title: Recognition with local features: the kernel recipe

abstract: Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support vector machines have been established as powerful learning algorithms with good generalization capabilities. We combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is, suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.


Face Painting: querying art with photos


title: The Cross-Culture Management of Chinese Enterprises in Poland Under the Belt and Road Initiative—Based on PEST Model

abstract: “The Belt and Road Initiative” not only provides great opportunities but also poses enormous challenges to Chinese enterprises for further development. Along the Belt and the Road, there are different countries with unique culture characteristics, which will be the difficult challenges Chinese enterprises have to face in the overseas investment. The present study will combine PEST (Political, Economic, Social and Technological) model with Hofstede’s culture dimensions as the theoretical basis for analyzing the potential opportunities and challenges Chinese enterprises tend to confront in Poland. Based on a detailed analysis of the opportunities and challenges, this writing proposes three tentative cross-culture management strategies: (1) Investigating the local markets and identifying the culture differences; (2) Cultivating intercultural communication competence of the cross-culture employees; (3) Acculturating to the local society and making innovation based on culture fusion, which would be referential for Chinese enterprises to seek investment opportunities in the countries along the Belt and the Road.


title: On perspective of security and privacy-preserving solutions in the internet of things

abstract: The Internet of Things (IoT) brings together a large variety of devices of different platforms, computational capacities and functionalities. The network heterogeneity and the ubiquity of IoT devices introduce increased demands on both security and privacy protection. Therefore, the cryptographic mechanisms must be strong enough to meet these increased requirements but, at the same time, they must be efficient enough for the implementation on constrained devices. In this paper, we present a detailed assessment of the performance of the most used cryptographic algorithms on constrained devices that often appear in IoT networks. We evaluate the performance of symmetric primitives, such as block ciphers, hash functions, random number generators, asymmetric primitives, such as digital signature schemes, and privacy-enhancing schemes on various microcontrollers, smart-cards and mobile devices. Furthermore, we provide the analysis of the usability of upcoming schemes, such as the homomorphic encryption schemes, group signatures and attribute-based schemes.


title: An Inductive Power and Data Telemetry Subsystem With Fast Transient Low Dropout Regulator for Biomedical Implants

abstract: This paper presents a capacitorless low-dropout (LDO) regulator with fast transient response and data reverse telemetry circuit for fully implantable wireless transmission applications. We propose a novel hybrid feedback structure using high-frequency compensation technology to achieve a rapid transient response for the LDO regulator. To reduce the size of the implant and transmit neural recordings through the same coil without interfering with power transmission, the load-shift-key (LSK) modulation technique is adopted for back data telemetry. The proposed implantable chip, fabricated using commercial $0.18~\mu{\rm m}$ complementary metal oxide semiconductor technology, yielded an output power of 15 mW. Under 1.15 V operation voltage, the maximum overshoot and undershoot voltages were less than 45 mV and 55 mV, respectively, for a 15 mA full-load current whose rising and falling time were less than 100 ns. The proposed LSK transceiver uses a digitized demodulator to improve bandwidth efficiency for low carrier frequency operation.


title: On the Class Imbalance Problem

abstract: The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range of instances. This paper reviewed academic activities special for the class imbalance problem firstly. Then investigated various remedies in four different levels according to learning phases. Following surveying evaluation metrics and some other related factors, this paper showed some future directions at last.


title: A unified Tabu Search heuristic for vehicle routing problems with time windows

abstract: This article reviews some of the most important tabu search heuristics for the vehicle routing problem. Some of the main tabu search features are first described: neighbourhood structures, short term memory, long term memory, intensification. The tabu search algorithms are then described, followed by computational results and the conclusion.


Improvement of Software Reliabil i


title: Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data

abstract: Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis.


title: Sustained agile usage: a systematic literature review

abstract: Context: Agile methodologies (AM) emerged as a popular alternative to formalized methods, and have gained widespread acceptance in both the academic and industrial contexts. While most research has focused on their early stages of adoption and use, there is little evidence that agile methodologies have been effectively used beyond the adoption stage. Objective: The objective of this paper is to gain a comprehensive understanding of the various factors that impact the sustained usage of agile methods. Method: We performed a systematic literature review to identify empirical studies relating to agile usage. We followed the guidelines provided for conducting systematic literature reviews in software engineering [ 1, 2 ] using the following distinct stages: 1) protocol development and the search strategy, 2) exclusion process, 3) data extraction, and 4) data synthesis. Results: Our review identified nine critical factors viz., Management Support, Attitude, Motivation, Team Composition, Training, Agile Mindset, Technical Competence and Expertise, Agile Engineering Practices, and Methodology Champion. Conclusions: Our review highlights the significance of nine critical factors that impact sustained usage of AM in organizations. Our findings also indicate that the right balance and combination of various factors with an emphasis on continuous improvement will be crucial for achieving true agile sustainability in organizations.


title: Development of an innovative 3-stage steady-bed gasifier for municipal solid waste and biomass

abstract: Abstract Gasification of biomass, municipal solid waste, waste-derived fuels and residues has been lately gaining in attractiveness as an alternative thermal treatment method to produce power and heat. Presented in this paper is a new, recently patented, 3-stage gasification scheme, designed for all aforementioned types of fuels and for producing a synthesis gas free of tar and dioxins. The proposed 3-stage gasification scheme comprises of three stages: i) pyrolysis, ii) combustion and iii) gasification. The proposed 3-stage gasification scheme is valid for municipal solid waste and any type of biomass despite differences in chemical composition. The innovation of this 3-stage gasification scheme is based on the fact that the transition between normal and reverse operation and vice versa is achieved only by the proper rotation settings of four air blowers, thus creating a new model of gaseous flow management between the three aforementioned stages. The presented model can achieve a safe industrial-scale operation while producing a gas free of harmful components. The proposed gasification model is validated as suitable for small-to-medium scale capacities, achieving an overall electrical efficiency of 30% and minimum environmental impacts well below all existing thresholds, including those set by the Directive 2000/76/EC on solid waste incineration.


title: 2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges

abstract: Image quality is important not only for the viewing experience, but also for the performance of image processing algorithms. Image quality assessment (IQA) has been a topic of intense research in the fields of image processing and computer vision. In this paper, we first analyze the factors that affect two-dimensional (2D) and three-dimensional (3D) image quality, and then provide an up-to-date overview on IQA for each main factor. The main factors that affect 2D image quality are fidelity and aesthetics. Another main factor that affects stereoscopic 3D image quality is visual comfort. We also describe the IQA databases and give the experimental results on representative IQA metrics. Finally, we discuss the challenges for IQA, including the influence of different factors on each other, the performance of IQA metrics in real applications, and the combination of quality assessment, restoration, and enhancement.


title: Slimness is Associated with Greater Intercourse and Lesser Masturbation Frequency

abstract: I examined the relationship of recalled and diary recorded frequency of penile-vaginal intercourse (FSI), noncoital partnered sexual activity, and masturbation to measured waist and hip circumference in 120 healthy adults aged 19–38. Slimmer waist (in men and in the sexes combined) and slimmer hips (in men and women) were associated with greater FSI. Slimmer waist and hips were associated with rated importance of intercourse for men. Noncoital partnered sexual activity had a less consistent association with slimness. Slimmer waist and hips were associated with less masturbation (in men and in the sexes combined). I discuss the results in terms of differences between different sexual behaviors, attractiveness, emotional relatedness, physical sensitivity, sexual dysfunction, sociobiology, psychopharmacological aspects of excess fat and carbohydrate consumption, and implications for sex therapy.


title: Realizing digital transformation through a digital business model design process

abstract: Business model is an abstract representation of a business description, and is formed to realize the value potential of a business operation. When companies are hoping to become more digital, creation of new business models may get inhibited by the vast amount of opportunities digitalization can bring and lack of streamlined digital transformation processes. Therefore, a structured and practical approach is presented here as a way to coordinate digital transformation. Best practices from business models, innovation processes, and digital businesses are brought together to establish a methodology on digital business model design. The key characteristics of digital business models derived from applying this process are represented through Digital Business Model Innovation Loops, and are validated by looking at digital initiatives in established companies.


title: Development of a Novel, Compact, Balanced, Micropower Impulse Radar for Nondestructive Applications

abstract: In this paper, we present a novel development of compact micropower ultrawideband (UWB) radar by integrating ultrashort balanced transmitter, sampling receiver, and antennas into a single package. The developed transmitter produces a pair of Gaussian pulses with 1 MHz of PRF, 400 ps of duration, and 8.3 V for peak-to-peak amplitude. The receiver down-converts microwave signals with a conversion gain of 3 dB over a 4.5-GHz bandwidth. The integrated UWB micropower radar system allows for the short-range detection with high range resolution and accuracy, thus enhance effectively the capability of target detection and classification. The high performances of such radar system for detecting objects are demonstrated through the real-field tests of various target scenes.


