title: Energy-efficient execution of data-parallel applications on heterogeneous mobile platforms

abstract: State-of-the-art mobile system-on-chips (SoC) include heterogeneity in various forms for accelerated and energy-efficient execution of diverse range of applications. The modern SoCs now include programmable cores such as CPU and GPU with very different functionality. The SoCs also integrate performance heterogeneous cores with different power-performance characteristics but the same instruction-set architecture such as ARM big.LITTLE. In this paper, we first explore and establish the combined benefits of functional heterogeneity and performance heterogeneity in improving power-performance behavior of data parallel applications. Next, given an application specified in OpenCL, we present a static partitioning strategy to execute the application kernel across CPU and GPU cores along with voltage-frequency setting for individual cores so as to obtain the best power-performance tradeoff. We achieve over 19% runtime improvement by exploiting the functional and performance heterogeneities concurrently. In addition, energy saving of 36% is achieved by using appropriate voltage-frequency setting without significantly degrading the runtime improvement from concurrent execution.


HYPE – TOWARDS A BLOCKCHAIN MARKET ENGINEERING APPROACH


Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer


title: A case study of project management practices in virtual settings: lessons from working in and managing virtual teams

abstract: In this paper we report a case study examining the communication processes engaged in by virtual project teams and their management. Twenty-two teams, using widely available groupware to communicate, work together, share documents, discuss ideas, and solve problems, designed and implemented a database. These teams were managed by a geographically-distributed management team. The case study is analyzed qualitatively and quantitatively, from two perspectives--working in, and managing, virtual teams--using a framework that integrates virtual team dynamics and project management practices. Through the critical examination of communication content from the longitudinal experiences of multiple virtual project teams and their virtual management team, we identify successful project practices and uncover underlying interaction processes. Specifically, we found that high performing project teams differed from low performing teams in terms of process management, relational development, and proactive technology use behaviors. The five-person management team paralleled the project teams in evolving its own process management and relational development over time.


title: Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs

abstract: Subgraph Isomorphism is a fundamental problem in graph data processing. Most existing subgraph isomorphism algorithms are based on a backtracking framework which computes the solutions by incrementally matching all query vertices to candidate data vertices. However, we observe that extensive duplicate computation exists in these algorithms, and such duplicate computation can be avoided by exploiting relationships between data vertices. Motivated by this, we propose a novel approach, BoostIso, to reduce duplicate computation. Our extensive experiments with real datasets show that, after integrating our approach, most existing subgraph isomorphism algorithms can be speeded up significantly, especially for some graphs with intensive vertex relationships, where the improvement can be up to several orders of magnitude.


title: Effect of Frame Aggregation on the Throughput Performance of IEEE 802.11n

abstract: The IEEE 802.11n standard aims at providing a data transmission rate of up to 600 Mbps. The frame aggregation techniques of IEEE 802.11n MAC layer play an important role in the enhancement of channel utilization. In this paper, we investigate the effect of IEEE 802.11n frame aggregation for two aggregation mechanisms: MAC protocol data unit aggregation (A-MPDU) and MAC service data unit aggregation (A-MSDU). We propose an analytical model based on an enhanced discrete time Markov chain (DTMC) model in order to describe the post- backoff behavior due to frame aggregation, and then evaluate the throughput performance. Analytical results based on the mathematical model are verified through simulation. The throughput performance of the A-MSDU mechanism outperforms the A-MPDU mechanism as the frame aggregation size becomes larger under an error-free environment. In addition, the throughput performance is significantly improved as the frame aggregation size increases.


title: Language-independent data set annotation for machine learning-based sentiment analysis

abstract: Social media platforms provide large amounts of user-generated text which can be utilized for text-based Sentiment Analysis in order to obtain insights about opinions on many aspects of life. Current approaches that are based on supervised learning require manually annotated data sets that are time-consuming to create and are specific to a single language. In this work, we present an approach to generate ground truth sentiment values for a data set from Twitter. We use a sentiment emoji lexicon and distribute known polarities of hashtags over neighbors in a graph we build on them. This approach is language-independent. Native speakers of five different languages evaluate the accuracy of the sentiment values assigned by our method on a corpus of Tweets. Our experiments show that the quality of our automatically assigned sentiment values is sufficiently high to be used for training of machine learning-based sentiment analysis.


title: Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus

abstract: Robotic systems for disease detection in greenhouses are expected to improve disease control, increase yield, and reduce pesticide application. We present a robotic detection system for combined detection of two major threats of greenhouse bell peppers: Powdery mildew (PM) and Tomato spotted wilt virus (TSWV). The system is based on a manipulator, which facilitates reaching multiple detection poses. Several detection algorithms are developed based on principal component analysis (PCA) and the coefficient of variation (CV). Tests ascertain the system can successfully detect the plant and reach the detection pose required for PM (along the side of the plant), yet it has difficulties in reaching the TSWV detection pose (above the plant). Increasing manipulator work–volume is expected to solve this issue. For TSWV, PCA-based classification with leaf vein removal, achieved the highest classification accuracy (90%) while the accuracy of the CV methods was also high (85% and 87%). For PM, PCA-based pixel-level classification was high (95.2%) while leaf condition classification accuracy was low (64.3%) since it was determined based on the upper side of the leaf while disease symptoms start on its lower side. Exposure of the lower side of the leaf during detection is expected to improve PM condition detection.


title: Minimum Disclosure Proofs of Knowledge

abstract: Abstract Protocols are given for allowing a “prover” to convince a “verifier” that the prover knows some verifiable secret information, without allowing the verifier to learn anything about the secret. The secret can be probabilistically or deterministically verifiable, and only one of the prover or the verifier need have constrained resources. This paper unifies and extends models and techniques previously put forward by the authors, and compares some independent related work.


title: Split-Gate 1.2-kV 4H-SiC MOSFET: Analysis and Experimental Validation

abstract: The 1.2-kV-rated 4H-SiC Split Gate MOSFET (SG-MOSFET) structure is demonstrated to have a superior high-frequency figures-of-merit (HF-FOMs) by numerical simulations, with experimental validation for the first time. Excellent electrical characteristics (specific on-resistance, threshold voltage, breakdown voltage, reverse transfer capacitance, and gate-to- drain charge) were measured from devices fabricated on a 6-in SiC wafer. Compared with the conventional MOSFET, the SG-MOSFET has $2.4\times $ smaller HF-FOM [ $\text{R}_{\text {on}}\times \text{Q}_{\text {gd}}$ ] due to the reduced gate-to-drain charge.


