title: Forensic Analysis and Anonymisation of Printed Documents

abstract: Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.


title: Analyzing and evaluating the performance of 6L0WPAN and RPL using CONTIKI

abstract: Recent trends of universal computing and the Internet of Things require everyday objects to be IP addressable and Internet accessible. The 6LoWPAN network protocol, data link layer which lies in an adaptation layer and make use of IEEE 802.15.4 standard for constructing the constrained networks. Moreover, this standard not only provides short-range radio frequency transmissions, low-power and low-data-rate, which is appropriate for wireless communication devices such as actuators and sensors, but also provides intelligence, connectivity, reliability, sensing and expressing of this technology are used in various applications like Smart Garbage Smart Cans, Smart Healthcare and Smart Meters etc. RPL is based on IPv6 routing protocol which is used low power, lossy Networks (LLNs). Our work is a based on and Contiki OS using Cooja simulator, with the help of IOT we are calculating temperature and light of wireless sensor nodes. Then compared trace packets, it shows capturing the packets through wire shark with 6LoWPAN analyzer consumes more power consumption, rather than with 6LoWPAN analyzer consumes less power consumption.


title: Radiation Analysis Approaches for Reflectarray Antennas [Antenna Designer's Notebook]

abstract: This paper compares two basic methods for analysis of the radiation performance of reflectarray antennas. Two different approaches - array theory and aperture field - are first described, and numerical results are then presented for various reflectarray configurations. The advantages and limitations for each method are discussed, and the numerical results are compared with each other, showing very good agreement. Comparison with full-wave simulations showed that these approaches are time-efficient methods that can accurately calculate the reflectarray antenna's pattern shape, main-beam direction, beamwidth, and sidelobe and cross-polarization levels. As such, these methods can be efficient tools for antenna engineers for designing and analyzing reflectarray antennas.


title: Critical Hyper-Parameters: No Random, No Cry

abstract: The selection of hyper-parameters is critical in Deep Learning. Because of the long training time of complex models and the availability of compute resources in the cloud,"one-shot"optimization schemes - where the sets of hyper-parameters are selected in advance (e.g. on a grid or in a random manner) and the training is executed in parallel - are commonly used. It is known that grid search is sub-optimal, especially when only a few critical parameters matter, and suggest to use random search instead. Yet, random search can be"unlucky"and produce sets of values that leave some part of the domain unexplored. Quasi-random methods, such as Low Discrepancy Sequences (LDS) avoid these issues. We show that such methods have theoretical properties that make them appealing for performing hyperparameter search, and demonstrate that, when applied to the selection of hyperparameters of complex Deep Learning models (such as state-of-the-art LSTM language models and image classification models), they yield suitable hyperparameters values with much fewer runs than random search. We propose a particularly simple LDS method which can be used as a drop-in replacement for grid or random search in any Deep Learning pipeline, both as a fully one-shot hyperparameter search or as an initializer in iterative batch optimization.


title: Unsupervised learning of invariant representations

abstract: The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples ( n ź ∞ ). The next phase is likely to focus on algorithms capable of learning from very few labeled examples ( n ź 1 ), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a "good" representation for supervised learning, characterized by small sample complexity. We consider the case of visual object recognition, though the theory also applies to other domains like speech. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translation, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and selective signature can be computed for each image or image patch: the invariance can be exact in the case of group transformations and approximate under non-group transformations. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such signature. The theory offers novel unsupervised learning algorithms for "deep" architectures for image and speech recognition. We conjecture that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and selective for recognition-and show how this representation may be continuously learned in an unsupervised way during development and visual experience.


title: Comparison of high-performance packet processing frameworks on NUMA

abstract: Recent studies have been concentrated on comparison of forwarding ability in multiple dimensions among different high-speed packet processing frameworks, such as PF_RING and DPDK on the whole where transmitting and receiving impact each other, without enough study on frameworks' ability for receiving or transmitting separately. Characteristics of multithreading is another hot spot as multi-core platforms play more important roles. It requires more microscopic insight into comparison of these frameworks' characteristics on multi-core platform, especially on Non-Uniform Memory Access Architecture (NUMA). This paper mainly compared DPDK and PF_RING's processing ability focusing on receiving and multithreading characteristics without transmitting in run-to-completion model and pipeline model, i.e. a kind of load balancing model. Some characteristics about how performance of PF_RING ZC and DPDK in pipeline model specifically on NUMA varies with distribution type of threads among NUMA nodes, were made insight into and concluded. For studying above, a new indicator, ρ, giving the ratio of CPU time consumed on processing per unit packets, was put forward and played an important role in above analysis. By means of ρ, one of this paper's discovery shows that two frameworks' threads' distributions types among NUMA nodes in the pipeline model contribute to different performance of traffic processing.


