title: A climbing robot based on Hot Melt Adhesion

abstract: Robust climbing in unstructured environment has been one of the long-standing challenges in robotics research. Among others, the control of large adhesion forces is still an important problem that significantly restricts the locomotion performance of climbing robots. The main contribution of this paper is to propose a novel approach to autonomous robot climbing which makes use of Hot Melt Adhesion (HMA). The HMA material is known as a very economical solution to achieve large adhesion forces, and the forces can be varied by controlling its material temperature. For locomotion in both inclined and vertical walls, this paper investigates the basic characteristics of HMA material, and proposes a design and control of climbing robot that uses the HMA material for attaching and detaching its body to the environment. The robot is equipped with servomotors and thermal control units to actively vary the temperature of the material, and the coordination of these components enables the robot to walk against the gravitational forces even with a relatively large body weight. A real-world platform is used to demonstrate locomotion on a vertical wall, and the experimental result explains feasibility and overall performances of this approach.


title: VLSI testing based security metric for IC camouflaging

abstract: An Integrated Circuit (IC) can be reverse engineered by imaging its layout and reconstructing the netlist. IC camouflaging is a layout-level technique that hampers imaging-based reverse engineering by using, in one embodiment, functionally different standard cells that look alike. Reverse engineering will fail if the functionality of a camouflaged gate cannot be correctly resolved. We adapt VLSI testing principles (justification and sensitization) to quantify the ability of a reverse engineer to unambiguously resolve the functionality of look-alike camouflaged gates. We evaluate the security of look-alike standard cells based IC camouflaging by applying it on the controllers in OpenSPARC T1 processor.


title: Delving into adversarial attacks on deep policies

abstract: Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.


title: A Novel Broadband Dual-Polarized Antenna Element for LTE700 MHz/GSM850 MHz/GSM900 MHz Applications

abstract: This paper presents a novel broadband and ±45° slant orthogonal dual-polarized antenna element designed for LTE700 MHz/GSM850 MHz/GSM900 MHz applications. The proposed antenna element consists of an annular base, four couples of baluns, three kinds of parasitic plastic fasteners ( $3\times 4=12$ , four for each kind of fastener), and four dipoles that are orthogonal with each other. Compared with the prototype without parasitic plastic fasteners, the impedance bandwidth of the proposed antenna element can be significantly improved. Measured results show that the proposed antenna element can operate from 698 to 960 MHz with VSWR 25 dB, a stable radiation pattern with half-power beamwidth of 65.92° ± 5.44° at $H$ plane and $V$ plane, and a relatively stable gain of 9.41 ± 0.48 (dBi). In addition, the size of the proposed antenna element is $0.469\lambda _{0} \times 0.469\lambda _{0}\times 0.22\lambda _{0}$ ( $\lambda _{0}$ is the free-space wavelength referring to the antenna center frequency). Good agreement is observed between simulated and measured results.


title: Understanding Other Minds: Linking Developmental Psychology and Functional Neuroimaging

abstract: Evidence from developmental psychology suggests that understanding other minds constitutes a special domain of cognition with at least two components: an early-developing system for reasoning about goals, perceptions, and emotions, and a later-developing system for representing the contents of beliefs. Neuroimaging reinforces and elaborates upon this view by providing evidence that (a) domain-specific brain regions exist for representing belief contents, (b) these regions are apparently distinct from other regions engaged in reasoning about goals and actions (suggesting that the two developmental stages reflect the emergence of two distinct systems, rather than the elaboration of a single system), and (c) these regions are distinct from brain regions engaged in inhibitory control and in syntactic processing. The clear neural distinction between these processes is evidence that belief attribution is not dependent on either inhibitory control or syntax, but is subserved by a specialized neural system for th...


title: A general framework for image feature matching without geometric constraints

abstract: We propose a general framework of 4 algorithms for image feature matching.We prove that the traditional Ratio-Match is the worst performer.We confirm the theoretical results experimentally on over 3000 image pairs.20 percentage-point higher precision without increased computation time. Computer vision applications that involve the matching of local image features frequently use Ratio-Match as introduced by Lowe and others, but is this really the optimal approach? We formalize the theoretical foundation of Ratio-Match and propose a general framework encompassing Ratio-Match and three other matching methods. Using this framework, we establish a theoretical performance ranking in terms of precision and recall, proving that all three methods consistently outperform or equal Ratio-Match. We confirm the theoretical results experimentally on over 3000 image pairs and show that matching precision can be increased by up to 20 percentage-points without further assumptions about the images we are using. These gains are achieved by making only a few key changes of the Ratio-Match algorithm that do not affect computation times.


A Smart Stick for Assisting Blind People


title: Potential Use of the Conservation Security Program to Encourage Diverse Crop Rotations in Eastern South Dakota

abstract: There are provided new 3-hydroxy-4-aryl-5-oxo-pyrazoline derivatives of the formula (I) (I) in which A and B are identical or different and independently of one another in each case represent hydrogen, alkyl, alkenyl, alkynyl, alkoxyalkyl, alkylthioalkyl, cycloalkyl or optionally substituted aryl or A and B together represent the bivalent radical of a saturated or unsaturated, optionally substituted, mono-, bi-, tri- or polycyclic system, X represents alkyl, halogen or alkoxy, Y represents hydrogen, alkyl, halogen, alkoxy or halogenoalkyl, Z represents alkyl, halogen or alkoxy n represents a number 0, 1, 2 or 3, G represents hydrogen (a) or the groups COR1, (b) (c) SO2R3, (d) (e) (f) or E, (g) in which E represents a metal ion equivalent or an ammonium ion, L and M represents oxygen and/or sulphur, and R1, R2, R3, R4, R5, R6 and R7 have the meaning given in the text of the application. The new 3-hydroxy-4-aryl-5-oxo-pyrazoline derivatives of the formula (I) have a particularly pronounced activity against animal pests, in particular against insects and arachnids, and against weeds.


title: Faster deterministic integer factorization

abstract: The best known unconditional deterministic complexity bound for computing the prime factorization of an integer N is O(M_int(N^(1/4) log N)), where M_int(k) denotes the cost of multiplying k-bit integers. This result is due to Bostan--Gaudry--Schost, following the Pollard--Strassen approach. We show that this bound can be improved by a factor of (log log N)^(1/2).


title: Facial expression recognition using deep convolutional neural networks

abstract: Facial expressions convey non-verbal information between humans in face-to-face interactions. Automatic facial expression recognition, which plays a vital role in human-machine interfaces, has attracted increasing attention from researchers since the early nineties. Classical machine learning approaches often require a complex feature extraction process and produce poor results. In this paper, we apply recent advances in deep learning to propose effective deep Convolutional Neural Networks (CNNs) that can accurately interpret semantic information available in faces in an automated manner without hand-designing of features descriptors. We also apply different loss functions and training tricks in order to learn CNNs with a strong classification power. The experimental results show that our proposed networks outperform state-of-the-art methods on the well-known FERC-2013 dataset provided on the Kaggle facial expression recognition competition. In comparison to the winning model of this competition, the number of parameters in our proposed networks intensively decreases, that accelerates the overall performance speed and makes the proposed networks well suitable for real-time systems.


title: What are good parts for hair shape modeling?

abstract: Hair plays an important role in human appearance. However, hair segmentation is still a challenging problem partially due to the lack of an effective model to handle its arbitrary shape variations. In this paper, we present a part-based model robust to hair shape and environment variations. The key idea of our method is to identify local parts by promoting the effectiveness of the part-based model. To this end, we propose a measurable statistic, called Subspace Clustering Dependency (SC-Dependency), to estimate the co-occurrence probabilities between local shapes. SC-Dependency guarantees output reasonability and allows us to evaluate the effectiveness of part-wise constraints in an information-theoretic way. Then we formulate the part identification problem as an MRF that aims to optimize the effectiveness of the potential functions. Experiments are performed on a set of consumer images and show our algorithm's capability and robustness to handle hair shape variations and extreme environment conditions.


