title: Human posture recognition and classification

abstract: Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more general problem of video sequence interpretation. This paper presents a novel an intelligent human posture recognition system for video surveillance using a single static camera. The training and testing were performed using four different classifiers. The recognition rates (accuracies) of those classifiers were then compared and results indicate that MLP gives the highest recognition rate. Moreover, results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition. Furthermore, for each individual classifier, the recognition rate has been found to be proportional to the number of postures trained and evaluated. Performance comparisons between the proposed systems and existing systems were also carried out.


title: All-Digital Self-interference Cancellation Technique for Full-duplex Systems

abstract: Full-duplex systems are expected to double the spectral efficiency compared to conventional half-duplex systems if the self-interference signal can be significantly mitigated. Digital cancellation is one of the lowest complexity self-interference cancellation techniques in full-duplex systems. However, its mitigation capability is very limited, mainly due to transmitter and receiver circuit's impairments (e.g., phase noise, nonlinear distortion, and quantization noise). In this paper, we propose a novel digital self-interference cancellation technique for full-duplex systems. The proposed technique is shown to significantly mitigate the self-interference signal as well as the associated transmitter and receiver impairments, more specifically, transceiver nonlinearities and phase noise. In the proposed technique, an auxiliary receiver chain is used to obtain a digital-domain copy of the transmitted Radio Frequency (RF) self-interference signal. The self-interference copy is then used in the digital-domain to cancel out both the self-interference signal and the associated transmitter impairments. Furthermore, to alleviate the receiver phase noise effect, a common oscillator is shared between the auxiliary and ordinary receiver chains. A thorough analytical and numerical analysis for the effect of the transmitter and receiver impairments on the cancellation capability of the proposed technique is presented. Finally, the overall performance is numerically investigated showing that using the proposed technique, the self-interference signal could be mitigated to ~3 dB higher than the receiver noise floor, which results in up to 76% rate improvement compared to conventional half-duplex systems at 20 dBm transmit power values.


title: A Mid-level Planning System for Object Reorientation

abstract: This paper presents a mid-level planning system for object reorientation. It includes a grasp planner, a placement planner, and a regrasp sequence solver. Given the initial and goal poses of an object, the mid-level planning system finds a sequence of hand configurations that reorient the object from the initial to the goal. This mid-level planning system is open to low-level motion planning algorithm by providing two end-effector poses as the input. It is also open to high-level symbolic planners by providing interface functions like placing an object to a given position at a given rotation. The planning system is demonstrated with several simulation examples and real-robot executions using a Kawada Hiro robot and Robotiq 85 grippers.


title: A Model-Driven Framework to Develop Personalized Health Monitoring

abstract: Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.


title: Scalable model predictive control of demand for ancillary services

abstract: In this paper, we develop an integrated decision making framework for the planning and real-time control decisions made by a Load Serving Entity (LSE) providing ancillary services to the wholesale market. Due to the multi-settlement structure of the energy market, planning decisions by the LSE are naturally made at multiple temporal stages. The tight interdependence among decisions demands an integrated approach to minimize the overall costs of operation. In order to model the dynamics of the load at large-scales when making these decisions, we propose a classification-based model that captures the effect of scheduling decisions made for individual appliances at aggregate levels, with reasonable effort. To provide a tangible example of how this load aggregation technique can be applied, we study the case of Electric Vehicle (EV) charging in detail.


Analysis and Comparison of different performance index factor for Conventional PID & GA plus PID Controller


title: Optimizing dual half bridge converter for full range soft switching and high efficiency

abstract: This paper is concerned with finding the optimum operating points of a soft-switched dual half bridge bidirectional dc-dc converter. Phase shift modulation (θ) with a fixed duty cycle is used to control the converter [1] but the soft switching range is limited. In this paper the soft switching range and efficiency of the converter are highly improved by recruiting duty cycle (d) as the second control parameter. It is shown that for a wide voltage range on both dc buses, soft switching can be achieved for any power level including zero to full load in both directions. The regions of soft switching on the (θ,d) plane are analyzed and the trajectories of the optimal (θ,d) values for highest efficiencies are extracted. A precise simulated model of the converter is developed to verify and fine tune the analytic results. Finally a 250 KHz, 1.5Kw prototype is designed and implemented to verify the results in practice.


title: Webpage Load Speed: ASP.NET vs. PHP

abstract: As data transmission speeds over the Internet continue to increase, it is necessary to research and identify technologies that can take advantage of the increased speeds by enhancing the loading speed of webpages. One area of consideration is found in the type of framework that is utilized for a website. There are numerous frameworks that can be chosen from to be used to support a website, each with their distinctive advantages. There are many different opinions that have been tested on which framework should be used to fully realize the optimization of page load speed. This manuscript examines and implements testing methods for two popular frameworks, Active Server Pages .net and PHP, to make a final determination of which framework is most beneficial for webpage load speeds.


title: A Rule Based Taxonomy of Dirty Data

abstract: There is a growing awareness that high quality of data is a key to today’s business success and that dirty data existing within data sources is one of the causes of poor data quality. To ensure high quality data, enterprises need to have a process, methodologies and resources to monitor, analyze and maintain the quality of data. Nevertheless, research shows that many enterprises do not pay adequate attention to the existence of dirty data and have not applied useful methodologies to ensure high quality data for their applications. One of the reasons is a lack of appreciation of the types and extent of dirty data. In practice, detecting and cleaning all the dirty data that exists in all data sources is quite expensive and unrealistic. The cost of cleaning dirty data needs to be considered for most of enterprises. This problem has not attracted enough attention from researchers. In this paper, a rule-based taxonomy of dirty data is developed. The proposed taxonomy not only provides a mechanism to deal with this problem but also includes more dirty data types than any of existing such taxonomies.


title: Virtual network functions orchestration in wireless networks

abstract: Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.


title: Icons, symbols, and signs: visible languages to facilitate communication

abstract: Graphical user-interfaces rely on visual signs, that is, icons and symbols, to speed communication, take up less visual space, and provide a distinctive visual style for products and services. Are icons/symbols being used effectively? Can they help improve human communication? Some proposed systems, like LoCoS, seek to develop additional forms of universal communication, like a visual Esperanto. What is the future for icons and symbols? Read on…


title: Evaluation of Feature Extractors and Psycho-Acoustic Transformations for Music Genre Classification

abstract: We present a study on the importance of psycho-acoustic transformations for effective audio feature calculation. From the results, both crucial and problematic parts of the algorithm for Rhythm Patterns feature extraction are identified. We furthermore introduce two new feature representations in this context: Statistical Spectrum Descriptors and Rhythm Histogram features. Evaluation on both the individual and combined feature sets is accomplished through a music genre classification task, involving 3 reference audio collections. Results are compared to published measures on the same data sets. Experiments confirmed that in all settings the inclusion of psycho-acoustic transformations provides significant improvement of classification accuracy.


title: Improved backing-off for M-gram language modeling

abstract: In stochastic language modeling, backing-off is a widely used method to cope with the sparse data problem. In case of unseen events this method backs off to a less specific distribution. In this paper we propose to use distributions which are especially optimized for the task of backing-off. Two different theoretical derivations lead to distributions which are quite different from the probability distributions that are usually used for backing-off. Experiments show an improvement of about 10% in terms of perplexity and 5% in terms of word error rate.