title: Feature-based survey of model transformation approaches

abstract: Model transformations are touted to play a key role in Model Driven DevelopmentTM. Although well-established standards for creating metamodels such as the Meta-Object Facility exist, there is currently no mature foundation for specifying transformations among models. We propose a framework for the classification of several existing and proposed model transformation approaches. The classification framework is given as a feature model that makes explicit the different design choices for model transformations. Based on our analysis of model transformation approaches, we propose a few major categories in which most approaches fit.


title: A fast hybrid time-synchronous/event approach to parallel discrete event simulation of queuing networks

abstract: The trend in computing architectures has been toward multi-core central processing units (CPUs) and graphics processing units (GPUs). An affordable and highly parallelizable GPU is practical example of Single Instruction, Multiple Data (SIMD) architectures oriented toward stream processing. While the GPU architectures and languages are fairly easily employed for inherently time-synchronous based simulation models, it is less clear if or how one might employ them for queuing model simulation, which has an asynchronous behavior. We have derived a two-step process that allows SIMD-style simulation on queuing networks, by initially performing SIMD computation over a cluster and following this research with a GPU experiment. The two-step process simulates approximate time events synchronously and then reduces the error in output statistics by compensating for it based on error analysis trends. We present our findings to show that, while the outputs are approximate, one may obtain reasonably accurate summary statistics quickly.


title: Don't Trust Satellite Phones: A Security Analysis of Two Satphone Standards

abstract: There is a rich body of work related to the security aspects of cellular mobile phones, in particular with respect to the GSM and UMTS systems. To the best of our knowledge, however, there has been no investigation of the security of satellite phones (abbr. sat phones). Even though a niche market compared to the G2 and G3 mobile systems, there are several 100,000 sat phone subscribers worldwide. Given the sensitive nature of some of their application domains (e.g., natural disaster areas or military campaigns), security plays a particularly important role for sat phones. In this paper, we analyze the encryption systems used in the two existing (and competing) sat phone standards, GMR-1 and GMR-2. The first main contribution is that we were able to completely reverse engineer the encryption algorithms employed. Both ciphers had not been publicly known previously. We describe the details of the recovery of the two algorithms from freely available DSP-firmware updates for sat phones, which included the development of a custom disassembler and tools to analyze the code, and extending prior work on binary analysis to efficiently identify cryptographic code. We note that these steps had to be repeated for both systems, because the available binaries were from two entirely different DSP processors. Perhaps somewhat surprisingly, we found that the GMR-1 cipher can be considered a proprietary variant of the GSM A5/2 algorithm, whereas the GMR-2 cipher is an entirely new design. The second main contribution lies in the cryptanalysis of the two proprietary stream ciphers. We were able to adopt known A5/2 cipher text-only attacks to the GMR-1 algorithm with an average case complexity of 232 steps. With respect to the GMR-2 cipher, we developed a new attack which is powerful in a known-plaintext setting. In this situation, the encryption key for one session, i.e., one phone call, can be recovered with approximately 50-65 bytes of key stream and a moderate computational complexity. A major finding of our work is that the stream ciphers of the two existing satellite phone systems are considerably weaker than what is state-of-the-art in symmetric cryptography.


title: Privacy, accuracy, and consistency too: a holistic solution to contingency table release

abstract: The contingency table is a work horse of official statistics, the format of reported data for the US Census, Bureau of Labor Statistics, and the Internal Revenue Service. In many settings such as these privacy is not only ethically mandated, but frequently legally as well. Consequently there is an extensive and diverse literature dedicated to the problems of statistical disclosure control in contingency table release. However, all current techniques for reporting contingency tables fall short on at leas one of privacy, accuracy, and consistency (among multiple released tables). We propose a solution that provides strong guarantees for all three desiderata simultaneously. Our approach can be viewed as a special case of a more general approach for producing synthetic data: Any privacy-preserving mechanism for contingency table release begins with raw data and produces a (possibly inconsistent) privacy-preserving set of marginals. From these tables alone-and hence without weakening privacy--we will find and output the "nearest" consistent set of marginals. Interestingly, this set is no farther than the tables of the raw data, and consequently the additional error introduced by the imposition of consistency is no more than the error introduced by the privacy mechanism itself. The privacy mechanism of [20] gives the strongest known privacy guarantees, with very little error. Combined with the techniques of the current paper, we therefore obtain excellent privacy, accuracy, and consistency among the tables. Moreover, our techniques are surprisingly efficient. Our techniques apply equally well to the logical cousin of the contingency table, the OLAP cube.


