title: Ploidy-dependent growth and binucleation in cultured rat hepatocytes.

abstract: The proliferative activity of rat hepatocytes, cultured in the presence of epidermal growth factor (EGF) and insulin, was examined by immunostaining of S-phase cells labeled with bromodeoxyuridine (BrdU) in culture. Proliferation rates of the different hepatocellular ploidy and nuclearity classes were measured by fluorescence image cytometry or by microscope counting of immunostained cells. Effects of EGF and insulin were largely additive, the binuclear cells being more growth factor-dependent (showing less growth in the absence of factors) than the mononuclear cells. A serial warm-washing procedure was used to remove excess BrdU from the culture medium, allowing the study of hepatocellular binucleation by a BrdU pulse-chase approach. A high rate of binucleation was detected (50%, possibly suggesting a quantal mechanism), indicating that the hormones induce a binucleating (polyploidizing) type of growth similar to that normally observed in the liver of growing rats. The highest proliferative activity (labeling index) in the hepatocyte cultures was found among the diploid cells, independent of the degree of mitogenic stimulation. The labeling index was inversely correlated with ploidy, suggesting that the ability of hepatocytes to proliferate decreases with increasing polyploidization.


title: Robust Decision Trees: Removing Outliers from Databases

abstract: Finding and removing outliers is an important problem in data mining. Errors in large databases can be extremely common, so an important property of a data mining algorithm is robustness with respect to errors in the database. Most sophisticated methods in machine learning address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. In this paper we examine C4.5, a decision tree algorithm that is already quite robust - few algorithms have been shown to consistently achieve higher accuracy. C4.5 incorporates a pruning scheme that partially addresses the outlier removal problem. In our ROBUST-C4.5 algorithm we extend the pruning method to fully remove the effect of outliers, and this results in improvement on many databases.


title: An empirical study of operating systems errors

abstract: We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previous studies that consider errors found by manual inspection of logs, testing, and surveys because static analysis is applied uniformly to the entire kernel source, though our approach necessarily considers a less comprehensive variety of errors than previous studies. In addition, automation allows us to track errors over multiple versions of the kernel source to estimate how long errors remain in the system before they are fixed.We found that device drivers have error rates up to three to seven times higher than the rest of the kernel. We found that the largest quartile of functions have error rates two to six times higher than the smallest quartile. We found that the newest quartile of files have error rates up to twice that of the oldest quartile, which provides evidence that code "hardens" over time. Finally, we found that bugs remain in the Linux kernel an average of 1.8 years before being fixed.


title: Delay bounded spray and wait in delay tolerant networks

abstract: Recently, application-specific requirement becomes one of main research challenges in the area of routing for delay tolerant networks. Among various requirements, in this paper, we focus on achieving the desired delivery ratio within bounded given deadline. For this goal, we use analytical model and develop a new forwarding scheme in respective phase. The proposed protocol dynamically adjusts the number of message copies by analytical model and the next hop node is determined depending on the delivery probability and the inter-meeting time of the encountering nodes as well as remaining time. Simulation results demonstrate that our proposed algorithm meets bounded delay with lower overhead than existing protocols in an adaptive way to varying network conditions.


title: Study on the Causes of Misbehavior among South-East Asian Children

abstract: The study investigates the major causes of misbehavior among South-East Asian children. Phenomenological research design was used as research model for this study. The population of the study consisted of married men/women from South-East Asia. In all, 70 respondents were selected through purposive sampling method where 36 married males and 34 females participated from India, Pakistan, Malaysia, Singapore and United Arab Emirates. The phenomenological interviews were conducted through face-to-face interaction and video conferencing where the respondents were asked to share the reasons that encourage children to misbehave. Five themes and multiple sub-themes were derived from the interviews. The results drawn from the study reveal that the main causes of misbehavior among South-East Asian children are (1) family and social environment which include lack of inculcation of ethical values, harsh behavior of parents, use of abusive language, imposition of strict rules and regulations, bad friend circle, nagging children in front of others, imbalance development and undue pampering;(2)lack of attention which includes ignorance of parents, communication gap due to lack of time, lack of understanding, love and care and lack of counseling; (3)media which includes change in lifestyle, western culture and modernism;(4) de-motivation which includes discouragement and avoiding children’s opinion; high expectations; (5) favoritism which includes impartiality and discrimination among children. Recommendations are supplemented at the end of the research paper.


title: Lines, Blobs, Crosses and Arrows: Diagrammatic Communication with Schematic Figures

abstract: In producing diagrams for a variety of contexts, people use a small set of schematic figures to convey certain context specific concepts, where the forms themselves suggest meanings. These same schematic figures are interpreted appropriately in context. Three examples will support these conclusions: lines, crosses, and blobs in sketch maps; bars and lines in graphs; and arrows in diagrams of complex systems.


title: Exploring the Determinants of Knowledge Exchange in Virtual Communities

abstract: Virtual communities have become ubiquitous and vital in nearly all professions. These communities typically involve voluntary participation, and given that these communities are not organization specific, community members’ motivations for participation vary. In this study, we investigate the motivations of the members of a professional virtual community to engage in knowledge exchange. We synthesize social exchange theory and the theory of reasoned action to identify critical determinants of attitudes toward knowledge exchange in virtual communities, namely, trust among participants, anticipated reciprocal relationships, and the relevance of the community to participants’ jobs. Additionally, we posit that attitudes will influence the intention to use the virtual community and that this relationship will be moderated by the perceived quality of the information exchanged within the community. We test our research model using data compiled from a community of research scientists in South Korea. Our findings indicate that trust among participants has a positive influence on attitudes toward both sharing and acquiring knowledge. The anticipation of a reciprocal relationship has a positive effect on attitudes toward knowledge acquisition, and job relevance has a positive effect on attitudes toward knowledge sharing. Furthermore, attitudes toward knowledge acquisition affect attitudes toward knowledge sharing, and attitudes toward knowledge sharing positively influence intentions to use a virtual community. We also find that perceived information quality negatively moderates the relationship between attitudes toward knowledge sharing and user intentions to use a virtual community. We interpret and discuss these findings, and their implications for research and practice.


title: Piezoelectric vs. Capacitive Based Force Sensing in Capacitive Touch Panels

abstract: High sensitivity force sensing is a desirable function of capacitance touch screen panels. In this paper, we report on a dynamic force detection technique by utilizing piezoelectric materials. The force-voltage responsivities are investigated for four widely used stack-ups, with various panel thicknesses and touch locations. Based on the theoretical analysis and simulation results, the maximum responsivity and the signal-to-noise ratio are achieved at 0.42 V/N and 59.1 dB, respectively. The proposed technique implements force sensing successfully, enhancing the human–machine interactivity experience.


title: Using Neural Network Model Predictive Control for Controlling Shape Memory Alloy-Based Manipulator

abstract: This paper presents a new setup and investigates neural model predictive and variable structure controllers designed to control the single-degree-of-freedom rotary manipulator actuated by shape memory alloy (SMA). SMAs are a special group of metallic materials and have been widely used in the robotic field because of their particular mechanical and electrical characteristics. SMA-actuated manipulators exhibit severe hysteresis, so the controllers should confront this problem and make the manipulator track the desired angle. In this paper, first, a mathematical model of the SMA-actuated robot manipulator is proposed and simulated. The controllers are then designed. The results set out the high performance of the proposed controllers. Finally, stability analysis for the closed-loop system is derived based on the dissipativity theory.


title: Big Data and Big Data Technologies

abstract: The term Big Data often refers to the massive amount of digital information that companies and government organizations collect about entities (people and things), interactions of entities with each other and operations of systems in physical or virtual living environments.


title: Do Petri Nets Provide the Right Representational Bias for Process Mining?

abstract: Process discovery is probably the most challenging process mining task. Given an event log, i.e., a set of example traces, it is difficult to automatically construct a process model explaining the behavior seen in the log. Many process discovery techniques use Petri nets as a language to describe the discovered model. This implies that the search space—often referred to as the representational bias—includes many inconsistent models (e.g., models with deadlocks and livelocks). Moreover, the low-level nature of Petri nets does not help in finding a proper balance between overfitting and underfitting. Therefore, we advocate a new representation more suitable for process discovery: causal nets. Causal nets are related to the representations used by several process discovery techniques (e.g., heuristic mining, fuzzy mining, and genetic mining). However, unlike existing approaches, C-nets use declarative semantics tailored towards process mining.