title: Full dimension MIMO for LTE-Advanced and 5G

abstract: Elevation beamforming and Full Dimension MIMO (FD-MIMO) has been an active area of research and standardization in 3GPP LTE-Advanced. In an FD-MIMO system, a base station with 2-dimensional (2D) active array supports multi-user joint elevation and azimuth beamforming (a.k.a. 3D beamfoming), which results in much higher cell capacity compared to conventional systems. Recent study has shown that with these new FD-MIMO technologies, we can achieve promising 3–5× gain in both cell capacity as well as cell-edge throughput. In this paper, we will provide a brief summary of recent 3GPP activities, including the recently completed 3D channel model, ongoing study on FD-MIMO scenarios, antenna/RF (radio frequency) transceiver architectures, as well as potential network performance benefits. In addition, we also discuss some methods for reducing CSI (channel state information) feedback overhead and ensuring efficient operation of large size FD-MIMO for both TDD and FDD systems.


title: Acupuncture in De Quervain—s Disease: A Treatment Proposal

abstract: De Quervain's disease is a painful stenosing tenosynovitis of the first dorsal compartment of the hand affecting the tendons of the abductor pollicis longus and extensor pollicis brevis, caused mainly by overuse. Conventional treatments include rest, immobilisation, oral anti-inflammatory drugs, corticosteroid injection and even surgery, but none of these is established as clearly effective. Acupuncture is rarely mentioned and the points suggested are rather general-regional, tender and ah shi points. Tendinopathy is almost always associated with problems in the relevant muscles and this paper calls attention to the correct identification and needling of the affected muscles, in order to increase the specificity of acupuncture treatment.


title: A probabilistic theory of clustering

abstract: Abstract Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is different with pattern classification, for which two fundamental characteristics can be stated: (1) the error of a classifier can be estimated using “test data,” and (2) a classifier can be learned using “training data.” This paper presents a probabilistic theory of clustering, including both learning (training) and error estimation (testing). The theory is based on operators on random labeled point processes. It includes an error criterion in the context of random point sets and representation of the Bayes (optimal) cluster operator for a given random labeled point process. Training is illustrated using a nearest-neighbor approach, and trained cluster operators are compared to several classical clustering algorithms.


abstract: Abstract A series of Sb-, Dy-, and Eu-doped oxyfluoride silicate glasses for light emitting diodes (LEDs) applications were prepared via the melt-quenching method, and studied by a) photoluminescence emission and excitation spectra, b) decay curves, c) Commission Internationale de L'Eclairage (CIE) chromaticity coordinates, and d) correlated color temperatures (CCTs). We discover the energy transfer from Sb 3+ to Dy 3+ ions occurs in Sb/Dy co-doped glass. We also find the emission behavior of Sb 3+ single doped glass is dependent on the excitation wavelength. Furthermore, the white light emission can be achieved in Sb/Dy/Eu co-doped oxyfluoride silicate glasses under ultraviolet (UV) light excitation. The results presented here demonstrate that the as-prepared Sb/Dy/Eu doped oxyfluoride silicate glasses may serve as a potential candidate for LEDs-based lighting.


title: Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

abstract: Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control — developed in lockstep with advances in experimental neurotechnology — promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.


title: Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

abstract: The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such knowledge to provide better services e.g., public transport planning, optimized resource allocation and safer environment. Call Detail Record CDR data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we propose a methodology that is able to detect unusual events from CDR data, which typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10$$\times $$ higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.


title: A Discriminative Model for Age Invariant Face Recognition

abstract: Aging variation poses a serious problem to automatic face recognition systems. Most of the face recognition studies that have addressed the aging problem are focused on age estimation or aging simulation. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face by designing a densely sampled local feature description scheme, in which scale invariant feature transform (SIFT) and multi-scale local binary patterns (MLBP) serve as the local descriptors. By densely sampling the two kinds of local descriptors from the entire facial image, sufficient discriminatory information, including the distribution of the edge direction in the face image (that is expected to be age invariant) can be extracted for further analysis. Since both SIFT-based local features and MLBP-based local features span a high-dimensional feature space, to avoid the overfitting problem, we develop an algorithm, called multi-feature discriminant analysis (MFDA) to process these two local feature spaces in a unified framework. The MFDA is an extension and improvement of the LDA using multiple features combined with two different random sampling methods in feature and sample space. By random sampling the training set as well as the feature space, multiple LDA-based classifiers are constructed and then combined to generate a robust decision via a fusion rule. Experimental results show that our approach outperforms a state-of-the-art commercial face recognition engine on two public domain face aging data sets: MORPH and FG-NET. We also compare the performance of the proposed discriminative model with a generative aging model. A fusion of discriminative and generative models further improves the face matching accuracy in the presence of aging.


title: Emotions and Emotional Communication in Infants

abstract: Important advances have recently been made in studying emotions in infants and the nature of emotional communication between infants and adults. Infant emotions and emotional communications are far more organized than previously thought. Infants display a variety of discrete affective expressions that are appro- priate to the nature of events and their context. They also appreciate the emotional meaning of the affective displays of caretakers. The emotional expressions of the infant and the caretaker function to allow them to mutually regulate their interactions. Indeed, it appears that a major deter- minant of children's development is related to the oper- ation of this communication system. Positive development may be associated with the experience of coordinated in- teractions characterized by frequent reparations of inter- active errors and the transformation of negative affect into positive affect, whereas negative development appears to be associated with sustained periods of interactive failure and negative affect. How is it that some children become sad, withdrawn, and lacking in self-esteem, whereas others become angry, un- focused, and brittlely self-assertive, whereas still others become happy, curious, affectionate, and self-confident? As clinicians, researchers, and polieymakers, our goal must be to understand the processes that lead to these outcomes, not just to generate indexes of them, so that problematic and compromised developmental outcomes can be prevented and remediated. Although the nature of these processes is not yet known, an answer is taking shape on the basis of recent work on the nature of infant- caretaker emotional communication. The emerging answer is that the infant and adult are participants in an alfective communication system. A central hypothesis is that the operation of this system has a major influence on how well the infant accomplishes his or her goals, the emotions the infant experiences, and the infant's developmental outcome. If this hypothesis is correct, then the key issue is to understand how this sys- tem works. We need to explore the inextricable links among infant emotions and behavior, caretaker emotions and behavior, and the success, failure, and reparation of interactive errors that the infant experiences when striving to accomplish his or her goals. Two contrasting examples of infant-mother interaction drawn from the work of Brazelton (Brazelton, Koslowski, & Main, 1974) will serve as a base for the initial exploration of the functioning of this affective communication system. Imagine two infant-mother pairs playing the game of peek-a-boo. In the first, the infant abruptly turns away from his mother as the game reaches its "peek" of inten- sity and begins to suck on his thumb and stare into space with a dull facial expression. The mother stops playing and sits back watching her infant. After a few seconds the infant turns back to her with an interested and inviting expression. The mother moves closer, smiles, and says in a high-pitched, exaggerated voice, "Oh, now you're back!" He smiles in response and vocalizes. As they finish crow- ing together, the infant reinserts his thumb and looks away. The mother again waits. After a few seconds the infant turns back to her, and they greet each other with big smiles. Imagine a second similar situation except that after this infant turns away, she does not look back at her mother. The mother waits but then leans over into the infant's line of vision while clicking her tongue to attract her attention. The infant, however, ignores the mother and continues to look away. Undaunted, the mother per- sists and moves her head closer to the infant. The infant grimaces and fusses while she pushes at the mother's face. Within seconds she turns even further away from her mother and continues to suck on her thumb. I will not yet focus on the issue of who is responsible


