title: Visualization-Aware Sampling for Very Large Databases

abstract: Interactive visualizations are crucial in ad hoc data exploration and analysis. However, with the growing number of massive datasets, generating visualizations in interactive timescales is increasingly challenging. One approach for improving the speed of the visualization tool is via data reduction in order to reduce the computational overhead, but at a potential cost in visualization accuracy. Common data reduction techniques, such as uniform and stratified sampling, do not exploit the fact that the sampled tuples will be transformed into a visualization for human consumption. We propose a visualization-aware sampling (VAS) that guarantees high quality visualizations with a small subset of the entire dataset. We validate our method when applied to scatter and map plots for three common visualization goals: regression, density estimation, and clustering. The key to our sampling method's success is in choosing a set of tuples that minimizes a visualization-inspired loss function. While existing sampling approaches minimize the error of aggregation queries, we focus on a loss function that maximizes the visual fidelity of scatter plots. Our user study confirms that our proposed loss function correlates strongly with user success in using the resulting visualizations. Our experiments show that (i) VAS improves user's success by up to 35% in various visualization tasks, and (ii) VAS can achieve a required visualization quality up to 400× faster.


title: FENNEL: streaming graph partitioning for massive scale graphs

abstract: Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of non-neighbors. In this work, we introduce a framework which unifies the two seemingly orthogonal heuristics and allows us to quantify the interpolation between them. More generally, the framework enables a well principled design of scalable, streaming graph partitioning algorithms that are amenable to distributed implementations. We derive a novel one-pass, streaming graph partitioning algorithm and show that it yields significant performance improvements over previous approaches using an extensive set of real-world and synthetic graphs. Surprisingly, despite the fact that our algorithm is a one-pass streaming algorithm, we found its performance to be in many cases comparable to the de-facto standard offline software METIS and in some cases even superiror. For instance, for the Twitter graph with more than 1.4 billion of edges, our method partitions the graph in about 40 minutes achieving a balanced partition that cuts as few as 6.8% of edges, whereas it took more than 81/2 hours by METIS to produce a balanced partition that cuts 11.98% of edges. We also demonstrate the performance gains by using our graph partitioner while solving standard PageRank computation in a graph processing platform with respect to the communication cost and runtime.


title: A highly linear and accurate touch data extraction algorithm based on polar coordinates for large-sized capacitive touch screen panels

abstract: In this paper, a polar coordinate-based touch data extraction (PCTDE) algorithm is proposed to enhance the linearity and accuracy of touch coordinates for large-sized capacitive touch screen panels (TSPs) with a wide electrode pitch. The proposed PCTDE algorithm accurately extracts the touch coordinates by calculating the distance and directional angle between the touch point and the intersection of the transmitter and receiver electrodes. In addition, the interference between two touch points in multi-touch is reduced by decreasing the differential rate of touch data so that the exact touch coordinates can be determined. We evaluated the proposed algorithm using a 46-inch TSP with an 8.5 mm electrode pitch. To verify the linearity and accuracy of touch coordinates, the standard deviation of the slope of the touch coordinates and the average distance error are calculated, respectively. In single-touch, the linearity and accuracy of touch coordinates are improved by 56.7 % and 57.8 %, respectively, compared with those using the best previously-reported touch-sensing algorithm. In multi-touch, the linearity and accuracy of touch coordinates are improved by 56.7 % and 64.2 %, respectively.


title: View Planning for Objects Modeling using UAVs

abstract: View planning is an important part of achieving full robotic autonomy. The ability to incorporate the view of a robot as well as the ability of evaluating and choosing the best view are highly desi ...


title: Optimizing Instructional Policies

abstract: Psychologists are interested in developing instructional policies that boost student learning. An instructional policy specifies the manner and content of instruction. For example, in the domain of concept learning, a policy might specify the nature of exemplars chosen over a training sequence. Traditional psychological studies compare several hand-selected policies, e.g., contrasting a policy that selects only difficult-to-classify exemplars with a policy that gradually progresses over the training sequence from easy exemplars to more difficult (known as fading). We propose an alternative to the traditional methodology in which we define a parameterized space of policies and search this space to identify the optimal policy. For example, in concept learning, policies might be described by a fading function that specifies exemplar difficulty over time. We propose an experimental technique for searching policy spaces using Gaussian process surrogate-based optimization and a generative model of student performance. Instead of evaluating a few experimental conditions each with many human subjects, as the traditional methodology does, our technique evaluates many experimental conditions each with a few subjects. Even though individual subjects provide only a noisy estimate of the population mean, the optimization method allows us to determine the shape of the policy space and to identify the global optimum, and is as efficient in its subject budget as a traditional A-B comparison. We evaluate the method via two behavioral studies, and suggest that the method has broad applicability to optimization problems involving humans outside the educational arena.


title: A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications

abstract: In recent few years, the antenna and sensor communities have witnessed a considerable integration of radio frequency identification (RFID) tag antennas and sensors because of the impetus provided by internet of things (IoT) and cyber-physical systems (CPS). Such types of sensor can find potential applications in structural health monitoring (SHM) because of their passive, wireless, simple, compact size, and multimodal nature, particular in large scale infrastructures during their lifecycle. The big data from these ubiquitous sensors are expected to generate a big impact for intelligent monitoring. A remarkable number of scientific papers demonstrate the possibility that objects can be remotely tracked and intelligently monitored for their physical/chemical/mechanical properties and environment conditions. Most of the work focuses on antenna design, and significant information has been generated to demonstrate feasibilities. Further information is needed to gain deep understanding of the passive RFID antenna sensor systems in order to make them reliable and practical. Nevertheless, this information is scattered over much literature. This paper is to comprehensively summarize and clearly highlight the challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view. Future trends are also discussed. The future research and development in UK are suggested as well.


title: Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

abstract: This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments.


title: Comparative Longevity of Pet Dogs and Humans: Implications for Gerontology Research

abstract: The effect of breed and body weight on longevity in the pet dog was analyzed, and a method was developed to standardize the chronological age of dogs in terms of physiological time, using human year equivalents. Mortality data from 23,535 pet dogs were obtained from a computerized data base of North American veterinary teaching hospitals, and the median age at death was determined for pure and mixed breed dogs of different body weight. Body size in the dog was inversely related to longevity. Within each body weight category, the median age at death was lower for pure breed dogs compared with mixed breed dogs. The difference between the standardized physiological ages of mixed breed dogs of the same chronological age in the smallest and largest body weight categories varied from 8 to > 15 years, and between large and small pure breed dogs, the disparity was even greater. Laboratory research to explore the biological basis for these breed and body weight specific differences in life span among dogs may provide additional clues to genetic factors influencing senescence.


title: Network dismantling

abstract: We study the problem of network dismantling, that is of finding a minimal set of vertices whose removal leaves the network broken in connected components of sub-extensive size. For a large class of random graphs this problem is tightly connected to the decycling problem (the removal of vertices leaving the graph acyclic). Exploiting this connection and recent works on epidemic spreading we present precise predictions for the minimal size of a dismantling set in a large random graph with a prescribed (light-tailed) degree distribution. Building on the statistical mechanics perspective we propose a three-stage Min-Sum algorithm for efficiently dismantling networks, including heavy-tailed ones for which the dismantling and decycling problems are not equivalent. We also provide insight into the dismantling problem concluding that it is an intrinsically collective problem and optimal dismantling sets cannot be viewed as a collection of individually well performing nodes.


title: Effective Emergency Management: Reconsidering the Bureaucratic Approach

abstract: The command and control approach is compared with the Emergent Human Resources Model (EHRM) approach to emergency management. Four decades of systematic research shows that a rigid, bureaucratic command and control approach to emergency management generally leads to an ineffective emergency response. Previous studies and our own research suggest that flexible, malleable, loosely coupled, organizational configurations can create a more effective disaster response.