title: Motivating mobility: designing for lived motivation in stroke rehabilitation

abstract: How to motivate and support behaviour change through design is becoming of increasing interest to the CHI community. In this paper, we present our experiences of building systems that motivate people to engage in upper limb rehabilitation exercise after stroke. We report on participatory design work with four stroke survivors to develop a holistic understanding of their motivation and rehabilitation needs, and to construct and deploy engaging interactive systems that satisfy these. We reflect on the limits of motivational theories in trying to design for the lived experience of motivation and highlight lessons learnt around: helping people articulate what motivates them; balancing work, duty, fun; supporting motivation over time; and understanding the wider social context. From these we identify design guidelines that can inform a toolkit approach to support both scalability and personalisability.


Privacy risks in recommender systems


title: Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

abstract: Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.


title: Item Silk Road: Recommending Items from Information Domains to Social Users

abstract: Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.


title: The roles of melatonin and light in the pathophysiology and treatment of circadian rhythm sleep disorders

abstract: Desynchronization of circadian rhythms is a common occurrence in individuals presenting with sleep disorders. In this Review, Pandi-Perumal et al. highlight the roles of light and endogenous melatonin in the synchronization of sleep–wake rhythms and rest–activity cycles with the light–dark cycle, and they discuss how light therapy and exogenous melatonin might be used to treat circadian rhythm sleep disorders.


title: Association of VEGF Gene Polymorphisms with Diabetic Retinopathy in a South Indian Cohort

abstract: Background: Polymorphisms in vascular endothelial growth factor (VEGF) gene have been associated with diabetic retinopathy (DR) in various populations. A promoter polymorphism and a 3′UTR variation are studied for association with DR. Materials and Methods: Type 2 diabetic patients with and without retinopathy were recruited. The −634C/G and 936C/T polymorphisms were genotyped by direct sequencing and their frequencies were analyzed using relevant statistical tests. Results: No significant association was observed between genotypes, alleles and haplotypes of −634C/G and 936C/T polymorphisms and DR or its severity. However, C(−634)G genotype was found to increase the risk for DR in patients with microalbuminuria (OR: 8.9, 95% CI: 1.4, 58.3). Conclusion: Our study broadly suggests lack of association of VEGF gene polymorphisms with DR.


title: Exploring students and teachers activities, experiences and impact of Opón Ìmò mobile learning device on teaching and learning

abstract: There has been series of introduction of low-cost learning technologies worldwide such as laptop and tablet computers, especially in developing countries to address uneven standard education which has attracted attention from many researchers and practitioners as reflected in myriad studies. The State Government of Osun in Nigeria following the trend of technology globally introduced Opon Imo Initiative to tackle the learning problem through its Opon Imo Technology Enhanced Learning System. This study examined the students' and teachers' activities, experience, and impact of using Opon Imo for teaching and learning. Quantitative method was employed using 111 samples including teachers and secondary school leavers. The result shows that learners' activities influence learning experiences, while teachers' activities on the device negatively affect teaching experiences as surprisingly, teachers ICT experience negatively affect teaching with the device.


The Endocrinology of pregnancy and parturition


title: Design and implementation of a distributed IoT system for the monitoring of water quality in aquaculture

abstract: In this work we present the prototype and proof of concept of a distributed monitoring system of the most important variables in aquaculture water quality. This is of great importance because aquaculture is a lagging area of technology compared to other areas such as agriculture. So it is important to solve the problems that are in this area with the support of technology. Among the problems is the slow response time in the care of water quality, the waste of resources and losses. The system proposed in this work monitors the water quality based on wireless sensor networks and on the Internet of Things (IoT). This information is important for the development of this area, since it allows sharing the different conditions in the breeding of aquatic organisms between different breeders and organizations. This information is useful to know the conditions in which there is a better development of a product, worse development, what conditions can mean a possible disaster in the environment and how to optimize resources for the care of the pond.


title: A condition monitoring tool based on a FMECA and FMMEA combined approach in Oil&Gas applications

abstract: A new approach that combines up both FMECA and FMMEA techniques has been analyzed and apply in the Oil&Gas field. This method guarantees its efficiency along the product/system development cycle and assures a proper maintenance scheduling according to the current condition of the product/system.


title: Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

abstract: Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.


title: Improved Techniques for Training GANs

abstract: We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.


title: Teaching the Female Brain: How Girls Learn Math and Science.

