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In this era, use of credit has increased extensively. People are using credit cards more frequently in their daily lives. Millions of dollars were lost in forgery, however fraud detection methods have been developed to fix such types of problems. However, Nevertheless, we still face such problems through imbalance data. There is a requirement for a centralized claims database to collect views of holistic view of fraudulent characteristic behaviour. Credit card supervised learning are widely used to detect fraud based on the assumption that the pattern of fraud would depend on past transaction. This paper will give succinct understanding to avoid such problems. And will elaborate how we can use unsupervised techniques in forgery or fraud detection. In the beginning all the supervised techniques are given, types of anomalies and classification of anomaly detection techniques are given. In addition to that a project is done with the use of raw bank data and forgery is detected with unsupervised techniques.

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving the economy of small communities and the stability of the Indonesian economy. However, they still face various problems such as capital, raw materials, distribution, licensing, and others. This study developed a special treatment recommendation system to increase the independence of MSMEs by adopting a ranking-based method to produce recommendations related to problem-solving steps from MSMEs. The ranking method used Normalized Rating Frequency (NRF). This method performed an aggregation process of the ratings given by MSMEs in looking at the priority level of problem-solving based on previous experience. The result of the experiment showed that an average NDCG value was 0.7916. This explained that the quality of the ranking was good so that it was feasible to be recommended in solving problems in the form of special treatment to acquire to the independence of MSMEs.

With the rise of Deep Learning (DL), numerous DL-based SAR ship detectors, represented by Faster R-CNN, is constantly breaking the record of detection accuracy. However, these detectors still face huge challenges in modeling the geometric transformation of shape-changeable ships, due to their used conventional convolution kernels whose structure is fixed. Therefore, to address this problem, we propose an improved Faster R-CNN by using deformable convolution kernels for SAR ship detection. We substitute some conventional shape-changeless convolution kernels in Faster R-CNN with deformable convolution ones that can adaptively learn additional 2-D offsets of the raw convolution kernels, to better model the geometric transformation of shape-changeable ships. Finally, the experimental results on the open SAR Ship Detection Dataset (SSDD) reveal that our improved Faster R-CNN achieves a 2.02% mean Average Precision (mAP) improvement than the raw Faster R-CNN.

Intelligent warehouse management system (IWMS) attracts lots of interest by increasing the productivity and efficiency, minimizing the number of human workers and decreasing errors. As an important part of IWMS, warehouse portal in-out registration still face with enormous challenge. Usually, multiple UHF RFID reader antennas are installed on the portal, in-out registration can be performed by estimating the locations of RFID tags which are attached on the goods. Even though the precise localization accuracy is not required, it's still hard to distinguish the import and export directions. In this paper, we analyzed the fluctuation of received signal strength indicator (RSSI) backscattered from the RFID tags and proposed a movement direction judging method based on double-tag with an exact spacing distance. Experimental results have shown that the double-tag method can distinguish the moving direction exactly in portal environments and realize efficient in-out registration.

In the current expanding digital society, healthcare systems still face digitalization challenges due to legacy systems and the complex workflows for processing medical data. Adoption of internal Enterprise Architectures and Inter-Enterprise Architectures can provide seamless integration by allowing all participating parties to contribute to the achievement of an integrated system in accordance with the organization strategy. The authors propose a study of enabling paradigms and technologies for the design of a health 4.0 systems architecture.

Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallow-learning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What's more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images.

Machine reading and comprehension using differentiable reasoning models has recently been studied extensively, and memory networks have demonstrated promising performance on some reasoning tasks such as factual reasoning and basic deduction. However, as a natural language understanding model, memory networks still face challenges on the numeric representations for sentences, particularly the text representation method and the effectiveness of learned vector representations. In this paper, inspired by the convolution mechanism in the computer vision domain, a raw text representation architecture for question answering problem named convolutional end-to-end memory networks(CMemN2N) architecture is proposed. The convolutional architecture of the proposed model allows us to abstract the useful local information for reasoning to get the significant numeric sentence representation passed to the follow-up sub-tasks. Our experiments show that CMemN2N achieves better results on most of the 20 bAbI task dataset, yielding improvements for the average result compared to the state-of-the-art.