title: Consensus clustering in complex networks

abstract: The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.


title: Tracing Research Paradigm Change Using Terminological Methods. A Case Study on "Machine Translation" in the ACL Anthology Reference Corpus

abstract: This paper explores the use of terminology extraction methods for detecting paradigmatic changes in scientific articles. We use a statistical method for identifying salient nouns and adjectives that signal these paradigmatic changes. We then employ the extracted lexical units for discovering terms that are assumed to be central in characterising paradigm shifts. To assess the method’s performance, in this pilot study, we work on “machine translation” (MT) research articles sampled from the ACL anthology reference corpus. We analyse this corpus to check whether the proposed approach can trace the dramatic changes that machine translation research has experienced in the last decades: from transformational rule-based methods to statistical machine learning-based techniques.


title: Estimation of static and Dynamic characteristics for 4-bit Flash ADC

abstract: Analog to Digital converter (ADC) finds the major role in Analog Circuit Design. Analog signal is characterized by the signal whose amplitude is continuously changing with respect to time while the amplitude and time is discrete in case of digital signal. This paper describes the design of high speed FLASH ADC using clocked digital comparator with 4-bit resolution. Analog signal is characterized by the signal whose amplitude is continuously changing with respect to time. The Clock Digital Comparator (CDC) is designed in TSMC 0.18 µm CMOS technology with supply voltage of 1.8 V. The length of transistor is fixed and depending upon the width of transistor, internal references voltages are generated in the range of 0.653 to 1.02 V. The proposed 4-bit flash ADC using CDC is designed using multiplexer based Decoder and simulated with the help of Tanner-EDA tool in TSMC 0.18 µm CMOS technology.


title: COSMETIC The Tear Trough Ligament : Anatomical Basis for the Tear Trough Deformity

abstract: BACKGROUND ::: The exact anatomical cause of the tear trough remains undefined. This study was performed to identify the anatomical basis for the tear trough deformity. ::: ::: ::: METHODS ::: Forty-eight cadaveric hemifaces were dissected. With the skin over the midcheek intact, the tear trough area was approached through the preseptal space above and prezygomatic space below. The origins of the palpebral and orbital parts of the orbicularis oculi (which sandwich the ligament) were released meticulously from the maxilla, and the tear trough ligament was isolated intact and in continuity with the orbicularis retaining ligament. The ligaments were submitted for histologic analysis. ::: ::: ::: RESULTS ::: A true osteocutaneous ligament called the tear trough ligament was consistently found on the maxilla, between the palpebral and orbital parts of the orbicularis oculi, cephalad and caudal to the ligament, respectively. It commences medially, at the level of the insertion of the medial canthal tendon, just inferior to the anterior lacrimal crest, to approximately the medial-pupil line, where it continues laterally as the bilayered orbicularis retaining ligament. Histologic evaluation confirmed the ligamentous nature of the tear trough ligament, with features identical to those of the zygomatic ligament. ::: ::: ::: CONCLUSIONS ::: This study clearly demonstrated that the prominence of the tear trough has its anatomical origin in the tear trough ligament. This ligament has not been isolated previously using standard dissection, but using the approach described, the tear trough ligament is clearly seen. The description of this ligament sheds new light on considerations when designing procedures to address the tear trough and the midcheek.


title: Application of SOR Iteration for Poisson Image Blending

abstract: Image blending is one of the important techniques in image processing. In this paper, a method based on solving the Poisson equation using Jacobi, Gauss-Seidel and SOR iterative methods is proposed. The aim is to compare and analyze the effectiveness of the three iterative methods in solving Poisson image blending problem. The evaluation criteria are based on the number of iterations and computational time to solve the Poisson equation. It is shown that SOR iterative method is more effective than the other two methods with a fewer number of iterations and requiring lesser computational time.


title: Comprehensive Review of Medicinal Marijuana, Cannabinoids, and Therapeutic Implications in Medicine and Headache: What a Long Strange Trip It's Been …

abstract: BACKGROUND ::: The use of cannabis, or marijuana, for medicinal purposes is deeply rooted though history, dating back to ancient times. It once held a prominent position in the history of medicine, recommended by many eminent physicians for numerous diseases, particularly headache and migraine. Through the decades, this plant has taken a fascinating journey from a legal and frequently prescribed status to illegal, driven by political and social factors rather than by science. However, with an abundance of growing support for its multitude of medicinal uses, the misguided stigma of cannabis is fading, and there has been a dramatic push for legalizing medicinal cannabis and research. Almost half of the United States has now legalized medicinal cannabis, several states have legalized recreational use, and others have legalized cannabidiol-only use, which is one of many therapeutic cannabinoids extracted from cannabis. Physicians need to be educated on the history, pharmacology, clinical indications, and proper clinical use of cannabis, as patients will inevitably inquire about it for many diseases, including chronic pain and headache disorders for which there is some intriguing supportive evidence. ::: ::: ::: OBJECTIVE ::: To review the history of medicinal cannabis use, discuss the pharmacology and physiology of the endocannabinoid system and cannabis-derived cannabinoids, perform a comprehensive literature review of the clinical uses of medicinal cannabis and cannabinoids with a focus on migraine and other headache disorders, and outline general clinical practice guidelines. ::: ::: ::: CONCLUSION ::: The literature suggests that the medicinal use of cannabis may have a therapeutic role for a multitude of diseases, particularly chronic pain disorders including headache. Supporting literature suggests a role for medicinal cannabis and cannabinoids in several types of headache disorders including migraine and cluster headache, although it is primarily limited to case based, anecdotal, or laboratory-based scientific research. Cannabis contains an extensive number of pharmacological and biochemical compounds, of which only a minority are understood, so many potential therapeutic uses likely remain undiscovered. Cannabinoids appear to modulate and interact at many pathways inherent to migraine, triptan mechanisms ofaction, and opiate pathways, suggesting potential synergistic or similar benefits. Modulation of the endocannabinoid system through agonism or antagonism of its receptors, targeting its metabolic pathways, or combining cannabinoids with other analgesics for synergistic effects, may provide the foundation for many new classes of medications. Despite the limited evidence and research suggesting a role for cannabis and cannabinoids in some headache disorders, randomized clinical trials are lacking and necessary for confirmation and further evaluation.


title: A RF to DC Voltage Conversion Model for Multi-Stage Rectifiers in UHF RFID Transponders

abstract: This paper presents a RF to DC conversion model for multi-stage rectifiers in UHF RFID transponders. An equation relating the RF power available from the antenna to the DC output voltage produced by a multi-stage rectifier is presented. The proposed model includes effects of the nonlinear forward voltage drop in diodes and impedance matching conditions of the antenna to rectifier interface. Fundamental frequency impedance approximation is used to analyze the resistance of rectifying diodes; parasitic resistive loss components are also included in the analysis of rectifier input resistance. The closed form equation shows insights into design parameter tradeoffs, such as power available from the antenna, antenna radiation resistance, the number of diodes, DC load current, parasitic resistive loss components, diode and capacitor sizes, and frequency of operation. Therefore, it enables the optimization of rectifier parameters for impedance matching with a low-cost printed antenna and shunt tuning inductor, in order to improve the RF to DC conversion efficiency and the operational distance of UHF RFID transponders. Three diode doublers and three multistage rectifiers were fabricated in a 130 nm CMOS process with custom no-mask added Schottky diodes. Measurements of the test IC are in good agreement with the proposed model.


