title: The urban sustainable development goal: Indicators, complexity and the politics of measuring cities

abstract: Abstract As part of the post-2015 United Nations sustainable development agenda, the world has its first urban sustainable development goal (USDG) “to make cities and human settlements inclusive, safe, resilient and sustainable”. This paper provides an overview of the USDG and explores some of the difficulties around using this goal as a tool for improving cities. We argue that challenges emerge around selecting the indicators in the first place and also around the practical use of these indicators once selected. Three main practical problems of indicator use include 1) the poor availability of standardized, open and comparable data 2) the lack of strong data collection institutions at the city scale to support monitoring for the USDG and 3) “localization” - the uptake and context specific application of the goal by diverse actors in widely different cities. Adding to the complexity, the USDG conversation is taking place at the same time as the proliferation of a bewildering array of indicator systems at different scales. Prompted by technological change, debates on the “data revolution” and “smart city” also have direct bearing on the USDG. We argue that despite these many complexities and challenges, the USDG framework has the potential to encourage and guide needed reforms in our cities but only if anchored in local institutions and initiatives informed by open, inclusive and contextually sensitive data collection and monitoring.


title: Synthesis of silicated hydroxyapatite Ca10(PO4)6-x(SiO4)x(OH)2-x

abstract: Abstract The preparation of silicated hydroxyapatite Ca 10 (PO 4 ) 6− x (SiO 4 ) x (OH) 2− x (SiHA) with 0⩽ x ⩽2 was investigated using a wet precipitation method followed by a heat treatment. X-ray diffraction and Rietveld refinement, Fourier transformed IR (FTIR) spectroscopy, elemental analyses, transmission electron microscopy and thermal analyses were used to characterize the samples. The raw materials were composed of a partially silicated and carbonated apatite and a secondary minor phase containing the excess silicon. Single phase silicated hydroxyapatites, with 0⩽ x ⩽1, could be synthesized after a thermal treatment of the raw powders above 700 °C. The presence of carbonate groups in the raw apatite played an important role in the incorporation of silicates during heating. From the different results, the mechanisms of formation of SiHA are discussed.


title: QALink: Enriching Text Documents with Relevant Q&A Site Contents

abstract: With rapid development of Q&A sites such as Quora and StackExchange, high quality question-answer pairs have been produced by users. These Q&A contents cover a wide range of topics, and they are useful for users to resolve queries and obtain new knowledge. Meanwhile, when people are reading digital documents, they may encounter reading problems such as lack of background information and unclear illustration of concepts. We believe that Q&A sites offer high-quality contents which can serve as rich supplements to digital documents. In this paper, we devise a rigorous formulation of the novel text enrichment problem, and design an end-to-end system named QALink which assigns the most relevant Q&A contents to the corresponding section of the document. We first present a new segmentation approach to model each document with a hierarchical structure. Based on the hierarchy, queries are constructed to retrieve and rank related question-answer pairs. Both syntactical and semantic features are adopted in our system. The empirical evaluation results indicate that QALink is able to effectively enrich text documents with relevant Q&A contents to help people better understand the documents.


title: A DNN-based object detection system on mobile cloud computing

abstract: With the development of big data and the improvement of computing power, deep learning has made a very prominent breakthrough in computer vision. However, the computational overhead of the Deep Neural Network (DNN) for video processing in mobile devices is extremely high. To address the problem above, this paper combines smartphones with the cloud to realize a DNN-based object detection system. The main contributions are three-fold. (i) A model scheduling algorithm is proposed to adaptively select the operating environment (cloud or mobile) according to the conditions of network and mobile devices. (ii) To meet the hardware requirements of mobile devices, the compact model variants are trained and generated with a small loss of precision. (iii) To reduce the latency, the outputs of DNN models are used to process (add bounding boxes and annotations) the video directly. Test results for runtime and precision show that our system outperforms the state-of-the-art in both detection accuracy and running speed.


title: Small neural nets are beautiful: enabling embedded systems with small deep-neural-network architectures

abstract: Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications. As a result, Deep Neural Networks have supplanted other approaches to solving problems in these areas, and enabled many new applications. While the design of Deep Neural Nets is still something of an art form, in our work we have found basic principles of design space exploration used to develop embedded microprocessor architectures to be highly applicable to the design of Deep Neural Net architectures. In particular, we have used these design principles to create a novel Deep Neural Net called SqueezeNet that requires as little as 480KB of storage for its model parameters. We have further integrated all these experiences to develop something of a playbook for creating small Deep Neural Nets for embedded systems.


ON THE COMPATIBILITY OF CONNECTIONISM AND COGNITIVE LINGUISTICS


title: Scene Text Synthesis for Efficient and Effective Deep Network Training

abstract: A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest in the recent deep learning research. We develop an innovative image synthesis technique that composes annotated training images by realistically embedding foreground objects of interest (OOI) into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. The first is context-aware semantic coherence which ensures that the OOI are placed around semantically coherent regions within the background image. The second is harmonious appearance adaptation which ensures that the embedded OOI are agreeable to the surrounding background from both geometry alignment and appearance realism. The proposed technique has been evaluated over two related but very different computer vision challenges, namely, scene text detection and scene text recognition. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.


title: Broadcast Disks: Data Management for Asymmetric Communication Environments

abstract: This paper proposes the use of repetitive broadcast as a way of augmenting the memory hierarchy of clients in an asymmetric communication environment. We describe a new technique called "Broadcast Disks" for structuring the broadcast in a way that provides improved performance for non-uniformly accessed data. The Broadcast Disk superimposes multiple disks spinning at different speeds on a single broadcast channel--in effect creating an arbitrarily fine-grained memory hierarchy. In addition to proposing and defining the mechanism, a main result of this work is that exploiting the potential of the broadcast structure requires a re-evaluation of basic cache management policies. We examine several "pure" cache management policies and develop and measure implementable approximations to these policies. These results and others are presented in a set of simulation studies that substantiates the basic idea and develops some of the intuitions required to design a particular broadcast program.


title: Programmable Packet Scheduling at Line Rate

abstract: Switches today provide a small menu of scheduling algorithms. While we can tweak scheduling parameters, we cannot modify algorithmic logic, or add a completely new algorithm, after the switch has been designed. This paper presents a design for a {\em programmable} packet scheduler, which allows scheduling algorithms---potentially algorithms that are unknown today---to be programmed into a switch without requiring hardware redesign. Our design uses the property that scheduling algorithms make two decisions: in what order to schedule packets and when to schedule them. Further, we observe that in many scheduling algorithms, definitive decisions on these two questions can be made when packets are enqueued. We use these observations to build a programmable scheduler using a single abstraction: the push-in first-out queue (PIFO), a priority queue that maintains the scheduling order or time. We show that a PIFO-based scheduler lets us program a wide variety of scheduling algorithms. We present a hardware design for this scheduler for a 64-port 10 Gbit/s shared-memory (output-queued) switch. Our design costs an additional 4% in chip area. In return, it lets us program many sophisticated algorithms, such as a 5-level hierarchical scheduler with programmable decisions at each level.


title: Learning to Rank Features for Recommendation over Multiple Categories

abstract: Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation has became a popular meth-od due to its explainable property for latent features and high prediction accuracy. However, the inherent limitations of the existing model make it difficult to (1) effectively distinguish the features that are most interesting to users, (2) maintain the recommendation performance especially when the set of items is scaled up to multiple categories, and (3) model users' implicit feedbacks on the product features. In this paper, motivated by these shortcomings, we first introduce a tensor matrix factorization algorithm to Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM for short), and then by combining this technique with Collaborative Filtering (CF) method, we propose a novel model called LRPPM-CF to boost the performance of recommendation. Thorough experiments on two real-world datasets demonstrate that our proposed model is able to improve the performance in the tasks of capturing users' interested features and item recommendation by about 17%-24% and 7%-13%, respectively, as compared with several state-of-the-art methods.


title: A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices

abstract: An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition. It includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features. A Bayes linear classifier and an improved dynamic time-warping algorithm are utilized in the framework. In addition, a prototype system, including a wearable gesture sensing device (embedded with a three-axis accelerometer and four SEMG sensors) and an application program with the proposed algorithmic framework for a mobile phone, is developed to realize gesture-based real-time interaction. With the device worn on the forearm, the user is able to manipulate a mobile phone using 19 predefined gestures or even personalized ones. Results suggest that the developed prototype responded to each gesture instruction within 300 ms on the mobile phone, with the average accuracy of 95.0% in user-dependent testing and 89.6% in user-independent testing. Such performance during the interaction testing, along with positive user experience questionnaire feedback, demonstrates the utility of the framework.