title: Active hashing and its application to image and text retrieval

abstract: In recent years, hashing-based methods for large-scale similarity search have sparked considerable research interests in the data mining and machine learning communities. While unsupervised hashing-based methods have achieved promising successes for metric similarity, they cannot handle semantic similarity which is usually given in the form of labeled point pairs. To overcome this limitation, some attempts have recently been made on semi-supervised hashing which aims at learning hash functions from both metric and semantic similarity simultaneously. Existing semi-supervised hashing methods can be regarded as passive hashing since they assume that the labeled pairs are provided in advance. In this paper, we propose a novel framework, called active hashing, which can actively select the most informative labeled pairs for hash function learning. Specifically, it identifies the most informative points to label and constructs labeled pairs accordingly. Under this framework, we use data uncertainty as a measure of informativeness and develop a batch mode algorithm to speed up active selection. We empirically compare our method with a state-of-the-art passive hashing method on two benchmark data sets, showing that the proposed method can reduce labeling cost as well as overcome the limitations of passive hashing.


title: Active former of monocycle high-voltage subnanosecond pulses

abstract: Experiments have been performed to investigate the operating modes of a generator of 2/spl times/500-ps bipolar high-voltage, nanosecond pulses with the double amplitude (270 kV) close to that of the charge pulse of the RADAN-303 nanosecond driver. The generator contains an additional peaker shortening the risetime of the starting pulse and a pulse-forming line with two untriggered gas gaps operating with a total jitter of 200 ps.


title: Packet Injection Attack and Its Defense in Software-Defined Networks

abstract: Software-defined networks (SDNs) are novel networking architectures that decouple the network control and forwarding functions from the data plane. Unlike traditional networking, the control logic of SDNs is implemented in a logically centralized controller which provides a global network view and open programming interface to the applications. While SDNs have become a hot topic among both academia and industry in recent years, little attention has been paid on the security aspect. In this paper, we introduce a novel attack, namely, packet injection attack, in SDNs. By maliciously injecting manipulated packets into SDNs, attackers can affect the services and networking applications in the control plane, and largely consume the resources in the data plane. The consequences could be the disruption of applications built on the top of the topology manager service and rest API, as well as a huge consumption of network resources, such as the bandwidth of the OpenFlow channel. To defend against the packet injection attack, we present PacketChecker, a lightweight extension module on SDN controllers to effectively detect and mitigate the flooding of falsified packets. We implement a prototype of PacketChecker in floodlight controller and conduct experiments to evaluate the efficiency of the defense mechanism. The evaluation shows that the PacketChecker module can effectively mitigate the attack with a minor overhead to the SDN controller.


title: EM Wave Propagation Speed, Comments on "Measurement of Time Delay of Alternating Electrical Field in Wires" and "Physical Principles of Measuring the Speed of Alternating Electrical Field"

abstract: The time shift of an electromagnetic wave at a single frequency between a transmitter and a receiver can be used to determine the phase velocity of the wave propagation only if there is no reflection at the receiver or the reflection is very small. The reflection adds additional phase shifts to the composted wave of an incident wave and a reflected wave so that the time difference of the composted wave is shifted between the transmitter and the receiver. This time difference may be either decreased or increased and even negative in a certain condition. Ignoring the phase shift and time shift induced by the reflection, the authors of two articles recently published on “Modern Physics” wrongly claim of “the speed of alternating electric field can be 20 times faster than the speed of light”. The two articles are: “Measurement of Time Delay of Alternating Electrical Field in Wires” (Modern Physics, 2015, 5, 29-34) and “Physical Principles of Measuring the Speed of Alternating Electrical Field” (Modern Physics, 2015, 5, 35-39). In this communication note, theory and experiments are presented to falsify their claim.


title: Decentralized Caching for Content Delivery Based on Blockchain: A Game Theoretic Perspective

abstract: Blockchains enable tamper-proof, ordered logging for transactional data in a decentralized manner over open-access, overlay peer-to-peer networks. In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains. We employ the blockchain-based smart contracts to construct an autonomous content caching market. In the market, the cache helpers are able to autonomously adapt their caching strategies according to the market statistics obtained from the blockchain, and the truthfulness of trustless nodes are financially enforced by smart contract terms. Further, we propose an incentive-compatible consensus mechanism based on proof-of-stake to financially encourage the cache helpers to stay active in service. We model the interaction between the cache helpers and the content providers as a Chinese restaurant game. Based on the theoretical analysis regarding the Nash equilibrium of the game, we propose a decentralized strategy-searching algorithm using sequential best response. The simulation results demonstrate both the efficiency and reliability of the proposed equilibrium searching algorithm.


title: Cohort profile: The Hertfordshire Ageing Study (HAS)

abstract: We have previously described the discovery of a unique set of infant records collected in Hertfordshire between 1911 and 1948.1 The historical data for births between 1911 and 1930 were linked to the NHS central register and showed that small size at birth and during infancy was associated with increased risk of cardiovascular mortality in men and women.2,3 These were the first studies based on individual data to suggest that adverse environmental influences acting in utero and during infancy might increase the risk of cardiovascular disease mortality in later life. Detailed physiological investigations of surviving men and women born in Hertfordshire between 1920 and 1930 and still living there in the early 1990s were subsequently conducted and allowed investigation of morbidity. Studies based on men and women resident in East Hertfordshire demonstrated that small size at birth and during infancy was associated with increased risk of developing coronary heart disease


title: Fuzzy logic steering control of autonomous vehicles inside roundabouts

abstract: Graphical abstractDisplay Omitted HighlightsThis paper shows a real experiment with autonomous vehicles in urban roundabouts.A parametric curve generation in used in the path planning, which in divided in three stages: entrance, inside and exit of the roundabout.Lane changes experiments show that the controller proposed (based on fuzzy logic) is stable.The range of speed used in the experiment is between 8km/h and 24km/h. The system was designed for 20km/h max.The real situation experiments show that our proposal is valid for urban scenarios. The expansion of roads and the development of new road infrastructures have increased in recent years, linked to the population growing in large cities. In the last two decades, roundabouts have largely replaced traditional intersections in many countries. They have the advantage of allowing drivers continuous flow when traffic is clear, without the usual delay caused by traffic lights. Although roundabouts with and without traffic-signal control have been widely used and considered in the literature, driverless control on roundabouts has not been studied in depth yet. The behavior of autonomous vehicles in roundabouts can be divided into three stages: entrance, inside, and exit. The first and last may be handled as an extension of intersections. However, autonomous driving on the roundabout requires special attention. In this paper, the design and implementation of a fuzzy logic system for the steering control of autonomous vehicles inside the roundabout is proposed. Cascade architecture for lateral control and parametric trajectory generation are used. Fuzzy control has proved to be easy to define using expert knowledge. Experiments with a real prototype have been carried out, taking into account different speed profiles and lane change maneuvers inside the roundabout, with very satisfactory results.


title: A Behavioral Economic Approach to Green Consumerism: Demand for Reusable Shopping Bags

abstract: The success of behavioral economics is underscored by its emerging role in informing public policy. While applied behavioral economics has almost exclusively investigated pharmacological issues or the reinforcing efficacy of stimuli used in educational and clinical settings, we propose that this technology can be applied to the understanding and promotion of sustainable purchases in the American marketplace. Many communities have begun to adopt measures promoting such sustainable purchasing, but a conceptual understanding of proper pricing for these “green” products is lacking. An empirical foundation upon which to model prices can aid in generating revenue for distributors while simultaneously nudging sustainable decision-making on the part of the consumer. This report describes a study that successfully translates behavioral economic concepts to demand for sustainable products. A secondary contribution is the demonstration of relations between behavioral economic demand indices and scores on the New Ecological Paradigm Scale (i.e., construct validity), a renowned scale used by environmental psychologists and ecological economists.