title: Nested Named Entity Recognition

abstract: Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recognition, but due to technological, rather than ideological reasons. In this paper, we present a new technique for recognizing nested named entities, by using a discriminative constituency parser. To train the model, we transform each sentence into a tree, with constituents for each named entity (and no other syntactic structure). We present results on both newspaper and biomedical corpora which contain nested named entities. In three out of four sets of experiments, our model outperforms a standard semi-CRF on the more traditional top-level entities. At the same time, we improve the overall F-score by up to 30% over the flat model, which is unable to recover any nested entities.


title: Topic model based behaviour modeling and clustering analysis for wireless network users

abstract: User behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.


title: Rotational symmetry field design on surfaces

abstract: Designing rotational symmetries on surfaces is a necessary task for a wide variety of graphics applications, such as surface parameterization and remeshing, painterly rendering and pen-and-ink sketching, and texture synthesis. In these applications, the topology of a rotational symmetry field such as singularities and separatrices can have a direct impact on the quality of the results. In this paper, we present a design system that provides control over the topology of rotational symmetry fields on surfaces. ::: As the foundation of our system, we provide comprehensive analysis for rotational symmetry fields on surfaces and present efficient algorithms to identify singularities and separatrices. We also describe design operations that allow a rotational symmetry field to be created and modified in an intuitive fashion by using the idea of basis fields and relaxation. In particular, we provide control over the topology of a rotational symmetry field by allowing the user to remove singularities from the field or to move them to more desirable locations. ::: At the core of our analysis and design implementations is the observations that N-way rotational symmetries can be described by symmetric N-th order tensors, which allows an efficient vector-based representation that not only supports coherent definitions of arithmetic operations on rotational symmetries but also enables many analysis and design operations for vector fields to be adapted to rotational symmetry fields. ::: To demonstrate the effectiveness of our approach, we apply our design system to pen-and-ink sketching and geometry remeshing.


title: Multi-robot multi-task allocation for hospital logistics

abstract: A number of materials are delivered inside a hospital, and a fleet of autonomous navigating mobile robots are applied to replace these heavy mobility works. To increase the efficiency of using multiple robots, more than one delivery tasks can be assigned to a robot instead of delivering only one package at a time. This multi-task allocation algorithm (MTA) is introduced in this paper and a simulator is developed to measure the performance of the algorithm. The simulator models the hospital environment and enables to compare the delivery productivity by changing the number of robot. It shows that the efficiency of MTA increases dramatically compared to the single task allocation when using the same number of robot.


title: Body Area Networks for Ubiquitous Healthcare Applications: Opportunities and Challenges

abstract: Body Area Networks integrated into mHealth systems are becoming a mature technology with unprecedented opportunities for personalized health monitoring and management. Potential applications include early detection of abnormal conditions, supervised rehabilitation, and wellness management. Such integrated mHealth systems can provide patients with increased confidence and a better quality of life, and promote healthy behavior and health awareness. Automatic integration of collected information and user's inputs into research databases can provide medical community with opportunity to search for personalized trends and group patterns, allowing insights into disease evolution, the rehabilitation process, and the effects of drug therapy. A new generation of personalized monitoring systems will allow users to customize their systems and user interfaces and to interact with their social networks. With emergence of first commercial body area network systems, a number of system design issues are still to be resolved, such as seamless integration of information and ad-hoc interaction with ambient sensors and other networks, to enable their wider acceptance. In this paper we present state of technology, discuss promising new trends, opportunities and challenges of body area networks for ubiquitous health monitoring applications.


Signature Verification Using a Convolutional Neural Network


title: Nuclear and mitochondrial DNA quantification of various forensic materials.

abstract: Due to the different types and quality of forensic evidence materials, their DNA content can vary substantially, and particularly low quantities can impact the results in an identification analysis. In this study, the quantity of mitochondrial and nuclear DNA was determined in a variety of materials using a previously described real-time PCR method. DNA quantification in the roots and distal sections of plucked and shed head hairs revealed large variations in DNA content particularly between the root and the shaft of plucked hairs. Also large intra- and inter-individual variations were found among hairs. In addition, DNA content was estimated in samples collected from fingerprints and accessories. The quantification of DNA on various items also displayed large variations, with some materials containing large amounts of nuclear DNA while no detectable nuclear DNA and only limited amounts of mitochondrial DNA were seen in others. Using this sensitive real-time PCR quantification assay, a better understanding was obtained regarding DNA content and variation in commonly analysed forensic evidence materials and this may guide the forensic scientist as to the best molecular biology approach for analysing various forensic evidence materials.


title: A Machine Learning Based Intrusion Detection System for Software Defined 5G Network

abstract: As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function mod- ules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance.


title: Design and implementation of a robotic arm using ROS and MoveIt!

abstract: In this paper is presented an interfacing of a robotic arm with a motion planner and a 3D viewer (RViz). The main purpose of this work, is to manipulate and grip objects to transport them from one location to another using Robot Operating System (ROS). In order to do this task, a motion planner, who deal with constraints, i.e. collisions among the arm joints and objects, is used. Also a 3D viewer, called RViz, is used to visualize (online and offline) the motion of the arm. To easily interface the 3 degree of freedom (DOF) arm, it was designed using FreeCAD, as the output files used to print the arm, are simultaneously exported to create the Unified Robot Description Format (URDF) files required for the planner and viewer. A position-force controller for the gripper, previously presented, has been used to pick up and hold the object during the manipulation. Some experiments where carried out to asses the result of the interface, the execution of the motion planning avoiding collision and the grasp to the object. These results are shown and discussed.


Noisy Covert Channels through VPNs Project by Isakov Yehiel under the supervision of Dr


abstract: In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. ::: Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition ::: DOI: 10.25165/j.ijabe.20181104.4475 ::: ::: Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.


title: A Survey on the Internet of Things Security

abstract: The security issues of the Internet of Things (IoT) are directly related to the wide application of its system. Beginning with introducing the architecture and features of IoT security, this paper expounds several security issues of IoT that exist in the three-layer system structure, and comes up with solutions to the issues above coupled with key technologies involved. Among these safety measures concerned, the ones about perception layer are particularly elaborated, including key management and algorithm, security routing protocol, data fusion technology, as well as authentication and access control, etc.


title: The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing

abstract: As human computation on crowdsourcing systems has become popular and powerful for performing tasks, malicious users have started misusing these systems by posting malicious tasks, propagating manipulated contents, and targeting popular web services such as online social networks and search engines. Recently, these malicious users moved to Fiverr, a fast-growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run by crowd workers) and malicious customers can purchase those tasks for only $5. In this paper, we present a comprehensive analysis of Fiverr. First, we identify the most popular types of crowdturfing tasks found in this marketplace and conduct case studies for these crowdturfing tasks. Then, we build crowdturfing task detection classifiers to filter these tasks and prevent them from becoming active in the marketplace. Our experimental results show that the proposed classification approach effectively detects crowdturfing tasks, achieving 97.35% accuracy. Finally, we analyze the real world impact of crowdturfing tasks by purchasing active Fiverr tasks and quantifying their impact on a target site. As part of this analysis, we show that current security systems inadequately detect crowdsourced manipulation, which confirms the necessity of our proposed crowdturfing task detection approach.


title: Evaluation of Community Detection Methods

abstract: Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proven to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect. Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, and then use kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the advantages of our method.


title: Practical regular expression matching free of scalability and performance barriers

abstract: Regular expressions (regexes) provide rich expressiveness to specify the signatures of intrusions and are widely used in contemporary network security systems for signature-based intrusion detection. To perform very fast regex matching, deterministic finite automata (DFA) has been the first choice because its time complexity is constant O ( 1 ) . Unfortunately, DFA often suffers the well known state explosion problem and, consequently, tends to require prohibitive memory overhead in practical applications. To address the problem, a wide variety of DFA compression techniques have been proposed; however, few can keep up with the ever increasing network traffic bandwidth and regex set complexity. This paper proposes that the DFA problem is rooted in regexes (rather than in DFA), i.e., semantic overlapping of regexes, and accordingly presents a complete algorithmic solution PaCC (Partition, Compression, and Combination), that can transform the given large-scale set of complex regexes into a compact and fast matching engine using DFA as its core. PaCC fundamentally defuses state explosion for DFA by partitioning complex regexes into overlapping-free segments. By exploiting the massive repetitiveness among the resulting segments, PaCC can further deflate corresponding DFA in terms of the number of states. Moreover, on the basis of the characteristics of these segments, PaCC takes a tailor-made compression approach and reduces over 96% of the state transitions for the corresponding DFA. In the final matching engine, the combination of DFA and a small relation mapping table, built from segments and their syntagmatic relations, respectively, guarantees high performance and semantic equivalence. Experimental evaluation shows that PaCC produces succinct matching engines with memory usage proportional to the size of the real-world Snort and Bro regex sets, with speeds of up to 1.7Gbps per core on a HP Z220 SFF workstation with a 3.40GHz Intel Core i7-3770.