A 14-bit Hybrid Incremental Sigma-Delta/Cyclic ADC for X-ray Linear Array Sensor


title: Design of a Myoelectric Transhumeral Prosthesis

abstract: This paper describes a transhumeral prosthesis prototype intended for the purpose of experimentally investigating design features and control strategies for the control of transhumeral prostheses. This paper specifically focuses on the design and performance characterization of a powered wrist rotator and powered elbow joint, in addition to the embedded system that controls them. In addition to outlining design objectives associated with the wrist and elbow joints, this paper describes the design of both joints, and the embedded system that provides control of them and the arm system. Experimental data are presented that characterizes the performance characteristics of both joints, including data associated with electrical power consumption and audible noise. The arm prosthesis described here is intended to be used with a multigrasp hand prosthesis, previously published by the authors.


title: How programming environment shapes perception, learning and goals: logo vs. scratch

abstract: This study compares the attitudinal and learning outcomes of sixth grade students programming in either Logo or Scratch. Given proposed affordances of the visual programming language, Scratch, I hypothesized that those students learning Scratch would demonstrate greater competence in interpreting loops and conditional statements and would have more positive attitudes towards programming. However, differences in performance between the two groups appeared only in the greater ability of the students that learned Scratch to interpret conditional statements. Contrary to our hypothesis, we found that students that learned Logo had on average higher confidence in their ability to program and students were no more likely to plan to continue to program after the course or view the learning of topics as difficult if they learned Logo or Scratch.


title: Implement network security control solutions in BYOD environment

abstract: Bring Your Own Device (BYOD) security is the way to protect organization's network against Variety of threats which come through mobile devices and access channels. This research paper explains the implementation of BYOD security solution in higher education institution in Oman. This security solution will help to protect the network data from unauthorized access, as well as, controlling unmanaged devices which are smartphones and mobile devices. This research will follow these steps starting with literature review, data collection, analysis, design the network structure with suggested solution and implementation for BYOD security solutions. As well as, monitoring the network performance with the implanted solutions to keep track if traffic flow with high availability and security. This research paper will help to facilitate the work to the network users through allowing BYOD as well as increase the network availability, ability and security through 802.1x, CA and RADius.


title: Meme extraction and tracing in crisis events

abstract: The proliferation of social media has increased the competition among different memes, which can be free texts, trending catchphrases, or micro media. As human attention is limited, these memes compete with each other, and go in and out of popularity at a rapid pace, sometimes even faster than we can recognize. Popular memes often shape the mindsets of online communities, and also shed light on their future tendencies. Considering the huge volume of memes generated and their continuous mutations, extracting and tracing online memes automatically is rather challenging. In this paper, we propose an automatic meme extraction algorithm. The proposed algorithm extracts massive memes based on phrases independency, and clusters phrase variants of a single meme efficiently. Evaluation on measles outbreak in the USA in 2015 indicates that the proposed algorithm could extract typical memes reflecting the fierce campaign between the pro-vaccination community and the anti-vaccination community. In both communities, memes are power-law distributed, and popular ones have many variants that appear more frequently. By tracing the evolution of online memes, we uncover that popular memes converge and generate peaks at times. Though the pro-vaccination community and the anti-vaccination community may focus on similar memes, they comprehend memes from totally different perspectives and deliver opposing opinions of measles vaccination.


title: The use of instability to train the core musculature.

abstract: Training of the trunk or core muscles for enhanced health, rehabilitation, and athletic performance has received renewed emphasis. Instability resistance exercises have become a popular means of training the core and improving balance. Whether instability resistance training is as, more, or less effective than traditional ground-based resistance training is not fully resolved. The purpose of this review is to address the effectiveness of instability resistance training for athletic, nonathletic, and rehabilitation conditioning. The anatomical core is defined as the axial skeleton and all soft tissues with a proximal attachment on the axial skeleton. Spinal stability is an interaction of passive and active muscle and neural subsystems. Training programs must prepare athletes for a wide variety of postures and external forces, and should include exercises with a destabilizing component. While unstable devices have been shown to be effective in decreasing the incidence of low back pain and increasing the sensory efficiency of soft tissues, they are not recommended as the primary exercises for hypertrophy, absolute strength, or power, especially in trained athletes. For athletes, ground-based free-weight exercises with moderate levels of instability should form the foundation of exercises to train the core musculature. Instability resistance exercises can play an important role in periodization and rehabilitation, and as alternative exercises for the recreationally active individual with less interest or access to ground-based free-weight exercises. Based on the relatively high proportion of type I fibers, the core musculature might respond well to multiple sets with high repetitions (e.g., >15 per set); however, a particular sport may necessitate fewer repetitions.


title: Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease

abstract: In this paper, we present an assessment of the practical value of existing traditional and nonstandard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, pitch period entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected ten highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that nonstandard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected nonstandard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well suited to telemonitoring applications.


title: Neural Network for Graphs: A Contextual Constructive Approach

abstract: This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.


title: Sensorless control of PM motor drives — A technology status review

abstract: The control of Brushless Permanent Magnet Motors require rotor position information that can be measured by means of a position sensor. The motion sensorless control aims at eliminating the motor position sensor and its corresponding electronic conditioning circuits, cabling and connectors. Besides a clear cost reduction, the motion sensorless drives have better reliability when compared with their position sensor-based counterparts. The research conducted in the last two decades has provided many different solutions that are used in many applications. Nevertheless, the literature reports just a few survey papers dealing with this important topic. This paper intends providing a comprehensive review of the sensorless control solutions, including the machine influence on the sensorless drive performance.


title: Estimating vigilance level by using EEG and EMG signals

abstract: We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.


title: Intent-aware search result diversification

abstract: Search result diversification has gained momentum as a way to tackle ambiguous queries. An effective approach to this problem is to explicitly model the possible aspects underlying a query, in order to maximise the estimated relevance of the retrieved documents with respect to the different aspects. However, such aspects themselves may represent information needs with rather distinct intents (e.g., informational or navigational). Hence, a diverse ranking could benefit from applying intent-aware retrieval models when estimating the relevance of documents to different aspects. In this paper, we propose to diversify the results retrieved for a given query, by learning the appropriateness of different retrieval models for each of the aspects underlying this query. Thorough experiments within the evaluation framework provided by the diversity task of the TREC 2009 and 2010 Web tracks show that the proposed approach can significantly improve state-of-the-art diversification approaches.


title: Object-Part Attention Model for Fine-grained Image Classification

abstract: Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to large variance in the same subcategory and small variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have two limitations : 1) relying on object or part annotations which are heavily labor consuming; and 2) ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the object-part attention model (OPAM) for weakly supervised fine-grained image classification and the main novelties are: 1) object-part attention model integrates two level attentions: object-level attention localizes objects of images, and part-level attention selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotion; and 2) Object-part spatial constraint model combines two spatial constraints: object spatial constraint ensures selected parts highly representative and part spatial constraint eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Compared with more than ten state-of-the-art methods on four widely-used datasets, our OPAM approach achieves the best performance.


title: Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

abstract: This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables, such as bad lighting and wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables, such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem, we propose a method called semi-supervised sparse representation-based classification. This is based on recent work on sparsity, where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions and different glasses). The main idea is that: 1) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework and 2) prototype face images are estimated as a gallery dictionary via a Gaussian mixture model, with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.


title: A Statistical NLG Framework for Aggregated Planning and Realization

abstract: We present a hybrid natural language generation (NLG) system that consolidates macro and micro planning and surface realization tasks into one statistical learning process. Our novel approach is based on deriving a template bank automatically from a corpus of texts from a target domain. First, we identify domain specific entity tags and Discourse Representation Structures on a per sentence basis. Each sentence is then organized into semantically similar groups (representing a domain specific concept) by k-means clustering. After this semi-automatic processing (human review of cluster assignments), a number of corpus‐level statistics are compiled and used as features by a ranking SVM to develop model weights from a training corpus. At generation time, a set of input data, the collection of semantically organized templates, and the model weights are used to select optimal templates. Our system is evaluated with automatic, non‐expert crowdsourced and expert evaluation metrics. We also introduce a novel automatic metric ‐ syntactic variability ‐ that represents linguistic variation as a measure of unique template sequences across a collection of automatically generated documents. The metrics for generated weather and biography texts fall within acceptable ranges. In sum, we argue that our statistical approach to NLG reduces the need for complicated knowledge-based architectures and readily adapts to different domains with reduced development time. *Ravi Kondadadi is now affiliated with Nuance Commu


title: Learning to Explain Entity Relationships in Knowledge Graphs

abstract: We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method significantly improves over state-of-the-art baseline models.