Revisiting past challenges and evaluating certificate trust model enhancements Extended Version


title: Universal quantum gates for hybrid system assisted by atomic ensembles embedded in double-sided optical cavities

abstract: We propose deterministic schemes for controlled-NOT (CNOT), Toffoli, and Fredkin gates between flying photon qubits and the collective spin wave (magnon) of an atomic ensemble inside double-sided optical microcavities. All the gates can be accomplished with 100% success probability in principle and no additional qubit is required. Atomic ensemble is employed so that light-matter coupling is remarkably improved by collective enhancement. We qualified the performance of the gates and the results show that they can be faithfully constituted with current experimental techniques.


title: Tune: A Research Platform for Distributed Model Selection and Training

abstract: Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have been proposed for improving the efficiency of model selection, however their adaptation to the distributed compute environment is often ad-hoc. We propose Tune, a unified framework for model selection and training that provides a narrow-waist interface between training scripts and search algorithms. We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation. We demonstrate the implementation of several state-of-the-art hyperparameter search algorithms in Tune. Tune is available at http://ray.readthedocs.io/en/latest/tune.html.


title: Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors

abstract: Orientation field (OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e.g., by scars, contamination, moisture, or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients-based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor-based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' Website for comparative tests.


title: Identifying SCADA vulnerabilities using passive and active vulnerability assessment techniques

abstract: Critical infrastructure such as power plants, oil refineries, and sewage are at the core of modern society. Supervisory Control and Data Acquisition (SCADA) systems were designed to allow human operators supervise, maintain, and control critical infrastructure. Recent years has seen an increase in connectivity of SCADA systems to the Internet. While this connectivity provides an increased level of convenience, it also increases their susceptibility to cyber-attacks. Given the potentially severe ramifications of exploiting SCADA systems, the purpose of this study is to utilize passive and active vulnerability assessment techniques to identify the vulnerabilities of Internet enabled SCADA systems. Specifically, we collect a large testbed of SCADA devices from Shodan, a search engine for the IoT, and assess their vulnerabilities with Nessus and against the National Vulnerability Database (NVD). Results of this study indicate that many SCADA systems from major vendors such as Rockwell Automation and Siemens are vulnerable to default credential, man-in-the-middle, and SSH exploit attacks.


title: The promises and challenges of human brain organoids as models of neuropsychiatric disease

abstract: Neuropsychiatric disorders such as autism spectrum disorder (ASD), schizophrenia (SCZ) and bipolar disorder (BPD) are of great societal and medical importance, but the complexity of these diseases and the challenges of modeling the development and function of the human brain have made these disorders difficult to study experimentally. The recent development of 3D brain organoids derived from human pluripotent stem cells offers a promising approach for investigating the phenotypic underpinnings of these highly polygenic disorders and for understanding the contribution of individual risk variants and complex genetic background to human pathology. Here we discuss the advantages, limitations and future applications of human brain organoids as in vitro models of neuropsychiatric disease.


title: Survival and complication rates of implant-supported fixed partial dentures with cantilevers: a systematic review

abstract: Objective: The objective of the present systematic review was to analyze the potential effect of incorporation of cantilever extensions on the survival rate of implant-supported fixed partial dental prostheses (FPDPs) and the incidence of technical and biological complications, as reported in longitudinal studies with at least 5 years of follow-up. Methods: A MEDLINE search was conducted up to and including November 2008 for longitudinal studies with a mean follow-up period of at least 5 years. Two reviewers performed screening and data abstraction independently. Prosthesis-based data on survival/ failure rate, technical complications (prosthesis-related problems, implant loss) and biological complications (marginal bone loss) were analyzed. Results: The search provided 103 titles with abstract. Full-text analysis was performed of 12 articles, out of which three were finally included. Two of the studies had a prospective or retrospective case‐control design, whereas the third was a prospective cohort study. The 5year survival rate of cantilever FPDPs varied between 89.9% and 92.7% (weighted mean 91.9%), with implant fracture as the main cause for failures. The corresponding survival rate for FPDPs without cantilever extensions was 96.3‐96.2% (weighted mean 95.8%). Technical complications related to the supra-constructions in the three included studies were reported to occur at a frequency of 13‐26% (weighted mean 20.3%) for cantilever FPDPs compared with 0‐12% (9.7%) for non-cantilever FPDPs. The most common complications were minor porcelain fractures and bridge-screw loosening. For cantilever FPDPs, the 5-year event-free survival rate varied between 66.7% and 79.2% (weighted mean 71.7%) and between 83.1% and 96.3% (weighted mean 85.9%) for noncantilever FPDPs. No statistically significant differences were reported with regard to peri-implant bone-level change between the two prosthetic groups, either at the prosthesis or at the implant level. Conclusion: Data on implant-supported FPDPs with cantilever extensions are limited and therefore survival and complication rates should be interpreted with caution. The incorporation of cantilevers into implant-borne prostheses may be associated with a higher


title: Algorithms for Packet Classification

abstract: The process of categorizing packets into "flows" in an Internet router is called packet classification. All packets belonging to the same flow obey a predefined rule and are processed in a similar manner by the router. For example, all packets with the same source and destination IP addresses may be defined to form a flow. Packet classification is needed for non-best-effort services, such as firewalls and quality of service; services that require the capability to distinguish and isolate traffic in different flows for suitable processing. In general, packet classification on multiple fields is a difficult problem. Hence, researchers have proposed a variety of algorithms which, broadly speaking, can be categorized as basic search algorithms, geometric algorithms, heuristic algorithms, or hardware-specific search algorithms. In this tutorial we describe algorithms that are representative of each category, and discuss which type of algorithm might be suitable for different applications.


title: A UAS-based ultra-wideband radar system for soil moisture measurements

abstract: A 2–18 GHz ultra-wideband radar system was designed for soil moisture measurements. We are integrating the system into a small-unmanned helicopter, with a maximum payload capacity of 5 kg. The radar's transmitter generates a highly linear frequency sweep signal based on a fast phase-locked loop and frequency multiplication. The antenna is a four-element array consisting of linearly polarized microstrip monopoles. A custom-designed 1: 4 Wilkinson power divider network feeds the array. In this paper, we present an overview of the radar system and a summary of current development activities, focusing on the antenna structures and initial measurements to show the utility of the system to infer soil moisture characteristics.


title: SiC-MOSFET based single phase AC to DC PWM converter

abstract: Single phase AC to DC converters are essential parts of any modern electrical, electronic, computer, communication and automation systems. These units will be powering the systems all the day through the year. Millions of such units are in operation through the world. So these units must be compact, efficient and reliable. This research paper presents efficient AC to DC PWM converters based on modern devices like Silicon Carbide-MOSFET. The SiC-MOSFET is highly efficient and also allows higher switching frequency compared to Silicon-MOSFET. Novel closed loop vector control algorithm on synchronous rotating d-q axes is developed and presented. The proposed closed loop vector control AC to DC converter system is simulated and results are presented. Switching and conduction losses for the SiC-MOSFETs are estimated and compared with that of Silicon based device to demonstrate the improved efficiency.


title: ‘Who Likes What and, Why?’ Insights into Modeling Users’ Personality Based on Image ‘Likes’

abstract: The increased proliferation of data production technologies (e.g., cameras) and consumption avenues (e.g., social media) has led to images and videos being utilized by users to convey innate preferences and tastes. This has opened up the possibility of using multimedia as a source for user-modeling. This work attempts to model personality traits (based on the Five Factor Theory) of users using a collection of images they tag as ‘favorite’ (or like) on Flickr. First, a set of semantic features are proposed to be used for representing different concepts in images which influence users to like them. The addition of the proposed features led to improvement over state-of-the-art by 12 percent. Second, a novel machine learning approach is developed to model users’ personality based on the image features (resulting in upto 15 percent improvement). Third, efficacy of the semantic features and the modeling approach is shown in recommending images based on personality modeling. Using the modeling approach, recommendations are made regarding the factors that might influence users with different personality traits to like an image.


title: Dialyzer reuse--part II: advantages and disadvantages.

