title: Tip Refinement Grafts: The Designer Tip

abstract: In cosmetic rhinoplasty, the patient's satisfaction is most often determined by the quality of the tip surgery, but perfecting a technique for consistently attractive tips can be challenging. As a result, rhinoplasty surgery is now entering a new era of “designer tip” operations, wherein surgeons can employ a combination of open suture tip techniques and tip refinement grafts to achieve consistent results. The grafts are made from excised lateral crural cartilage and, depending upon the specific aesthetic goals, the shape can include the following: domal, shield, diamond, folded, or combination. It is possible to alter dome-defining points, tip point, projection, definition, volume, and size and shape. A study of 100 consecutive female rhinoplasties indicated that tip sutures alone were used in 36% of cases, while a tip refinement graft was added to a sutured tip in 59% of cases.


title: Density Based k-Nearest Neighbors Clustering Algorithm for Trajectory Data

abstract: With widespread availability of low cost GPS, cellular phones, satellite imagery, robotics, Web traffic monitoring devices, it is becoming possible to record and store data about the movement of people and objects at a large amount. While these data hide important knowledge for the enhancement of location and mobility oriented infrastructures and services, by themselves, they demand the necessary semantic embedding which would make fully automatic algorithmic analysis possible. Clustering algorithm is an important task in data mining. Clustering algorithms for these moving objects provide new and helpful information, such as Jam detection and significant Location identification. In this paper we present augmentation of relative density-based clustering algorithm for movement data or trajectory data. It provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves the problem of being very sensitive to the user-defined parameters in DBSCAN. In this paper we consider two real datasets of moving vehicles in Milan (Italy) and Athens (Greece) and extensive experiments were conducted.


title: Medical imaging and computers in the diagnosis of breast cancer

abstract: Computer-aided diagnosis (CAD) and quantitative image analysis (QIA) methods (i.e., computerized methods of analyzing digital breast images: mammograms, ultrasound, and magnetic resonance images) can yield novel image-based tumor and parenchyma characteristics (i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer management plans). The role of QIA/CAD has been expanding beyond screening programs towards applications in risk assessment, diagnosis, prognosis, and response to therapy as well as in data mining to discover relationships of image-based lesion characteristics with genomics and other phenotypes; thus, as they apply to disease states. These various computer-based applications are demonstrated through research examples from the Giger Lab.


title: Low cost ambient monitoring using ESP8266

abstract: A different language is different vision of life. Micropython is the open source scripting language and an interpreter that expresses the concepts in fewer lines of codes. This high level language possesses clear and expressive codes which is designed to respond to the actions. It is a lean and efficient implementation of python 3 programming language that comes with the quick language feedback and is optimized for interactive input to run on microcontroller or embedded systems. This functional and dynamic programming language is ported to ESP8266 board. The DHT11 sensor is interfaced with ESP8266 board to sense the ambient conditions like temperature and humidity. The dht module which is confined to DHT sensor is being imported to perform its programming task. The OLED display used here have dimensions of 128×64, it has an I2C interface and can be driven by SSD1306 driver. Firstly, the library for the SSD1306 driver of the chip needs to be imported. The ESP8266 board have two connections, one with the DHT11 sensor to sense the temperature and humidity, and the second with OLED display to show the collected information of temperature and humidity from DHT11 sensor on to the screen. These all are implemented using lean and efficient Micropython language and thus it is easy to control hardware and connected devices using Micropython.


title: Density Based Smart Traffic System with Real Time Data Analysis Using IoT

abstract: This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.


title: Activating elicited agent knowledge: How robot and user features shape the perception of social robots

abstract: A recent theoretical framework on anthropomorphism emphasizes the role of elicited agent knowledge in anthropomorphic inferences about nonhuman entities. According to the Three-Factor Model of psychological Anthropomorphism, people use anthropocentric knowledge structures when judging unfamiliar objects (e.g., robots). In the present research, our goal was to manipulate the accessibility of such elicited agent knowledge by varying features of a robot's voice: Specifically, we examined effects of vocal cues that reflected both gender of robot (i.e., a male vs. female voice) and voice type (i.e., a human-like vs. robot-like voice). This was done to test the impact of these vocal features on anthropomorphic inferences about the robot and on human-robot interaction (HRI) acceptance. Our results demonstrate that a robot's vocal cues clearly influence subsequent judgments of the robot and particularly so, when participant gender taken into account. Implications of our research for robotics will be discussed.


title: Bandwidth- and Gain-Enhanced Circularly Polarized Antenna Array Using Sequential Phase Feed

abstract: A technique is developed in this letter to enhance the impedance and axial-ratio (AR) bandwidths as well as broadside gain of a sequential phase (SP) feed 2 × 2 patch array. The proposed antenna is composed of a probe-fed patch, a 2 × 2 corner truncated patch array with a loop, and a slotted metal wall surrounding the feeding structure. The proposed antenna combines the attractive features such as wide impedance and AR bandwidths, low profile, and easiness of design and manufacture. The antenna operating at 5-GHz band is designed and fabricated with overall volume of 1.5 λ ::: 0 ::: ×1.5 λ ::: 0 ::: ×0.06 λ ::: 0 ::: . Measured results show that the antenna array achieves an impedance bandwidth of about 20.8% for |S ::: 11 ::: |<;-10 dB, a 3-dB AR bandwidth of about 17.6%, a peak gain of 11.5 dBi, and a 3-dB gain bandwidth of 13%, which is completely within the impedance and AR bandwidths.


title: DRAM − efficient adaptive MCMC

abstract: We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples. We present situations where the combination outperforms the original methods: adaptation clearly enhances efficiency of the delayed rejection algorithm in cases where good proposal distributions are not available. Similarly, delayed rejection provides a systematic remedy when the adaptation process has a slow start.


The Patch Transform Citation


title: What do drug transporters really do?

abstract: Potential drug–drug interactions mediated by ATP-binding cassette (ABC) and solute carrier (SLC) transporters are of clinical and regulatory concern, but the endogenous function of these drug transporters is unclear. Nigam describes the evidence that these transporters transport diverse endogenous substrates and could potentially be important in remote communication. Understanding such functions could clarify the roles of these transporters in disease and in drug–metabolite interactions.


title: A survey on expert finding techniques

abstract: Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.


title: Moving object detection using background subtraction for a moving camera with pronounced parallax

abstract: This paper proposes a moving object detection algorithm which can handle videos taken by a moving camera in the presence of pronounced parallax. The paper considers the idea that objects in a image can be considered to be spatially distributed across multiple planes, the movement of each of which can be estimated using a Visual Odometry (VO) algorithm. For each plane, a Homography matrix between consecutive frames is calculated to facilitate motion compensation. The approach also identifies regions where parallax is pronounced and where the most recent data are then assumed to provide the background. Moving objects can then be extracted by an existing background subtraction algorithm. Since the camera states are known (from the VO), the detections on the image can be projected to a global space and tracked using a nearly constant velocity model. This approach is evaluated using a simulated video of a city and real video from an outdoor benchmark.


title: Meta-Interpretive Learning from noisy images

abstract: Statistical machine learning is widely used in image classification. However, most techniques (1) require many images to achieve high accuracy and (2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. This paper describes an Inductive Logic Programming approach called Logical Vision which overcomes some of these limitations. LV uses Meta-Interpretive Learning (MIL) combined with low-level extraction of high-contrast points sampled from the image to learn recursive logic programs describing the image. In published work LV was demonstrated capable of high-accuracy prediction of classes such as regular polygon from small numbers of images where Support Vector Machines and Convolutional Neural Networks gave near random predictions in some cases. LV has so far only been applied to noise-free, artificially generated images. This paper extends LV by (a) addressing classification noise using a new noise-telerant version of the MIL system Metagol, (b) addressing attribute noise using primitive-level statistical estimators to identify sub-objects in real images, (c) using a wider class of background models representing classical 2D shapes such as circles and ellipses, (d) providing richer learnable background knowledge in the form of a simple but generic recursive theory of light reflection. In our experiments we consider noisy images in both natural science settings and in a RoboCup competition setting. The natural science settings involve identification of the position of the light source in telescopic and microscopic images, while the RoboCup setting involves identification of the position of the ball. Our results indicate that with real images the new noise-robust version of LV using a single example (i.e. one-shot LV) converges to an accuracy at least comparable to a thirty-shot statistical machine learner on both prediction of hidden light sources in the scientific settings and in the RoboCup setting. Moreover, we demonstrate that a general background recursive theory of light can itself be invented using LV and used to identify ambiguities in the convexity/concavity of objects such as craters in the scientific setting and partial obscuration of the ball in the RoboCup setting.