abstract: Foreword by Monica M. Gillespie Acknowledgments About the Author Introduction 1. The Influence of Cognitive Gender Differences Quiz Part I: The Brain and Senses Part II: The Mind 2. Differences in Learning Approaches Quiz Learning Modalities Group Size Fairness Learning Disabilities Synthesizing vs. Analyzing 3. Dealing With Stress Quiz Stress Management of Test Anxiety Ability vs. Effort 4. Teaching Math to the Female Brain Quiz Performance in Math Why Girls Don't Like Math What Can Be Done to Help? 5. Teaching Science to the Female Brain Quiz Why Girls Don't Like Science Why Girls Should Do Well in Science What Can Be Done to Help? 6. Teaching Math and Science to Girls in a Coed School Sociocultural Issues Role Models Verbal vs. Visual Approach Singe-Sex Classes or Programs Practical Applications 7. Gendered Instruction Virginia Standards of Learning Differentiated Instruction Multiple Intelligences Learning Modalities Unit Design Empowering Girls as Learners Test-Taking Strategies Final Words 8. Resources and Other Helps Math Techniques Books Web Sites Learning Style Assessments References Index


title: Design of a new independently-mobile reconfigurable modular robot

abstract: A new self-reconfigurable robot is presented. The robot is a hybrid chain/lattice design with several novel features. An active mechanical docking mechanism provides inter-module connection, along with optical and electrical interface. The docking mechanisms function additionally as driven wheels. Internal slip rings provide unlimited rotary motion to the wheels, allowing the modules to move independently by driving on flat surfaces, or in assemblies negotiating more complex terrain. Modules in the system are mechanically homogeneous, with three identical docking mechanisms within a module. Each mechanical dock is driven by a high torque actuator to enable movement of large segments within a multi-module structure, as well as low-speed driving. Preliminary experimental results demonstrate locomotion, mechanical docking, and lifting of a single module.


title: Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding

abstract: Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which measure human's verbal ability including the understanding of the words with multiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by artificial intelligence technologies, especially the deep learning technologies that are recently developed and successfully applied in a number of fields. However, we found that the task was quite challenging, and simply applying existing technologies (e.g., word embedding) could not achieve a good performance, mainly due to the multiple senses of words and the complex relations among words. To tackle these challenges, we propose a novel framework consisting of three components. First, we build a classifier to recognize the specific type of a verbal question (e.g., analogy, classification, synonym, or antonym). Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries. Third, for each type of questions, we propose a specific solver based on the obtained distributed word representations and relation representations. Experimental results have shown that the proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of the Amazon Mechanical Turk workers involved in the study. The results indicate that with appropriate uses of the deep learning technologies we might be a further step closer to the human intelligence.


title: Compact CNN for Indexing Egocentric Videos

abstract: While egocentric video is becoming increasingly popular, browsing it is very difficult. In this paper we present a compact 3D Convolutional Neural Network (CNN) architecture for long-term activity recognition in egocentric videos. Recognizing long-term activities enables us to temporally segment (index) long and unstructured egocentric videos. Existing methods for this task are based on hand tuned features derived from visible objects, location of hands, as well as optical flow. Given a sparse optical flow volume as input, our CNN classifies the camera wearer's activity. We obtain classification accuracy of 89%, which outperforms the current state-of-the-art by 19%. Additional evaluation is performed on an extended egocentric video dataset, classifying twice the amount of categories than current state-of-the-art. Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99.2% accuracy, up by 24% from current state-of-the-art. To better understand what the network actually learns, we propose a novel visualization of CNN kernels as flow fields.


title: Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.


title: iFPNA: A Flexible and Efficient Deep Neural Network Accelerator with a Programmable Data Flow Engine in 28nm CMOS

abstract: The paper presents iFPNA, instruction-and-fabric programmable neuron array: a general-purpose deep learning accelerator that achieves both energy efficiency and flexibility. The iFPNA has a programmable data flow engine with a custom instruction set, and 16 configurable neuron slices for parallel neuron operations of different bit-widths. Convolutional neural networks of different kernel sizes are implemented by choosing data flows among input stationary, row stationary and tunnel stationary, etc. Recurrent neural networks with element-wise operations are implemented by a universal activation engine. Measurement results show that the iFPNA achieves a peak energy efficiency of 1.72 TOPS/W running at 30 MHz clock rate and 0.63 V voltage supply. The measured latency on AlexNet is 60.8 ms and on LSTM-512 is 40 ms at 125 MHz clock rate.


title: Amplitude and time skew aware equalization of 100-Gb/s PAM4 signals at the transmitter side for VCSEL-based short reach optical interconnects

abstract: A digital amplitude and time skew aware equalizer at the transmitter side is proposed to enhance the performance of a VCSEL-based 100-Gb/s PAM-4 transmission system. Approximately 0.9-dB and 2.5-dB power penalty improvement can be observed respectively in the case of 20°C and 40°C by applying proposed equalization after 50-m MMF transmission.


