title: Building Software-Defined Radios in MATLAB Simulink - A Step Towards Cognitive Radios

abstract: Software Defined Radio (SDR) is a flexible architecture which can be configured to adapt various wireless standards, waveforms, frequency bands, bandwidths, and modes of operations. This paper presents a detailed survey of the existing hardware and software platform for SDRs. It also presents prototype system for designing and testing of software defined radios in MATLAB/Simulink and briefly discusses the salient functions of the prototype system for Cognitive Radio (CR). A prototype system for wireless personal area network is built and interfaced with a Universal Software Radio Peripheral-2 (USRP2) main-board and RFX2400 daughter board from Ettus Research LLC. The philosophy behind the prototype is to do all waveform-specific processing such as channel coding, modulation, filtering etc. on a host (PC) and general purpose high-speed operations like digital up and down conversion, decimation and interpolation etc. inside FPGA on an USRP2. MATLAB has a rich family of toolboxes that allows building software-defined and cognitive radio to explore various spectrum sensing, prediction and management techniques.


title: A case-based decision theory based process model to aid product conceptual design

abstract: In new product development, the rapid proposal of innovative solutions represents an important phase. This in turn relies on creative ideas, their evaluation, refinement and embodiment of worthwhile directions. This study aims to describe a CBDT based process model for product conceptual design that concentrates on rapidly generating innovations with the support of decision-making rationale. Case-based decision theory (CBDT), derived from case-based reasoning, is applied in this paper as a core method to aid design engineers to make an informed decision quickly, thus accelerating the design process. In the process of utilizing CBDT to support a decision, as for the similarity function, the proper value assignment methods to the selected attribute set for calculation are discussed. In order to assist with innovative solution, aspects of the theory of inventive problem solving (TRIZ) are integrated into the case-based reasoning process. Accordingly, a CBDT-TRIZ model is developed. Quality-function deployment is used to translate customer wants into relevant engineering design requirements and thus formulating the design specification. Image-Scale is used to offer an orthogonal coordinates system to aid evaluation. Finally, a case study is used to demonstrate the validity of the proposed process model based on the design of a cordless hand-tool for garden and lawn applications.


title: Dynamic scheduling methods for computational grid environments

abstract: In this paper, we propose an adaptive and dynamic scheduling method, called most fit task first (MFTF), for a class of computational grids, which are characterized by heterogeneous computing nodes and dynamic task arrivals. Some existing static scheduling methods assume that tasks arrive statically and may not perform well in the case of dynamic task arrivals. Our method can get stable task execution times whether tasks arrive statically or dynamically. We compare the task execution time with other methods to show the performance of the scheduling method.


title: Externalising the autobiographical self: sharing personal memories online facilitated memory retention

abstract: ABSTRACTInternet technology provides a new means of recalling and sharing personal memories in the digital age. What is the mnemonic consequence of posting personal memories online? Theories of transactive memory and autobiographical memory would make contrasting predictions. In the present study, college students completed a daily diary for a week, listing at the end of each day all the events that happened to them on that day. They also reported whether they posted any of the events online. Participants received a surprise memory test after the completion of the diary recording and then another test a week later. At both tests, events posted online were significantly more likely than those not posted online to be recalled. It appears that sharing memories online may provide unique opportunities for rehearsal and meaning-making that facilitate memory retention.


title: Compact Wideband Linear CMOS Variable Gain Amplifier for Analog-Predistortion Power Amplifiers

abstract: To predistort the signal of an analog-predistortion power amplifier system, this paper proposes a variable gain amplifier (VGA) topology that gives an optimized linearity and bandwidth performance while having a continuous, linear-in-decibel gain control curve. The design employs a self-biased differential amplifier with a dynamic current source to provide gain and linearity. The overall VGA gain is then controlled by a highly linear variable attenuator connected at the output of the amplifier. This separated controlling stage allows the VGA to have a continuous and linear-in-decibel gain curve without sacrificing the bandwidth and linearity of its amplifier stage. A VGA designed with such topology has been fabricated in a standard 0.18- μm CMOS technology. Targeted for wideband code-division multiple-access applications, the VGA has a maximum gain tuning range from -13.5 to 13.5 dB, a bandwidth better than 1.8 GHz (380 MHz to 2.2 GHz), and a worst case input P1dB better than -5 and -3.6 dBm, respectively, at 850 MHz and 1.95 GHz. The post-attenuated structure also gives the VGA a noise figure as low as of 4.9 dB at 1.95 GHz. To our knowledge, the design demonstrates the best linearity performance and has other characteristics that are equivalent with other VGAs.


title: Encryption and Secure Computer Networks

abstract: There is increasing growth in the number of computer networks in use and in the kinds of distributed computing applications available on these networks This increase, together with concern about privacy, security, and integrity of information exchange, has created considerable interest in the use of encryptlon to protect information in the networks This survey is directed at the reader who ts knowledgeable about varmus network designs and who now wishes to consider incorporating encryption methods into these designs. It is also directed at developers of encryption algorithms who wish to understand the characteristics of such algorithms useful in network applications. Key management, network encryption protocols, digital signatures, and the utility of conventionalor public-key encryptlon methods are each discussed. A case study of how encryption was integrated into an actual network, the Arpanet, illustrates many issues present m the design of a network encryption facdity.


Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM


title: Gray matter alterations in visual cortex of patients with loss of central vision due to hereditary retinal dystrophies

abstract: Abstract In patients with central visual field scotomata a large part of visual cortex is not adequately stimulated. Over time this lack of input could lead to a reduction of gray matter in the affected cortical areas. We used Voxel Based Morphometry to investigate structural brain changes in patients with central scotomata due to hereditary retinal dystrophies and compared their results to those of normal sighted subjects. Additionally we correlated clinical and demographic characteristics like duration of disease, scotoma size, visual acuity, fixation stability and reading speed to the amount of gray matter in whole brain analyses within the patient group. We found a decrease in gray matter around the lesion projection zone in visual cortex of patients in comparison to controls. Gray matter loss along the posterior and middle portions of the calcarine sulcus is also correlated with scotoma size, indicating that indeed the lack of functional input provokes the gray matter alterations. In whole brain regression analyses within the patient group we found an additional cluster in the right superior and middle frontal gyri, slightly anterior to the frontal eye fields, where gray matter correlated positively with fixation stability. This could be regarded as a consequence of oculomotor learning.


title: Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance

abstract: The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner. We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery.


title: Defeating Image Obfuscation with Deep Learning

abstract: We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The obfuscation techniques considered in this paper are mosaicing (also known as pixelation), blurring (as used by YouTube), and P3, a recently proposed system for privacy-preserving photo sharing that encrypts the significant JPEG coefficients to make images unrecognizable by humans. We empirically show how to train artificial neural networks to successfully identify faces and recognize objects and handwritten digits even if the images are protected using any of the above obfuscation techniques.


title: The Clinical Utility of CA 19-9 in Pancreatic Adenocarcinoma: Diagnostic and Prognostic Updates

abstract: CA 19-9 and CEA are the most commonly used biomarkers for diagnosis and management of patients with pancreatic cancer. Since the original compendium by Steinberg in 1990, numerous studies have reported the use of CA 19-9 and, to a lesser extent, CEA in the diagnosis of pancreatic cancer. Here we update an evaluation of the accuracy of CA 19-9 and CEA, and, unlike previous reviews, focus on discrimination between malignant and benign disease instead of normal controls. In 57 studies involving 3,285 pancreatic carcinoma cases, the combined sensitivity of CA 19-9 was 78.2% and in 37 studies involving 1,882 cases with benign pancreatic disease the specificity of CA 19-9 was 82.8%. From the combined analysis of studies reporting CEA, the sensitivity was 44.2% (1,324 cases) and the specificity was 84.8% (656 cases). These measurements more appropriately reflect the expected biomarker accuracy in the differential diagnosis of patients with periampullary diseases. We also present a summary of the use of CA 19-9 as a prognostic tool and evaluate CA 19-9 diagnostic and prognostic utility in a 10-year, single institution experience.


title: Beyond sensory images: Object-based representation in the human ventral pathway

abstract: We investigated whether the topographically organized, category-related patterns of neural response in the ventral visual pathway are a representation of sensory images or a more abstract representation of object form that is not dependent on sensory modality. We used functional MRI to measure patterns of response evoked during visual and tactile recognition of faces and manmade objects in sighted subjects and during tactile recognition in blind subjects. Results showed that visual and tactile recognition evoked category-related patterns of response in a ventral extrastriate visual area in the inferior temporal gyrus that were correlated across modality for manmade objects. Blind subjects also demonstrated category-related patterns of response in this “visual” area, and in more ventral cortical regions in the fusiform gyrus, indicating that these patterns are not due to visual imagery and, furthermore, that visual experience is not necessary for category-related representations to develop in these cortices. These results demonstrate that the representation of objects in the ventral visual pathway is not simply a representation of visual images but, rather, is a representation of more abstract features of object form.