Under the background of Big Data, small and medium-sized enterprises in Anhui Province still face many problems. The outstanding problem is financing. Based on qualitative analysis and quantitative analysis, this paper studies the current situation of small and medium-sized enterprises in Anhui Province and the financing difficulties of small and medium-sized enterprises under the background of Big Data, this paper puts forward some countermeasures to solve the financing problems of small and medium-sized enterprises in Anhui province under the background of Big Data: strengthening the financing consciousness and self-management system construction of small and medium-sized enterprises; We will improve the financial financing system for small and medium-sized enterprises, create new channels for their financing, strengthen the construction of the concept of credit guarantee and upgrade their credit rating, and make full use of state policies.

Locomotion is essential for a person’s ability to function in society. When an individual has a condition that limits locomotion, such as a lower limb amputation, the performance of a prosthetic often determines the quality of life an individual regains. In recent years, powered prosthetic devices have shown nearly identical replication for human leg motion on non-compliant terrains. However, they still face numerous functional deficits such as increased metabolic cost and instability for walking on surfaces of varying compliance and complexity. This paper proposes joint angles of the biological leg are uniquely altered by surface compliance regardless of a subject’s individual walking pattern. These differences are then displayed and quantified as a way to better characterize able-bodied walking compensation typical with three common terrains: sand, grass and gravel. This study also collects data outdoors using IMU sensors and is not limited by lab setup and conditions. These results are important since better understanding of joint angle kinematics on varying terrains could enable the formulation of advanced controllers for current prosthetic devices allowing them to anticipate surface changes and adapt accordingly.

In Brazil, only 15% of deaf people are fluent in Brazilian Portuguese. Although Brazilian law says that public spaces should provide access in Libras, deaf still face lots of issues accessing customer services. Also, there is no tool able to allow them to talk in Libras via customer service communication channels, such as chats. Thus, this work aims to help deaf to access the customer services of a big computer manufacturer by providing a way for them to communicate in Libras while the call center attendant receives a translation in Brazilian Portuguese. The method to translate Libras to Portuguese includes the use of a CNN. The results in recognizing and classifying signs (captured from video) reached over 90% of the accuracy in the training phase.

The automatic dialogue system is an intelligent system built by combining various artificial intelligence technologies. In recent years, with the introduction of multi-turn dialogue generation systems, semantic relevance and topic consistency between different dialogue turns have become important evaluation criteria for the success of the model. However, these problems have not been resolved and still face many challenges. In this paper, we propose a sequence to sequence (seq2seq) model based on multi-encoder structure and theme-oriented decoder, and these innovations enable the proposed model to obtain semantic correlation between different turns of conversation and maintain the consistency of topics. Meanwhile, aiming at the disadvantages of the basic seq2seq model, BiLSTM cells, attention mechanism and beam search algorithm are adopted to solve the problem of long-distance dependence and obtain richer semantic information. Experiments show that the proposed model can generate coherent, appropriate and diverse replies on multi-turn dialogue datasets.

Nowadays, distance education is becoming more and more important as a way of recharging learning. Tencent Classroom is based on online education and is the leading online education platform in China. This paper takes Tencent Classroom as an example to analyze the service innovation of distance education platform under the background of coronavirus. It is found that though it has made brilliant achievements, Tencent Classroom still face some problems, such as the week profitability of the courses, relatively low specialization level, and the lack of humanization in client interaction. According to these problems, the innovation profit model, the implementation of “expert program”, the improvement of the specialization and the client interaction from the perspective of the users are future service innovation strategies for Tencent Classroom. In the 5G era, artificial intelligence and big data are also necessary for Tencent Classroom.

Many enterprises run Network Function Virtualization (NFV) services on public clouds to relieve management burdens and reduce costs. However, NFV operators still face the burden of choosing the right types of virtual machines (VMs) for various network functions (NFs), as well as the cost of renting VMs at a granularity of months or years while many VMs remain idle during valley hours. A recent computing model named serverless computing automatically executes user-defined functions on requests arrival, and charges users based on the number of processed requests. For NFV operators, serverless computing has the potential of completely relieving NF management burden and significantly reducing costs. Nevertheless, naively exploring existing serverless platforms for NFV introduces significant performance overheads in three aspects, including high remote state access latency, long NF launching time, and high packet delivery latency between NFs. To address these problems, we propose Serpens, a high-performance serverless platform for NFV. Firstly, Serpens designs a novel state management mechanism to support local state access. Secondly, Serpens proposes an efficient NF execution model to provide fast NF launching and avoid extra packet delivery. We have implemented a prototype of Serpens. Evaluation results demonstrate that Serpens could significantly improve performance for NFs and service function chains (SFCs) comparing to existing serverless platforms.