title: DaRe: Data Recovery through Application Layer Coding for LoRaWAN

abstract: Internet of Things (IoT) solutions are increasingly being deployed for smart applications. To provide good communication for the increasing number of smart applications, there is a need for low cost and long range Low Power Wide Area Network (LPWAN) technologies. LoRaWAN is an energy efficient and inexpensive LPWAN solution that is rapidly being adopted all around the world. However, LoRaWAN does not guarantee reliable communication in its basic configuration. Transmitted frames can be lost due to the channel effects and mobility of the end-devices. In this study, we perform extensive measurements on a new LoRaWAN network to characterise spatial and temporal properties of the LoRaWAN channel. The empirical outage probability for the farthest measured distance from the closest gateway of 7.5 km in our deployment is as low as 0.004, but the frame loss measured at this distance was up to 70%. Furthermore, we show that burstiness in frame loss can be expected for both mobile and stationary scenarios. Frame loss results in data loss, since in the basic configuration frames are only transmitted once. To reduce data loss in LoRaWAN, we design a novel coding scheme for data recovery called DaRe, which extends frames with redundant information that is calculated from the data from previous frames. DaRe combines techniques from convolutional codes and fountain codes. We develop an implementation for DaRe and show that 99% of the data can be recovered with a code rate of 1/2 for up to 40% frame loss. Compared to repetition coding DaRe provides 21% more data recovery, and can save up to 42% energy consumption on transmission for 10 byte data units. DaRe also provides better resilience to bursty frame loss. This study provides useful results to both LoRaWAN network operators as well as developers of LoRaWAN applications. Network operators can use the characterisation results to identify possible weaknesses in the network, and application developers are offered a tool to prevent possible data loss.


Media Effects on Body Image: Examining Media Exposure in the Broader Context of Internal and Other Social Factors


title: Electronic commerce: structures and Issues

abstract: Electronic commerce (E-commerce) is sharing business information, maintaining business relationships, and conducting business transactions by means of telecommunications networks. Traditional E-commerce, conducted with the use of information technologies centering on electronic data interchange (EDI) over proprietary value-added networks, is rapidly moving to the Internet. The Internet's World Wide Web has become the prime driver of contemporary E-commerce. This paper presents a hierarchical framework of E-commerce, consisting of three meta-levels: infrastructure, services, and products and structures, which, in turn, consist of seven functional levels. These levels of E-commerce development, as well as of analysis, range from the wide-area telecommunications infrastructure to electronic marketplaces and electronic hierarchies enabled by E-commerce. Several nodal problems are discussed that will define future development in E-commerce, including integrating electronic payment into the buying process, building a consumer marketplace, the governance of electronic business, and the new intermediation. The paper also introduces the International Journal of Electronic Commerce, which will provide an integrated view of the new E-commerce.


title: Evaluation of Dependency Parsers on Unbounded Dependencies

abstract: We evaluate two dependency parsers, MSTParser and MaltParser, with respect to their capacity to recover unbounded dependencies in English, a type of evaluation that has been applied to grammar-based parsers and statistical phrase structure parsers but not to dependency parsers. The evaluation shows that when combined with simple post-processing heuristics, the parsers correctly recall unbounded dependencies roughly 50% of the time, which is only slightly worse than two grammar-based parsers specifically designed to cope with such dependencies.


title: Evaluation of Graph Sampling: A Visualization Perspective

abstract: Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have been proposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing is the impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies that investigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used in the graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results show that depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view to metric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies.


title: The Inscription of Charms in Anglo-Saxon Manuscripts

abstract: Anglo-Saxon charms constitute a definable oral genre that may be distinguished from other kinds of traditionally oral materials such as epic poetry because texts of charms include explicit directions for performance. Scribes often specify that a charm be spoken (cwean) or sung (singan). In some cases a charm is to be written on some object. But inscribing an incantation on an object does not necessarily diminish or contradict the orality of the genre. An incantation written on an amulet manifests the appropriation of the technology of writing for the purposes of a traditionally oral activity. 1 Unlike epic poetry, riddles, or lyrics, charms are performed toward specific practical ends and their mode of operation is performative, so that uttering the incantation accomplishes a purpose. The stated purpose of an incantation also determines when and under what circumstances a charm will be performed. Charms inscribed in manuscripts are tagged according to the needs they answer-whether eye pain, insomnia, childbirth, theft of property, or whatever. Some charms ward off troubles (toothache, bees swarming); others, such as those for bleeding or swellings, relieve physical troubles. This specificity of purpose markedly distinguishes the genre from other traditional oral genres that are less specifically utilitarian. Given the specific circumstances of need that call for their performance, the social contexts in which charms are performed create the conditions felicitous for performative speech acts in Austin's sense (1975:6-7, 12-15). The assumption underlying charms is that the incantations (whether words or symbols or phonetic patterns) of a charm can effect a change in the state of the person or persons or inanimate object (a salve, for example, or a field for crops). The performer, the beneficiary, and the community of hearers or believers affirm the power of the words to create a new, hoped-for reality among them. The efficacy of the speech-act


title: Connectionism and cognitive architecture: A critical analysis

abstract: Abstract This paper explores differences between Connectionist proposals for cognitive architecture and the sorts of models that have traditionally been assumed in cognitive science. We claim that the major distinction is that, while both Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a number of the standard arguments that have been offered in favor of Connectionism, and conclude that they are coherent only on this interpretation.


title: Rule-Based Prediction of Medical Claims' Payments: A Method and Initial Application to Medicaid Data

abstract: Imperfections in healthcare revenue cycle management systems cause discrepancies between submitted claims and received payments. This paper presents a method for deriving attributional rules that can be used to support the preparation and screening of claims prior to their submission to payers. The method starts with unsupervised analysis of past payments to determine normal levels of payments for services. Then, supervised machine learning is used to derive sets of attributional rules for predicting potential discrepancies in claims. New claims can be then classified using the created models. The method was tested on a subset of Obstetrics claims for payment submitted by one hospital to Medicaid. One year of data was used to create models, which were tested using the following year's data. Results indicate that rule-based models are able to detect abnormal claims prior to their submission.


title: Panel 5: Application of Healthcare Intervention Economic Evaluations in Healthcare Decision-Making

abstract: Summary Applying the knowledge provided by health eco-nomic studies to healthcare decisions at all levelscan only be an asset to healthcare systems. Toachieve this, it is paramount that the informationneeds of potential users of health economics bedetermined, that training is provided to facilitatebetter use of this information, and that the resultsof health economic studies be presented in an eas-ily interpreted and comparable way. Allowing us-ers of this information access to experts in thefield when additional support is needed willensure that the information is not dismissed be-cause it is not understood. A relevant associationsuch as ISPOR has been proposed as a facilitator,bringing representatives of drug companies, man-aged care organizations, and regulatory agenciestogether to discuss areas of compromise, whichwill allow performance of more efficient and use-ful studies. ISPOR should also participate in thedesign and funding of educational programs andin the development of standardized data reportingformats.


title: A strategic approach to recharging lithium-sulphur batteries for long cycle life

abstract: The success of rechargeable lithium-ion batteries has brought indisputable convenience to human society for the past two decades. However, unlike commercialized intercalation cathodes, high-energy-density sulphur cathodes are still in the stage of research because of the unsatisfactory capacity retention and long-term cyclability. The capacity degradation over extended cycles originates from the soluble polysulphides gradually diffusing out of the cathode region. Here we report an applicable way to recharge lithium-sulphur cells by a simple charge operation control that offers tremendous improvement with various lithium-sulphur battery systems. Adjusting the charging condition leads to long cycle life (over 500 cycles) with excellent capacity retention (>99%) by inhibiting electrochemical reactions along with severe polysulphide dissolution. This charging strategy and understanding of the reactions in different discharge steps will advance progress in the development of lithium-sulphur batteries.


title: A community effort to assess and improve drug sensitivity prediction algorithms

abstract: Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.


