title: 63.5–65.5-GHz Transmit/Receive Phased-Array Communication Link With 0.5–2 Gb/s at 100–800 m and ± 50° Scan Angles

abstract: This paper presents a 32-element phased array centered at 64 GHz using multiple SiGe chips on a single printed-circuit board. The antenna element is a series-fed patch array, which provides directivity in the elevation plane. The transmit array results in an effective isotropic radiated power of 42± 2 dBm at 63–65.5 GHz, while the receive array provides an electronic gain of 24 dB and a system noise figure <7.7 dB, including antenna loss, T/R switch, beamformer, and transceiver. The arrays can be scanned to ±50° in the azimuth using a 5-bit phase shifter on the SiGe chip without degradation in sidelobes and maintaining a near-ideal pattern. A communication link between two phased arrays at 100, 300, and 800 m is also demonstrated, and employs one array on the transmit side and another array on the receive side, together with external mixers and IF amplifiers. The link performance was measured for different scan angles and modulation formats. Data rates of 0.5–2 Gb/s using 16-QAM and QPSK waveforms are demonstrated at 100–800 m, with 2-Gb/s data rate at 300 m and ~500-Mb/s data rate at 800 m. To the best of our knowledge, this is the first system-level demonstration of a silicon-based Gb/s 60-GHz phased array over hundreds of meters. Application areas are in point-to-multipoint links, back-haul and front-haul links, and reconfigurable mesh networks for advanced communication systems.


title: Real-time stereo visual odometry for autonomous ground vehicles

abstract: This paper describes a visual odometry algorithm for estimating frame-to-frame camera motion from successive stereo image pairs. The algorithm differs from most visual odometry algorithms in two key respects: (1) it makes no prior assumptions about camera motion, and (2) it operates on dense disparity images computed by a separate stereo algorithm. This algorithm has been tested on many platforms, including wheeled and legged vehicles, and has proven to be fast, accurate and robust. For example, after 4000 frames and 400 m of travel, position errors are typically less than 1 m (0.25% of distance traveled). Processing time is approximately 20 ms on a 512times384 image. This paper includes a detailed description of the algorithm and experimental evaluation on a variety of platforms and terrain types.


5-Aza-2＇-deoxycytidine Activates the p53／p21^waf1／Cip1 Pathway to Inhibit Cell Proliferation


Integrating ICTs into the Teaching process : Issues in Pedagogical Practices in Teacher Education


title: How to Learn Effectively in Medical School: Test Yourself, Learn Actively, and Repeat in Intervals

abstract: Students in medical school often feel overwhelmed by the excessive amount of factual knowledge they are obliged to learn. Although a large body of research on effective learning methods is published, scientifically based learning strategies are not a standard part of the curriculum in medical school. Students are largely unaware of how to learn successfully and improve memory. This review outlines three fundamental methods that benefit learning: the testing effect, active recall, and spaced repetition. The review summarizes practical learning strategies to learn effectively and optimize long-term retention of factual knowledge.


title: A Distributed Nash Equilibrium Seeking in Networked Graphical Games

abstract: This paper considers a distributed gossip approach for finding a Nash equilibrium in networked games on graphs. In such games a player's cost function may be affected by the actions of any subset of players. An interference graph is employed to illustrate the partially-coupled cost functions and the asymmetric information requirements. For a given interference graph, network communication between players is considered to be limited. A generalized communication graph is designed so that players exchange only their required information. An algorithm is designed whereby players, with possibly partially-coupled cost functions, make decisions based on the estimates of other players' actions obtained from local neighbors. It is shown that this choice of communication graph guarantees that all players' information is exchanged after sufficiently many iterations. Using a set of standard assumptions on the cost functions, the interference and the communication graphs, almost sure convergence to a Nash equilibrium is proved for diminishing step sizes. Moreover, the case when the cost functions are not known by the players is investigated and a convergence proof is presented for diminishing step sizes. The effect of the second largest eigenvalue of the expected communication matrix on the convergence rate is quantified. The trade-off between parameters associated with the communication graph and the ones associated with the interference graph is illustrated. Numerical results are presented for a large-scale networked game.


title: SHEF-Multimodal: Grounding Machine Translation on Images

abstract: This paper describes the University of ::: Sheffield’s submission for the WMT16 ::: Multimodal Machine Translation shared ::: task, where we participated in Task 1 to ::: develop German-to-English and Englishto-German ::: statistical machine translation ::: (SMT) systems in the domain of image ::: descriptions. Our proposed systems are ::: standard phrase-based SMT systems based ::: on the Moses decoder, trained only on the ::: provided data. We investigate how image ::: features can be used to re-rank the n-best ::: list produced by the SMT model, with the ::: aim of improving performance by grounding ::: the translations on images. Our submissions ::: are able to outperform the strong, ::: text-only baseline system for both directions


title: Super high frequency reflective biquad antenna

abstract: This research is focused on the reflective biquad antenna and how it's simple, but interesting, design allows for a much more powerful antenna than similar antennas in multiple situations. Through research it was confirmed that the reflective biquad antenna follows square-loop antenna theory. It also generates multiple peak frequencies (multiband), wide bandwidths, and strong directivity.


title: Taking the Charisma Out of Transformational Leadership

abstract: Transformational and cliarismatic leadership are critically assessed. A theoretical exploration of each reveals contradictions and research limitations for the two constructs. Charisma is defined in its origins as a social phenomenon and as a magical gift" possessed by leaders. Followers of cliarismatic leaders typically emulate or strongly identify with the leader. The transformational leader, in contrast, inspires followers to pursue organizational goals in lieu of self-interests. Followers oftransfonnational leaders are empowered to pursue organizational goals. The paper moves towards a reconsideration of transformational leadership, by not requiring charisma" and suggests that transformational leaders can operate and effectively transform " an organization using inspiration, intellectual stimulation, and individual consideration. Research implications are discussed.


SEGMENTATION OF OPTIC DISK AND OPTIC CUP FROM RETINAL IMAGE


title: Learning a Predictable and Generative Vector Representation for Objects

abstract: What is a good vector representation of an object? We believe that it should be generative in 3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in the sense that it can be perceived from 2D images. We propose a novel architecture, called the TL-embedding network, to learn an embedding space with these properties. The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable. This enables tackling a number of tasks including voxel prediction from 2D images and 3D model retrieval. Extensive experimental analysis demonstrates the usefulness and versatility of this embedding.


title: Much Ado About Time: Exhaustive Annotation of Temporal Data

abstract: Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to perceive. In contrast, we investigate and determine the most cost-effective way of obtaining high-quality multi-label annotations for temporal data such as videos. Watching even a short 30-second video clip requires a significant time investment from a crowd worker; thus, requesting multiple annotations following a single viewing is an important cost-saving strategy. But how many questions should we ask per video? We conclude that the optimal strategy is to ask as many questions as possible in a HIT (up to 52 binary questions after watching a 30-second video clip in our experiments).We demonstrate that while workers may not correctly answer all questions, the cost-benefit analysis nevertheless favors consensus from multiple such cheap-yet-imperfect iterations over more complex alternatives. When compared with a one-question-per-video baseline, our method is able to achieve a 10% improvement in recall (76.7% ours versus 66.7% baseline) at comparable precision (83.8% ours versus 83.0% baseline) in about half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline). We demonstrate the effectiveness of our method by collecting multi-label annotations of 157 human activities on 1,815 videos.


title: Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries

abstract: We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories is preferable, are much easier for users, and have emerged as an effective alternative. Unfortunately, comparisons are far less informative. We propose that there is much richer information that users can easily provide and that robots ought to leverage. We focus on augmenting comparisons with feature queries, and introduce a unified formalism for treating all answers as observations about the true desired reward. We derive an active query selection algorithm, and test these queries in simulation and on real users. We find that richer, feature-augmented queries can extract more information faster, leading to robots that better match user preferences in their behavior.