Technological and institutional ‘ lock-in ’ as a barrier to sustainable innovation


title: Detecting Unknown Insider Threat Scenarios

abstract: This paper reports results from a set of experiments that evaluate an insider threat detection prototype on its ability to detect scenarios that have not previously been seen or contemplated by the developers of the system. We show the ability to detect a large variety of insider threat scenario instances imbedded in real data with no prior knowledge of what scenarios are present or when they occur. We report results of an ensemble-based, unsupervised technique for detecting potential insider threat instances over eight months of real monitored computer usage activity augmented with independently developed, unknown but realistic, insider threat scenarios that robustly achieves results within 5% of the best individual detectors identified after the fact. We explore factors that contribute to the success of the ensemble method, such as the number and variety of unsupervised detectors and the use of prior knowledge encoded in scenario-based detectors designed for known activity patterns. We report results over the entire period of the ensemble approach and of ablation experiments that remove the scenario-based detectors.


title: A cloudlet architecture using mobile devices

abstract: With the increasing ubiquity of mobile devices with computational capabilities comparable to many personal computers, they became the dominant personal computing devices nowadays. However, mobile devices still suffer short battery life and many of them are not capable of running rich-media, data-intensive and compute-intensive applications. Offloading to the cloud is a promising solution, however, long WAN latencies and the large amount of consumed energy using cellular data connectivity guided researchers to bring cloud capabilities to LANs to form cloudlets. On the other hand, in the last few years, there has been a new trend of making use of the computing resources of the vast amount of mobile devices available everywhere. In this paper, we introduce DroidCloudlet as a cloudlet architecture that is based on mobile devices. DroidCloudlet is empowered by available resource-rich mobile devices, while resource-constrained mobile devices can offload compute-intensive workloads to them. Offloading is carried out dynamically at runtime according to specific policies that target reducing execution time or saving energy. We also propose a lightweight cost model that is used to make offloading decision based on both context parameters and historical offloading performance results. To make use of all reachable mobile devices, we introduce a multilevel architecture in which offloading can bubble recursively from local cloudlet servers to other remote servers in any reachable cloudlet. For evaluation, we have built a prototype of the proposed architecture and performance results show that it succeeded to save up to 72% of execution time and 98% of consumed energy.


title: Life Event Dimensions of Loss , Humiliation , Entrapment , and Danger in the Prediction of Onsets of Major Depression and Generalized Anxiety

abstract: Background Although substantial evidence suggests that stressful life events predispose to the onset of episodes of depression and anxiety, the essential features of these events that are depressogenic and anxiogenic remain uncertain. Methods High contextual threat stressful life events, assessed in 98 592 person-months from 7322 male and female adult twins ascertained from a population-based registry, were blindly rated on the dimensions of humiliation, entrapment, loss, and danger and their categories. Onsets of pure major depression (MD), pure generalized anxiety syndrome (GAS) (defined as generalized anxiety disorder with a 2-week minimum duration), and mixed MD-GAS episodes were examined using logistic regression. Results Onsets of pure MD and mixed MD-GAS were predicted by higher ratings of loss and humiliation. Onsets of pure GAS were predicted by higher ratings of loss and danger. High ratings of entrapment predicted only onsets of mixed episodes. The loss categories of death and respondent-initiated separation predicted pure MD but not pure GAS episodes. Events with a combination of humiliation (especially other-initiated separation) and loss were more depressogenic than pure loss events, including death. No sex differences were seen in the prediction of episodes of illness by event categories. Conclusions In addition to loss, humiliating events that directly devalue an individual in a core role were strongly linked to risk for depressive episodes. Event dimensions and categories that predispose to pure MD vs pure GAS episodes can be distinguished with moderate specificity. The event dimensions that preceded mixed MD-GAS episodes were largely the sum of those that preceded pure MD and pure GAS episodes.


title: Analysis of low voltage bulk-driven self-biased high swing cascode current mirror

abstract: A novel low voltage self-biased high swing cascode current mirror (SHCCM) employing bulk-driven NMOS transistors is proposed in this paper. The comparison with the conventional circuit reveals that the proposed bulk-driven circuit operates at lower voltages and provides enhanced bandwidth with improved output resistance. The proposed circuit is further modified by replacing the passive resistance by active MOS realization. Small signal analysis of the proposed and conventional SHCCM are carried out to show the improvement achieved through the proposed circuit. The circuits are simulated in standard SPICE [email protected] CMOS technology and simulated results are compared with the theoretically obtained results. To ensure robustness of the proposed SHCCM, simulation results of component tolerance and process variation have also been included.


title: The Implications of Noncompliance with Antihypertensive Medication

abstract: Given the clear evidence that reducing blood pressure decreases the vascular complications of hypertension, loss of efficacy represents the principal complication of noncompliance with antihypertensive therapy. Withdrawal symptoms are also important and occur after abruptly stopping beta-blockers and centrally-acting antihypertensive drugs. Very few studies have been conducted to assess the impact of missing 1 or 2 doses of an antihypertensive agent on short term control of blood pressure. A high trough to peak ratio (> 50%) for a once-daily medication suggests a long duration of action. However, methodological problems in the design of the studies to determine trough to peak ratios make comparisons between various medications very difficult. In general, however, stopping a drug with a low through to peak ratio is more likely to result in loss of antihypertensive effect than a drug with a high ratio. Poor compliance in dose-escalating studies with antihypertensive agents may have resulted in excessively high dose recommendations in clinical trials.


title: Panopticon: reaping the benefits of incremental SDN deployment in enterprise networks

abstract: The operational challenges posed in enterprise networks present an appealing opportunity for automated orchestration by way of Software-Defined Networking (SDN). The primary challenge to SDN adoption in the enterprise is the deployment problem: How to deploy and operate a network consisting of both legacy and SDN switches, while benefiting from simplified management and enhanced flexibility of SDN. ::: ::: This paper presents the design and implementation of Panopticon, an architecture for operating networks that combine legacy and SDN switches. Panopticon exposes an abstraction of a logical SDN in a partially upgraded legacy network, where SDN benefits can extend over the entire network. We demonstrate the feasibility and evaluate the efficiency of our approach through both testbed experiments with hardware switches and through simulation on real enterprise campus network topologies entailing over 1500 devices. Our results suggest that when as few as 10% of distribution switches support SDN, most of an enterprise network can be operated as a single SDN while meeting key resource constraints.


title: Wireless Communications in the Era of Big Data

abstract: The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all aspects of wireless system design, such as spectrum efficiency, computing capabilities, and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace the big data era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing big data traffic in wireless networks. On the other hand, instead of viewing mobile big data as an unwanted burden, we introduce methods to capitalize on the vast data traffic, for building a big-data-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile big data era.