abstract: Although single dialyzer use and reuse by chemical reprocessing are both associated with some complications, there is no definitive advantage to either in this respect. Some complications occur mainly at the first use of a dialyzer: a new cellophane or cuprophane membrane may activate the complement system, or a noxious agent may be introduced to the dialyzer during production or generated during storage. These agents may not be completely removed during the routine rinsing procedure. The reuse of dialyzers is associated with environmental contamination, allergic reactions, residual chemical infusion (rebound release), inadequate concentration of disinfectants, and pyrogen reactions. Bleach used during reprocessing causes a progressive increase in dialyzer permeability to larger molecules, including albumin. Reprocessing methods without the use of bleach are associated with progressive decreases in membrane permeability, particularly to larger molecules. Most comparative studies have not shown differences in mortality between centers reusing and those not reusing dialyzers, however, the largest cluster of dialysis-related deaths occurred with single-use dialyzers due to the presence of perfluorohydrocarbon introduced during the manufacturing process and not completely removed during preparation of the dialyzers before the dialysis procedure. The cost savings associated with reuse is substantial, especially with more expensive, high-flux synthetic membrane dialyzers. With reuse, some dialysis centers can afford to utilize more efficient dialyzers that are more expensive; consequently they provide a higher dose of dialysis and reduce mortality. Some studies have shown minimally higher morbidity with chemical reuse, depending on the method. Waste disposal is definitely decreased with the reuse of dialyzers, thus environmental impacts are lessened, particularly if reprocessing is done by heat disinfection. It is safe to predict that dialyzer reuse in dialysis centers will continue because it also saves money for the providers. Saving both time for the patient and money for the provider were the main motivations to design a new machine for daily home hemodialysis. The machine, developed in the 1990s, cleans and heat disinfects the dialyzer and lines in situ so they do not need to be changed for a month. In contrast, reuse of dialyzers in home hemodialysis patients treated with other hemodialysis machines is becoming less popular and is almost extinct.


title: Extended SMAS dissection as an approach to midface rejuvenation.

abstract: The objective in rhytidectomy is to rejuvenate and improve facial appearance. To obtain consistent results, facelifting should be approached not just as a tightening or lifting procedure but also as a reconstructive procedure, reversing the anatomic changes that occur in aging. The ability to bring aesthetic harmony back into the aging face requires the blending of surgical technique, anatomic knowledge, and artistic sensitivity to individualize the surgical approach for a given patient. To obtain surgical rejuvenation while minimizing signs of surgical distortion remains the ultimate goal of our facelifting procedures.


title: Problems With Evaluation of Word Embeddings Using Word Similarity Tasks

abstract: Lacking standardized extrinsic evaluation methods for vector representations of words, the NLP community has relied heavily on word similarity tasks as a proxy for intrinsic evaluation of word vectors. Word similarity evaluation, which correlates the distance between vectors and human judgments of semantic similarity is attractive, because it is computationally inexpensive and fast. In this paper we present several problems associated with the evaluation of word vectors on word similarity datasets, and summarize existing solutions. Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.


title: An Interactive Framework for Data Cleaning

abstract: Cleaning organizational data of discrepancies in structure and content is important for data warehousing and Enterprise Data Integration (EDI). Current commercial solutions for data cleaning involve many iterations of time-consuming "data quality" analysis to find errors, and long-running transformations to fix them. Users need to endure long waits and often write complex transformation programs. We present an interactive framework for data cleaning that tightly integrates transformation and discrepancy detection. Users gradually build transformations by adding or undoing transforms, in a intu-itive, graphical manner through a spreadsheet-like interface; the effect of a transformis shown at once on records visible on screen. In the background, the system incrementally searches for discrepancies on the latest transformed version of data, flagging them as they are found. This allows users to gradually construct a transformation as discrepancies are found, and clean the data without writing complex programs or enduring long delays. Balancing the goals of power, ease of specification, and interactive application, we choose a set of transforms that can be used for transformations within data records as well as for higher-order transformations. We also present initial work on optimizing a sequence of transforms.


title: Causal Inference and Causal Explanation with Background Knowledge

abstract: This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation consistent with a set of background knowledge which explains all of the observed independence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such causal explanation?


title: gMark: Schema-Driven Generation of Graphs and Queries

abstract: Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of $\mathsf {gMark}$ , a domain- and query language-independent graph instance and query workload generator. A core contribution of $\mathsf {gMark}$ is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of $\mathsf {gMark}$ by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.


title: Survey of Insurance Fraud Detection Using Data Mining Techniques

abstract: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.


title: GNU Radio + USRP2 Implementation of a Single-Carrier Zero-Correlation-Zone CDMA System

abstract: GNU Radio is a software defined radio in which components implemented in hardware are implemented using the software. GNU Radio works along with the USRP board. The USRP board is used to transmit and receive the signal in the wireless channel.CDMA system using the ZCZ (zero Correlation Zone) code is needed to be implemented in the GNU Radio and USRP board. Primary implementation and testing of CDMA using the ZCZ code is done with Matlab. The ZCZ come with the property of ideal impulsive auto correlation and zero cross correlation property within its ZCZ. However the Matlab implementation is not enough to make the system work using the USRP board. It is needed to be modified and implemented in GNU Radio to make the USRP board work.Unlike the simulation done in the Matlab there are lot of problems involved in real-time implementation of the idea in GNU Radio. In Matlab there are lot of in built functions which are needed to be implemented manually in C++. In GNU Radio a chunk of data received at a particular instant may not be of same length all the time and it keeps varying, should be handled properly. For the feedback implementation such as code tracking needed to be handled carefully and all the feedback variables needed to be present in a single module. ZCZ code for single user system, that is one transmitter and one receiver has worked well, but the ZCZ code for multi user uplink had to be tested too.This thesis aims at converting the Matlab implementation of ZCZ codes into real time GNU Radio implementation that works using the USRP board. This thesis will check the real time implementation of ZCZ code using USRP board and GNU Radio. This is done by implementing spreading of data at the sender side using ZCZ code, code synchronization at the receiver, code despreading at the receiver without code tracking and code despreading with code tracking are all done using ZCZ codes. This thesis also checks the feasibility of an asynchronous multi user communication without strict timing synchronization among the users with good results.


title: Cloud-Assisted Computation Offloading to Support Mobile Services

abstract: The widespread use and increasing capabilities of mobiles devices are making them a viable platform for offering mobile services. However, the increasing resource demands of mobile services and the inherent constraints of mobile devices limit the quality and type of functionality that can be offered, preventing mobile devices from exploiting their full potential as reliable service providers. Computation offloading offers mobile devices the opportunity to transfer resource-intensive computations to more resourcefulcomputing infrastructures. We present a framework for cloud-assisted mobile service provisioning to assist mobile devices in delivering reliable services. The framework supports dynamic offloading based on the resource status of mobile systems and current network conditions, while satisfying the user-defined energy constraints. It also enables the mobile provider to delegate the cloud infrastructure to forward the service response directly to the user when no further processing is required by the provider. Performance evaluation shows up to 6x latency improvement for computation-intensive services that do not require large data transfer. Experiments show that the operation of the cloud-assisted service provisioning framework does not pose significant overhead on mobile resources, yet it offers robust and efficient computation offloading.


title: An efficient search algorithm for motion data using weighted PCA

abstract: Good motion data is costly to create. Such an expense often makes the reuse of motion data through transformation and retargetting a more attractive option than creating new motion from scratch. Reuse requires the ability to search automatically and efficiently a growing corpus of motion data, which remains a difficult open problem. We present a method for quickly searching long, unsegmented motion clips for subregions that most closely match a short query clip. Our search algorithm is based on a weighted PCA-based pose representation that allows for flexible and efficient pose-to-pose distance calculations. We present our pose representation and the details of the search algorithm. We evaluate the performance of a prototype search application using both synthetic and captured motion data. Using these results, we propose ways to improve the application's performance. The results inform a discussion of the algorithm's good scalability characteristics.