title: Virtual resistor based active damping of LC filter in standalone voltage source inverter

abstract: Output LC filter effectively mitigates higher order harmonics in the output of voltage source inverters employing pulse width modulation. The presence of the LC filter introduces stability concern due to the resonance, with options for damping by passive or active methods. The active methods feature lossless damping without the hardware change. Virtual resistance based active damping methods emulate the physical behavior of a resistor in the control loop. Conventional design methods involve sequential selection of the virtual resistance loop and voltage loop controller parameters. This paper jointly analyses the relation between voltage controller parameters and virtual resistance, and their effect on the open loop gain and phase margin. Such an approach for controller design results in increased system bandwidth. Based on the transfer function analysis, a novel control design methodology is proposed. Experimental results are provided that validate the proposed control methodology.


title: A multi basin SWAT model analysis of runoff and sedimentation in the Blue Nile, Ethiopia

abstract: Abstract. A multi basin analysis of runoff and erosion in the Blue Nile Basin, Ethiopia was conducted to elucidate sources of runoff and sediment. Erosion is arguably the most critical problem in the Blue Nile Basin, as it limits agricultural productivity in Ethiopia, degrades benthos in the Nile, and results in sedimentation of dams in downstream countries. A modified version of the Soil and Water Assessment Tool (SWAT) model was developed to predict runoff and sediment losses from the Ethiopian Blue Nile Basin. The model simulates saturation excess runoff from the landscape using a simple daily water balance coupled to a topographic wetness index in ways that are consistent with observed runoff processes in the basin. The spatial distribution of landscape erosion is thus simulated more correctly. The model was parameterized in a nested design for flow at eight and sediment at three locations in the basin. Subbasins ranged in size from 1.3 to 174 000 km2, and interestingly, the partitioning of runoff and infiltrating flow could be predicted by topographic information. Model predictions showed reasonable accuracy (Nash Sutcliffe Efficiencies ranged from 0.53–0.92) with measured data across all sites except Kessie, where the water budget could not be closed; however, the timing of flow was well captured. Runoff losses increased with rainfall during the monsoonal season and were greatest from areas with shallow soils and large contributing areas. Analysis of model results indicate that upland landscape erosion dominated sediment delivery to the main stem of the Blue Nile in the early part of the growing season when tillage occurs and before the soil was wetted up and plant cover was established. Once plant cover was established in mid August landscape erosion was negligible and sediment export was dominated by channel processes and re-suspension of landscape sediment deposited early in the growing season. These results imply that targeting small areas of the landscape where runoff is produced can be the most effective at controlling erosion and protecting water resources. However, it is not clear what can be done to manage channel erosion, particularly in first order streams in the basin.


title: The Carolina HPV Immunization Attitudes and Beliefs Scale (CHIAS): Scale Development and Associations With Intentions to Vaccinate

abstract: BACKGROUND ::: No standardized instruments, to our knowledge, exist to assess attitudes and beliefs about human papillomavirus (HPV) vaccination. ::: ::: ::: METHODS ::: We developed the Carolina HPV Immunization Attitudes and Beliefs Scale (CHIAS), using data collected on 783 parents who had not yet vaccinated their daughters against HPV. We conducted a principal components analysis of 16 HPV vaccine attitude and belief items, assessed the scale's psychometric properties, and used linear regression to examine the relationship of CHIAS factors and parents' vaccination intentions. ::: ::: ::: RESULTS ::: Analyses identified 4 CHIAS factors, all of which had acceptable scale alphas and 1-year test-retest reliability. In multivariate models, higher vaccination intentions were associated with: believing HPV vaccine is effective (β = 0.06) or has less harmful effects (β = -0.47), perceiving more barriers to access (β = 0.18), and having less uncertainty about the vaccine (β = -0.23) (all P < 0.05). ::: ::: ::: CONCLUSIONS ::: Findings suggest that parent attitudes about HPV vaccine are important to their intentions to vaccinate their adolescent daughters against HPV. The CHIAS offers researchers a compact, standardized measure of important HPV vaccine attitudes and beliefs.


title: Compliance of IEEE 802.22 WRAN for field area network in smart grid

abstract: Distributed power system network is going to be complex, and it will require high-speed, reliable and secure communication systems for managing intermittent generation with coordination of centralised power generation, including load control. Cognitive Radio (CR) is highly favourable for providing communications in Smart Grid by using spectrum resources opportunistically. The IEEE 802.22 Wireless Regional Area Network (WRAN) having the capabilities of CR use vacant channels opportunistically in the frequency range of 54 MHz to 862 MHz occupied by TV band. A comprehensive review of using IEEE 802.22 for Field Area Network in power system network using spectrum sensing (CR based communication) is provided in this paper. The spectrum sensing technique(s) at Base Station (BS) and Customer Premises Equipment (CPE) for detecting the presence of incumbent in order to mitigate interferences is also studied. The availability of backup and candidate channels are updated during “Quite Period” for further use (spectrum switching and management) with geolocation capabilities. The use of IEEE 802.22 for (a) radio-scene analysis, (b) channel identification, and (c) dynamic spectrum management are examined for applications in power management.


title: Clustering articles in bahasa Indonesia using self-organizing map

abstract: One of the text explosion in the Internet is the availability of any articles published by any institution at any time. As the articles are widely available in the Internet, there are many observations that can be done by utilizing the available articles. One of the basic things to do is categorizing the articles. As the number of the articles are massive, categorizing them manually is not an option. Therefore, we propose the self-organizing map (SOM) algorithm with the combination of Term Frequency — Inverse Document Frequency (TF-IDF) and multiword expressions to categorize articles in Bahasa Indonesia. This research clusters health articles into several topics according to keywords of each articles. The process is divided into three sections, such as text pre-processing, keywords extraction, and the implementation of self-organizing maps. Number of keywords that are used as parameters to self-organizing maps algorithm will influence the number of resulted clusters.


title: Wearable Photoplethysmographic Sensor System with PSoC Microcontroller

abstract: This paper presents a wearable PPG (photoplethysmographic) sensor system with a PSoC micro controller unit (Cypress Semiconductor Corp., USA). We have developed a pulse wave monitoring system on earlobe, which has a PIC micro controller and analog circuits. However, analog sensors need some discrete parts, resulting in difficulty in reducing the system size. The PSoC micro controller includes a CPU, analog and digital blocks to configure mixed-signal circuits, and an internal oscillator in a single IC package. Therefore, PSoC allows designers to reduce the number of external discrete devices and the system size. The novel system consists of a photo interrupter, the PSoC, and the Bluetooth module, which are on PCBs (Printed Circuit Board) of 18, 13, and 10 mm in diameter, respectively. The photo interrupter detects the pulse wave due to the change in volume of blood vessels. The PSoC includes an analog filter, amplifiers, and digital circuits to calculate HR (heart rate) from the pulse wave of human body. The system was able to detect the pulse wave and transmit the HR data to a PC wirelessly. The power supply voltage is 3.3 V, and the total current consumption is about 51 mA. In comparison with the previous work, the area of discrete RCL (resistor, capacitor, and inductor) devices is reduced from 1230 mm2 to 466 mm2, and the number of such devices is reduced from 26 to 6.


title: Eigenvalues and eigenvectors of generalized DFT, generalized DHT, DCT-IV and DST-IV matrices

abstract: In this paper, the eigenvalues and eigenvectors of the generalized discrete Fourier transform (GDFT), the generalized discrete Hartley transform (GDHT), the type-IV discrete cosine transform (DCT-IV), and the type-IV discrete sine transform (DST-IV) matrices are investigated in a unified framework. First, the eigenvalues and their multiplicities of the GDFT matrix are determined, and the theory of commuting matrices is applied to find the real, symmetric, orthogonal eigenvectors set that constitutes the discrete counterpart of Hermite Gaussian function. Then, the results of the GDFT matrix and the relationships among these four unitary transforms are used to find the eigenproperties of the GDHT, DCT-IV, and DST-IV matrices. Finally, the fractional versions of these four transforms are defined, and an image watermarking scheme is proposed to demonstrate the effectiveness of fractional transforms.