title: Real-time and robust object tracking in video via low-rank coherency analysis in feature space

abstract: Object tracking in video is vital for security surveillance, pattern and motion recognition, traffic control, augmented reality, human-computer interaction, etc. Despite the rapid growth of various techniques in recent years, certain technical challenges still exist in terms of efficiency, accuracy, and robustness. To ameliorate, this paper suggests a novel video object tracking approach by first collecting both local and global information from consecutive video observations (i.e., frames) and then exploring the low-rank coherency in the accompanying feature space of targeting objects, which enables real-time and robust object tracking in video while combating certain technical difficulties due to occlusion, deformation, transient illumination, rapid movement, and scale change. Our central idea is to integrate local space-distinctive candidate features and global time-continuous target coherency into a smart low-rank analysis model. For local candidate representation, we propose a simple yet efficient patch-level feature descriptor based on compressive sensing, which is directly derived from the frame color distribution available from video frames. Building upon this powerful local representation, we further organize all the candidates in the frame cache and the yet-to-be-processed new frame to form a space-time feature set, we then employ the low-rank decomposition to enable global coherency voting. Since the low-rank coherency implies the intrinsic co-occurring parts of different target observations, robust tracking can be achieved by employing this principle as the matching criterion even for objects with drastically varying appearance. Furthermore, we progressively incorporate the prior-frames? tracking results into the low-rank approximation in the current frame, which can greatly reduce the most time-consuming computation and guarantee real-time performance. We conduct extensive experiments on several well-known yet challenging benchmarks, and make comprehensive and quantitative evaluations with state-of-the-art methods. All the results demonstrate the superiority of our method in terms of accuracy, efficiency, robustness, and versatility. HighlightsWe propose a versatile, real-time, and robust video object tracking method.We define an efficient discriminative appearance model based on compressive sensing in a low dimensional feature space.We propose a novel low-rank decomposition based coherency analysis model for tracking and updating.We formulate a series of sparsity-measuring based criteria to handle various challenges of object tracking.


Clique relaxation models in social network analysis


title: Extending the Technology Acceptance Model in Healthcare: Identifying the Role of Trust and Shared Information

abstract: We propose extensions to the TAM for the adoption of integrated electronic health records that are shared by multiple health care providers. In particular, we propose a conceptual model in which we incorporate two new factors, trust and access to shared information, into the TAM. Preliminary results confirm the statistically significant and positive impact of perceived usefulness on behavioral intent to use integrated electronic health records and a significant and positive impact of perceived ease of use on perceived usefulness. We also find a statistically significant effect of shared information on behavioral intent to use integrated health records. We expect that further analyses of additional data we are collecting will offer improved insights into the effects of these factors on intention to use integrated electronic health records.


title: A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects

abstract: From half a million hectares at the turn of the century, Philippine mangroves have declined to only 120,000 ha while fish/shrimp culture ponds have increased to 232,000 ha. Mangrove replanting programs have thus been popular, from community initiatives (1930s–1950s) to government-sponsored projects (1970s) to large-scale international development assistance programs (1980s to present). Planting costs escalated from less than US$100 to over $500/ha, with half of the latter amount allocated to administration, supervision and project management. Despite heavy funds for massive rehabilitation of mangrove forests over the last two decades, the long-term survival rates of mangroves are generally low at 10–20%. Poor survival can be mainly traced to two factors: inappropriate species and site selection. The favored but unsuitable Rhizophora are planted in sandy substrates of exposed coastlines instead of the natural colonizers Avicennia and Sonneratia. More significantly, planting sites are generally in the lower intertidal to subtidal zones where mangroves do not thrive rather than the optimal middle to upper intertidal levels, for a simple reason. Such ideal sites have long been converted to brackishwater fishponds whereas the former are open access areas with no ownership problems. The issue of pond ownership may be complex and difficult, but such should not outweigh ecological requirements: mangroves should be planted where fishponds are, not on seagrass beds and tidal flats where they never existed. This paper reviews eight mangrove initiatives in the Philippines and evaluates the biophysical and institutional factors behind success or failure. The authors recommend specific protocols (among them pushing for a 4:1 mangrove to pond ratio recommended for a healthy ecosystem) and wider policy directions to make mangrove rehabilitation in the country more effective.


title: The VoiceXML dialog system for the e-commerce ordering service

abstract: The Web-based application by VoiceXML service on the Internet is gradually accepted for human-machine interaction because it provides the speech-enabled functionality and makes the telephone access a reality. However, it is not cost efficient to build voice only stand-alone Web implementation and is more reasonable that voice interfaces should be retrofitted to be compatible or collaborated with the existing HTML or XML based Web applications. Therefore, this paper considers that the functionality of the Web site should enable multiple access modalities so that human beings can perceive and interact with either visual or speech response simultaneously. Under this principle, our research develops an integrated Web based Mandarin dialog system which adopts ASR, TTS, VoiceXML browser, and VoIP technologies to create user friendly interfaces of GUI and VUI. The users can use either traditional telephone, cellular phone connection, or even VoIP by personal computer to interact with the VoiceXML server. In the mean time, the users browse the Web and access the same content. The prototype system shows excellent performance and can be easily implemented into e-commerce ordering service for wide accessibility.


title: Survey on fog computing : architecture , key technologies , applications and open issues

abstract: The emergence of Internet of Things (IoT) has enabled the interconnection and intercommunication among massive ubiquitous things, which caused an unprecedented generation of huge and heterogeneous amount of data, known as data explosions. On the other hand, although that cloud computing has served as an efficient way to process and store these data, however, challenges, such as the increasing demands of real time or latency-sensitive applications and the limitation of network bandwidth, still cannot be solved by using only cloud computing. Therefore, a new computing paradigm, known as fog computing, has been proposed as a complement to the cloud solution. Fog computing extends the cloud services to the edge of network, and makes computation, communication and storage closer to edge devices and end-users, which aims to enhance low-latency, mobility, network bandwidth, security and privacy. In this paper, we will overview and summarize fog computing model architecture, key technologies, applications, challenges and open issues. Firstly, we will present the hierarchical architecture of fog computing and its characteristics, and compare it with cloud computing and edge computing to emphasize the similarities and differences. Then, the key technologies like computing, communication and storage technologies, naming, resource management, security and privacy protection are introduced to present how to support its deployment and application in a detailed manner. Several application cases like health care, augmented reality, brain machine interface and gaming, smart environments and vehicular fog computing are also presented to further explain fog computing application scenarios. Finally, based on the observation, we propose some challenges and open issues which are worth further in-depth study and research in fog computing development.


title: Fusion of stereo and Lidar data for dense depth map computation

abstract: Creating a map is a necessity in a lot of robotic applications, and depth maps are a way to estimate the position of other objects or obstacles. In this paper, an algorithm to compute depth maps is proposed. It operates by fusing information from two types of sensor: a stereo camera, and a LIDAR scanner. The strategy is to estimate reliably the disparities of a sparse set of points, then a bilateral filter is used to interpolate the missing disparities. Finally, the interpolation is refined. Our method is tested on the KITTI dataset and is compared against several other methods which fuse those modalities, or are extended to perform this fusion. Those tests show that our method is competitive with other fusion methods.


title: Dual band inkjet printed bow-tie slot antenna on leather

abstract: A new dual band CPW fed bow-tie slot antenna on leather substrate is presented. The antenna is fabricated using inkjet printing directly on leather substrate. The dual band operation is achieved by loading the bow-tie slot antenna with bend slot stubs on the edges. The antenna operates at around 1.7 GHz and 2.9 GHz. The measured impedance bandwidth of the antenna in the two bands is 13.8% and 16.6%. It is the first ever antenna fabricated using inkjet printing on leather. The proposed antenna design is attractive for wearable and flexible electronic systems. A new concept of shoe radar for antipersonnel mine detection is presented which can utilize the proposed antenna.