title: QGrid: Q-learning based routing protocol for vehicular ad hoc networks

abstract: In Vehicular Ad Hoc Networks (VANETs), moving vehicles are considered as mobile nodes in the network and they are connected to each other via wireless links when they are within the communication radius of each other. Efficient message delivery in VANETs is still a very challenging research issue. In this paper, a Q-learning based routing protocol (i.e., QGrid) is introduced to help to improve the message delivery from mobile vehicles to a specific location. QGrid considers both macroscopic and microscopic aspects when making the routing decision, while the traditional routing methods focus on computing meeting information between different vehicles. QGrid divides the region into different grids. The macroscopic aspect determines the optimal next-hop grid and the microscopic aspect determines the specific vehicle in the optimal next-hop grid to be selected as next-hop vehicle. QGrid computes the Q-values of different movements between neighboring grids for a given destination via Q-learning. Each vehicle stores Q-value table learned offline, then selects optimal next-hop grid by querying Q-value table. Inside the selected next-hop grid, we either greedily select the nearest neighboring vehicle to the destination or select the neighboring vehicle with highest probability of moving to the optimal next-hop grid predicted by the two-order Markov chain. The performance of QGrid is evaluated by using real life trajectory GPS data of Shanghai taxies. Simulation comparison among QGrid and other existing position-based routing protocols confirms the advantages of proposed QGrid routing protocol for VANETs.


title: Review: A review on mobility management and vertical handover solutions over heterogeneous wireless networks

abstract: In the nowadays heterogeneous wireless environment, a plethora of access networks have to be inter-connected in an optimal manner to meet the always best connected paradigm. This paper highlights some of the main technical challenges in heterogeneous wireless networks underlying seamless vertical handover making as a fundamental feature to all future networking endeavors. It provides a survey on the vertical mobility management process and mainly focuses on decision making mechanisms. After presenting the related standards, the main vertical handover approaches in the literature are analyzed and compared. The paper also points out the importance of multihoming in a such heterogeneous environment and provides an overview of the most known supporting mobility protocols. Finally, the main research trends and challenges are discussed.


title: Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies

abstract: We explore how to organize large text databases hierarchically by topic to aid better searching, browsing and filtering. Many corpora, such as internet directories, digital libraries, and patent databases are manually organized into topic hierarchies, also called taxonomies. Similar to indices for relational data, taxonomies make search and access more efficient. However, the exponential growth in the volume of on-line textual information makes it nearly impossible to maintain such taxonomic organization for large, fast-changing corpora by hand. We describe an automatic system that starts with a small sample of the corpus in which topics have been assigned by hand, and then updates the database with new documents as the corpus grows, assigning topics to these new documents with high speed and accuracy. To do this, we use techniques from statistical pattern recognition to efficiently separate the feature words, or discriminants, from thenoise words at each node of the taxonomy. Using these, we build a multilevel classifier. At each node, this classifier can ignore the large number of “noise” words in a document. Thus, the classifier has a small model size and is very fast. Owing to the use of context-sensitive features, the classifier is very accurate. As a by-product, we can compute for each document a set of terms that occur significantly more often in it than in the classes to which it belongs. We describe the design and implementation of our system, stressing how to exploit standard, efficient relational operations like sorts and joins. We report on experiences with the Reuters newswire benchmark, the US patent database, and web document samples from Yahoo!. We discuss applications where our system can improve searching and filtering capabilities.


title: Role of Requirements Elicitation & Prioritization to Optimize Quality in Scrum Agile Development

abstract: One of most common aspect with traditional software development is managing requirements. As requirements emerge throughout the software development process and thus are needed to be addressed through proper communication and integration between stakeholders, developers and documentation. Agile methodology is an innovative and iterative process that supports changing requirements and helps in addressing changes throughout the development process. ::: Requirements are elicited at the beginning of every software development process and project (product) and latter are prioritized according to their importance to the market and to the product itself. One of the most important and influencing steps while making a software product is requirements prioritization. Prioritizing requirements helps the software team to understand the existence and importance of a particular requirement, its importance of use and its urgency to time to market. There are many requirements prioritization techniques with their relative strength and weaknesses. Otherwise many of them fail to take account all the factors that must be considered while prioritizing requirements such as cost, value, risk, time to market, number of requirements and effect of non-functional requirements on functional requirements. ::: There are several requirements prioritization methodologies that aid in decision making but importantly many lacks to account the important factors that have significant influence in prioritizing requirements. A requirement prioritization methodology based on several factors such as time to market, cost, risk etc has been proposed. The proposed model is expected to overcome this lack. In sprints, requirements will be prioritized both on the basis of influencing factors such as cost, value, risk, time to market etc. and through the effect of non-functional requirements over functional requirements. This will improve the overall quality of software product when it is included in the development process of scrum. Requirements will not only be prioritized based on sprints, human decision but by critically analyzing the factors (sub characteristics) that can cause the product to success/ fail repeatedly thus ensuring the consistency in right requirements and hence the right prioritized requirements will be selected for a particular sprint at a time.


title: Liquid Leakage Thin Film-Tape Sensor

abstract: A liquid leakage sensor on adhesive tape with alarming function has been developed. The film sensor tape consists of three layers: a base film layer, a substrate film layer, and a protective film layer. Three conductive lines and one resistive line are printed on the substrate film layer with a nano-sized silver conductive ink by the electronic gravure printing method. The location of liquid leakage has been found to be monitored accurately by applying the pulse voltage to the conductive and resistance lines in a periodic polarity switching mode. The leakage tests for various electrolyte liquids have been performed and the accuracy of leakage error position has been achieved to be about 0.25% on 200 meter long tape length.


title: GPSF: General-Purpose Scheduling Framework for Container Based on Cloud Environment

abstract: Currently, container technology has increasingly grown up in cloud environment. However, the research related with resource management for container has not been considered actively. To manage resources on cloud with container efficiently, we present the GPSF, General-Purpose Scheduling Framework. This framework provides useful resource management functions, and more importantly it is possible to apply customized scheduling in local environment. By using the framework, cloud providers or researchers can optimize resources for their purpose. We implement a prototype of the GPSF and evaluate it by applying customized scheduling. Our results show that it works correctly and it is easy to use enough. So we believe that it has a great potential as optimizing resources for container technology.


title: Predicting Risk from Financial Reports with Regression

abstract: We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text's meaning. In this work, the text is an SEC-mandated financial report published annually by a publicly-traded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply well-known regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.


title: Multiobjective Optimization Using Ideas from the Clonal Selection Principle

abstract: The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.


Using dialogue corpora to train a chatbot Bayan Abu Shawar


title: Barrel menu: a new mobile phone menu for feature rich devices

abstract: Mobile phones have ceased to be devices that people merely use to make phone calls. Today, modern mobile phones offer their users a large selection of features which are accessed via hierarchical menus. These hierarchical menus typically result in deep nested menus that require numerous clicks to navigate through, often resulting in usability issues. This paper presents a prototype of a new menu style (Barrel menu) for mobile phones, and compares the usability of this menu style with that of a traditional hierarchical (Hub-and-Spoke) design. The Barrel menu was found to be as efficient as the Hub-and-Spoke menu in terms of time-on-task and key presses, but was found to cause fewer user errors. In addition, the Barrel menu was found to be better in terms of ease of use, orientation, user satisfaction and learnability. Thus, the Barrel menu shows the potential to be a feasible alternative for mobile phone manufacturers to overcome some of the usability issues associated with Hub-and-Spoke menus.


title: A study on crop yield forecasting using classification techniques

abstract: India is generally an agricultural country. Agriculture is the single most important contributor to the Indian economy. Agriculture crop production depends on the season, biological, and economic cause. The prognosticating of agricultural yield is a challenging and desirable task for every nation. Nowadays, Farmers are struggling to produce the yield because of unpredictable climatic changes and drastically reduce in water resource so; we are creating an agriculture data. This data could be gathered, stored and analyzed for useful information. It is used to promote new advanced methods and approaches such as data mining that can give the information of the previous results to the crop yield estimation. In this paper, we have demonstrated to estimate the crop yield, choose the most excellent crop, thereby improves the value and gain of the farming area using data mining techniques.


title: Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature

abstract: Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims.


title: Why Your Encrypted Database Is Not Secure

abstract: Encrypted databases, a popular approach to protecting data from compromised database management systems (DBMS's), use abstract threat models that capture neither realistic databases, nor realistic attack scenarios. In particular, the "snapshot attacker" model used to support the security claims for many encrypted databases does not reflect the information about past queries available in any snapshot attack on an actual DBMS. We demonstrate how this gap between theory and reality causes encrypted databases to fail to achieve their "provable security" guarantees.