title: Home modifications to reduce injuries from falls in the Home Injury Prevention Intervention (HIPI) study: a cluster-randomised controlled trial

abstract: Summary Background Despite the considerable injury burden attributable to falls at home among the general population, few effective safety interventions have been identified. We tested the safety benefits of home modifications, including handrails for outside steps and internal stairs, grab rails for bathrooms, outside lighting, edging for outside steps, and slip-resistant surfacing for outside areas such as decks and porches. Methods We did a single-blind, cluster-randomised controlled trial of households from the Taranaki region of New Zealand. To be eligible, participants had to live in an owner-occupied dwelling constructed before 1980 and at least one member of every household had to be in receipt of state benefits or subsidies. We randomly assigned households by electronic coin toss to either immediate home modifications (treatment group) or a 3-year wait before modifications (control group). Household members in the treatment group could not be masked to their assigned status because modifications were made to their homes. The primary outcome was the rate of falls at home per person per year that needed medical treatment, which we derived from administrative data for insurance claims. Coders who were unaware of the random allocation analysed text descriptions of injuries and coded injuries as all falls and injuries most likely to be affected by the home modifications tested. To account for clustering at the household level, we analysed all injuries from falls at home per person-year with a negative binomial generalised linear model with generalised estimating equations. Analysis was by intention to treat. This trial is registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12609000779279. Findings Of 842 households recruited, 436 (n=950 individual occupants) were randomly assigned to the treatment group and 406 (n=898 occupants) were allocated to the control group. After a median observation period of 1148 days (IQR 1085–1263), the crude rate of fall injuries per person per year was 0·061 in the treatment group and 0·072 in the control group (relative rate 0·86, 95% CI 0·66–1·12). The crude rate of injuries specific to the intervention per person per year was 0·018 in the treatment group and 0·028 in the control group (0·66, 0·43–1·00). A 26% reduction in the rate of injuries caused by falls at home per year exposed to the intervention was estimated in people allocated to the treatment group compared with those assigned to the control group, after adjustment for age, previous falls, sex, and ethnic origin (relative rate 0·74, 95% CI 0·58–0·94). Injuries specific to the home-modification intervention were cut by 39% per year exposed (0·61, 0·41–0·91). Interpretation Our findings suggest that low-cost home modifications and repairs can be a means to reduce injury in the general population. Further research is needed to identify the effectiveness of particular modifications from the package tested. Funding Health Research Council of New Zealand.


title: Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces

abstract: We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.


title: Some New Observations on the SMS4 Block Cipher in the Chinese WAPI Standard

abstract: SMS4 is a 128-bit block cipher used in the WAPI standard in wireless networks in China. The cipher has attracted much attention in the past two years. This paper consists of two parts. The first part is on the design of the linear diffusion layer L of SMS4. Some new observations on L are present, which open out the design rationales of L and such class functions to a great extent. The second part is on the differential attack against SMS4. A class of 18-round differential characteristics with a higher probability is given. Then a simple differential attack on 22-round SMS4 is present, which is an improvement of the previous work, thus our attack becomes the best known one on SMS4. Furthermore, we make a remark on the construction of differential characteristics of SMS4.


title: A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot

abstract: In developing a humanoid robot, there are two major objectives. One is developing a physical robot having body, hands and feet resembling those of human beings and being able to similarly control them. The other is to develop a control system that works similarly to our brain, to feel, think, act, and learn like ours. In this paper, an architecture of a control system with a brain-oriented logical structure for the second objective is proposed. The proposed system autonomously adapts to the environment, and implements a clearly defined “consciousness” function, through which both habitual behavior as well as goal-directed behavior are realized. Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life. The binding problem and the basic causes of delay in Libet's experiment are also explained by capturing awareness in this manner. The goal is set as an image in the system, and efficient actions towards achieving this goal are selected in the goal-directed behavior process. The system is designed as an artificial neural network, and aims at achieving consistent and efficient system behavior, through the interaction of highly independent neural nodes. The proposed architecture is based on a two-level design. The first level, which we call the “basic-system” is an artificial neural network system which realizes consciousness, habitual behavior and explains the binding problem. The second level, which we call the “extended-system” is an artificial neural network system which realizes goal-directed behavior.


title: Neurophysiological Tools to Investigate Consumer's Gender Differences during the Observation of TV Commercials

abstract: Neuromarketing is a multidisciplinary field of research whose aim is to investigate the consumers' reaction to advertisements from a neuroscientific perspective. In particular, the neuroscience field is thought to be able to reveal information about consumer preferences which are unobtainable through conventional methods, including submitting questionnaires to large samples of consumers or performing psychological personal or group interviews. In this scenario, we performed an experiment in order to investigate cognitive and emotional changes of cerebral activity evaluated by neurophysiologic indices during the observation of TV commercials. In particular, we recorded the electroencephalographic (EEG), galvanic skin response (GSR), and heart rate (HR) in a group of 28 healthy subjects during the observation of a series of TV advertisements that have been grouped by commercial categories. Comparisons of cerebral indices have been performed to highlight gender differences between commercial categories and scenes of interest of two specific commercials. Findings show how EEG methodologies, along with the measurements of autonomic variables, could be used to obtain hidden information to marketers not obtainable otherwise. Most importantly, it was suggested how these tools could help to analyse the perception of TV advertisements and differentiate their production according to the consumer's gender.


title: Green vehicle routing in urban zones - A neuro-fuzzy approach

abstract: Proposes a model for routing of green vehicles in urban areas.The model takes into account user costs, operator costs and the environment state.As the method used is a neuro-fuzzy logic and Kruskal's algorithm for network design.The model was tested on a real network of Belgrade sity. Local city authorities are making a serious effort to expand the number of low-greenhouse gas vehicles (green vehicles) at home. There is no reliable methodology, however, to support the implementation of this passenger transportation concept. In order to optimize the green capacity, a system has been developed to support decision making in urban green vehicle routing. The objective of this paper is to propose a green vehicle distribution model in a public transportation network. The problem has been defined as a problem of non-linear optimization with dispersed input parameters, requiring neuro-fuzzy logic. An adaptive neural network was developed, taking into account the costs to be borne by operators and users, and the environmental parameters along the observed vehicle route. Each input parameter of the neuro-fuzzy model has been placed in a complex context. They were divided into the elements describing in more detail the environmental status, the operator and passenger costs. The advantage of the model is that several factors shaping the input parameters have been taken into consideration. On the other hand, the complexity of urban systems management makes it a considerable challenge, and the surrounding circumstances are difficult to predict accurately. Accordingly, the inputs of the green vehicle model were fuzzified. The Index of Performance (IP) is the output, associated with each branch of the passenger transportation network. The model has been tested on a part of the public transport system in central Belgrade. The results have proven a practical application possible, and a calibration of input parameters allows for full implementation in public transport vehicle routing.


Making MDD Agile : The Agile Model-Driven Method


title: Fine-grained Recognition Datasets for Biodiversity Analysis

abstract: In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis. We not only give details about two challenging new datasets suitable for computer vision research with up to 675 highly similar classes, but also present first results with localized features using convolutional neural networks (CNN). We conclude with a list of challenging new research directions in the area of visual classification for biodiversity research.


title: Experiences in a 3G network: interplay between the wireless channel and applications

abstract: We present an experimental characterization of the physical and MAC layers in CDMA 1xEV-DO and their impact on transport layer performance. The 1xEV-DO network is currently the fastest mobile broadband cellular network, offering data rates of up to 3.1 Mbps for both stationary and mobile users. These rates are achieved by using novel capacity enhancement techniques at the lower layers. Specifically, 1xEV-DO incorporates rapid channel rate adaptation in response to signal conditions, and opportunistic scheduling to exploit channel fluctuations. Although shown to perform well in isolation, there is no comprehensive literature that examines the impact of these features on transport layer and application performance in real networks. We take the first step in addressing this issue through a large set of experiments conducted on a commercial 1xEV-DO network. Our evaluation includes both stationary and mobile scenarios wherein we transferred data using four popular transport protocols: TCPReno, TCP-Vegas, TCP-Westwood, and TCP-Cubic, and logged detailed measurements about wireless channel level characteristics as well as transport layer performance. We analyzed data from several days of experiments and inferred the properties of the physical, MAC and transport layers, as well as potential interactions between them. We find that the wireless channel data rate shows significant variability over long time scales on the order of hours, but retains high memory and predictability over small time scales on the order of milliseconds. We also find that loss-based TCP variants are largely unaffected by channel variations due to the presence of large buffers, and hence able to achieve in excess of 80% of the system capacity.