A Mission to Civilize: The Republican Idea of Empire in France and West Africa


title: Aerobic endurance training improves weight loss, body composition, and co-morbidities in patients after laparoscopic Roux-en-Y gastric bypass.

abstract: BACKGROUND ::: One of the most effective treatments of patients with morbid obesity is laparoscopic Roux-en-Y gastric bypass (RYGB). Sudden weight loss after RYGB for morbid obesity can result in a concurrent decrease in the lean body mass. However, the long-term results (weight reduction and reduced co-morbidities) depend on the postoperative long-term therapy. Aerobic physical exercise (APE) has been considered conservative treatment of obesity and type 2 diabetes mellitus. The aim of the present study was to assess the efficacy of APE on weight loss, body composition, and co-morbidities in patients after laparoscopic RYGB. The study was performed at a university hospital in Germany. ::: ::: ::: METHODS ::: A total of 60 consecutive morbidly obese patients underwent laparoscopic RYGB. The patients were prospectively randomized into a low-exercise group (APE 1 time for 1 hr/wk) or a multiple-exercise group (APE 2 times for 1 hr/wk). The following prospective data were collected: age, gender, length of hospital stay, operative details, co-morbidities, postoperative complications, initial body weight and height, postoperative weight, and body composition. The patients' body composition was assessed every 8 weeks during the 24-month follow-up period. ::: ::: ::: RESULTS ::: The average body mass index (52 kg/m(2)) and other baseline characteristics were distributed equally in the 2 groups. No major complications and no significant differences in the minor complications were found postoperatively between the 2 groups. The multiple exercise group had a significantly more rapid reduction of body mass index, excess weight loss, and fat mass compared with the low-exercise group. The initial loss of body cell mass and lean body mass was significantly lower in the multiple exercise group and was regained more rapidly in the low-exercise group. In addition, the multiple exercise group showed significantly earlier resolution or improvement of co-morbidities. ::: ::: ::: CONCLUSION ::: APE positively influenced weight loss, body composition, and co-morbidity resolution after RYGB for obesity. Additional controlled studies and longer follow-up are needed to confirm these positive findings.


title: The Influence of Tribolayer Formation on Tribological Performance of Rolling/Sliding Contacts

abstract: In the present study, a series of dedicated experiments has been performed to observe and measure the build-up of ZDDP tribolayer in rolling/sliding heavily loaded mixed-lubricated contacts. The experiments were carried out using several test configurations. First, ball-on-disc tests were run to investigate the effects of the contact pressure, temperature, and roughness on tribolayer formation. Then, micropitting tests were carried out, with bearing components and with rolling bearings, in order to evaluate the influence of tribolayer build-up on the tribological performance of the rolling/sliding contacts. The obtained experimental data were used first to calibrate a specially developed thermomechanical model and secondly to validate it under different operating conditions and at different levels, up to assessment of the tribological performance, by measuring the level of micropitting on the surfaces. The results show that a relatively simple thermomechanical model can account for tribolayer formation and removal and their effects on the contact performance in a relatively consistent way.


title: A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

abstract: We propose a new type-2 fuzzy set of linguistic variable for the Analytic Hierarchy Process..We use the concept of ranking trapezoidal interval type-2 fuzzy set to normalized two different matrices.The third and fourth bullets are remained..The proposed method is tested to work safety and early warning rating of hot and humid environments.A comparison results are also presented to check the feasibility of the proposed method. The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers' linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.


An Efficient Architecture for Resource Provisioning in Fog Computing


title: GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory

abstract: People travel in the real world and leave their location history in a form of trajectories. These trajectories do not only connect locations in the physical world but also bridge the gap between people and locations. This paper introduces a social networking service, called GeoLife, which aims to understand trajectories, locations and users, and mine the correlation between users and locations in terms of usergenerated GPS trajectories. GeoLife offers three key applications scenarios: 1) sharing life experiences based on GPS trajectories; 2) generic travel recommendations, e.g., the top interesting locations, travel sequences among locations and travel experts in a given region; and 3) personalized friend and location recommendation.


title: Towards meaningful augmentation of the cityscape : new challenges for mobile GeoHCI

abstract: Augmenting the physical environment with virtual georeferenced information is a current research theme in both the Geo-Information and Human-Computer Interaction domains. In this paper we draw from our work on augmentation of urban tourism destinations in order to identify the most important open questions, issues and challenges for the GeoHCI field. Author Keywords


title: A Generalized Active Learning Approach for Unsupervised Anomaly Detection

abstract: This work formalizes the new framework for anomaly detection, called active anomaly detection. This framework has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. We show that unsupervised anomaly detection is an undecidable problem and that a prior needs to be assumed for the anomalies probability distribution in order to have performance guarantees. Finally, we also present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active anomaly detection method, presenting results on both synthetic and real anomaly detection datasets.


title: Septum-Enhanced Mammaplasty in Inferocentral Pedicled Breast Reduction for Macromastia and Gigantomastia Patients

abstract: Background ::: Inferior pedicle and free nipple grafting are commonly used as breast reduction techniques for patients with breast hypertrophy and gigantomastia. Limitations of these techniques are, respectively, possible vascular compromise and total/partial necrosis of the nipple–areola complex (NAC). The authors describe the innovative inferocentral pedicled reduction mammaplasty (ICPBR) enhanced by preservation of Wuringer’s septum for severe hypertrophic breasts.


title: A hardware platform for network intrusion detection and prevention

abstract: The need for building high-speed NIDS that can reliably generate alerts as intrusions occur and have the intrinsic ability to scale as network infrastructure and attack sophistication evolves has been discussed in this chapter. The key design principles are analyzed and it has been argued that network intrusion-detection functions should be carried out by distributed and collaborative NNIDS at the end hosts. It is shown that an NNIDS running on the network interface instead of the host operating system can provide increased protection, reduced vulnerability to circumvention, and much lower overhead. The chapter also describes the experience in implementing a prototype NNIDS, based on Snort, an Intel IXP 1200, and a Xilinx Virtex-1000 FPGA. These experiments help to identify the performance bottlenecks and give insights on how to improve the design. System stress tests shows that the embedded NNIDS can handle high-speed traffic without packet drops and achieve the same performance as the Snort software running on a dedicated high-end computer system. Ongoing work includes optimizing the performance of NNIDS, developing strategies for sustainable operation of the NNIDS under attacks through adaptation and active countermeasures, studying algorithms for distributed and collaborative intrusion detection, and further developing the analytical models for buffer and processor allocation. Also tested were FPGA pattern-matching designs that approach 10 Gbps throughput with the entire Snort ruleset using a Xilinx Virtex2 device. A better understanding of the design principles and implementation techniques for building high-speed has been provided, along with reliable, and scalable network intrusion detection systems.


title: The proactive brain: memory for predictions

abstract: It is proposed that the human brain is proactive in that it continuously generates predictions that anticipate the relevant future. In this proposal, analogies are derived from elementary information that is extracted rapidly from the input, to link that input with the representations that exist in memory. Finding an analogical link results in the generation of focused predictions via associative activation of representations that are relevant to this analogy, in the given context. Predictions in complex circumstances, such as social interactions, combine multiple analogies. Such predictions need not be created afresh in new situations, but rather rely on existing scripts in memory, which are the result of real as well as of previously imagined experiences. This cognitive neuroscience framework provides a new hypothesis with which to consider the purpose of memory, and can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's 'default mode' to a host of mental disorders.