title: Time and cognitive load in working memory

abstract: According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.


title: Human Tracking With Wireless Distributed Pyroelectric Sensors

abstract: This paper presents a wireless pyroelectric sensor system, composed of sensing modules (slaves), a synchronization and error rejection module (master), and a data fusion module (host), to perform human tracking. The computation workload distribution among slave, master, and host is investigated. The performances and costs of different signal-processing and target-tracking algorithms are discussed. A prototype system is described containing pyroelectric sensor modules that are able to detect the angular displacement of a moving thermal target. Fresnel lens arrays are used to modulate the sensor field of view. The sensor system has been used to track a single human target


title: Simultaneous Local Binary Feature Learning and Encoding for Face Recognition

abstract: In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which is automatically learned from raw pixels. Unlike existing binary face descriptors such as the LBP and discriminant face descriptor (DFD) which use a two-stage feature extraction approach, our SLBFLE jointly learns binary codes for local face patches and the codebook for feature encoding so that discriminative information from raw pixels can be simultaneously learned with a one-stage procedure. Experimental results on four widely used face datasets including LFW, YouTube Face (YTF), FERET and PaSC clearly demonstrate the effectiveness of the proposed method.


title: Intrafascicular stimulation of monkey arm nerves evokes coordinated grasp and sensory responses

abstract: High-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss. In this study, we implanted Utah Slanted Electrode Arrays (USEAs) intrafascicularly at the elbow or shoulder in arm nerves of rhesus monkeys (n = 4) under isoflurane anesthesia. Input-output curves indicated that pulse-width-modulated single-electrode stimulation in each arm nerve could recruit single muscles with little or no recruitment of other muscles. Stimulus trains evoked specific, natural, hand movements, which could be combined via multielectrode stimulation to elicit coordinated power or pinch grasp. Stimulation also elicited short-latency evoked potentials (EPs) in primary somatosensory cortex, which might be used to provide sensory feedback from a prosthetic limb. These results demonstrate a high-resolution, high-channel-count interface to the peripheral nervous system for restoring hand function after neural injury or disruption or for examining nerve structure.


title: Research on Lossless Predictive Coding Methods for Stereo Images

abstract: There exists very high correlation between left-view images and right-view images of stereo image pairs. The correlation can help to reduce the stereoscopic image coding bit rate. Proposed methods first estimate the disparity by using causal neighborhood pixels. And then a prediction method which combines inter-view prediction with intra-view prediction is used to obtain a better predictive coding method for target-view images. The reference-view images are then encoded by using those commonly used prediction coding method. The experimental results show that compared with those methods using only intra-view correlation, the proposed method can obtain lower code rate.


title: Robust and Efficient Surface Reconstruction From Range Data

abstract: We describe a robust but simple algorithm to reconstruct a surface from a set of merged range scans. Our key contribution is the formulation of the surface reconstruction problem as an energy minimisation problem that explicitly models the scanning process. The adaptivity of the Delaunay triangulation is exploited by restricting the energy to inside/outside labelings of Delaunay tetrahedra. Our energy measures both the output surface quality and how well the surface agrees with soft visibility constraints. Such energy is shown to perfectly fit into the minimum s − t cuts optimisation framework, allowing fast computation of a globally optimal tetrahedra labeling, while avoiding the “shrinking bias” that usually plagues graph cuts methods. ::: ::: ::: ::: The behaviour of our method confronted to noise, undersampling and outliers is evaluated on several data sets and compared with other methods through different experiments: its strong robustness would make our method practical not only for reconstruction from range data but also from typically more difficult dense point clouds, resulting for instance from stereo image matching. Our effective modeling of the surface acquisition inverse problem, along with the unique combination of Delaunay triangulation and minimum s − t cuts, makes the computational requirements of the algorithm scale well with respect to the size of the input point cloud.


Algorithmic patterns for H-matrices on many-core processors


title: CARBON FIBER-REINFORCED POLYMER WRAPS FOR CORROSION CONTROL AND REHABILITATION OF REINFORCED CONCRETE COLUMNS

abstract: This paper discusses the use of carbon fiber-reinforced polymer (CFRP) wraps as a rehabilitation technique for corroded reinforced concrete columns. The effect of applying the wraps at early ages of the columns and its effect on corrosion propagation is addressed. Interaction between corrosion phenomena and the CFRP wrapping is studied through simple combinations of both factors. The lab work included testing of 52 reinforced concrete cylinders. The testing procedures are described. It was concluded that applying CFRP wraps significantly decreases corrosion activity when applied over the entire specimen. The application of the wraps before corrosion propagation will prevent corrosion from taking place, while the application of the wraps after corrosion occurrence will drop the rate of corrosion sharply. This effect is likely due to the epoxy saturant used to apply the CFRP sheets and not from the fibers themselves.


title: Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond

abstract: Reading is one of the most frequent activities of knowledge workers. Eye tracking can provide information on what document parts users read, and how they were read. This article aims at generating implicit relevance feedback from eye movements that can be used for information retrieval personalization and further applications. We report the findings from two studies which examine the relation between several eye movement measures and user-perceived relevance of read text passages. The results show that the measures are generally noisy, but after personalizing them we find clear relations between the measures and relevance. In addition, the second study demonstrates the effect of using reading behavior as implicit relevance feedback for personalizing search. The results indicate that gaze-based feedback is very useful and can greatly improve the quality of Web search. The article concludes with an outlook introducing attentive documents keeping track of how users consume them. Based on eye movement feedback, we describe a number of possible applications to make working with documents more effective.


title: Semantic hashing

abstract: We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than Latent Semantic Analysis. When the deepest layer is forced to use a small number of binary variables (e.g. 32), the graphical model performs ''semantic hashing'': Documents are mapped to memory addresses in such a way that semantically similar documents are located at nearby addresses. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method. By using semantic hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying TF-IDF to the entire document set.


title: Exploring the Application of Blockchain Technology to Combat the Effects of Social Loafing in Cross Functional Group Projects

abstract: Today, many multi-national organisations operate in a dispersed geographical environment. Teams consisting of members from around the globe can be assembled on an as-needed basis. However, this can prove to be a complex managerial task. Individuals, who believe that their efforts are not being effectively monitored by upper management, lose their motivation to fully contribute to the best of their abilities as they do not believe there is any correlation between the effort they exert and the reward they receive. With low levels of intrinsic involvement among employees, a lack of task visibility from upper management and limited social interaction among group members, many organisations struggle to combat the issue of social loafing in cross functional working groups. Blockchain technology, widely acknowledged as enabling openness, can facilitate the development of an immutable, transparent, secure and verifiable application for capturing individuals Intellectual Property as they work. This would motivate employees to more openly contribute to group work, safe in the knowledge that their contribution will be recognised, enabling management to maintain a high level of task visibility over their employees work without requiring their physical presence.


title: Discovery of Critical Nodes in Road Networks Through Mining From Vehicle Trajectories

abstract: Road networks are extremely vulnerable to cascading failure caused by traffic accidents or anomalous events. Therefore, accurate identification of critical nodes, whose failure may cause a dramatic reduction in the road network transmission efficiency, is of great significance to traffic management and control schemes. However, none of the existing approaches can locate city-wide critical nodes in real road networks. In this paper, we propose a novel data-driven framework to rank node importance through mining from comprehensive vehicle trajectory data, instead of analysis solely on the topology of the road network. In this framework, we introduce a trip network modeled by a tripartite graph to characterize the dynamics of the road network. Furthermore, we present two algorithms, integrating the origin-destination entropy with flow (ODEF) algorithm and the crossroad-rank (CRRank) algorithm, to better exploit the information included in the tripartite graph and to effectively assess the node importance. ODEF absorbs the idea of the information entropy to evaluate the centrality of a node and to calculate its importance rating by integrating its centrality with the traffic flow. CRRank is a ranking algorithm based on eigenvector centrality that captures the mutual reinforcing relationships among the OD-pair, path, and intersection. In addition to the factors considered in ODEF, CRRank considers the irreplaceability of a node and the spatial relationships between neighboring nodes. We conduct a synthetic experiment and a real case study based on a real-world dataset of taxi trajectories. Experiments verify the utility of the proposed algorithms.