title: Learning object detection from a small number of examples: the importance of good features

abstract: Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.


title: Rotten Apples or Bad Harvest? What We Are Measuring When We Are Measuring Abuse

abstract: Internet security and technology policy research regularly uses technical indicators of abuse in order to identify culprits and to tailor mitigation strategies. As a major obstacle, readily available data are often misaligned with actual information needs. They are subject to measurement errors relating to observation, aggregation, attribution, and various sources of heterogeneity. More precise indicators such as size estimates are costly to measure at Internet scale. We address these issues for the case of hosting providers with a statistical model of the abuse data generation process, using phishing sites in hosting networks as a case study. We decompose error sources and then estimate key parameters of the model, controlling for heterogeneity in size and business model. We find that 84\,\% of the variation in abuse counts across 45,358 hosting providers can be explained with structural factors alone. Informed by the fitted model, we systematically select and enrich a subset of 105 homogeneous "statistical twins" with additional explanatory variables, unreasonable to collect for \emph{all} hosting providers. We find that abuse is positively associated with the popularity of websites hosted and with the prevalence of popular content management systems. Moreover, hosting providers who charge higher prices (after controlling for level differences between countries) witness less abuse. These factors together explain a further 77\,\% of the remaining variation, calling into question premature inferences from raw abuse indicators on security efforts of actors, and suggesting the adoption of similar analysis frameworks in all domains where network measurement aims at informing technology policy.


title: The Microsoft 2017 Conversational Speech Recognition System

abstract: We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.


title: Automatic Detection of Sentence Fragments

abstract: We present and evaluate a method for automatically detecting sentence fragments in English texts written by non-native speakers. Our method combines syntactic parse tree patterns and parts-of-speech information produced by a tagger to detect this phenomenon. When evaluated on a corpus of authentic learner texts, our best model achieved a precision of 0.84 and a recall of 0.62, a statistically significant improvement over baselines using non-parse features, as well as a popular grammar checker.


title: Internet of things forensics: Challenges and Case Study

abstract: Today is the era of Internet of Things (IoT), millions of machines such as cars, smoke detectors, watches, glasses, webcams, etc. are being connected to the Internet. The number of machines that possess the ability of remote access to monitor and collect data is continuously increasing. This development makes, on one hand, the human life more comfort- able, convenient, but it also raises on other hand issues on security and privacy. However, this development also raises challenges for the digital investigator when IoT devices involve in criminal scenes. Indeed, current research in the literature focuses on security and privacy for IoT environments rather than methods or techniques of forensic acquisition and analysis for IoT devices. Therefore, in this paper, we discuss firstly different aspects related to IoT forensics and then focus on the cur- rent challenges. We also describe forensic approaches for a IoT device smartwatch as a case study. We analyze forensic artifacts retrieved from smartwatch devices and discuss on evidence found aligned with challenges in IoT forensics


title: Automatic Selection of Sparse Matrix Representation on GPUs

abstract: Sparse matrix-vector multiplication (SpMV) is a core kernel in numerous applications, ranging from physics simulation and large-scale solvers to data analytics. Many GPU implementations of SpMV have been proposed, targeting several sparse representations and aiming at maximizing overall performance. No single sparse matrix representation is uniformly superior, and the best performing representation varies for sparse matrices with different sparsity patterns. In this paper, we study the inter-relation between GPU architecture, sparse matrix representation and the sparse dataset. We perform extensive characterization of pertinent sparsity features of around 700 sparse matrices, and their SpMV performance with a number of sparse representations implemented in the NVIDIA CUSP and cuSPARSE libraries. We then build a decision model using machine learning to automatically select the best representation to use for a given sparse matrix on a given target platform, based on the sparse matrix features. Experimental results on three GPUs demonstrate that the approach is very effective in selecting the best representation.


title: Thinking in Words: Language as an Embodied Medium of Thought

abstract: Recently, there has been a great deal of interest in the idea that natural language enhances and extends our cognitive capabilities. Supporters of embodied cognition have been particularly interested in the way in which language may provide a solution to the problem of abstract concepts. Toward this end, some have emphasized the way in which language may act as form of cognitive scaffolding and others have emphasized the potential importance of language-based distributional information. This essay defends a version of the cognitive enhancement thesis that integrates and builds on both of these proposals. I argue that the embodied representations associated with language processing serve as a supplementary medium for conceptual processing. The acquisition of a natural language provides a means of extending our cognitive reach by giving us access to an internalized combinatorial symbol system that augments and supports the context-sensitive embodied representational systems that exist independently of language.


title: The Computer Industry

abstract: Acknowledgements Introduction The Prehistory of the Computer Industry, 1880-1939 The Advent of the Mainframe Digital Computer, 1940-1957 Broadening Scale and Scope of the Mainframe Computer Industry, 1957-1965 The Industry's Supercomputing and Minicomputing Sectors, 1957-1975 The Rise of Software as a Service, Product, Business, and Industry, 1958-1975 Infrastructure for Long-Term Change, 1962-1975 The Personal Computer and PC Software, 1975-1990 The Computer Networking Revolution and the Computer Industry, 1990-2004 Conclusion: Looking Backwards and Looking Ahead


title: ν-support vector machine as conditional value-at-risk minimization

abstract: The ν-support vector classification (ν-SVC) algorithm was shown to work well and provide intuitive interpretations, e.g., the parameter ν roughly specifies the fraction of support vectors. Although ν corresponds to a fraction, it cannot take the entire range between 0 and 1 in its original form. This problem was settled by a non-convex extension of ν-SVC and the extended method was experimentally shown to generalize better than original ν-SVC. However, its good generalization performance and convergence properties of the optimization algorithm have not been studied yet. In this paper, we provide new theoretical insights into these issues and propose a novel ν-SVC algorithm that has guaranteed generalization performance and convergence properties.


title: Microglia Promote Learning-Dependent Synapse Formation through Brain-Derived Neurotrophic Factor

abstract: Microglia are the resident macrophages of the CNS, and their functions have been extensively studied in various brain pathologies. The physiological roles of microglia in brain plasticity and function, however, remain unclear. To address this question, we generated CX3CR1(CreER) mice expressing tamoxifen-inducible Cre recombinase that allow for specific manipulation of gene function in microglia. Using CX3CR1(CreER) to drive diphtheria toxin receptor expression in microglia, we found that microglia could be specifically depleted from the brain upon diphtheria toxin administration. Mice depleted of microglia showed deficits in multiple learning tasks and a significant reduction in motor-learning-dependent synapse formation. Furthermore, Cre-dependent removal of brain-derived neurotrophic factor (BDNF) from microglia largely recapitulated the effects of microglia depletion. Microglial BDNF increases neuronal tropomyosin-related kinase receptor B phosphorylation, a key mediator of synaptic plasticity. Together, our findings reveal that microglia serve important physiological functions in learning and memory by promoting learning-related synapse formation through BDNF signaling.


title: Super-Resolution Reconstruction of Image Sequences

abstract: In an earlier work (1999), we introduced the problem of reconstructing a super-resolution image sequence from a given low resolution sequence. We proposed two iterative algorithms, the R-SD and the R-LMS, to generate the desired image sequence. These algorithms assume the knowledge of the blur, the down-sampling, the sequences motion, and the measurements noise characteristics, and apply a sequential reconstruction process. It has been shown that the computational complexity of these two algorithms makes both of them practically applicable. In this paper, we rederive these algorithms as approximations of the Kalman filter and then carry out a thorough analysis of their performance. For each algorithm, we calculate a bound on its deviation from the Kalman filter performance. We also show that the propagated information matrix within the R-SD algorithm remains sparse in time, thus ensuring the applicability of this algorithm. To support these analytical results we present some computer simulations on synthetic sequences, which also show the computational feasibility of these algorithms.