title: Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy

abstract: Throughout life, new neurons are continuously added to the dentate gyrus. As this continuous addition remodels hippocampal circuits, computational models predict that neurogenesis leads to degradation or forgetting of established memories. Consistent with this, increasing neurogenesis after the formation of a memory was sufficient to induce forgetting in adult mice. By contrast, during infancy, when hippocampal neurogenesis levels are high and freshly generated memories tend to be rapidly forgotten (infantile amnesia), decreasing neurogenesis after memory formation mitigated forgetting. In precocial species, including guinea pigs and degus, most granule cells are generated prenatally. Consistent with reduced levels of postnatal hippocampal neurogenesis, infant guinea pigs and degus did not exhibit forgetting. However, increasing neurogenesis after memory formation induced infantile amnesia in these species.


title: Analysing user trust in electronic banking using data mining methods

abstract: Abstract The potential fraud problems, international economic crisis and the crisis of trust in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain these levels of trust they have been forced to make significant adjustments to economic structures, in efforts to recoup their investments and maintain the loyalty of their customers. To achieve these objectives, the implementation of electronic banking for customers has been considered a successful strategy. The use of electronic banking in Spain in the last decade has been fostered due to its many advantages, giving rise to real integration of channels in financial institutions. This paper reviews different methods and techniques to determine which variables could be the most important to financial institutions in order to predict the likely levels of trust among electronic banking users including socio-demographic, economic, financial and behavioural strategic variables that entities have in their databases. To do so, the most recent advances in machine learning and soft-computing have been used, including a new selection operator for multiobjective genetic algorithms. The results obtained by the algorithms were validated by an expert committee, ranking the quality of them. The new methodology proposed, obtained the best results in terms of optimisation as well as the highest punctuation given by the experts.


title: 60-GHz Millimeter-Wave Identification Reader on 90-nm CMOS and LTCC

abstract: A reader module at 60 GHz for high data-rate short-range backscattering-based communications is presented. The reader consists of a CMOS-based oscillator, amplifiers, and a mixer on a low-temperature co-fired ceramic (LTCC) substrate. The filter, power splitter, and antennas are directly patterned on the LTCC. All millimeter-wave components are contained within the module and the only interfaces to the module are the IF and bias lines. Transmit power of the module is +11.6-dBm effective isotropic radiated power with an IF bandwidth of 400 MHz. The LTCC module measures 13×24 mm2 and has a dc power consumption of 130 mW. Reception of a 20-MHz square wave from a tag 5 cm apart from the reader is demonstrated; the suggested millimeter-wave identification concept enables a 102- 103-fold data-rate increase in comparison to the present near-field communication technique, with similar size, range, and power consumption of the reader.


title: Analyzing Biases in Perception of Truth in News Stories and Their Implications for Fact Checking

abstract: Recently, social media sites like Facebook and Twitter have been severely criticized by policy makers, and media watchdog groups for allowing fake news stories to spread unchecked on their platforms. In response, these sites are encouraging their users to report any news story they encounter on the site, which they perceive as fake. Stories that are reported as fake by a large number of users are prioritized for fact checking by (human) experts at fact checking organizations like Snopes and PolitiFact. Thus, social media sites today are relying on their users' perceptions of the truthfulness of news stories to select stories to fact check. However, few studies have focused on understanding how users perceive truth in news stories, or how biases in their perceptions might affect current strategies to detect and label fake news stories. To this end, we present an in-depth analysis on users' perceptions of truth in news stories. Specifically, we analyze users' truth perception biases for 150 stories fact checked by Snopes. Based on their ground truth and the truth value perceived by users, we can classify the stories into four categories -- (i) C1: false stories perceived as false by most users, (ii) C2: true stories perceived as false by most users, (iii) C3: false stories perceived as true by most users, and (iv) C4: true stories perceived as true by most users. The stories that are likely to be reported (flagged) for fact checking are from the two classes C1 and C2 that have the lowest perceived truth levels. We argue that there is little to be gained by fact checking stories from C1 whose truth value is correctly perceived by most users. Although stories in C2 reveal the cynicality of users about true stories, social media sites presently do not explicitly mark them as true to resolve the confusion. On the contrary, stories in C3 are false stories, yet perceived as true by most users. Arguably, these stories are more damaging than C1 because the truth values of the the story in former situation is incorrectly perceived while truth values of the latter is correctly perceived. Nevertheless, the stories in C1 is likely to be fact checked with greater priority than the stories in C3! In fact, in today's social media sites, the higher the gullibility of users towards believing a false story, the less likely it is to be reported for fact checking. In summary, we make the following contributions in this work. 1. Methodological: We develop a novel method for assessing users' truth perceptions of news stories. We design a test for users to rapidly assess (i.e., at the rate of a few seconds per story) how truthful or untruthful the claims in a news story are. We then conduct our truth perception tests on-line and gather truth perceptions of 100 US-based Amazon Mechanical Turk workers for each story. 2. Empirical: Our exploratory analysis of users' truth perceptions reveal several interesting insights. For instance, (i) for many stories, the collective wisdom of the crowd (average truth rating) differs significantly from the actual truth of the story, i.e., wisdom of crowds is inaccurate, (ii) across different stories, we find evidence for both false positive perception bias (i.e., a gullible user perceiving the story to be more true than it is in reality) and false negative perception bias (i.e., a cynical user perceiving a story to be more false than it is in reality), and (iii) users' political ideologies influence their truth perceptions for the most controversial stories, it is frequently the result of users' political ideologies influencing their truth perceptions. 3. Practical: Based on our observations, we call for prioritizing stories to fact check in order to achieve the following three important goals: (i) Remove false news stories from circulation, (ii) Correct the misperception of the users, and (iii) Decrease the disagreement between different users' perceptions of truth. Finally, we provide strategies which utilize users' truth perceptions (and predictive analysis of their biases) to achieve the three goals stated above while prioritizing stories for fact checking. The full paper is available at: https://bit.ly/2T7raFO


title: Automatic video summarization by graph modeling

abstract: We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. The clusters, together with the computed attention values, form a temporal graph similar to Markov chain that inherently describes the evolution and perceptual importance of video clusters. In our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate subshots in scenes for summarization.


title: Nonverbal Behavior Generator for Embodied Conversational Agents

abstract: Believable nonverbal behaviors for embodied conversational agents (ECA) can create a more immersive experience for users and improve the effectiveness of communication. This paper describes a nonverbal behavior generator that analyzes the syntactic and semantic structure of the surface text as well as the affective state of the ECA and annotates the surface text with appropriate nonverbal behaviors. A number of video clips of people conversing were analyzed to extract the nonverbal behavior generation rules. The system works in real-time and is user-extensible so that users can easily modify or extend the current behavior generation rules.


title: Predicting Motivations of Actions by Leveraging Text

abstract: Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.