title: What is so special about embodied simulation?

abstract: Simulation theories of social cognition abound in the literature, but it is often unclear what simulation means and how it works. The discovery of mirror neurons, responding both to action execution and observation, suggested an embodied approach to mental simulation. Over the past few years this approach has been hotly debated and alternative accounts have been proposed. We discuss these accounts and argue that they fail to capture the uniqueness of embodied simulation (ES). ES theory provides a unitary account of basic social cognition, demonstrating that people reuse their own mental states or processes represented with a bodily format in functionally attributing them to others.


title: Pseudo-Random Number Generators for Vector Processors and Multicore Processors

abstract: Large scale Monte Carlo applications need a good pseudo-random number generator capable of utilizing both the vector processing capabilities and multiprocessing capabilities of modern computers in order to get the maximum performance. The requirements for such a generator are discussed. New ways of avoiding overlapping subsequences by combining two generators are proposed. Some fundamental philosophical problems in proving independence of random streams are discussed. Remedies for hitherto ignored quantization errors are offered. An open source C++ implementation is provided for a generator that meets these needs.


title: A Generic Formulated KID Model for Pragmatic Processing of Data, Information, and Knowledge

abstract: Data without interpretation have no value and only attain value when they are assessed and interpreted with existing knowledge. Information is the core element in forming knowledge and knowledge is essential to the processing of data and information. Existing knowledge can be used not only for interpreting data but also assimilating of and entailing new knowledge with fresh information, i.e. Newly-interpreted data relevant in a specific context. This paper is focused on understanding fundamental concepts of data, information, knowledge and their relations, and proposes a generic formulated (knowledge-information-data) KID model for pragmatic transformation processing of data, information, and knowledge. In this paper, the KID model is described with emphasis on formalization of data, information and knowledge and formulation of their interrelations. Three abstract functions, i.e., Interpretation(), assimilation() and instantiation() are explained how they pragmatically perform the transformation from data to information and from information to knowledge with a retail business scenario.


title: Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors

abstract: Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.


title: Basic Notions of Information Structure

abstract: This article takes stock of the basic notions of Information Structure (IS). It first provides a general characterization of IS—following Chafe (1976)—within a communicative model of Common Ground (CG), which distinguishes between CG content and CG management. IS is concerned with those features of language that affect the local CG. Second, this paper defines and discusses the notions of Focus (as indicating alternatives) and its various uses, Givenness (as indicating that a denotation is already present in the CG), and Topic (as specifying what a statement is about). It also proposes a new notion, Delimitation, which comprises contrastive topics and frame setters, and indicates that the current conversational move does not entirely satisfy the local communicative needs. It also points out that rhetorical structuring partly belongs to IS.


title: An Image Matching Algorithm Based on SIFT and Improved LTP

abstract: SIFT is one of the most robust and widely used image matching algorithms based on local features. But the key-points descriptor of SIFT algorithm have 128 dimensions. Aiming to the problem of its high dimension and complexity, a novel image matching algorithm is proposed. The descriptors of SIFT key-points are constructed by the rotation invariant LTP, city-block distance is also employed to reduce calculation of key-points matching. The experiment is achieved through different lighting, blur changes and rotation of images, the results show that this method can reduce the processing time and raise image matching efficiency.


title: SMS of Death: From Analyzing to Attacking Mobile Phones on a Large Scale

abstract: Mobile communication is an essential part of our daily lives. Therefore, it needs to be secure and reliable. In this paper, we study the security of feature phones, the most common type of mobile phone in the world. We built a framework to analyze the security of SMS clients of feature phones. The framework is based on a small GSM base station, which is readily available on the market. Through our analysis we discovered vulnerabilities in the feature phone platforms of all major manufacturers. Using these vulnerabilities we designed attacks against end-users as well as mobile operators. The threat is serious since the attacks can be used to prohibit communication on a large scale and can be carried out from anywhere in the world. Through further analysis we determined that such attacks are amplified by certain configurations of the mobile network. We conclude our research by providing a set of countermeasures.


Autonomous Robots for Agricultural Tasks and Farm Assignment and Future Trends in Agro Robots


title: Analysis of the current state of Industry 4.0 in purchasing : a multiple case study based on the purchasing year cycle

abstract: The much-discussed topic, Industry 4.0 receives more and more attention. However, literature mainly focuses on Industry 4.0 in coherence with production and the practical application. Due to the minimal attention in literature for purchasing in connection with the fourth industrial revolution, this research pursues the subject. A multiple case study has been conducted with companies from different industries to explore the status quo of applications regarding Industry 4.0 technologies in procurement. The theoretical framework of the purchasing year cycle was adapted regarding Industry 4.0 and used to analyse the interviews. Qualitative data obtained from nine conducted interviews was analysed and compared with corresponding literature. The analysis revealed that Industry 4.0 in procurement is still in its infancy. Most companies market new technological developments within their firm as Industry 4.0 applications. However, some of the implementations can be considered as advanced technologies of the third industrial revolution and not as Industry 4.0 in procurement. Therefore, companies need to fully adapt the third industrial revolution before being able to successfully carry on with the fourth industrial revolution. It was figured that the companies share similar visions. They aspire to make use of Industry 4.0 applications to lead the firm to more transparency, efficiency, and enable more precise forecasting. It was analysed that interconnected cyber-physical systems can be considered as the organisational antecedents for Industry 4.0 in procurement. Following the adapted framework could help companies to overcome uncertainties that appear due to the novelty of the topic. Hence, the concept of a company-wide interconnected ERP system received special attention in this paper. The ERP system is suggested as a unifying system for the companies encouraging the connection to their suppliers.


title: Channel Equalization and Interference Analysis for Uplink Narrowband Internet of Things (NB-IoT)

abstract: We derive the uplink system model for In-band and Guard-band narrowband Internet of Things (NB-IoT). The results reveal that the actual channel frequency response (CFR) is not a simple Fourier transform of the channel impulse response, due to sampling rate mismatch between the NB-IoT user and long term evolution (LTE) base station. Consequently, a new channel equalization algorithm is proposed based on the derived effective CFR. In addition, the interference is derived analytically to facilitate the co-existence of NB-IoT and LTE signals. This letter provides an example and guidance to support network slicing and service multiplexing in the physical layer.


title: An improved algorithm on Viola-Jones object detector

abstract: In image processing, Viola-Jones object detector [1] is one of the most successful and widely used object detectors. A popular implementation used by the community is the one in OpenCV. The detector shows its strong power in detecting faces, but we found it hard to be extended to other kinds of objects. The convergence of the training phase of this algorithm depends a lot on the training data. And the prediction precision stays low. In this paper, we have come up with new ideas to improve its performance for diverse object categories. We incorporated six different types of feature images into the Viola and Jones' framework. The integral image [1] used by the Viola-Jones detector is then computed on these feature images respectively instead of only on the gray image. The stage classifier in Viola-Jones detector is now trained on one of these feature images. We also present a new stopping criterion for the stage training. In addition, we integrate a key points based SVM [2] predictor into the prediction phase to improve the confidence of the detection result.


title: A High-Efficiency Positive Buck–Boost Converter With Mode-Select Circuit and Feed-Forward Techniques

abstract: This paper presents a high-efficiency positive buck- boost converter with mode-select circuits and feed-forward techniques. Four power transistors produce more conduction and more switching losses when the positive buck-boost converter operates in buck-boost mode. Utilizing the mode-select circuit, the proposed converter can decrease the loss of switches and let the positive buck-boost converter operate in buck, buck-boost, or boost mode. By adding feed-forward techniques, the proposed converter can improve transient response when the supply voltages are changed. The proposed converter has been fabricated with TSMC 0.35-μm CMOS 2P4M processes. The total chip area is 2.59 × 2.74 mm2 (with PADs), the output voltage is 3.3 V, and the regulated supply voltage range is from 2.5-5 V. Its switching frequency is 500 kHz and the maximum power efficiency is 91.6% as the load current equals 150 mA.