title: ESS ET AL.: SEGMENTATION-BASED URBAN TRAFFIC SCENE UNDERSTANDING 1 Segmentation-Based Urban Traffic Scene Understanding

abstract: Recognizing the traffic scene in front of a car is an important asset for autonomous driving, as well as for safety systems. While GPS-based maps abound and have reached an incredible level of accuracy, they can still profit from additional, image-based information. Especially in urban scenarios, GPS reception can be shaky, or the map might not contain the latest detours due to constructions, demonstrations, etc. Furthermore, such maps are static and cannot account for other dynamic traffic agents, such as cars or pedestrians. In this paper, we therefore propose an image-based system that is able to recognize both the road type (straight, left/right curve, crossing, ...) as well as a set of often encountered objects (car, pedestrian, pedestrian crossing). The obtained information could then be fused with existing maps and either assist the driver directly (e.g., a pedestrian crossing is ahead: slow down) or help in improving object tracking (e.g., where are possible entrance points for pedestrians or cars?). Starting from a video sequence obtained from a car driving through urban areas, we employ a two-stage architecture termed SegmentationBased Urban Traffic Scene Understanding (SUTSU) that first builds an intermediate representation of the image based on a patch-wise image classification. The patch-wise segmentation is inspired by recent work [3, 4, 5] and assigns class probabilities to every 8× 8 image patch. As a feature set, we use the coefficients of the Walsh-Hadamard transform (a decomposition of the image into square waves), and, if available, additional information from the depth map. These are then used in a oneversus-all training using AdaBoost for feature selection, where we choose 13 texture classes that we found to be representative of typical urban scenes. This yields a meta representation of the scene that is more suitable for further processing, Fig. 1 (b,c). In recent publications, such a segmentation was used for a variety of purposes, such as improvement of object detection [1, 5], analysis of occlusion boundaries, or 3D reconstruction. In this paper, we will investigate the use of a segmentation for urban scene analysis. We infer another set of features from the segmentation’s probability maps, analyzing repetitivity, curvature, and rough structure. This set is then again used with a one-versus-all training to infer both the type of road segment ahead, as well the additional presence of pedestrians, cars, or pedestrian crossing. A Hidden Markov Model is used for temporally smoothing the result. SUTSU is tested on two challenging sequences, spanning over 50 minutes video of driving through Zurich. The experiments show that while a state-of-the-art scene classifier [2] can keep global classes such as road types, similarly well apart, a manually crafted feature set based on a segmentation clearly outperforms it on object classes. Example images are shown in Fig. 2. The main contribution of this paper is the application of recent research efforts in scene categorization research to do vision “in the wild”, driving through urban scenarios. We furthermore show the advantage of a segmentation-based approach over a global descriptor, as the intermediate representation can easily be adapted to other underlying image data (e.g. dusk, rain, ...), without having to change the high-level classifier.


title: Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

abstract: Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented. However, FCNs based on a 16 layer VGGNet architecture have limited capacity to add additional layers. Therefore, it is challenging to learn more discriminative features among different classes for FCNs. In this study, we overcome these limitations using deep residual networks (ResNet) to segment liver lesions. ResNet contain skip connections between convolutional layers, which solved the problem of the training degradation of training accuracy in very deep networks and thereby enables the use of additional layers for learning more discriminative features. In addition, we achieve more precise boundary definitions through a novel cascaded ResNet architecture with multi-scale fusion to gradually learn and infer the boundaries of both the liver and the liver lesions. Our proposed method achieved 4th place in the ISBI 2017 Liver Tumor Segmentation Challenge by the submission deadline.


title: General Design of Multiway Multisection Power Dividers by Interconnecting Two-Way Dividers

abstract: It is common practice to design multiway power dividers by interconnecting two-way power dividers. Transmission lines are used to link the two-way dividers. This paper investigates the performance of the interconnected power divider and the effects of the interconnecting transmission lines. In particular, it will be shown that the performance of multiway dividers constructed by interconnecting even-number-section two-way dividers deteriorates significantly as the number of output ports increases. The interconnecting lines can be used to improve the performance of such dividers.


Natural Resources Proceedings of the Vertebrate Pest Conference Title Evaluation of methyl anthranilate as a bird repellent in fruit crops


title: Photorealistic Facial Texture Inference Using Deep Neural Networks

abstract: We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input, we introduce a multi-scale detail analysis technique based on mid-layer feature correlations extracted from a deep convolutional neural network. We demonstrate that fitting a convex combination of feature correlations from a high-resolution face database can yield a semantically plausible facial detail description of the entire face. A complete and photorealistic texture map can then be synthesized by iteratively optimizing for the reconstructed feature correlations. Using these high-resolution textures and a commercial rendering framework, we can produce high-fidelity 3D renderings that are visually comparable to those obtained with state-of-the-art multi-view face capture systems. We demonstrate successful face reconstructions from a wide range of low resolution input images, including those of historical figures. In addition to extensive evaluations, we validate the realism of our results using a crowdsourced user study.


title: The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment

abstract: Background: Advancements in mobile phone technology have led to the development of smartphones with the capability to run apps. The availability of a plethora of health- and fitness-related smartphone apps has the potential, both on a clinical and public health level, to facilitate healthy behavior change and weight management. However, current top-rated apps in this area have not been extensively evaluated in terms of scientific quality and behavioral theory evidence base. Objective: The purpose of this study was to evaluate the quality of the most popular dietary weight-loss smartphone apps on the commercial market using comprehensive quality assessment criteria, and to quantify the behavior change techniques (BCTs) incorporated. Methods: The top 200-rated Health & Fitness category apps from the free and paid sections of Google Play and iTunes App Store in Australia (n=800) were screened in August 2014. To be included in further analysis, an app had to focus on weight management, include a facility to record diet intake (self-monitoring), and be in English. One researcher downloaded and used the eligible apps thoroughly for 5 days and assessed the apps against quality assessment criteria which included the following domains: accountability, scientific coverage and content accuracy of information relevant to weight management, technology-enhanced features, usability, and incorporation of BCTs. For inter-rater reliability purposes, a second assessor provided ratings on 30% of the apps. The accuracy of app energy intake calculations was further investigated by comparison with results from a 3-day weighed food record (WFR). Results: Across the eligible apps reviewed (n=28), only 1 app (4%) received full marks for accountability. Overall, apps included an average of 5.1 (SD 2.3) out of 14 technology-enhanced features, and received a mean score of 13.5 (SD 3.7) out of 20 for usability. The majority of apps provided estimated energy requirements (24/28, 86%) and used a food database to calculate energy intake (21/28, 75%). When compared against the WFR, the mean absolute energy difference of apps which featured energy intake calculations (23/28, 82%) was 127 kJ (95% CI -45 to 299). An average of 6.3 (SD 3.7) of 26 BCTs were included. Conclusions: Overall, the most popular commercial apps for weight management are suboptimal in quality, given the inadequate scientific coverage and accuracy of weight-related information, and the relative absence of BCTs across the apps reviewed. With the limited regulatory oversight around the quality of these types of apps, this evaluation provides clinicians and consumers an informed view of the highest-quality apps in the current popular app pool appropriate for recommendation and uptake. Further research is necessary to assess the effectiveness of apps for weight management. [JMIR Mhealth Uhealth 2015;3(4):e104]