title: Head and Neck Carcinomas in Patients under the Age of Forty Years

abstract: From 1973 to 1988, 1, 104 patients with head and neck carcinomas (excluding thyroid carcinoma) were admitted to the Yokohama City University Hospital, and among them, 48 (4.3%) were under the age of 40 years. We made a retrospective survey of these young patients. There were 36 males and 12 females. An increasing ratio of male to female was seen with increasing age. The primary site was most frequently the nasopharynx (29 cases), followed by the maxillary sinus (9 cases), and the tongue (4 cases). Squamous cell carcinoma in these young patients characteristically showed a low grade of differentiation.


title: Arbitrary Style Transfer with Deep Feature Reshuffle

abstract: This paper introduces a novel method by reshuffling deep features (i.e., permuting the spacial locations of a feature map) of the style image for arbitrary style transfer. We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods. This simple idea can effectively address the challenging issues in existing style transfer methods. On one hand, it can avoid distortions in local style patterns, and allow semantic-level transfer, compared with neural parametric methods. On the other hand, it can preserve globally similar appearance to the style image, and avoid wash-out artifacts, compared with neural non-parametric methods. Based on the proposed loss, we also present a progressive feature-domain optimization approach. The experiments show that our method is widely applicable to various styles, and produces better quality than existing methods.


title: Access Control for IoT Nodes With Energy and Fidelity Constraints

abstract: The presence of many battery-powered sensors in the Internet of Things paradigm calls for the design of energy-aware protocols. Source coding techniques make it possible to save some energy by compressing the packets sent over the network, but at the cost of poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining a scheduling strategy with the twofold goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a time division multiple access-based access scheme that efficiently allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry energy costs. We consider both full and statistical knowledge of the wireless channels and derive communication policies for the two cases. The overall problem is structured in modules and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters taken from real sensors.


Recent Developments of Magnetoresistive Sensors for Industrial Applications


title: Aspergillus cibarius sp. nov., from traditional meju in Korea

abstract: Aspergillus cibarius sp. nov. isolated from meju, a brick of dried fermented soybeans in Korea, is described. The species was also found from black bean, bread and salami in the Netherlands. It is characterized by abundant yellow to reddish brown ascomata and small lenticular ascospores (4.5–5.5 μm) with a wide furrow, low equatorial crests and tuberculate or reticulate convex surface. The species was resolved as phylogenetically distinct from the other reported Aspergillus species with an Eurotium teleomorph based on multilocus sequence typing using partial fragments of the β-tubulin, calmodulin, ITS and RNA polymerase II genes.


title: Multiple-Pronunciation Lexical Modeling In A Speaker Independent Speech Understanding System

abstract: Over the past 40 years, significant progress has been made in the fields of speech recognition and speech understanding. Current state-of-the-art speech recognition systems are capable of achieving word-level accuracies of 90% to 95% on continuous speech recognition tasks using 5000 words. Even larger systems, capable of recognizing 20,000 words are just now being developed. Speech understanding systems have recently been developed that perform fairly well within a restricted domain. ::: While the size and performance of modern speech recognition and understanding systems are impressive, it is evident to anyone who has used these systems that the technology is primitive compared to our own human ability to understand speech. Some of the difficulties hampering progress in the fields of speech recognition and understanding stem from the many sources of variation that occur during human communication. ::: One of the sources of variation that occurs in human communication is the different ways that words can be pronounced. There are many causes of pronunciation variation, such as: the phonetic environment in which the word occurs, the dialect of the speaker, the speaker's age, the speaker's gender, and the speaking rate. Some researchers have shown improvements in speech recognition performance on a read-speech task through the use of explicit pronunciation modeling, while others have not shown any significant improvements. ::: This thesis presents an algorithm for the construction of models that attempt to capture the variation that occurs in the pronunciations of words in spontaneous (i.e., non-read) speech. A technique for developing alternate pronunciations of words and then estimating the probabilities of the alternate pronunciations is presented. Additionally, we describe the development and implementation of a spoken-language understanding system called the Berkeley Restaurant Project (BeRP). Multiple pronunciation word models constructed using the algorithm proposed in this thesis are evaluated within the context of the BeRP system. The results of this evaluation show that the explicit modeling of variation in the pronunciation of words improves the performance of both the speech recognition and the speech understanding components of the BeRP system.


title: Wavelet neural networks: A practical guide

abstract: Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of applications. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thoroughly examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.


title: Efficiently Identifying a Well-Performing Crowd Process for a Given Problem

abstract: With the increasing popularity of crowdsourcing and crowd computing, the question of how to select a well-performing crowd process for a problem at hand is growing ever more important. Prior work casted crowd process selection to an optimization problem, whose solution is the crowd process performing best for a user's problem. However, existing approaches require users to probabilistically model aspects of the problem, which may entail a substantial investment of time and may be error-prone. We propose to use black-box optimization instead, a family of techniques that do not require probabilistic modelling by the end user. Specifically, we adopt Bayesian Optimization to approximate the maximum of a utility function quantifying the user's (business-) objectives while minimizing search cost. Our approach is validated in a simulation and three real-world experiments. The black-box nature of our approach may enable us to reduce the entry barrier for efficiently building crowdsourcing solutions.


title: Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance

abstract: Images are the standard input for vision algorithms, but one-shot in-field reflectance measurements are creating new opportunities for recognition and scene understanding. In this work, we address the question of what reflectance can reveal about materials in an efficient manner. We go beyond the question of recognition and labeling and ask the question: What intrinsic physical properties of the surface can be estimated using reflectance? We introduce a framework that enables prediction of actual friction values for surfaces using one-shot reflectance measurements. This work is a first of its kind vision-based friction estimation. We develop a novel representation for reflectance disks that capture partial BRDF measurements instantaneously. Our method of deep reflectance codes combines CNN features and fisher vector pooling with optimal binary embedding to create codes that have sufficient discriminatory power and have important properties of illumination and spatial invariance. The experimental results demonstrate that reflectance can play a new role in deciphering the underlying physical properties of real-world scenes.


title: Neuropsychological assessment: Principles, rationale, and challenges

abstract: Neuropsychological assessments are increasingly in demand for a wide range of patients. This paper offers a survey of the basic aspects of neuropsychological assessment that are of greatest importance for professionals (e.g., psychologists, psychiatrists, social workers, and lawyers) who are not trained in neuropsychological testing, but who refer clients for neuropsychological assessment. This survey could also serve neuropsychologists in their early stages of training, by addressing some of the major issues related to the assessment process. The range of goals that neuropsychological assessment may attain is first outlined. Next, a model is presented that explains the rationale enabling generalization from assessment to real-world functions that are the focus of interest and the target of prediction. Issues that need to be considered before deciding to conduct a neuropsychological evaluation are then introduced, and sources of information available to the assessor are described. A description is provide...


title: Localized air-conditioning with occupancy control in an open office

abstract: Conditioning and ventilating open office spaces requires innovations as new buildings aim to be more intelligent, energy efficient and healthy. This paper explores the possibility of using a localized airflow to divide an open cubicle office into zones without partition walls. Computational fluid dynamics (CFD) model was used to simulate localized airflow in a cubicle office and both the energy and the indoor air quality concerns were addressed. The findings suggest that (1) localized airflow is plausible for zoning purposes, (2) localized airflow can result in both temperature and pollutant concentration segregations, (3) temperature segregations provide possible energy savings if coupled with occupancy-based HVAC control, and finally (4) limited air mixing between zones provide a novel way for better ventilation and indoor contaminant control.


title: LEARNING MUSICAL PITCH STRUCTURES WITH HIERARCHICAL HIDDEN MARKOV MODELS

abstract: In this paper we attempt to demonstrate the strengths of Hierarchical Hidden Markov Models (HHMMs) in the representation and modelling of musical structures. We show how relatively simple HHMMs, containing a minimum of expert knowledge, use their advantage of having multiple layers to perform well on tasks where flat Hidden Markov Models (HMMs) struggle. The examples in this paper show a HHMM’s performance at extracting higherlevel musical properties through the construction of simple pitch sequences, correctly representing the data set on which it was trained.