title: Model based pose estimation using SURF

abstract: Estimation of a camera pose (position and orientation) from an image, given a 3d model of the world, is a topic of great interest in many current fields of research. When aiming for a model based pose estimation approach, several questions arise: What is the model? How do we acquire a model? How is the image linked to the model? How is a pose computed and verified using the latter information? In this paper we present a new approach towards model based pose estimation based solely on SURF features. We give a formal definition of our model, show how to build such a model from image data automatically, how to integrate two partial models, and how pose estimation for new images works.


title: A Neural Basis for Social Cooperation

abstract: Cooperation based on reciprocal altruism has evolved in only a small number of species, yet it constitutes the core behavioral principle of human social life. The iterated Prisoner's Dilemma Game has been used to model this form of cooperation. We used fMRI to scan 36 women as they played an iterated Prisoner's Dilemma Game with another woman to investigate the neurobiological basis of cooperative social behavior. Mutual cooperation was associated with consistent activation in brain areas that have been linked with reward processing: nucleus accumbens, the caudate nucleus, ventromedial frontal/orbitofrontal cortex, and rostral anterior cingulate cortex. We propose that activation of this neural network positively reinforces reciprocal altruism, thereby motivating subjects to resist the temptation to selfishly accept but not reciprocate favors.


title: Radar and vision based data fusion - Advanced filtering techniques for a multi object vehicle tracking system

abstract: The robust and reliable detection of objects in the path of a vehicle is an important prerequisite for collision avoidance and collision mitigation systems. In this paper, an ego-motion compensated tracking approach is presented which combines radar observations with the results of a contour-based image processing algorithm. The approach is able to handle all uncertainties of the system in a unified way without analytical linearization by using the Unscented transform. By that, the covariances of the system can be estimated more accurately. The paper describes both the image processing and the state estimation algorithms. Furthermore, results of several practical tests are presented.


title: Optimizing Census-based Semi Global Matching by genetic algorithms

abstract: Recent years have shown a great progress in self-driving vehicles and stereovision has proven to be a key aspect towards this goal. Semi-Global Matching (SGM) algorithm is among the best stereo solutions, capable of producing reliable results at reasonable cost. Census transform is generally preferred as a cost metric due to its robustness and invariance to lighting conditions. This paper proposes an original methodology for finding both the optimal Census mask and the best values for the penalties P 1 and P 2 in SGM by using genetic algorithms (GA). The obtained census masks are thoroughly analyzed and the best ones can be combined in a weighted center-symmetric census to increase the performance of SGM. Kitti test cases show that our GA-based censuses as well as our novel weighted center-symmetric census outperform dense, sparse and center-symmetric counterparts for Census only and SGM.


TAG Parsing with Neural Networks and Vector Representations of Supertags


title: Virtualized Congestion Control

abstract: New congestion control algorithms are rapidly improving datacenters by reducing latency, overcoming incast, increasing throughput and improving fairness. Ideally, the operating system in every server and virtual machine is updated to support new congestion control algorithms. However, legacy applications often cannot be upgraded to a new operating system version, which means the advances are off-limits to them. Worse, as we show, legacy applications can be squeezed out, which in the worst case prevents the entire network from adopting new algorithms. Our goal is to make it easy to deploy new and improved congestion control algorithms into multitenant datacenters, without having to worry about TCP-friendliness with non-participating virtual machines. This paper presents a solution we call virtualized congestion control. The datacenter owner may introduce a new congestion control algorithm in the hypervisors. Internally, the hypervisors translate between the new congestion control algorithm and the old legacy congestion control, allowing legacy applications to enjoy the benefits of the new algorithm. We have implemented proof-of-concept systems for virtualized congestion control in the Linux kernel and in VMware’s ESXi hypervisor, achieving improved fairness, performance, and control over guest bandwidth allocations.


title: Hardware-Acceleration of Short-Read Alignment Based on the Burrows-Wheeler Transform

abstract: The alignment of millions of short DNA fragments to a large genome is a very important aspect of the modern computational biology. However, software-based DNA sequence alignment takes many hours to complete. This paper proposes an FPGA-based hardware accelerator to reduce the alignment time. We apply a data encoding scheme that reduces the data size by 96 percent, and propose a pipelined hardware decoder to decode the data. We also design customized data paths to efficiently use the limited bandwidth of the DDR3 memories. The proposed accelerator can align a few hundred million short DNA fragments in an hour by using 80 processing elements in parallel. The proposed accelerator has the same mapping quality compared to the software-based methods.


title: Broadband Printed-Dipole Antenna and Its Arrays for 5G Applications

abstract: In this letter, we propose a broadband printed-dipole antenna and its arrays for fifth-generation (5G) wireless cellular networks. To realize a wide frequency range of operation, the proposed antenna is fed by an integrated balun, which consists of a folded microstrip line and a rectangular slot. For compactness, the printed dipole is angled at 45°. The single-element antenna yields an |S ::: 11 ::: | <;-10-dB bandwidth of 36.2% (26.5-38.2 GHz) and a gain of 4.5-5.8 dBi. We insert a stub between two printed-dipole antennas and obtain a low mutual coupling of <;-20 dB for a 4.8-mm center-to-center spacing (0.42-0.61 λ at 26-38 GHz). We demonstrate the usefulness of this antenna as a beamforming radiator by configuring 8-element linear arrays. Due to the presence of the stubs, the arrays resulted in a wider scanning angle, a higher gain, and a lower sidelobe level in the low-frequency region.


title: The Good, the Bad, and the Misguided: How Managers Inadvertently Encourage Deviant Behaviors

abstract: Executive Overview Recent estimates of the costs associated with deviant behavior in the workplace are staggering. While part of the managerial function requires the establishment of rules and policies that promote good customer service and product consistency, managers who lead with a firm hand or place too much pressure on sales quotas, may be unknowingly contributing to their employees' deviant behaviors. Managers must learn to identify the role that they play in triggering employee deviance. Once recognized, there is much that managers can do to ameliorate the triggers that encourage otherwise honest employees to engage in deviant behavior.


title: Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

abstract: Introduction ::: In pharmaceutical drug development and manufacturing, the amount and complexity of information of different types, ranging from raw experimental data to lab reports to complex mathematical models that needs to be stored, accessed, validated, manipulated, managed, and used for decision making is staggering. The information is often in different formats, used in different computer tools, making smooth interaction between these tools difficult. A common, explicit, and platform-independent vocabulary that is both machine accessible and human usable is needed to streamline the flow of information and knowledge generation.


Semantic File Systems


title: SDRIFE (baboon syndrome) due to paracetamol: case report.

abstract: The term "baboon syndrome" (BS) (recently known as symmetrical drug related intertriginous and flexural exanthema, SDRIFE) was introduced in 1984 to describe a specific skin eruption (resembling the red gluteal area of baboons) that occurred after systemic exposure to contact allergens. The crucial characteristics include a sharply defined symmetric erythema in the gluteal area and in the flexural or intertriginous folds without any systemic symptoms or signs. Because the term BS does not reflect the complete range of symptoms and is ethically problematic, it was replaced with a new term of SDRIFE. This term specifically refers to the distinctive clinical pattern of drug eruption induced by exposure to a systemically administered drug, presented as sharply demarcated symmetric erythematous areas of the gluteal/perianal area and/or V-shaped erythema of the inguinal/perigenital area (at least one other intertriginous/flexural localization) and absence of systemic symptoms and signs. We present a case of a 33-year-old man with SDRIFE due to Panadol® tablets (paracematol). On admission, there was a densely disseminated, symmetric, livid to erythematous maculopapular exanthema present in both axillae, the sides of the trunk, inguinally spreading towards the thighs, in cubital and popliteal fossae, on the back sides of the upper legs, and in the gluteal regions. Awareness of SDRIFE (BS) as an unusual drug reaction is especially important since the connection between skin eruption and drug exposure may easily be overlooked or misdiagnosed.


title: Offloading in Heterogeneous Networks: Modeling, Analysis, and Design Insights

abstract: Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of $M$ different RATs, each deploying up to $K$ different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize $\SINR$ coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.