Supersizing the Penis Following Penile Prosthesis


title: RFID: A 7-POINT PLAN TO ENSURE PRIVACY

abstract: This paper gives an overview of consumer fears associated with the introduction of RFID technology. It analyzes the motivation and technical viability of these fears and derives suggestions for privacy-friendly technology design. The analysis shows that all consumer fears currently debated are essentially justified, because from a technical perspective they can all be implemented in the short- or mid-term. A 7-point plan of technological measures is presented that should be taken into consideration and developed further by standardization bodies, researchers and governments in order to impede potential abuses of the technology in the long term.


title: Continuing medical education in cardiology. Proposal for a national program. Committee of Medical Residency and Training in Cardiology--SBC/FUNCOR.

abstract: In the last decades, the evolution of knowledge has increased the volume of information in an exponential manner, particularly in the area of biomedicine, where there are 30,000 periodicals, and two million manuscripts and 17 thousand books are published annually . For a cardiologist to stay abreast of this great volume of published material, it is estimated that he/she would have to read about 26 scientific manuscripts each day. This challenge plus the scarce time physicians have to read at all explains the significant development and acceptance of medical educational programs throughout the world . Another important aspect is the process by which physicians incorporate new knowledge into their daily practice. As recently as two decades ago, the instructions provided in medical school were used for a long period of time in a physician’s life. Today, the validity period of certain diagnostic management techniques and treatment approaches gets shorter and shorter. Although the need to stay current is mandatory, this is turning into something more and more difficult to achieve . Many studies show that even though physicians say they keep up to date by consulting books, specialized periodicals and attending congresses, in reality, what they do is consult the closest colleague . On the other hand, to change a medical management method is not always an entirely rational process. Some studies on the influence of continuing medical education that used the methods of clinical investigation and meta analysis have confirmed its value for updating health care professionals . However, the global results are deceiving when the real impact obtained in clinical practice 7,8 is considered. Among the innumerable strategies for updating medical knowledge, the most significant ones are the supervision of or access to prestigious specialists, usually through visits in work places. However, the strategy commonly employed by institutions dedicated to continuing medical education is still the use of combined formulas that consider the differences of interests between general clinicians and specialists 9 (table I). The Sociedade Brasileira de Cardiologia (SBC Brazilian Society of Cardiology) and FUNCOR (Foundation of Research and Studies in Cardiology) should decisively get involved in this question, stimulating and supporting all existing initiatives, besides contributing in a conclusive way to the organization of a national program of continuing education in cardiology. This program should consider the large regional diversity of Brazil and provide all interested cardiologists with general pathways of access to new information, updating and training.


title: Matching service strategies, business models and modular business processes

abstract: Purpose – In recent decades, supply chains have diverged and new types of services and operators have emerged in the logistics sector. The purpose of this paper is to focus on analyzing service strategies and service‐related business models, as well as their modular business processes in logistic services. The aims are to describe these three levels and to match strategic service positioning with business models and modular business processes. Different types of services are analyzed and the analyses are conducted on both the industry and corporate levels.Design/methodology/approach – The theoretical focus of the paper illustrates frameworks for service strategy, service positioning, business models, and business processes. The corporate level approach aims to describe the efficiency and quality of services and their processes, while the industry level approach focuses on service strategies in an industry and on the organization of business, i.e. business models. A case study is used to illustrate the str...


title: A Flexible Architecture for Precise Gamma Correction

abstract: We present a flexible hardware architecture for precise gamma correction via piece-wise linear polynomial approximations. Arbitrary gamma values, input bit widths, and output bit widths are supported. The gamma correction curve is segmented via a combination of uniform segments and segments whose sizes vary by powers of two. This segmentation method minimizes the number of segments required, while providing an efficient way for indexing the polynomial coefficients. The outputs are guaranteed to be accurate to one unit in the last place through an analytical bit-width analysis methodology. Hardware realizations of various gamma correction designs are demonstrated on a Xilinx Virtex-4 field-programmable gate array (FPGA). A pipelined 12-bit input/8-bit output design on an XC4VLX100-12 FPGA occupies 146 slices and one digital signal processing slice. It is capable of performing 378 million gamma correction operations per second


title: An Identity Based Encryption Scheme Based on Quadratic Residues

abstract: We present a novel public key cryptosystem in which the public key of a subscriber can be chosen to be a publicly known value, such as his identity. We discuss the security of the proposed scheme, and show that this is related to the difficulty of solving the quadratic residuosity problem.


title: On-Road Motion Planning for Autonomous Vehicles

abstract: We present a motion planner for autonomous on-road driving, especially on highways. It adapts the idea of a on-road state lattice. A focused search is performed in the previously identified region in which the optimal trajectory is most likely to exist. The main contribution of this paper is a computationally efficient planner which handles dynamic environments generically. The Dynamic Programming algorithm is used to explore in spatiotemporal space and find a coarse trajectory solution first that encodes desirable maneuvers. Then a focused trajectory search is conducted using the "generate-and-test" approach, and the best trajectory is selected based on the smoothness of the trajectory. Analysis shows that our scheme provides a principled way to focus trajectory sampling, thus greatly reduces the search space. Simulation results show robust performance in several challenging scenarios.


A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data


title: Connected Component Labeling in CUDA

abstract: Publisher Summary This chapter presents a new algorithm for connected component labeling in 2D images implemented in CUDA. First, a brief overview of the CCL problem together with existing CPU-oriented algorithms has been provided. The goal of a CCL algorithm is to find a unique label for every set of connected elements in input data. In other words, CCL is used to divide input elements, for example, pixels of a raster image, into groups where all elements from a single group represent a connected object. CCL can be thought of as topological clustering based on connectivity. The CCL problem is usually solved on the CPU using sequential algorithms, but the speed of these algorithms is often not sufficient for real-time applications. Certain CCL algorithms can be parallelized, but as has been pointed out in, the sequential algorithms often outperform the parallel ones in real applications. The complexity of a CCL problem depends on the structure of the connected components in the input data and the performance of each method can differ dramatically for different datasets. Hence, to perform an unbiased analysis, CCL algorithms should be tested on a variety of different input cases. There is no standardized set of input data designed for the performance testing. One drawback of this method is that it does not generate consecutive final labels because each label has a value of some element from a given connected component.


title: A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines

abstract: The detection of fraudulent financial statements FFS is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. Although nonfinancial ratios are generally acknowledged as the key factor contributing to the FFS of a corporation, they are usually excluded from early detection models. The objective of this study is to increase the accuracy of FFS detection by integrating the rough set theory RST and support vector machines SVM approaches, while adopting both financial and nonfinancial ratios as predictive variables. The results showed that the proposed hybrid approach RST+SVM has the best classification rate as well as the lowest occurrence of Types I and II errors, and that nonfinancial ratios are indeed valuable information in FFS detection.