Joint projection filling method for occlusion handling


title: A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives.

abstract: The use of manipulatives to teach mathematics is often prescribed as an efficacious teaching strategy. To examine the empirical evidence regarding the use of manipulatives during mathematics instruction, we conducted a systematic search of the literature. This search identified 55 studies that compared instruction with manipulatives to a control condition where math instruction was provided with only abstract math symbols. The sample of studies included students from kindergarten to college level (N = 7,237). Statistically significant results were identified with small to moderate effect sizes, as measured by Cohen's d, in favor of the use of manipulatives when compared with instruction that only used abstract math symbols. However, the relationship between teaching mathematics with concrete manipulatives and student learning was moderated by both instructional and methodological characteristics of the studies. Additionally, separate analyses conducted for specific learning outcomes of retention (k = 53, N = 7,140), problem solving (k = 9, N = 477), transfer (k = 13, N = 3,453), and justification (k = 2, N = 109) revealed moderate to large effects on retention and small effects on problem solving, transfer, and justification in favor of using manipulatives over abstract math symbols.


title: Hepatitis B virus X-protein binds damaged DNA and sensitizes liver cells to ultraviolet irradiation.

abstract: The mechanism which is responsible for the association of chronic hepatitis B virus (HBV) infection with hepatocellular carcinoma (HCC) is poorly understood. The protein encoded by the HBV X-gene (HBx) has been identified as potentially oncogenic. HBx is a promiscuous indirect trans-activator of a wide range of cellular and viral cis-elements and may disrupt the maintenance of genomic integrity by inhibiting p53 function and binding a putative DNA repair protein (XAP-1). In this report, we show that there is preferential binding of recombinant HBx to damaged DNA through an association with nuclear proteins. We have used the transcriptional activation by HBx of the beta-actin promoter of a beta-galactosidase reporter cassette to label cultured Chang liver cells expressing HBx. We demonstrate that cells expressing HBx are sensitised to the lethal effects of low dose ultraviolet irradiation. These data indicate that HBx interferes with liver cell DNA repair by binding damaged DNA and may predispose to the accumulation of potentially lethal or carcinogenic mutations.


Procedures for reducing the size of coverage-based test sets


title: Lifted Tree-Reweighted Variational Inference

abstract: We analyze variational inference for highly symmetric graphical models such as those arising from first-order probabilistic models. We first show that for these graphical models, the tree-reweighted variational objective lends itself to a compact lifted formulation which can be solved much more efficiently than the standard TRW formulation for the ground graphical model. Compared to earlier work on lifted belief propagation, our formulation leads to a convex optimization problem for lifted marginal inference and provides an upper bound on the partition function. We provide two approaches for improving the lifted TRW upper bound. The first is a method for efficiently computing maximum spanning trees in highly symmetric graphs, which can be used to optimize the TRW edge appearance probabilities. The second is a method for tightening the relaxation of the marginal polytope using lifted cycle inequalities and novel exchangeable cluster consistency constraints.


title: Synthesizing Java expressions from free-form queries

abstract: We present a new code assistance tool for integrated development environments. Our system accepts as input free-form queries containing a mixture of English and Java, and produces Java code expressions that take the query into account and respect syntax, types, and scoping rules of Java, as well as statistical usage patterns. In contrast to solutions based on code search, the results returned by our tool need not directly correspond to any previously seen code fragment. As part of our system we have constructed a probabilistic context free grammar for Java constructs and library invocations, as well as an algorithm that uses a customized natural language processing tool chain to extract information from free-form text queries. We present the results on a number of examples showing that our technique (1) often produces the expected code fragments, (2) tolerates much of the flexibility of natural language, and (3) can repair incorrect Java expressions that use, for example, the wrong syntax or missing arguments.


title: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging

abstract: The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers. Ann Neurol 1999;45:265–269


title: A Novel Episode Mining Methodology for Stock Investment

abstract: In this paper, we present a novel methodology for stock investment using episode mining and technical indicators. The time-series data of stock price and the derived moving average, a class of well-known technical indicators, are used for the construction of complex episode events and rules. Our objective is to devise a profitable episode-based investment model to mine associated events in the stock market. Using Taiwan Capitalization Weighted Stock Index (TAIEX), the empirical results show that our proposed model significantly outperforms the benchmark in terms of cumulative total returns. We also show that the level of the precision by our model is close to 60%, which is better than random guessing. Based upon the results obtained, we expect this novel episode-based methodology will advance the research in data mining for computational finance and provide an alternative to stock investment in practice.


title: Challenges to the Adoption of E-commerce Technology for Supply Chain Management in a Developing Economy: A Focus on Nigerian SMEs

abstract: The evolution of Information Technology has enhanced consumers' and organisations’ ability to gather information along with purchasing goods and services. Information Technology offers businesses increased competition, lower prices of goods and services, the choice of comparing products from different vendors and easy access to various vendors anywhere, anytime. Therefore, Information Technology facilitates the global nature of commerce. In developing countries, e-commerce dominates their economic activities. E-commerce is one of the leading non-oil sectors in Nigeria, 18.9 % GDP. In contrast with developed nations, e-commerce has not been as successfully adopted in developing countries. This chapter addresses the challenges and benefits of e-commerce technology in relation to SMEs in Nigeria. It presents quantitative evidence of SMEs perceptions of e-commerce technology, benefits, and barriers. A number of hypotheses are presented and assessed. Recommendations to mitigate barriers are suggested.


title: Temperature control of micro heater using Pt thin film temperature sensor embedded in micro gas sensor

abstract: Pt thin film temperature sensors (Pt T sensors) are embedded in micro gas sensors to measure and control the working temperature. We characterized electrical resistances of Pt T sensors and micro heaters with temperature changing in the oven and by Joule heating. In order to enhance the accuracy of temperature measurement by the Pt T sensors, we investigated the correlation among the Pt T sensor, micro heater, and the working temperature, which was linear proportional relation expressed as the equation: T2 = 6.466R1–642.8, where T2 = temperature of the Pt micro heater and R1 = the electrical resistance of the Pt T sensor. As the error by physically separated gap between Pt T sensor and micro heater calibrated, measuring and controlling temperature of micro heater in micro gas sensors were possible through the Pt T sensors. For the practical use of Pt T sensor in micro gas sensor, the gas sensing properties of fabricated micro gas sensors to 25 ppm CO and 1 ppm HCHO gases were characterized.


title: WATERMELON PLANT CLASSIFICATION BASED ON SHAPE AND TEXTURE FEATURE LEAF USING SUPPORT VECTOR MACHINE (SVM)

abstract: Nowadays, some efforts are used to increase results of agriculture production. One of those is utilizing herbisides to exterminate the weeds. However, there are some of the weeds having resemblance with the plant, with the result that we need to classify the plant and the weeds before utilizing herbisides as an extermination weeds. In this paper, we use watermelon plant classification as case study. The recognition of the plant owned by the similarity of leaves of these plants are divided into three phases. At the first phase we perform preprocessing to convert the RGB image into a grayscale images. Further, the grayscale images are changed into segmentation of edge detection using Canny operator. In the second, we use feature extraction to retrieve important informations for the recognition of those leaves. The last phase we classify that leaves as watermelon plants or weeds using Support Vector Machine (SVM) algorithm. The results of early trials indicate that this method has an accuracy of 91,3%. ::: Keywords : image, leaf, edge detection, feature extraction, and plant classification esults of early trials


title: Probabilistic Social Sequential Model for Tour Recommendation

abstract: The pervasive growth of location-based services such as Foursquare and Yelp has enabled researchers to incorpo- rate better personalization into recommendation models by leveraging the geo-temporal breadcrumbs left by a plethora of travelers. In this paper, we explore Travel path recommendation, which is one of the applications of intelligent urban navigation that aims in recommending sequence of point of interest (POIs) to tourists. Currently, travelers rely on a tedious and time-consuming process of searching the web, browsing through websites such as Trip Advisor, and reading travel blogs to compile an itinerary. On the other hand, people who do not plan ahead of their trip find it extremely difficult to do this in real-time since there are no automated systems that can provide personalized itinerary for travelers. To tackle this problem, we propose a tour recommendation model that uses a probabilistic generative framework to incorporate user's categorical preference, influence from their social circle, the dynamic travel transitions (or patterns) and the popularity of venues to recommend sequence of POIs for tourists. Through comprehensive experiments over a rich dataset of travel patterns from Foursquare, we show that our model is capable of outperforming the state-of-the-art probabilistic tour recommendation model by providing contextual and meaningful recommendation for travelers.