title: Emotional sentence identification in a story

abstract: In this paper, we investigate the methods of fusing different classifiers to identify emotional sentences in text. The Extreme Learning Machine (ELM) and Support Vector Machines (SVM) are two classifiers used to predict a sentence neutral or emotional. We use the UniGram, subjective words, and special punctuations, etc. as features. A method of calculating emotion value of a word is presented, and the values are employed to compose the features of an emotional sentence. To further enhance the system performance, we divide the features into three subsets, and train different models of the two classifiers on each feature set. The six models are then combined through a weighted summation fusion method and FoCal fusion method. We evaluate the system performance on a corpus of children's tales, and the experimental results demonstrate that the fusion of models can improve system performance.


title: Targeting horror via level and soundscape generation

abstract: Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.


title: Using laser projectors for augmented reality

abstract: The paper explores the use of laser projectors as an alternative to head-mounted displays for Augmented Reality. We describe the development of an Augmented Reality Laser Projector and report on experiences setting up AR systems that use laser projectors, reasoning about several design criteria.


title: Detecting spam tweets in Twitter using a data stream clustering algorithm

abstract: Social network sites are becoming great through millions of users and information have been gathered from the users. This information have equal advantages to their friends and spammers. Twitter is one of the most popular social networks that, users can send short textual messages namely tweet. Researches have shown that this network is subject to spammer's invasion more than other social networks and more than six percent of its tweets are spam. So diagnose of the spam tweets is very important. Firstly, in this research, we determine various features for spam detection and then by using a clustering algorithm based on the data stream, we identify spam tweets. previous works in the field of spam tweets were done by classification algorithms. it is the first time that for spam tweets detection, an data stream clustering algorithm is used. Den stream Algorithm can cluster tweets and consider Outliers as spam. Results show, when this algorithm is set properly the amount of accuracy and precision of spam tweets detection will improve and false positive rate will reach to the minimum value in comparison with previous works.


title: Design of RFID Packaging for Construction Materials

abstract: RFID (Radio Frequency Identification), which is a kind of the electronic tag, is a wireless access device using the radio frequency for recognizing the ID information. It has a variety of application such as the bus card, gate access card, distribution industry, and management of construction materials. The performance and size of RFID depend on the penetrability, recognition ratio, memory size, multi tag recognition, external pollution dust, and exterior impact, and RFID requires the packaging to protect itself considered above factors. Recently, RFID is diversely employed to effectively manage construction materials and the RFID packaging, which is robust to the external impact, is required to attach RFID on construction materials. In this paper, we propose the construction material RFID packaging designed to be robust for the external impact and to be practicable for change of the broken RFID. For the change of RFID, we separate the cast and body of the packaging. Also, we present the detail drawing for the proposed construction material RFID packaging and implement the performance evaluation of the packaging manufactured using 3D printer.


Simulation Gone Wild: SIM OUT


title: Mobile IP and wide area wireless data

abstract: In this paper we present techniques for allowing Mobile IP to interwork with wide area cellular networks. As an example, we illustrate a network consisting of a GSM general packet radio service (GPRS) air interface and a Mobile IP backbone network. The advantage of such a solution is that the backbone network may easily be used to support integrated wireless and wired data transfer, and the leveraging of standard data networking protocols and equipment will reduce the cost of wireless data networks. We present issues and suggested solutions for mobility management functions, such as detecting changes in points of attachment to a network, micromobility, roaming, and paging, and security functions, such as authentication and ciphering. We show which elements of each network, GSM and Mobile IP, may be re-used, combined, or eliminated to provide a single, unified network.


title: A Personalized Researcher Recommendation Approach in Academic Contexts: Combining Social Networks and Semantic Concepts Analysis

abstract: The rapid proliferation of information technologies especially the web 2.0 techniques have changed the fundamental ways how things can be done in many areas, including how researchers could communicate and collaborate with each other. The presence of the sheer volume of researcher and topical research information on the Web has led to the problem of information overload. There is a pressing need to develop researcher recommender systems such that users can be provided with personalized recommendations of the researchers they can potentially collaborate with for mutual research benefits. In an academic context, recommending suitable research partners to researchers can facilitate knowledge discovery and exchange, and ultimately improve the research productivity of both sides. Existing expertise recommendation research usually investigates into the expert finding problem from two independent dimensions, namely, the social relations and the common expertise. The main contribution of this paper is that we propose a novel researcher recommendation approach which combines the two dimensions of social relations and common expertise in a unified framework to improve the effectiveness of personalized researcher recommendation. Moreover, how our proposed framework can be applied to the real-world academic contexts is explained based on two case studies.


title: Multioccupant Activity Recognition in Pervasive Smart Home Environments

abstract: Human activity recognition in ambient intelligent environments like homes, offices, and classrooms has been the center of a lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multioccupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey article provides an overview of existing approaches and current practices for activity recognition in multioccupant smart homes. It presents the latest developments and highlights the open issues in this field.


title: A Functional Architecture for Autonomous Driving

abstract: As the Technology Readiness Levels (TRLs) of self-driving vehicles increase, it is necessary to investigate the Electrical/Electronic(E/E) system architectures for autonomous driving, beyond proof-of-concept prototypes. Relevant patterns and anti-patterns need to be raised into debate and documented. This paper presents the principal components needed in a functional architecture for autonomous driving, along with reasoning for how they should be distributed across the architecture. A functional architecture integrating all the concepts and reasoning is also presented.


title: A study on object detection method from manga images using CNN

abstract: Japanese comics (manga) are popular content worldwide. In order to acquire metadata from manga images, techniques automatic recognition of manga content have been studied. Recently, Convolutional Neural Network (CNN) has been applied to object detection in manga images. R-CNN and Fast R-CNN generate region proposals by Selective Search. Faster R-CNN generates them using CNN layers called Region Proposal Network (RPN). Single Shot MultiBox Detector (SSD), the latest detection method, performs object classification and box adjustment for small regions in an image. These methods are effective to natural images. However, it is unclear whether such methods work properly to manga images or not, since those image features are different from natural images. In this paper, we examine the effectiveness of manga object detection by comparing Fast R-CNN, Faster R-CNN, and SSD. Here, manga objects are panel layout, speech balloon, character face, and text. Experimental results show that Fast R-CNN is effective for panel layout and speech balloon, whereas Faster R-CNN is effective for character face and text.


title: Geometric Alignment by Deep Learning for Recognition of Challenging License Plates

abstract: In this paper, we explore the problem of license plate recognition in-the-wild (in the meaning of capturing data in unconstrained conditions, taken from arbitrary viewpoints and distances). We propose a method for automatic license plate recognition in-the-wild based on a geometric alignment of license plates as a preceding step for holistic license plate recognition. The alignment is done by a Convolutional Neural Network that estimates control points for rectifying the image and the following rectification step is formulated so that the whole alignment and recognition process can be assembled into one computational graph of a contemporary neural network framework, such as Tensorflow. The experiments show that the use of the aligner helps the recognition considerably: the error rate dropped from 9.6 % to 2.1 % on real-life images of license plates. The experiments also show that the solution is fast - it is capable of real-time processing even on an embedded and low-power platform (Jetson TX2). We collected and annotated a dataset of license plates called CamCar6k, containing 6,064 images with annotated corner points and ground truth texts. We make this dataset publicly available.


title: A Rapid Webcam-Based Eye Tracking Method for Human Computer Interaction

abstract: This study proposes a rapid eye tracking method, to respond to a situation that require a high processing speed but less accuracy. Unlike other studies, this study uses a webcam with a low resolution of 640 × 480, which decreased the cost of devices considerably. We also developed the corresponding algorithm to suit the low-quality image. We use an efficient algorithm to detect the pupils which is based on color intensity change to decrease the calculation load. The processing speed exceeds the requirement of eye tracking for saccade eyeball movement. The result of experiment shows that the proposed method is a fast and low-cost method for eye tracking.


title: Engraved ochres from the Middle Stone Age levels at Blombos Cave, South Africa.

abstract: Powerful categories of evidence for symbolically mediated behaviour, variously described as 'modern' or 'cognitively modern' human behaviour, are geometric or iconographic representations. After 40,000 years ago such evidence is well documented in much of the Old World and is widely considered as typifying 'modern human culture,' but earlier evidence is rare. In Africa, this includes two deliberately engraved ochre pieces from c. 75,000 year old levels at Blombos Cave, Western Cape, South Africa and the greater than 55,000 year old incised ostrich egg shell from the Diepkloof shelter, located in the same province. Here we report on thirteen additional pieces of incised ochre recovered from c. 75,000-100,000 year old levels at Blombos Cave. These finds, taken together with other engraved objects reported from other southern African sites, suggest that symbolic intent and tradition were present in this region at an earlier date than previously thought.