Multilingual native language identification


The Guaiac—Test Correlation With Clinical Findings


title: Optimizing web search using web click-through data

abstract: The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.


title: Malware Detection Approach for Android systems Using System Call Logs

abstract: Static detection technologies based on signature-based approaches that are widely used in Android platform to detect malicious applications. It can accurately detect malware by extracting signatures from test data and then comparing the test data with the signature samples of virus and benign samples. However, this method is generally unable to detect unknown malware applications. This is because, sometimes, the machine code can be converted into assembly code, which can be easily read and understood by humans. Furthuremore, the attacker can then make sense of the assembly instructions and understand the functioning of the program from the same. Therefore we focus on observing the behaviour of the malicious software while it is actually running on a host system. The dynamic behaviours of an application are conducted by the system call sequences at the end. Hence, we observe the system call log of each application, use the same for the construction of our dataset, and finally use this dataset to classify an unknown application as malicious or benign.


Histopathological Variations in Generalized Inflammatory Gingival Enlargements and Normal Gingiva : A Randomized Case-Control Study


title: A Wavenet for Speech Denoising

abstract: Currently, most speech processing techniques use magnitude spectrograms as front-end and are therefore by default discarding part of the signal: the phase. In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet. The proposed model adaptation retains Wavenet's powerful acoustic modeling capabilities, while significantly reducing its time-complexity by eliminating its autoregressive nature. Specifically, the model makes use of non-causal, dilated convolutions and predicts target fields instead of a single target sample. The discriminative adaptation of the model we propose, learns in a supervised fashion via minimizing a regression loss. These modifications make the model highly parallelizable during both training and inference. Both computational and perceptual evaluations indicate that the proposed method is preferred to Wiener filtering, a common method based on processing the magnitude spectrogram.


title: Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm

abstract: Abstract The high penetration rate of new technologies in all the activities of everyday life is fostering the belief that for any new societal challenge there is always an ICT solution able to successfully deal with it. Recently, the solution that is proposed almost anytime is the “Internet of Things” (IoT). This apparent panacea of the ICT world takes different aspects on and, actually, is identified with different (often very different) technological solutions. As a result, many think that IoT is just RFIDs, others think that it is sensor networks, and yet others that it is machine-to-machine communications. In the meanwhile, industrial players are taking advantage of the popularity of IoT to use it as a very trendy brand for technology solutions oriented to the consumer market. The scientific literature sometimes does not help much in clarifying, as it is rich in definitions of IoT often discordant between them. Objective of this paper is to present the evolutionary stages, i.e., generations, that have characterized the development of IoT, along with the motivations of their triggering. Besides, it analyzes the role that IoT can play in addressing the main societal challenges and the set of features expected from the relevant solutions. The final objective is to give a modern definition of the phenomenon, which de facto shows a strong pervasive nature, and, if not well understood in its theories, technologies, methodologies, and real potentials, then runs the risk of being regarded with suspicion and, thus, rejected by users.


title: Real-time regex matching with apache spark

abstract: Network Monitoring Systems (NMS) are an important part of protecting Army and enterprise networks. As governments and corporations grow, the amount of traffic data collected by NMS grows proportionally. To protect users against emerging threats, it is common practice for organizations to maintain a series of custom regular expression (regex) patterns to run on NMS data. However, the growth of network traffic makes it increasingly difficult for network administrators to perform this process quickly. In this paper, we describe a novel algorithm that leverages Apache Spark to perform regex matching in parallel. We test our approach on a dataset of 31 million Bro HTTP log events and 569 regular expressions provided by the Army Engineer Research & Development Center (ERDC). Our results indicate that we are able to process 1, 250 events in 1.047 seconds, meeting the desired definition of real-time.


title: Phrase Dependency Parsing for Opinion Mining

abstract: In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase level. This concept is then implemented for extracting relations between product features and expressions of opinions. Experimental evaluations show that the mining task can benefit from phrase dependency parsing.


title: Adapting nlp and corpus analysis techniques to structured imagery analysis in classical chinese poetry

abstract: This paper describes some pioneering work as a joint research project between City University of Hong Kong and Yuan Ze University in Taiwan to adapt language resources and technologies in order to set up a computational framework for the study of the creative language employed in classical Chinese poetry. ::: ::: In particular, it will first of all describe an existing ontology of imageries found in poems written during the Tang and the Song dynasties (7th -14th century AD). It will then propose the augmentation of such imageries into primary, complex, extended and textual imageries. A rationale of such a structured approach is that while poets may use a common dichotomy of primary imageries, creative language use is to be found in the creation of complex and compound imageries. This approach will not only support analysis of inter-poets stylistic similarities and differences but will also effectively reveal intra-poet stylistic characteristics. This article will then describe a syntactic parser designed to produce parse trees that will eventually enable the automatic identification of possible imageries and their subsequent structural analysis and classification. Finally, a case study will be presented that investigated the syntactic properties found in two lyrics written by two stylistically different lyric writers in the Song Dynasty.