Mobile Crowdsensing (MCS) is a new paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user’s probability or frequency of covering the task locations. However, we argue that, for the cooperative MCS task, the completion effect depends not only on the user’s objective ability, but also on their subjective collaboration likelihood with each other. In other words, in each single round, we prefer to recruit users with not only a strong objective ability but also good collaboration likelihood. Moreover, even though we could find a well-behaved group of users in a single round, in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting unknown user groups (exploration). To address these problems, in this paper, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the optimal group of users. Furthermore, in the multi-round scenario, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy based on the combinatorial Multi-armed Bandit model (URMB) and prove that it can achieve a tight regret bound. Finally, extensive experiments on three real-world datasets validate that the users recruited by URMB result in a better task completion effect than the state-of-the-art strategy.

Researchers and practitioners in numerous fields yearn to understand human behavior and use that knowledge to improve the quality of life for individuals and populations. Until recently, theories of behavior and its relationship with genetics, health, and the environment have been based on selfreport or human-observed information. Given the meteoric rise of pervasive and machine learning technologies, researchers now have the necessary tools to map a personalized human behaviorome.Despite these recent technology advances, we still face numerous challenges that limit our current ability to learn reliable, complete personal profiles. The ability to perform machine learning-based behavior observation and analysis in uncontrolled environments, where the physical twin lives, is a fundamental requirement. This capability is not only a key to understanding the relationship between behavior and its influences, but also represents an unmet need.In this talk, we highlight methods that have been designed at the WSU CASAS lab to lay the foundation for digitally mapping a human behaviorome. We mention hurdles that were faced along the way, which unexpectedly created new research opportunities. We also tour some of the applications of the human behaviorome that the group is exploring, specifically to impact health and quality of life for adults as they age.

Industrial symbiosis is recognized as a practice that through collaboration between traditionally separate industries, enables sustainable development with environmental, economic and social benefits. Extending them to adjacent urban areas maximizes these potential benefits. However, these synergies still face many challenges, especially regarding the incorporation of the latest technological advances. Thus, this paper aims to present a conceptual framework that represents the main flows of waste and resources between eco-industrial parks and urban areas, focusing on the capture of carbon dioxide and its transformation into fuel. It also aims to present the main benefits and impacts that industrial and urban symbiosis can provide.

The goal of mobile manipulators is to perform complex operational tasks autonomously in a structured and unstructured environment and to work or closely interact with humans. Mobile manipulators brings more complicated engineering problems and deeper scientific problems. It is not easy to simply combine the research results of mobile platforms and robot arms to solve the unique and complex problems: hand-eye-foot coordination for mobile manipulator. Firstly, this paper mainly presented a survey of mobile manipulator from three aspects: autonomous grasping strategy, mobile platform and path planning. Especially, we discussed the basic principle of space trajectory planning for mobile manipulators and its practical application combined with vision, and further divided them into six categories according to different modeling principles. In conclusion, we suggest that mobile manipulators still face three major challenges: uncertainty in high-dimensional space, highly coupled dynamic nonlinearity, real-time performance of dynamic task planning.

Online reviews are considered as one of the most essential sources of client opinion. In current scenario, consumers can learn about the products and services using online review resources to make decisions. The customer reviews for numerous products plays a very vital role not only for purchasers, But also for the firms. Consumer reviews are used by companies as feedback in their product development strategies and in the management of consumer relations. Due to their disorganized nature, consumer reviews with valuable information still face difficulties in navigating information. Driven by a need for profit, some organizations may generate spam reviews regarding different products or their own product which may mislead customers to buying unworthy product. Because of its promising commercial benefits, Sentiment Analysis has become one of the most interesting subjects in text analysis. One of its main problem is the detection of false negate reviews. In addition, the user reviews can be classified as positive or negative reviews so that a consumer can use the false review to select a product. In this paper, we examine various levels of Sentiment Analysis and compare different existing techniques.

With the increasing odds in the availability of conventional sources of energy like coal and petroleum, the search for alternative sources has become a top priority in many aspects. While the common renewable energy sources like solar and hydro energy are already being put into great implementation, the uncommon ones like biomass energy still face a long way for its complete usage. Sikkim, being a densely vegetated state, account for a large supply of biomass regularly. A Q-GIS (Quantum Geographical Information System) based system has been used along with an MCDA (Multi Criteria Decision Analysis) approach to figure out the optimum locations for biomass facility within the state of Sikkim and its further usage as a source of energy. The approach used in this analysis can also be implemented in any further studies of similar nature.