title: A comparative experimental study of software rejuvenation overhead

abstract: In this paper we present a comparative experimental study of the main software rejuvenation techniques developed so far to mitigate the software aging effects. We consider six different rejuvenation techniques with different levels of granularity: (i) physical node reboot, (ii) virtual machine reboot, (iii) OS reboot, (iv) fast OS reboot, (v) standalone application restart, and (vi) application rejuvenation by a hot standby server. We conduct a set of experiments injecting memory leaks at the application level. We evaluate the performance overhead introduced by software rejuvenation in terms of throughput loss, failed requests, slow requests, and memory fragmentation overhead. We also analyze the selected rejuvenation techniques' efficiency in mitigating the aging effects. Due to the growing adoption of virtualization technology, we also analyze the overhead of the rejuvenation techniques in virtualized environments. The results show that the performance overheads introduced by the rejuvenation techniques are related to the granularity level. We also capture different levels of memory fragmentation overhead induced by the virtualization demonstrating some drawbacks of using virtualization in comparison with non-virtualized rejuvenation approaches. Finally, based on these research findings we present comprehensive guidelines to support decision making during the design of rejuvenation scheduling algorithms, as well as in selecting the appropriate rejuvenation mechanism.


title: Version-Aware Rating Prediction for Mobile App Recommendation

abstract: With the great popularity of mobile devices, the amount of mobile apps has grown at a more dramatic rate than ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation—that is, an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grain version-aware app recommendation problem. Instead of directly learning the users’ preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-app rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of version-based correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large dataset demonstrate the superiority of our approach over several competitive methods.


title: BEE2: a high-end reconfigurable computing system

abstract: The Berkeley Emulation Engine 2 (BEE2) project is developing a reusable, modular, and scalable framework for designing high-end reconfigurable computers, including a processing-module building block and several programming models. Using these elements, BEE2 can provide over 10 times more computing throughput than a DSP-based system with similar power consumption and cost and over 100 times that of a microprocessor-based system.


title: WhirlingFuzzwork: a taint-analysis-based API in-memory fuzzing framework

abstract: Fuzz testing is widely used as an automatic solution for discovering vulnerabilities in binary programs that process files. Restricted by their high blindness and low code path coverage, fuzzing tests typically provide quite low efficiencies. In this paper, a novel API in-memory fuzz testing technique for eliminating the blindness of existing techniques is discussed. This technique employs dynamic taint analyses to locate the routines and instructions that belong to the target binary executables, and it consists of parsing and processing the input data. Within the testing phase, binary instrumentation is used to construct loops around such routines, in which the contained taint memory values are mutated in each loop. According to experiments using the prototype tool, this technique could effectively detect defects such as stack overflows. Compared with traditional fuzzing tools, this API in-memory fuzzing eliminated the bottleneck of interrupting execution paths and gained a greater than 95 % enhancement in execution speed.


title: DBSCAN Clustering Algorithm Applied to Identify Suspicious Financial Transactions

abstract: Money laundering refers to disguise or conceal the source and nature of variety ill-gotten gains, to make it legalization. In this paper, we design and implement the anti-money laundering regulatory application system (AMLRAS), which can not only automate sorting and counting the money laundering cases in comprehension and details, but also collect, analyses and count the large cash transactions. We also adopt data mining techniques DBSCAN clustering algorithm to identify suspicious financial transactions, while using link analysis (LA) to mark the suspicious level. The presumptive approach is tested on large cash transaction data which is provided by a bank where AMLRAS has already been applied. The result proves that this method is automatable to detect suspicious financial transaction cases from mass financial data, which is helpful to prevent money laundering from occurring.


title: Learning swing-free trajectories for UAVs with a suspended load

abstract: Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are not only subject to unknown system dynamics, but also to specific task constraints. This paper presents a motion planning method for generating trajectories with minimal residual oscillations (swing-free) for rotorcraft carrying a suspended loads. We rely on a finite-sampling, batch reinforcement learning algorithm to train the system for a particular load. We find criteria that allow the trained agent to be transferred to a variety of models, state and action spaces and produce a number of different trajectories. Through a combination of simulations and experiments, we demonstrate that the inferred policy is robust to noise and the unmodeled dynamics of the system. The contributions of this work are 1) applying reinforcement learning to solve the problem of finding swing-free trajectories for rotorcraft, 2) designing a problem-specific feature vector for value function approximation, 3) giving sufficient conditions for successful learning transfer to different models, state and action spaces, and 4) verification of the resulting trajectories in both simulation and autonomous control of quadrotors with suspended loads.


title: A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS From the Proposal to its Preliminary Validation for Efficiency

abstract: This paper enhances the currently available formal risk management models and related frameworks by providing an independent mechanism for checking out their results. It provides a way to compare the historical data on the risks identified by similar projects to the risk found by each framework Based on direct queries to stakeholders, existing approaches provide a mechanism for estimating the probability of achieving software project objectives before the project starts (Prior probability). However, they do not estimate the probability that objectives have actually been achieved, when risk events have occurred during project development. This involves calculating the posterior probability that a project missed its objectives, or, on the contrary, the probability that the project has succeeded. This paper provides existing frameworks with a way to calculate both prior and posterior probability. The overall risk evaluation, calculated by those two probabilities, could be compared to the evaluations that each framework has found within its own process. Therefore, the comparison is performed between what those frameworks assumed and what the historical data suggested both before and during the project. This is a control mechanism because, if those comparisons do not agree, further investigations could be carried out. A case study is presented that provides an efficient way to deal with those issues by using Artificial Neural Networks (ANN) as a statistical tool (e.g., regression and probability estimator). That is, we show that ANN can automatically derive from historical data both prior and posterior probability estimates. This paper shows the verification by simulation of the proposed approach.


title: Dietetics students' cultural food knowledge and experiences with various cultures

abstract: Academic insƟtutes are invesƟgaƟng benefits of integraƟng cultural diversity learning objecƟves into classroom learning outcomes and creaƟng an environment supporƟve of students’ interacƟons and experiences with diverse cultures. The purpose of this study was to invesƟgate dieteƟcs students’ cultural awareness by measuring food knowledge, percepƟons, and learning experiences with various cultures and differences in cultural awareness based on their gender, age, career choice, graduaƟon date and amount of nutriƟon educaƟon. Results indicate that students’ believe it is more beneficial to learn about other cultures through direct cultural experiences than through classroom interacƟons with other cultures or classroom instrucƟon on other cultures


title: Neural Adaptive Video Streaming with Pensieve

abstract: Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art ABR algorithms suffer from a key limitation: they use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and QoE objectives. We propose Pensieve, a system that generates ABR algorithms using reinforcement learning (RL). Pensieve trains a neural network model that selects bitrates for future video chunks based on observations collected by client video players. Pensieve does not rely on pre-programmed models or assumptions about the environment. Instead, it learns to make ABR decisions solely through observations of the resulting performance of past decisions. As a result, Pensieve automatically learns ABR algorithms that adapt to a wide range of environments and QoE metrics. We compare Pensieve to state-of-the-art ABR algorithms using trace-driven and real world experiments spanning a wide variety of network conditions, QoE metrics, and video properties. In all considered scenarios, Pensieve outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%--25%. Pensieve also generalizes well, outperforming existing schemes even on networks for which it was not explicitly trained.


title: ASCII Art Synthesis from Natural Photographs

abstract: While ASCII art is a worldwide popular art form, automatic generating structure-based ASCII art from natural photographs remains challenging. The major challenge lies on extracting the perception-sensitive structure from the natural photographs so that a more concise ASCII art reproduction can be produced based on the structure. However, due to excessive amount of texture in natural photos, extracting perception-sensitive structure is not easy, especially when the structure may be weak and within the texture region. Besides, to fit different target text resolutions, the amount of the extracted structure should also be controllable. To tackle these challenges, we introduce a visual perception mechanism of non-classical receptive field modulation ( non-CRF modulation ) from physiological findings to this ASCII art application, and propose a new model of non-CRF modulation which can better separate the weak structure from the crowded texture, and also better control the scale of texture suppression. Thanks to our non-CRF model, more sensible ASCII art reproduction can be obtained. In addition, to produce more visually appealing ASCII arts, we propose a novel optimization scheme to obtain the optimal placement of proportional-font characters. We apply our method on a rich variety of images, and visually appealing ASCII art can be obtained in all cases.