title: Delay-encoded transmission and image reconstruction method in synthetic transmit aperture imaging

abstract: Synthetic transmit aperture (STA) imaging systems usually have a lower SNR compared with conventional Bmode ultrasound systems because only one or a small number of elements are selected for each transmission in STA. Here we propose delay-encoded synthetic transmit aperture (DE-STA) imaging to encode all the transmission elements to increase the SNR of the pre-beamformed RF signals. The encoding scheme is similar to the Hadamard encoding. However, in each transmission of DE-STA imaging, selected transmitting elements are delayed by a half period of the ultrasound wave relative to the rest transmitting elements, rather than using a pulse inversion as in the Hadamard encoding sequence. After all the transmission events, a decoding process in the temporal frequency domain is applied to the acquired RF signals to recover the equivalent traditional STA signals with a better SNR. The proposed protocol is tested with simulated data (using Field II) and experimental data acquired with a commercial linear array imaging system (Ultrasonix RP). The results from both the simulations and the experiments demonstrate increased SNR of pre-beamformed RF signals and improved image quality in terms of peak signal-to-noise ratio (PSNR), resolution and contrast-to-noise ratio compared with traditional STA. The lateral resolution (as assessed by a wire target) of DESTA imaging is improved by 28% and the PSNR of the wire is increased by 7 dB, respectively, compared with traditional STA imaging. The proposed image reconstruction framework can also be extended to other transmission protocols.


title: Actor and Action Video Segmentation from a Sentence

abstract: This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.


title: Demo of FaceVR: real-time facial reenactment and eye gaze control in virtual reality

abstract: We introduce FaceVR, a novel method for gaze-aware facial reenactment in the Virtual Reality (VR) context. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. In addition to these face reconstruction components, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions, change gaze directions, or remove the VR goggles in realistic re-renderings. In a live setup with a source and a target actor, we apply these newly-introduced algorithmic components. We assume that the source actor is wearing a VR device, and we capture his facial expressions and eye movement in real-time. For the target video, we mimic a similar tracking process; however, we use the source input to drive the animations of the target video, thus enabling gaze-aware facial reenactment. To render the modified target video on a stereo display, we augment our capture and reconstruction process with stereo data. In the end, FaceVR produces compelling results for a variety of applications, such as gaze-aware facial reenactment, reenactment in virtual reality, removal of VR goggles, and re-targeting of somebody's gaze direction in a video conferencing call.


title: New perspectives and methods in link prediction

abstract: This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse networks presents a significant challenge due to the inherent disproportion of links that can form to links that do form. Previous research has typically approached this as an unsupervised problem. While this is not the first work to explore supervised learning, many factors significant in influencing and guiding classification remain unexplored. In this paper, we consider these factors by first motivating the use of a supervised framework through a careful investigation of issues such as network observational period, generality of existing methods, variance reduction, topological causes and degrees of imbalance, and sampling approaches. We also present an effective flow-based predicting algorithm, offer formal bounds on imbalance in sparse network link prediction, and employ an evaluation method appropriate for the observed imbalance. Our careful consideration of the above issues ultimately leads to a completely general framework that outperforms unsupervised link prediction methods by more than 30% AUC.


title: OTPR - Optimum Transmission Power Routing against Black Hole Attacks in MANETs

abstract: In general, a security is a necessary factor in wireless ad-hoc network to have safety in data transmitting packets between two wireless sensor nodes. The nodes unit a unique characteristics and it leads to insignificant challenges to security design. Mobile nodes operate with limited battery power which needs to be visual utilized in order to perform network activities such as routing, developed network lifetime and network connectivity. Many network protocols have been proposed to reduce energy consumption rate of nodes to maximize the network functioning. I proposed one of the classical and the main attacks of wireless sensor network is Black Hole Attack. Residual power based optimum Transmission Power Routing path which handling volatile transmission power model with calculated received signal strength and low residual power framework to complete energy efficiency, and to develop the network lifetime and connectivity. The operating system and has been used for enforce the proposed proceed. Results were compared with Energy Consumption Routing and Max-Min Battery Cost Routing, revenue and their environment which showed that the proposed approach provide better conduct than other protocols in terms of energy expenditure rate, network lifetime and end to end delay metrics.


title: Advances in millimeter-wave technology for wireless LAN/PAN applications

abstract: This paper describes emerging millimetre-wave technologies for 60-GHz-band wireless LAN/PAN (Local Area Network/Personal Area Network) applications. Multiple use cases are supported for them, and thus, a variety of technical requirements are considered, which include modulation schemes, power consumptions, ranges and propagation environments. The advances in power amplifiers, frequency synthesizers and antennas are also discussed related to the requirements.


UML 2.0 in a Nutshell: A Desktop Quick Reference


title: AlGaN UV focal plane arrays

abstract: This paper presents characterization data, including UV imagery, for 256 x 256 AlGaN UV Focal Plane Arrays (FPAs). The UV-FPAs have 30 x 30 μm 2 unit cells, and use back-illuminated arrays of AlGaN p-i-n photodiodes operating at zero bias voltage. The photodiode arrays were fabricated from multilayer AlGaN films grown by MOCVD on sapphire substrates. Data are also presented for individual AlGaN photodiodes and variable-area diagnostic arrays.


title: On some Properties of DNA Graphs

abstract: Molecular biology which aims to study DNA and protein structure and functions, has stimulated research in different scientific disciplines, discrete mathematics being one of them. One of the problems considered is that of recognition of DNA primary structure. It is known that some methods for solving this problem may be reduced (in their computational part) to graph-theoretic problems involving labeled graphs. Each vertex in such graphs has a label of length k written with an alphabet of size α, where k and α are two parameters. This paper is concerned with studying properties of these graphs (referred to as DNA graphs). More precisely, we give recognition algorithms and compare graphs labeled with different values of k and α.


title: Sidedness, commercial intent and expertise in blog advertising

abstract: Currently, the internet is the main product information source for many consumer goods, mainly because of the irruption of consumer reviews based on blogging. Messages placed on them are usually perceived as a reliable and independent source of information, compared to traditional advertising. Also, blogs are websites that have a high personal nature perception. Using a 2×2×2 factorial design, this study seeks to evaluate the effectiveness of blogging commercial messages, measuring the influence of the message sidedness, communicator expertise and advertising intent and tests their influence over advertising effectiveness (measured as credibility and behavioral intention). Results show the relevance of using two-sided messages, expert sources and unbiased (non-sponsored) messages in terms of increase blog credibility and behavioral intention toward the reviewed product. Interactions of the variables are also examined.


title: Serverless computation with openLambda

abstract: We present OpenLambda, a new, open-source platform for building next-generation web services and applications in the burgeoningmodel of serverless computation. We describe the key aspects of serverless computation, and present numerous research challenges that must be addressed in the design and implementation of such systems. We also include a brief study of current web applications, so as to better motivate some aspects of serverless application construction.


title: Aspect Level Sentiment Classification with Deep Memory Network

abstract: We introduce a deep memory network for aspect level sentiment classification. Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect. Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures. On both datasets we show that multiple computational layers could improve the performance. Moreover, our approach is also fast. The deep memory network with 9 layers is 15 times faster than LSTM with a CPU implementation.


title: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

abstract: Stock prices are formed based on short and/or long-term commercial and trading activities that reflect different frequencies of trading patterns. However, these patterns are often elusive as they are affected by many uncertain political-economic factors in the real world, such as corporate performances, government policies, and even breaking news circulated across markets. Moreover, time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Inspired by Discrete Fourier Transform (DFT), the SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading pattern underlying the fluctuation of stock price. Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion. Modeling multi-frequency trading patterns can enable more accurate predictions for various time ranges: while a short-term prediction usually depends on high frequency trading patterns, a long-term prediction should focus more on the low frequency trading patterns targeting at long-term return. Unfortunately, no existing model explicitly distinguishes between various frequencies of trading patterns to make dynamic predictions in literature. The experiments on the real market data also demonstrate more competitive performance by the SFM as compared with the state-of-the-art methods.


title: Cell replacement therapies for central nervous system disorders

abstract: In animal models, immature neural precursors can replace lost neurons, restore function and promote brain self-repair. Clinical trials in Parkinson's disease suggest that similar approaches may also work in the diseased human brain. But how realistic is it that cell replacement can be developed into effective clinical therapy?


title: Bayesian Probabilistic Numerical Methods

abstract: Over forty years ago average-case error was proposed in the applied mathematics literature as an alternative criterion with which to assess numerical methods. In contrast to worst-case error, this ...