title: Teaching The English Tense: Integrating Naive And Formal Grammars In An Intelligent Tutor For Foreign Language Teaching

abstract: A basic problem that must be dealt with in order to build an intelligent tutoring system (ITS) in the domain of foreign language teaching is that of establishing what kind of grammatical knowledge has to be included in the domain expert module. Two basic options are possible: (i) to use a naive or pedagogical grammar, comprising knowledge derived from textbooks and school grammars or (ii) to use one of the formal grammars developed by theoretical and computational linguists. The paper discusses the relationships between naive and formal grammars in foreign language teaching and presents, as a case study, an attempt to integrate the two approaches within ET (English Tutor), an ITS aimed at helping Italian students master English verb usage. More particularly, the paper focuses on the possibility of integrating a naive grammar into a systemic framework. The reliability of the proposed approach is currently being evaluated by means of a series of computational experiments with the Verb Generation Expert of ET.


title: IMITATION IN INFANCY: THE WEALTH OF THE STIMULUS

abstract: Imitation requires the initiator to solve the correspondence problem - to translate visual information from modelled action into matching motor output. It has been widely accepted for some 30 years that the correspondence problem is solved by a specialized, innate cognitive mechanism. This is the conclusion of a poverty of the stimulus argument, realized in the active intermodal matching model of imitation, which assumes that human neonates can imitate a range of body movements. An alternative, wealth of the stimulus argument, embodied in the associative sequence learning model of imitation, proposed that the correspondence problem is solved by sensorimoter learning, and that the experience necessary for this kind of learning is provided by the sociocultural environment during human development. In a detailed and wide-ranging review of research on imitation and imitation-relevant behaviour in infancy and beyond, we find substantially more evidence in favour of the wealth argument than of the poverty argument.


title: Integrating classification and association rule mining

abstract: Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). An efficient algorithm is also given for building a classifier based on the set of discovered CARs. Experimental results show that the classifier built this way is, in general, more accurate than that produced by the state-of-the-art classification system C4.5. In addition, this integration helps to solve a number of problems that exist in the current classification systems.


title: The 24-Core POWER9 Processor With Adaptive Clocking, 25-Gb/s Accelerator Links, and 16-Gb/s PCIe Gen4

abstract: The POWER9TM family of chips is fabricated in 14-nm silicon-on-insulator finFET technology using 17 levels of copper interconnect. The 695-mm2 24-core microprocessor features a new core based on an execution slice microarchitecture. The chip contains 8 billion transistors and has 120 MB of eDRAM L3 cache. The processor features an adaptive clock strategy to reduce timing margin needed during power supply droop events by embedding analog voltage-droop monitors that direct a digital phase-locked loop to immediately reduce clock frequency in response to a droop event. The scale-out chip IO subsystem supports up to 300-GB/s accelerator bandwidth using new 25-Gb/s links, 48 lanes of PCIeGen4 totaling 192 GB/s, eight ports of 2667 MT/s DDR4, and 256 GB/s of symmetric multiprocessor (SMP) interconnect. The scale-up chip adds additional SMP bandwidth and replaces the DDR4 memory interface with eight ports of differential memory interfaces with 230 GB/s of bandwidth resulting in 12.9 Tb/s of total off-chip bandwidth.


title: Gate charge control of high-voltage Silicon-Carbide (SiC) MOSFET in power converter applications

abstract: Switching devices such as Silicon Carbide (SiC) MOSFET present significant performance improvement in high frequency switching power converter applications. This paper presents a gate charge profile investigation of a Silicon Carbide MOSFET and gate driver circuit solutions to optimize both switching speed, power losses and EMI requirements. Main technology issues of Silicon carbide MOSFET are presented in order to understand the correlation between the technology solutions and the switching performances. Driver circuit requirements are highlighted and discussed. A Miller plateau identification method is presented. An independently circuit control principle of drain current and drain-source voltage is presented and discussed. The circuital technique allows a positive impact on the reduction of power losses and electromagnetic interferences level.


title: A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retriewal operations using a web/android application to explore load balancing — Sharding in MongoDB and its advantages

abstract: Our world has evolved to an optimal point of advancement. The extravagant growth has helped in the invention of technologies, industry standards, gadgets, and devices that produce enormous amount of data that all require an essential data management and manipulation system. The data acquired from the various input and output sources are indulged in providing a certain infrastructure are also susceptible to damages if not treated well which may result in loss of data. To overcome this loss, various strategies that run parallel to prevent such loss are being used, one such example is the NoSQL MongoDB. MongoDb is a cross-platform, document oriented database that provides, high performance and easy scalability ensuring effective data management with its prominent feature of auto sharding. Sharding splits the database across multiple servers, increasing the capacity and scalability as required. This feature handles distribution of data in different nodes to maximize disk space and dynamically load balance queries. Partitioning the databases appropriately is a major step that determines the efficiency of sharding. This involves choosing an index of the MongoDB, competently as a shared key for further horizontal scaling of the database. Our current research involves the study of this load balancer. This paper intends to ascertain the need for NoSQL databases in the present situation and emphasize advancement of document-oriented database — MongoDB in particular by describing with a quantitative example that SQL databases are prone to deterioration when data is over loaded and MongoDB comes with inbuilt load balancer which makes it a better solution in applications with high data load. We describe the technology of sharding — auto load balancing feature of MongoDB and hope to provide a comprehensive insight of the process.


title: Indications for Rotator Cuff Repair

abstract: Despite the popularity of surgical repair of rotator cuff tears, literature regarding the indications for and timing of surgery are sparse. We performed a systematic review of the literature to investigate factors influencing the decision to surgically repair symptomatic, full-thickness rotator cuff tears. Specifically, how do demographic variables, duration of symptoms, timing of surgery, physical examination findings, and size of tear affect treatment outcome and indications for surgery? We reviewed the best available evidence, which offers some guidelines for surgical decision making. Variables suggest earlier surgical intervention may be needed in the setting of weakness and substantial functional disability. With regard to demographic variables, the evidence is unclear regarding their association with treatment outcome. However, older chronological age does not seem to portend a worse outcome. Pending worker's compensation claims does seem to negatively affect treatment results. Further research is required to define the indications for surgery for full thickness rotator cuff tears. However, the design and conduct of an ethical study to obtain Level I evidence on this issue will be a major challenge. Level of Evidence: Level IV, systematic review. See Guidelines for Authors for a complete description of levels of evidence.


title: State of the Art in Research on Microgrids: A Review

abstract: The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.


An optimized approach for simultaneous horizontal data fragmentation and allocation in Distributed Database Systems (DDBSs)


title: The worst-case execution-time problem—overview of methods and survey of tools

abstract: The determination of upper bounds on execution times, commonly called worst-case execution times (WCETs), is a necessary step in the development and validation process for hard real-time systems. This problem is hard if the underlying processor architecture has components, such as caches, pipelines, branch prediction, and other speculative components. This article describes different approaches to this problem and surveys several commercially available tools1 and research prototypes.


title: Estimation of drivers' drowsiness level using a Neural Network Based ‘Error Correcting Output Coding’ method

abstract: This paper presents an improved method for estimating a driver's drowsiness level using eyelid closure and heart rate variability. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain an electrocardiogram (ECG) and eye-blink video sequences. The drivers were also monitored through a video camera. The changes in facial expression of the drivers were used as a standard index of drowsiness level. Error-Correcting Output Coding (ECOC) was employed as a multi-class classifier to estimate drowsiness level. The ordinary ECOC method was extended using a loss function for the decoding procedure to obtain class tendencies of each drowsiness level. We used a Neural Network-Based Decoding method to classify the drowsiness level. As a result, we obtained an extraordinarily high accuracy for estimation of drowsiness level. Comparisons were also made with the result of those from using the Loss-based Decoding ECOC (LD-ECOC).