Multi-Layered Network Embedding


title: Scalable software-defined networking through hybrid switching

abstract: Traditional networks rely on aggregate routing and decentralized control to achieve scalability. On the contrary, software-defined networks achieve near optimal network performance and policy-based management through per-flow routing and centralized control, which however face scalability challenge due to (1) limited TCAM and on-die memory for storing the forwarding table and (2) per-flow communication/computation overhead at the controller. This paper presents a novel hybrid switching design, which integrates traditional switching and SDN switching for the purpose of achieving both scalability and optimal performance. We show that the integration also leads to unexpected benefits of making both types of switching more efficient under the hybrid design. Numerical evaluation demonstrates the superior performance of hybrid switching when comparing with the state-of-the-art SDN design.


title: Efficient Parallel Skyline Evaluation Using MapReduce

abstract: This research develops an advanced two-phase MapReduce solution that is able to efficiently address skyline queries on large datasets. Unlike existing parallel skyline approaches, our scheme considers data partitioning, filtering, and parallel skyline evaluation as a holistic query process. In particular, we apply filtering techniques and angle-based partitioning in the first phase, in which unqualified objects are discarded and the processed objects are partitioned by their angles to the origin.In the second phase, local skyline objects in each partition are calculated in parallel, and global skyline objects are output after a merging skyline process. To improve the parallel local skyline calculation, we propose two partition-aware filtering methods that keep skyline candidates in a balanced manner. The aggressive partition-aware filtering aggressively eliminates objects in the partition with the greatest population of candidate objects, whereas the proportional partition-aware filtering slows down the growth of partition population proportionally.Recognizing the lack of studies that incorporate the MapReduce framework into parallel skyline processing, we propose a partial-presort grid-based partition skyline algorithm that is able to significantly improve the merging skyline computation on large datasets. The presort process can be completed in the shuffle phase with little overhead. Our experimental results show the efficiency and effectiveness of the proposed parallel skyline solution utilizing MapReduce on large-scale datasets.


title: Merging real and virtual worlds: An analysis of the state of the art and practical evaluation of Microsoft Hololens

abstract: Achieving a symbiotic blending between reality and virtuality is a dream that has been lying in the minds of many people for a long time. Advances in various domains constantly bring us closer to making that dream come true. Augmented reality as well as virtual reality are in fact trending terms and are expected to further progress in the years to come. ::: This master's thesis aims to explore these areas and starts by defining necessary terms such as augmented reality (AR) or virtual reality (VR). Usual taxonomies to classify and compare the corresponding experiences are then discussed. ::: In order to enable those applications, many technical challenges need to be tackled, such as accurate motion tracking with 6 degrees of freedom (positional and rotational), that is necessary for compelling experiences and to prevent user sickness. Additionally, augmented reality experiences typically rely on image processing to position the superimposed content. To do so, "paper" markers or features extracted from the environment are often employed. Both sets of techniques are explored and common solutions and algorithms are presented. ::: After investigating those technical aspects, I carry out an objective comparison of the existing state-of-the-art and state-of-the-practice in those domains, and I discuss present and potential applications in these areas. As a practical validation, I present the results of an application that I have developed using Microsoft HoloLens, one of the more advanced affordable technologies for augmented reality that is available today. Based on the experience and lessons learned during this development, I discuss the limitations of current technologies and present some avenues of future research.


title: Context in the clinic: how well do cognitive-behavioral therapies and medications work in combination?

abstract: Abstract Cognitive-behavioral therapy (CBT) and pharmacotherapy demonstrate efficacy across the anxiety disorders, but recognition of their limitations has sparked interest in combining modalities to maximize benefit. This article reviews the empirical literature to examine whether combining treatments influences efficacy of either monotherapy. We conducted a comprehensive literature search of published randomized trials that compared combined treatment with pharmacologic or CBT monotherapies. Ten studies that met our inclusion criteria were reviewed in detail, and within-subjects effect sizes were calculated to compare treatment conditions within and across studies. At posttreatment and follow-up, effect size and percentage responder data failed to clearly demonstrate an advantage or disadvantage of combined treatment over CBT alone for obsessive-compulsive disorder, social phobia, and generalized anxiety disorder. Some advantage of combined treatment over pharmacotherapy alone emerged from the few studies that allowed for such a direct comparison. In contrast, combined treatment for panic disorder seems to provide an advantage over CBT alone at posttreatment, but is associated with greater relapse after treatment discontinuation. The advantage of combined treatment may vary across the anxiety disorders. The potential differences in usefulness of combined treatment are discussed, directions for future research are suggested, and implications for clinical practice are considered.


title: Governing Platforms in the Internet of Things

abstract: The ambivalent paradigm Internet of Things (IoT) is gaining importance in today’s industries. To manage the various devices built on different technologies and to apply complex event-triggered business rules to the data streams, platforms are necessary tools for almost all use cases. In the recent years, hundreds of vendors entered the intransparent IoT platform market, from small startups focusing on niches, to large enterprise vendors offering professional solutions. These platforms need tools to orchestrate the interactions between the different sides involved, so-called platform governance mechanisms. The purpose of this multiple case study analysis is to explore the platform governance mechanisms applied in IoT platforms. To achieve this goal, we explored the governance concepts of eight selected platforms in a multiple case study analysis, resulting in a description of the important aspects and differences regarding platform governance. Moreover, the four main trade-offs that platform vendors must be aware of are subsequently discussed. In a last step, an evaluation and discussion of the contribution to theory and practice is provided.


title: Masking of electrical vibration sensation using mechanical vibration for presentation of pressure sensation

abstract: Electrical stimulation produces both vibration and pressure sensations, and it is difficult to control these sensations individually. It is known, however, that the vibrotactile sensation on an index finger can be masked using another vibration at the same frequency that is presented to the forearm. In our study, we use this phenomenon to mask the electrical vibration sensation in an index finger and retain the pressure sensation only. In the experiments, we applied a 50 Hz mechanical vibration to the test participant's palm and a 50 pulses per second (pps) cathodic or anodic electrical stimulation to the index finger of the same hand. The results showed that it was possible to present electrical pressure sensation only, and that the pressure sensation produced by cathodic stimulation remained stronger than that produced by anodic stimulation. We also conducted experiment to investigate the possibility of controlling the intensity of electrical pressure sensation of cathodic stimulation during masking. The result showed that, when the intensity of electrical current increased, both intensity of electrical pressure sensation and acceleration amplitude of mechanical vibration for masking also increased.


title: Lane Change Maneuvers for Automated Vehicles

abstract: By considering a lane change maneuver as primarily a longitudinal motion planning problem, this paper presents a lane change maneuver algorithm with a pragmatic approach to determine an inter-vehicle traffic gap and time instance to perform the maneuver. The proposed approach selects an appropriate inter-vehicle traffic gap and time instance to perform the lane change maneuver by simply estimating whether there might exist a longitudinal trajectory that allows the automated vehicle to safely perform the maneuver. The lane change maneuver algorithm then proceeds to solve two loosely coupled convex quadratic programs to obtain the longitudinal trajectory to position the automated vehicle in the selected inter-vehicle traffic gap at the desired time instance and the corresponding lateral trajectory. Simulation results demonstrate the capability of the proposed approach to select an appropriate inter-vehicle traffic gap and time instance to initialize the lateral motion of a lane change maneuver in various traffic scenarios. The real-time ability of the lane change maneuver algorithm to generate safe and smooth trajectories is shown by experimental results of a Volvo V60 performing automated lane change maneuvers on a test track.