title: Surface free energy and flotability changes of coal

abstract: Abstract The flotation activity of coal rank 33 (Polish standards) was studied in water as a function of the particle size, n-alkane precoverage (pentane to hexadecane series) and degree of the surface precoverage with n-nonane. The surface free energy components (dispersive, γ s d , and non-dispersive γ s n ) were determined for coal fractions γ s d = 53.7 mJ m −2 and γ s n = 16.5 mJ m −2 . From these values, the results gave a negative value of work of spreading and therefore a high natural flotability of this fraction of coal occurs. However, the natural flotability of particles 0.385–0.49 mm in size is only 5%. The alkanes influence coal flotation depending on their chain length and the film specific volume. In the case of nonane, the largest flotation activity was found for a precoverage of 1–4 statistical monolayers.


title: Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies

abstract: Over the past quarter of a century, there has been rapid development in structural biology, which now can provide solid evidence for understanding the functions of proteins. Concurrently, computational approaches with particular relevance to the chemical biology and drug design (CBDD) field have also incrementally and steadily improved. Today, these methods help elucidate detailed working mechanisms and accelerate the discovery of new chemical modulators of proteins. In recent years, integrating computational simulations and predictions with experimental validation has allowed for more effective explorations of the structure, function and modulation of important therapeutic targets. In this review, we summarize the main advancements in computational methodology development, which are then illustrated by several successful applications in CBDD. Finally, we conclude with a discussion of the current major challenges and future directions in the field.


title: A Dual-Mode Large-Arrayed CMOS ISFET Sensor for Accurate and High-Throughput pH Sensing in Biomedical Diagnosis

abstract: Goal: The existing ISFET-based DNA sequencing detects hydrogen ions released during the polymerization of DNA strands on microbeads, which are scattered into microwell array above the ISFET sensor with unknown distribution. However, false pH detection happens at empty microwells due to crosstalk from neighboring microbeads. In this paper, a dual-mode CMOS ISFET sensor is proposed to have accurate pH detection toward DNA sequencing. Methods: Dual-mode sensing, optical and chemical modes, is realized by integrating a CMOS image sensor (CIS) with ISFET pH sensor, and is fabricated in a standard 0.18-μm CIS process. With accurate determination of microbead physical locations with CIS pixel by contact imaging, the dual-mode sensor can correlate local pH for one DNA slice at one location-determined microbead, which can result in improved pH detection accuracy. Moreover, toward a high-throughput DNA sequencing, a correlated-double-sampling readout that supports large array for both modes is deployed to reduce pixel-to-pixel nonuniformity such as threshold voltage mismatch. Results: The proposed CMOS dual-mode sensor is experimentally examined to show a well correlated pH map and optical image for microbeads with a pH sensitivity of 26.2 mV/pH, a fixed pattern noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/s. Conclusion: A dual-mode CMOS ISFET sensor with suppressed FPN for accurate large-arrayed pH sensing is proposed and demonstrated with state-of-the-art measured results toward accurate and high-throughput DNA sequencing. Significance: The developed dual-mode CMOS ISFET sensor has great potential for future personal genome diagnostics with high accuracy and low cost.


title: Development and Validation of a Smartphone Addiction Scale (SAS)

abstract: OBJECTIVE ::: The aim of this study was to develop a self-diagnostic scale that could distinguish smartphone addicts based on the Korean self-diagnostic program for Internet addiction (K-scale) and the smartphone's own features. In addition, the reliability and validity of the smartphone addiction scale (SAS) was demonstrated. ::: ::: ::: METHODS ::: A total of 197 participants were selected from Nov. 2011 to Jan. 2012 to accomplish a set of questionnaires, including SAS, K-scale, modified Kimberly Young Internet addiction test (Y-scale), visual analogue scale (VAS), and substance dependence and abuse diagnosis of DSM-IV. There were 64 males and 133 females, with ages ranging from 18 to 53 years (M = 26.06; SD = 5.96). Factor analysis, internal-consistency test, t-test, ANOVA, and correlation analysis were conducted to verify the reliability and validity of SAS. ::: ::: ::: RESULTS ::: Based on the factor analysis results, the subscale "disturbance of reality testing" was removed, and six factors were left. The internal consistency and concurrent validity of SAS were verified (Cronbach's alpha = 0.967). SAS and its subscales were significantly correlated with K-scale and Y-scale. The VAS of each factor also showed a significant correlation with each subscale. In addition, differences were found in the job (p<0.05), education (p<0.05), and self-reported smartphone addiction scores (p<0.001) in SAS. ::: ::: ::: CONCLUSIONS ::: This study developed the first scale of the smartphone addiction aspect of the diagnostic manual. This scale was proven to be relatively reliable and valid.