REFERENCES


Decomposing the effect of color on memory: How red and blue affect memory differently


title: Matrix Completion Under Monotonic Single Index Models

abstract: Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces. In real-world settings, however, the linear structure underlying these models is distorted by a (typically unknown) nonlinear transformation. This paper addresses the challenge of matrix completion in the face of such nonlinearities. Given a few observations of a matrix that are obtained by applying a Lipschitz, monotonic function to a low rank matrix, our task is to estimate the remaining unobserved entries. We propose a novel matrix completion method that alternates between low-rank matrix estimation and monotonic function estimation to estimate the missing matrix elements. Mean squared error bounds provide insight into how well the matrix can be estimated based on the size, rank of the matrix and properties of the nonlinear transformation. Empirical results on synthetic and real-world datasets demonstrate the competitiveness of the proposed approach.


title: A Portable 2-Transistor Picowatt Temperature-Compensated Voltage Reference Operating at 0.5 V

abstract: Sensing systems such as biomedical implants, infrastructure monitoring systems, and military surveillance units are constrained to consume only picowatts to nanowatts in standby and active mode, respectively. This tight power budget places ultra-low power demands on all building blocks in the systems. This work proposes a voltage reference for use in such ultra-low power systems, referred to as the 2T voltage reference, which has been demonstrated in silicon across three CMOS technologies. Prototype chips in 0.13 μm show a temperature coefficient of 16.9 ppm/°C (best) and line sensitivity of 0.033%/V, while consuming 2.22 pW in 1350 μm2. The lowest functional Vdd 0.5 V. The proposed design improves energy efficiency by 2 to 3 orders of magnitude while exhibiting better line sensitivity and temperature coefficient in less area, compared to other nanowatt voltage references. For process spread analysis, 49 dies are measured across two runs, showing the design exhibits comparable spreads in TC and output voltage to existing voltage references in the literature. Digital trimming is demonstrated, and assisted one temperature point digital trimming, guided by initial samples with two temperature point trimming, enables TC <; 50 ppm/°C and ±0.35% output precision across all 25 dies. Ease of technology portability is demonstrated with silicon measurement results in 65 nm, 0.13 μm, and 0.18 μm CMOS technologies.


title: Learning to Refine Object Segments

abstract: Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge but are invariant to factors such as pose and appearance. In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach. The resulting bottom-up/top-down architecture is capable of efficiently generating high-fidelity object masks. Similarly to skip connections, our approach leverages features at all layers of the net. Unlike skip connections, our approach does not attempt to output independent predictions at each layer. Instead, we first output a coarse ‘mask encoding’ in a feedforward pass, then refine this mask encoding in a top-down pass utilizing features at successively lower layers. The approach is simple, fast, and effective. Building on the recent DeepMask network for generating object proposals, we show accuracy improvements of 10–20% in average recall for various setups. Additionally, by optimizing the overall network architecture, our approach, which we call SharpMask, is 50 % faster than the original DeepMask network (under .8 s per image).


title: Integrated perception with recurrent multi-task neural networks

abstract: Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for \emph{all} perceptual problems together, solving them efficiently and coherently in an \emph{integrated manner}. In order to capture some of these advantages in machine perception, we ask two questions: whether deep neural networks can learn universal image representations, useful not only for a single task but for all of them, and how the solutions to the different tasks can be integrated in this framework. We answer by proposing a new architecture, which we call \emph{multinet}, in which not only deep image features are shared between tasks, but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data. In this manner, we show that the performance of individual tasks in standard benchmarks can be improved first by sharing features between them and then, more significantly, by integrating their solutions in the common representation.


Similarity as Inference


title: Image Segmentation by Clustering Methods: Performance Analysis

abstract: segmentation plays a significant role in computer vision. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation. Clustering is a powerful technique that has been reached in image segmentation. The cluster analysis is to partition an image data set into a number of disjoint groups or clusters. The clustering methods such as k means, improved k mean, fuzzy c mean (FCM) and improved fuzzy c mean algorithm (IFCM) have been proposed. K means clustering is one of the popular method because of its simplicity and computational efficiency. The number of iterations will be reduced in improved K compare to conventional K means. FCM algorithm has additional flexibility for the pixels to belong to multiple classes with varying degrees of membership. Demerit of conventional FCM is time consuming which is overcome by improved FCM. The experimental results exemplify that the proposed algorithms yields segmented gray scale image of perfect accuracy and the required computer time reasonable and also reveal the improved fuzzy c mean achieve better segmentation compare to others. The quality of segmented image is measured by statistical parameters: rand index (RI), global consistency error (GCE), variations of information (VOI) and boundary displacement error (BDE).


title: Understanding and Modeling of WiFi Signal Based Human Activity Recognition

abstract: Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.


title: Contextual descriptors improving credibility of keypoint matching: Harris-Affine, Hessian-Affme and SIFT feasibility study

abstract: The paper proposes CONSIFT descriptors which are the rotation-variant modification of SIFT (primarily for affine-invariant keypoints). CONSIFT of a keypoint K is its SIFT computed relatively to the orientation defined by the location of another keypoint L (and concatenated with similarly computed SIFT for keypoint L relatively to the location of K). It is additionally recommended that K and L are extracted by different detectors of complementary properties (e.g. Harris-Affine and Hessian-Affine). The paper evaluates CONSIFT performances over the benchmark dataset. Surprisingly, performances of CONSIFT are nearly equivalent to SIFT. However, the intersection of SIFT-based and CONSIFT-based matches is a highly discriminative tool which rejects most of correspondences which are incorrect in a wider context. Since the method does not require any consistency verification over groups of preliminary matched keypoints (and its memory usage is acceptable) it can be instrumental in large-scale CBVIR systems.


title: Table Header Detection and Classification

abstract: In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeerX to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.


title: Behavioral effects of auditory stimulation on kenneled dogs

abstract: KEYWORDS: dog; auditory; music; kennel; welfare Abstract Dogs are kenneled in professional facilities for a variety of reasons; however, the kennel environment, even for short periods, is a potential psychogenic stressor for most dogs. Continual stress and the resultant anxiety are undesirable for both ethical and physiological reasons. One growing area of research pertaining to the welfare of kenneled dogs is environmental enrichment, including auditory stimulation. The current study investigated the impact of music (classical, heavy metal, and specifically designed/altered classical) on activity level, vocalization, and body shaking of 117 kenneled dogs. Results suggest that classical music leads to kenneled dogs spending more time sleeping (F8,354 5 12.24, P. 0.0001) and less time vocalizing (F8,354 5 3.61, P. 0.0005) than when exposed to other music types or no music. Heavy metal music, compared with other music types, appears to increase body shaking (F8,354 5 96.97, P. 0.0001), a behavior suggestive of nervousness. It is suggested that playing classical music in a shelter environment may help mitigate some of the stress inherent for many kenneled dogs.


title: SeSQLite: Security Enhanced SQLite: Mandatory Access Control for Android databases

abstract: SQLite is the most widely deployed in-process library that implements a SQL database engine. It offers high storage efficiency, fast query operation and small memory needs. Due to the fact that a complete SQLite database is stored in a single cross-platform disk file and SQLite does not support multiple users, anyone who has direct access to the file can read the whole database content. SELinux was originally developed as a Mandatory Access Control (MAC) mechanism for Linux to demonstrate how to overcome DAC limitations. However, SELinux provides per-file protection, thus the database file is treated as an atomic unit, impeding the definition of a fine-grained mandatory access control (MAC) policy for database objects. We introduce SeSQLite, an SQLite extension that integrates SELinux access controls into SQLite with minimal performance and storage overhead. SeSQLite implements labeling and access control at both schema level (for tables and columns) and row level. This permits the management of a fine-grained access policy for database objects. A prototype has been implemented and it has been used to improve the security of Android Content Providers.


title: Applications of SAR Interferometry in Earth and Environmental Science Research

abstract: This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions.