title: Poly(lactic acid)/Poly(ethylene glycol) Polymer Nanocomposites: Effects of Graphene Nanoplatelets

abstract: Graphene nanoplatelets (xGnP) were investigated as a novel nano-reinforcement filler in poly(lactic acid)(PLA)/poly(ethylene glycol)(PEG) blends by the melt blending method. PLA was first plasticized by PEG in order to improve its flexibility and thereby overcome its problem of brittleness. Then, xGnP was incorporated into the PLA/PEG blend. The prepared nanocomposites exhibited a significant improvement in tensile properties at a low xGnP loading. The tensile properties demonstrated the addition of 0.3 wt% of xGnP led to an increase of up to 32.7%, 69.5% and 21.9% in tensile strength, tensile modulus and elongation at break of the nanocomposites respectively, compared to PLA/PEG blend. X-ray diffraction (XRD) patterns showed the presence of a peak around 26.5○ in PLA/PEG/xGnP nanocomposites which corresponds to the characteristic peak of xGnP. The nanocomposites also shows enhanced thermal stability compared with PLA/PEG blend in thermogravimetry analysis (TGA). The enhancement to some extent of the tensile properties of the PLA/PEG/xGnP nanocomposites can be ascribed to the homogeneous dispersion and orientation of the xGnP nanoplatelets in the polymer matrix and strong interfacial interaction between both components. The scanning electron microscopy (SEM) image of PLA/PEG/0.3 wt% xGnP displays good uniformity and more homogenous morphology. Good uniformity of composites indicates a good degree of dispersion of the xGnp and therefore results in good tensile and thermal properties.


title: A New Layered Model on Emotional Intelligence

abstract: Emotional Intelligence (EI) has been an important and controversial topic during the last few decades. Its significance and its correlation with many domains of life has made it the subject of expert study. EI is the rudder for feeling, thinking, learning, problem-solving, and decision-making. In this article, we present an emotional–cognitive based approach to the process of gaining emotional intelligence and thus, we suggest a nine-layer pyramid of emotional intelligence and the gradual development to reach the top of EI.


title: Narratives: A visualization to track narrative events as they develop

abstract: Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories.


title: Visual Script and Language Identification

abstract: In this paper we introduce a script identification method based on hand-crafted texture features and an artificial neural network. The proposed pipeline achieves near state-of-the-art performance for script identification of video-text and state-of-the-art performance on visual language identification of handwritten text. More than using the deep network as a classifier, the use of its intermediary activations as a learned metric demonstrates remarkable results and allows the use of discriminative models on unknown classes. Comparative experiments in video-text and text in the wild datasets provide insights on the internals of the proposed deep network.


Real-Time Keyword Extraction from Conversations


title: What's Really New with NewSQL?

abstract: A new class of database management systems (DBMSs) called NewSQL tout their ability to scale modern on-line transaction processing (OLTP) workloads in a way that is not possible with legacy systems. The term NewSQL was first used by one of the authors of this article in a 2011 business analysis report discussing the rise of new database systems as challengers to these established vendors (Oracle, IBM, Microsoft). The other author was working on what became one of the first examples of a NewSQL DBMS. Since then several companies and research projects have used this term (rightly and wrongly) to describe their systems. Given that relational DBMSs have been around for over four decades, it is justifiable to ask whether the claim of NewSQL's superiority is actually true or whether it is simply marketing. If they are indeed able to get better performance, then the next question is whether there is anything scientifically new about them that enables them to achieve these gains or is it just that hardware has advanced so much that now the bottlenecks from earlier years are no longer a problem. To do this, we first discuss the history of databases to understand how NewSQL systems came about. We then provide a detailed explanation of what the term NewSQL means and the different categories of systems that fall under this definition.


title: Learning to Play Board Games using Temporal Difference Methods

abstract: A promising approach to learn to play board games is to use reinforcement learning algorithms that can learn a game position evaluation function. In this paper we examine and compare three dieren t methods for generating training games: (1) Learning by self-play, (2) Learning by playing against an expert program, and (3) Learning from viewing experts play against themselves. Although the third possibility generates highquality games from the start compared to initial random games generated by self-play, the drawback is that the learning program is never allowed to test moves which it prefers. We compared these three methods using temporal dierence methods to learn the game of backgammon. For particular games such as draughts and chess, learning from a large database containing games played by human experts has as a large advantage that during the generation of (useful) training games, no expensive lookahead planning is necessary for move selection. Experimental results in this paper show how useful this method is for learning to play chess and draughts.


title: Community-based anomaly detection in evolutionary networks

abstract: Networks of dynamic systems, including social networks, the World Wide Web, climate networks, and biological networks, can be highly clustered. Detecting clusters, or communities, in such dynamic networks is an emerging area of research; however, less work has been done in terms of detecting community-based anomalies. While there has been some previous work on detecting anomalies in graph-based data, none of these anomaly detection approaches have considered an important property of evolutionary networks--their community structure. In this work, we present an approach to uncover community-based anomalies in evolutionary networks characterized by overlapping communities. We develop a parameter-free and scalable algorithm using a proposed representative-based technique to detect all six possible types of community-based anomalies: grown, shrunken, merged, split, born, and vanished communities. We detail the underlying theory required to guarantee the correctness of the algorithm. We measure the performance of the community-based anomaly detection algorithm by comparison to a non---representative-based algorithm on synthetic networks, and our experiments on synthetic datasets show that our algorithm achieves a runtime speedup of 11---46 over the baseline algorithm. We have also applied our algorithm to two real-world evolutionary networks, Food Web and Enron Email. Significant and informative community-based anomaly dynamics have been detected in both cases.


title: Digital image forgery detection using passive techniques: A survey

abstract: Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software packages and high resolution capturing devices. Verifying the integrity of images and detecting traces of tampering without requiring extra prior knowledge of the image content or any embedded watermarks is an important research field. An attempt is made to survey the recent developments in the field of digital image forgery detection and complete bibliography is presented on blind methods for forgery detection. Blind or passive methods do not need any explicit priori information about the image. First, various image forgery detection techniques are classified and then its generalized structure is developed. An overview of passive image authentication is presented and the existing blind forgery detection techniques are reviewed. The present status of image forgery detection technique is discussed along with a recommendation for future research.


title: ARINC 653 role in Integrated Modular Avionics (IMA)

abstract: The air transport industry has developed ARINC Specification 653 as a standardized Real-Time Operating System (RTOS) interface definition. The document specifies the interface boundary between avionics software applications and the core executive software. The standardization effort was sponsored by the airline user community and involved many interested parties, including airframe manufacturers, avionics suppliers, RTOS suppliers, government and academia. ARINC 653 is a key enabler in the development of Integrated Modular Avionics (IMA). In many ways it represents a paradigm shift for avionics development; in particular it recognizes the RTOS as key component of an IMA system. The commitment shown by industry to IMA could not be more evident than that shown by the Airbus A380 and the Boeing 787 avionics suites. This paper will provide top-level overview of IMA software architecture, the key elements of the ARINC 653 standard and its current development status.


Analysis of participation in an online photo-sharing community: A multidimensional perspective


title: Modular design of mechatronic systems with function modeling

abstract: Abstract This paper develops a modularization scheme based on the functional model of a system. The modularization approach makes use of the function–behavior–state (FBS) model of the system to derive the entity relations. The design structure matrix (DSM) is automatically constructed based on the FBS model. In this way, the tedious work of filling the DSM entries based on expert knowledge is avoided. The approach makes use of k-means clustering algorithm to allow the user to try different number of clusters in a fast way. The k-means clustering is adopted for DSM based modularization by defining a proper entity representation, relation measure and objective function. Two modularization schemes are performed, one based on the immediate relations and one on the deeper behavioral relations between the components. Considering the application on the shifting system of the Delft University of Technology (DUT) Formula Student car, the latter modularization resulted in more mechatronic behavior based modules, while the former resulted in modules based on mere disciplinary and spatial closeness.


title: A Dual-Band Inverted-F MIMO Antenna With Enhanced Isolation for WLAN Applications

abstract: This letter presents a dual-band inverted-F multiple-input–multiple-output (MIMO) antenna with improved isolation, covering the 2.4/5-GHz wireless local networks (WLAN) band. The proposed MIMO antenna is composed of two symmetrical winding inverted-F antenna elements. The two antenna elements are closely spaced with about 0.115 λ 0 of the lower band. The high isolation is achieved by building two decoupling devices, a meandering resonant branch and an inverted T-shaped slot etched on the ground for the higher band and the lower band, respectively. Furthermore, two U-shaped slits achieving better impedance matching are etched on the 50-Ω feeding lines to broaden the bandwidth of the high band. The impedance bandwidth ( $S_{11}$ < –10 dB) of the proposed antenna covers 2.4–2.48 GHz in the lower band and 5.15–5.825 GHz in the upper band, and the proposed configuration obtains 15-dB isolation within the 2.4- and 5-GHz WLAN bands, which shows a significant improvement compared to the initial design of the MIMO antenna. The simulation and measurement results indicate that the proposed inverted-F MIMO antenna system is quite suitable for WLAN applications.