LEADSHEETJS: A JAVASCRIPT LIBRARY FOR ONLINE LEAD SHEET EDITING


title: A Probabilistic Model for the Estimation of Pedestrian Crossing Behavior at Signalized Intersections

abstract: Active safety systems which assess highly dynamic traffic situations including pedestrians are required with growing demands in autonomous driving and ADAS. In this paper, we focus on one of the most hazardous traffic situations: the possible collision between a pedestrian and a turning vehicle at intersections. This paper presents a probabilistic model of pedestrian behavior to signalized crosswalks. For this purpose, we take not only pedestrian physical states but also contextual information into account. We propose a model based on the Dynamic Bayesian Network (DBN) which integrates relations among the intersection context information and the pedestrian behavior in the same way as human. Afterwards, the model jointly estimates their states by the particle filter. Experimental evaluation using real traffic data shows that this model is able to recognize the pedestrian crossing decision in advance from the traffic signal and pedestrian position information.


title: Establishing Smart City Technical Standards and Guidance: A Way Forward

abstract: Solving technical problems with complex systems and integrating the many technologies employed in these multifaceted structures has been a recurring theme in Smart Cities research. This paper presents an analysis of the reason this problem has been so well explored but persists with no solution widely available. The problem is viewed as a combination of Smart City needs, governance, and increasingly technically difficult decisions. The paper describes the requirements that must be met to develop a framework that can address this seeming intractable and expanding integration concern, identifies the governance processes that can be used to address this problem, and to manage integration in Smart Cities, The solution proposed is a formalized accepted and managed technology regulated environment introduced by governance groups composed of city planners/managers, citizen, stake holders, and technology delivery organizations. The solution requirements dictate the establishment of a standard that would guide the development and usage of automated, autonomous components, integrating dynamically with software agents. All of this working to rapidly optimize shared resources through error handling processes executing largely at no cost except those of processing time, meeting safety guidelines, satisfying operational monitoring needs, and meeting post issue liability guidelines. This technical standard would obligate developers and vendors to meet safety standards and accept liability for malfeasance. As initiating steps, Smart City managers must come together and establish a basic understanding of the goals and regulations, and the methodologies for implementing them.


title: Gamification framework for designing online training and collaborative working system in Statistics Indonesia

abstract: Gamification could be used to increase employee's learning motivation in an online training environment. While in the business world, gamification could be implemented as a service in an enterprise information system which is used to engage employees to finish their work well. Since training is conducted to improve the quality of employees, the integration of gamification's implementation in training and working environment could make a better organization in the end. This paper explores related works on gamification design process, instructional learning design, collaborative working and then making a conceptual framework which implements gamification design in integration of online training and collaborative working environment. Proposed framework is used to implement gamified enterprise information system which support survey's activity in Statistics Indonesia.


Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks


An End-to-End Text-Independent Speaker Identification System on Short Utterances


title: ERP C250 shows the elderly (cognitively normal, Alzheimer’s disease) store more stimuli in short-term memory than Young Adults do

abstract: Abstract Objective To determine how aging and dementia affect the brain’s initial storing of task-relevant and irrelevant information in short-term memory. Methods We used brain Event-Related Potentials (ERPs) to measure short-term memory storage (ERP component C250) in 36 Young Adults, 36 Normal Elderly, and 36 early-stage AD subjects. Participants performed the Number–Letter task, a cognitive paradigm requiring memory storage of a first relevant stimulus to compare it with a second stimulus. Results In Young Adults, C250 was more positive for the first task-relevant stimulus compared to all other stimuli. C250 in Normal Elderly and AD subjects was roughly the same to relevant and irrelevant stimuli in Intratrial Parts 1–3 but not 4. The AD group had lower C250 to relevant stimuli in part 1. Conclusions Both normal aging and dementia cause less differentiation of relevant from irrelevant information in initial storage. There was a large aging effect involving differences in the pattern of C250 responses of the Young Adult versus the Normal Elderly/AD groups. Also, a potential dementia effect was obtained. Significance C250 is a candidate tool for measuring short-term memory performance on a biological level, as well as a potential marker for memory changes due to normal aging and dementia.


title: Compatibility of Surfactants and Thermally Activated Persulfate for Enhanced Subsurface Remediation.

abstract: Limited aqueous availability of hydrophobic organic contaminants and nonaqueous phase liquids in subsurface environment may seriously impair the effectiveness of traditional in situ chemical oxidation (ISCO). To tackle the issue, a combination of surfactants and thermally activated persulfate was proposed to enhance the aqueous availability and consequent oxidation of organic contaminants. The compatibility of eight representative nonionic, monovalent anionic, and divalent anionic surfactants with persulfate at various temperatures was first studied, to identify suitable surfactants that have high aqueous stability and low oxidant demands to couple with thermally activated persulfate. C12-MADS (sodium dodecyl diphenyl ether disulfonate, a representative divalent anionic surfactant) stands out as the most compatible surfactant. Batch treatability study with coal tar, an example of challenging scenarios for traditional ISCO, was then conducted. The results show that C12-MADS can significantly enhance not only the oxidation of polyaromatic hydrocarbons contained in coal tar but also oxidant utilization efficiency, indicating the potential of the proposed coupling process for the treatment of organic contaminants with low aqueous availability.


title: Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

abstract: This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.


title: Infographics applied in design education

abstract: Infographics, the word information graphics, use graphic visual representations to show information, data or knowledge. Infographics applied in education, especially the design education, become more challenging to design teaching. This paper analyzes the relationship between infographics and design education. Design education prepares designers for business and society. It takes time to develop and innovate, but this paper will show some researches on how to apply infographics in design education and in the end, forecast the combination of both.


title: Observing presenters' use of visual aids to inform the design of classroom presentation software

abstract: Large classrooms have traditionally provided multiple blackboards on which an entire lecture could be visible. In recent decades, classrooms were augmented with a data projector and screen, allowing computer-generated slides to replace hand-written blackboard presentations and overhead transparencies as the medium of choice. Many lecture halls and conference rooms will soon be equipped with multiple projectors that provide large, high-resolution displays of comparable size to an old fashioned array of blackboards. The predominant presentation software, however, is still designed for a single medium-resolution projector. With the ultimate goal of designing rich presentation tools that take full advantage of increased screen resolution and real estate, we conducted an observational study to examine current practice with both traditional whiteboards and blackboards, and computer-generated slides. We identify several categories of observed usage, and highlight differences between traditional media and computer slides. We then present design guidelines for presentation software that capture the advantages of the old and the new and describe a working prototype based on those guidelines that more fully utilizes the capabilities of multiple displays.


title: The Design and Implementation of Datagram TLS

abstract: A number of applications have emerged over recent years that use datagram transport. These applications include real time video conferencing, Internet telephony, and online games such as Quake and StarCraft. These applications are all delay sensitive and use unreliable datagram transport. Applications that are based on reliable transport can be secured using TLS, but no compelling alternative exists for securing datagram based applications. In this paper we present DTLS, a datagram capable version of TLS. DTLS is extremely similar to TLS and therefore allows reuse of pre-existing protocol infrastructure. Our experimental results show that DTLS adds minimal overhead to a previously non-DTLS capable application.


title: Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution

abstract: In this paper, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual (DEGREE) network to progressively recover the high-frequency details. Different from most of the existing methods that aim at predicting high-resolution (HR) images directly, the DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning. DEGREE further augments the SR process with edge-preserving capability, namely the LR image and its edge map can jointly infer the sharp edge details of the HR image during the recurrent recovery process. To speed up its training convergence rate, by-pass connections across the multiple layers of DEGREE are constructed. In addition, we offer an understanding on DEGREE from the view-point of sub-band frequency decomposition on image signal and experimentally demonstrate how the DEGREE can recover different frequency bands separately. Extensive experiments on three benchmark data sets clearly demonstrate the superiority of DEGREE over the well-established baselines and DEGREE also provides new state-of-the-arts on these data sets. We also present addition experiments for JPEG artifacts reduction to demonstrate the good generality and flexibility of our proposed DEGREE network to handle other image processing tasks.


title: Microfluidic liquid metal based mechanically reconfigurable antenna using reversible gecko adhesive based bonding

abstract: This paper describes a new fabrication method for flexible electronics made of microfluidic structures filled with liquid metal in a thermoplastic elastomer, styrene ethylene butylene styrene (SEBS). The devices can reversibly be bonded to a larger system with gecko inspired dry adhesives. To demonstrate the feasibility of the method, a mechanically reconfigurable folded dipole has been fabricated and 55% frequency tuning with a 20% to 40% bandwidth for various frequencies has been achieved.