title: An integrated design of power converters for electric vehicles

abstract: This paper proposes a novel integrated power system for electric vehicles (EVs) to improve the power density and efficiency at a less cost. It integrates the on-board charger (OBC) and the low-voltage DC/DC converter (LDC) by common use of semiconductor devices, capacitors and magnetic components. Therefore, the volume and cost of the integrated system is reduced compared with the conventional two independent converters. The proposed system can operate in three modes: OBC standalone mode, LDC standalone mode and OBC-LDC simultaneous mode. The phase-shift control is the main control strategy for this system. For a light load, a PWM plus phase shift (PPS) control is added to provide expending soft switching range. As the most important integrated component, the transformer is designed by considering the soft switching ranges. The theoretical analysis of topology, operating modes, control strategy and transformer design are presented. A prototype integrating a 3.3-kW OBC and a 2-kW LDC is built to demonstrate the validity and advantages of the proposed system.


title: Impact of sleep debt on metabolic and endocrine function

abstract: BACKGROUND ::: Chronic sleep debt is becoming increasingly common and affects millions of people in more-developed countries. Sleep debt is currently believed to have no adverse effect on health. We investigated the effect of sleep debt on metabolic and endocrine functions. ::: ::: ::: METHODS ::: We assessed carbohydrate metabolism, thyrotropic function, activity of the hypothalamo-pituitary-adrenal axis, and sympathovagal balance in 11 young men after time in bed had been restricted to 4 h per night for 6 nights. We compared the sleep-debt condition with measurements taken at the end of a sleep-recovery period when participants were allowed 12 h in bed per night for 6 nights. ::: ::: ::: FINDINGS ::: Glucose tolerance was lower in the sleep-debt condition than in the fully rested condition (p<0.02), as were thyrotropin concentrations (p<0.01). Evening cortisol concentrations were raised (p=0.0001) and activity of the sympathetic nervous system was increased in the sleep-debt condition (p<0.02). ::: ::: ::: INTERPRETATION ::: Sleep debt has a harmful impact on carbohydrate metabolism and endocrine function. The effects are similar to those seen in normal ageing and, therefore, sleep debt may increase the severity of age-related chronic disorders.


title: BlockSci: Design and applications of a blockchain analysis platform

abstract: Analysis of blockchain data is useful for both scientific research and commercial applications. We present BlockSci, an open-source software platform for blockchain analysis. BlockSci is versatile in its support for different blockchains and analysis tasks. It incorporates an in-memory, analytical (rather than transactional) database, making it several hundred times faster than existing tools. We describe BlockSci's design and present four analyses that illustrate its capabilities. ::: This is a working paper that accompanies the first public release of BlockSci, available at this https URL We seek input from the community to further develop the software and explore other potential applications.


title: Decision Tree Classification with Differential Privacy: A Survey

abstract: Data mining information about people is becoming increasingly important in the data-driven society of the 21st century. Unfortunately, sometimes there are real-world considerations that conflict with the goals of data mining; sometimes the privacy of the people being data mined needs to be considered. This necessitates that the output of data mining algorithms be modified to preserve privacy while simultaneously not ruining the predictive power of the outputted model. Differential privacy is a strong, enforceable definition of privacy that can be used in data mining algorithms, guaranteeing that nothing will be learned about the people in the data that could not already be discovered without their participation. In this survey, we focus on one particular data mining algorithm -- decision trees -- and how differential privacy interacts with each of the components that constitute decision tree algorithms. We analyze both greedy and random decision trees, and the conflicts that arise when trying to balance privacy requirements with the accuracy of the model.


title: The Relationships Between IT Flexibility, IT-Business Strategic Alignment, and IT Capability

abstract: What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.


Software-Defined Networking-The New Norm for Networks


title: Restoration of TET2 Function Blocks Aberrant Self-Renewal and Leukemia Progression

abstract: Loss-of-function mutations in TET2 occur frequently in patients with clonal hematopoiesis, myelodysplastic syndrome (MDS), and acute myeloid leukemia (AML) and are associated with a DNA hypermethylation phenotype. To determine the role of TET2 deficiency in leukemia stem cell maintenance, we generated a reversible transgenic RNAi mouse to model restoration of endogenous Tet2 expression. Tet2 restoration reverses aberrant hematopoietic stem and progenitor cell (HSPC) self-renewal in vitro and in vivo. Treatment with vitamin C, a co-factor of Fe2+ and α-KG-dependent dioxygenases, mimics TET2 restoration by enhancing 5-hydroxymethylcytosine formation in Tet2-deficient mouse HSPCs and suppresses human leukemic colony formation and leukemia progression of primary human leukemia PDXs. Vitamin C also drives DNA hypomethylation and expression of a TET2-dependent gene signature in human leukemia cell lines. Furthermore, TET-mediated DNA oxidation induced by vitamin C treatment in leukemia cells enhances their sensitivity to PARP inhibition and could provide a safe and effective combination strategy to selectively target TET deficiency in cancer. PAPERCLIP.


title: A survey of hierarchical classification across different application domains

abstract: In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.


title: Schmetrics ! How The Heck Do You Determine A UAV ’ s Autonomy Anyway ?

abstract: Abstract : The recently released DoD Unmanned Aerial Vehicles Roadmap discusses advancements in UAV autonomy in terms of autonomous control levels (ACL). The ACL concept was pioneered by researchers in the Air Force Research Laboratory's Air Vehicles Directorate who are charged with developing autonomous air vehicles. In the process of developing intelligent autonomous agents for UAV control systems we were constantly challenged to "tell us how autonomous a UAV is, and how do you think it can be measured?" Usually we hand-waved away the argument and hoped the questioner will go away since this is a very subjective, and complicated, subject, but within the last year we've been directed to develop national intelligent autonomous UAV control metrics - an IQ test for the flyborgs, if you will. The ACL chart is the result. We've done this via intense discussions with other government labs and industry, and this paper covers the agreed metrics (an extension of the OODA - observe, orient, decide, and act - loop) as well as the precursors, "dead-ends", and out-and-out flops investigated to get there.


title: An approach to spacecraft anomaly detection problem using kernel feature space

abstract: Development of advanced anomaly detection and failure diagnosis technologies for spacecraft is a quite significant issue in the space industry, because the space environment is harsh, distant and uncertain. While several modern approaches based on qualitative reasoning, expert systems, and probabilistic reasoning have been developed recently for this purpose, any of them has a common difficulty in obtaining accurate and complete a priori knowledge on the space systems from human experts. A reasonable alternative to this conventional anomaly detection method is to reuse a vast amount of telemetry data which is multi-dimensional time-series continuously produced from a number of system components in the spacecraft.This paper proposes a novel "knowledge-free" anomaly detection method for spacecraft based on Kernel Feature Space and directional distribution, which constructs a system behavior model from the past normal telemetry data from a set of telemetry data in normal operation and monitors the current system status by checking incoming data with the model.In this method, we regard anomaly phenomena as unexpected changes of causal associations in the spacecraft system, and hypothesize that the significant causal associations inside the system will appear in the form of principal component directions in a high-dimensional non-linear feature space which is constructed by a kernel function and a set of data.We have confirmed the effectiveness of the proposed anomaly detection method by applying it to the telemetry data obtained from a simulator of an orbital transfer vehicle designed to make a rendezvous maneuver with the International Space Station.


title: Renal Excretion of Sodium During Oral Water Loading in Man

abstract: The renal excretion of sodium following an oral sustained 20 ml/kg water load was studied in 10 normal volunteers and in 13 patients without evidence of renal, hepatic, cardiovascular or endocrine disease. The study was performed under conditions controlled for sodium content of the diet, time of the day and posture. Under the described conditions the diuresis following the administration of an oral water load was not accompanied by enhanced excretion of sodium. Sodium excretion was also studied in nine normal volunteers given an oral sustained 10 ml/kg water load and ethyl alcohol. Sodium excretion did not increase with increasing flow.


title: Dual Policy Iteration

abstract: Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at test time, and a slow, non-reactive policy (e.g., Tree Search), that can plan multiple steps ahead. The reactive policy is updated under supervision from the non-reactive policy, while the non-reactive policy is improved with guidance from the reactive policy. In this work we study this Dual Policy Iteration (DPI) strategy in an alternating optimization framework and provide a convergence analysis that extends existing API theory. We also develop a special instance of this framework which reduces the update of non-reactive policies to model-based optimal control using learned local models, and provides a theoretically sound way of unifying model-free and model-based RL approaches with unknown dynamics. We demonstrate the efficacy of our approach on various continuous control Markov Decision Processes.