1 Memristance can explain Spike-Time- Dependent-Plasticity in Neural Synapses


title: An Empirical Evaluation of Similarity Measures for Time Series Classification

abstract: Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure 'families', and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared.


title: Introduction of a LIDAR-based obstacle detection system on the LineScout power line robot

abstract: This paper is a sequel of an earlier paper that featured a thorough characterization of the Hokuyo UTM-30LX laser range finder, which showed promise for a specific application: allowing a power line robot to detect obstacles in its path. After a quick summary of the earlier conclusions, this paper pushes the validation farther by assessing for the first time this popular LIDAR's performance when subjected to the particularly challenging, outdoor, power line environmental conditions: large temperature range, changes in lighting, strong magnetic fields, and oscillating or vibrating targets. Use of return signal intensity, predictably affected by the angle of incidence on the target and by target surface finish, is also investigated as a means to detect variations due to an obstacle. Scanning results with LineScout traveling at maximum speed on a full-scale power line span are then analyzed to validate the proposed detection thresholds.


title: Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

abstract: Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.


SVM BASED DECISION SUPPORT SYSTEM FOR HEART DISEASE CLASSIFICATION WITH INTEGER-CODED GENETIC ALGORITHM TO SELECT CRITICAL FEATURES


title: Evaluation of symptom checkers for self diagnosis and triage: audit study

abstract: OBJECTIVE ::: To determine the diagnostic and triage accuracy of online symptom checkers (tools that use computer algorithms to help patients with self diagnosis or self triage). ::: ::: ::: DESIGN ::: Audit study. ::: ::: ::: SETTING ::: Publicly available, free symptom checkers. ::: ::: ::: PARTICIPANTS ::: 23 symptom checkers that were in English and provided advice across a range of conditions. 45 standardized patient vignettes were compiled and equally divided into three categories of triage urgency: emergent care required (for example, pulmonary embolism), non-emergent care reasonable (for example, otitis media), and self care reasonable (for example, viral upper respiratory tract infection). ::: ::: ::: MAIN OUTCOME MEASURES ::: For symptom checkers that provided a diagnosis, our main outcomes were whether the symptom checker listed the correct diagnosis first or within the first 20 potential diagnoses (n=770 standardized patient evaluations). For symptom checkers that provided a triage recommendation, our main outcomes were whether the symptom checker correctly recommended emergent care, non-emergent care, or self care (n=532 standardized patient evaluations). ::: ::: ::: RESULTS ::: The 23 symptom checkers provided the correct diagnosis first in 34% (95% confidence interval 31% to 37%) of standardized patient evaluations, listed the correct diagnosis within the top 20 diagnoses given in 58% (55% to 62%) of standardized patient evaluations, and provided the appropriate triage advice in 57% (52% to 61%) of standardized patient evaluations. Triage performance varied by urgency of condition, with appropriate triage advice provided in 80% (95% confidence interval 75% to 86%) of emergent cases, 55% (47% to 63%) of non-emergent cases, and 33% (26% to 40%) of self care cases (P<0.001). Performance on appropriate triage advice across the 23 individual symptom checkers ranged from 33% (95% confidence interval 19% to 48%) to 78% (64% to 91%) of standardized patient evaluations. ::: ::: ::: CONCLUSIONS ::: Symptom checkers had deficits in both triage and diagnosis. Triage advice from symptom checkers is generally risk averse, encouraging users to seek care for conditions where self care is reasonable.


title: Treatment of Class II malocclusion with mandibular skeletal anchorage

abstract: INTRODUCTION ::: The aim of this case report was to present the dentofacial changes obtained with bone anchorage in a Class II patient with moderate to severe crowding. ::: ::: ::: METHODS ::: A boy, aged 14.5 years, with a dolichofacial type, convex profile, and skeletal and dental Class II relationships was examined. After evaluation, functional treatment with bone anchorage and 4 first premolar extractions was decided as the treatment approach. Miniplates were placed on the buccal shelves of the mandibular third molars. The hook of the anchor was revealed from the first molar level. After surgery, the 4 first premolars were extracted to retract the protrusive mandibular incisors. The maxillary and mandibular first molars were banded, and a lip bumper was inserted to apply elastics and to help distalize the maxillary first molars. Orthodontic forces of 300 to 500 g were applied immediately after placement, originating from the miniscrews to the hooks of the appliance to advance the mandible. ::: ::: ::: RESULTS ::: After 20 months of treatment, the patient had a dental and skeletal Class I relationship, the mandible was advanced, the maxilla was restrained, and overjet was decreased. ::: ::: ::: CONCLUSIONS ::: The combination of a bone anchor, Class II elastics, and an inner bow is a promising alternative to functional treatment, along with extractions, in Class II patients.


A Touchless Human-machine Interface for the Control of an Elevator


title: Within-brain classification for brain tumor segmentation

abstract: Purpose ::: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.


A Comparative Analysis on indoor positioning Techniques and Systems


title: Optimization of CO₂ bio-mitigation by Chlorella vulgaris.

abstract: Biofixation of CO2 by microalgae has been recognized as an attractive approach to CO2 mitigation. The main objective of this work was to maximize the rate of CO2 fixation ( [Formula: see text] ) by the green microalga Chlorella vulgaris P12 cultivated photoautotrophically in bubble column photobioreactors under different CO2 concentrations (ranging from 2% to 10%) and aeration rates (ranging from 0.1 to 0.7 vvm). Results showed that the maximum [Formula: see text] (2.22 gL(-1)d(-1)) was obtained by using 6.5% CO2 and 0.5 vvm after 7 days of cultivation at 30°C. Although final biomass concentration and maximum biomass productivity of microalgae were affected by the different cultivation conditions, no significant differences were obtained in the biochemical composition of microalgal cells for the evaluated levels of aeration and CO2. The present study demonstrated that optimization of microalgal cultivation conditions can be considered a useful strategy for maximizing CO2 bio-mitigation by C. vulgaris.


Diving into Haze-Lines : Color Restoration of Underwater Images


title: Substructure counting graph kernels for machine learning from RDF data

abstract: In this paper we introduce a framework for learning from RDF data using graph kernels that count substructures in RDF graphs, which systematically covers most of the existing kernels previously defined and provides a number of new variants. Our definitions include fast kernel variants that are computed directly on the RDF graph. To improve the performance of these kernels we detail two strategies. The first strategy involves ignoring the vertex labels that have a low frequency among the instances. Our second strategy is to remove hubs to simplify the RDF graphs. We test our kernels in a number of classification experiments with real-world RDF datasets. Overall the kernels that count subtrees show the best performance. However, they are closely followed by simple bag of labels baseline kernels. The direct kernels substantially decrease computation time, while keeping performance the same. For the walks counting kernel this decrease in computation time is so large that it thereby becomes a computationally viable kernel to use. Ignoring low frequency labels improves the performance for all datasets. The hub removal algorithm increases performance on two out of three of our smaller datasets, but has little impact when used on our larger datasets. Systematic graph kernel framework for RDF.Fast computation algorithms.Low frequency labels and hub removal on RDF to enhance machine learning.