title: A virtual machine placement algorithm for balanced resource utilization in cloud data centers

abstract: Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.


title: Irreversibilities and nonidealities in desalination systems

abstract: Energy requirements for desalination systems must be ::: reduced to meet increasing global demand for fresh water. This ::: thesis identifies thermodynamic limits for the energetic ::: performance of desalination systems and establishes the importance ::: of irreversibilities and solution composition to the actual ::: performance obtained. Least work of separation for a desalination ::: system is derived and generalized to apply to all chemical ::: separation processes driven by some combination of work, heat, and ::: chemical energy (fuel) input. At infinitesimal recovery, least work ::: reduces to the minimum least work of separation: the true exergetic ::: value of the product and a useful benchmark for evaluating ::: energetic efficiency of separation processes. All separation ::: processes are subject to these energy requirements; several cases ::: relevant to established and emerging desalination technologies are ::: considered. The effect of nonidealities in electrolyte solutions on ::: least work is analyzed through comparing the ideal solution ::: approximation, Debye-Huckel theory, Pitzer's ionic interaction ::: model, and Pitzer-Kim's model for mixed electrolytes. Error ::: introduced by using incorrect property models is quantified. Least ::: work is a strong function of ionic composition; therefore, standard ::: property databases should not be used for solutions of different or ::: unknown composition. Second Law efficiency for chemical separation ::: processes is defined using the minimum least work and characterizes ::: energetic efficiency. A methodology is shown for evaluating Second ::: Law efficiency based on primary energy inputs. Additionally, ::: entropy generation mechanisms common in desalination processes are ::: analyzed to illustrate the effect of irreversibility. Formulations ::: for these mechanisms are applied to six desalination systems and ::: primary sources of loss are identified. An economics-based Second ::: Law efficiency is defined by analogy to the energetic parameter. ::: Because real-world systems are constrained by economic factors, a ::: performance parameter based on both energetics and economics is ::: useful. By converting all thermodynamic quantities to economic ::: quantities, the cost of irreversibilities can be compared to other ::: economic factors including capital and operating expenses. By ::: applying these methodologies and results, one can properly ::: characterize the energetic performance and thermodynamic ::: irreversibilities of chemical separation processes, make better ::: decisions during technology selection and design of new systems, ::: and critically evaluate claimed performance improvements of novel ::: systems.


title: Open Domain Targeted Sentiment

abstract: We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them. We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource. By leveraging linguisticallyinformed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90% accuracy on the combined task of named entity recognition and sentiment prediction. Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines.


title: Design and application of 2D illusory vibrotactile feedback for hand-held tablets

abstract: In this study, we investigate whether the “out of body” vibrotactile illusion known as funneling could be applied to enrich and thereby improve the interaction performance on a tablet-sized media device. First, a series of pilot tests was taken to determine the appropriate operational conditions and parameters (such as the tablet size, holding position, minimal required vibration amplitude, and the effect of matching visual feedback) for a two-dimensional (2D) illusory tactile rendering method. Two main experiments were then conducted to validate the basic applicability and effectiveness of the rendering method, and to further demonstrate how the illusory tactile feedback could be deployed in an interactive application and actually improve user performance. Our results showed that for a tablet-sized device (e.g., iPad mini and iPad), illusory perception was possible (localization performance of up to 85%) using a rectilinear grid with a resolution of 5 \(\times \) 7 (grid size: 2.5 cm) with matching visual feedback. Furthermore, the illusory feedback was found to be a significant factor in improving the user performance in a 2D object search/attention task.


title: Deep Convolutional Bidirectional LSTM Based Transportation Mode Recognition

abstract: Traditional machine learning approaches for recognizing modes of transportation rely heavily on hand-crafted feature extraction methods which require domain knowledge. So, we propose a hybrid deep learning model: Deep Convolutional Bidirectional-LSTM (DCBL) which combines convolutional and bidirectional LSTM layers and is trained directly on raw sensor data to predict the transportation modes. We compare our model to the traditional machine learning approaches of training Support Vector Machines and Multilayer Perceptron models on extracted features. In our experiments, DCBL performs better than the feature selection methods in terms of accuracy and simplifies the data processing pipeline. The models are trained on the Sussex-Huawei Locomotion-Transportation (SHL) dataset. The submission of our team, Vahan, to SHL recognition challenge uses an ensemble of DCBL models trained on raw data using the different combination of sensors and window sizes and achieved an F1-score of 0.96 on our test data.