title: Interprofessional experiences of recent healthcare graduates: A social psychology perspective on the barriers to effective communication, teamwork, and patient-centred care

abstract: AbstractAchieving safe, quality health care is highly dependent on effective communication between all members of the healthcare team. This study explored the attitudes and experiences of recent healthcare graduates regarding interprofessional teamwork and communication within a clinical setting. A total of 68 pharmacy, nursing, and medicine graduates participated in 12 semi-structured focus group discussions in clinical workplaces across three Australian states. Discussion focussed on graduates’ experiences of interprofessional education and its impact on their capacity for interprofessional teamwork and communication. The Social Identity and Realistic Conflict theories were used as a framework for qualitative data analysis. A consistent pattern of profession-focussed, rather than patient- or team-focussed goals was revealed along with reports of negative stereotyping, hierarchical communication, and competition for time with the patient. Graduates acknowledged the importance of communication, teamwork, ...


title: DNA probe immobilisation using 3D hydrogel matrix in a hollow core Photonic Crystal Fibre

abstract: In this paper, we show for the first time the integration of a 3D hydrogel matrix within a hollow core Photonic Crystal ::: Fibre (HC-PCF) and we also show the fluorescence propagation of a Cy5-labelled DNA probe immobilisation within the ::: hydrogel formed in two different types of HC-PCF. The 3D hydrogel matrix is designed to bind with the amino-groups ::: of biomolecules, providing higher sensitivity and selectivity than the standard 2D coverage, enabling a greater number of ::: probe molecules to be available per unit area. The HC-PCFs, on the other hand, are designed to maximise the capture of ::: fluorescence to improve sensitivity, providing long interaction lengths, being easily integrated with light sources and ::: detectors, and it can also be implemented in point-of-care or remote systems.


title: Experimental Characterization, Optimization, and Recovery of Data Retention Errors in MLC NAND Flash Memory

abstract: This paper summarizes our work on experimentally characterizing, mitigating, and recovering data retention errors in multi-level cell (MLC) NAND flash memory, which was published in HPCA 2015, and examines the work's significance and future potential. Retention errors, caused by charge leakage over time, are the dominant source of flash memory errors. Understanding, characterizing, and reducing retention errors can significantly improve NAND flash memory reliability and endurance. In this work, we first characterize, with real 2Y-nm MLC NAND flash chips, how the threshold voltage distribution of flash memory changes with different retention ages -- the length of time since a flash cell was programmed. We observe from our characterization results that 1) the optimal read reference voltage of a flash cell, using which the data can be read with the lowest raw bit error rate (RBER), systematically changes with its retention age, and 2) different regions of flash memory can have different retention ages, and hence different optimal read reference voltages. Based on our findings, we propose two new techniques. First, Retention Optimized Reading (ROR) adaptively learns and applies the optimal read reference voltage for each flash memory block online. The key idea of ROR is to periodically learn a tight upper bound of the optimal read reference voltage, and from there approach the optimal read reference voltage. Our evaluations show that ROR can extend flash memory lifetime by 64% and reduce average error correction latency by 10.1%. Second, Retention Failure Recovery (RFR) recovers data with uncorrectable errors offline by identifying and probabilistically correcting flash cells with retention errors. Our evaluation shows that RFR essentially doubles the error correction capability.


title: Learning to Estimate 3D Human Pose and Shape from a Single Color Image

abstract: This work addresses the problem of estimating the full body 3D human pose and shape from a single color image. This is a task where iterative optimization-based solutions have typically prevailed, while Convolutional Networks (ConvNets) have suffered because of the lack of training data and their low resolution 3D predictions. Our work aims to bridge this gap and proposes an efficient and effective direct prediction method based on ConvNets. Central part to our approach is the incorporation of a parametric statistical body shape model (SMPL) within our end-to-end framework. This allows us to get very detailed 3D mesh results, while requiring estimation only of a small number of parameters, making it friendly for direct network prediction. Interestingly, we demonstrate that these parameters can be predicted reliably only from 2D keypoints and masks. These are typical outputs of generic 2D human analysis ConvNets, allowing us to relax the massive requirement that images with 3D shape ground truth are available for training. Simultaneously, by maintaining differentiability, at training time we generate the 3D mesh from the estimated parameters and optimize explicitly for the surface using a 3D per-vertex loss. Finally, a differentiable renderer is employed to project the 3D mesh to the image, which enables further refinement of the network, by optimizing for the consistency of the projection with 2D annotations (i.e., 2D keypoints or masks). The proposed approach outperforms previous baselines on this task and offers an attractive solution for direct prediction of 3D shape from a single color image.


title: Solving Sudoku with Ant Colony Optimization

abstract: In this paper we present a new algorithm for the well-known and computationally-challenging Sudoku puzzle game. Our Ant Colony Optimization-based method significantly out-performs the state-of-the-art algorithm on the hardest, large instances of Sudoku. We provide evidence that – compared to traditional backtracking methods – our algorithm offers a much more efficient search of the solution space, and demonstrate the utility of a novel anti-stagnation operator. This work lays the foundation for future work on a general-purpose puzzle solver, and establishes Japanese pencil puzzles as a suitable platform for benchmarking a wide range of algorithms.


title: ©2011 WAP journal Solution To Multi-Depot Vehicle Routing Problem Using Genetic Algorithms

abstract: Abstract: The Multi-Depot Vehicle Routing Problem (MDVRP), an extension of classical VRP, is a NP-hard problem for simultaneously determining the routes for several vehicles from multiple depots to a set of customers and then return to the same depot. The objective of the problem is to find routes for vehicles to service all the customers at a minimal cost in terms of number of routes and total travel distance, without violating the capacity and travel time constraints of the vehicles. The solution to the MDVRP, in this paper, is obtained through Genetic Algorithm (GA). The customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method. Further the routes are scheduled and optimized using GA. A set of five different Cordeau’s benchmark instances (p01, p02, p03, p04, p06) from the online resource of University of Malaga, Spain were experimented using MATLAB R2008b software. The results were evaluated in terms of depot’s route length, optimal route, optimal distance, computational time, average distance, and number of vehicles. Comparison of the experimental results with state-of-the-art techniques shows that the performance of GA is feasible and effective for solving the multi-depot vehicle routing problem.


title: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data

abstract: DBSCAN (density-based spatial clustering of applications with noise) is an important spatial clustering technique that is widely adopted in numerous applications. As the size of datasets is extremely large nowadays, parallel processing of complex data analysis such as DBSCAN becomes indispensable. However, there are three major drawbacks in the existing parallel DBSCAN algorithms. First, they fail to properly balance the load among parallel tasks, especially when data are heavily skewed. Second, the scalability of these algorithms is limited because not all the critical sub-procedures are parallelized. Third, most of them are not primarily designed for shared-nothing environments, which makes them less portable to emerging parallel processing paradigms. In this paper, we present MR-DBSCAN, a scalable DBSCAN algorithm using MapReduce. In our algorithm, all the critical sub-procedures are fully parallelized. As such, there is no performance bottleneck caused by sequential processing. Most importantly, we propose a novel data partitioning method based on computation cost estimation. The objective is to achieve desirable load balancing even in the context of heavily skewed data. Besides, We conduct our evaluation using real large datasets with up to 1.2 billion points. The experiment results well confirm the efficiency and scalability of MR-DBSCAN.