title: MBMF: Model-Based Priors for Model-Free Reinforcement Learning

abstract: Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time data-efficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. Preliminary results demonstrate that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.


title: SHREC'15 Track: 3D Object Retrieval with Multimodal Views

abstract: This paper reports the results of the SHREC’15 track: 3D Object Retrieval with Multimodal Views, which goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 505 objects is generated and both the color images and the depth images are provided for each object. 311 objects are selected as the queries and average retrieval performance is measured. The track attracted six participants and the submission of 26 runs, to two tasks. The evaluation results show a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data.


title: A Survey of Social-Aware Routing Protocols in Delay Tolerant Networks: Applications, Taxonomy and Design-Related Issues

abstract: In the past few years, more and more researchers have paid close attention to the emerging field of delay tolerant networks (DTNs), in which network often partitions and end-to-end paths do not exist nearly all the time. To cope with these challenges, most routing protocols employ the "store-carry-forward" strategy to transmit messages. However, the difficulty of this strategy is how to choose the best relay node and determine the best time to forward messages. Fortunately, social relations among nodes can be used to address these problems. In this paper, we present a comprehensive survey of recent social-aware routing protocols, which offer an insight into how to utilize social relationships to design efficient and applicable routing algorithms in DTNs. First, we review the major practical applications of DTNs. Then, we focus on understanding social ties between nodes and investigating some design-related issues of social-based routing approaches, e.g., the ways to obtain social relations among nodes, the metrics and approaches to identify the characteristics of social ties, the strategies to optimize social-aware routing protocols, and the suitable mobility traces to evaluate these protocols. We also create a taxonomy for social-aware routing protocols according to the sources of social relations. Finally, we outline several open issues and research challenges.


title: NodeXL: Simple network analysis for social media

abstract: NodeXL (http://nodexl.codeplex.com/) is an application that simplifies basic network analysis tasks and supports analysis of social media networks. Using the widely familiar framework of a spreadsheet, NodeXL integrates the features needed to collect, store, analyze, visualize and publish network datasets. The tool connects to a wide range of data sources and to other SNA packages through GraphML and other network file formats. Special support is provided for social media data sources including Twitter, Facebook, YouTube, flickr, email, blogs, wikis, and the WWW. NodeXL is focused on end user ease of use, allowing automation of frequently repeated processes that are commonly applied to network data. NodeXL simplifies network analysis by automatically applying several processes that result in usable visualizations without requiring human engagement and refinement. Users can publish and share NodeXL data sets via the NodeXL Graph Gallery, a web archive of networks and visualizations contributed by the community of users. Many diverse networks have been contributed to the archive, enabling insights into a wide range of networks in general and social networks in particular. Review of these networks has led to identification of six major types of network pattern, described here, which have emerged as major types of self-organized structures in social media.


title: Text detection and restoration in natural scene images

abstract: A new method for text detection and recognition in natural scene images is presented in this paper. In the detection process, color, texture, and OCR statistic features are combined in a coarse-to-fine framework to discriminate texts from non-text patterns. In this approach, color feature is used to group text pixels into candidate text lines. Texture feature is used to capture the ''dense intensity variance'' property of text pattern. Statistic features from OCR (Optical Character Reader) results are employed to further reduce detection false alarms empirically. After the detection process, a restoration process is used. This process is based on plane-to-plane homography. It is carried out to refine the background plane of text when an affine transformation is detected on a located text and independent of camera parameters. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical.


title: Analysis of a GMR-based plethysmograph transducer and its utility for real-time Blood Pressure measurement

abstract: The paper presents study and analysis of a Giant Magneto Resistance (GMR)-based magneto plethysmograph and illustrates its efficacy as a tool for real-time cuff-less measurement of Blood Pressure (BP). The proposed scheme employs two GMR sensors and associated biasing and signal conditioning in its architecture. The delay between output of the GMR sensors is used to estimate the BP. The methodology, circuits and signal processing stages used are described in the paper. A prototype of the GMR-sensing solution is developed and tested. Initially, tests are carried out to determine the quality and characteristics of the plethysmographs produced by developed sensor unit, in different conditions such as various body positions, bias current etc. Good quality bio-signals were obtained during the above tests. Then, the experiments were conducted on 29 volunteers to find the feasibility of developed scheme as a BP monitor. The results obtained show that the performance of developed BP monitor is within acceptable limits.


title: Placement of distributed stream processing over heterogeneous infrastructures: doctoral symposium

abstract: Data Stream Processing (DSP) applications can extract, in a timely manner, valuable information from distributed data sources (e.g., sensing devices, social networks). These applications are subject to unpredictable and varying workloads and have to satisfy strict quality requirements, usually expressed in terms of latency, availability, and throughput. To successfully execute DSP applications, recent trends investigate the possibility of exploiting decentralized computing resources, which nonetheless pose new challenges due to the network and system heterogeneity, geographic distribution, and non-negligible network latencies. The doctorate work, presented in this paper, investigates the deployment of DSP applications with Quality of Service (QoS) requirements over a distributed infrastructure of heterogeneous computing and networking resources. Specifically, to support our study, we extend an open-source DSP system, Apache Storm, by providing mechanisms for executing distributed QoS-aware placement policies and self-adaptation. Then, we provide a general formulation of the optimal placement problem for DSP applications, modeling the heterogeneity of the execution environment. The ongoing research aims at investigating the following directions. First, we will design heuristics able to determine the best placement in a feasible amount of time. Second, we will investigate runtime adaptation strategies and online placement algorithms. Third, to prove the generality of our approach, we will customize the designed solutions for similar problems (e.g., service selection, container deployment).


title: Suppression of microwave magnetic envelope solitons by continuous wave magnetostatic wave signals

abstract: The influence of continuous wave (cw) magnetostatic wave signals on microwave magnetic envelope soliton pulse formation and propagation in magnetic films has been examined. Pulsed and cw microwave signals were applied to the input of a single crystal yttrium–iron–garnet film magnetostatic wave delay line. The nominal operating frequency was 4.8 GHz. The pulse signals served to form solitons with no cw power present. Under suitable conditions, the cw signal served to inhibit or eliminate the soliton formation and propagation. The suppression effect was measured as a function of the cw signal frequency and power. The suppression is maximized when the cw signal frequency coincides with the pulse carrier frequency. At this frequency, an input cw power of 80 mW is sufficient to suppress completely a soliton pulse formed from a 10 ns wide, 500 mW peak power input pulse.


title: Elements affect online repurchase intentions of Malaysia's online shoppers

abstract: Today, the internet is the significant tool of online shopping in all around the world. Online transaction systems make shopper possible to make payment for services or products utilizing the internet. The current paper purpose is to focus on the elements that can affect online repurchase intentions. In the current paper we utilized survey questionnaire to gather 102 in Malaysia from those that at least done one online shopping. In addition, data collection is using regression model to determine the significance relationship between the trust and privacy and online repurchase intentions. Moreover, the current research provides functional information for managing online businesses.


title: Modeling and Learning Distributed Word Representation with Metadata for Question Retrieval

abstract: Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved.


title: Age and second language acquisition and processing: A selective overview

abstract: This article provides a selective overview of theoretical issues and empirical findings relating to the question of age and second language acquisition (L2A). Both behavioral and brain-based data are discussed in the contexts of neurocognitive aging and cognitive neurofunction in the mature individual. Moving beyond the classical notion of "deficient" L2 processing and acquisition, we consider the complementary question of learner potential in postadolescent L2A.


title: Citation recommendation without author supervision

abstract: Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (without a bibliography) and our system automatically finds locations where citations are needed. We show that naive approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.


