samples. Comparison shows a good agreement between simulation and experiments. Results for glycerine indicates that the available attenuation models for high viscosity liquids is inappropriate.


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title: Interaction between basal ganglia and limbic circuits in learning and memory processes

abstract: Hippocampus and striatum play distinctive roles in memory processes since declarative and non-declarative memory systems may act independently. However, hippocampus and striatum can also be engaged to function in parallel as part of a dynamic system to integrate previous experience and adjust behavioral responses. In these structures the formation, storage, and retrieval of memory require a synaptic mechanism that is able to integrate multiple signals and to translate them into persistent molecular traces at both the corticostriatal and hippocampal/limbic synapses. The best cellular candidate for this complex synthesis is represented by long-term potentiation (LTP). A common feature of LTP expressed in these two memory systems is the critical requirement of convergence and coincidence of glutamatergic and dopaminergic inputs to the dendritic spines of the neurons expressing this form of synaptic plasticity. In experimental models of Parkinson's disease abnormal accumulation of α-synuclein affects these two memory systems by altering two major synaptic mechanisms underlying cognitive functions in cholinergic striatal neurons, likely implicated in basal ganglia dependent operative memory, and in the CA1 hippocampal region, playing a central function in episodic/declarative memory processes.


Article abstract dentification of an eye movement disorder


title: An enhanced context-sensitive proximity model for probabilistic information retrieval

abstract: We propose to enhance proximity-based probabilistic retrieval models with more contextual information. A term pair with higher contextual relevance of term proximity is assigned a higher weight. Several measures are proposed to estimate the contextual relevance of term proximity. We assume the top ranked documents from a basic weighting model are more relevant to the query, and calculate the contextual relevance of term proximity using the top ranked documents. We propose a context-sensitive proximity model, and the experimental results on standard TREC data sets show the effectiveness of our proposed model.


title: Dynamic Auto-Encoders for Semantic Indexing

abstract: We present a new algorithm for topic modeling, text classification and retrieval, tailored to sequences of time-stamped documents. Based on the auto-encoder architecture, our nonlinear multi-layer model is trained stage-wise to produce increasingly more compact representations of bags-of-words at the document or paragraph level, thus performing a semantic analysis. It also incorporates simple temporal dynamics on the latent representations, to take advantage of the inherent structure of sequences of documents, and can simultaneously perform a supervised classification or regression on document labels. Learning this model is done by maximizing the joint likelihood of the model, and we use an approximate gradient-based MAP inference. We demonstrate that by minimizing a weighted cross-entropy loss between histograms of word occurrences and their reconstruction, we directly minimize the topic-model perplexity, and show that our topic model obtains lower perplexity than Latent Dirichlet Allocation on the NIPS and State of the Union datasets. We illustrate how the dynamical constraints help the learning while enabling to visualize the topic trajectory. Finally, we demonstrate state-of-the-art information retrieval and classification results on the Reuters collection, as well as an application to volatility forecasting from financial news.


title: Stochastic Modeling of Multienergy Carriers Dependencies in Smart Local Networks With Distributed Energy Resources

abstract: In a multienergy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage, and are managed by the system operator through the control strategies applied to the equipment. External dependencies (EDs) are due to the choice of the energy supply according to customer preferences when alternative solutions are available. This paper introduces a new model of EDs within a multigeneration representation based on energy hubs. EDs are addressed through a stochastic model in order to take into account the possible uncertainty in the customers’ decisions. This model is then used to introduce carrier-based demand response (DR) in which the user participates in DR programs aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach.


title: Microcystins and two new micropeptin cyanopeptides produced by unprecedented Microcystis aeruginosa blooms in North Carolina's Cape Fear River.

abstract: The Cape Fear River is the largest river system in North Carolina. It is heavily used as a source of drinking water for humans and livestock as well as a source of irrigation water for crops, and production water for industry. It also serves as a major fishery for both commercial and recreational use. In recent years, possibly related to increased eutrophication of the river, massive blooms of cyanobacteria, identified as Microcystis aeruginosa have been observed. Bloom samples collected in 2009 and 2012 were chemically analyzed to determine if they contained cyanobacterial toxins known as microcystins. Both blooms were found to produce microcystins in high yields. Microcystins are potent hepatotoxins that can be bio-accumulated in the food chain. Recent biological studies have also shown a host of other potentially harmful effects of low level microcystin exposure. Detailed chemical analysis of these blooms led us to discover that these blooms produce an additional family of cyanobacterial peptides know as the micropeptins, including two new members named micropeptins 1106 and 1120. The biological activities of these new molecules have not yet been determined, although protease activity has been well documented for this peptide group. These data indicate a need for thorough monitoring of toxin levels especially during bloom events in addition to additional biological testing of other cyanopeptides present in blooms.


title: Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning

abstract: Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.