title: Game design principles for engaging cooperative play: core mechanics and interfaces for non-mimetic simulation of fire emergency response

abstract: Core mechanics are the activities that players repeat to play a game, the central aspects of play constrained by rules. Interfaces mediate play experiences, impacting engagement with core mechanics. We design core mechanics for gathering, integrating, and sharing information, based on team coordination practices of fire emergency responders. We connect these mechanics with interfaces that impact player engagement. Mechanics and interfaces combine into a non-mimetic simulation game, which eschews fire and smoke, in favor of re-creating information flows and team structures. We describe the iteration of mechanics and interface components as shaped by practice, pilot games, participatory redesign sessions, and long-term user studies. The result is integrated core mechanics that we develop from work practice and interface components that support engagement with them. From this data, we construct game design principles for engaging cooperative play: information distribution, modulating visibility, providing the right information in the right time, making predictable, and understandable representations for shared mental models.


title: Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements

abstract: PSI-BLAST is an iterative program to search a database for proteins with distant similarity to a query sequence. We investigated over a dozen modifications to the methods used in PSI-BLAST, with the goal of improving accuracy in finding true positive matches. To evaluate performance we used a set of 103 queries for which the true positives in yeast had been annotated by human experts, and a popular measure of retrieval accuracy (ROC) that can be normalized to take on values between 0 (worst) and 1 (best). The modifications we consider novel improve the ROC score from 0.758 +/- 0.005 to 0.895 +/- 0.003. This does not include the benefits from four modifications we included in the 'baseline' version, even though they were not implemented in PSI-BLAST version 2.0. The improvement in accuracy was confirmed on a small second test set. This test involved analyzing three protein families with curated lists of true positives from the non-redundant protein database. The modification that accounts for the majority of the improvement is the use, for each database sequence, of a position-specific scoring system tuned to that sequence's amino acid composition. The use of composition-based statistics is particularly beneficial for large-scale automated applications of PSI-BLAST.


title: Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images

abstract: Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task.


title: Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

abstract: We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome. However, synthetic images do not resemble real images exactly, and using them for training can result in suboptimal performance. It was recently shown that for exemplar-based approaches, it is possible to learn a mapping from the exemplar representations of real images to the exemplar representations of synthetic images. In this paper, we show that this approach is more general, and that a network can also be applied after the mapping to infer a 3D pose: At run-time, given a real image of the target object, we first compute the features for the image, map them to the feature space of synthetic images, and finally use the resulting features as input to another network which predicts the 3D pose. Since this network can be trained very effectively by using synthetic images, it performs very well in practice, and inference is faster and more accurate than with an exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for 3D object pose estimation from color images, and the NYU dataset for 3D hand pose estimation from depth maps. We show that it allows us to outperform the state-of-the-art on both datasets.


title: A Framework for Cyberbullying Detection in Social Network

abstract: Due to tremendous growth and popularity of current web technologies and social networking, online presence of the person has become permanent now. Through social networking connections, people usually express feelings, opinions, emotions and the most commonly seen is discussing the daily incidences which may include private or public talks. Especially in teenagers and young adults, expansion of social connections in terms of friendships has become fast without careful consideration of what is being shared and with whom. Such environment has become an avenue for cyber bullying, where abusive contents in terms of text messages or images are posted on the social network. For victims, this kind of behavior can lead to depression and other severe life-threatening problems. Therefore, measures need to be put in place to monitor and detect potentially harmful online activities. Manual check for all posts on social network is highly impossible because of large number of users. Therefore, in this paper, we have proposed a framework deployed for the detecting negative online interactions in terms of abusive contents carried out through text messages as well as images. The combination of text & image analysis techniques is considered as a suitable platform for the detection of potential cyber bullying threats.


title: A lattice-based approach to multi-robot motion planning for non-holonomic vehicles

abstract: Coordinating fleets of autonomous, non-holonomic vehicles is paramount to many industrial applications. While there exists solutions to efficiently calculate trajectories for individual vehicles, a ...


title: A Density-Based Clustering Algorithm for High-Dimensional Data with Feature Selection

abstract: The dsensity-based spatial clustering of applications with noise (DBSCAN) is a kind of the density-based representative algorithms. It has been widely used in more and more fields due to its ability to detect clusters of different sizes and shapes. However, the algorithm becomes unstable when dealing with the high dimensional data. To solve the problem, an improved DBSCAN algorithm based on feature selection (FS-DBSCAN) is proposed. The performance of this algorithm is testified by a series of simulations on real world datasets. Comparisons with the DBSCAN algorithm demonstrate the superiority of the proposed algorithm.


title: Driver Status Monitoring Systems for Smart Vehicles Using Physiological Sensors: A safety enhancement system from automobile manufacturers

abstract: Automobiles provide a convenient form of transportation, and the number of automobiles in the world has been increasing rapidly, from 193 million in 1970 to more than 796 million in recent years [1]. However, automobiles have created a number of serious problems, such as accidents, traffic congestion, and air pollution, among which traffic accidents are one of the most serious and urgent problems threatening the safety of automobile users.


title: A bottom-up oblique decision tree induction algorithm

abstract: Decision tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes, whereas univariate trees can only perform axis-parallel splits. The majority of the oblique and univariate decision tree induction algorithms perform a top-down strategy for growing the tree, relying on an impurity-based measure for splitting nodes. In this paper, we propose a novel bottom-up algorithm for inducing oblique trees named BUTIA. It does not require an impurity-measure for dividing nodes, since we know a priori the data resulting from each split. For generating the splitting hyperplanes, our algorithm implements a support vector machine solution, and a clustering algorithm is used for generating the initial leaves. We compare BUTIA to traditional univariate and oblique decision tree algorithms, C4.5, CART, OC1 and FT, as well as to a standard SVM implementation, using real gene expression benchmark data. Experimental results show the effectiveness of the proposed approach in several cases.


title: Summary Transfer: Exemplar-Based Subset Selection for Video Summarization

abstract: Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category/genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings.


title: Microstrip antenna array's gain enhancement via Electromagnetic Band Gap structures

abstract: For a point-to-point communication system, an antenna array with high gain is essential and in this work, by incorporating electromagnetic band gap (EBG) structures at a specific location within the antenna's peripherals, the gain of the antenna could be enhanced further. The EBG structures reduced mutual couplings between the adjacent patches thus make them radiates effectively. On the other hand, the EBG structures located at the edge of the antenna suppressed surface waves and prevent it from propagating to the edges, thus reduced the side and back lobes. This further enhances the front radiation characteristics of the antenna and also increased the gain. The techniques and guidelines on how to achieve the objectives is presented and the simulated and measured results complement each other.


Solving Steiner tree problems in graphs to optimality


title: Underwater image enhancement by wavelet based fusion

abstract: The image captured in water is hazy due to the several effects of the underwater medium. These effects are governed by the suspended particles that lead to absorption and scattering of light during image formation process. The underwater medium is not friendly for imaging data and brings low contrast and fade color issues. Therefore, during any image based exploration and inspection activity, it is essential to enhance the imaging data before going for further processing. This paper presents a wavelet-based fusion method to enhance the hazy underwater images by addressing the low contrast and color alteration issues. The publicly available hazy underwater images are enhanced and analyzed qualitatively with some state of the art methods. The quantitative study of image quality depicts promising results.


title: LikeMiner: a system for mining the power of 'like' in social media networks

abstract: Social media is becoming increasingly ubiquitous and popular on the Internet. Due to the huge popularity of social media websites, such as Facebook, Twitter, YouTube and Flickr, many companies or public figures are now active in maintaining pages on those websites to interact with online users, attracting a large number of fans/followers by posting interesting objects, e.g., (product) photos/videos and text messages. 'Like' has now become a very popular social function by allowing users to express their like of certain objects. It provides an accurate way of estimating user interests and an effective way of sharing/promoting information in social media. In this demo, we propose a system called LikeMiner to mine the power of 'like' in social media networks. We introduce a heterogeneous network model for social media with 'likes', and propose 'like' mining algorithms to estimate representativeness and influence of objects. The implemented prototype system demonstrates the effectiveness of the proposed approach using the large scale Facebook data.


title: OCEANUS: a spatio-temporal data stream system prototype

abstract: Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (moving objects tracking, sensor networks, fleet management, real-time intelligent transportation systems, etc.) process huge volume of continuous streaming data, i.e. data that is produced incrementally over time, rather than being available in full before processing. Data stream management systems (DSMS) have been developed to manage continuous data streams. Usually based on relational paradigm, they have rudimentary support for spatial data. Recent research efforts in data stream management systems focus mainly on processing continuous queries over traditional data streams, and only a few papers addressed spatio-temporal continuous queries. In this paper we present OCEANUS, an ongoing effort to extend TelegraphCQ DSMS with spatial support providing a platform for spatio-temporal streaming applications.