A Survey on Intrusion Detection Systems and Classification Techniques


title: A Minimal Span-Based Neural Constituency Parser

abstract: In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).


title: Fault Detection and Isolation in Manufacturing Systems with an Identified Discrete Event Model

abstract: In this article a generic method for fault detection and isolation FDI in manufacturing systems considered as discrete event systems DES is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.


title: Procedural Generation of Parcels in Urban Modeling

abstract: We present a method for interactive procedural generation of parcels within the urban modeling pipeline. Our approach performs a partitioning of the interior of city blocks using user-specified subdivision attributes and style parameters. Moreover, our method is both robust and persistent in the sense of being able to map individual parcels from before an edit operation to after an edit operation – this enables transferring most, if not all, customizations despite small to large-scale interactive editing operations. The guidelines guarantee that the resulting subdivisions are functionally and geometrically plausible for subsequent building modeling and construction. Our results include visual and statistical comparisons that demonstrate how the parcel configurations created by our method can closely resemble those found in real-world cities of a large variety of styles. By directly addressing the block subdivision problem, we intend to increase the editability and realism of the urban modeling pipeline and to become a standard in parcel generation for future urban modeling methods. © 2012 Wiley Periodicals, Inc.


title: Longitudinal Analysis of Android Ad Library Permissions

abstract: This paper investigates changes over time in the behavior of Android ad libraries. Taking a sample of 100,000 apps, we extract and classify the ad libraries. By considering the release dates of the applications that use a specific ad library version, we estimate the release date for the library, and thus build a chronological map of the permissions used by various ad libraries over time. We find that the use of most permissions has increased over the last several years, and that more libraries are able to use permissions that pose particular risks to user privacy and security.


title: Countering Browser Fingerprinting Techniques: Constructing a Fake Profile with Google Chrome

abstract: While Web browsers are fundamental components in the Internet nowadays, the widespread availability of several techniques that can be used to detect the individual browser connected to a server raises privacy issues that need to be adequately addressed. Browser fingerprinting uses a combination of attributes, whose values are silently obtained during normal navigation, to identify, with high likelihood, a browser and, consequently, who is controlling such browser. Although providing contrived or random values for specific single attributes or attribute sets can be at least partly successful in interfering with the operation of fingerprinting, such approach may also have adverse effects. The objective of this work is to suggest an alternative: supplying coherent data designed in such a way as to mimick as closely as possible a different browser. A proof-of-concept implementation as an extension for the Chrome browser is presented and discussed.


title: Learning to Represent Mechanics via Long-term Extrapolation and Interpolation

abstract: While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters. In order to adopt such models for artificial intelligence, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortunately, such approaches can be unsuitable for modeling complex real-world scenarios, where manually authoring relevant state spaces tend to be challenging. In this work, we investigate if neural networks can implicitly learn physical states of real-world mechanical processes only based on visual data, and thus enable long-term physical extrapolation. We develop a recurrent neural network architecture for this task and also characterize resultant uncertainties in the form of evolving variance estimates. We evaluate our setup to extrapolate motion of a rolling ball on bowl of varying shape and orientation using only images as input, and report competitive results with approaches that assume access to internal physics models and parameters.


title: Cognitive load theory in health professional education: design principles and strategies

abstract: Context Cognitive load theory aims to develop instructional design guidelines based on a model of human cognitive architecture. The architecture assumes a limited working memory and an unlimited long-term memory holding cognitive schemas; expertise exclusively comes from knowledge stored as schemas in long-term memory. Learning is described as the construction and automation of such schemas. Three types of cognitive load are distinguished: intrinsic load is a direct function of the complexity of the performed task and the expertise of the learner; extraneous load is a result of superfluous processes that do not directly contribute to learning, and germane load is caused by learning processes that deal with intrinsic cognitive load. ::: ::: Objectives This paper discusses design guidelines that will decrease extraneous load, manage intrinsic load and optimise germane load. ::: ::: Discussion Fifteen design guidelines are discussed. Extraneous load can be reduced by the use of goal-free tasks, worked examples and completion tasks, by integrating different sources of information, using multiple modalities, and by reducing redundancy. Intrinsic load can be managed by simple-to-complex ordering of learning tasks and working from low- to high-fidelity environments. Germane load can be optimised by increasing variability over tasks, applying contextual interference, and evoking self-explanation. The guidelines are also related to the expertise reversal effect, indicating that design guidelines for novice learners are different from guidelines for more experienced learners. Thus, well-designed instruction for novice learners is different from instruction for more experienced learners. Applications in health professional education and current research lines are discussed. ::: ::: Medical Education 2010: 44: 85–93


Experimenting with blockchain: Can one technology boost both data integrity and patients' pocketbooks?


title: Clickstream Analytics: An Experimental Analysis of the Amazon Users' Simulated Monthly Traffic

abstract: Online shopping in recent years demonstrate a constant increase and as a result the study of user behavior through clickstream has attracted again the interest of the research community. This increase though requires novel approaches to clickstream analytics since the volume of the products available online and the corresponding transactions is huge. In this paper, a sequential frequent itemsets detection methodology (SAFID) is adopted to solve a clickstream analytics problem by analyzing a composite dataset which simulates monthly traffic of Amazon U.S. online retail shop. It is shown that the methodology can perform the analysis very efficiently in a simple desktop and detect all the frequently bought together products which can provide valuable knowledge to marketers of online retail stores. The methodology used can further be improved to handle larger datasets by considering a cloud computing environment.


title: A Dual-Layer Planar Leaky-Wave Antenna Designed for Linear Scanning Through Broadside

abstract: A low-cost planar leaky-wave antenna (LWA) offering directive antenna beam patterns as well as linear beam scanning through broadside is proposed. The design is based on a one-sided annular slot grating placed on a dual-layer grounded dielectric slab with an integrated TM $_0$ antenna feed system in the bottom ground plane. By appropriate selection of the width and the period of the top radiating annular slots, as well as the substrates, the structure is optimized to excite and perturb the dominant TM mode for the generation of directive beam patterns that can scan with frequency in the far field. In particular, the antenna parameters are chosen to obtain a narrow open stopband frequency range at broadside as well as control the dispersive scanning behavior. A method-of-moments dispersion analysis has also been developed to assist in the design and fully characterize the proposed antenna. These results are complemented by full-wave simulations and measurements of a LWA prototype offering realized gain values in excess of 14 dBi with scanning through broadside at 23.5 GHz.