title: Effects of Varying Doses of a Probiotic Supplement Fed to Healthy Dogs Undergoing Kenneling Stress

abstract: Dogs undergoing stress may experience gastrointestinal disturbances that can manifest as loose stool or diarrhea. The objective of this study was to examine the efficacy of feeding varying doses of a canine-derived probiotic supplement (Bifidobacterium animalis strain AHC7) for reducing stressrelated gastrointestinal disturbances in dogs that were relocated from homes to a kennel environment. Healthy, young adult dogs (n = 134) were randomly assigned to one of four supplements containing either < 103 (control), 1.5 x 107, 1.5 x 108, or 1.5 x 109 CFU of AHC7. Dogs received an oral supplement one time per day for 5 weeks prior to relocation and for the first 3 weeks following relocation. Fecal scores, number of defecations per day, fecal microbial populations, and serum cortisol levels were measured before and after kennel relocation. Fecal populations of AHC7 were significantly elevated in all three treatment groups when compared with the control group, and concentrations suggested a dose-response effect. Fecal scores were significantly higher (closer to optimal) in dogs supplemented with AHC7 when compared with the control group during Week 3 and over the entire 3-week relocation period. Significantly fewer dogs that were supplemented with AHC7 passed unacceptable stools during the first week of relocation when compared with the control group. Supplementing healthy dogs with Bifidobacterium animalis AHC7 prior to and during kennel relocation at doses between 107 and 109 CFU/day supported optimal stool production and may help to prevent stress-related gastrointestinal upsets and


title: Local Receptive Fields Based Extreme Learning Machine

abstract: Extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of feature learning, clustering, regression and classification. Different from the common understanding and tenet that hidden neurons of neural networks need to be iteratively adjusted during training stage, ELM theories show that hidden neurons are important but need not be iteratively tuned. In fact, all the parameters of hidden nodes can be independent of training samples and randomly generated according to any continuous probability distribution. And the obtained ELM networks satisfy universal approximation and classification capability. The fully connected ELM architecture has been extensively studied. However, ELM with local connections has not attracted much research attention yet. This paper studies the general architecture of locally connected ELM, showing that: 1) ELM theories are naturally valid for local connections, thus introducing local receptive fields to the input layer; 2) each hidden node in ELM can be a combination of several hidden nodes (a subnetwork), which is also consistent with ELM theories. ELM theories may shed a light on the research of different local receptive fields including true biological receptive fields of which the exact shapes and formula may be unknown to human beings. As a specific example of such general architectures, random convolutional nodes and a pooling structure are implemented in this paper. Experimental results on the NORB dataset, a benchmark for object recognition, show that compared with conventional deep learning solutions, the proposed local receptive fields based ELM (ELM-LRF) reduces the error rate from 6.5% to 2.7% and increases the learning speed up to 200 times.


title: Reflectance and Natural Illumination from a Single Image

abstract: Estimating reflectance and natural illumination from a single image of an object of known shape is a challenging task due to the ambiguities between reflectance and illumination. Although there is an inherent limitation in what can be recovered as the reflectance band-limits the illumination, explicitly estimating both is desirable for many computer vision applications. Achieving this estimation requires that we derive and impose strong constraints on both variables. We introduce a probabilistic formulation that seamlessly incorporates such constraints as priors to arrive at the maximum a posteriori estimates of reflectance and natural illumination. We begin by showing that reflectance modulates the natural illumination in a way that increases its entropy. Based on this observation, we impose a prior on the illumination that favors lower entropy while conforming to natural image statistics. We also impose a prior on the reflectance based on the directional statistics BRDF model that constrains the estimate to lie within the bounds and variability of real-world materials. Experimental results on a number of synthetic and real images show that the method is able to achieve accurate joint estimation for different combinations of materials and lighting.


title: The queuing theory application in bank service optimization

abstract: Lines of waiting customers are always very long in most of banks. The essence of this phenomenon is the low efficiency of queuing system. In this paper, the queuing number, the service windows number and the optimal service rate are investigated by means of the queuing theory. In technology, the optimal problem of the bank queuing is solved. The time of customer queuing is reduced. The customer satisfaction is increased. It was proved that this optimal model of the queuing is feasible. By the example, the results are effective and practical.


title: A Tutorial on Principal Component Analysis

abstract: Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.


title: Preservation and collection of biological evidence.

abstract: As the courts have placed greater emphasis on physical evidence during the past few decades, the initial stages of evidence examination have become increasingly important to the successful resolution of many criminal investigations. This emphasis on evidence collection and preservation is often manifested by many rigorous court challenges. This article reviews how the ability to introduce DNA test results in court is affected by methods used to recognize, document, collect, and preserve biological evidence.


title: Boosting in the presence of label noise

abstract: Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling.


Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation based on Face Images using Raspberry Pi Processor


title: Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems

abstract: We tackle the problem of scheduling task graphs onto a heterogeneous set of machines, where each processor has a probability of failure governed by an exponential law. The goal is to design algorithms that optimize both makespan and reliability. First, we provide an optimal scheduling algorithm for independent unitary tasks where the objective is to maximize the reliability subject to makespan minimization. For the bi-criteria case, we provide an algorithm that approximates the Pareto-curve. Next, for independent non-unitary tasks, we show that the product {failure rate}x {unitary instruction execution time} is crucial to distinguish processors in this context. Based on these results we are able to let the user choose a trade-off between reliability maximization and makespan minimization. For general task graphs we provide a method for converting scheduling heuristics on heterogeneous cluster into heuristics that take reliability into account. Here again, we show how we can help the user to select a trade-off between makespan and reliability.


title: Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks

abstract: In this paper, we present the results of a long-term study of ransomware attacks that have been observed in the wild between 2006 and 2014. We also provide a holistic view on how ransomware attacks have evolved during this period by analyzing 1,359 samples that belong to 15 different ransomware families. Our results show that, despite a continuous improvement in the encryption, deletion, and communication techniques in the main ransomware families, the number of families with sophisticated destructive capabilities remains quite small. In fact, our analysis reveals that in a large number of samples, the malware simply locks the victim's computer desktop or attempts to encrypt or delete the victim's files using only superficial techniques.i?źOur analysis also suggests that stopping advanced ransomware attacks is not as complex as it has been previously reported. For example, we show that by monitoring abnormal file system activity, it is possible to design a practical defense system that could stop a large number of ransomware attacks, even those using sophisticated encryption capabilities. A close examination on the file system activities of multiple ransomware samples suggests that by looking at I/O requests and protecting Master File Table MFT in the NTFS file system, it is possible to detect and prevent a significant number of zero-day ransomware attacks.


title: Multi-scale image hashing using adaptive local feature extraction for robust tampering detection

abstract: The main problem addressed in this paper is the robust tampering detection of the image received in a transmission under various content-preserving attacks. To this aim the multi-scale image hashing method is proposed by using the location-context information of the features generated by adaptive and local feature extraction techniques. The generated hash is attached to the image before transmission and analyzed at destination to filter out the geometric transformations occurred in the received image by image restoration firstly. Based on the restored image, the image authentication using the global and color hash component is performed to determine whether the received image has the same contents as the trusted one or has been maliciously tampered, or just different. After regarding the received image as being tampered, the tampered regions will be localized through the multi-scale hash component. Lots of experiments are conducted to indicate that our tampering detection scheme outperforms the existing state-of-the-art methods and is very robust against the content-preserving attacks, including both common signal processing and geometric distortions. Address the problem of Image tamper detection.Novel Multi-scale image hashing using adaptive local feature extraction is proposed.Novel robust tampering detection scheme is proposed.Novel image authentication and tampering localization are proposed.


title: A General Model and Analysis of Physical Layer Capture in 802.11 Networks

abstract: While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.


title: On Clustering Induced Voronoi Diagrams

abstract: In this paper, we study a generalization of the classical Voronoi diagram, called clustering induced Voronoi diagram (CIVD). Different from the traditional model, CIVD takes as its sites the power set U of an input set P of objects. For each subset C of P, CIVD uses an influence function F(C, q) to measure the total (or joint) influence of all objects in C on an arbitrary point q in the space ℝd, and determines the influence-based Voronoi cell in ℝd for C. This generalized model offers a number of new features (e.g., simultaneous clustering and space partition) to Voronoi diagram which are useful in various new applications. We investigate the general conditions for the influence function which ensure the existence of a small-size (e.g., nearly linear) approximate CIVD for a set P of n points in ℝd for some fixed d. To construct CIVD, we first present a standalone new technique, called approximate influence (AI) decomposition, for the general CIVD problem. With only O(n log n) time, the AI decomposition partitions the space ℝd into a nearly linear number of cells so that all points in each cell receive their approximate maximum influence from the same (possibly unknown) site (i.e., a subset of P). Based on this technique, we develop assignment algorithms to determine a proper site for each cell in the decomposition and form various (1-e)-approximate CIVDs for some small fixed € > 0. Particularly, we consider two representative CIVD problems, vector CIVD and density-based CIVD, and show that both of them admit fast assignment algorithms; consequently, their (1 - €)-approximate CIVDs can be built in O(n logd+1 n) and O(n log2 n) time, respectively.