Hierarchical Variational Autoencoders for Music


title: Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques

abstract: We propose an image forgery localization technique which fuses the outputs of three complementary tools, based on sensor noise, machine-learning and block-matching, respectively. To apply the sensor noise tool, a preliminary camera identification phase was required, followed by estimation of the camera fingerprint, and then forgery detection and localization. The machine-learning is based on a suitable local descriptor, while block-matching relies on the PatchMatch algorithm. A decision fusion strategy is then implemented, based on suitable reliability indexes associated with the binary masks. The proposed technique ranked first in phase 2 of the first Image Forensics Challenge organized in 2013 by the IEEE Information Forensics and Security Technical Committee (IFS-TC).


System Reliability Theory: Models, Statistical Methods, and Applications


title: Artificial bee colony algorithm on distributed environments

abstract: Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large problem size, we introduce a distributed version of ABC. In our parallel algorithm, the entire bee colony is decomposed into several subgroups. Each subgroup then performs a local search concurrently on each processor node. The local best solutions are then exchanged among processor nodes. The algorithm implementation utilizes the message passing technique as a communication paradigm. We then empirically assess the performance based on both result accuracy and algorithm's efficiency. The experimental results show improvement in both solution quality and computing time when comparing to the sequential ABC algorithm.


title: High-voltage high-frequency power supply using a phase-shifted PWM full bridge inverter fed ozone generator with constant applied electrode voltage

abstract: This paper proposes the high-voltage high-frequency power supply for an ozone generator using a phase-shift pulse width modulation (PWM) full bridge inverter with constant applied electrode voltage. The circuit operation is fully described. The high-frequency transformer and ozone generator mathematical models are also included for preliminarily calculating of instantaneous voltages and currents. The proposed system simulation using the MATLAB/SIMULINK software package is given. In order to ensure that the zero voltage switch (ZVS) is always operated over a certain range of a frequency variation, a series-compensated resonant inductor is included. The advantage of the proposed system is a capability of varying ozone gas production quantity by varying the frequency and phase shift angle of the converter whilst the applied voltage to the electrodes is kept constant. The correctness of the proposed system is verified by both simulation and experimental results.


title: Design and development of low-cost EMG amplifier for assistive technology

abstract: The importance of EMG signal is very widespread in many areas of biomedical application due to its distinct output characteristics. However, the small amplitude and noise vulnerability nature of EMG signal necessitates amplification and filtering through properly designed electronic circuitry. So far, a suitable design of an EMG amplifier with an optimum signal-to-noise ratio (SNR), and biosafety feature is a challenging issue. Besides, the high cost of a medical grade bioelectric amplifier also limits the open source approach to clinical research and development. Our work delineates a simple, affordable, but efficient method to design an EMG amplifier with a clinical safety feature of optical isolation compared to other standard bio amplifiers. In addition, the research also focuses on the development of an efficient signal processing algorithm for the post processing of the bio-signals which can be easily implemented into any embedded system dedicated for human-computer interaction.


title: Computing methods for fast and precise body surface area estimation of selected body parts

abstract: Currently used body surface area (BSA) formulas give satisfactory results only for individuals with typical physique, while for elderly, obese or anorectic people accurate results cannot be expected. Particularly noteworthy are the results for individuals with severe obesity (body-mass index greater than 35 kg/m2), for which BSA estimation errors reached 80%. The main goal of our study is the development of precise BSA models for specific body parts. We have achieved satisfactory results for a wide range of patients. Using regression models, such as: support vector regression, multilayer perceptron regressor, stochastic gradient descent, or ridge regression, a fourfold decrease in errors proportion is achieved. Machine learning algorithms led to reduction from 1.2 to 8 times for mean estimation error.


title: TCEP: Adapting to Dynamic User Environments by Enabling Transitions between Operator Placement Mechanisms

abstract: Operator placement has a profound impact on the performance of a distributed complex event processing system (DCEP). Since the behavior of a placement mechanism strongly depends on its environment; a single placement mechanism is often not enough to fulfill stringent performance requirements under environmental changes. In this paper, we show how DCEP can benefit from the adaptive use of multiple placement mechanisms. We propose Tcep, a DCEP system to integrate multiple placement mechanisms. By enabling transitions, Tcep can seamlessly exchange distinct operator mechanisms at runtime. We make two main contributions that are highly important for a cost-efficient transition: i) a transition strategy for efficiently scheduling state migrations and ii) a lightweight learning algorithm to adaptively select an appropriate placement mechanism as a consequence of a transition. Our evaluations for important decentralized placement mechanisms in the context of an IoT scenario show that transitions can better fulfill QoS demands in a dynamic environment. Thereby efficient scheduling of state migrations can help to faster complete transitions by up to 94 %.


title: Precharged Pneumatic Soft Actuators and Their Applications to Untethered Soft Robots.

abstract: The past decade has witnessed tremendous progress in soft robotics. Unlike most pneumatic-based methods, we present a new approach to soft robot design based on precharged pneumatics (PCP). We propose a PCP soft bending actuator, which is actuated by precharged air pressure and retracted by inextensible tendons. By pulling or releasing the tendons, the air pressure in the soft actuator is modulated, and hence, its bending angle. The tendons serve in a way similar to pressure-regulating valves that are used in typical pneumatic systems. The linear motion of tendons is transduced into complex motion via the prepressurized bent soft actuator. Furthermore, since a PCP actuator does not need any gas supply, complicated pneumatic control systems used in traditional soft robotics are eliminated. This facilitates the development of compact untethered autonomous soft robots for various applications. Both theoretical modeling and experimental validation have been conducted on a sample PCP soft actuator design. A fully untethered autonomous quadrupedal soft robot and a soft gripper have been developed to demonstrate the superiority of the proposed approach over traditional pneumatic-driven soft robots.


title: Autogynephilia: A Paraphilic Model of Gender Identity Disorder

abstract: SUMMARY Autogynephilia is defined as a male's propensity to be sexually aroused by the thought or image of himself as female. Autogynephilia explains the desire for sex reassignment of some male-to-female (MTF) transsexuals. It can be conceptualized as both a paraphilia and a sexual orientation. The concept of autogynephilia provides an alternative to the traditional model of transsexualism that emphasizes gender identity. Autogynephilia helps explain mid-life MTF gender transition, progression from transvestism to transsexualism, the prevalence of other paraphilias among MTF transsexuals, and late development of sexual interest in male partners. Hormone therapy and sex reassignment surgery can be effective treatments in autogynephilic transsexualism. The concept of autogynephilia can help clinicians better understand MTF transsexual clients who recognize a strong sexual component to their gender dysphoria.


title: Attention-Guided Unified Network for Panoptic Segmentation

abstract: This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks.


title: Towards text knowledge engineering

abstract: We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual "quality" of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.


title: Evaluating Youtube Platform Usability by People with Intellectual Disabilities (A User Experience Case Study Performed in a Six-Month Period)

abstract: A comparison study of the Web interaction evolution of a group of people with intellectual disabilities, when performing search tasks using the YouTube platform, is presented. For the effect, we compare results in two assessment moments (the second assessment moment was performed, within the 6 months after the first one). ::: We aimed at evaluating the evolution of their digital skills by comparing two assessment moments through the following usability variables: effectiveness, we register the rate of successful complete conclusion of Web search activities using the YouTube; efficiency, where it was register: time of task conclusion, number and type of difficulties found and errors made; satisfaction, the rate of acceptance and comfort when performing the tasks; and, autonomy indicators. ::: The results revealed that participants depicted more learning skills when performing the second assessment moment, which is indicated by the number of participants who were able to conclude the activities and it's also confirmed by the prominent results of the second assessment moment. When it comes to errors, they made more errors in the first assessment moment in general. While handling the input devices the participants had a better performance with the mouse than the keyboard however in the second assessment moment they improved their skills with the mouse and keyboard input device. However, we believed that the keyboard will never be a device that they can be autonomous with due to their reading/ writing difficulties, which itself represents a didactic challenge when it comes to the very presentation of appropriate methodologies and techniques that may help them overcome such problem. Overall, users with intellectual disabilities had a good experience with the YouTube interface because they were motivated and satisfied during the execution of the tasks, however they were not autonomy with the search option.