title: Multimedia forensics is not computer forensics

abstract: The recent popularity of research on topics of multimedia forensics justifies reflections on the definition of the field. This paper devises an ontology that structures forensic disciplines by their primary domain of evidence. In this sense, both multimedia forensics and computer forensics belong to the class of digital forensics, but they differ notably in the underlying observer model that defines the forensic investigator's view on (parts of) reality, which itself is not fully cognizable. Important consequences on the reliability of probative facts emerge with regard to available counter-forensic techniques: while perfect concealment of traces is possible for computer forensics, this level of certainty cannot be expected for manipulations of sensor data. We cite concrete examples and refer to established techniques to support our arguments.


title: Relational Data Mining in the Era of Big Data

abstract: The aim of this article is to synthetically describe a sample of distinct approaches and applications of Relational Data Mining, which address the issue of managing complex, and possibly big, amounts of data. Specifically, we report a brief review of the literature on Relational Data Mining in the fields of Spatial Data Mining, Process Mining, Network Data Analysis and Stream Data Mining, with an emphasis on the Italian research. For each field, we describe the milestones that have been reached, as well as the future research trends that are fuelled by the emergent ubiquity of Big Data.


title: Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

abstract: The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the L2 error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space SE(3). By exploiting the special structure of SE(3) geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.


title: Interleukin-1-induced ether-linked diglycerides inhibit calcium-insensitive protein kinase C isotypes. Implications for growth senescence.

abstract: It is hypothesized that inflammatory cytokines and vasoactive peptides stimulate distinct species of diglycerides that differentially regulate protein kinase C isotypes. In published data, we demonstrated that interleukin-1, in contrast to endothelin, selectively generates ether-linked diglyceride species (alkyl, acyl- and alkenyl, acylglycerols) in rat mesangial cells, a smooth muscle-like pericyte in the glomerulus. We now demonstrate both in intact cell and in cell-free preparations that these interleukin-1 receptor-generated ether-linked diglycerides inhibit immunoprecipitated protein kinase C delta and epsilon but not zeta activity. Neither interleukin-1 nor endothelin affect de novo protein expression of these protein kinase C isotypes. As down-regulation of calcium-insensitive protein kinase C isotypes has been linked to antimitogenic activity, we investigated growth arrest as a functional correlate for IL-1-generated ether-linked diglycerides. Cell-permeable ether-linked diglycerides mimic the effects of interleukin-1 to induce a growth-arrested state in both G-protein-linked receptor- and tyrosine kinase receptor-stimulated mesangial cells. This signaling mechanism implicates cytokine receptor-induced ether-linked diglycerides as second messengers that inhibit the bioactivity of calcium-insensitive protein kinase C isotypes resulting in growth arrest.


Candidate Model Problems in Software Architecture


title: CrossNet: a framework for crossover with network-based chromosomal representations

abstract: We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graph-based) chromosomal representation. We designed CrossNet with the intent of providing a framework for creating crossover operators that take advantage of domain-specific knowledge for solving problems. Specifically, GA users supply a network which defines the epistatic relationships between genes in the genotype. CrossNet-based crossover uses this information with the goal of improving linkage. We performed two experiments that compared CrossNet-based crossover with one-point and uniform crossover. The first experiment involved the density classification problem for cellular automata (CA), and the second experiment involved fitting two randomly generated hyperplane-defined functions (hdf's). Both of these exploratory experiments support the hypothesis that CrossNet-based crossover can be useful, although performance improvements were modest. We discuss the results and remain hopeful about the successful application of CrossNet to other domains. We conjecture that future work with the CrossNet framework will provide a useful new perspective for investigating linkage and chromosomal representations.


title: Shape matching via quotient spaces

abstract: We introduce a novel method for non-rigid shape matching, designed to address the symmetric ambiguity problem present when matching shapes with intrinsic symmetries. Unlike the majority of existing methods which try to overcome this ambiguity by sampling a set of landmark correspondences, we address this problem directly by performing shape matching in an appropriate quotient space, where the symmetry has been identified and factored out. This allows us to both simplify the shape matching problem by matching between subspaces, and to return multiple solutions with equally good dense correspondences. Remarkably, both symmetry detection and shape matching are done without establishing any landmark correspondences between either points or parts of the shapes. This allows us to avoid an expensive combinatorial search present in most intrinsic symmetry detection and shape matching methods. We compare our technique with state-of-the-art methods and show that superior performance can be achieved both when the symmetry on each shape is known and when it needs to be estimated.


title: Comparing Image Representations for Training a Convolutional Neural Network to Classify Gender

abstract: In this work, we evaluated the effect of different image representations on the classification performance of a convolutional neural network. Several different methods for normalization of the input data were also considered. The network was discriminatively trained for the task of gender classification of pedestrians. A publicly available dataset was used for training, containing both frontal and rear views of pedestrians. The best result was obtained using grayscale representation as compared to RGB and YUV, giving cross-validated accuracy of 81.5% on the dataset. The performance of the convolutional neural network is competitive and comparable to previous works on the same dataset.


title: Propeller-hull interaction in a single-screw vessel

abstract: Unfavorable wake and separated flow from the hull might cause a dramatic decay of the propeller performance in single-screw propelled vessels such as tankers, bulk carriers and containers. For these types of vessels, special attention has to be paid to the design of the stern region, the occurrence of a good flow towards the propeller and rudder being necessary to avoid separation and unsteady loads on the propeller blades and, thus, to minimize fuel consumption and the risk for cavitation erosion and vibrations. The present work deals with the analysis of the propeller inflow in a single-screw chemical tanker vessel affected by massive flow separation in the stern region. Detailed flow measurements by Laser Doppler Velocimetry (LDV) were performed in the propeller region at model scale, in the Large Circulating Water Channel of CNR-INSEAN. Tests were undertaken with and without propeller in order to investigate its effect on the inflow characteristics and the separation mechanisms. In this regard, the study concerned also a phase locked analysis of the propeller perturbation at different distances upstream of the propulsor. The study shows the effectiveness of the 3 rd order statistical moment (i.e. skewness) for describing the topology of the wake and accurately identifying the portion affected by the detached flow.


title: A study on features of social recommender systems

abstract: Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems.


title: Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution

abstract: Hyperspectral image is very useful for many computer vision tasks, however it is often difficult to obtain high-resolution hyperspectral images using existing hyperspectral imaging techniques. In this paper, we propose a deep residual convolutional neural network to increase the spatial resolution of hyperspectral image. Our network consists of 18 convolution layers and requires only one low-resolution hyperspectral image as input. The super-resolution is achieved by minimizing the difference between the estimated image and the ground truth high resolution image. Besides the mean square error between these two images, we introduce a loss function which calculates the angle between the estimated spectrum vector and the ground truth one to maintain the correctness of spectral reconstruction. In experiments on two public datasets we show that the proposed network delivers improved hyperspectral super-resolution result than several state-of-the-art methods.


title: Degree vs. manner well: A case study in selective binding

abstract: We present a semantic analysis of the adverb well that captures its degree and manner readings in a principled fashion via the Generative Lexicon Selective Binding composition rule. The analysis integrates Kennedy and McNally’s (Language, 81:345–381, 2005) treatment of scale structure with Generative Lexicon theory, and embeds the resulting semantics in HPSG.