Efficient Storage and Analysis of Genome Data in Databases


title: Modic changes following lumbar disc herniation

abstract: Only a small proportion (20%) of patients with LBP can be diagnosed based on a patho-anatomical entity. Therefore, the identification of relevant subgroups, preferably on a patoanatomical basis, is strongly needed. Modic changes have been described by several authors as being closely linked with LBP. The aims of this study were to describe the prevalence of Modic changes, their development as well as their association to LBP, previous disc contour, and surgery in patients with previous severe sciatica. This is a longitudinal cohort study where the patients were recruited from an RCT comparing two active conservative treatments, the 181 patients, who at baseline had radicular pain in or below the knee; all underwent a physical examination and MRI. MRI's, pain history and physical examination of 166 patients were obtained at follow-up 14 months later. The prevalence of Modic changes type 1 increased from 9% at baseline to 29% at follow-up. At that time, a strong association between Modic changes and non-specific LBP was noted. Apparently, Modic changes type 1 was more strongly associated with non-specific lumbar pain than Modic changes type 2. The development of new Modic changes was closely related to the level of a previous disc herniation. A lumbar disc herniation is a strong risk factor for developing Modic changes (especially type 1) during the following year. Furthermore, Modic changes are strongly associated with LBP.


title: Recovering free space of indoor scenes from a single image

abstract: In this paper we consider the problem of recovering the free space of an indoor scene from its single image. We show that exploiting the box like geometric structure of furniture and constraints provided by the scene, allows us to recover the extent of major furniture objects in 3D. Our “boxy” detector localizes box shaped objects oriented parallel to the scene across different scales and object types, and thus blocks out the occupied space in the scene. To localize the objects more accurately in 3D we introduce a set of specially designed features that capture the floor contact points of the objects. Image based metrics are not very indicative of performance in 3D. We make the first attempt to evaluate single view based occupancy estimates for 3D errors and propose several task driven performance measures towards it. On our dataset of 592 indoor images marked with full 3D geometry of the scene, we show that: (a) our detector works well using image based metrics; (b) our refinement method produces significant improvements in localization in 3D; and (c) if one evaluates using 3D metrics, our method offers major improvements over other single view based scene geometry estimation methods.


title: Personalized Recommendation of Twitter Lists using Content and Network Information

abstract: Lists in social networks have become popular tools to orga-nize content. This paper proposes a novel framework for rec-ommending lists to users by combining several features thatjointly capture their personal interests. Our contribution is oftwo-fold. First, we develop a ListRec model that leveragesthe dynamically varying tweet content, the network of twitterers and the popularity of lists to collectively model the users’preference towards social lists. Second, we use the topicalinterests of users, and the list network structure to developa novel network-based model called the LIST-PAGERANK.We use this model to recommend auxiliary lists that are morepopular than the lists that are currently subscribed by theusers. We evaluate our ListRec model using the Twitterdataset consisting of 2988 direct list subscriptions. Using au-tomatic evaluation technique, we compare the performanceof the ListRec model with different baseline methods andother competing approaches and show that our model deliversbetter precision in terms of the prediction of the subscribedlists of the twitterers. Furthermore, we also demonstrate the importance of combining different weighting schemes andtheir effect on capturing users’ interest towards Twitter lists.To evaluate the LIST-PAGERANK model, we employ a user-study based evaluation to show that the model is effective inrecommending auxiliary lists that are more authoritative thanthe lists subscribed by the users.


title: Ageing of ion-exchange membranes in electrodialysis: A structural and physicochemical investigation

abstract: Abstract Understanding the ageing mechanisms of ion-exchange membranes (IEMs) used in electrodialysis (ED) for food industry applications constitutes a major challenge. In this regard, four used membranes (two cationic ones and two anionic ones) were analyzed at the end of their useful life (2 years of ED operation) and compared with their respective fresh new samples to assess the evolution of their structural and physicochemical characteristics, and explore their deterioration. The conductivity, ion-exchange capacity, water content, contact angle and counterion transport number were determined for each new and used membrane. Scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and FTIR analyses were also performed. The used cation-exchange membranes (CEMs) suffered significant degradation; nonetheless, they were generally more robust and resistant than the anion-exchange counterparts, which were more unstable. A significant degradation in the polymer matrix of both membrane types was found. Both used CEMs and anion-exchange membranes (AEMs) lost a part of ion-exchange sites, and their specific electrical conductivity decreased. However, the CEMs became denser with lower water content and higher surface hydrophobicity, while the water content and the thickness of the AEMs increased in about two times. The permselectivity of used CEMs decreased, while that of AEM analogues was not modified. A physicochemical model schematically describing the time evolution of membrane structure and properties was built on the basis of the microheterogeneous model and the experimental data. The main idea was to present colloid aggregates adsorbed by the membrane as nanoparticles occupying a part of membrane pores. This model was used to explain the variation of the totality of equilibrium and transport properties, and in particular, the apparent contradiction between the loss of ion-exchange capacity and the increase in permselectivity of the used AEMs.


title: An Ontological Analysis of Value Propositions

abstract: In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore, it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.


title: Method for enumerating feasible LDC parameters for OLTC and SVR in distribution networks

abstract: This paper presents a method for enumerating the feasible load drop compensator (LDC) parameters of on-load tap changer (OLTC) and step voltage regulators (SVRs) in distribution networks utilizing data acquired by SCADA. Deriving the feasible combinations of LDC parameters is becoming important because voltage control is becoming difficult due to the introduction of photovoltaic generation systems, and the voltage control effectiveness of OLTC and SVRs depends on their three LDC parameters: the target voltage, the dead-band, and the impedance. However, an exhaustive search takes a lot of time and heuristics or metaheuristics provide no guarantee on the quality of the solution. The proposed method derives all the feasible LDC parameters, with which tap operation keeping voltage within the proper range is performed, within practical time. To evaluate the performance of the proposed method, the numerical simulation results of the proposed method will be compared with those of the metaheuristics.


title: An embedded, GSM based, multiparameter, realtime patient monitoring system and control — An implementation for ICU patients

abstract: Wireless, remote patient monitoring system and control using feedback and GSM technology is used to monitor the different parameters of an ICU patient remotely and also control over medicine dosage is provided. Measurement of vital parameters can be done remotely and under risk developing situation can be conveyed to the physician with alarm triggering systems in order to initiate the proper control actions. In the implemented system a reliable and efficient real time remote patient monitoring system that can play a vital role in providing better patient care is developed. This system enables expert doctors to monitor vital parameters viz body temperature, blood pressure and heart rate of patients in remote areas of hospital as well as he can monitor the patient when he is out of the premises. The system in addition also provides a feedback to control the dosage of medicine to the patient as guided by the doctor remotely, in response to the health condition message received by the doctor. Mobile phones transfer measured parameters via SMS to clinicians for further analysis or diagnosis. The timely manner of conveying the real time monitored parameter to the doctor and control action taken by him is given high priority which is very much needed and which is the uniqueness of the developed system. The system even facilitates the doctor to monitor the patient's previous history from the data in memory inbuilt in the monitoring device. Also data can be sent to several doctors incase a doctor fails to respond urgently.


title: Characterization of 4×4 high gain microstrip array antenna for 3.3GHz WiMAX application

abstract: This paper presents the design of microstrip array antenna for 3.3GHz WiMAX (Worldwide Interoperability Mobile Access) application. The proposed antenna comprises of 16 rectangular patches with microstrip line feeding network established using a quarter-wavelength transformer impedance matching technique. To avoid grating lobes affected by the spacing between patches, the patch separation to the adjacent patches is set to be 0.6 of wavelength. Hence, in order to achieve high gain required by the WiMAX specifications, the array consisted of 16 patches is applied where the patches are structured in 4×4 (columns x rows) formation. The antenna structure is then deployed on an Arlon DiClad 527 substrate which the thickness, dielectric constant and loss tangent are 1.6mm, 2.5 and 0.0022, respectively. The characterization result of return loss, VSWR (Voltage Standing Wave Ratio), radiation pattern, and gain are presented consecutively. From the result, it shows that the proposed antenna has the overall gain of 16.02dB and the return loss of 29.85dB that corresponds to VSWR of 1.07 at center frequency of 3.35GHz, whilst the working bandwidth is 150MHz ranges from 3.25–3.40GHz.


title: fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

abstract: Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.