title: A Study on Sentiment Analysis: Methods and Tools

abstract: The purpose of social media has created many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment Analysis is a case of natural language processing which could mark the mood of the people about any specific product by analysis. Sentiment Analysis is a process of automatic extraction of features by mode of notions of others about specific product, services or experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features. Sentiment analysis is also called Opinion mining due to the significant volume of opinion. Analyzing customer opinion is very important to rate the product. To automate rate the opinions in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools used.


title: FUNCTIONAL MRI OF LANGUAGE: New Approaches to Understanding the Cortical Organization of Semantic Processing

abstract: ▪ Abstract Until recently, our understanding of how language is organized in the brain depended on analysis of behavioral deficits in patients with fortuitously placed lesions. The availability of functional magnetic resonance imaging (fMRI) for in vivo analysis of the normal brain has revolutionized the study of language. This review discusses three lines of fMRI research into how the semantic system is organized in the adult brain. These are (a) the role of the left inferior frontal lobe in semantic processing and dissociations from other frontal lobe language functions, (b) the organization of categories of objects and concepts in the temporal lobe, and (c) the role of the right hemisphere in comprehending contextual and figurative meaning. Together, these lines of research broaden our understanding of how the brain stores, retrieves, and makes sense of semantic information, and they challenge some commonly held notions of functional modularity in the language system.


title: LDIF- A Framework for Large-Scale Linked Data Integration

abstract: While the Web of Linked Data grows rapidly, the development of Linked Data applications is still cumbersome and hampered due to the lack of software libraries for accessing, integrating and cleansing Linked Data from the Web. In order to make it easier to develop Linked Data applications, we provide the LDIF - Linked Data Integration Framework. LDIF can be used as a component within Linked Data applications to gather Linked Data from the Web and to translate the gathered data into a clean local target representation while keeping track of data provenance. LDIF provides a Linked Data crawler as well as components for accessing SPARQL endpoints and remote RDF dumps. It provides an expressive mapping language for translating data from the various vocabularies that are used on the Web to a consistent, local target vocabulary. LDIF includes an identity resolution component which discovers URI aliases in the input data and replaces them with a single target URI based on exible, user-provided matching heuristics. For provenance tracking, the LDIF framework employs the Named Graphs data model. LDIF contains a data quality assessment and a data fusion module which allow Web data to be ltered according to dierent data quality assessment policies and provide for fusing Web data using dierent conict resolution methods. In order to deal with use cases of dierent sizes, we provide an in-memory implementation of the LDIF framework as well as an RDF-store-backed implementation and a Hadoop implementation that can be deployed on Amazon EC2.


title: Semi-Supervised Learning for Monocular Gaze Redirection

abstract: We present a new approach to monocular learning-based gaze redirection problem in images that is able to train on raw sequences of eye images with unknown gaze directions and a small amount of eye images, where the gaze direction is known. The proposed approach is based on a pair of deep networks, where the first encoder-like network maps eye images to a latent space, while the second network maps pairs of latent representations to warping fields implementing the transformation between the pair of the original images. In the proposed system, both networks are trained in an unsupervised manner, while the gaze-annotated images are only used to estimate displacements in the latent space that are characteristic to certain gaze redirections. Quantitative and qualitative evaluation suggests that such characteristic displacement vectors in the learned latent space can be learned from few examples and are transferable across different people and different imaging conditions.


title: Averaging of motion capture recordings for movements’ templates generation

abstract: In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates’ preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave - one - out cross - validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.


title: Diaphragmatic Eventration in Sisters with Asparagine Synthetase Deficiency: A Novel Homozygous ASNS Mutation and Expanded Phenotype.

abstract: BACKGROUND ::: Asparagine Synthetase Deficiency (ASNSD; OMIM #615574) is a newly described rare autosomal recessive neurometabolic disorder, characterised by congenital microcephaly, severe psychomotor delay, encephalopathy and progressive cerebral atrophy. To date, seven families and seven missense mutations in the ASNSD disease causing gene, ASNS, have been published. ::: ::: ::: METHODS ::: We report two further affected infant sisters from a consanguineous Indian family, who in addition to the previously described features had diaphragmatic eventration. Both girls died within the first 6 months of life. Whole exome sequencing (WES) was performed for both sisters to identify the pathogenic mutation. The clinical and biochemical parameters of our patient are compared to previous reports. ::: ::: ::: RESULTS ::: WES demonstrated a homozygous novel missense ASNS mutation, c.1019G > A, resulting in substitution of the highly conserved arginine residue by histidine (R340H). ::: ::: ::: CONCLUSION ::: This report expands the phenotypic and mutation spectrum of ASNSD, which should be considered in neonates with congenital microcephaly, seizures and profound neurodevelopmental delay. The presence of diaphragmatic eventration suggests extracranial involvement of the central nervous system in a disorder that was previously thought to exclusively affect the brain. Like all previously reported patients, these cases were diagnosed with WES, highlighting the clinical utility of next generation sequencing in the diagnosis of rare, difficult to recognise disorders.


title: An Historical Review of African Freshwater Ichthyology

abstract: Abstract The history of ichthyology in Africa is reviewed. From the time of the Ancient Egyptians to the present, more than 3200 species have been discovered, drawn and described. Michel Adanson initiated the first material collections during the 18th century. During the 19th century, the work of travelling scientists (Etienne Geoffroy Saint-Hilaire, Andrew Smith) and explorers (including Mungo Park, Pierre Savorgnan de Brazza and Henry Morton Stanley) added substantially to developing zoological collections from their field trips. At that time, many species descriptions were based on fish preserved in these collections. Towards the end of the 19th and into the early part of the 20th century, knowledge of African fishes was greatly enhanced, especially through the work of Georges A. Boulenger, Albert C.L.G. Gunther and Franz Steindachner, who, respectively, described 640, 119 and 53 species. The majority of the naturalists working in Africa during the middle and later parts of the 20th century tended to s...


title: Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

abstract: Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers. To overcome this problem, we present a novel method without the pure-pixel assumption, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data. Two important facts are exploited: First, the spectral data are nonnegative; second, the simplex volume determined by the endmembers is the minimum among all possible simplexes that circumscribe the data scatter space. The proposed method takes advantage of the fast convergence of NMF schemes, and at the same time eliminates the pure-pixel assumption. The experimental results based on a set of synthetic mixtures and a real image scene demonstrate that the proposed method outperforms several other advanced endmember detection approaches


title: Mapping moods: Geo-mapped sentiment analysis during hurricane sandy

abstract: Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users’ concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users’ locations, but also based on the distance from the disaster.


title: Modeling and nonlinear control of magnetic levitation systems

abstract: In this paper, the authors propose a nonlinear model for magnetic levitation systems which is validated with experimental measurements. Using this model, a nonlinear control law based on differential geometry is firstly synthesized. Then, its real-time implementation is developed. In order to highlight the performance of the proposed control law, experimental results are given.


title: Active learning from noisy and abstention feedback

abstract: An active learner is given an instance space, a label space and a hypothesis class, where one of the hypotheses in the class assigns ground truth labels to instances. Additionally, the learner has access to a labeling oracle, which it can interactively query for the label of any example in the instance space. The goal of the learner is to find a good estimate of the hypothesis in the hypothesis class that generates the ground truth labels while making as few interactive queries to the oracle as possible. This work considers a more general setting where the labeling oracle can abstain from providing a label in addition to returning noisy labels. We provide a model for this setting where the abstention rate and the noise rate increase as we get closer to the decision boundary of the ground truth hypothesis. We provide an algorithm and an analysis of the number of queries it makes to the labeling oracle; finally we provide matching lower bounds to demonstrate that our algorithm has near-optimal estimation accuracy.