Multi-Chart Geometry Images


title: Ontology-Based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP

abstract: Ontology-based data access is concerned with querying incomplete data sources in the presence of domain-specific knowledge provided by an ontology. A central notion in this setting is that of an ontology-mediated query, which is a database query coupled with an ontology. In this paper, we study several classes of ontology-mediated queries, where the database queries are given as some form of conjunctive query and the ontologies are formulated in description logics or other relevant fragments of first-order logic, such as the guarded fragment and the unary-negation fragment. The contributions of the paper are three-fold. First, we characterize the expressive power of ontology-mediated queries in terms of fragments of disjunctive datalog. Second, we establish intimate connections between ontology-mediated queries and constraint satisfaction problems (CSPs) and their logical generalization, MMSNP formulas. Third, we exploit these connections to obtain new results regarding (i) first-order rewritability and datalog-rewritability of ontology-mediated queries, (ii) P/NP dichotomies for ontology-mediated queries, and (iii) the query containment problem for ontology-mediated queries.


title: Secure and authentic communication on existing in-vehicle networks

abstract: Because of the increasing number of electronic components, the automotive manufacturers introduced data bus systems to decrease the number of discrete lines. Inside modern vehicles there are several bus systems that are used for communication to provide many safety-relevant functions with direct impact to the vehicle's behaviour. Due to missing security services, these in-car networks are unprotected against malicious attacks. Exemplarily focussing CAN, this article explains that the missing of authenticity and confidentiality are the most important issues concerning security risks for in-car communication. A flexible and adaptive solution using trusted communication groups is presented that enables confidential communication between components of a vehicle and guarantees that only authentic controllers - holding a certificate signed by the manufacturer - are able to be part of these closed communication groups.


title: Interactions of viruses and microparticles with apical plasma membranes of M cells: implications for human immunodeficiency virus transmission.

abstract: In the rectal mucosa, specialized M cells of the lymphoid follicle-associated epithelium conduct vesicular transport of antigens from the mucosal surface into organized mucosal lymphoid tissues, where immune responses are induced. Bacteria and viruses may exploit this mechanism to initiate mucosal or systemic infection. Viral pathogens, including reovirus, poliovirus, and possibly human immunodeficiency virus (HIV), can enter the intestinal or rectal mucosa by adhering to apical membranes of M cells, but the membrane components involved in these interactions are unknown. Glycoprotein coats on the apical surfaces of epithelial cells act as diffusion barriers that limit access of particles and microorganisms to membrane glycolipids and to certain oligosaccharide epitopes of enterocytes but allow selective adherence to M cells. The accessibility of membrane glycolipids of M cells, along with their active endocytic activity, could promote entry of HIV into the rectal mucosa.


title: Awakening professionals' critical awareness of health literacy issues within a francophone linguistic-minority population in Ontario.

abstract: INTRODUCTION ::: We carried out a qualitative evaluation of immediate learning and attitudinal change among health care and social services professionals who attended a workshop promoting critical reflection about health literacy among linguistic-minority Franco-Ontarians. ::: ::: ::: METHODS ::: The study involved 41 francophone health care and social services professionals. The workshop facilitator used evocative objects to elicit reflection on health literacy. Data sources were audio-recordings of group discussions and feedback forms completed by participants. ::: ::: ::: RESULTS ::: The study found that the workshop awakened participants' awareness of health literacy and stimulated them to promote health literacy in their professional practice. The workshop also broadened participants' vision of health literacy as a social determinant of health that interacts synergistically with culture, age, immigration status, social support, and socioeconomic status. ::: ::: ::: CONCLUSION ::: Professionals expressed their awakened awareness of health literacy as collective accountability. This corroborates our claim that critical pedagogy applied to in-service education effectively stimulates professionals' awareness of their potential to change their practice and work environment.


title: Deep Pyramidal Residual Networks with Separated Stochastic Depth

abstract: On general object recognition, Deep Convolutional Neural Networks (DCNNs) achieve high accuracy. In particular, ResNet and its improvements have broken the lowest error rate records. In this paper, we propose a method to successfully combine two ResNet improvements, ResDrop and PyramidNet. We confirmed that the proposed network outperformed the conventional methods; on CIFAR-100, the proposed network achieved an error rate of 16.18% in contrast to PiramidNet achieving that of 18.29% and ResNeXt 17.31%.


title: Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

abstract: Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.


title: A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval

abstract: Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept. Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document’s text to a keyword style query. Limited by a relatively small amount of training data, the model uses pre-trained word embeddings. With these, the model first computes a variable-length Delta matrix between the query and document, representing a difference between the two texts, which is then passed through a deep convolution stage followed by a deep feed-forward network to compute a relevance score. This results in a fast model suitable for use in an online search engine. The model is robust and outperforms comparable state-of-the art deep learning approaches.


title: Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

abstract: Photoplethysmography (PPG) is a noninvasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of the conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.


title: StarMap for Category-Agnostic Keypoint and Viewpoint Estimation

abstract: Semantic keypoints provide concise abstractions for a variety of visual understanding tasks. Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices. As a result, this keypoint representation is in-feasible when objects have a varying number of parts, e.g. chairs with varying number of legs. We propose a category-agnostic keypoint representation, which combines a multi-peak heatmap (StarMap) for all the keypoints and their corresponding features as 3D locations in the canonical viewpoint (CanViewFeature) defined for each instance. Our intuition is that the 3D locations of the keypoints in canonical object views contain rich semantic and compositional information. Using our flexible representation, we demonstrate competitive performance in keypoint detection and localization compared to category-specific state-of-the-art methods. Moreover, we show that when augmented with an additional depth channel (DepthMap) to lift the 2D keypoints to 3D, our representation can achieve state-of-the-art results in viewpoint estimation. Finally, we show that our category-agnostic keypoint representation can be generalized to novel categories.


title: The truth about lies: What works in detecting high-stakes deception?

abstract: In this paper, we provide our view of the current understanding of high-stakes lies often occurring in forensic contexts. We underscore the importance of avoiding widespread pitfalls of deception detection and challenging prevailing assumptions concerning strategies for catching liars. The promise and limitations of each of non-verbal/body language, facial, verbal/linguistic, and physiological channels in detecting deception are discussed. In observing the absence of a single cue or behavioural channel that consistently reveals deception, a holistic approach with concurrent attention to multiple channels of a target's behaviour (ideally videotaped for review) and changes from baseline behaviour is recommended whenever possible. Among the best-validated cues to be considered together include: illustrators, blink and pause rate, speech rate, vague descriptions, repeated details, contextual embedding, reproduction of conversations, and emotional ‘leakage’ in the face. While advocating a reliance on empirical evidence, we observe that few studies of high-stakes deception yet have been conducted. Further, some manifestations of lying are highly idiosyncratic and difficult to address in quantitative research, pointing to the need for keen observation skills, and psychological insight. A recurring theme is the need for the field to devise innovative approaches for studying high-stakes lies to promote ecological validity. Ultimately, such work will provide a strong foundation for the responsible application of deception research in forensic and security settings.


title: An implemented model of punning riddles

abstract: In this paper, we discuss a model of simple question-answer punning, implemented in a program, JAPE-1, which generates riddles from humour-independent lexical entries. The model uses two main types of structure: schemata, which determine the relationships between key words in a joke, and templates, which produce the surface form of the joke. JAPE-1 succeeds in generating pieces of text that are recognizably jokes, but some of them are not very good jokes. We mention some potential improvements and extensions, including post—production heuristics for ordering the jokes according to quality.


title: Amplitude-Based Filtering for Video Magnification in Presence of Large Motion

abstract: Video magnification reveals important and informative subtle variations in the world. These signals are often combined with large motions which result in significant blurring artifacts and haloes when conventional video magnification approaches are used. To counter these issues, this paper presents an amplitude-based filtering algorithm that can magnify small changes in video in presence of large motions. We seek to understand the amplitude characteristic of small changes and large motions with the goal of extracting accurate signals for visualization. Based on spectrum amplitude filtering, the large motions can be removed while small changes can still be magnified by Eulerian approach. An advantage of this algorithm is that it can handle large motions, whether they are linear or nonlinear. Our experimental results show that the proposed method can amplify subtle variations in the presence of large motion, as well as significantly reduce artifacts. We demonstrate the presented algorithm by comparing to the state-of-the-art and provide subjective and objective evidence for the proposed method.