The dexterous prosthetic hand is capable of grasping many objects with different size and shape such as precision, power, and lateral grips. However, most of these prosthetic hands haven't the ability of the tactile. Recent advances in EMG pattern recognition techniques are useful for creating hand prostheses with more intuitive control, but amputees still face the problem of lost tactile feedback. In this paper, a biomimetic tactile data driven closed-loop control of myoelectric prosthetic hand is proposed. The problem addressed in this paper is myoelectric prosthetic hand how to consistently grasp delicate objects without causing damage and dropping. The peculiarity of the proposed controller will offer the hand prostheses the ability to make rapid and accurate adjustments the grasping force based on the tactile feedback, which is similar to our very own reflex pathway. The experiments results show that the proposed controller can significantly improve the grasping function.

Although there has been an immense advancement in the robustness of IT infrastructure, the organizations still face downtimes, lasting for short periods and even continuing for days. This could be due to malicious requests, deliberately sent to disrupt the services or the volume of legitimate users trying to access the website. Detection of these network traffic anomalies and then categorizing them as DDoS Attack or Flash event, is a challenging task in the front of network security researchers. This paper reviews and summarizes the details and scenarios of these anomalies. It highlights the various features, parameters and metrics used in research till date to detect these anomalies and distinguish the legitimate request from the malicious one. The intent of paper is to guide researchers in selecting efficient parameters in order to obtain accurate results with low false positives. It highlights the list of characteristics to discriminate the two traffic patterns has been proposed for better understanding of the problem. Thus, the paper is expected to help academicians and researchers find effective solution for the above-mentioned anomalous behavior of the network traffic.

Big Data has become an important technical force to advance many industries. Many big data techniques have been invented and open-sourced for public use. The small businesses, which form the major part of the whole business world, still face great challenges in applying big data solutions to their own businesses. However, there are few cases that have been published to report the procedure of engineering big data in small businesses. This fact leads to insufficient references for small businesses to take to figure out their own scenarios of applying big data. In this paper, we report a pilot case study on a small business applying big data to an electric signal process project. It describes in detail the procedure of analyzing the business logics, identifying big data requirements, and selecting appropriate big data techniques for engineering solutions. We also share lessons learned from this case study, which can be a general reference for those small businesses on trying their own big data application cases.

Model-Driven Software Engineering (MDSE) is a development method in which models are used to generate software. Despite documented advantages, projects employing MDSE may fail due to development challenges. In this paper, we study and document these challenges by conducting an up-to-date systematic mapping that goes beyond reviewing MDSE studies: we also include two derived paradigms (Model-Oriented Programming and Models at Run-time). Therefore, we present a systematic mapping with two objectives: The first objective was to identify specific domains in which MDSE is successful, while the second objective was to identify what are the challenges to apply this methodology to general purpose development processes. Following the review of 4859 studies (3727 are unique), we have identified the application and technological domains in which MDSE projects are more likely to succeed. We also discuss challenges presented by 17 primary studies. The analysis of the results indicate that MDSE application is consolidated in specific domains. A common feature identified among studies related to general purpose MDSE processes is that, initially, authors reported lack of proper methods and training. After new techniques have risen, it has been pointed that MDSE projects still face maintenance problems that can discourage their usage in other domains.

One of the most significant procedures for getting the principal food to newborns within six months is nutritive sucking. On the other hand, developing the precise coordination of feeding skills actions under oral feeding is usually critical for premature infants and may affect the body and neural development of them. Thus, data collecting related to the oral feeding is useful for doctors who are seeking to make more accurate clinical diagnoses for related issues to oral feeding skills in preterm infants. There are different factors involved in the process of evaluating the feeding skills of infants, clinical experience of medical staffs is one of the most important ones. However, we still face a deficiency of monitoring systems for evaluating the feeding skills of infants quantitatively and objectively. In this study, an original wireless monitoring system is introduced to facilitate the process of evaluating the feeding behavior of infants both quantitatively and objectively. This unique monitoring system will also assess the process of analyzing the characteristics of oral feeding skills. Here, a specific measurer system was applied to monitor the sucking and expression pressure actions noninvasively. Based on the experimental results, the sucking rate and intensity increased by growing the infants. In contrast, the positive expression pressure decreased under the same condition. The proposed system successfully assessed the feeding behavior of infants. Furthermore, it can perform as an objective tool for evaluating oral feeding ability and identifying the risk of infant developmental delays in the future.