title: Sweet Taste Receptors Mediated ROS-NLRP3 Inflammasome Signaling Activation: Implications for Diabetic Nephropathy

abstract: Previous studies demonstrated that ROS-NLRP3 inflammasome signaling activation was involved in the pathogenesis of diabetic nephropathy (DN). Recent research has shown that sweet taste receptors (STRs) are important sentinels of innate immunity. Whether high glucose primes ROS-NLRP3 inflammasome signaling via STRs is unclear. In this study, diabetic mouse model was induced by streptozotocin (STZ) in vivo; mouse glomerular mesangial cells (GMCs) and human proximal tubular cells were stimulated by high glucose (10, 20, and 30 mmol/L) in vitro; STR inhibitor lactisole was used as an intervention reagent to evaluate the role and mechanism of the STRs in the pathogenesis of DN. Our results showed that the expression of STRs and associated signaling components (Gα-gustducin, PLCβ2, and TRPM5) was obviously downregulated under the condition of diabetes in vivo and in vitro. Furthermore, lactisole significantly mitigated the production of intracellular ROS and reversed the high glucose-induced decrease of Ca2


title: Multimodal language grounding for improved human-robot collaboration: exploring spatial semantic representations in the shared space of attention

abstract: There is an increased interest in artificially intelligent technology that surrounds us and takes decisions on our behalf. This creates the need for such technology to be able to communicate with humans and understand natural language and non-verbal behaviour that may carry information about our complex physical world. Artificial agents today still have little knowledge about the physical space that surrounds us and about the objects or concepts within our attention. We are still lacking computational methods in understanding the context of human conversation that involves objects and locations around us. Can we use multimodal cues from human perception of the real world as an example of language learning for robots? Can artificial agents and robots learn about the physical world by observing how humans interact with it and how they refer to it and attend during their conversations? This PhD project’s focus is on combining spoken language and non-verbal behaviour extracted by multi-party dialogue in order to increase context awareness and spatial understanding for artificial agents.


title: Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks

abstract: Because of the complicity of consensus control of nonlinear multiagent systems in state time-delay, most of previous works focused only on linear systems with input time-delay. An adaptive neural network (NN) consensus control method for a class of nonlinear multiagent systems with state time-delay is proposed in this paper. The approximation property of radial basis function neural networks (RBFNNs) is used to neutralize the uncertain nonlinear dynamics in agents. An appropriate Lyapunov–Krasovskii functional, which is obtained from the derivative of an appropriate Lyapunov function, is used to compensate the uncertainties of unknown time delays. It is proved that our proposed approach guarantees the convergence on the basis of Lyapunov stability theory. The simulation results of a nonlinear multiagent time-delay system and a multiple collaborative manipulators system show the effectiveness of the proposed consensus control algorithm.


Simulating Human Behavior in Built Environments


Experimental and Statistical Design issues in Human Movement Research


title: Real-time hand posture and gesture-based touchless automotive user interface using deep learning

abstract: In this study, a vision based in-car entertainment user interface is presented. The user interface is designed using a hand posture and gesture recognition algorithm in deep learning framework. The hand posture recognition algorithm is formulated using the convolutional neural network to perform the fundamental tasks in the user interface. The hand gesture recognition algorithm is formulated using the long-term recurrent convolutional neural network to intuitively interact with the touchless automotive user interface in a detailed manner. In the recurrent deep learning framework, typically, the gesture frames are taken from a uniformly sampled image sequence. In this work, the recurrent structure is enhanced using a reduced number of input frames captured from the image sequence. The reduced input frames or key frames represent the action present in the video sequence. Sparse dictionary learning provide reliable key frame extraction from video sequences. However, sparse dictionary learning is computationally expensive, and are individually optimized for every video sequence. In this paper, we propose to approximate sparse dictionary learning using a non-linear regression framework. The multilayer perceptron is utilized to model the non-linear regression framework. The optimal neural network architecture is identified after a detailed evaluation. We evaluate the proposed recognition methods on public datasets. The proposed methods yield a recognition accuracy of 92% and 90% for pose and gestures, respectively. The combined hand posture and gesture recognition takes 82ms which is a reasonable for real time implementation.


title: Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark

abstract: Criminals are using every means available to launder the profits from their illegal activities into ostensibly "legitimate" assets. Meanwhile, most commercial anti-money laundering systems are still rule-based, which cannot adapt to the ever-changing tricks. Although some machine learning methods have been proposed, they are mainly focused on the perspective of abnormal behavior for single accounts. Considering money laundering activities are often involved in gang criminals, these methods are still not intelligent enough to crack down on criminal gangs all-sidedly. In this paper, a systematic solution is presented to find suspicious money laundering gangs. A temporal-directed Louvain algorithm has been proposed to detect communities according to relevant anti-money laundering patterns. All processes are implemented and optimized on Spark platform. This solution can greatly improve the efficiency of anti-money laundering work for financial regulation agencies.


title: Real time security framework for detecting abnormal events at ATM installations

abstract: Automated Teller Machines (ATM) transactions are quick and convenient, but the machines and the areas surrounding them make people and ATM vulnerable to felonious activities if not properly put under the protection. Responsibility for providing security needs to be fixed, however, most machines have very less or no security. It is imminent to develop security framework that would identify event as their happening. In this paper we propose a robust computer vision approach for identifying abnormal activity at ATM premises in real time. For effective identification of activity, we propose a novel method in which different Window size is used to record magnitude of pixel intensity using root of sum of square method. To describe this pattern, histogram of gradients is used. Further random forest is applied to infer the most likely class. The average accuracy of our security system is 93.1 %. For validation of our approach we have tested it on two standard datasets, HMDB and Caviar. Our approach achieved 52.12 % accuracy on HMDB dataset and 81.48 % on Caviar dataset.


title: Accelerating Differential Evolution Using an Adaptive Local Search

abstract: We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.


title: A Flexible Heterogeneous Multi-Core Architecture

abstract: Multi-core processors naturally exploit thread-level parallelism (TLP). However, extracting instruction-level parallelism (ILP) from individual applications or threads is still a challenge as application mixes in this environment are nonuniform. Thus, multi-core processors should be flexible enough to provide high throughput for uniform parallel applications as well as high performance for more general workloads. Heterogeneous architectures are a first step in this direction, but partitioning remains static and only roughly fits application requirements. This paper proposes the Flexible Heterogeneous Mul-tiCore processor (FMC), the first dynamic heterogeneous multi-core architecture capable of reconfiguring itself to fit application requirements without programmer intervention. The basic building block of this microarchitecture is a scalable, variable-size window microarchitecture that exploits the concept of Execution Locality to provide large-window capabilities. This allows to overcome the memory wall for applications with high memory-level parallelism (MLP). The microarchitecture contains a set of small and fast cache processors that execute high locality code and a network of small in-order memory engines that together exploit low locality code. Single-threaded applications can use the entire network of cores while multi-threaded applications can efficiently share the resources. The sizing of critical structures remains small enough to handle current power envelopes. In single-threaded mode this processor is able to outperform previous state-of-the-art high-performance processor research by 12% on SpecFP. We show how in a quad- threaded/quad-core environment the processor outperforms a statically allocated configuration in both throughput and harmonic mean, two commonly used metrics to evaluate SMTperformance, by around 2-4%. This is achieved while using a very simple sharing algorithm.


title: Smart Grid Architecture Model use case management in a large European Smart Grid project

abstract: On European level the EU Mandate M/490 including the final reports of the respective working groups are gaining momentum in the community. Two of the mandate's core results deal with architecture description and use case management. As a response to the mandate, among other deliverables, the Smart Grid Architecture Model (SGAM) has been developed to provide a framework for Smart Grid architectures. Additionally, use case templates as well as a comprehensive methodology for use case management have been specified. The focus of this contribution is to demonstrate how use case management and the SGAM, and thus recommended by the EU, can be applied together in practice to specific use cases provided by the DISCERN project. The very purpose of the SGAM application is to share information among projects that implement similar use cases based on different technical solutions. Finally, open issues and research gaps to be addressed in order to utilize the SGAM will be presented.


title: Design and prototyping of a soft earthworm-like robot targeted for GI tract inspection

abstract: In this paper we present a pneumatic driven soft earthworm robot targeted for gastrointestinal (GI) tract inspection. The robot contains two expanding sections and one extending section with bending function. This allows the robot crawling through tubular environment such as the colon. Pressure inside the actuators are monitored as well, which enables to regulate the PWM delivered to the robot. Three different versions of our robots are prototyped and tested using PVC tube and ex vivo pig colon. Results show that all the robots could crawl effectively in PVC tube with the speed of 1.34 mm/s, 0.7 mm/s and 1.5mm/s respectively. The robot could also crawl through steep slopes without slowing down. The maximum drag force of the soft robot in PVC tube is 4.7 N.