title: What is dyslexia? An exploration of the relationship between teachers' understandings of dyslexia and their training experiences

abstract: Given that an estimated 5–10% of the worldwide population is said to have dyslexia, it is of great importance that teachers have an accurate understanding of what dyslexia is and how it effects their students. Using results from a large‐scale survey of teachers in England and in Wales (N ≈ 2,600), this paper demonstrates that teachers held a basic understanding of dyslexia, based on the behavioural issues that it is associated with. Teachers lacked the knowledge of the biological (i.e., neurological) and cognitive (i.e., processing) aspects of dyslexia. Moreover, a number of teachers mentioned visual factors in their description of dyslexia, despite there being inconclusive evidence to suggest a direct relationship between visual functioning and dyslexia. Further findings demonstrate the importance of good‐quality teacher training in increasing teachers' confidence working with those with dyslexia, while increasing their knowledge of the cognitive aspects of dyslexia. This paper argues that evidence‐based teacher training, which informs teachers of the up‐to‐date research on the biological, cognitive, and behavioural aspects of dyslexia, is essential to combat misconceptions and ensure that teachers have more nuanced and informed understandings of dyslexia.


title: Switched-Capacitor/Switched-Inductor Structures for Getting Transformerless Hybrid DC–DC PWM Converters

abstract: A few simple switching structures, formed by either two capacitors and two-three diodes (C-switching), or two inductors and two-three diodes (L-switching) are proposed. These structures can be of two types: ldquostep-downrdquo and ldquostep-up.rdquo These blocks are inserted in classical converters: buck, boost, buck-boost, Cuk, Zeta, Sepic. The ldquostep-downrdquo C- or L-switching structures can be combined with the buck, buck-boost, Cuk, Zeta, Sepic converters in order to get a step-down function. When the active switch of the converter is on, the inductors in the L-switching blocks are charged in series or the capacitors in the C-switching blocks are discharged in parallel. When the active switch is off, the inductors in the L-switching blocks are discharged in parallel or the capacitors in the C-switching blocks are charged in series. The ldquostep-uprdquo C- or L-switching structures are combined with the boost, buck-boost, Cuk, Zeta, Sepic converters, to get a step-up function. The steady-state analysis of the new hybrid converters allows for determing their DC line-to-output voltage ratio. The gain formula shows that the hybrid converters are able to reduce/increase the line voltage more times than the original, classical converters. The proposed hybrid converters contain the same number of elements as the quadratic converters. Their performances (DC gain, voltage and current stresses on the active switch and diodes, currents through the inductors) are compared to those of the available quadratic converters. The superiority of the new, hybrid converters is mainly based on less energy in the magnetic field, leading to saving in the size and cost of the inductors, and less current stresses in the switching elements, leading to smaller conduction losses. Experimental results confirm the theoretical analysis.


title: Challenges, design and analysis of a large-scale p2p-vod system

abstract: P2P file downloading and streaming have already become very popular Internet applications. These systems dramatically reduce the server loading, and provide a platform for scalable content distribution, as long as there is interest for the content. P2P-based video-on-demand (P2P-VoD) is a new challenge for the P2P technology. Unlike streaming live content, P2P-VoD has less synchrony in the users sharing video content, therefore it is much more difficult to alleviate the server loading and at the same time maintaining the streaming performance. To compensate, a small storage is contributed by every peer, and new mechanisms for coordinating content replication, content discovery, and peer scheduling are carefully designed. In this paper, we describe and discuss the challenges and the architectural design issues of a large-scale P2P-VoD system based on the experiences of a real system deployed by PPLive. The system is also designed and instrumented with monitoring capability to measure both system and component specific performance metrics (for design improvements) as well as user satisfaction. After analyzing a large amount of collected data, we present a number of results on user behavior, various system performance metrics, including user satisfaction, and discuss what we observe based on the system design. The study of a real life system provides valuable insights for the future development of P2P-VoD technology.


title: Diversified set monitoring over distributed data streams

abstract: Data monitoring over distributed streams is a fundamental problem, as represented by modern applications, e.g., sensor network and financial data monitoring. Such applications need a technique which continuously monitors user-requiring data and achieves not only time and space efficiencies but also communication efficiency. In addition, result diversification is also required to increase user satisfaction, thus has been receiving significant attention recently. This motivates us to consider a problem of monitoring k-diverse data over distributed streams. Result diversification is well known to be NP-hard, so the natures of NP-hardness and dynamic distributed data bring non-trivial challenges, e.g., impracticably of centralized approaches. In this paper, we propose a novel algorithm that monitors k-diverse data with time, space, and communication efficiencies. The results of our experiments using both real and synthetic data confirm the effectiveness of our algorithm.


title: SDN-enabled network virtualization for industry 4.0 based on IoTs and cloud computing

abstract: With the rapid development of digital and network technologies, Industry 4.0 is having a huge impact on commerce. In the manufacturing sector, enterprises that produce many customized products, and those that produce a few and different of products, have the opportunity to achieve the same production standards and costs as each other. In an intelligent factory, workers and operators are shifting from traditional machine operators to production process controllers, and staff who make adjustments to management and decisions, to optimize production processes. This work proposes a software-defined infrastructure, which is then deployed in an Industry 4.0 network environment. This infrastructure improves the overall operations of the network environment, the rate of data transmission and the quality of services provided.


title: Optimization of performance and assessment of material cost of the refrigerator condenser

abstract: The condenser of the domestic refrigerator model NST200, manufactured by Regnis Lanka Plc was modified by adopting proper design procedures. The modified condenser was tested and compared with pres ...


title: Diastereoselective synthesis of functionalized pyrrolidines through N-bromosuccinimide-induced aziridine ring expansion cascade of cinnamylaziridine

abstract: An efficient aziridine ring expansion cascade of cinnamylaziridine has been developed. N-Bromosuccinimide was used as the promoter. The resulting functionalized pyrrolidines are the fundamental units of many useful molecules.


title: Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

abstract: Since the amount of information on the internet is growing rapidly, it is not easy for a user to find relevant information for his/her query. To tackle this issue, much attention has been paid to Automatic Document Summarization. The key point in any successful document summarizer is a good document representation. The traditional approaches based on word overlapping mostly fail to produce that kind of representation. Word embedding, distributed representation of words, has shown an excellent performance that allows words to match on semantic level. Naively concatenating word embeddings makes the common word dominant which in turn diminish the representation quality. In this paper, we employ word embeddings to improve the weighting schemes for calculating the input matrix of Latent Semantic Analysis method. Two embedding-based weighting schemes are proposed and then combined to calculate the values of this matrix. The new weighting schemes are modified versions of the augment weight and the entropy frequency. The new schemes combine the strength of the traditional weighting schemes and word embedding. The proposed approach is experimentally evaluated on three well-known English datasets, DUC 2002, DUC 2004 and Multilingual 2015 Single-document Summarization for English. The proposed model performs comprehensively better compared to the state-of-the-art methods, by at least 1% ROUGE points, leading to a conclusion that it provides a better document representation and a better document summary as a result.


title: Design and embedded control of a soft elbow exosuit

abstract: The use of soft materials to transmit power to the human body has numerous advantages, amongst which safety and kinematic transparency stand out. In previous work we showed that a tethered fabric-based exosuit for the elbow joint, driven by an electric motor through a Bowden cable transmission, reduces the muscular effort associated with flexion movements by working in parallel with its wearer's muscles. We herein propose a refined design of the suit and present an untethered control architecture for gravity compensation and motion-intention detection. The architecture comprises four interconnected modules for power management, low-level motor control and high-level signal processing and data streaming. The controller uses a silicone stretch sensor and a miniature load cell, integrated in the fabric frame, to estimate and minimise the torque that its user needs to exert to perform a movement. We show that the device relieves its wearer from an average of 77% of the total moment required to sustain and move a light weight, with a consequent average reduction in muscular effort of 64.5%.