title: Alternative approaches to mixed conifer forest restoration: partitioning the competitive neighborhood

abstract: Forest restoration efforts in the intermountain west of North America generally seek to promote the continuation of pine dominance, enhance wildlife habitat, and decrease hazardous fuels, thereby mitigating catastrophic losses from various stressors and disturbances. We propose a method of focal tree release thinning that partitions the competitive neighborhood to provide alternative approaches to managers. Specifically, we sought to examine how competition index (CI) derived harvest simulations alter forest structure, composition, and variability, and evaluate the ecological implications and efficacy of achieving management recommendations. We used a tree inventory collected across 38 experimental plots in the mixed conifer forest of the Sierra Nevada and simulated post-harvest structure using common silvicultural prescriptions to the ownership and cover type. We calculated competition values for all trees using 10 CIs and simulated harvests from two defined integrals of each corresponding probability density function to compare with the standard marking scenario, for a total of 21 harvest simulations. We assessed post-harvest structure through tree density and diameter distributions, basal area by species, and a structural diversity index. Post-harvest conditions exhibited differences in levels of structural diversity and species dominance; however we did not detect any influence on tree density across diameter classes. Most simulations resulted in a decline of non-pine species basal area relative to the default, while only 3 thinning scenarios showed concomitant increases in pine. Every simulation resulted in greater variance of structural diversity than the default marking guidelines. Review of this method highlighted the variability in tree ranked competitive status across indices. We emphasize that no harvest simulation by CI was clearly superior in all aspects of achieving desired objectives, and there was no clear benefit to incorporating inter-tree distances to calculate CI. We consistently observed favorable outcomes in harvest simulations derived from an open-grown crown width parameter which averts the need for tree distance measurements. This approach can be tailored across multiple individual plots, each maintaining unique competitive environments that reflect local tree neighborhoods.


Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction


Image-level to Pixel-wise Labeling: From Theory to Practice


title: Directional antennas in FANETs: A performance analysis of routing protocols

abstract: Flying Ad-hoc Networks (FANETs) [1] are mobile ad-hoc networks formed by small and medium-sized UAVs. Nowadays, most UAVs are equipped with omnidirectional antennas. In addition, most of the existing routing protocols were designed assuming the use of omnidirectional antennas. Directional antennas have the potential to increase spatial reuse, save the battery's energy, and substantially increase the transmission range. However, these benefits come with a few challenges. Existing directional MAC protocols deal with these challenges, mostly in static ad-hoc networks. We define DA-FANETs as FANETs where directional antennas are used. In this paper, we investigate the performance of existing routing protocols in DA-FANETs. First we implement an 802.11b-based directional MAC protocol that embodies common directional MAC protocol features. Then we evaluate the following routing protocols in DA-FANET scenarios: AODV, OLSR, RGR, and GRP. The results show that each antenna beamwidth has an optimal network size that gives the highest amount of routing. In addition, RGR is the best option for DA-FANETs while GRP is the worst. Although RGR shows the best performance of all, it still leaves a lot of room for improvement with a PDR sitting at just 85% and a relatively high amount of overhead in terms of the number of transmissions performed.


title: ADAPTIVE CONTROL METHOD OF RF-COMBINING DIVERSITY ANTENNA WITH REACTANCE CONTROL CIRCUITS

abstract: The authors have proposed an RF adaptive array antenna loaded with reactance control circuits conflgured with variable capacitors. In this paper, the optimization algorithms for the variable circuit constants are considered. As a result, it is shown that the steepest gradient method provides faster convergence than the perturbation method.


title: How long is the coast of britain? Statistical self-similarity and fractional dimension.

abstract: Geographical curves are so involved in their detail that their lengths are often infinite or, rather, undefinable. However, many are statistically "selfsimilar," meaning that each portion can be considered a reduced-scale image of the whole. In that case, the degree of complication can be described by a quantity D that has many properties of a "dimension," though it is fractional; that is, it exceeds the value unity associated with the ordinary, rectifiable, curves.


title: A deep convolutional neural network module that promotes competition of multiple-size filters

abstract: Abstract We introduce a new deep convolutional neural network (ConvNet) module that promotes competition amongst a set of convolutional filters of multiple sizes. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multiple size filter outputs) by a competitive pooling represented by a maxout activation unit. This extension has the following two objectives: 1) the selection of the maximum response amongst the multiple size filters prevents filter co-adaptation and allows the formation of multiple sub-networks within the same model, which has been shown to facilitate the training of complex learning problems; and 2) the maxout unit reduces the dimensionality of the outputs from the multiple size filters. We show that the use of our proposed module in typical deep ConvNets produces classification results that are competitive with the state-of-the-art results on the following benchmark datasets: MNIST, CIFAR-10, CIFAR-100, SVHN, and ImageNet ILSVRC 2012.


title: Feasibility study of a slack enabling actuator for actuating tendon-driven soft wearable robot without pretension

abstract: A soft wearable robot with a tendon drive is a promising technology that enables a wearable robot to be compact and lightweight. A soft tendon routing system was previously proposed to apply a tendon drive to a soft wearable robot.


title: Agile in Public Administration: Oxymoron or Reality? An Experience Report

abstract: In the last 10 years, Agile methods and practices have emerged as an alternative for software development. Different "flavors" of Agile have appeared ranging from project management to tests organization. These approaches have being gaining popularity and involve now a solid option for organizations developing software, but what about Public Administrations? Is Agile a suitable option for developing software in Public Administrations? Even if Public Administrations have been traditionally regarded as changeresistant, Agile approach can also provide them with the benefits of quick adaptation and frequent value delivery. This paper presents the results of two different projects, which use an Agile framework based on Scrum, developed by a Spanish Public Administration. Additionally, after considering the obtained results, it takes out some relevant learned lessons on the suitability of applying Agile approaches to Public Administration environments.


title: Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

abstract: Many lakes in boreal and arctic regions have high concentrations of CDOM (coloured dissolved organic matter). Remote sensing of such lakes is complicated due to very low water leaving signals. Ther ...


title: Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones

abstract: In this paper, we discuss wireless sensor and networking technologies for swarms of inexpensive aquatic surface drones in the context of the HANCAD project. The goal is to enable the swarm to perform maritime tasks such as sea-border patrolling and environmental monitoring, while keeping the cost of each drone low. Communication between drones is essential for the success of the project. Preliminary experiments show that XBee modules are promising for energy efficient multi-hop drone-to-drone communication.


title: SiRiUS: Securing Remote Untrusted Storage

abstract: This paper presents SiRiUS, a secure file system designed to be layered over insecure network and P2P file systems such as NFS, CIFS, OceanStore, and Yahoo! Briefcase. SiRiUS assumes the network storage is untrusted and provides its own read-write cryptographic access control for file level sharing. Key management and revocation is simple with minimal out-of-band communication. File system freshness guarantees are supported by SiRiUS using hash tree constructions. SiRiUS contains a novel method of performing file random access in a cryptographic file system without the use of a block server. Extensions to SiRiUS include large scale group sharing using the NNL key revocation construction. Our implementation of SiRiUS performs well relative to the underlying file system despite using cryptographic operations.


title: The 'Green Keyhole' nutritional campaign in Sweden: do women with more knowledge have better dietary practices?

abstract: INTRODUCTION ::: In 1989 the National Food Administration in Sweden introduced a food-marking symbol called the 'Green Keyhole'. The aim of the campaign is to help consumers make lowfat, high-fibre food choices. ::: ::: ::: OBJECTIVE ::: To describe the knowledge of the Green Keyhole symbol in a general female population, and to examine whether knowledge is associated with reported intake of dietary fat and fibre. ::: ::: ::: SUBJECTS ::: 616 randomly selected women were examined and interviewed. ::: ::: ::: METHODS ::: Intakes of dietary fat and fibre were analysed in relation to knowledge of the Green Keyhole symbol. Knowledge was assessed by means of an open-ended question. ::: ::: ::: RESULTS ::: 62% of the women adequately understood the meaning of the Green Keyhole. These women were significantly younger (P < 0.0001). Mean body mass index (kg/m2+/-s.e.m.) was significantly higher among the women with more knowledge of the Green Keyhole symbol than those with less knowledge: 26.4 +/- 0.2 vs 25.6 +/- 0.2, (P = 0.002). There were no major differences in total fat intake or total fibre intake between the women with less and more knowledge. However the ratios of polyunsaturated fatty acids to saturated fatty acids and fibre intake per 4200 kJ (1000 kcal) were higher (P = 0.05 and P = 0.03, respectively) and intake of saturated fatty acids was lower (P = 0.05) in the diet among women with better knowledge. ::: ::: ::: CONCLUSIONS ::: The female population described here understands the campaign but their dietary behaviours do not appear to conform to the low-fat message.