title: Cannabinoids: New Promising Agents in the Treatment of Neurological Diseases

abstract: Nowadays, Cannabis sativa is considered the most extensively used narcotic. Nevertheless, this fame obscures its traditional employ in native medicine of South Africa, South America, Turkey, Egypt and in many regions of Asia as a therapeutic drug. In fact, the use of compounds containing Cannabis and their introduction in clinical practice is still controversial and strongly limited by unavoidable psychotropic effects. So, overcoming these adverse effects represents the main open question on the utilization of cannabinoids as new drugs for treatment of several pathologies. To date, therapeutic use of cannabinoid extracts is prescribed in patients with glaucoma, in the control of chemotherapy-related vomiting and nausea, for appetite stimulation in patients with anorexia-cachexia syndrome by HIV, and for the treatment of multiple sclerosis symptoms. Recently, researcher efforts are aimed to employ the therapeutic potentials of Cannabis sativa in the modulation of cannabinoid receptor activity within the central nervous system, particularly for the treatment of neurodegenerative diseases, as well as psychiatric and non-psychiatric disorders. This review evaluates the most recent available data on cannabinoids utilization in experimental and clinical studies, and highlights their beneficial effects in the prevention of the main neurological diseases and for the clinical treatment of symptoms with them correlated.


title: Faster R-CNN for Marine Organism Detection and Recognition Using Data Augmentation

abstract: Recently, Faster Region-based CNN(Faster R-CNN) has achieved marvelous accomplishment in object detection and recognition. In this paper, Faster R-CNN is applied to marine organism detection and recognition. However, the training of Faster R-CNN requires a mass of labeled samples which are difficult to obtain for marine organism. Therefore, three data augmentation methods are proposed dedicated to underwater-imaging. Specifically, the inverse process of underwater image restoration is used to simulate different marine turbulence environments. Perspective transformation is proposed to simulate different view of camera shooting. Illumination synthesis is used to simulate different marine illuminating environments. The performance of each data augmentation method, together with Faster R-CNN is evaluated by experiments on real world underwater dataset, which validate the effectiveness of the proposed method for marine organism detection and recognition.


title: A region-based image caption generator with refined descriptions

abstract: A region based deep learning approach is proposed for image description generation.It employs a regional object detector and RNN-based attribute prediction.It also embeds encoderdecoder based description generation.It shows superiority over other methods when dealing with cross-domain images.Evaluated with multiple databases, it outperforms state-of-the-art methods. Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoderdecoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images.


title: MIXGAN: Learning Concepts from Different Domains for Mixture Generation

abstract: In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-the-shelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models.


title: Privacy-preserving Machine Learning through Data Obfuscation

abstract: As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.


Classifiers and Machine Learning Techniques for Image Processing and Computer Vision


title: Mixed crop-livestock systems: an economic and environmental-friendly way of farming?

abstract: Intensification and specialisation of agriculture in developed countries enabled productivity to be improved but had detrimental impacts on the environment and threatened the economic viability of a huge number of farms. The combination of livestock and crops, which was very common in the past, is assumed to be a viable alternative to specialised livestock or cropping systems. Mixed crop-livestock systems can improve nutrient cycling while reducing chemical inputs and generate economies of scope at farm level. Most assumptions underlying these views are based on theoretical and experimental evidence. Very few assessments of their environmental and economic advantages have nevertheless been undertaken in real-world farming conditions. In this paper, we present a comparative assessment of the environmental and economic performances of mixed crop-livestock farms v. specialised farms among the farm population of the French ‘Coteaux de Gascogne’. In this hilly region, half of the farms currently use a mixed crop-livestock system including beef cattle and cash crops, the remaining farms being specialised in either crops or cattle. Data were collected through an exhaustive survey of farms located in our study area. The economic performances of farming systems were assessed on 48 farms on the basis of (i) overall gross margin, (ii) production costs and (iii) analysis of the sensitivity of gross margins to fluctuations in the price of inputs and outputs. The environmental dimension was analysed through (i) characterisation of farmers’ crop management practices, (ii) analysis of farm land use diversity and (iii) nitrogen farm-gate balance. Local mixed crop-livestock farms did not have significantly higher overall gross margins than specialised farms but were less sensitive than dairy and crop farms to fluctuations in the price of inputs and outputs considered. Mixed crop-livestock farms had lower costs than crop farms, while beef farms had the lowest costs as they are grass-based systems. Concerning crop management practices, our results revealed an intensification gradient from low to high input farming systems. Beyond some general trends, a wide range of management practices and levels of intensification were observed among farms with a similar production system. Mixed crop-livestock farms were very heterogeneous with respect to the use of inputs. Nevertheless, our study revealed a lower potential for nitrogen pollution in mixed crop-livestock and beef production systems than in dairy and crop farming systems. Even if a wide variability exists within system, mixed crop-livestock systems appear to be a way for an environmental and economical sustainable agriculture.


title: Liver histological changes in chronic hepatitis B patients with elevated ALT less than 2 x ULN

abstract: Abstract To investigate the relevant factors of liver histological changes in chronic hepatitis B (CHB) patients with mildly elevated ALT and to explore the clinical values of these factors on anti-viral treatment. A total of 152 CHB patients with mildly elevated ALT (less than 2 x ULN) who underwent liver biopsy were included in the study. Correlations between routine laboratory markers, liver histological inflammation grade and fibrosis stage were statistically assessed by Spearman correlation analysis, one-way ANOVA, area under the curve (AUC) of the receiver operating characteristic curves (ROC) and Logistic regression statistical analysis. All patients in the study showed various hepatic histological damages. Among the 152 patients 50 (32.9%) were found with inflammation grade 1 (G1), 42 (27.6%) with G2, 46 (30.3%) with G3 and 14 (9.2%) with G4. 16 patients (10.5%) were found with fibrosis stage 2 (S2), 25 (16.5%) with S3 and 41 (27.0%) with S4. Routine laboratory markers Alb, BPC and WBC were significantly correlated with hepatic histological inflammation grade and fibrosis stage. Marked liver fibrosis and moderate to severe liver damage were significantly higher in patients aged more than 40 years as compared to those less than 40 years of age (P = 0.002, P = 0.010). The regression equation P = 1/[1+e-(9.36250-1625Alb-0.0234BPC)] was established with sensitivity and specificity of 83.3% and 65.0%, respectively. 67.8% of CHB patients with mildly elevated ALT have significant injury to the liver tissue. CHB patients aged more than 40 years have a significant increase of marked liver fibrosis and moderate to severe liver damage. The regression equation is valuable to predict whether CHB patients need antiviral therapy or not.


title: 6W Ka Band Power Amplifier and 1.2dB NF X-Band Amplifier Using a 100nm GaN/Si Process

abstract: This paper presents the measured results of two circuits fabricated with a millimeter wave 100 nm Gallium Nitride on Silicon (GaN/Si) process. The first circuit is a 27-34 GHz power amplifier, presenting 6 W of output power in pulsed operation and 5.6 W in CW operation. The second circuit, using exactly the same process, is a 8-12 GHz Low Noise Amplifier presenting 1.3 dB noise figure from 11 to 13 GHz.


title: YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames

abstract: YAGO is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. The project combines information from Wikipedias in 10 different languages into a coherent whole, thus giving the knowledge a multilingual dimension. It also attaches spatial and temporal information to many facts, and thus allows the user to query the data over space and time. YAGO focuses on extraction quality and achieves a manually evaluated precision of 95 %. In this paper, we explain how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it.


title: Regularized extreme learning machine for regression with missing data

abstract: This paper proposes a method which is the advanced modification of the original extreme learning machine with a new tool for solving the missing data problem. It uses a cascade of L"1 penalty (LARS) and L"2 penalty (Tikhonov regularization) on ELM (TROP-ELM) to regularize the matrix computations and hence makes the MSE computation more reliable, and on the other hand, it estimates the expected pairwise distances between samples directly on incomplete data so that it offers the ELM a solution to solve the missing data issues. According to the experiments on five data sets, the method shows its significant advantages: fast computational speed, no parameter need to be tuned and it appears more stable and reliable generalization performance by the two penalties. Moreover, it completes ELM with a new tool to solve missing data problem even when half of the training data are missing as the extreme case.