title: Enzymatic resolution of 2-fluoro-2-arylacetic acid derivatives

abstract: Abstract Optical resolutions of 2-fluoro-2-arylacetic acids, ArC * FRCOOH (R=CN, H, and Me), were performed by enantioselective hydrolysis of the corresponding esters using Candida rugosa lipase (CRL). The enantioselectivity of commercial CRL toward the esters was greatly improved when commercial CRL was treated with 2-propanol solution. In the enantioselective hydrolysis of these esters, as represented by ArC * SM COOR ( S and M are small and medium substituents, respectively), the active site of the 2-propanol-treated CRL recognized fluorine as S in both PhCF(CH 3 )COOR and p -TolCF(CN)COOR but recognized fluorine as M in PhCHFCOOR.


title: Critical factors for knowledge management in project business

abstract: Purpose – The purpose of this study is to identify and examine various factors that influence the success or failure of knowledge management (KM) initiatives in project‐based companies.Design/methodology/approach – Following a literature review, the study proposes a conceptual model of six factors of potential importance to the success of KM initiatives. The model is then examined through an online survey of project managers and assistant managers from project‐based businesses in Finland.Findings – The study finds that a lack of incentives and the absence of an appropriate information system are the most significant barriers to successful KM initiatives in projects.Research limitations/implications – The findings of the study may be restricted in terms of generalisability because of the limited empirical study.Practical implications – Project managers should formulate an attractive incentive package to encourage project members to participate in KM initiatives and to suggest ideas for new KM opportunities...


title: The Mental Canvas: A Tool for Conceptual Architectural Design and Analysis

abstract: We describe a computer graphics system that supports conceptual architectural design and analysis. We use as a starting point the traditional sketchbook drawings that architects use to experiment with various views, sections, and details. Rather than interpret or infer 3D structure from drawings, our system is designed to allow the designer to organize concept drawings in 3D, and gradually fuse a series of possibly geometrically-inconsistent sketches into a set of 3D strokes. Our system uses strokes and planar "canvases" as basic primitives; the basic mode of input is traditional 2D drawing. We introduce methods for the user to control stroke visibility and transfer strokes between canvases. We also introduce methods for the user to position and orient the canvases that have infinite extent. We demonstrate the use of the system to analyze existing structures and conceive new designs.


title: focus reports from the field Using Extreme Programming in a Maintenance Environment

abstract: In response to problems experienced by the Orbix Generation 3 maintenance and enhancement team, Iona Technologies tried to introduce industry-level best practices by adopting extreme programming. The issues discussed are common for companies moving from startup mode to those supporting numerous customers in need of bug fixes and application enhancements for existing deployment scenarios.


title: Emotional Human Machine Conversation Generation Based on SeqGAN

abstract: In recent years, artificial intelligence has made a significant breakthrough and progress in the field of humanmachine conversation. However, how to generate high-quality, emotional and subhuman conversation still a troublesome work. The key factor of man-machine dialogue is whether the chatbot can give a good response in content and emotional level. How to ensure that the robot understands the user’s emotions, and consider the user’s emotions then give a satisfactory response. In this paper, we add the emotional tags to the post and response from the dataset respectively. The emotional tags, as the emotional tags of post and response, represent the emotions expressed by this sentence. The purpose of our emotional tags is to make the chatbot understood the emotion of the input sequence more directly so that it has a recognition of the emotional dimension. In this paper, we apply the mechanism of GAN network on our conversation model. For the generator: We make full use of Encoder-Decoder structure form a seq2seq model, which is used to generate a sentence’s response. For the discriminator: distinguish between the human-generated dialogues and the machine-generated ones.The outputs from the discriminator are used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. We cast our task as an RL(Reinforcement Learning) problem, using a policy gradient method to reward more subhuman conversational sequences, and in addition we have added an emotion tags to represent the response we want to get, which we will use as a rewarding part of it, so that the emotions of real responses can be closer to the emotions we specify. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion, which can be used to control and adjust users emotion. Compared with our previous work, we get a better performance on the same data set, and we get less ’’safe’’ response than before, but there will be a certain degree of existence.


title: The combined use of cross-section analysis and other stratigraphic recording systems in the cleaning of two panel paintings from the fifteenth- and sixteenth-century

abstract: AbstractCross sections are frequently used in the stratigraphic study of pictorial structures. Thanks to cross sections, it is possible to study and record original and non-original strata that may provide important information regarding the artist's technique and later restoration processes. This information helps conservators design different strategies in processes such as cleaning. However, it is often in cleaning where the advantages and limitations of cross sections become obvious. When dealing with a complex structure, cross sections may not be enough to record in a comprehensive and accurate manner all the strata removed during cleaning. In some cases, the conservator may obtain during cleaning a great amount of stratigraphic information that is not visible in the cross sections. Therefore, it may be necessary to resort to other recording systems, such as the stratigraphic unit recording sheet and the stratigraphic diagram, which are frequently used in archaeological stratigraphy. This article dem...


title: DF-C2M2: A Capability Maturity Model for Digital Forensics Organisations

abstract: The field of digital forensics has emerged as one of the fastest changing and most rapidly developing investigative specialisations in a wide range of criminal and civil cases. Increasingly there is a requirement from the various legal and judicial authorities throughout the world, that any digital evidence presented in criminal and civil cases should meet requirements regarding the acceptance and admissibility of digital evidence, e.g., Daubert or Frye in the US. There is also increasing expectation that digital forensics labs are accredited to ISO 17025 or the US equivalent ASCLD-Lab International requirements. On the one hand, these standards cover general requirements and are not geared specifically towards digital forensics. On the other hand, digital forensics labs are mostly left with costly piece-meal efforts in order to try and address such pressing legal and regulatory requirements. In this paper, we address these issues by proposing DF-C^2M^2, a capability maturity model that enables organisations to evaluate the maturity of their digital forensics capabilities and identify roadmaps for improving it in accordance with business or regulatory requirements. The model has been developed through consultations and interviews with digital forensics experts. The model has been evaluated by using it to assess the digital forensics capability maturity of a lab in a law enforcement agency.


title: Design Considerations of Miniaturized Least Dispersive Periodic Slow-Wave Structures

abstract: Slow-wave structures using distributed periodic inductive and capacitive loadings have found many microwave circuit applications as left-handed (bandpass) or right-handed (low-pass) transmission lines. A large slow-wave factor (SWF) could result in a much smaller passive component, but also a much lower bandgap (cutoff) frequency and a larger dispersion. This paper addresses the issues and the design tradeoff between the SWF, group delay (dispersion), and the cutoff frequency of a right-handed (low-pass) quasi-lumped transmission line. A new two-layer transmission line structure using 3-D substrate metallization with an SWF of 5.8 is designed. A prototype of a 3-GHz branch-line coupler with a 70% size reduction using such a transmission line structure is fabricated and tested.


title: Cyber Conflicts as a New Global Threat

abstract: In this paper, an attempt is made to analyze the potential threats and consequences of cyber conflicts and, in particular, the risks of a global cyber conflict. The material is based on a comprehensive analysis of the nature of cyber conflict and its elements from both technical and societal points of view. The approach used in the paper considers the societal component as an essential part of cyber conflicts, allowing basics of cyber conflicts often disregarded by researchers and the public to be highlighted. Finally, the conclusion offers an opportunity to consider cyber conflict as the most advanced form of modern warfare, which imposes the most serious threat and whose effect could be comparable to weapons of mass destruction.