title: Applying Recommender Systems and Adaptive Hypermedia for e-Learning Personalizatio

abstract: Learners learn differently because they are different -- and they grow more distinctive as they mature. Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper we present our design and implementation of an adaptive and intelligent web-based programming tutoring system -- Protus, which applies recommendation and adaptive hypermedia techniques. This system aims at automatically guiding the learner's activities and recommend relevant links and actions to him/her during the learning process. Experiments on real data sets show the suitability of using both recommendation and hypermedia techniques in order to suggest online learning activities to learners based on their preferences, knowledge and the opinions of the users with similar characteristics.


title: In the Mood for Being Influential on Twitter

abstract: Researchers have widely studied how information diffuses in Twitter and have often done so by modeling the social-networking site as a communication graph in which tweets spread depending on its nodes' graph properties (e.g., degree, centrality). The resulting models are tractable but make a crucial assumption: that the human being behind an account is a node and that, consequently, human expression in Twitter can be modeled as a set of abstract nodes communicating with each other. We set out to test whether Twitter users can be reduced to look-alike nodes or, instead, whether they show individual differences that impact their popularity and influence. One aspect that may differentiate users is their character and personality. The problem is that personality is difficult to observe and quantify on Twitter. It has been shown, however, that personality is linked to what is unobtrusively observable in tweets: the use of language. We thus carry out a study of tweets - more specifically, we compare five different categories of user (one of which is influencer) and look at their language use. We find that popular and influential users linguistically structure their tweets in specific ways, and that influential users tend to be individuals who express negative sentiment in part of their tweets. These findings suggest that the popularity and influence of a Twitter account cannot be simply traced back to the graph properties of the network within which it is embedded, but also depends on the personality and emotions of the human being behind it.


title: An IEEE 802.11a/g/p OFDM receiver for GNU radio

abstract: Experimental research on wireless communication protocols frequently requires full access to all protocol layers, down to and including the physical layer. Software Defined Radio (SDR) hardware platforms, together with real-time signal processing frameworks, offer a basis to implement transceivers that can allow such experimentation and sophisticated measurements. We present a complete Orthogonal Frequency Division Multiplexing (OFDM) receiver implemented in GNU Radio and fitted for operation with an Ettus USRP N210. To the best of our knowledge, this is the first prototype of a GNU Radio based OFDM receiver for this technology. Our receiver comprises all layers up to parsing the MAC header and extracting the payload of IEEE 802.11a/g/p networks. It supports both WiFi with a bandwidth of 20 MHz and IEEE 802.11p DSRC with a bandwidth of 10 MHz. We validated and verified our implementation by means of interoperability tests, and present representative performance measurements. By making the code available as Open Source we provide an easy-to-access system that can be readily used for experimenting with novel signal processing algorithms.


title: Comparison of dimensionality reduction methods for wood surface inspection

abstract: Dimensionality reduction methods for visualization map the original high-dimensional data typically into two dimensions. Mapping preserves the important information of the data, and in order to be useful, fulfils the needs of a human observer. ::: We have proposed a self-organizing map (SOM)- based approach for visual surface inspection. The method provides the advantages of unsupervised learning and an intuitive user interface that allows one to very easily set and tune the class boundaries based on observations made on visualization, for example, to adapt to changing conditions or material. There are, however, some problems with a SOM. It does not address the true distances between data, and it has a tendency to ignore rare samples in the training set at the expense of more accurate representation of common samples. In this paper, some alternative methods for a SOM are evaluated. These methods, PCA, MDS, LLE, ISOMAP, and GTM, are used to reduce dimensionality in order to visualize the data. Their principal differences are discussed and performances quantitatively evaluated in a few special classification cases, such as in wood inspection using centile features. ::: For the test material experimented with, SOM and GTM outperform the others when classification performance is considered. For data mining kinds of applications, ISOMAP and LLE appear to be more promising methods.


title: A Hidden Markov Model based driver intention prediction system

abstract: Awareness of other vehicle's intention may help human drivers or autonomous vehicles judge the risk and avoid traffic accidents. This paper proposed an approach to predicting driver's intentions using Hidden Markov Model (HMM) which is able to access the control and the state of the vehicle. The driver performs maneuvers including stop/non-stop, change lane left/right and turn left/right in a simulator in both highway and urban environments. Moreover, the structure of the road (curved road) is also taken into account for classification. Experiments were conducted with different input sets (steering wheel data with and without vehicle state data) to compare the system performance.


title: Clearing the Skies: A deep network architecture for single-image rain removal

abstract: We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.


title: Object recognition using the OpenCV Haar cascade-classifier on the iOS platform

abstract: Augmented reality (AR), the compiling of layered computer-generated information to real-time stream data, has recently become a buzzword in the mobile application communities, as real-time vision c ...


title: 14 nm chip package interaction development with Cu pillar bump flip chip package

abstract: This paper presents the 14 nm chip and package interaction (CPI) challenges and development by using 130 um pitch Cu pillar bump in flip chip BGA package. We evaluated 14 nm back-end-of-line (BEOL) film strength / structure / adhesion by passing all the torture tests and CPI reliability tests (Precon, uHAST, TCT, TST and HTST) and following the JEDEC standard. SAM (Scanning Acoustic Microscope) and typical electrical test were used to confirm any failures. Furthermore, SEM (Scanning Electron Microscope) and FIB (Focused Ion Beam) were used to confirm the defect mode. A 3D thermal-mechanical finite element model was built to analyze the stress field for early material assessment, selection and structure optimization, and the results were confirmed by experiments. Two types of BEOL film structure results showed that the film stack change does not affect CPI reliability because the change in package structure is minor. In addition, we can clearly and easily see from the results that PI (Polyimide) acted as package stress buffer and protected the passivation layer (PASV) from cracking in multiple reflow and QTC (Quick Temperature Cycling Test). The simulation results for different bump size and underfill material revealed that small bump, large PI opening, and low Tg (Glass Transition Temperature) underfill induced more stress on BEOL film stack. This conclusion is comparable with simulation data.


title: Tebyan: Interactive Spelling Correction Application for Quranic Verse

abstract: Spelling errors when writing Quranic verses require special handling in terms of detection and correction. This paper aims to demonstrate the significance of the character-level errors correction approach for written Quranic text. It concentrates mainly on discussing the Algorithm and the implementation of the system.


title: Generating Highly Realistic Images of Skin Lesions with GANs

abstract: As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.


title: A prudent based approach for compromised user credentials detection

abstract: Compromised user credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting compromised legitimate users’ credentials. This is because cyber-criminals can gain access to legitimate users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the user’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about compromised users’ activities for decision or policy makers as to which user is more critical and requires more consideration as compared to less crucial user based prevalence value.


title: Spatiotemporal data mining in the era of big spatial data: algorithms and applications

abstract: Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies emphasize the need for developing new and computationally efficient methods tailored for analyzing big data. In this paper, we review major spatial data mining algorithms by closely looking at the computational and I/O requirements and allude to few applications dealing with big spatial data.