title: A Fully Time-domain Neural Model for Subband-based Speech Synthesizer

abstract: This paper introduces a deep neural network model for subband-based speech synthesizer. The model benefits from the short bandwidth of the subband signals to reduce the complexity of the time-domain speech generator. We employed the multi-level wavelet analysis/synthesis to decompose/reconstruct the signal into subbands in time domain. Inspired from the WaveNet, a convolutional neural network (CNN) model predicts subband speech signals fully in time domain. Due to the short bandwidth of the subbands, a simple network architecture is enough to train the simple patterns of the subbands accurately. In the ground truth experiments with teacher-forcing, the subband synthesizer outperforms the fullband model significantly in terms of both subjective and objective measures. In addition, by conditioning the model on the phoneme sequence using a pronunciation dictionary, we have achieved the fully time-domain neural model for subband-based text-to-speech (TTS) synthesizer, which is nearly end-to-end. The generated speech of the subband TTS shows comparable quality as the fullband one with a slighter network architecture for each subband.


title: Character-Level Intrusion Detection Based On Convolutional Neural Networks

abstract: Deep learning models are increasingly applied in the intrusion detection system (IDS) and propel its development nowadays. In this paper, a new character-level IDS is proposed based on convolutional neural networks and obtains better performance. Different from other models which are in the feature level, this model is in the character level, which views network traffic records as sequences of characters. Each character of a record is encoded into a vector based on an alphabet. The vectors of the characters in the record are aggregated into a matrix as the input of the convolutional neural networks with an improved structure. This model simplifies the preprocessing without considering the priori knowledge about the data. Experiments on the dataset NSL-KDD show that the binary classification and the multi-classification of our model have good performance. Compared with other machine learning algorithms in term of accuracy, our model outperforms those algorithms. The comparison with the model using convolutional neural networks with the preprocessing in the feature level reveals that our model performs much better with high accuracy, high detection rate and low false alarm rate.


title: Allozyme variation and an estimate of the inbreeding coefficient in the coffee berry borer, Hypothenemus hampei (Coleoptera: Scolytidae)

abstract: Seven presumed Mendelian enzyme loci (Est-2, Est-3, Gpi, Idh-I, Idh-2, Mdh-2 and Mpi) were characterized and tested for polymorphism in coffee berry borers, Hypothenemus hanzpei (Ferrari), sampled in Cote d'Ivoire, Mexico and New Caledonia. The average genetic diversity was H=0.080. Two loci, Mdh-2 and Mpi were poly- morphic, and thus usable as genetic markers. The population structure of H. hampei was analysed using Weir & Cockerham's estimators of Wright's F-statistics. A high degree of inbreeding (f=0.298) characterized the elementary geographic sampling unit, the coffee field. The estimate of gene flow between fields within a country was Nm =10.6 and that between countries was Nm =2. The population genetic structure in H. hampei could be related to its known population biological features and history. 21


title: Excess of glucocorticoid induces myocardial remodeling and alteration of calcium signaling in cardiomyocytes

abstract: Ventricular dysfunction is one of the important side effects of the anti-inflammatory agent, glucocorticoid (GC). The present study was undertaken to examine whether abnormal calcium signaling is responsible for cardiac dysfunction due to an excess of GC hormone. The synthetic GC drug, dexamethasone (DEX), significantly (P!0 . 001, nZ20) increased heart weight to body weight ratio, left ventricular remodeling, and fibrosis. The microarray analysis showed altered expression of several genes encoding calcium cycling/ion channel proteins in DEXtreated rat heart. The altered expression of some of the genes was validated by real-time PCR and western blotting analyses. The expression of the L-type calcium channels and calsequestrin was increased, whereas sarcoendoplasmic reticulum calcium transport ATPase 2a (SERCA2a) and junctin mRNAs were significantly reduced in DEX-treated rat left ventricular tissues. In neonatal rat ventricular cardiomyocytes, DEX also increased the level of mRNAs of atrial- and brain natriuretic peptides, L-type calcium channels, and calsequestrin after 24 h of treatment, which were mostly restored by mifepristone. The caffeine-induced calcium release was prolonged by DEX compared to the sharp release in control cardiomyocytes. Taken together, these data show that impaired calcium kinetics may be responsible for cardiac malfunction by DEX. The results are important in understanding the pathophysiology of the heart in patients treated with excess GC.


title: Predicting Flu Trends using Twitter data

abstract: Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from “sentinel” medical practices. Typically there is a 1–2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to “sentinel” physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity.


title: Evolution-Preserving Dense Trajectory Descriptors

abstract: Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets. This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a novel type of video descriptor that significantly outperforms Trajectory-pooled Deep-learning Descriptors. EPT descriptors are defined based on dense trajectories, and they provide complimentary benefits to video descriptors that are not based on trajectories. In particular, we show that the combination of EPT descriptors and VideoDarwin leads to state-of-the-art performance on Hollywood2 and UCF101 datasets.


title: Accuracy of Deception Judgments

abstract: We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individual...


title: Linked Data Platform as a novel approach for Enterprise Application Integration

abstract: Enterprises are increasingly using a wide range of heterogeneous information systems for executing and governing their business activities. Even if the adoption of service orientation has improved loose coupling and reusability, applications are still isolated data silos requiring complex transformation and mediation for integrating them. The W3C Linked Data Platform (LDP) Working Group aims to standardize a RESTful way to read and write Linked Data. This opens the door for industry to benefit more effectively from Linked Data by building interoperable LDP applications and implementing new approaches for Enterprise Application Integration (EAI). For a wide industrial adoption of LDP-based EAI, the advantages of LDP have to be clearly illustrated and LDP needs to be assessed for enterprise readiness. This paper analyses LDP as an EAI approach by considering both its advantages over existing approaches and identifying the gaps and challenging enterprise EAI requirements it has to satisfy. The paper also presents lessons learned from a project that uses LDP for integrating open source Application Lifecycle Management tools.


Neural Headline Generation on Abstract Meaning Representation


title: Exacting Eccentricity for Small-World Networks

abstract: This paper studies the efficiency issue on computing the exact eccentricity-distribution of a small-world network. Eccentricity-distribution reflects the importance of each node in a graph, which is beneficial for graph analysis. Moreover, it is key to computing two fundamental graph characters: diameter and radius. Existing eccentricity computation algorithms, however, are either inefficient in handling large-scale networks emerging nowadays in practice or approximate algorithms that are inappropriate to small-world networks. We propose an efficient approach for exact eccentricity computation. Our approach is based on a plethora of insights on the bottleneck of the existing algorithms — one-node eccentricity computation and the upper/lower bounds update. Extensive experiments demonstrate that our approach outperforms the state-of-the-art up to three orders of magnitude on real large small-world networks.


title: Integrating Transformer and Paraphrase Rules for Sentence Simplification

abstract: Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from ma- chine translation studies and implicitly learned simplification mapping rules from normal- simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we pro- pose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state- of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification.


title: Protecting the self: A descriptive qualitative exploration of how registered nurses cope with working in surgical areas.

abstract: Abstract Aims This paper aims to explore and describe how qualified nurses working with in, in-patient surgical areas cope with the daily experiences they are exposed to. Background It has long been recognised that many aspects of nursing work can result in high levels of stress, with negative consequences for the individual nurse and patient care. Difficulties in coping with nursing work can also result in burnout, as well as raising concerns about cognitive dissonance, emotional labour and the use of emotional barriers. Why some nurses are more prone to experience these phenomena than others, is unclear. Method A descriptive qualitative approach is taken using a purposive, theoretically congruent sample of 16 qualified registered nurses all of whom participated in a semi-structured interview during 2002. All interviews were tape recorded and transcribed verbatim and then analysed using the four stages outlined by Morse and Field [Morse, J.M., Field, P.A., 1996. Nursing Research: The Application of Qualitative Approaches. Chapman & Hall, London]. Findings Three key themes emerged from analysis; relationships with patients, being a person and the effect of experience. All three interlink to describe a process whereby the individual switches off from the environment around them by adopting a working persona which is different but related to their own personal persona and is beneficially enhanced as a consequence of experience. Conclusion Working as a nurse results in exposure to potentially distressing and stressful events from which it is important to protect the self. Participants in this study achieve protection by the development of a working persona which facilitates switching off and is beneficially enhanced by experience.