title: Trimmomatic: a flexible trimmer for Illumina sequence data

abstract: Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. ::: ::: Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. ::: ::: Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic ::: ::: Contact: ed.nehcaa-htwr.1oib@ledasu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Distributed optimization of deeply nested systems

abstract: In science and engineering, intelligent processing of complex signals such as images, sound or language is often performed by a parameterized hierarchy of nonlinear processing layers, sometimes biologically inspired. Hierarchical systems (or, more generally, nested systems) offer a way to generate complex mappings using simple stages. Each layer performs a different operation and achieves an ever more sophisticated representation of the input, as, for example, in an deep artificial neural network, an object recognition cascade in computer vision or a speech front-end processing. Joint estimation of the parameters of all the layers and selection of an optimal architecture is widely considered to be a difficult numerical nonconvex optimization problem, difficult to parallelize for execution in a distributed computation environment, and requiring significant human expert effort, which leads to suboptimal systems in practice. We describe a general mathematical strategy to learn the parameters and, to some extent, the architecture of nested systems, called the method of auxiliary coordinates (MAC). This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, and is competitive with state-of-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations.


title: A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

abstract: In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based top-N recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons.


title: Construction et exploitation d'un corpus français pour l'analyse de sentiment

abstract: Building and exploiting a French corpus for sentiment analysis This work introduces a French corpus for sentiment analysis. We describe the construction and organization of the corpus. We then apply machine learning techniques to automatically predict whether a text is positive or negative (the opinion classification task). Two techniques are used : logistic regression and classification based onSupport Vector Machines(SVM). Finally, we briefly evaluate the merits of applying feature selection algorithms to our models (via elastic net


title: The genetic epidemiology of neurodegenerative disease

abstract: Gene defects play a major role in the pathogenesis of degenerative disorders of the nervous system. In fact, it has been the very knowledge gained from genetic studies that has allowed the elucidation of the molecular mechanisms underlying the etiology and pathogenesis of many neurodegenerative disorders. In this review, we discuss the current status of genetic epidemiology of the most common neurodegenerative diseases: Alzheimer disease, Parkinson disease, Lewy body dementia, frontotemporal dementia, amyotrophic lateral sclerosis, Huntington disease, and prion diseases, with a particular focus on similarities and differences among these syndromes.


title: Royal Australian and New Zealand College of Psychiatrists clinical practice guideline for the management of deliberate self-harm

abstract: Objective:To provide guidance for the organisation and delivery of clinical services and the clinical management of patients who deliberately self-harm, based on scientific evidence supplemented by expert clinical consensus and expressed as recommendations.Method:Articles and information were sourced from search engines including PubMed, EMBASE, MEDLINE and PsycINFO for several systematic reviews, which were supplemented by literature known to the deliberate self-harm working group, and from published systematic reviews and guidelines for deliberate self-harm. Information was reviewed by members of the deliberate self-harm working group, and findings were then formulated into consensus-based recommendations and clinical guidance. The guidelines were subjected to successive consultation and external review involving expert and clinical advisors, the public, key stakeholders, professional bodies and specialist groups with interest and expertise in deliberate self-harm.Results:The Royal Australian and New Ze...


title: Multimodal emotion recognition using deep learning architectures

abstract: Emotion analysis and recognition has become an interesting topic of research among the computer vision research community. In this paper, we first present the emoF-BVP database of multimodal (face, body gesture, voice and physiological signals) recordings of actors enacting various expressions of emotions. The database consists of audio and video sequences of actors displaying three different intensities of expressions of 23 different emotions along with facial feature tracking, skeletal tracking and the corresponding physiological data. Next, we describe four deep belief network (DBN) models and show that these models generate robust multimodal features for emotion classification in an unsupervised manner. Our experimental results show that the DBN models perform better than the state of the art methods for emotion recognition. Finally, we propose convolutional deep belief network (CDBN) models that learn salient multimodal features of expressions of emotions. Our CDBN models give better recognition accuracies when recognizing low intensity or subtle expressions of emotions when compared to state of the art methods.


title: Cores of Swirling Particle Motion in Unsteady Flows

abstract: In nature and in flow experiments particles form patterns of swirling motion in certain locations. Existing approaches identify these structures by considering the behavior of stream lines. However, in unsteady flows particle motion is described by path lines which generally gives different swirling patterns than stream lines. We introduce a novel mathematical characterization of swirling motion cores in unsteady flows by generalizing the approach of Sujudi/Haimes to path lines. The cores of swirling particle motion are lines sweeping over time, i.e., surfaces in the space-time domain. They occur at locations where three derived 4D vectors become coplanar. To extract them, we show how to re-formulate the problem using the parallel vectors operator. We apply our method to a number of unsteady flow fields.


title: Pinch-to-zoom-plus: an enhanced pinch-to-zoom that reduces clutching and panning

abstract: Despite its popularity, the classic pinch-to-zoom gesture used in modern multi-touch interfaces has drawbacks: specifically, the need to support an extended range of scales and the need to keep content within the view window on the display can result in the need to clutch and pan. In two formative studies of unimanual and bimanual pinch-to-zoom, we found patterns: zooming actions follows a predictable ballistic velocity curve, and users tend to pan the point-of-interest towards the center of the screen. We apply these results to design an enhanced zooming technique called Pinch-to-Zoom-Plus (PZP) that reduces clutching and panning operations compared to standard pinch-to-zoom behaviour.


Bangladeshi Vehicle Digital License Plate Recognition for Metropolitan Cities Using Support Vector Machine


title: Collaborative video reindexing via matrix factorization

abstract: Concept-based video indexing generates a matrix of scores predicting the possibilities of concepts occurring in video shots. Based on the idea of collaborative filtering, this article presents unsupervised methods to refine the initial scores generated by concept classifiers by taking into account the concept-to-concept correlation and shot-to-shot similarity embedded within the score matrix. Given a noisy matrix, we refine the inaccurate scores via matrix factorization. This method is further improved by learning multiple local models and incorporating contextual-temporal structures. Experiments on the TRECVID 2006--2008 datasets demonstrate relative performance gains ranging from 13p to 52p without using any user annotations or external knowledge resources.


SPECIAL ISSUE Heart Rate Variability Biofeedback for Major


title: Solving the K-of-N Lifetime Problem by PSO

abstract: In wireless sensor networks, application nodes (ANs) may be produced by different manufacturers and may own different data transmission rates, initial energies and parameter values. When different kinds of ANs exist in a wireless network, it is hard to find the optimal base-station (BS) locations for minimizing power consumption and prolonging network lifetime. In this paper, a heuristic algorithm based on PSO is thus proposed to solve the K-of-N lifetime problem under general power-consumption constraints in wireless sensor networks. Give N ANs, the network survives as long as there are at least K ANs alive. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results. The proposed algorithm can thus help maximize the K-of-N network lifetime in wireless sensor networks. Keywords: wireless sensor network, network lifetime, energy consumption, particle swarm optimization


title: Information retrieval on the web

abstract: In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information. The same surveys show, however, that users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results (e.g., noise and broken links) are commonly cited problems. We discuss the development of new techniques targeted to resolve some of the problems associated with Web-based information retrieval and speculate on future trends.


title: Spatial data extension for Cassandra NoSQL database

abstract: The big data phenomenon is becoming a fact. Continuous increase of digitization and connecting devices to Internet are making current solutions and services smarter, richer and more personalized. The emergence of the NoSQL databases, like Cassandra, with their massive scalability and high availability encourages us to investigate the management of the stored data within such storage system. In our present work, we harness the geohashing technique to enable spatial queries as extension to Cassandra query language capabilities while preserving the native syntax. The developed framework showed the feasibility of this approach where basic spatial queries are underpinned and the query response time is reduced by up to 70 times for a fairly large area.