title: Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series

abstract: With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks, and in particular Long Short-Term Memory (LSTM) networks have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model using LSTMs on subgroups of similar time series, which are identified by time series clustering techniques. The proposed methodology is able to consistently outperform the baseline LSTM model, and it achieves competitive results on benchmarking datasets, in particular outperforming all other methods on the CIF2016 dataset.


title: Optimizing Area Under Roc Curve with SVMs

abstract: For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassifi- cation costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more mean- ingful performance measures. In this paper, we propose a quadratic programming based algorithm for AUC maximization and show that under certain conditions 2-norm soft margin Support Vector Ma- chines can also maximize AUC. We present experiments that com- pare SVMs performances to those of other AUC maximization based algorithms and provide empirical analysis of SVMs behavior with regards to ROC- based metrics. Our main conclusion is that SVMs can maximize both AUC and accuracy compared to other algorithms like RankBoost that optimize only AUC 2 .


title: Low-noise transimpedance amplifier designg procedure for optical communications

abstract: Here, we report on design and measurement results of a state of the art low-noise and high-gain transimpedance amplifier (TIA) implemented in 0.18 μm TSMC CMOS technology. In depth design methodology for design of high gain and low noise TIA for 2.5 Gb/s optical communication family is presented. A novel noiseless capacitive feedback is proposed and implemented as a noise efficient feedback for TIA circuits. Besides, analytical noise calculations in this family of TIA circuits are presented and optimum noise criteria are derived. The saturation and instability problem of TIA circuits resulted from DC dark current of the input photodiodes (PDs) is addressed and a circuit-level solution is proposed. The measurement results of 0.18 μm chip shows bandwidth of 52 kHz to 1.62 GHz, and transimpedance gain of 75.5 dBΩ) while dissipating 26.3 mW from a 2.2 V power supply, including the output buffer. Taking advantage of proposed capacitive feedback network and optimum noise criteria, noise measurement results show average input referred current noise of 3.18 pA/√Hz for this TIA in the bandwidth of operation.


title: Discriminating self from non-self in nucleic acid sensing

abstract: The detection of viruses by the immune system is mediated predominantly by the sensing of nucleic acids. Here, the authors review our current understanding of how this complex immune sensory system discriminates self from non-self nucleic acids to reliably detect pathogenic viruses, and discuss the future perspectives and implications for human disease.


title: CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension

abstract: We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.


title: Prototypicality Effects in Global Semantic Description of Objects

abstract: In this paper, we introduce a novel approach for semantic description of object features based on the prototypicality effects of the Prototype Theory. Our prototype-based description model encodes and stores the semantic meaning of an object, while describing its features using the semantic prototype computed by CNN-classifications models. Our method uses semantic prototypes to create discriminative descriptor signatures that describe an object highlighting its most distinctive features within the category. Our experiments show that: i) our descriptor preserves the semantic information used by the CNN-models in classification tasks; ii) our distance metric can be used as the object's typicality score; iii) our descriptor signatures are semantically interpretable and enables the simulation of the prototypical organization of objects within a category.


title: TruffleC: dynamic execution of C on a Java virtual machine

abstract: This paper presents TruffleC, a C interpreter that allows the dynamic execution of C code on top of a Java Virtual Machine (JVM). Rather than producing a static build of a C application, TruffleC is a self-optimizing abstract syntax tree (AST) interpreter combined with a just-in-time (JIT) compiler. Our self-optimizing interpreter specializes the AST based on run-time feedback. AST specialization relies on optimistic assumptions and allows us to build inline caches for polymorphic function pointer calls, to profile runtime values and to potentially replace them with constants, or to speculatively remove code that was not executed yet. After AST specialization, the JIT compiler translates the AST to highly optimized machine code. The machine code uses dynamic deoptimization points at which the control switches back to the interpreter in case of a violated assumption. We evaluated TruffleC using a C micro-benchmark in terms of peak performance. This evaluation showed that TruffleC outperforms the code produced by industry standard compilers such as GCC or Clang/LLVM. The evaluation of other benchmarks ("Computer Language Benchmarks Game") showed that the TruffleC performance is on average only 7% slower than the best performance out of the GCC and Clang/LLVM performances.


title: A recurrent neural network approach for visual servoing of manipulators

abstract: The control of robotic manipulators has received significant amount of attention in the control and robotics communities. In this paper, we investigate the kinematic control of a manipulator with an eye-in-hand camera, which is referred to as visual servoing. The visual servoing problem is formulated as a constrained optimization problem, which is then solved via a recurrent neural network. By this approach, the visual servoing with respect to a static point object is achieved with the feature coordinate errors in the image space converging to zero. Besides, joint angle and velocity limits of the manipulator are satisfied, which thus enhances the safety of the manipulator during the visual servoing process. The performance of the approach is guaranteed via theoretical analysis and verified via a simulative example.


title: Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression

abstract: Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors’ decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices of Taiwan construction companies one step ahead. It may be of great interest to home brokers who do not possess sufficient knowledge to invest in such companies. The system has a graphical user interface and functions as a stand-alone application. The developed hybrid system exhibited outstanding prediction performance and it improves overall profit for investment performance. The proposed model is a promising predictive technique for highly nonlinear time series, whose patterns are difficult to capture by traditional models.


title: An automatic RTL compiler for high-throughput FPGA implementation of diverse deep convolutional neural networks

abstract: Convolutional neural networks (CNNs) are rapidly evolving and being applied to a broad range of applications. Given a specific application, an increasing challenge is to search the appropriate CNN algorithm and efficiently map it to the target hardware. The FPGA-based accelerator has the advantage of reconfigurability and flexibility, and has achieved high-performance and low-power. Without a general compiler to automate the implementation, however, significant efforts and expertise are still required to customize the design for each CNN model. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The implementation of each module is optimized at the RTL level. Given a CNN algorithm, its structure is abstracted to a directed acyclic graph (DAG) and then complied with RTL modules in the library. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. ResNet, can be compiled. The proposed methodology is demonstrated with end-to-end FPGA implementations of various CNN algorithms (e.g. NiN, VGG-16, ResNet-50, and ResNet-152) on two standalone Intel FPGAs, Stratix V and Arria 10. The performance and overhead of the automated compilation are evaluated. The compiled FPGA accelerators exhibit superior performance compared to state-of-the-art automation-based works by >2× for various CNNs.


title: On-demand multicast routing protocol

abstract: This paper presents a novel multicast routing protocol for mobile ad hoc wireless networks. The protocol, termed ODMRP (on-demand multicast routing protocol), is a mesh-based, rather than a conventional tree-based multicast scheme and uses a forwarding group concept (only a subset of nodes forwards the multicast packets via scoped flooding). It applies on-demand procedures to dynamically build routes and maintain multicast group membership. ODMRP is well suited for ad hoc wireless networks with mobile hosts where bandwidth is limited, topology changes frequently, and power is constrained. We evaluate ODMRP's scalability and performance via simulation.


title: Causal Decision Trees

abstract: Uncovering causal relationships in data is a major objective of data analytics. Currently, there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree (CDT) where nodes have causal interpretations. Our method follows a well-established causal inference framework and makes use of a classic statistical test to establish the causal relationship between a predictor variable and the outcome variable. At the same time, by taking the advantages of normal decision trees, a CDT provides a compact graphical representation of the causal relationships, and the construction of a CDT is fast as a result of the divide and conquer strategy employed, making CDTs practical for representing and finding causal signals in large data sets. Experiment results demonstrate that CDTs can identify meaningful causal relationships and the CDT algorithm is scalable.