title: A Systematic Review of Learning Analytics Intervention Contributing to Student Success in Online Learning

abstract: Recent developments in Learning Analytics have attracted much attention from researchers and practitioners, as well as diverse stakeholders, in exploring the potential for Learning Analytics to enhance learning and teaching practices. Hence, this paper presents a literature review of the use of the Learning Analytics Intervention implemented in various educational institutions in order to improve students' success and engagement in their learning, particularly for at-risk students. The aim of this paper is to provide an overview of how Learning Analytics Intervention has been utilised in the educational institution, the types of theory applied in the intervention and what tools are used in the intervention. Additionally, the application of Learning Analytics Intervention, its purpose, effects and examples are also discussed in this paper. The findings show that most of the Learning Analytics Interventions have an impact on students' success in their learning, although some of them have little impact or no significant effect, and various Learning Analytics tools and strategies are discovered. Lastly, it is suggested that further Learning Analytics Interventions should be developed in order to meet the needs of students.


title: Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance

abstract: The continued call for twenty-first century skills renders computational thinking a topical subject of study, as it is increasingly recognized as a fundamental competency for the contemporary world. Yet its relationship to academic performance is poorly understood. In this paper, we explore the association between computational thinking and academic performance. We test a structural model—employing a partial least squares approach—to assess the relationship between computational thinking skills and academic performance. Surprisingly, we find no association between computational thinking skills and academic performance (except for a link between cooperativity and academic performance). These results are discussed respecting curricular mandated instruction in higher-order thinking skills and the importance of curricular alignment between instructional objectives and evaluation approaches for successfully teaching and learning twenty-first-century skills.


title: Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

abstract: As a result of the recent trend towards digitization-- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis --growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charite University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naive Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.


title: A Descriptive Literature Review and Classification of Cloud Computing Research

abstract: We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 ::: refereed journal articles published since the inception of cloud computing research. The articles are classified based ::: on a scheme that consists of four main categories: technological issues, business issues, domains and applications, ::: and conceptualising cloud computing. The results show that although current research is still skewed towards ::: technological issues, new research themes regarding social and organisational implications are emerging. This ::: review provides a reference source and classification scheme for IS researchers interested in cloud computing, and ::: to indicate under-researched areas as well as future directions.


title: Automatic Database Management System Tuning Through Large-scale Machine Learning

abstract: Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that they are not standardized (i.e., two DBMSs use a different name for the same knob), not independent (i.e., changing one knob can impact others), and not universal (i.e., what works for one application may be sub-optimal for another). Worse, information about the effects of the knobs typically comes only from (expensive) experience. To overcome these challenges, we present an automated approach that leverages past experience and collects new information to tune DBMS configurations: we use a combination of supervised and unsupervised machine learning methods to (1) select the most impactful knobs, (2) map unseen database workloads to previous workloads from which we can transfer experience, and (3) recommend knob settings. We implemented our techniques in a new tool called OtterTune and tested it on two DBMSs. Our evaluation shows that OtterTune recommends configurations that are as good as or better than ones generated by existing tools or a human expert.


title: Knowledge creation: absorptive capacity, organizational mechanisms, and knowledge storage/retrieval capabilities:

abstract: Drawing on a knowledge-based perspective of the firm (KBV), this study develops a framework that delineates the interrelationships among 'the roles of individuals as well as organizations,' 'the IT capabilities of knowledge storage/ retrieval,' and 'knowledge creation.' In order to test the feasibility of this framework, we conducted an empirical study. This study employed a survey instrument, which collected data from 1000 respondents from organizations in manufacturing, trade, transportation, service industries, and academic institutions. A total of 271 useable responses were analyzed. The major contributions of this research are to: (a) develop a knowledge management framework based on individual and organizational perspectives; (b) identify the impact of individuals' absorptive capacity, and organizational mechanisms, on knowledge creation; and (c) specify the moderating effect of organizational memory. The implications of the study are provided, and further research directions are proposed.


title: Augmented Feedback in Semantic Segmentation Under Image Level Supervision

abstract: Training neural networks for semantic segmentation is data hungry. Meanwhile annotating a large number of pixel-level segmentation masks needs enormous human effort. In this paper, we propose a framework with only image-level supervision. It unifies semantic segmentation and object localization with important proposal aggregation and selection modules. They greatly reduce the notorious error accumulation problem that commonly arises in weakly supervised learning. Our proposed training algorithm progressively improves segmentation performance with augmented feedback in iterations. Our method achieves decent results on the PASCAL VOC 2012 segmentation data, outperforming previous image-level supervised methods by a large margin.


PLANTS OF THE TOURAN PROTECTED AREA


A Reservoir Computing Approach for Human Gesture Recognition from Kinect Data


title: Word Level Language Identification and Back-Transliteration

abstract: In this paper, I describe a Rule based and List-Searching system for Word-Level Language Identification and Named Entity recognition in bilingual text. My method uses dictionary search, rules for LI and CRF++, character n-gram for NER. The model does not use any Language specific rules, therefore can easily be replicated on most languages having mixed pair with English. The model also does back-transliteration of words into native language script and recognizes named entity. The model performance is carried on the test sets provided by the shared task on language Identification for English Hindi (En-Hi) Pair, Microsoft Research India. The experimental results show a consistent performance with high precision.


title: Social Welfare Program Administration and Evaluation and Policy Analysis Using Knowledge Discovery and Data Mining (KDD) on Administrative Data

abstract: New technology in knowledge discovery and data mining (KDD) make it possible to extract valuable information from operational data. Private businesses already use the technology for better management, planning, and marketing. Social welfare government agencies have a wealth of information about the experiences of families and individuals that are the most needy in our society in their administrative databases. These data too can be mined and analyzed with proper application of KDD technology. Such social science research could be priceless for better welfare program administration, program evaluation, and policy analysis. In this paper, we discuss a successful case study involving research in computer science as well as social welfare. In a long standing collaboration between the North Carolina DHHS and the University of North Carolina, we have (1) successfully built a longitudinal information system that tracks the experiences of families and individuals on welfare in NC since 1995 (2) developed a dynamic website reporting on the various aspects of the welfare program at the county level in order to assist county staff in the administration of the welfare program and (3) developed a new method to analyze sequential data, which can detect common patterns of welfare services given over time.


title: Learning to Rank

abstract: New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learned function.


title: Comment on Preston and Campbell's "Differential Fertility and the Distribution of Traits"

abstract: Samuel Preston and Cameron Campbell have modelled the processes of assortative mating differential fertility and heritability of IQ to show that the common belief that higher reproduction rates by those who score lower on a particular measurable trait will lead to a reduction in the mean value of that trait from one generation to the next is often not correct. Reed and Reed in 1965 stated that the larger the family size the lower the average intelligence of the children in it although low-IQ persons are less likely to marry and have children. Preston and Campbell explain this Cattell paradox by modeling that negative reproductive differentials are compatible with a constant improving or deteriorating IQ distribution in the populations. 1) Preston and Campbell implicitly assume that the negative relation between IQ and fertility has been at least as great in the past as in the present although a positive relation before the 19th century is evidenced; 2) they are correct that the trait distribution will not move to that of the most prolific subgroup when there is intermarriage between subgroups; however they seriously underestimate the effect of a negative association between IQ and fertility; 3) Preston and Campbell are incorrect in stating that trait distribution is consistent with the current fertility matrix and the current heritability matrix; it will more likely move to a heritability matrix of the subgroup with the highest fertility; 4) the greater the endogamy within heritability types the more the long-term trait distribution will appropriate that of the subgroup with highest fertility; 5) when there are panel data on heritability of a trait over 3 generations inferences can be made about multigenerational trait trends. As to the Cattell paradox the dependence of IQ on an intellectually stimulating social environment has prevented decline in average IQ and even led to slight increases.