title: Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach

abstract: Efficient fastest path computation in the presence of varying speed conditions on a large scale road network is an essential problem in modern navigation systems. Factors affecting road speed, such as weather, time of day, and vehicle type, need to be considered in order to select fast routes that match current driving conditions. Most existing systems compute fastest paths based on road Euclidean distance and a small set of predefined road speeds. However, "History is often the best teacher". Historical traffic data or driving patterns are often more useful than the simple Euclidean distance-based computation because people must have good reasons to choose these routes, e.g., they may want to avoid those that pass through high crime areas at night or that likely encounter accidents, road construction, or traffic jams. ::: ::: In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for important driving and speed patterns mined from a large set of traffic data. The algorithm is based on the following observations: (1) The hierarchy of roads can be used to partition the road network into areas, and different path pre-computation strategies can be used at the area level, (2) we can limit our route search strategy to edges and path segments that are actually frequently traveled in the data, and (3) drivers usually traverse the road network through the largest roads available given the distance of the trip, except if there are small roads with a significant speed advantage over the large ones. Through an extensive experimental evaluation on real road networks we show that our algorithm provides desirable (short and well-supported) routes, and that it is significantly faster than competing methods.


title: Modeling Context Information in Pervasive Compututing Systems

abstract: As computing becomes more pervasive, the nature of applications must change accordingly. In particular, applications must become more flexible in order to respond to highly dynamic computing environments, and more autonomous, to reflect the growing ratio of applications to users and the corresponding decline in the attention a user can devote ::: to each. That is, applications must become more context-aware. To facilitate the programming of such applications, infrastructure is required to gather, manage, and disseminate context information to applications. This paper is concerned with the development of appropriate context modeling concepts for pervasive computing, which can form the basis for such a context management infrastructure. This model overcomes prob- ::: lems associated with previous context models, including their lack of formality and generality, and also tackles issues such as wide variations in information quality, the existence of complex relationships amongst context information and temporal aspects of context.


title: A Topic-based Reviewer Assignment System

abstract: Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.


title: A low cost implementation of MQTT using ESP8266

abstract: Technology is great growling engine of the change and Internet of Things (IoT) is the backbone of such revolutionary engines. Basically, in the real world the things having sensor capability, sufficient power supply and connectivity to internet makes field like Internet of Things (IoT) possible. For such rapid growing technology, it is the necessity to have very light, inexpensive and minimum bandwidth protocol like Message Queuing Telemetry Transport (MQTT) Protocol. Because of such non-established protocol it is easy for the clients to publish or/and subscribe the desire topic through the host acting as server of the network also known to be the broker. In this paper, it is shown that communication between the low power ESP8266 WiFi as client with the clients on smartphones and laptop using an MQTT protocol becomes easier and more reliable. The WiFi enabled ESP8266 board interfaces with DHT11 sensor and LDR sensor to monitor the ambient condition and according to the light intensity level the brightness level of 8∗8 Neopixel matrix is controlled. The adafruit.io is the MQTT server i.e, broker which also provides the facility of monitoring system through the dashboard. The clients on smartphone and laptop subscribes to the topics of temperature, humidity and light intensity level gets the respective update. The main objective of the paper is to implement the idea for the smart home appliance, street light system for the smart cities, fire alert systems, whether monitoring system and so.


title: An auto-tuning framework for parallel multicore stencil computations

abstract: Although stencil auto-tuning has shown tremendous potential in effectively utilizing architectural resources, it has hitherto been limited to single kernel instantiations; in addition, the large variety of stencil kernels used in practice makes this computation pattern difficult to assemble into a library. This work presents a stencil auto-tuning framework that significantly advances programmer productivity by automatically converting a straightforward sequential Fortran 95 stencil expression into tuned parallel implementations in Fortran, C, or CUDA, thus allowing performance portability across diverse computer architectures, including the AMD Barcelona, Intel Nehalem, Sun Victoria Falls, and the latest NVIDIA GPUs. Results show that our generalized methodology delivers significant performance gains of up to 22× speedup over the reference serial implementation. Overall we demonstrate that such domain-specific auto-tuners hold enormous promise for architectural efficiency, programmer productivity, performance portability, and algorithmic adaptability on existing and emerging multicore systems.


title: Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation

abstract: This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global events and their issues on predicting stock markets. From this survey, we found that incorporating event information with prediction model plays very important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required to provide better performance in financial time series prediction


title: Single-Shot Object Detection with Enriched Semantics

abstract: We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a location-agnostic module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a location-agnostic module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.6 on VOC2007 test and an mmAP of 32.8 on COCO test-dev with an inference speed of 36.7 milliseconds per image on a Titan X Pascal GPU. With a lower resolution version, we achieve an mAP of 79.5 on VOC2007 with an inference speed of 14.7 milliseconds per image.


title: College students' text messaging, use of textese and literacy skills

abstract: In this study, I examined reported frequency of text messaging, use of textese and literacy skills (reading accuracy, spelling and reading fluency) in a sample of American college students. Participants reported using text messaging, social networking sites and textese more often than was reported in previous (2009) research, and their frequency of textese use varied across contexts. Correlational analyses revealed significant, positive relationships between text messaging frequency and literacy skills (spelling and reading fluency), but significant, negative relationships between textese usage in certain contexts (on social networking sites such as MySpace™ and Facebook™ and in emails to professors) and literacy (reading accuracy). These findings differ from findings reported in recent studies with Australian college students, British schoolchildren and American college students. Explanations for these differences are discussed, and future directions for research are presented.


Knowledge Base Question Answering via Encoding of Complex Query Graphs


title: IT Governance: Reviewing 17 IT Governance Tools and Analysing the Case of Novozymes A/S

abstract: Complexity in information technology architectures and infrastructures, and an increasing need for executives to verify and secure value generation processes in private as well as public organisations, call for an increasing awareness and understanding of Corporate Governance in general and IT Governance in particular. The paper investigates how IT Governance is adopted in the case company Novozymes A/S, which is a biotech-based world leader in industrial enzymes and microorganisms. Based on a review of 17 IT Governance Tools, the paper analyses the challenges of the adopted IT Governance arrangements and mechanisms. Finally, the paper point to future development directions in order to further unfold the potential of IT Governance at Novozymes A/S.


title: Antenna-in-package technology: The key to success of 60-GHz radio

abstract: We introduce the exposure and interference regulations for the 60-GHz band in terms of allowable level of transmit power and antenna gain for different regions of the world. We examine the current development of 60-GHz radio chipsets with an interest in antenna-circuit interfaces and emphasize importance of low-loss interconnection between a chip and an antenna, leading to the Antenna-in-Package (AiP) technology. We evaluate various substrate materials and processes for mass production of AiPs. We demonstrate that LTCC-based AiPs have achieved excellent performance. Hence, we believe that the AiP technology has a great potential as the mainstream antenna solution to the 60-GHz radio chipsets for short-range high-speed wireless communications.


title: Planar composite right/left-handed leaky-wave antenna featuring dual beam

abstract: A novel single-layer leaky-wave antenna utilizing composite right/left-handed transmission-line sections has been proposed. The proposed antenna is a 1-D periodic structure composed of identical unit cells. Each unit cell is constituted by a quasi-distributed series capacitance and two open stubs at opposite sides of a unit cell. The unique feature of this antenna is that main beam is scanned in one of the principal cut-planes from back-fire through broadside to end-fire, whereas in the other principal cut-plane the main beam is scanned as well, having different scanning angles in left-handed and right-handed regions. The design, simulation and measurement results of a proposed antenna operating at approximately 5-5.6 GHz are presented showing interesting features of the antenna.


title: Use of Analytic Hierarchy Process (AHP) For Selection of Sustainable Rail Transport Proposal

abstract: With increasing greenhouse gas emissions, traffic congestion and oil prices, the concept of sustainability in transport infrastructure planning is receiving more and more attention. Decision and policy makers are urged to make decisions that are not only economically efficient but also sustainable. For that reason, new approaches are needed for comprehensive assessment that will take various economic, social, and environmental impacts into account. This paper introduces a decision support framework involving Analytic Hierarchy Process (AHP) to undertake a comprehensive sustainability assessment of a large transport infrastructure project. The AHP Framework is illustrated through a case study to find the best sustainable option for the construction of the Rail Baltica railway line through the Baltic countries and Poland. The AHP approach has been proved able to provide valid and useful decision support for making decisions that can contribute to promote sustainable transport development.