title: Applying the Uses and Gratifications Theory to Compare Higher Education Students’ Motivation for Using Social Networking Sites: Experiences from Iran, Malaysia, United Kingdom, and South Africa

abstract: Drawing from the Uses and Gratifications Theory, this study examined the Gratification Sought and the Gratification Obtained from using Social Networking Sites among Iranian, Malaysian, British, and South African higher education students. This comparison allowed to drawing conclusions about how social networking sites fulfill users’ needs with different cultures. Data were collected through a quantitative study applying online and paperbased questionnaire carried out in 2013, using a representative sample (N=320). Findings showed differences in students’ motivation to join to and to use of social network sites in each country. Interestingly, the researchers found that cultural differences may determine the uses and gratifications of social networking sites.


title: Challenges in Delivering Software in the Cloud as Microservices

abstract: Microservices can be broadly defined as the design of service-oriented software using a set of small services. In a microservice architecture, application complexity is distributed among narrowly focused and independently deployable units of computation. Such complexity can result in security vulnerabilities. Trustworthiness is also an issue when dealing with microservices. Moreover, there may be gaps in existing legal frameworks with regard to this technology. Solutions to these issues must seek balance between security and performance.


title: Network bandwidth predictor (NBP): a system for online network performance forecasting

abstract: The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (network bandwidth predictor) for rapid network performance prediction. NBP is a new system that employs a neural network based approach for network bandwidth forecasting. This system is designed to integrate with most advanced technologies. It employs the NWS (network weather service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.


title: A LIE NEVER LIVES TO BE OLD: THE EFFECTS OF FAKE SOCIAL INFORMATION ON CONSUMER DECISION- MAKING IN CROWDFUNDING

abstract: The growing success of social media led to a strong presence of social information such as customer product reviews and product ratings in electronic markets. While this information helps consumers to better assess the quality of goods before purchase, its impact on consumer decision-making also incentivizes sellers to game the system by creating fake data in favor of specific goods in order to deliberately mislead consumers. As a consequence, consumers could make suboptimal choices or could choose to disregard social information altogether. In this exploratory study, we assess the effects nongenuine social information has on the consumer’s decision-making in the context of reward-based crowdfunding. Specifically, we capture unnatural peaks in the number of Facebook Likes that a specific crowdfunding campaign receives on the platforms Kickstarter and Indiegogo and observe subsequent campaign performance. Our results show that fake Facebook Likes have a very short-term positive effect on the number of backers participating in the respective crowdfunding campaign. However, this peak in participation is then followed by a period in which participation is lower than prior to the existence of the non-genuine social information. We further discuss circumstances that foster this artificial manipulation of quality signals.


title: Learning Privately from Multiparty Data

abstract: Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data? We propose to transfer the `knowledge' of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global $\epsilon$-differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by $O(\epsilon^{-2}M^{-2})$ where $M$ is the number of parties. This allows strong privacy without performance loss when $M$ is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.


title: #Healthy Selfies: Exploration of Health Topics on Instagram

abstract: BACKGROUND ::: Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity. ::: ::: ::: OBJECTIVE ::: This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research. ::: ::: ::: METHODS ::: The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool. ::: ::: ::: RESULTS ::: We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts). ::: ::: ::: CONCLUSIONS ::: A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter.


title: Detecting figures and part labels in patents: competition-based development of graphics recognition algorithms

abstract: Most United States Patent and Trademark Office (USPTO) patent documents contain drawing pages which describe inventions graphically. By convention and by rule, these drawings contain figures and parts that are annotated with numbered labels but not with text. As a result, readers must scan the document to find the description of a given part label. To make progress toward automatic creation of `tool-tips' and hyperlinks from part labels to their associated descriptions, the USPTO hosted a monthlong online competition in which participants developed algorithms to detect figures and diagram part labels. The challenge drew 232 teams of two, of which 70 teams (30 %) submitted solutions. An unusual feature was that each patent was represented by a 300-dpi page scan along with an HTML file containing patent text, allowing integration of text processing and graphics recognition in participant algorithms. The design and performance of the top-5 systems are presented along with a system developed after the competition, illustrating that the winning teams produced near state-of-the-art results under strict time and computation constraints. The first place system used the provided HTML text, obtaining a harmonic mean of recall and precision (F-measure) of 88.57 % for figure region detection, 78.81 % for figure regions with correctly recognized figure titles, and 70.98 % for part label detection and recognition. Data and source code for the top-5 systems are available through the online UCI Machine Learning Repository to support follow-on work by others in the document recognition community.


Using VIKOR Method for Evaluating Service Quality of Online Auction under Fuzzy Environment


title: Fast 3D edge detection by using decision tree from depth image

abstract: T3D edge detection from a depth image is an important technique of 3D object recognition in preprocessing. There are three types of 3D edges in a depth image called jump, convex roof, and concave roof edges. Conventional 3D edge detection based on ring operators has been proposed. The conventional ring operator can detect three types of 3D edges by classifying the response of Fourier transforms. Since the conventional method needs to apply Fourier transforms to all pixels of a depth image, real-time processing cannot be done due to high computational cost. Therefore, this paper presents a fast and reliable method of detecting three types of 3D edges by using a decision tree. The decision tree is trained under supervised learning from numerous synthesized depth images and labels by capturing depth relations between candidate pixels and pixels on a ring operator to classify 3D edges. The experimental results revealed that the proposed method has 25 times faster than the conventional method. This paper also presents some examples of 3D line and 3D convex corner detection based on results obtained with the proposed method.


title: Full electro-thermal model of a 6.5kV field-stop IGBT module

abstract: As the integration level of power electronics equipment increases, the coupling between multi-domain physical effects becomes more and more relevant for design optimization. At the same time, virtual analysis capability acquires a critical importance and is conditioned by the achievement of an adequate compromise between accuracy and computational effort. This paper proposes the compact model development of a 6.5 kV field-stop IGBT module, for use in a circuit simulation environment. The model considers the realistic connection of IGBT and anti-parallel freewheeling diode pairs: the description of semiconductor physics is coupled with self-heating effects, both at device and module level; electro-magnetic phenomena associated with the package and layout are also taken into account. The modeling approach follows a mixed physical and behavioral description, resulting in an ideal compromise for realistic analysis of multi-chip structures. Finally, selected examples, derived from a railway traction application scenario, demonstrate the validity of the proposed solution, both for simulation of short transients and periodic operation, qualifying the model as a support tool of general validity, from system design development to reliability investigations.