Nowadays, hot and cold rolling of steel strips is mainly monitored by optical or radiometric measurement systems. In some production stages, however, conventional systems still face challenges due to harsh environmental conditions or other influencing factors. To meet some of these challenges, a prototype system consisting of eight radar sensors for thickness measurement of steel strips in hot and cold rolling mills is being developed. It provides an accuracy in the micrometre range and is being optimized for the conditions in the mill. After finishing the laboratory tests, the radar sensors will be implemented in an industrial measurement gauge and tested in the field. In the following, the system setup and results of laboratory measurements are presented.

Inkjet printing is known as a main technology to manufacture flexible electronic interconnect structures due to its ability to print different types of inks on flexible substrate in a controllable and fast way. However, current inkjet printing processes on manufacturing flexible electronic interconnects still face many challenges (e.g. fracture, rough etc.). In view these serious problems, the following research has been carried out in this paper. First of all, printed a series of single ink drop array on the flexible substrate, and measured its average diameter to select the appropriate distance spacing. Second, manufactured flexible interconnect structures with different substrate-ink groups by inkjet printing. Third, made a compare on the effect with different pre-treatment of the flexible substrate. And finally, discussed the effect on surface topography and resistivity of interconnect structures with different layers, curing temperature and time. After all above experiments, we obtained flexible interconnects with uniform surface morphology and good electrical conductivity.

While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.

Many IoT and wearable devices require an on-chip small-to-mid-capacity nonvolatile memory (NVM) with a fast read-access time (T AC ) and reliable read operations: for applications including data-logging, configurable look-up tables (LUT), eFuse, and physically unclonable functions (PUF). STT-MRAM [1-4] is a good candidate for these applications due to its fast write speed, low-voltage write, and high endurance. However, STT-MRAM suffers from a small read-signal margin (RSM) due to a small tunnel magnetoresistance ratio (TMR: (R AP -R P )/R P ) between the cell resistance of parallel (P, R P ) and anti-parallel (AP, R AP ) states [1-6]. Moreover, the read-disturb behavior of STT-MRAM cells is sensitive to the BL read voltage (V BL_RD ) and the stress/read time. Compact 1T1MTJ arrays are suitable for high-density applications [5-6]; however, they use a power-hungry current-mode read scheme with a slow read speed due to the small RSM. Researchers have proposed 2T2MTJ (Fig. 30.3.1) arrays [1-4] with differential bitlines (BL and BLB) and a voltage-mode read scheme, with an enlarged RSM (V RSM ), for fast, low-power read operations. V RSM refers to the voltage difference between BL (V BL ) and BLB (V BLB ). 2T2MTJ STT-MRAM read operations still face the following challenges: (1) V BL and V BLB both drop from V BL_RD to 0V quite quickly due to the large cell read current (V P and V AP ) or low R-value in both R P and R AP , resulting in small sensing window (T SMW ), which is the period when V RSM >offset; (2) the maximum V RSM (V RSM_MAX ) occurs at different times (t RSM_MAX ) for different cells due to TMR (R AP /R P ) variation; and (3) a degraded V RSM due to the use of a low V BL_RD to avoid read disturbs for high data-reliability applications. (1) and (2) lead to a decrease in V RSM after reaching its peak (t RSM_MAX ), despite an increase in BL development time (t BL ). When using a conventional voltage-mode sense amplifier (CNV-VSA) with a common activated (SAEN=1) timing (f SAEN ) under the effects of (1)-(3), the signal to be amplified (ΔV IN „ RSM_MAX ) is subject to degradation at the VSA's differential inputs, resulting in a sensing failure at a low VBL_RD.

Quality of face images may be degraded as they are captured under varying capturing conditions such as illumination and speed of moving subject in videos. Performance of a typical face recognition systems is sensitive to the quality of input face images. Face image quality assessment is necessary for accurate face recognition systems both in the enrollment and recognition stages. Face image quality assessment is considered as a complex task as some of quality factors are in contrast to each other in different environmental conditions. In this study, a face image quality assessment based on photometric quality factors using classification techniques is proposed to justify applicability of used quality factors. The proposed method has three main phases namely, quality factor measurement, feature normalization, and classification. Evaluation of the proposed method on modified NLPR face dataset demonstrates all of the used classifiers have almost equal performance but, MLP classifier outperforms other classifiers in terms of f-score and accuracy measures slightly. Experimental results revealed that brightness, contrast, focus, and illumination are effective factors for purpose of still face image quality assessment. The proposed method also has better performance with comparison with some of the existing methods based on the mentioned dataset.