title: Multivariate outlier detection in medicare claims payments applying probabilistic programming methods

abstract: The rising elderly population continues to demand more cost-effective healthcare programs. In particular, Medicare is a vital program serving the needs of the elderly in the United States. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for, and risk of, fraudulent activities. Out of the many possible factors for the rising cost of healthcare, fraud is a major contributor, but its impacts can be lessened through the use of fraud detection methods. In this paper, we assess possible illegitimate activities by looking at the amounts paid to providers for services rendered to patients. We propose a novel method for fraud detection that focuses on discovering outliers in Medicare payment data using multiple predictors as model inputs. Our multivariate outlier detection approach is twofold: (1) create a Multivariate Adaptive Regression Splines model to produce studentized residuals and, (2) use the residuals as input into a general univariate outlier detection model, based on full Bayesian inference, using probabilistic programming. Using this approach, we are able to incorporate multiple variables to detect outliers with a model that provides probability distributions, with credible intervals, rather than just point values, as with most common outlier detection methods. Additionally, these credible intervals further enhance confidence that the detected outliers should in fact be considered outlying values, thus possibly fraudulent activities. Our results show that the successful detection of these possibly fraudulent activities can provide effective and meaningful results for further investigation within various medical specialties.


title: Optimized Load Modulation Network for Doherty Power Amplifier Performance Enhancement

abstract: In this paper, a new load modulation network (LMN) is proposed for the Doherty power amplifier (DPA). By adjusting the transmission line characteristic impedances in the LMN, efficiency degradation due to the incomplete load modulation caused by the current unbalance between sub-amplifiers can be alleviated. Comprehensive theoretical analysis is conducted to investigate the impact of imperfect current profile on the overall efficiency and give rise to the judicious solution. To validate the new theory, two DPA prototypes based on GaAs pseudomorphic HEMTs are implemented for comparative purposes. Measurement results show that 8% and 13% greater power-added efficiency values are achieved at saturation and 6-dB output back-off point with the proposed method when compared with that of the conventional one. Additionally, the output 1-dB compression point is also improved by 2.9 dB. In particular, to the best of the authors' knowledge, this work provides a solution to address the inherent lower current driving ability for the peaking amplifier directly at the output part of a single-input DPA.


title: Object-Centric Representation Learning from Unlabeled Videos

abstract: Supervised (pre-)training currently yields state-of-the-art performance for representation learning for visual recognition, yet it comes at the cost of (1) intensive manual annotations and (2) an inherent restriction in the scope of data relevant for learning. In this work, we explore unsupervised feature learning from unlabeled video. We introduce a novel object-centric approach to temporal coherence that encourages similar representations to be learned for object-like regions segmented from nearby frames. Our framework relies on a Siamese-triplet network to train a deep convolutional neural network (CNN) representation. Compared to existing temporal coherence methods, our idea has the advantage of lightweight preprocessing of the unlabeled video (no tracking required) while still being able to extract object-level regions from which to learn invariances. Furthermore, as we show in results on several standard datasets, our method typically achieves substantial accuracy gains over competing unsupervised methods for image classification and retrieval tasks.


title: Clinical and Laboratory Evaluation of Specific Chemiluminescence Assays for Intact and Total Proinsulin

abstract: Measurement of proinsulin is an important tool in the assessment of pancreatic β cell function in patients with type 2 diabetes. The goal of this study was to perform a technical and clinical evaluation of two specific chemiluminescence assays (CLIA) for the determination of intact and total proinsulin in comparison to a radioimmunoassay (RIA) method for the measurement of total proinsulin. A total of 191 serum samples from patients with type 2 diabetes were used to perform a regression analysis. The total proinsulin CLIA showed higher proinsulin levels than the two other proinsulin assays (mean ± SD: 55.9±58.1 μmol/l, p < 0.001 in both cases). The intact proinsulin CLIA (22.5±20.9 pmol/l) gave lower values than the RIA for total proinsulin (31.9±25.4 pmol/l, p < 0.001 vs. CLIA, r = 0.948). The RIA has a 95% cross-reactivity to des31,32-proinsulin, which is secreted during the process of β cell deterioration. The intact proinsulin CLIA has virtually no cross-reactivity with des31,32-proinsulin (1.4%) and is therefore more specific for intact proinsulin than the RIA. This test does not measure further degradation products, in contrast to the total proinsulin CLIA. The CLIA is, therefore, more specific for total proinsulin measurement than the RIA. Both CLIAs could be performed much faster (4 hours) than the RIA method (75 hours/ 4 days). In conclusion, the CLIA methods show improved qualitative outcomes, higher specificity and several technical advantages over the RIA method.


title: ON SUPERVISED AND SEMI-SUPERVISED k-NEAREST NEIGHBOR ALGORITHMS

abstract: The k-nearest neighbor (kNN) is one of the simplest classifl- cation methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this pa- per kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classiflcation settings. Semi-supervision is performed by using data-dependent kernels.


title: Simple effective image and video color correction using quaternion distance metric

abstract: Color correction or stabilization methods for image and video are important in signal processing areas. We can obtain image and video with better quality and more pleasing appearance by applying these correction methods. Besides, color corrected image and video also can be used to enhance performance of many post-processing techniques, such as pattern recognition, object tracking, human face matching, etc. In this paper, we have proposed a color correction algorithm based on quaternion algebra for color image and video. First, our method uses quaternion color distance metric to compute the pixel-based color differences between target image (or video frame) and reference image/video frame with similar appearance but slightly different illumination condition. After the color differences of image pixels are obtained, we use these pixels to calculate a correction matrix and use this matrix to correct target image and video frame. Our method can be applied to image and video with fast processing speed and has good performance when compared it with state-of-the-art method in the literature. The algorithm is also simple, effective, and easy to be implemented.


title: Input Match and Output Balance Improvement of Marchand Balun With Connecting Line

abstract: A novel method allowing for improvement of input match and output balance of Marchand balun with connecting line has been presented. The proposed compensation technique together with the ideal Marchand balun design formulas allow for achieving an arbitrary impedance transformation ratio even for the circuit having a long connecting line. The theoretical analysis has been verified by EM simulations and measurements of a Marchand balun composed of 3 dB coupled-line sections and 30° transmission line operating at the center frequency f 0 = 1.15 GHz and terminated at input and outputs with 50 Ω and 100 Ω, respectively.


title: Segmentation from Natural Language Expressions

abstract: In this paper we approach the novel problem of segmenting an image based on a natural language expression. This is different from traditional semantic segmentation over a predefined set of semantic classes, as e.g., the phrase “two men sitting on the right bench” requires segmenting only the two people on the right bench and no one standing or sitting on another bench. Previous approaches suitable for this task were limited to a fixed set of categories and/or rectangular regions. To produce pixelwise segmentation for the language expression, we propose an end-to-end trainable recurrent and convolutional network model that jointly learns to process visual and linguistic information. In our model, a recurrent neural network is used to encode the referential expression into a vector representation, and a fully convolutional network is used to a extract a spatial feature map from the image and output a spatial response map for the target object. We demonstrate on a benchmark dataset that our model can produce quality segmentation output from the natural language expression, and outperforms baseline methods by a large margin.


title: Surveying stock market portfolio optimization techniques

abstract: Optimizing a stock market portfolio requires decision making at two distinct stages, first is to select the stocks and second is to assign distribution of investment amount among these selected stocks. Given the historical data of stocks, the role of optimization models is to select stocks and assign portfolio proportion to the selected stocks. Selection and weight assignment to stocks are co-occurring activities. Investors prime motive is to maximize the return and minimize the risk of portfolio. Stock market is uncertain and volatile and therefore, Artificial Intelligence, Machine Learning and Soft Computing techniques are viable candidates which can help in optimization and making decisions using such data. This paper surveys the research carried out in the domain of stock market portfolio optimization. Paper compares research efforts in the domain on the basis of techniques used, risk models and stock markets considered. It is observed from the surveyed papers that Artificial Intelligence, Machine Learning and Soft Computing techniques are widely accepted for studying and evaluating stock market behavior and optimizing portfolios.