title: Cognitive perspectives of information retrieval interaction: elements of a cognitive IR theory

abstract: The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full‐text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data fusion in IR. By explicitly incorporating all the cognitive structures participating in the interactive communication processes during IR, the cognitive theory provides a comprehensive view of these processes. It encompasses the ad hoc theories of text retrieval and IR techniques hitherto developed in mainstream retrieval research. It has elements in common with van Rijsbergen and Lalmas' logical uncertainty theory and may be regarded as compatible with that conception of IR. Epistemologically speaking, the theory views IR interaction as processes of cognition, potentially occurring in all the information processing components of IR, that may be applied, in particular, to the user in a situational context. The theory draws upon basic empirical results from information seeking investigations in the operational online environment, and from mainstream IR research on partial matching techniques and relevance feedback. By viewing users, source systems, intermediary mechanisms and information in a global context, the cognitive perspective attempts a comprehensive understanding of essential IR phenomena and concepts, such as the nature of information needs, cognitive inconsistency and retrieval overlaps, logical uncertainty, the concept of ‘document’, relevance measures and experimental settings. An inescapable consequence of this approach is to rely more on sociological and psychological investigative methods when evaluating systems and to view relevance in IR as situational, relative, partial, differentiated and non‐linear. The lack of consistency among authors, indexers, evaluators or users is of an identical cognitive nature. It is unavoidable, and indeed favourable to IR. In particular, for full‐text retrieval, alternative semantic entities, including Salton et al.'s ‘passage retrieval’, are proposed to replace the traditional document record as the basic retrieval entity. These empirically observed phenomena of inconsistency and of semantic entities and values associated with data interpretation support strongly a cognitive approach to IR and the logical use of polyrepresentation, cognitive overlaps, and both data fusion and data diffusion.


title: Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

abstract: Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.


title: Predicting DRAM reliability in the field with machine learning

abstract: Uncorrectable errors in dynamic random access memory (DRAM) are a common form of hardware failure in server clusters. Failures are costly both in terms of hardware replacement costs and service disruption. While a large body of work exists on analyzing DRAM reliability in large production clusters, little has been reported on the automatic prediction of such errors ahead of time. In this paper, we present a highly accurate predictive model, based on daily event logs and sensor measurements, in a large fleet of commodity servers going back to 2014. By correlating correctable errors with sensor metrics, we can use ensemble machine learning techniques to predict uncorrectable errors weeks in advance. In addition, we show how such models can be applied in the wild and consumed by customer support teams. Our goal is to minimize false positives, as healthy DRAMs should not be replaced, while accounting for common limitations, such as missing data points and rare occurences of uncorrectable errors.


title: Force-directed layout of origin-destination flow maps

abstract: ABSTRACT This paper introduces a force-directed layout method for creating origin-destination flow maps. Design principles derived from manual cartography and automated graph drawing to increase readability of flow maps and graph layouts are taken into account. The origin-destination flow maps produced with our algorithm show flows with quadratic Bézier curves that reduce flow-on-flow and flow-on-node overlaps, and avoid sharp or irregular bends in flow lines. A survey of expert cartographers found that flow maps created with our automated method are similar in quality to manually produced flow maps.


title: Attacking smart card systems: Theory and practice

abstract: Smart card technology has evolved over the last few years following notable improvements in the underlying hardware and software platforms. Advanced smart card microprocessors, along with robust smart card operating systems and platforms, contribute towards a broader acceptance of the technology. These improvements have eliminated some of the traditional smart card security concerns. However, researchers and hackers are constantly looking for new issues and vulnerabilities. In this article we provide a brief overview of the main smart card attack categories and their corresponding countermeasures. We also provide examples of well-documented attacks on systems that use smart card technology (e.g. satellite TV, EMV, proximity identification) in an attempt to highlight the importance of the security of the overall system rather than just the smart card.


title: Critical thinking, cognitive presence, and computer conferencing in distance education

abstract: Abstract This article describes a practical approach to judging the nature and quality of critical discourse in a computer conference. A model of a critical community of inquiry frames the research. A core concept in defining a community of inquiry is cognitive presence. In turn, the practical inquiry model operationalizes cognitive presence for the purpose of developing a tool to assess critical discourse and reflection. The authors present encouraging empirical findings related to an attempt to create an efficient and reliable instrument to assess the nature and quality of critical discourse and thinking in a text‐based educational context. Finally, the authors suggest that cognitive presence (i.e., critical, practical inquiry) can be created and supported in a computer‐conference environment with appropriate teaching and social presence.


title: Application of machine learning techniques to sentiment analysis

abstract: Today, we live in a ‘data age’. Due to rapid increase in the amount of user-generated data on social media platforms like Twitter, several opportunities and new open doors have been prompted for organizations that endeavour hard to keep a track on customer reviews and opinions about their products. Twitter is a huge fast emergent micro-blogging social networking platform for users to express their views about politics, products sports etc. These views are useful for businesses, government and individuals. Hence, tweets can be used as a valuable source for mining public's opinion. Sentiment analysis is a process of automatically identifying whether a user-generated text expresses positive, negative or neutral opinion about an entity (i.e. product, people, topic, event etc). The objective of this paper is to give step-by-step detail about the process of sentiment analysis on twitter data using machine learning. This paper also provides details of proposed approach for sentiment analysis. This work proposes a Text analysis framework for twitter data using Apache spark and hence is more flexible, fast and scalable. Naive Bayes and Decision trees machine learning algorithms are used for sentiment analysis in the proposed framework.


title: Multiband LTE MIMO antenna for laptop applications

abstract: A slim multi-band LTE MIMO antenna covering the LTE12/LTE13/LTE14/LTE17 bands is proposed and designed for laptop applications. Each MIMO antenna mounted on the vertical side edges of a laptop consists of two PIFAs with a single proximity coupled-feed. The radiation efficiency of the proposed antenna is higher than 60% over the entire LTE band. The isolation between two ports is more than 15 dB, and the correlation coefficient is less than 0.2 over the entire operating LTE band.


title: Crossroads of Religions: Shrines, Mobility and Urban Space in Goa

abstract: Wayside shrines - representing Hindu and Catholic divinities and saints - show an astonishing dynamic in the cities of Goa and India. Not only do they persist in a milieu of drastic modern change that often seems to be at odds with their traditional locations, aesthetics and purposes, but also some of them surpass temples, churches and mosques in popularity. The popularity of these seemingly marginal religious monuments is a response to three forms of mobility characterizing modern Indian urban environments: cultural mobility - the diversification and fluctuation of religious ideas and practices; social mobility - the diversification and fluctuation of people from different castes, social classes and geographical regions, as well as the change of caste and class status due to socio-economic change; and physical mobility - the movement of and movement around increasingly dense and complex flows of motorized traffic. The shrines modify and transform the centuries-old spatio-religious system of Hindus and Catholics to fit the conditions of late-modern city life. They allow a culturally diversifying, socially changing and geographically fluctuating population to engage with a variety of personalized deities and saints whose charismatic authority is not only quite independent from formalized local social hierarchies, but often also cuts across orthodox divisions between religious traditions. Copyright (c) 2008 The Author. Journal Compilation (c) 2008 Joint Editors and Blackwell Publishing Ltd.


title: Improved removal of electrocardiogram baseline wandering

abstract: Baseline wandering interference causes inaccurate detection of electrocardiogram (ECG) characteristics such as detection of the R point. This paper presents a novel phase space-based approach for baseline wandering removal of ECG signals. The baseline wander contaminated ECG signal is transformed to the phase space by using the signal derivative. Baseline wandering was eliminated by shifting loops of the ECG phase portrait on a predefined fixed position. This resulted in signal discontinuities in time domain which were solved using linear correction. The method was evaluated on synthetized ECG signals and on signals from the MIT-BIH database. Results show an improvement of signal-to-noise ratio after baseline wandering removal in phase space, comparable with related works, and efficient elimination of time-domain discontinuities in time domain.