Inductor-capacitor (LC) passive wireless sensors use a transformer with loose coupling between an external readout coil and an inductor that receives power through this inductive coupling. This paper presents the overview and development of the LC sensors. The configuration and operation principle are first introduced simply. Then the advantages of the LC sensors are illustrated to manifest the importance of further studies. Through decades of study, the LC sensors still face two problems: increasing the readout distance and expanding into multifunction platform. Around these two questions, recent efforts by our group are introduced and analyzed in detail. The development prospect of the LC sensors is finally outlined.

The number of applications that rely on robust indoor localization is constantly growing. Conventional outdoor localization technologies are generally not suited for indoor use. WiFi-based indoor localization systems are a widely studied alternative since the necessary infrastructure is already available in most buildings. Existing WiFi-based localization algorithms, however, still face challenges such as high sensitivity to changes in the environment, temporal instability, a time consuming calibration process and low accuracy in non-line-of-sight (NLOS) multipath environments. In this paper, we propose a novel fingerprinting-based WiFi indoor localization scheme which operates by extracting and analyzing individual multipath propagation delays. We demonstrate through simulation the robustness of the proposed algorithm to changes in the multipath environment.

Science experiments and research are effective approaches for scientific education and developmental experiences in primary and secondary school students. However, developing countries still face the barrier of educational inequality and insufficiency of science laboratories in schools.In order to face this actual worldwide challenge, we designed a conceptual platform using smart cities framework for interfacing and supporting research programs, laboratories implementation, publishing and spreading science for youth.

In this paper, the different machine learning and data mining approaches used for Residential Energy Smart Management (RESM) will be discussed and classified according to some meaningful criteria. The proposed classification is an attempt to highlight the advantages and limitations of each category. Moreover, we emphasize the complementarity between approaches belonging to different categories and we point out the main challenges that still face RESM.

Online social networks (OSN) have become very popular during the last years. Every day millions of people generate and consume a huge amount information via these type of networks, which are useful to find former classmate or to improve our interaction with friends. Most popular online social networks are based on centralized servers, which are responsible for the management and storage all information. Although online social networks introduce several benefits, these networks still face many challenges such as central control, privacy or security. P2P infrastructures have emerged as an alternative platform to deploy decentralized online social networks. However, decentralized distributed systems are vulnerable to security attacks from malicious peers. In this work, we evaluate P2P infrastructures against spoofing attack. In particular, we simulate and evaluate hybrid and distributed P2P architectures.

In recent years, online social networks (OSN) have become very popular. These types of networks have been useful to find former classmates or to improve our interaction with friends. Currently, a huge amount of information is generated and consumed by millions of people from these types of networks. Most popular online social networks are based on centralized servers, which are responsible for the management and storage all information. Although online social networks introduce several benefits, these networks still face many challenges such as central control, privacy or security. P2P infrastructures have emerged as an alternative platform to deploy decentralized online social networks. However, decentralized distributed systems are vulnerable to malicious peers. In this work, we evaluate P2P infrastructures against Sybil attacks. In particular, we simulate and evaluate hybrid and distributed P2P architectures.

Despite the technological advances, healthcare systems still face several issues. One of the most important is the lack of communication between systems or the communication process speed. If the information about a patient is not promptly shared in time between services, it may jeopardise the practicioner-pacient relationship. In a worse scenario, the system can even become a handicap and turn the Healthcare processes down into a state of total uselessness. A lot has been done in Portugal to enable the interconnection of external healthcare applications with the ones used in the National Health Service. In this article we present an architecture to facilitate this interconnection. Based on a REST architecture and HL7 communication standards, it connects two current solutions, one for melanoma and one for blood donors, with a central healthcare data repository of the National Healthcare Service.

Despite 3D object recognition being an ongoing research field for many years, state-of-the-art methods still face problems in real-world situations with clutter, occlusion or non-textured objects. To overcome these problems, recent approaches use multi-view setups exploiting beneficial vantage points of the environment. Minimizing the assumptions on the scene and objects of interest made by these systems, we present an efficient online multi-view method, which integrates information of the captured environment merging individual single-view recognition outputs. Our method achieves state-of-the-art results for the Willow dataset at reduced computational time. Further evaluations on the more challenging TUW dataset show an increase in f-score and object pose accuracy over the number of observations.