title: Personalized recommender system based on social relations

abstract: Advent of the Social Web and the ever increasing popularity of Web 2.0 applications, has led to a massive amount of information. Therefore, users have difficulties in finding their desired information according to their interests and preferences. To address this issue, recommender systems have been emerged. These systems try to provide users with the most relevant and suitable information they need by investigating their preferences as well as their demographic information. With the growing development of social networks and the number of users in them, the value of information in these systems has also increased. This information in social networks can be used to improve the precision of recommender systems. In this paper we present a novel recommender system that makes use of user's social relationships in two levels: computing the similarity between them and identifying user's neighbors set. Our experimental results show that the proposed model outperforms Collaborative Filtering (CF) based recommender system in terms of recommendation accuracy.


title: Performance Analysis of Consensus Algorithm in Private Blockchain

abstract: Blockchain technology has the characteristics of decentralization, security and non-modifiable. The consensus algorithm is the core of blockchain framework, which solves the problem of mutual trust among nodes in the distributed network. Selecting the appropriate consensus algorithm or not directly affects the performance of blockchain. However, existing private blockchain platform lacks theory and data support for the performance analysis of the consensus algorithm. This paper proposes a method to evaluate the performance of consensus algorithm in private blockchain platforms of Ethereum and Hyperledger Fabric. Through quantitative analysis of latency and throughput, we obtain the performance evaluation results of consensus algorithms with different numbers of transactions. The results show that the consensus mechanism induces performance bottleneck. Besides, Practical Byzantine Fault Tolerance (PBFT) consistently outperforms Proof-of-Work (PoW) in terms of latency and throughput under varying workloads. This paper contributes to providing not only the quantitative data support for the further research of consensus algorithm, but also the guidance to the blockchain practitioners in the selection of consensus mechanism.


title: Sports Camera Calibration via Synthetic Data

abstract: Calibrating sports cameras is important for autonomous broadcasting and sports analysis. Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data. First, we develop a novel camera pose engine. The camera pose engine has only three significant free parameters so that it can effectively generate a lot of camera poses and corresponding edge (i.e, field marking) images. Then, we learn compact deep features via a siamese network from paired edge image and camera pose and build a feature-pose database. After that, we use a novel two-GAN (generative adversarial network) model to detect field markings in real images. Finally, we query an initial camera pose from the feature-pose database and refine camera poses using truncated distance images. We evaluate our method on both synthetic and real data. Our method not only demonstrates the robustness on the synthetic data but also achieves the state-of-the-art accuracy on a standard soccer dataset and very high performance on a volleyball dataset.


title: Head pose-free eye gaze prediction for driver attention study

abstract: Driver's gaze direction is an indicator of driver state and plays a significantly role in driving safety. Traditional gaze zone estimation methods based on eye model have disadvantages due to the vulnerability under large head movement. Different from these methods, an appearance-based head pose-free eye gaze prediction method is proposed in this paper, for driver gaze zone estimation under free head movement. To achieve this goal, a gaze zone classifier is trained with head vectors and eye image features by random forest. The head vector is calculated by Pose from Orthography and Scaling with ITerations (POSIT) where a 3D face model is combined with facial landmark detection. And the eye image features are derived from eye images which extracted through eye region localization. These features are presented as the combination of sparse coefficients by sparse encoding with eye image dictionary, having good potential to carry information of the eye images. Experimental results show that the proposed method is applicable in real driving environment.


title: An optimized MPPT circuit for thermoelectric energy harvester for low power applications

abstract: A novel approach of maximum power point tracking (MPPT) control method for thermoelectric generator (TEG) is reported in this paper. The maximum power point tracking is achieved by using impedance matching principle. Unlike the existing MPPT methods, the proposed implementation does not require any voltage and current sensors and computations of any nature. The implementation of the MPPT control circuit is carried out using buck-boost DC/DC converter and a simple gating signal generator using only one Op-amp. The proposed method needs the least power consumption of the MPPT control circuit — less than 250μW. Experimental tests results obtained from series connected 3×TEGs (24 mm × 6 mm) show an average power harvesting of 5.6mW at ΔT of 24°C. The proposed thermoelectric energy harvester is used to power a battery which subsequently feeds a sensor node. The low power thermoelectric energy harvester system provides an overall power management circuit efficiency of up to 85%. The efficiency calculation takes care of power losses in the MPPT circuit.


title: Compact Broadband Microstrip Crossover With Isolation Improvement and Phase Compensation

abstract: In this letter, we present a broadband microstrip crossover structure in two different configurations. Both structures have compact size, i.e., less than 10 mm $\,\times \,$ 10 mm. Across the measured band from dc to 10 GHz, the structure 1 has less than 1 dB insertion loss, more than 20 dB return loss and at least 23.3 dB isolation. Within the same band, the structure 2 has less than 1 dB insertion loss, more than 22 dB return loss and at least 18.9 dB isolation. Phase difference between two paths in both structures is kept in less than 5 $^{\circ}$ .


title: Language Identification for Creating Language-Specific Twitter Collections

abstract: Social media services such as Twitter offer an immense volume of real-world linguistic data. We explore the use of Twitter to obtain authentic user-generated text in low-resource languages such as Nepali, Urdu, and Ukrainian. Automatic language identification (LID) can be used to extract language-specific data from Twitter, but it is unclear how well LID performs on short, informal texts in low-resource languages. We address this question by annotating and releasing a large collection of tweets in nine languages, focusing on confusable languages using the Cyrillic, Arabic, and Devanagari scripts. This is the first publicly-available collection of LID-annotated tweets in non-Latin scripts, and should become a standard evaluation set for LID systems. We also advance the state-of-the-art by evaluating new, highly-accurate LID systems, trained both on our new corpus and on standard materials only. Both types of systems achieve a huge performance improvement over the existing state-of-the-art, correctly classifying around 98% of our gold standard tweets. We provide a detailed analysis showing how the accuracy of our systems vary along certain dimensions, such as the tweet-length and the amount of in- and out-of-domain training data.


title: Optic disc detection in color fundus images using ant colony optimization

abstract: Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age in developed countries. However, loss of vision could be prevented by an early detection of the disease and, therefore, by a regular screening program to detect retinopathy. Due to its characteristics, the digital color fundus photographs have been the easiest way to analyze the eye fundus. An important prerequisite for automation is the segmentation of the main anatomical features in the image, particularly the optic disc. Currently, there are many works reported in the literature with the purpose of detecting and segmenting this anatomical structure. Though, none of them performs as needed, especially when dealing with images presenting pathologies and a great variability. Ant colony optimization (ACO) is an optimization algorithm inspired by the foraging behavior of some ant species that has been applied in image processing with different purposes. In this paper, this algorithm preceded by anisotropic diffusion is used for optic disc detection in color fundus images. Experimental results demonstrate the good performance of the proposed approach as the optic disc was detected in most of all the images used, even in the images with great variability.


title: Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO).

abstract: Chronic kidney disease (CKD) is a worldwide public health problem, with adverse outcomes of kidney failure, cardiovascular disease (CVD), and premature death. A simple definition and classification of kidney disease is necessary for international development and implementation of clinical practice guidelines. Kidney Disease: Improving Global Outcomes (KDIGO) conducted a survey and sponsored a controversies conference to (1) provide a clear understanding to both the nephrology and nonnephrology communities of the evidence base for the definition and classification recommended by Kidney Disease Quality Outcome Initiative (K/DOQI), (2) develop global consensus for the adoption of a simple definition and classification system, and (3) identify a collaborative research agenda and plan that would improve the evidence base and facilitate implementation of the definition and classification of CKD. The K/DOQI definition and classification were accepted, with clarifications. CKD is defined as kidney damage or glomerular filtration rate (GFR) <60 mL/min/1.73 m(2) for 3 months or more, irrespective of cause. Kidney damage in many kidney diseases can be ascertained by the presence of albuminuria, defined as albumin-to-creatinine ratio >30 mg/g in two of three spot urine specimens. GFR can be estimated from calibrated serum creatinine and estimating equations, such as the Modification of Diet in Renal Disease (MDRD) Study equation or the Cockcroft-Gault formula. Kidney disease severity is classified into five stages according to the level of GFR. Kidney disease treatment by dialysis and transplantation should be noted. Simple, uniform classifications of CKD by cause and by risks for kidney disease progression and CVD should be developed.