title: On the use of ensemble of classifiers for accelerometer-based activity recognition

abstract: Proposed activity recognition approach. We propose and validate a novel activity recognition model.We examine the power of ensemble of classifiers approach experimentally.The model uses J48, Logistic Regression, and MLP.Proposed recognition model is superior to MLP-based recognition model suggested in a previous study.We suggest researchers to focus on ensemble of classifiers approach for activity recognition. Activity recognition aims to detect the physical activities such as walking, sitting, and jogging performed by humans. With the widespread adoption and usage of mobile devices in daily life, several advanced applications of activity recognition were implemented and distributed all over the world. In this study, we explored the power of ensemble of classifiers approach for accelerometer-based activity recognition and built a novel activity prediction model based on machine learning classifiers. Our approach utilizes from J48 decision tree, Multi-Layer Perceptrons (MLP) and Logistic Regression techniques and combines these classifiers with the average of probabilities combination rule. Publicly available activity recognition dataset known as WISDM (Wireless Sensor Data Mining) which includes information from thirty six users was used during the experiments. According to the experimental results, our model provides better performance than MLP-based recognition approach suggested in previous study. These results strongly suggest researchers applying ensemble of classifiers approach for activity recognition problem.


title: Thermal Phenomena in Fiber-reinforced Thermoplastic Tape Winding Process: Computational Simulations and Experimental Validations

abstract: This paper presents experimentally validated three-dimensional transient simulations of the thermal phenomena of the tape winding process, as well as a method to determine separately the heat transfer between the hot gas originating from a torch and the composite material. The computational model predicts the temperature of the incoming tape and the substrate during the winding process. Each numerical simulation is based on an explicit time integration scheme and covers the duration of the process. The simulation within each time step employs a steady-state model. This method takes into account the cyclic nonuniform heating of the material and the effect of the growing mass. The comparison of the simulation results with the experimental data shows good agreements. The experiments were performed with preconsolidated glass fiber-reinforced polypropylene tapes. The measurements were performed with infrared pyrometry. This technique can handle moving points during the entire process, and is nonintrusive.


title: An economical and SLO-guaranteed cloud storage service across multiple cloud service providers

abstract: It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service (ES3), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit; and (3) a dynamic request redirection method, which dynamically redirects a data request from a reservation-overutilized datacenter to a reservation-underutilized datacenter to further reduce the payment. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of ES3 in payment cost minimization and SLO guarantee in comparison with previous methods.


title: Parallel WaveNet: Fast High-Fidelity Speech Synthesis

abstract: The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices.


title: Harmonizing ventilation requirements for indoor lighter than air hazardous atmospheres

abstract: This paper compares the major concepts in ventilation requirements contained in API RP 500/505 and IEC 60079–10, specific to indoor, lighter than air, natural gas hazardous atmospheres. Engineering professionals rely on API 505 as the basis for area classifications. Previously, IEC and North American standards have been successfully adopted in the body of API 500/505 (79–10 Mod). However, the informative appendices of both standards covering ventilation calculations are not entirely consistent. This paper presents case studies for ventilation/classification and an attempt is made to harmonize ventilation calculations to offer further uniformity in global standards. This will be of interest to those involved with area classification activities, as the result may offer reduced mechanical ventilation demands, while maintaining equivalent levels of safety and reliability.


title: Fast Path Planning Algorithm for the RoboCup Small Size League

abstract: Plenty of work based on the Rapidly-exploring Random Trees (RRT) algorithm for path planning in real time has been developed recently. This is the most used algorithm by the top research teams in the Small Size League of RoboCup. Nevertheless, we have concluded that other simpler alternatives show better results under these highly dynamic environments. In this work, we propose a new path planning algorithm that meets all the robotic soccer challenges requirements, which has already been implemented in the STOx’s team for the RoboCup competition in 2013. We have evaluated the algorithm’s performance using metrics such as the smoothness of the paths, the traveled distance and the processing time and compared it with the RRT algorithm’s. The results showed improved performance over RRT when combined measures are used.


title: A non-isolated three-port converter for stand-alone renewable power system

abstract: A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.


title: Studies of regenerative braking in electric vehicle

abstract: Generally the braking system for a car is based on hydraulic braking technology. However, this traditional braking methodology causes a lot of energy wastage since it produces unwanted heat during braking. Thus, the invention of regenerative braking in electric car has overcome these disadvantages moreover it helps in save energy and provides higher efficiency for a car. In regenerative mode, the motor act as a generator, it transfers the kinetic to electrical energy to restore the batteries or capacitors. Meanwhile, the brake controller monitors the speed of the wheels and calculates the torque required plus the excessive energy from the rotational force that can be converted into electricity and fed back into the batteries during regenerative mode. The merits of regenerative braking over traditional braking are energy conservation, wear reduction, fuel consumption and more efficient in braking. Nowadays, the brilliant technology in automotive industry towards regenerative braking is improving. By using a flywheel and ultracapacitor with DC-DC converter it had enhance the regenerative performance. In this paper, the working principle and braking controller for the regenerative braking have been studied to promote the efficiency and realization of energy saving in the electric vehicle.


title: Assessing the Impact of Syntactic and Semantic Structures for Answer Passages Reranking

abstract: In this paper, we extensively study the use of syntactic and semantic structures obtained with shallow and deeper syntactic parsers in the answer passage reranking task. We propose several dependency-based structures enriched with Linked Open Data (LD) knowledge for representing pairs of questions and answer passages. We use such tree structures in learning to rank (L2R) algorithms based on tree kernel. The latter can represent questions and passages in a tree fragment space, where each substructure represents a powerful syntactic/semantic feature. Additionally since we define links between structures, tree kernels also generate relational features spanning question and passage structures. We derive very important findings, which can be useful to build state-of-the-art systems: (i) full syntactic dependencies can outperform shallow models also using external knowledge and (ii) the semantic information should be derived by effective and high-coverage resources, e.g., LD, and incorporated in syntactic structures to be effective. We demonstrate our findings by carrying out an extensive comparative experimentation on two different TREC QA corpora and one community question answer dataset, namely Answerbag. Our comparative analysis on well-defined answer selection benchmarks consistently demonstrates that our structural semantic models largely outperform the state of the art in passage reranking.


title: A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence

abstract: Many works related to Twitter aim at characterizing its users in some way: role on the service (spammers, bots, organizations, etc.), nature of the user (socio-professional category, age, etc.), topics of interest, and others. However, for a given user classification problem, it is very difficult to select a set of appropriate features, because the many features described in the literature are very heterogeneous, with name overlaps and collisions, and numerous very close variants. In this article, we review a wide range of such features. In order to present a clear state-of-the-art description, we unify their names, definitions and relationships, and we propose a new, neutral, typology. We then illustrate the interest of our review by applying a selection of these features to the offline influence detection problem. This task consists in identifying users who are influential in real life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, we propose several content-based approaches to label Twitter users as influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.


title: Personality classification based on Twitter text using Naive Bayes, KNN and SVM

abstract: Personality is a fundamental basis of human behavior. Personality affects the interaction and preferences of an individual. People are required to take a personality test to find out their personality. Social media is a place where users express themselves to the world. Posts made by users of social media can be analyzed to obtain their personal information. This experiment uses text classification to predict personality based on text written by Twitter users. The languages used are English and Indonesian. Classification methods implemented are Naive Bayes, K-Nearest Neighbors and Support Vector Machine. Testing results showed Naive Bayes slightly outperformed the other methods.