The recent RNA-Seq technology brings computational challenges in transcriptome assembly and analysis. Lasso-type methods were designed to address the ambiguity and unidentifiability issues in isoform discovery and abundance estimation from RNA-Seq data. However, typical Lasso-type methods are confined to taking the l 1 norm to approximate the desired l 0 norm, but such approximation has been shown to be limited in analytical performance and modeling of the specific computational problem. The isoform discovery and quantification tasks still face the challenge of high-level false positive/negative predictions. In this paper, we propose the Log-Sum Heuristic Recovery for Automated Isoform Discovery and Abundance Estimation method, which is attempted at a closer approximation to the l 0 norm and more effective modeling of the parsimony principle involved in isoform discovery. The method is applied to transcriptome analysis with RNA-Seq data. Both simulation and real data experiments demonstrate that the proposed method is promising to be an effective computational tool for isoform discovery and quantification.

The problem of effective teaching of programming is still open. Students still face problems with learning and especially understanding of even basic programming principles. In this paper we present our experiences with our modern teaching method for programming that is basically a combination of the task-driven methodology and case study approach. This new method is called task-driven case study approach. In the paper we share our own experiences from the viewpoint of teachers, but we also present results of a survey done with 112 of our students. Presented results show positive experience both from viewpoint of students and teachers.

Big-data systems are increasingly used in many disciplines for important tasks such as knowledge discovery and decision making by processing large volumes of data. Big-data systems rely on hard-disk drive (HDD) based storage to provide the necessary capacity. However, as big-data applications grow rapidly more diverse and demanding, HDD storage becomes insufficient to satisfy their performance requirements. Emerging solid-state drives (SSDs) promise great IO performance that can be exploited by big-data applications, but they still face serious limitations in capacity, cost, and endurance and therefore must be strategically incorporated into big-data systems. This paper presents BigCache, an SSD-based distributed caching layer for big-data systems. It is designed to be seamlessly integrated with existing big-data systems and transparently accelerate IOs for diverse big-data applications. The management of the distributed SSD caches in BigCache is coordinated with the job management of big-data systems in order to support cache-locality-driven job scheduling. BigCache is prototyped in Hadoop to provide caching upon HDFS for MapReduce applications. It is evaluated using typical MapReduce applications, and the results show that BigCache reduces the runtime of WordCount by 38% and the runtime of TeraSort by 52%. The results also show that BigCache is able to achieve significant speedup by caching only partial input for the benchmarks, owing to its ability to cache partial input and its replacement policy that recognizes application access patterns.

A forest resources and timber supply model with variables including forest stock volume, timber harvest volume and forest area was established. The regression results of the model indicate that China's plantation accelerates the growth rate of forest stock volume while forest tenure reform has negative effect on that; additionally, there has a positive correlation between timber supply and GDP, yet timber price has no significant effect on timber supply. By use of this model, China's forest resources and timber supply trend is predicted. The forecast results show that China's forest stock volume would increase about 380 million m3 and timber supply would increase by 3.5% annually over 2009–2020. Overall, although China's timber supply would maintain an increasing trend, China will still face the problem of structural imbalances and will continue to rely on import on aspects of large-diameter timber.

Over the years, a lot has been done in the field of medical technology to assist the elderly and physically challenged people. One such development is the robotic wheelchair. Many designs of the same have been proposed throughout the recent past. But such models still face difficulties in covering difficult terrains like steps, etc. We propose a design of wheelchair which will be able to mimic easy locomotion techniques available in nature. For example: serpentine locomotion, aquatic locomotion, etc. Various types of robots mimicking such movements have been made in the past. We suggest the inclusion of such robotic locomotors in the already existing robotic wheelchair designs to assist more efficient movement. A peep into the previous researches done in this field lays the foundation of our proposed design.

Interactive systems users still face several challenge. Besides current improvements in usability and intuitiveness users have to adapt to the systems proposed to satisfy their needs. For instance, they must learn how to achieve tasks, how to interact with the system, etc. This paper proposes a methodology to improve this situation supporting the use of interactive systems by users. To achieve this goal the approach is based on enriched task models and picture-driven computing. An example based on a text editor illustrates the approach.