title: A Mask Regional Convolutional Neural Network Model for Segmenting Real Time Traffic Images

abstract: This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captured by traffic cameras. The approach developed here comprises of image pre-processing, object detection followed by segmentation. Mask R-CNN model not only segments the image, but also surrounds the image with bounding boxes and assigns class names to the individual objects e.g. Car, Truck, Bus, Bicycle, Person etc. The model is trained with the annotated MS COCO Training dataset. To improve the performance of Mask R-CNN over noisy images, here pre-processing algorithms like Non Local Means (NLM) filter denoising and Median filter denoising are used. The testing is carried out on a subset of MS COCO Test dataset which comprises of only traffic images. The improved performance is demonstrated using parameters: increased correct object detections and corresponding confidence value, reduced incorrect object detections and corresponding confidence value, and an overall enhanced segment mask area accuracy.


title: Who is Authoritative? Understanding Reputation Mechanisms in Quora

abstract: As social Q&A sites gain popularity, it is important to understand how users judge the authoritativeness of users and content, build reputation, and identify and promote high quality content. We conducted a study of emerging social Q&A site Quora. First, we describe user activity on Quora by analyzing data across 60 question topics and 3917 users. Then we provide a rich understanding of issues of authority, reputation, and quality from in-depth interviews with ten Quora users. Our results show that primary sources of information on Quora are judged authoritative. Also, users judge the reputation of other users based on their past contributions. Social voting helps users identify and promote good content but is prone to preferential attachment. Combining social voting with sophisticated algorithms for ranking content might enable users to better judge others’ reputation and promote high quality content.


Negative and Positive Reappraisal After a Romantic Break-up


Extracting Persian-English Parallel Sentences from Document Level Aligned Comparable Corpus using Bi-Directional Translation


title: Classification of Filipino Speech Rhythm Using Computational and Perceptual Approach

abstract: This study incorporates computational and perceptual methods to classify Filipino speech rhythm. Speech rhythm may be described as a language‟s distinguishing durational sound pattern, resulting from the complexity of the language‟s syllable inventory. 1 Computational methods involve the correlation of rhythm-types to acoustic features such as the vocalic and consonantal intervals, one of which is the implementation of Multivariate Discriminant Analysis (MDA). Perceptual methods involve contrasting the rhythm of an unclassified language from prototype syllable-timed and stress-timed sentences. In order to isolate rhythm from speech, a data-stripping technique called flat sasasa resynthesis was implemented wherein the consonants are replaced with /s/ and vowels with /a/, producing a resynthesized alternating “sasasa” sounds at a constant pitch (F0). The rhythm discrimination and classification were closely examined for consistency between the data modeling and listening test results. The computational experiment was able to show that an MDA classifier trained to distinguish English and Japanese sentences tend to label Filipino sentences as Japanese 67% of the time, vis-a-vis the perceptual experiment showing that the listeners perceive Filipino to be more similar with Japanese, this study shows computational and perceptual validation that Filipino is syllable-timed, just like Japanese.


title: hatS: A Heterogeneity-Aware Tiered Storage for Hadoop

abstract: Hadoop has become the de-facto large-scale data processing framework for modern analytics applications. A major obstacle for sustaining high performance and scalability in Hadoop is managing the data growth while meeting the ever higher I/O demand. To this end, a promising trend in storage systems is to utilize hybrid and heterogeneous devices - Solid State Disks (SSD), ram disks and Network Attached Storage (NAS), which can help achieve very high I/O rates at acceptable cost. However, the Hadoop Distributed File System (HDFS) that is unable to exploit such heterogeneous storage. This is because HDFS works on the assumption that the underlying devices are homogeneous storage blocks, disregarding their individual I/O characteristics, which leads to performance degradation. In this paper, we present hatS, a Heterogeneity-Aware Tiered Storage, which is a novel redesign of HDFS into a multi-tiered storage system that seamlessly integrates heterogeneous storage technologies into the Hadoop ecosystem. hatS also proposes data placement and retrieval policies, which improve the utilization of the storage devices based on their characteristics such as I/O throughput and capacity. We evaluate hatS using an actual implementation on a medium-sized cluster consisting of HDDs and two types of SSDs (i.e., SATA SSD and PCIe SSD). Experiments show that hatS achieves 32.6% higher read bandwidth, on average, than HDFS for the test Hadoop jobs (such as Grep and Test DFSIO) by directing 64% of the I/O accesses to the SSD tiers. We also evaluate our approach with trace-driven simulations using synthetic Facebook workloads, and show that compared to the standard setup, hatS improves the average I/O rate by 36%, which results in 26% improvement in the job completion time.


title: Robustness of factors influencing social media usage/adoption amongst SMEs in developing countries: a case of Pakistan

abstract: The purpose of this study is to analyze the durability of the factors influencing social media usage or adoption by the SMEs of Pakistan. Considering the literature, this study integrates some factors in the model of TAM (Technology Acceptance Model) to analyze the durability of factors influencing the usage/adoption of famous social media i.e. Facebook by the SMEs in Pakistan. Using the bootstrapping technique through computer software (smartPLS3), we statistically analyzed the hypothesis developed for the survey-based data of 95 SMEs. Empirical findings show that the factors integrated in PU-Perceived Usefulness (Increase in Sales & Increase in Customers) are significantly impactful in Facebook usage/adoption by the SMEs presently as well as in the long run. Except Cost Effectiveness, other two factors integrated in PEOU-Previewed Ease of Use (i.e. Communication and Smartphones usage) are also the influencing factors not only presently but in long term as well. There are limited studies that focus on the technological factors such as diffusion of digital devices other than personal and organizational factors to assess the influencing factors of Facebook usage/adoption by the SMEs. Therefore we attempt to fill this gap in the literature and add smartphone diffusion as one of the core factors influencing Facebook adoption by the SMEs.


title: A Case Study of Downstream Baroclinic Development over the North Pacific Ocean. Part II: Diagnoses of Eddy Energy and Wave Activity

abstract: The sequential development of a western, and then an eastern, North Pacific cyclone is examined in terms of eddy energy and a phase-independent wave activity. Based on the propagation of both a contiguous wave activity center and eddy energy, the development of the western cyclone appears to influence its downstream neighbor. A quantitative comparison of these two diagnoses is made in terms of group velocity, and only minor differences are found during much of the initial evolution. It is only once the tropopause undulations lose their wavelike appearance (at which point, application of the group-velocity concept itself becomes quite tenuous) that the downstream propagation of eddy energy seems faster than that of wave activity. Conventional methods of tracking this wave packet are also briefly discussed.


title: Generalized Gompertz-power series distributions

abstract: In this paper, we introduce the generalized Gompertz-power series class of distributions which is obtained by compounding generalized Gompertz and power series distributions. This compounding procedure follows same way that was previously carried out by Silva et al. (2013) and Barreto-Souza et al. (2011) in introducing the compound class of extended Weibull-power series distribution and the Weibull-geometric distribution, respectively. This distribution contains several lifetime models such as generalized Gompertz, generalized Gompertz-geometric, generalized Gompertz-poisson, generalized Gompertz-binomial distribution, and generalized Gompertz-logarithmic distribution as special cases. The hazard rate function of the new class of distributions can be increasing, decreasing and bathtub-shaped. We obtain several properties of this distribution such as its probability density function, Shannon entropy, its mean residual life and failure rate functions, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented, and sub-models of the distribution are studied in details.


title: The Late Qing Dynasty Diplomatic Transformation: Analysis from an Ideational Perspective

abstract: During the 19th century, the Imperial Qing Dynasty gradually waned, as the power of Western capitalist states surged and consolidated. 1 The formidable military force commanded by the Western powers gained them entry into China’s vast territory, wherein to extend their power parameters. Yet, the constant pressure exerted by Western gunboats, and this eminently advanced civilization as a whole, did little to sway the Qing government from its traditional mode of Chinese Dynastic diplomacy. This raises the questions, why should the Qing government seriously imperil state security throughout its two-decade, persistent adherence to outmoded statecraft? And why did the transformation in foreign policy that lead to the Qing government’s diplomatic modernization occur so rapidly after the signing of the Treaty of Peking? A rationalist approach within the academic field of international relations (IR) generally cites power and interests as the primary causes or determinants of a state’s external behaviour, ideas being perceived as pre-determined factors that have little, or at most, epiphenomenal, impact. 2 The authors of this essay believe that ideas are an important variable, equal in significance to power and interests, when interpreting state behaviour. The late Qing Dynasty had fundamental reasons for transforming its diplomacy; one was the direct military challenge of the Western powers,


title: Deep Embedding Forest: Forest-based Serving with Deep Embedding Features

abstract: Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving. Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.