title: GFlink: An In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data

abstract: The increasing main memory capacity and the explosion of big data have fueled the development of in-memory big data management and processing. By offering an efficient in-memory parallel execution model which can eliminate disk I/O bottleneck, existing in-memory cluster computing platforms (e.g., Flink and Spark) have already been proven to be outstanding platforms for big data processing. However, these platforms are merely CPU-based systems. This paper proposes GFlink, an in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. Our proposed architecture extends the original Flink from CPU clusters to heterogeneous CPU-GPU clusters, greatly improving the computational power of Flink. Furthermore, we have proposed a programming framework based on Flink's abstract model, i.e., DataSet (DST), hiding the programming complexity of GPUs behind the simple and familiar high-level interfaces. To achieve high performance and good load-balance, an efficient JVM-GPU communication strategy, a GPU cache scheme, and an adaptive locality-aware scheduling scheme for three-stage pipelining execution are proposed. Extensive experiment results indicate that the high computational power of GPUs can be efficiently utilized, and the implementation on GFlink outperforms that on the original CPU-based Flink.


title: Elliptic Curve Public Key Cryptosystems - an introduction

abstract: We give a brief introduction to elliptic curve public-key cryptosystems. We explain how the discrete logarithm in an elliptic curve group can be used to construct cryptosystems. We also focus on practical aspects such as implementation, standardization and intellectual property.


title: Randomly Connected Networks Have Short Temporal Memory

abstract: The brain is easily able to process and categorize complex time-varying signals. For example, the two sentences, "It is cold in London this time of year" and "It is hot in London this time of year," have different meanings, even though the words hot and cold appear several seconds before the ends of the two sentences. Any network that can tell these sentences apart must therefore have a long temporal memory. In other words, the current state of the network must depend on events that happened several seconds ago. This is a difficult task, as neurons are dominated by relatively short time constants-tens to hundreds of milliseconds. Nevertheless, it was recently proposed that randomly connected networks could exhibit the long memories necessary for complex temporal processing. This is an attractive idea, both for its simplicity and because little tuning of recurrent synaptic weights is required. However, we show that when connectivity is high, as it is in the mammalian brain, randomly connected networks cannot exhibit temporal memory much longer than the time constants of their constituent neurons.


title: Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior

abstract: Summary Gene transcription is the process by which the genetic codes of organisms are read and interpreted as a set of instructions for cells to divide, differentiate, migrate, and mature. As cells function in their respective niches, transcription further allows mature cells to interact dynamically with their external environment while reliably retaining fundamental information about past experiences. In this Review, we provide an overview of the field of activity-dependent transcription in the vertebrate brain and highlight contemporary work that ranges from studies of activity-dependent chromatin modifications to plasticity mechanisms underlying adaptive behaviors. We identify key gaps in knowledge and propose integrated approaches toward a deeper understanding of how activity-dependent transcription promotes the refinement and plasticity of neural circuits for cognitive function.


title: Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games

abstract: Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.


title: On Non-Preemptive Scheduling of Periodic and Sporadic Tasks

abstract: A fundamental problem in the theory of real-time scheduling is examined: scheduling a set of periodic or sporadic tasks on a uniprocessor without preemption and without inserted idle time. The authors give a necessary and sufficient set of conditions C for a set of periodic or sporadic tasks to be schedulable for arbitrary release times of the tasks. They show that any set of periodic or sporadic tasks that satisfies C can be scheduled with an earliest-deadline-first (EDF) scheduling algorithm. The authors present the scheduling model, briefly review the literature in real-time scheduling, prove that the non-preemptive EDF algorithm is universal for sets of tasks, whether periodic or sporadic, and demonstrate the absence of a universal algorithm for periodic tasks with specified release times. It is proved that the problem of deciding schedulability of a set of concrete periodic tasks is intractable. >


Mid-face Rejuvenation Mr Dalvi Humzah and


Learning Non-Linear Functions for Text Classification.


title: A derivative-free implementation of the extended Kalman filter

abstract: A nonlinear estimation paradigm is developed to estimate the mean and covariance of a time-evolving state distribution. The approach represents uncertainty by an ensemble set of state vectors rather than by the traditional mean and covariance measures, avoiding the need for the calculation of matrix partial derivatives (Jacobian matrices). The paradigm is shown to be equivalent to the extended Kalman filter in a limiting case. Implementation of the new filtering approach is illustrated with a simple example and a step-by-step description. The paradigm is not significantly more computationally intensive than traditional filters and proves ideal for the rapid implementation of complex nonlinear system and observation models.


Developing a competence framework for psychological interventions in a multidisciplinary paediatric context


Mitosis can drive cell cannibalism through entosis


title: Research Article Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach 1 Abstract

abstract: Identifying causal relationships is an important aspect of scientific inquiry. Causal relationships help us to infer, predict, and plan. This research investigates the causal relationships between two constructs, perceived enjoyment (PE) and perceived ease of use (PEOU), within the nomological net of user technology acceptance. PE has been theorized and empirically validated as either an antecedent or a consequence of PEOU. We believe that there are two reasons that account for this ambiguity the conceptual coupling of PE and PEOU and the limitations of covariance-based statistical methods. Accordingly, we approach this inconsistency by providing more theoretical reasoning and employing an alternative statistical method, namely Cohen’s path analysis. Specifically, as suggested by previous research on the difference between utilitarian and hedonic systems, we propose the conditional dominance of causal directions. Empirical results from two studies using different technologies and user samples support the theoretical claim that the PEAEPEOU causal direction outweighs the PEOUAEPE direction for utilitarian systems. There are both theoretical and the methodical contributions of this research. The approach applied in this research can be generalized to study causal relationships between conceptually coupled variables, which otherwise may be overlooked by confirmatory methods. We encourage researchers to pay attention to causal directions in addition to causal connectedness.


title: An enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm in cloud environment

abstract: In cloud computing, de-duplication plays an essential role in detecting the de-duplication of encoded data with minimal computation and cost. De-duplication cleans the cloud datacentre’s unwanted storage and helps to identify the right owner of the content in the cloud. Even if there is only one copy of each data file stored in the cloud, the cloud has huge quantity of cloud users who own such data file. The existing method discussed a convergent encryption technique to solve the de-duplication problem. It also developed a system which does not allow storing any duplicate data in the cloud. However, the method does not assure consistency, reliability and confidentiality in cloud. Similar or different cloud users could store duplicated file in the cloud server, where cloud storage utilises high volume of storage. To find a solution for the above problems, the paper introduces enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm to enhance the file-level and content-level de-duplication detection of encoded data with reliability in cloud environment. Every cloud user’s files contain an independent master key for encryption using ESCDIP technique and outsourcing them into the cloud. It reduces the overheads that are associated with the interactive duplication detection and query processes. The proposed method identifies the unique data chunking to store in the cloud. Based on experimental result, the ESCDIP method reduces 2.3 data uploading time in seconds, 2.31 data downloading time in seconds and 32.66% communication cost compared to existing approaches.


title: Job recommender systems: A survey

abstract: The personalized recommender system is proposed to solve the problem of information overload and widely applied in many domains. The job recommender systems for job recruiting domain have emerged and enjoyed explosive growth in the last decades. User profiles and recommendation technologies in the job recommender system have gained attention and investigated in academia and implemented for some application cases in industries. In this paper, we introduce some basic concepts of user profile and some common recommendation technologies based on the existing research. Finally, we survey some typical job recommender systems which have been achieved and have a general comprehension of job recommender systems.


A Mood-Based Music Classification and Exploration System


title: Sensorless speed control of BLDC motor using six step square wave and rotor position detection

abstract: This paper presents a brushless DC motor (BLDCM) control system that does not require Hall sensor and speed encoder for an air conditioner fan motor. Using low cost circuits, the zero-crossing points are detected to estimate the commutated timing and speed information. Hall element shortcoming can therefore be eliminated. A modified six-step square wave pattern with pulse-width modulation is developed to simplify the conventional coordinate transformations technique. To verify the proposed method, a prototype fan motor controller is constructed and tested. Experimental results show that the proposed method has high efficiency and good performance.


title: Slice-to-volume medical image registration: a survey

abstract: During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.