title: Needle biomass turnover rates of Scots pine (Pinus sylvestris L.) derived from the needle-shed dynamics

abstract: To understand carbon cycle and flows of forests, accurate information on tree-component-specific litter production of trees is needed. In the ecosystem models, the litterfall of living trees is usually predicted by the biomass component by average amounts corresponding to site conditions or by multiplying the biomass of the growing stock by the component-specific biomass turnover rate. In this study, the rates of needle biomass turnover of Scots pine (Pinus sylvestris L.) were derived from the needle-shed dynamics. When the rates for needle litter production were modelled, the weighting and yellowing effects were taken into account. The annual biomass turnover rates of needles for southern and northern Finland are 0.21 and 0.10, respectively. Species-specific estimation of litter production is essential for understanding the carbon cycle and flows of forests. Biomass turnover rates can provide useful litter production estimates for large areas with average biomass values as a source of data.


title: ABACUS: A technique for automated behavioral synthesis of approximate computing circuits

abstract: Many classes of applications, especially in the domains of signal and image processing, computer graphics, computer vision, and machine learning, are inherently tolerant to inaccuracies in their underlying computations. This tolerance can be exploited to design approximate circuits that perform within acceptable accuracies but have much lower power consumption and smaller area footprints (and often better run times) than their exact counterparts. In this paper, we propose a new class of automated synthesis methods for generating approximate circuits directly from behavioral-level descriptions. In contrast to previous methods that operate at the Boolean level or use custom modifications, our automated behavioral synthesis method enables a wider range of possible approximations and can operate on arbitrary designs. Our method first creates an abstract synthesis tree (AST) from the input behavioral description, and then applies variant operators to the AST using an iterative stochastic greedy approach to identify the optimal inexact designs in an efficient way. Our method is able to identify the optimal designs that represent the Pareto frontier trade-off between accuracy and power consumption. Our methodology is developed into a tool we call ABACUS, which we integrate with a standard ASIC experimental flow based on industrial tools. We validate our methods on three realistic Verilog-based benchmarks from three different domains - signal processing, computer vision and machine learning. Our tool automatically discovers optimal designs, providing area and power savings of up to 50% while maintaining good accuracy.


3D Algebraic Iterative Reconstruction for Cone-Beam X-Ray Differential Phase-Contrast Computed Tomography


title: Analysis and Suppression of Leakage Current in Cascaded-Multilevel-Inverter-Based PV Systems

abstract: The transformerless cascaded multilevel inverter (CMI) is considered to be a promising topology alternative for low-cost and high-efficiency photovoltaic (PV) systems. However, the leakage current issue resulted from the parasitic capacitors between the PV panels and the earth remains a challenging in designing a reliable CMI-based PV system. In this paper, the leakage current paths in PV CMI are analyzed and the unique features are discussed. Two filter-based suppression solutions are then presented to tackle the leakage current issue in different PV CMI applications. Simplified leakage current analytical models are derived to study the suppression mechanisms and design the suppression filters. Study cases are demonstrated for each of the solutions with filter design example, simulation and experimental verifications.


title: Dynamic modeling of a skid-steered wheeled vehicle with experimental verification

abstract: Skid-steered vehicles are often used as outdoor mobile robots due to their robust mechanical structure and high maneuverability. Sliding along with rolling is inherent to general curvilinear motion, which makes both kinematic and dynamic modeling difficult. For the purpose of motion planning this paper develops and experimentally verifies dynamic models of a skid-steered wheeled vehicle for general planar (2D) motion and for linear 3D motion. These models are characterized by the coefficient of rolling resistance, the coefficient of friction, and the shear deformation modulus, which have terrain-dependent values. The dynamic models also include motor saturation and motor power limitations, which enable correct prediction of vehicle velocities when traversing hills. It is shown that the closed-loop system that results from inclusion of the dynamics of the (PID) speed controllers for each set of wheels does a much better job than the open loop model of predicting the vehicle linear and angular velocities. Hence, the closed-loop model is recommended for motion planning.


title: A deep learning approach to traffic lights: Detection, tracking, and classification

abstract: Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometry which perceives traffic lights in real-time. Within the scope of this work, we present three major contributions. The first is an accurately labeled traffic light dataset of 5000 images for training and a video sequence of 8334 frames for evaluation. The dataset is published as the Bosch Small Traffic Lights Dataset and uses our results as baseline. It is currently the largest publicly available labeled traffic light dataset and includes labels down to the size of only 1 pixel in width. The second contribution is a traffic light detector which runs at 10 frames per second on 1280×720 images. When selecting the confidence threshold that yields equal error rate, we are able to detect traffic lights as small as 4 pixels in width. The third contribution is a traffic light tracker which uses stereo vision and vehicle odometry to compute the motion estimate of traffic lights and a neural network to correct the aforementioned motion estimate.


title: Favored patterns in spike trains. I. Detection

abstract: Traditional spike-train analysis methods cannot identify patterns of firing that occur frequently but at arbitrary times. It is appropriate to search for recurring patterns because such patterns could be used for information transfer. In this paper, we present two methods for identifying "favored patterns" --patterns that occur more often than is reasonably expected at random. The quantized Monte Carlo method identifies and establishes significance for favored patterns whose detailed timing may vary but that do not have extra or missing spikes. The template method identifies favored patterns whose occurrences may have extra or missing spikes. This method is useful when employed after the results of the first method are known. Studies with simulated spike trains containing known interpolated patterns are used to establish the sensitivity and accuracy of the quantized Monte Carlo method. Certain trends with regard to parameters of the detected patterns and of the analysis methods are described. Application of these methods to neurophysiological data has shown that a large proportion of spike trains have favored patterns. These findings are described in the accompanying paper (3).


What would drivers like to know during automated driving? Information needs at different levels of automation.


title: The Effects of Taper-Angle on the Electrical Characteristics of Vertical NAND Flash Memories

abstract: The effects of taper angle of the string and the number of layers on the electrical characteristics of vertical NAND flash memories are investigated. Simulation results show that the transconductance and the threshold voltage distribution over the position of the cell along the string depend on taper angle and the number of layers. There is a taper angle that minimizes the spread of threshold voltage, and hence, the impact of the random telegraph noise and this angle depends on the number of layers. These results will be helpful in designing the vertical NAND flash memories.


Planning for Autonomous Cars that Leverage Effects on Human Actions


title: Epidemiology of infertility in China: a population‐based study

abstract: OBJECTIVE ::: To assess the current prevalence of and risk factors for infertility among couples of reproductive age in China. ::: ::: ::: DESIGN ::: Population-based cross-sectional study. ::: ::: ::: SETTING ::: We approached 25 270 couples in eight provinces/municipalities, of whom 18 571 (response rate 74%) were interviewed. ::: ::: ::: POPULATION ::: Couples living together and married for more than 1 year, of whom the female spouse was 20-49 years old. ::: ::: ::: METHODS ::: Women were approached via telephone and face-to-face conversation to complete the standardised and structured questionnaire by trained interviewers. ::: ::: ::: MAIN OUTCOME MEASURES ::: Prevalence of and risk factors for infertility. ::: ::: ::: RESULTS ::: Among women 'at risk' of pregnancy, the prevalence of infertility was 15.5% (2680/17 275). Among 10 742 women attempting to become pregnant, the prevalence of infertility was 25.0% (2680/10 742), which increased with age in the second population. Among women who failed to achieve pregnancy in the last 12 months, 3470 finished our questionnaire about fertility care, and 55.2% (1915/3470) of them had sought medical help. Sociodemographic risk factors for infertility included lower educational level [adjusted odds ratio (aOR) 3.4, 95% CI 2.0-5.5] and employment (aOR 2.3, 95% CI 1.9-2.9). Clinical risk factors were irregular menstrual cycle (aOR 1.8, 95% CI 1.2-2.5), light menstrual blood volume (aOR 1.6, 95% CI 1.2-2.0), history of cervicitis (aOR 1.5, 95% CI 1.2-2.0) and endometriosis (aOR 3.1, 95% CI 1.1-9.3), previous stillbirth (aOR 2.1, 95% CI 1.3-3.3) and miscarriage (aOR 2.7, 95% CI 2.1-3.5). In addition, history of operation was a significant risk factor of infertility. ::: ::: ::: CONCLUSIONS ::: Among couples of reproductive age in China, the prevalence of infertility was 25%, and almost half of the couples experiencing infertility had not sought medical help. ::: ::: ::: TWEETABLE ABSTRACT ::: In China, 25% of couples actively attempting to become pregnant suffered infertility.