title: The science of interpersonal touch: An overview

abstract: Abstract Surprisingly little scientific research has been conducted on the topic of interpersonal touch over the years, despite the importance of touch in our everyday social interactions from birth through to adulthood and old age. In this review, we critically evaluate the results of the research on this topic that have emerged from disciplines, such as cognitive and social psychology, neuroscience, and cultural anthropology. We highlight some of the most important advances to have been made in our understanding of this topic: For example, research has shown that interpersonal tactile stimulation provides an effective means of influencing people's social behaviors (such as modulating their tendency to comply with requests, in affecting people's attitudes toward specific services, in creating bonds between couples or groups, and in strengthening romantic relationships), regardless of whether or not the tactile contact itself can be remembered explicitly. What is more, interpersonal touch can be used to communicate emotion in a manner similar to that demonstrated previously in vision and audition. The recent growth of studies investigating the potential introduction of tactile sensations to long-distance communication technologies (by means of mediated or ‘virtual’ touch) are also reviewed briefly. Finally, we highlight the synergistic effort that will be needed by researchers in different disciplines if we are to develop a more complete understanding of interpersonal touch in the years to come.


title: Generic Sketch-Based Retrieval Learned without Drawing a Single Sketch

abstract: We cast the sketch-based retrieval as edge-map matching. A shared convolutional network is trained to extract descriptors from edge maps and sketches, which are treated as a special case of edge maps. The network is fine-tuned solely from edge maps of landmark images. The training images are acquired in a fully unsupervised manner from 3D landmark models obtained by an automated structure-from-motion pipeline. ::: The proposed method achieves the state-of-the-art results on a standard benchmark. On two other fine-grained sketch-based retrieval benchmarks, it performs on par with or comes just after the method specifically designed for the dataset.


title: Investigating users' query formulations for cognitive search intents

abstract: This study investigated query formulations by users with {\it Cognitive Search Intents} (CSIs), which are users' needs for the cognitive characteristics of documents to be retrieved, {\em e.g. comprehensibility, subjectivity, and concreteness. Our four main contributions are summarized as follows (i) we proposed an example-based method of specifying search intents to observe query formulations by users without biasing them by presenting a verbalized task description;(ii) we conducted a questionnaire-based user study and found that about half our subjects did not input any keywords representing CSIs, even though they were conscious of CSIs;(iii) our user study also revealed that over 50\% of subjects occasionally had experiences with searches with CSIs while our evaluations demonstrated that the performance of a current Web search engine was much lower when we not only considered users' topical search intents but also CSIs; and (iv) we demonstrated that a machine-learning-based query expansion could improve the performances for some types of CSIs.Our findings suggest users over-adapt to current Web search engines,and create opportunities to estimate CSIs with non-verbal user input.


title: Mining trailers data from youtube for predicting gross income of movies

abstract: YouTube is the most popular video contents sharing platform around the world. As a consequence, YouTube has become one of the most preferred choices to the movie producers and studios for connecting/communicating with their potential viewers through sharing trailers and teasers. Data regarding the trailers of a movie from YouTube can provide useful insights for predicting the gross income of movies. In this paper, we have prepared a dataset of 7988 movie trailers from YouTube. The dataset contains different attributes like opening income, number of views, number of likes, number of dislikes, number of comments. We prepared two prediction models and applied four regression techniques to find out the most suitable technique for predicting the gross income of a movie. The comparative analysis has depicted that linear regression is the most suitable method regarding the prediction of movies gross income using these attributes. Furthermore, we have provided future research issues from where our work has ended.


title: Placelessness, spacelessness, and formlessness: experiential qualities of virtual possessions

abstract: People worldwide are increasingly acquiring virtual possessions. While virtual possessions have become ubiquitous, little work exists on how people value them, and how their experiences of them differ from material possessions. In this paper, we reflect on and synthesize findings from five studies we conducted over the past five years that investigated people's perceptions of and practices with virtual possessions. Through the higher-level perspective we adopt, we propose three thematic qualities that help characterize people's experiences with virtual possessions, as compared to their material things: placelessness, spacelessness, and formlessness. We draw on these proposed qualities as lenses to help frame future research and practice opportunities for better supporting value construction activities with virtual possessions.


title: Intelligent Agriculture Greenhouse Environment Monitoring System Based on IOT Technology

abstract: In recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (Internet of Things) technology rapid development and wide application. This paper takes CC2530 chip as the core, presents the design and implementation of agriculture Greenhouse Environment monitoring system based on ZigBee technology, the wireless sensor and control nodes takes CC2530F256 as core to control the environment data. This system is made up of front-end data acquisition, data processing, data transmission and data reception. The ambient temperature is real-time processed by the temperature sensor of data terminal node. Processed data is send to the intermediate node through a wireless network. Intermediate node aggregates all data, and then sends the data to the PC through a serial port, at the same time, staff may view, analysis and storage the data by the PC that provide real-time data for agricultural greenhouse, fans and other temperature control equipment, and achieve automatic temperature control.


title: Sentiment Analysis of English Tweets Using Rapid Miner

abstract: Social networking sites these days are great source of communication for internet users. So these are important source for understanding the emotions of people. In this paper, we use data mining techniques for the purpose of classification to perform sentiment analysis on the views people have shared in Twitter. We collect dataset, i.e. The tweets from twitter that are in natual language and apply text mining techniques -- tokenization, stemming etc to convert them into useful form and then use it for building sentiment classifier that is able to predict happy, sad and neutral sentiments for a particular tweet. Rapid Miner tool is being used, that helps in building the classifier as well as able to apply it to the testing dataset. We are using two different classifiers and also compare their results in order to find which one gives better results.


title: Reinforcement Learning Versus Model Predictive Control: A Comparison on a Power System Problem

abstract: This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear and deterministic electrical power oscillations damping problem. Both families of methods are based on the formulation of the control problem as a discrete-time optimal control problem. The considered MPC approach exploits an analytical model of the system dynamics and cost function and computes open-loop policies by applying an interior-point solver to a minimization problem in which the system dynamics are represented by equality constraints. The considered RL approach infers in a model-free way closed-loop policies from a set of system trajectories and instantaneous cost values by solving a sequence of batch-mode supervised learning problems. The results obtained provide insight into the pros and cons of the two approaches and show that RL may certainly be competitive with MPC even in contexts where a good deterministic system model is available.


title: A survey of in-vehicle communications: Requirements, solutions and opportunities in IoT

abstract: As the automobile industry evolves, a number of in-vehicle communication protocols are developed for different in-vehicle applications. With the emerging new applications towards Internet of Things (IoT), a more integral solution is needed to enable the pervasiveness of intra- and inter-vehicle communications. In this survey, we first introduce different classifications of automobile applications with focus on their bandwidth and latency. Then we survey different in-vehicle communication bus protocols including both legacy protocols and emerging Ethernet. In addition, we highlight our contribution in the field to employ power line as the in-vehicle communication medium. We believe power line communication will play an important part in future automobile which can potentially reduce the amount of wiring, simplify design and reduce cost. Based on these technologies, we also introduce some promising applications in future automobile enabled by the development of in-vehicle network. Finally, We will share our view on how the in-vehicle network can be merged into the future IoT.


title: Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field

abstract: One of the primary challenges for autonomous robotics in uncertain and dynamic environments is planning and executing a collision-free path. Hybrid dynamic obstacles present an even greater challenge as the obstacles can change dynamics without warning and potentially invalidate paths. Artificial potential field (APF)-based techniques have shown great promise in successful path planning in highly dynamic environments due to their low cost at runtime. We utilize the APF framework for runtime planning but leverage a formal validation method, Stochastic Reachable (SR) sets, to generate accurate potential fields for moving obstacles. A small number of SR sets are computed a priori , then used to generate a potential field that represents the obstacle's stochastic motion for online path planning. Our method is novel and scales well with the number of obstacles, maintaining a relatively high probability of reaching the goal without collision, as compared to other traditional Gaussian APF methods. Here, we demonstrate our method with up to 900 hybrid dynamic obstacles and show that it outperforms the traditional Gaussian APF method by up to 60% in the holonomic case and up to 20% in the unicycle case.