title: Data-Driven Workflows in Multi-cloud Marketplaces

abstract: Cloud computing is emerging as a viable platform for scientific exploration. The ideas of on-demand access to resources, "unlimited" resources as well as interesting pricing models are making scientist to move their workflows into cloud computing. However, the amount of services and different pricing models offered by the providers often overwhelm users when deciding which option is best for them. Moreover, interoperability across providers remains an open topic that forces users to develop specific solutions for each provider. In this paper, we present a service framework that enables the autonomic execution of dynamic workflows in multi-cloud environments. It also allows users to customize scheduling policies to use those resources that best fit their needs. To demonstrate the benefits of this framework, we study the execution of a real scientific workflow, with data dependencies across stages, in a multi-cloud federation using different policies and objective functions.


title: A Language-Independent Proof System for Mutual Program Equivalence

abstract: Two programs are mutually equivalent if they both diverge or they both terminate with the same result. In this paper we introduce a language-independent proof system for mutual equivalence, which is parametric in the operational semantics of two languages and in a state-similarity relation. We illustrate it on two programs in two different languages (an imperative one and a functional one), that both compute the Collatz sequence.


title: Implementation of CRISP Methodology for ERP Systems

abstract: ERP systems contain huge amounts of data related to the actual execution of business processes. These systems have a particular way of recording activities which results in an unclear display of business processes in event logs. Several works have been conducted on ERP systems, most of them focusing on the development of new algorithms for the automatic discovery of business processes. We focused on addressing issues like, how can organizations with ERP systems apply process mining for analyzing their business processes in order to improve them. The data handling aspect of ERP systems contrasts with those of BPMS or workflow based systems, whose systematical storage of events facilitates the application of process mining techniques. CRISP-DM has emerged as the de facto standard for developing data mining and knowledge discovery projects. Successful data mining requires three families of analytical capabilities namely reporting, classification and forecasting. A data miner uses more than one analytical method to get the best results. The objective of this paper is to improve the usability and understandability of process mining techniques, by implementing CRISP-DM methodology for their application in ERP contexts, detailed in terms of specific implementation tools and step by step coordination. Our study confirms that data discovery from ERP system improves strategic and operational decision making.


title: Digital Humanities 2010 Does Size Matter? Authorship Attribution,

abstract: The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose) to 5,000 or so words (in most cases, including English, German, Polish, and Hungarian novels). Another observation is connected with the method of sampling: contrary to common sense, randomly excerpted ‘bags of words’ turned out to be much more effective than the classical solution, i.e. using original sequences of words (‘passages’) of desired size. Although the tests have been performed using the Delta method ( Burrows, J.F . (2002). ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing , 17 (3): 267–87) applied to the most frequent words, some additional experiments have been conducted for support vector machines and k -NN applied to most frequent words, character 3-grams, character 4-grams, and parts-of-speech-tag 3-grams. Despite significant differences in overall attributive success rate between particular methods and/or style markers, the minimal amount of textual data needed for reliable authorship attribution turned out to be method-independent.


Novel Variable Length Teager Energy Separation Based Instantaneous Frequency Features for Replay Detection.


Advances and prospects in quantum dots based backlights


title: Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation

abstract: Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.


Should Code-switching Models Be Asymmetric?


title: Game-Based Video-Context Dialogue

abstract: Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. However, several real-world human interactions also involve dynamic visual context (similar to videos) as well as dialogue exchanges among multiple speakers. To move closer towards such multimodal conversational skills and visually-situated applications, we introduce a new video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This challenging testbed allows us to develop visually-grounded dialogue models that should generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history. For strong baselines, we also present several discriminative and generative models, e.g., based on tridirectional attention flow (TriDAF). We evaluate these models via retrieval ranking-recall, automatic phrase-matching metrics, as well as human evaluation studies. We also present dataset analyses, model ablations, and visualizations to understand the contribution of different modalities and model components.


title: Knowledge Based Machine Reading Comprehension

abstract: Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability. To encourage the progress on knowledge-based reasoning in MRC, we present knowledge-based MRC in this paper, and build a new dataset consisting of 40,047 question-answer pairs. The annotation of this dataset is designed so that successfully answering the questions requires understanding and the knowledge involved in a document. We implement a framework consisting of both a question answering model and a question generation model, both of which take the knowledge extracted from the document as well as relevant facts from an external knowledge base such as Freebase/ProBase/Reverb/NELL. Results show that incorporating side information from external KB improves the accuracy of the baseline question answer system. We compare it with a standard MRC model BiDAF, and also provide the difficulty of the dataset and lay out remaining challenges.


title: 16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

abstract: The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.


title: Real-time rigid-body visual tracking in a scanning electron microscope

abstract: Robotics continues to provide researchers with an increasing ability to interact with objects at the nano scale. As micro- and nanorobotic technologies mature, more interest is given to computer-assisted or automated approaches to manipulation at these scales. Although actuators are currently available that enable displacements resolutions in the subnanometer range, improvements in feedback technologies have not kept pace. Thus, many actuators that are capable of performing nanometer displacements are limited in automated tasks by the lack of suitable feedback mechanisms. This paper proposes the use of a rigid-model-based method for end effector tracking in a scanning electron microscope to aid in enabling more precise automated manipulations and measurements. These models allow the system to leverage domain-specific knowledge to increase performance in a challenging tracking environment.


title: 3D textureless object detection and tracking: An edge-based approach

abstract: This paper presents an approach to textureless object detection and tracking of the 3D pose. Our detection and tracking schemes are coherently integrated in a particle filtering framework on the special Euclidean group, SE(3), in which the visual tracking problem is tackled by maintaining multiple hypotheses of the object pose. For textureless object detection, an efficient chamfer matching is employed so that a set of coarse pose hypotheses is estimated from the matching between 2D edge templates of an object and a query image. Particles are then initialized from the coarse pose hypotheses by randomly drawing based on costs of the matching. To ensure the initialized particles are at or close to the global optimum, an annealing process is performed after the initialization. While a standard edge-based tracking is employed after the annealed initialization, we employ a refinement process to establish improved correspondences between projected edge points from the object model and edge points from an input image. Comparative results for several image sequences with clutter are shown to validate the effectiveness of our approach.


Studies on the chemotherapy of malaria. III. The treatment of acute malaria with chloroquine.


title: ParseNet: Looking Wider to See Better

abstract: We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-the-art performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at this https URL .