Fraudulent Detection in Credit Card System Using SVM & Decision Tree


title: Stoichiometric Mismatch between Consumers and Resources Mediates the Growth of Rocky Intertidal Suspension Feeders

abstract: The concept of ecological stoichiometry – the balancing of elemental ratios in ecological interactions – has transformed our thinking about processes in natural systems. Here, this perspective is applied to rocky shore ecosystems to explore the consequences of variation in internal nutrient ratios across two trophic levels. Specifically, I measured the internal concentrations of carbon (C) and nitrogen (N) in mussels (Mytilus spp.) and particulate organic matter (POM) to evaluate the effects of stoichiometric mismatch – the difference in the carbon-to-nitrogen ratio (C:N) between a consumer and its resources – on mussel growth at sites on the coasts of Oregon, USA, and the South Island of New Zealand. As POM quality (i.e., Chl a, a proxy for phytoplankton availability in the POM) increased, C:N of the POM declined, but C:N of mussels increased. This resulted in a greater mismatch in C:N between mussels and their food source at low Chl a. Mussel growth across sites was positively associated with Chl a, particulate organic carbon (POC), and particulate organic nitrogen (PON) but negatively associated with stoichiometric mismatch. Overall, as the elemental ratios of consumers became more different from those of their resources, growth declined, likely due to the energetic cost associated with processing lower quality food. Furthermore, the effect of food quantity on growth depended on stoichiometric mismatch. In New Zealand, where mismatch was high – i.e., consumer C:N differed substantially from resource C:N – consumer growth was strongly affected by resource quantity (Chl a or POC). However, in Oregon, where mismatch was low, the relationship between resource quantity and growth was considerably weaker. This interaction between resource quantity and mismatch was not apparent for PON, which is consistent with variation in PON underlying variation in POM C:N and highlights the role of N in limiting growth. Previous research has neglected the importance of ecological stoichiometry as a mediator of consumer-resource interactions in rocky intertidal communities. I show that resource quality and quantity interact to determine consumer growth, highlighting the utility of ecological stoichiometry in understanding spatial subsidies in benthic marine systems.


title: A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.0

abstract: One of the challenges of Industry 4.0 is the creation of vertical networks that connect smart production systems with design teams, suppliers, and the front office. To achieve such a vision, information has to be collected from machines and products throughout a smart factory. Smart factories are defined as flexible and fully connected factories that are able to make use of constant streams of data from operations and production systems. In such scenarios, the arguably most popular way for identifying and tracking objects is by adding labels or tags, which have evolved remarkably over the last years: from pure hand-written labels to barcodes, QR codes, and RFID tags. The latest trend in this evolution is smart labels which are not only mere identifiers with some kind of internal storage, but also sophisticated context-aware tags with embedded modules that make use of wireless communications, energy efficient displays, and sensors. Therefore, smart labels go beyond identification and are able to detect and react to the surrounding environment. Moreover, when the industrial Internet of Things paradigm is applied to smart labels attached to objects, they can be identified remotely and discovered by other Industry 4.0 systems, what allows such systems to react in the presence of smart labels, thus triggering specific events or performing a number of actions on them. The amount of possible interactions is endless and creates unprecedented industrial scenarios, where items can talk to each other and with tools, machines, remote computers, or workers. This paper, after reviewing the basics of Industry 4.0 and smart labels, details the latest technologies used by them, their applications, the most relevant academic and commercial implementations, and their internal architecture and design requirements, providing researchers with the necessary foundations for developing the next generation of Industry 4.0 human-centered smart label applications.


title: Multiresolution models for object detection

abstract: Most current approaches to recognition aim to be scale-invariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from those for recognizing a 3 pixel tall object. We argue that for sensors with finite resolution, one should instead use scale-variant, or multiresolution representations that adapt in complexity to the size of a putative detection window. We describe a multiresolution model that acts as a deformable part-based model when scoring large instances and a rigid template with scoring small instances. We also examine the interplay of resolution and context, and demonstrate that context is most helpful for detecting low-resolution instances when local models are limited in discriminative power. We demonstrate impressive results on the Caltech Pedestrian benchmark, which contains object instances at a wide range of scales. Whereas recent state-of-the-art methods demonstrate missed detection rates of 86%-37% at 1 false-positive-per-image, our multiresolution model reduces the rate to 29%.


title: Validation of Computational Approaches for Antiretroviral Dose Optimization

abstract: Strategies for reducing antiretroviral doses and drug costs can support global access, and numerous options are being investigated. Efavirenz pharmacokinetic simulation data generated with a bottom-up physiologically based model were successfully compared with data obtained from the ENCORE I clinical trial (efavirenz at 400 mg once per day versus 600 mg once per day). These findings represent a pivotal paradigm for the prediction of pharmacokinetics resulting from dose reductions. Validated computational models constitute a valuable resource for optimizing therapeutic options and predicting complex clinical scenarios.


title: Rotation of the Sacrum During Bellyboard Pelvic Radiotherapy

abstract: Patients with cervical, uterine and rectum carcinomas are usually treated in the prone position using the bellyboard positioning device. Specific and uncomfortable prone position gives rise to the uncertainties in the daily set-up of the patients during the treatment. During the investigation of translational movements, rotational movements of the pelvis are observed and investigated. The film portal imaging is used in order to find patient positioning errors during the treatment. We defined the rotational set-up errors by angle deviations of the sacrum. Thirty six patients were included in the study ; 15 patients were followed during the whole treatment and 21 during the first 5 consecutive treatment days. The image acquisition was completed in 84%. Systematic and random positioning errors were analyzed in 725 images. Approximately half of the patients had adjusted to the bellyboard in the first few fractions, with sacrum angles remaining the same for the rest of the treatment. The other half had drifts of the sacrum angle during the whole treatment. The rotation of the patients’ sacrum during the treatment ranged up to 14o causing the usual set-up verification and correction procedure to result in errors up to 15 mm. Rotational movements of the patient pelvis during bellyboard pelvis radiotherapy can introduce considerable patient position error.


title: Achieving usable and privacy-assured similarity search over outsourced cloud data

abstract: As the data produced by individuals and enterprises that need to be stored and utilized are rapidly increasing, data owners are motivated to outsource their local complex data management systems into the cloud for its great flexibility and economic savings. However, as sensitive cloud data may have to be encrypted before outsourcing, which obsoletes the traditional data utilization service based on plaintext keyword search, how to enable privacy-assured utilization mechanisms for outsourced cloud data is thus of paramount importance. Considering the large number of on-demand data users and huge amount of outsourced data files in cloud, the problem is particularly challenging, as it is extremely difficult to meet also the practical requirements of performance, system usability, and high-level user searching experiences. In this paper, we investigate the problem of secure and efficient similarity search over outsourced cloud data. Similarity search is a fundamental and powerful tool widely used in plaintext information retrieval, but has not been quite explored in the encrypted data domain. Our mechanism design first exploits a suppressing technique to build storage-efficient similarity keyword set from a given document collection, with edit distance as the similarity metric. Based on that, we then build a private trie-traverse searching index, and show it correctly achieves the defined similarity search functionality with constant search time complexity. We formally prove the privacy-preserving guarantee of the proposed mechanism under rigorous security treatment. To demonstrate the generality of our mechanism and further enrich the application spectrum, we also show our new construction naturally supports fuzzy search, a previously studied notion aiming only to tolerate typos and representation inconsistencies in the user searching input. The extensive experiments on Amazon cloud platform with real data set further demonstrate the validity and practicality of the proposed mechanism.