title: Hindi speech corpora: A review

abstract: A benchmark dataset provides insight into the phenomena that generate the data. Hence, it is an essential requirement to conduct research that requires concept discovery from data. In this paper, we examine the current status of 26 (twenty-six) datasets for Hindi speech (or Hindi speech corpora). This paper also aims at studying their impacts on Hindi speech based computer mediated application development. During this study, we discovered that researchers have paid little attention to issues relating to data collection from a realistic environment through mobile phone. Out of the twenty-six Hindi speech corpora reviewed only one is created for speaker recognition, in which conversation speech samples are recorded through mobile phone for noisy as well as clear condition.


title: Thermal analysis of a segmented stator winding design

abstract: This paper presents a thermal analysis of a segmented stator winding design. As the thermal performance is one of the main factors limiting a machine's output capability, a thermal test on a complete prototype machine is an essential part of the design process. However, for the segmented stator winding design a test-informed thermal analysis on a single stator tooth can be performed prior to the manufacture of the full machine. This approach allows for a rapid and inexpensive assessment of the thermal performance of the complete machine and early identification of design modifications needed. The research has been applied to the design of a highly efficient and compact permanent magnet (PM) traction motor. A thermal model for a single tooth was developed and supported by tests to identify key heat transfer coefficients. A number of winding assemblies were compared and the most promising was selected for the final motor prototype. The results from the approach are compared with thermal test results from the complete machine.


title: HOGgles: Visualizing Object Detection Features

abstract: We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.


title: Sustainability Analysis and Resource Management for Wireless Mesh Networks with Renewable Energy Supplies

abstract: There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.


title: CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes

abstract: The CoNLL-2011 shared task involved predicting coreference using OntoNotes data. Resources in this field have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ace entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, and evaluation criteria, and presents and discusses the results achieved by the participating systems. Having a standard test set and evaluation parameters, all based on a new resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.


title: DESIGN OF A WIDE BAND EIGHT-WAY COMPACT SIW POWER COMBINER FED BY A LOW LOSS GCPW- TO-SIW TRANSITION

abstract: Ultra wideband components have been developed using SIW technology. The various newly developed components include a GCPW transition, Y and T-junctions. The GCPW transition covers over 10GHz bandwidth with less than 0.4dB insertion loss. The optimized T and Y-junctions have relatively wide bandwidths of greater than 40% that have less than 0.6dB insertion loss. The developed transition was utilized to design an X-band eight-way power divider that demonstrated excellent performance over a 4GHz bandwidth with less than §4 - and §0:9dB phase and amplitude imbalance, respectively. Theoretical and experimental results are presented and compared with previously designed SIW power dividers. The developed SIW power divider has a low proflle and is particularly suitable for circuits' integration.


title: Usefulness of information visualizations based on educational data

abstract: In this article, we introduce the results of a study concerning the usefulness of five educational data visualizations implemented in an online course. Information about the teachers' and students' opinions on the usefulness of the visualizations was collected by using a web survey and the focus group method. To ensure that the visualizations are in used correctly during the course, their use was also tracked by using a web analytics platform. Well-designed and usable visual analytic tools and visualizations can help teachers make decisions regarding potential pedagogical strategies, instructional guidance, actions, and interventions that may be used to promote and support students' participation and activity. According to the results of this research, most of the students seemed to prefer visualizations that offer concrete and practical support for their studies rather than visualizations that support them in monitoring and evaluating their own learning performance.


title: Feature based Summarization of Customers’ Reviews of Online Products

abstract: Abstract With the growing availability and popularity of opinion-rich resources such as review forums for the product sold online, choosing the right product from a large number of products have become difficult for the user. For trendy product, the number of customers’ opinions available can be in the thousands. It becomes hard for the customers to read all the reviews and if he reads only a few of those reviews, then he may get a biased view about the product. Makers of the products may also feel difficult to maintain, keep track and understand the customers’ views for the products. Several research works have been proposed in the past to address these issues, but they have certain limitations: The systems implemented are completely opaque, the reviews are not easier to perceive and are time consuming to analyze because of large irrelevant information apart from actual opinions about the features, the feature based summarization system that are implemented are more generic ones and static in nature. In this research, we proposed a dynamic system for feature based summarization of customers’ opinions for online products, which works according to the domain of the product. We are extracting online reviews for a product on periodic bases, each time after extraction, we carry out the following work: Firstly, identification of features of a product from customers’ opinions is done. Next, for each feature, its corresponding opinions’ are extracted and their orientation or polarity (positive/negative) is detected. The final polarity of feature-opinions pairs is calculated. At last, feature based summarizations of the reviews are generated, by extracting the relevant excerpts with respect to each feature-opinions pair and placing it into their respective feature based cluster. These feature based excerpts can easily be digested by the user.


Behavioural Finance


title: Adaptive RF Front-Ends Using Electrical-Balance Duplexers and Tuned SAW Resonators

abstract: This paper proposes an adaptive RF front-end (RFFE) architecture that uses an electrical-balance duplexer (EBD) and tuned surface acoustic wave (SAW) resonators to enable adaptive dual-frequency isolation suited for LTE’s frequency-division duplex (FDD) mode of operation. Alternatively, in the in-band full-duplex (IBFD) mode, the EBD cancels the in-band TX self-interference directly at RF for increased channel capacity. Furthermore, the EBD’s balance network is optimized to reduce the TX insertion loss (IL) below the nominal 3-dB limit in the FDD mode. A 0.18- $\mu \text{m}$ SOI CMOS prototype implements the EBD, low-noise amplifier, and SAW-tuning capacitor banks. The chip is mounted to a module substrate together with the SAWs and matching components. The RFFE achieves >50-dB dual-frequency isolation and 2.6–3.4-dB TX IL in FDD mode, has +58/+42-dBm TX/RX-path IIP3 for an increased IBFD-link budget and handles up to +27.5-dBm TX power in both modes.


title: (www.interscience.wiley.com) DOI: 10.1002/sim.2666 Flexible random-effects models using Bayesian semi-parametric models: Applications to institutional comparisons

abstract: Random effects models are used in many applications in medical statistics, including meta-analysis, cluster randomized trials and comparisons of health care providers. This paper provides a tutorial on the practical implementation of a flexible random effects model based on methodology developed in Bayesian non-parametrics literature, and implemented in freely available software. The approach is applied to the problem of hospital comparisons using routine performance data, and among other benefits provides a diagnostic to detect clusters of providers with unusual results, thus avoiding problems caused by masking in traditional parametric approaches. By providing code for Winbugs we hope that the model can be used by applied statisticians working in a wide variety of applications. Copyright © 2006 John Wiley & Sons, Ltd.


title: ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction

abstract: BackgroundWith the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias.MethodsCoders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality.ResultsCompared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1 – 100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994 – 2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81).ConclusionThe ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases.


title: A consensus on the medical treatment of acromegaly

abstract: The newly appreciated heterogeneity in type 1 diabetes mellitus implies that a personalized approach could be used to treat this disease; treatments that modify the immune system might be ideally suited to this purpose. This Review discusses the current knowledge on immune regulation and dysfunction in humans that could be used to develop new therapies.


title: Robust video denoising using low rank matrix completion

abstract: Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.