title: Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations

abstract: Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.


title: Challenges in Measuring, Understanding, and Achieving Social-Technical Congruence

abstract: Congruence, the state in which a software development organization harbors sufficient coordination capabilities to meet the coordination demands of the technical products under development, is increasingly recognized as critically important to the performance of an organization. To date, it has been shown that a variety of states of in congruence may exist in an organization, with possibly serious negative effects on product quality, development progress, cost, and so on. Exactly how to achieve congruence, or knowing what steps to take to achieve congruence, is less understood. In this paper, we introduce a series of key challenges that we believe must be comprehensively addressed in order for congruence research to result in wellunderstood approaches, tactics, and tools – so these can be infused in the day-to-day practices of development organizations to improve their coordination capabilities with better aligned social and technical structures.


title: Driver fatigue detection based on eye tracking and dynamk, template matching

abstract: A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.


title: Graph-Cut RANSAC

abstract: A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard CPU).


title: Efficient BSIF-based near-infrared iris recognition

abstract: Binarized statistical image features (BSIF) represents a general purpose texture descriptor originally designed for texture description and classification, such as local binary patterns (LBP) or local phase quantisation (LPQ). Recently, BSIF has extensively been applied for the purpose of biometric recognition, for instance based on face or palmprint images. While recognition accuracy reported for different biometric characteristics indicates its applicability to iris recognition, up till now BSIF has primarily been employed for iris spoofing detection in particular, fake contact lens detection. In this work we present an adaptation of BSIF for near-infrared iris recognition. In accordance with generic iris recognition schemes, a specific alignment procedure is introduced in order to achieve robustness against head tilts. Further, we propose a binarization method for BSIF-based feature histograms, to obtain a compact feature representation, which allows for a rapid comparison. On the CASIAv4-Interval iris database the proposed system achieves competitive biometric performance obtaining EERs below 0.6%, compared to traditional schemes based on Log-Gabor and quadratic spline wavelets revealing EERs of approximately 0.4%. Moreover, we show that BSIF-based feature vectors complement those extracted by traditional systems, yielding a significant performance gain in a multi-algorithm fusion scenario resulting in an EER below 0.2%, which further underlines the usefulness of the presented approach.


title: Photosynthetic pigments, nitrogen status, and flower behavior in eggplant exposed to different sources and levels of potassium

abstract: The aim of this study was to investigate (i) the influence of different sources and levels of potassium on photosynthetic pigments, (ii) measure nitrogen status in leaf, (iii) evaluate flower behavior in eggplant (Solanum melongena L.), and (iv) to identify a better potassium source to cultivation of this species. The experiment design used was in factorial scheme with randomized blocks, being composed by 2 potassium sources (KCl and K2SO4), combined with 4 levels of K2O (250, 500, 750 and 1000 kg ha ). Nitrogen accumulation in leaf at 50 days after transplanting had a higher value with KCl compared with K2SO4. Results linked to aborted fruits presented similar effects to KCl and K2SO4. This study revealed that eggplant exposed to excess potassium did not have a negative effect on chlorophyll a and b, and nitrogen concentration in leaf was significantly modified due to increase in potassium level. In addition, greater levels of potassium produced negative consequences linked to flower and fruit abortion. Therefore, potassium source indicated for eggplant is potassium sulfate.


title: YARDS: FPGA/MPU hybrid architecture for telecommunication data processing

abstract: This paper presents a novel system architecture applicable to high-performance and flexible transport data processing which includes complex protocol operation and a nehvork control algorithm. We developed a new tightly coupled Held Programmable Gate Array (FPGA) and Micro-Processing Unit (MPU) system named. Yet Another Re-Definable System (YARDS). It comprises three programmable devices which equateto high flexibility. These devices are the RISC-type MPU with memories, programmable inter-connection devices, and FPGAs. Using these, this system supports various styles of coupling between the FPGAs and the MPU which are suitable for constructing transport data processing. In this paper, two applications of the systemin the telecommunications field are given. One is an Operation, Administration, and Management (OAM) cell operations on an AsynchronousTransfer Mode (ATM) network. The other is a dynamic configuration protocol enables the updateor changeof the functions of the transport data processing system on-line. This is the first approach applying the FPGA/MPU hybrid system to the telecommunications field.


title: Large-Scale Question Answering with Joint Embedding and Proof Tree Decoding

abstract: Question answering (QA) over a large-scale knowledge base (KB) such as Freebase is an important natural language processing application. There are linguistically oriented semantic parsing techniques and machine learning motivated statistical methods. Both of these approaches face a key challenge on how to handle diverse ways natural questions can be expressed about predicates and entities in the KB. This paper is to investigate how to combine these two approaches. We frame the problem from a proof-theoretic perspective, and formulate it as a proof tree search problem that seamlessly unifies semantic parsing, logic reasoning, and answer ranking. We combine our word entity joint embedding learned from web-scale data with other surface-form features to further boost accuracy improvements. Our real-time system on the Freebase QA task achieved a very high F1 score (47.2) on the standard Stanford WebQuestions benchmark test data.


title: Stating the Obvious: Extracting Visual Common Sense Knowledge

abstract: Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common Objects in Context dataset. We show that many thousands of common sense facts can be extracted from such corpora at high quality. Furthermore, using WordNet and a novel submodular k-coverage formulation, we are able to generalize our initial set of common sense assertions to unseen objects and uncover over 400k potentially useful facts.


title: BRIEF-Gist - closing the loop by simple means

abstract: The ability to recognize known places is an essential competence of any intelligent system that operates autonomously over longer periods of time. Approaches that rely on the visual appearance of distinct scenes have recently been developed and applied to large scale SLAM scenarios. FAB-Map is maybe the most successful of these systems. Our paper proposes BRIEF-Gist, a very simplistic appearance-based place recognition system based on the BRIEF descriptor. BRIEF-Gist is much more easy to implement and more efficient compared to recent approaches like FAB-Map. Despite its simplicity, we can show that it performs comparably well as a front-end for large scale SLAM. We benchmark our approach using two standard datasets and perform SLAM on the 66 km long urban St. Lucia dataset.


title: SampleClean: Fast and Reliable Analytics on Dirty Data

abstract: An important obstacle to accurate data analytics is dirty data in the form of missing, duplicate, incorrect, or inconsistent values. In the SampleClean project, we have developed a new suite of techniques to estimate the results of queries when only a sample of data can be cleaned. Some forms of data corruption, such as duplication, can affect sampling probabilities, and thus, new techniques have to be designed to ensure correctness of the approximate query results. We first describe our initial project on computing statistically bounded estimates of sum, count, and avg queries from samples of cleaned data. We subsequently explored how the same techniques could apply to other problems in database research, namely, materialized view maintenance. To avoid expensive incremental maintenance, we maintain only a sample of rows in a view, and then leverage SampleClean to approximate aggregate query results. Finally, we describe our work on a gradient-descent algorithm that extends the key ideas to the increasingly common Machine Learning-based analytics.


title: Facial feature detection using distance vector fields

abstract: A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is calculated. The distance vector field of the face is extracted by assigning to every facial image pixel a vector pointing to the closest edge pixel. The x and y components of these vectors are used to detect the eyes and mouth regions. Luminance information is used for eye center localization, after removing unwanted effects, such as specular highlights, whereas the hue channel of the lip area is used for the detection of the mouth corners. The proposed method has been tested on the XM2VTS and BioID databases, with very good results.