Easier contagion and weaker ties make anger spread faster than joy in social media


title: Automatic Non-rigid 3D Modeling from Video

abstract: We present a robust framework for estimating non-rigid 3D shape and motion in video sequences. Given an input video sequence, and a user-specified region to reconstruct, the algorithm automatically solves for the 3D time-varying shape and motion of the object, and estimates which pixels are outliers, while learning all system parameters, including a PDF over non-rigid deformations. There are no user-tuned parameters (other than initialization); all parameters are learned by maximizing the likelihood of the entire image stream. We apply our method to both rigid and non-rigid shape reconstruction, and demonstrate it in challenging cases of occlusion and variable illumination.


title: A Survey on Data Security Issues in Cloud Computing: From Single to Multi-Clouds

abstract: Cloud computing usage has increased rapidly in many companies. Cloud computing offers many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing plays a major role in the cloud computing, as customers often store important information with cloud storage providers but these providers may be unsafe. Customers are wondering about attacks on the integrity and the availability of their data in the cloud from malicious insiders and outsiders, and from any collateral damage of cloud services. These issues are extremely significant but there is still much room for security research in cloud computing. Dealing with "single cloud" providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards "multi-clouds", or in other words, "interclouds" or "cloud- of-clouds" has increased recently. The purpose of this paper is to survey recent research related to single and multi-clouds security and to address possible solutions. It is found that the research into the use of multi-cloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aspires to promote the use of multi-clouds due to its ability to reduce security risks that affect the cloud computing consumer.


title: Car detection using deformable part models with composite features

abstract: A two stages car detection method using deformable part models with composite feature sets (DPM/CF) is proposed to recognize cars of various types and from multiple viewing angles. In the first stage, a HOG template is matched to detect the bounding box of the entire car of a certain type and viewed from a certain angle (called a t/a pair), which yields a region of interest (ROI). In the second stage, various part detectors using either HOG or the convolution neural network (CNN) features are applied to the ROI for validation. An optimization procedure based on latent logistic regression is adopted to select the optimal part detector's location, window size, and feature to use. Extensive experimental results indicate the proposed DPM/CF system can strike a balance between detection accuracy and training complexity.


Visualizing the Connection Among Convex Hull, Voronoi Diagram and Delaunay Triangulation


title: Weighted voting for replicated data

abstract: In a new algorithm for maintaining replicated data, every copy of a replicated file is assigned some number of votes. Every transaction collects a read quorum of r votes to read a file, and a write quorum of w votes to write a file, such that r + w is greater than the total number of votes assigned to the file. This ensures that there is a non-null intersection between every read quorum and every write quorum. Version numbers make it possible to determine which copies are current. The reliability and performance characteristics of a replicated file can be controlled by appropriately choosing r, w, and the file's voting configuration. The algorithm guarantees serial consistency, admits temporary copies in a natural way by the introduction of copies with no votes, and has been implemented in the context of an application system called Violet.


title: Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs

abstract: We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures, which are typically used at the coarse search stage of the most proximity graph techniques. Hierarchical NSW incrementally builds a multi-layer structure consisting from hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.


title: Driver Inattention Monitoring System for Intelligent Vehicles: A Review

abstract: In this paper, we review the state-of-the-art technologies for driver inattention monitoring, which can be classified into the following two main categories: 1) distraction and 2) fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has actively been carried out for decades, with the goal of precisely determining the drivers' state of mind. In this paper, we summarize these approaches by dividing them into the following five different types of measures: 1) subjective report measures; 2) driver biological measures; 3) driver physical measures; 4) driving performance measures; and 5) hybrid measures. Among these approaches, subjective report measures and driver biological measures are not suitable under real driving conditions but could serve as some rough ground-truth indicators. The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system. We also discuss some nonlinear modeling techniques commonly used in the literature.


title: A lightweight soft exosuit for gait assistance

abstract: In this paper we present a soft lower-extremity robotic exosuit intended to augment normal muscle function in healthy individuals. Compared to previous exoskeletons, the device is ultra-lightweight, resulting in low mechanical impedance and inertia. The exosuit has custom McKibben style pneumatic actuators that can assist the hip, knee and ankle. The actuators attach to the exosuit through a network of soft, inextensible webbing triangulated to attachment points utilizing a novel approach we call the virtual anchor technique. This approach is designed to transfer forces to locations on the body that can best accept load. Pneumatic actuation was chosen for this initial prototype because the McKibben actuators are soft and can be easily driven by an off-board compressor. The exosuit itself (human interface and actuators) had a mass of 3500 g and with peripherals (excluding air supply) is 7144 g. In order to examine the exosuit's performance, a pilot study with one subject was performed which investigated the effect of the ankle plantar-flexion timing on the wearer's hip, knee and ankle joint kinematics and metabolic power when walking. Wearing the suit in a passive unpowered mode had little effect on hip, knee and ankle joint kinematics as compared to baseline walking when not wearing the suit. Engaging the actuators at the ankles at 30% of the gait cycle for 250 ms altered joint kinematics the least and also minimized metabolic power. The subject's average metabolic power was 386.7 W, almost identical to the average power when wearing no suit (381.8 W), and substantially less than walking with the unpowered suit (430.6 W). This preliminary work demonstrates that the exosuit can comfortably transmit joint torques to the user while not restricting mobility and that with further optimization, has the potential to reduce the wearer's metabolic cost during walking.


title: A review of recent advances in wind turbine condition monitoring and fault diagnosis

abstract: The state-of-the-art advancement in wind turbine condition monitoring and fault diagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power electronics, as well as system-wise turbine diagnosis. There are several major trends observed through the survey. Due to the variable-speed nature of wind turbine operation and the unsteady load involved, time-frequency analysis tools such as wavelets have been accepted as a key signal processing tool for such application. Acoustic emission has lately gained much more attention in order to detect incipient failures because of the low-speed operation for wind turbines. There has been an increasing trend of developing model based reasoning algorithms for fault detection and isolation as cost-effective approach for wind turbines as relatively complicated system. The impact of unsteady aerodynamic load on the robustness of diagnostic signatures has been notified. Decoupling the wind load from condition monitoring decision making will reduce the associated down-time cost.


title: Kinect gait skeletal joint feature-based person identification

abstract: Gait not only defines the way a person walks, but also provides interesting cues on individuals daily routine, mental state, health condition or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, we develop a biometric-security system using gait-based skeletal information from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the distance of left and right ankle joints. We have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle for extraction. Mean and variance features are extracted from the distance feature vector. The K Nearest Neighbors (KNN) algorithm is used for classification purpose. The classification accuracy of our proposed approach is 93.33%. The effectiveness of the method is evaluated by comparing it with others existing approaches. Experimental results show that proposed approach is having better recognition accuracy compared to other approaches. Incorporating this biometric in situation awareness system that can identify the mental state of a human is the future direction of this research.


title: Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles

abstract: This work uses deep learning models for daily directional movements prediction of a stock price using financial news titles and technical indicators as input. A comparison is made between two different sets of technical indicators, set 1: Stochastic %K, Stochastic %D, Momentum, Rate of change, William’s %R, Accumulation/Distribution (A/D) oscillator and Disparity 5; set 2: Exponential Moving Average, Moving Average Convergence-Divergence, Relative Strength Index, On Balance Volume and Bollinger Bands. Deep learning methods can detect and analyze complex patterns and interactions in the data allowing a more precise trading process. Experiments has shown that Convolutional Neural Network (CNN) can be better than Recurrent Neural Networks (RNN) on catching semantic from texts and RNN is better on catching the context information and modeling complex temporal characteristics for stock market forecasting. So, there are two models compared in this paper: a hybrid model composed by a CNN for the financial news and a Long Short-Term Memory (LSTM) for technical indicators, named as SI-RCNN; and a LSTM network only for technical indicators, named as I-RNN. The output of each model is used as input for a trading agent that buys stocks on the current day and sells the next day when the model predicts that the price is going up, otherwise the agent sells stocks on the current day and buys the next day. The proposed method shows a major role of financial news in stabilizing the results and almost no improvement when comparing different sets of technical indicators.


title: Accurate Sparse Feature Regression Forest Learning for Real-Time Camera Relocalization

abstract: Camera relocalization is needed in several applications such as augmented reality or robot navigation. However, it is still challenging to have a both real-time and accurate method. In this paper, we present our hybrid method combing machine learning approach and geometric approach for real-time camera relocalization from a single RGB image. We introduce our sparse feature regression forest to improve the machine learning part. In our regression forest, we propose a novel split function, that uses a whole feature vector instead of classical binary test function to improve the accuracy of 2D-3D point correspondences. Moreover, we use sparse feature extraction (SURF features) to reduce time processing. The results indicate that our method is the only real-time hybrid method (50ms per frame). We also achieve results as accurate as the best state-of-the-art methods (hybrid methods) and outperform machine learning based and sparse feature based methods.