This paper describes in detail about 2D and 3D face and video face databases developed in the year from 1992 to 2009. The still face database discussed here includes: Our Laboratory Database of Faces (ORL), Yale face database, Yale face databaseB(B stands for Big), Yale face database extendedB, FERET(Face Recognition Technology) face database and CASPEAL is a large scale Chinese face database with varying PEAL(Pose Expression Accessory, and Lighting). The 3D face databases like MIT-CBCL (Centre for Biological and Computational Learning) face recognition database, Georgia Tech face database, Cohn-Kanade Action Unit Face database and 3D BFM (Basel Face Model) are also discussed in this paper. In CK AU, CK+AU(Cohn-Kanade Action Unit) coded facial expression database, a 3D model is developed from a 2D model which is obtained from the captured video of different subjects. The other Face databases and development of new Face databases are discussed at the end of this paper. The application of proposed concept is to develop a new small size face database which includes all the important criteria included in the previous face and video face databases.

This paper describes in detail about important 2D and 3D face and video face databases. The face and video face databases discussed here includes: Indian Institute of Technology Kanpur (IIT-K) database, Makeup dataset, University of North Carolina Wilmington Craniofacial Morphology (MORPH) non-commercial face database, Surveillance face (SCface) database, YouTube face database and University of Mialno Bicocca 3D face database (UMBDB). The facial image captured may be colour or gray, with or without makeup, same pose or different poses, occluded or not occluded, neutral expression or non-neutral expression (angry, sad, laughter, smile, sleepy), different illumination conditions, in the presence of visible light or Infrared captured. The subject may be Asian or African or American or European. For above said reasons, different face and video face databases developed in the past 20 years and few important databases are analyzed here.

Benefits through using RFID in retail organizations have been frequently discussed in academia and practice. However, implementers still face many problems and challenges in achieving the estimated benefits of this technology. To support practitioners in the resolution of various issues associated with the practical implementation of productive RFID systems, we developed a number of decision aids using the action research methodology. The present study provides an overview of these tools and experiences with their use in the context of an RFID project at a global fashion retailer. For the empirical evaluation, we collected and analyzed data from 10 pilot stores. In sum, we provide a methodological approach that supports practitioners in the optimization of RFID installations and consequently help to continuously improve the data quality of the respective system. As a consequence, more reliable data enable the generation of more performance indicators and reports for managers.

Over the last fifteen years, science gateways have proven to be fertile ground for cyberinfrastructure research, while at the same time dramatically increasing the usage and accessibility of cyberinfrastructure to scientists and educators. Gateway developers, however, still face many challenges in building and operating these complex infrastructures that address the dynamic and emerging challenges of computational infrastructure. Through the XSEDE Science Gateway Program, we propose to solicit information on the knowledge, experience, and software from widely used science gateways. We will compile the information into a “Science Gateway Cookbook” which will democratize the experiences in developing, operating, and sustaining science gateways. We hope this will be a good literature reference and also avoid some repetition in the development process.

Transmission power control (TPC) has the potential to reduce power consumption in Wireless Sensor Networks (WSNs). However, despite a multitude of existing protocols, they still face significant challenges in real-world deployments. A practical TPC protocol must be robust against complex and dynamic wireless properties, and efficient for resource-constrained sensors. This paper presents P-TPC, a practical TPC protocol designed on control-theoretic techniques. P-TPC features a highly efficient controller designed on a dynamic model that combines a theoretical link model with online parameter estimation. P-TPC's robustness and energy savings are demonstrated through trace-driven simulations and real-world experiments in a campus building and residential environments.

It is well-known that APIs can be hard to learn and use. Although search tools can help find related code examples, API novices still face other significant challenges such as evaluating the relevance of the search results. To help address the broad problems of finding, understanding, and debugging API-based solutions, we have built a critic system that offers recommendations, explanations, and criticisms for API client code. Our critic takes API usage rules as input, performs symbolic execution to check that the client code has followed these rules properly, and generates advice as output to help improve the client code. We demonstrate our critic by applying it to a real-world example derived from the Java Swing Forum.

In the search for competitiveness machining companies are applying concepts of lean manufacturing, like the fast setup, for the elimination of losses in the process. However, most of the companies users of CNC machines still face a significant barrier in this field: the presetting of tools is usually performed manually, and requires high times for the preparation of the machines. In this paper the losses in production caused by the manual presetting operations are detached, showing the results in terms of delays generated in the setup operations of machines, and also the reductions in the utilization rates of CNC machines.