A Rethink on Hyaluronidase Injection, Intraarterial Injection, and Blindness: Is There Another Option for Treatment of Retinal Artery Embolism Caused by Intraarterial Injection of Hyaluronic Acid?


title: Advocating an ethical memory model for artificial companions from a human-centred perspective

abstract: This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.


title: Chinese Open Relation Extraction for Knowledge Acquisition

abstract: This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition.


title: Self-Modification of Policy and Utility Function in Rational Agents

abstract: Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify – for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this ability. The question is: will they want to use it? For example, highly intelligent systems may find ways to change their goals to something more easily achievable, thereby ‘escaping’ the control of their creators. In an important paper, Omohundro (2008) argued that goal preservation is a fundamental drive of any intelligent system, since a goal is more likely to be achieved if future versions of the agent strive towards the same goal. In this paper, we formalise this argument in general reinforcement learning, and explore situations where it fails. Our conclusion is that the self-modification possibility is harmless if and only if the value function of the agent anticipates the consequences of self-modifications and use the current utility function when evaluating the future.


title: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

abstract: It is well known that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons (including biases) can learn N distinct samples (x(i),t(i)) with zero error, and the weights connecting the input neurons and the hidden neurons can be chosen "almost" arbitrarily. However, these results have been obtained for the case when the activation function for the hidden neurons is the signum function. This paper rigorously proves that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons and with any bounded nonlinear activation function which has a limit at one infinity can learn N distinct samples (x(i),t(i)) with zero error. The previous method of arbitrarily choosing weights is not feasible for any SLFN. The proof of our result is constructive and thus gives a method to directly find the weights of the standard SLFNs with any such bounded nonlinear activation function as opposed to iterative training algorithms in the literature.


title: Scippa: system-centric IPC provenance on Android

abstract: Google's Android OS provides a lightweight IPC mechanism called Binder, which enables the development of feature-rich apps that seamlessly integrate services and data of other apps. Whenever apps can act both as service consumers and service providers, it is inevitable that the IPC mechanism provides message receivers with message provenance information to establish trust. However, the Android OS currently fails in providing sufficient provenance information, which has led to a number of attacks. We present an extension to the Android IPC mechanism, called Scippa, that establishes IPC call-chains across application processes. Scippa provides provenance information required to effectively prevent recent attacks such as confused deputy attacks. Our solution constitutes a system-centric approach that extends the Binder kernel module and Android's message handlers. Scippa integrates seamlessly into the system architecture and our evaluation shows a performance overhead of only 2.23% on Android OS v4.2.2.


title: Camera Distance from Face Images

abstract: We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orienta- tion (yaw, pitch, and roll) and translations perpendicular to the cam- era principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera's principal axis. Our method uses a set of exemplar 3D human heads to estimate the dis- tance between a camera and a previously unseen head. The distance is estimated by solving for the camera pose using Effective Perspective n-Point (EPnP). We present promising experimental results using the Texas 3D Face Recognition Database.


title: Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis

abstract: Computerized algorithms and solutions in processing and diagnosis mammography X-ray, cardiovascular CT/MRI scans, and microscopy image play an important role in disease detection and computer-aided decision-making. Machine learning techniques have powered many aspects in medical investigations and clinical practice. Recently, deep learning is emerging a leading machine learning tool in computer vision and begins attracting considerable attentions in medical imaging. In this chapter, we provide a snapshot of this fast growing field specifically for mammography, cardiovascular, and microscopy image analysis . We briefly explain the popular deep neural networks and summarize current deep learning achievements in various tasks such as detection, segmentation, and classification in these heterogeneous imaging modalities. In addition, we discuss the challenges and the potential future trends for ongoing work.


title: A critical analysis of decision support systems research

abstract: This paper reports the preliminary results of a project that is investigating the theoretic foundations of decision support systems (DSS). The project is principally motivated by a concern for the direction and relevance of DSS research. The main areas of research focus are the decision and judgement theoretic base of the discipline, the research strategies used in published articles, and the professional relevance of DSS research. The project will analyse DSS articles published in 17 selected journals from 1990 to 2002. This preliminary report is based on the analysis of 358 papers. The findings indicate that DSS research is dominated by positivist research (in particular laboratory experiments and surveys), is heavily influenced by the work of Herbert Simon, and has a healthy balance of theory and application, but, it is poorly grounded in judgement and decision-making research, and falls down in the identification of the nature of clients and users.


title: Multiscale vessel enhancement filtering ⋆

abstract: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac and cerebral MRA data. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays.


title: Automatic labeling of multinomial topic models

abstract: Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way. We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model. Experiments with user study have been done on two text data sets with different genres.The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models. Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA, LDA, and their variations.


BETA-ADRENOCEPTOR ANTAGONISTS AND ESSENTIAL TREMOR


title: The World Health Organization Surgical Safety Checklist

abstract: The World Health Organization produced the Surgical Safety Checklist to improve patient safety. This review examines the supporting evidence, the results of a study of its worldwide trial and discusses the possible mechanisms of its effects.


title: Distributed Scrum: Agile Project Management with Outsourced Development Teams

abstract: Agile project management with Scrum derives from best business practices in companies like Fuji-Xerox, Honda, Canon, and Toyota. Toyota routinely achieves four times the productivity and 12 times the quality of competitors. Can Scrum do the same for globally distributed teams? Two agile companies, SirsiDynix and StarSoft development laboratories achieved comparable performance developing a Java application with over 1,000,000 lines of code. During 2005, a distributed team of 56 Scrum developers working from Provo, Utah; Waterloo, Canada; and St. Petersburg, Russia, delivered 671,688 lines of production Java code. At 15.3 function points per developer/month, this is the most productive Java project ever documented. SirsiDynix best practices are similar to those observed on distributed Scrum teams at IDX Systems, radically different than those promoted by PMBOK, and counterintuitive to practices advocated by the Scrum Alliance. This paper analyzes and recommends best practices for globally distributed agile teams


title: Latent Structures for Coreference Resolution

abstract: Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.


title: A Game Engine in Pure Python for CS1: Design, Experience, and Limits

abstract: Games are being increasingly used to create compelling assignments for students learning programming, and Python is often used as an initial programming language. To that end, we present a game engine written in pure Python. Not only does the engine integrate seamlessly with what students already know about Python, but the game engine code itself is not a "black box" -- it is readable and approachable for beginning students. We report on two years' worth of experience using our game engine in CS1 for both regular assignments as well as "master classes," the engine's design, and its limits.


title: Automatic Keyphrase Extraction via Topic Decomposition

abstract: Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into multiple random walks specific to various topics. We thus build a Topical PageRank (TPR) on word graph to measure word importance with respect to different topics. After that, given the topic distribution of the document, we further calculate the ranking scores of words and extract the top ranked ones as keyphrases. Experimental results show that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.


title: Efficient online spherical k-means clustering

abstract: The spherical k-means algorithm, i.e., the k-means algorithm with cosine similarity, is a popular method for clustering high-dimensional text data. In this algorithm, each document as well as each cluster mean is represented as a high-dimensional unit-length vector. However, it has been mainly used in hatch mode. Thus is, each cluster mean vector is updated, only after all document vectors being assigned, as the (normalized) average of all the document vectors assigned to that cluster. This paper investigates an online version of the spherical k-means algorithm based on the well-known winner-take-all competitive learning. In this online algorithm, each cluster centroid is incrementally updated given a document. We demonstrate that the online spherical k-means algorithm can achieve significantly better clustering results than the batch version, especially when an annealing-type learning rate schedule is used. We also present heuristics to improve the speed, yet almost without loss of clustering quality.