Realtime Ray Tracing


title: Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

abstract: Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~300Hz), without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo-realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.


title: A 60 mW Class-G Stereo Headphone Driver for Portable Battery-Powered Devices

abstract: A 60 mW 1.15 mA/channel Class-G stereo headphone driver primarily designed for demanding applications in mobile phones and other portable communication devices is described. The architecture of the driver has been chosen to overcome the various design challenges for audio amplifiers in a wireless environment. A high-order feed-forward loop topology provides high immunity to battery disturbance with extended correction range. In addition, a Class-G amplifier with a Class-AB/B driving stage improves the small-signal efficiency and extends music playback time. Implemented in 0.18 μm CMOS technology, the stereo headphone driver achieves a DR of 111 dB and PSRR of 120 dB at the GSM TDMA frequency of 217 Hz while occupying an area of 2.3 mm2.


title: Why Bother About Clumsiness? The Implications of Having Developmental Coordination Disorder (DCD)

abstract: Developmental coordination disorder (DCD) is a common motor problem affecting--even in rather severe formseveral percent of school age children. In the past, DCD has usually been called ’clumsy child syndrome’ or ’non-cerebralpalsy motor-perception dysfunction’. This disorder is more common in boys than in girls and is very often associated with psychopathology, particularly with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders/ autistic-type problems. Conversely, children


title: Mining message sequence graphs

abstract: Dynamic specification mining involves discovering software behavior from traces for the purpose of program comprehension and bug detection. However, mining program behavior from execution traces is difficult for concurrent/distributed programs. Specifically, the inherent partial order relationships among events occurring across processes pose a big challenge to specification mining. In this paper, we propose a framework for mining partial orders so as to understand concurrent program behavior. Our miner takes in a set of concurrent program traces, and produces a message sequence graph (MSG) to represent the concurrent program behavior. An MSG represents a graph where the nodes of the graph are partial orders, represented as Message Sequence Charts. Mining an MSG allows us to understand concurrent program behaviors since the nodes of the MSG depict important "phases" or "interaction snippets" involving several concurrently executing processes. To demonstrate the power of this technique, we conducted experiments on mining behaviors of several fairly complex distributed systems. We show that our miner can produce the corresponding MSGs with both high precision and recall.


title: On pinning synchronization of complex dynamical networks

abstract: There exist some fundamental and yet challenging problems in pinning control of complex networks: (1) What types of pinning schemes may be chosen for a given complex network to realize synchronization? (2) What kinds of controllers may be designed to ensure the network synchronization? (3) How large should the coupling strength be used in a given complex network to achieve synchronization? This paper addresses these technique questions. Surprisingly, it is found that a network under a typical framework can realize synchronization subject to any linear feedback pinning scheme by using adaptive tuning of the coupling strength. In addition, it is found that the nodes with low degrees should be pinned first when the coupling strength is small, which is contrary to the common view that the most-highly-connected nodes should be pinned first. Furthermore, it is interesting to find that the derived pinning condition with controllers given in a high-dimensional setting can be reduced to a low-dimensional condition without the pinning controllers involved. Finally, simulation examples of scale-free networks are given to verify the theoretical results.


title: Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter

abstract: In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors.


title: KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning

abstract: In imbalanced learning, most standard classification algorithms usually fail to properly represent data distribution and provide unfavorable classification performance. More specifically, the decision rule of minority class is usually weaker than majority class, leading to many misclassification of expensive minority class data. Motivated by our previous work ADASYN [1], this paper presents a novel kernel based adaptive synthetic over-sampling approach, named KernelADASYN, for imbalanced data classification problems. The idea is to construct an adaptive over-sampling distribution to generate synthetic minority class data. The adaptive over-sampling distribution is first estimated with kernel density estimation methods and is further weighted by the difficulty level for different minority class data. The classification performance of our proposed adaptive over-sampling approach is evaluated on several real-life benchmarks, specifically on medical and healthcare applications. The experimental results show the competitive classification performance for many real-life imbalanced data classification problems.


title: Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings

abstract: We present Walklets, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multiscale vertex relationships in a way that is analytically derivable. ::: Walklets generates these multiscale relationships by subsampling short random walks on the vertices of a graph. By `skipping' over steps in each random walk, our method generates a corpus of vertex pairs which are reachable via paths of a fixed length. This corpus can then be used to learn a series of latent representations, each of which captures successively higher order relationships from the adjacency matrix. ::: We demonstrate the efficacy of Walklets's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, DBLP, Flickr, and YouTube. Our results show that Walklets outperforms new methods based on neural matrix factorization. Specifically, we outperform DeepWalk by up to 10% and LINE by 58% Micro-F1 on challenging multi-label classification tasks. Finally, Walklets is an online algorithm, and can easily scale to graphs with millions of vertices and edges.


title: An expert diagnosis system for classification of human parasite eggs based on multi-class SVM

abstract: In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, classification stage, and testing stage. In pre-processing stage, the digital image processing methods, which are noise reduction, contrast enhancement, thresholding, and morphological and logical processes. In feature extraction stage, the invariant moments of pre-processed parasite images are calculated. Finally, in classification stage, the multi-class support vector machine (MCSVM) classifier is used for classification of features extracted feature extraction stage. We used MATLAB software for estimating the success classification rate of proposed approach in this study. For this aim, proposed approach was tested by using test data. At end of test, 97.70% overall success rates were obtained.


title: Lifelong Learning with Dynamically Expandable Networks

abstract: We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets under lifelong learning scenarios, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch counterparts with substantially fewer number of parameters. Further, the obtained network fine-tuned on all tasks obtained significantly better performance over the batch models, which shows that it can be used to estimate the optimal network structure even when all tasks are available in the first place.


title: Human-aligned artificial intelligence is a multiobjective problem

abstract: As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action---selection can overcome many of the issues which limit MEU's effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.


title: On the analysis of low output impedance characteristic of flipped voltage follower (FVF) and FVF LDOs

abstract: The flipped voltage follower (FVF), a variant of the common-drain transistor amplifier, comprising local feedback, finds application in circuits such as voltage buffers, current mirrors, class AB amplifiers, frequency compensation circuits and low dropout voltage regulators (LDOs). One of the most important characteristics of the FVF, is its low output impedance. In this tutorial-flavored paper, we perform a theoretical analysis of the transfer function, poles and zeros of the output impedance of the FVF and correlate it with transistor-level simulation results. Utilization of the FVF and its variants has wide application in the analog, mixed-signal and power management circuit design space.


title: Focused named entity recognition using machine learning

abstract: In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.


title: Left inferior frontal gyrus is critical for response inhibition

abstract: BackgroundLesion studies in human and non-human primates have linked several different regions of prefrontal cortex (PFC) with the ability to inhibit inappropriate motor responses. However, recent functional neuroimaging studies have specifically implicated right inferior PFC in response inhibition. Right frontal dominance for inhibitory motor control has become a commonly accepted view, although support for this position has not been consistent. Particularly conspicuous is the lack of data on the importance of the homologous region in the left hemisphere. To investigate whether the left inferior frontal gyrus (IFG) is critical for response inhibition, we used neuropsychological methodology with carefully characterized brain lesions in neurological patients.ResultsTwelve individuals with damage in the left IFG and the insula were tested in a Go/NoGo response inhibition task. In alternating blocks, the difficulty of response inhibition was easy (50% NoGo trials) or hard (10% NoGo trials). Controls showed the predicted pattern of faster reaction times and more false alarm errors in the hard condition. Left IFG patients had higher error rates than controls in both conditions, but were more impaired in the hard condition, when a greater degree of inhibitory control was required. In contrast, a patient control group with orbitofrontal cortex lesions showed intact performance.ConclusionRecent neuroimaging studies have focused on a highly specific association between right IFG and inhibitory control. The present results indicate that the integrity of left IFG is also critical for successful implementation of inhibitory control over motor responses. Our findings demonstrate the importance of obtaining converging evidence from multiple methodologies in cognitive neuroscience.


title: Multispectral pedestrian detection: Benchmark dataset and baseline

abstract: With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.