title: P-OLSR: Position-based optimized link state routing for mobile ad hoc networks

abstract: Optimized Link State Routing (OLSR) protocol is a table-driven proactive routing protocol for wireless mobile ad hoc networks. The key idea behind this protocol is to reduce the control message overheads by marking subset of neighbors as Multi-Point Relays (MPRs). In this paper, we discuss problems of OLSR or other routing protocols that are due to mobility of nodes. We integrate our solutions to OLSR protocol and call it as Position-based OLSR (P-OLSR). Performance evaluation of OLSR and P-OLSR is done through NS simulation. Simulation results show that P-OLSR outperforms OLSR with respect to packet delivery, throughput, latency and normalized overheads.


title: Social big data: Recent achievements and new challenges

abstract: The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.


title: Kinematic modeling for feedback control of an omnidirectional wheeled mobile robot

abstract: We have introduced a methodology for the kinematic modeling of wheeled mobile robots. In this paper, we apply our methodology to Uranus, an omnidirectional wheeled mobile robot which is being developed in the Robotics Institute of Carnegie Mellon University. We assign coordinate systems to specify the transformation matrices and write the kinematic equations-of-motion. We illustrate the actuated inverse and sensed forward solutions; i.e., the calculation of actuator velocities from robot velocities and robot velocities from sensed wheel velocities. We apply the actuated inverse and sensed forward solutions to the kinematic control of Uranus by: calculating in real-time the robot position from shaft encoder readings (i.e., dead reckoning); formulating an algorithm to detect wheel slippage; and developing an algorithm for feedback control.


title: Algebraic structures of soft sets associated with new operations

abstract: Recently new operations have been defined for soft sets. In this paper, we study some important properties associated with these new operations. A collection of all soft sets with respect to new operations give rise to four idempotent monoids. Then with the help of these monoids we can study semiring (hemiring) structures of soft sets. Some of these semirings (hemirings) are actually lattices. Finally, we show that soft sets with a fixed set of parameters are MV algebras and BCK algebras.


title: Historical Event Extraction from Text

abstract: In this paper, we report on how historical events are extracted from text within the Semantics of History research project. The project aims at the creation of resources for a historical information retrieval system that can handle the time-based dynamics and varying perspectives of Dutch historical archives. The historical event extraction module will be used for museum collections, allowing users to search for exhibits related to particular historical events or actors within time periods and geographic areas, extracted from accompanying text. We present here the methodology and tools used for the purpose of historical event extraction alongside with the first evaluation results.


title: Using Semantic Web Technologies for Exploratory OLAP: A Survey

abstract: This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP . When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.


Kyoto University Participation to WAT 2016


Music Generation from MIDI datasets


title: Resource Description Framework (RDF)

abstract: The Resource Description Framework (RDF) is the standard knowledge representation language for the Semantic Web, an evolution of the World Wide Web that aims to provide a well-founded infrastructure for publishing, sharing and querying structured data. This article provides an introduction to RDF and its related vocabulary definition language RDF Schema, and explains its relationship with the OWL Web Ontology Language. Finally, it provides an overview of the historical development of RDF and related languages for Web metadata.


title: MonetDB/X100 - A DBMS In The CPU Cache.

abstract: X100 is a new execution engine for the MonetDB system, that improves execution speed and overcomes its main memory limitation. It introduces the concept of in-cache vectorized processing that strikes a balance between the existing column-at-a-time MIL execution primitives of MonetDB and the tuple-at-a-time Volcano pipelining model, avoiding their drawbacks: intermediate result materialization and large interpretation overhead, respectively. We explain how the new query engine makes better use of cache memories as well as parallel computation resources of modern super-scalar CPUs. MonetDB/X100 can be one to two orders of magnitude faster than commercial DBMSs and close to hand-coded C programs for computationally intensive queries on in-memory datasets. To address larger disk-based datasets with the same efficiency, a new ColumnBM storage layer is developed that boosts bandwidth using ultra lightweight compression and cooperative scans.


title: Calculation of Output Voltage Ripple and Design Considerations of SEPIC Converter

abstract: In this paper, a design method is proposed for finding the equivalent inductance and capacitance of the single-ended primary-inductor converter (SEPIC). The relations of the output voltage ripple (OVR) of the SEPIC converter are obtained in complete inductor supply mode-continuous conduction mode (CISM-CCM), incomplete inductor supply mode-CCM (IISM-CCM), and IISM-discontinuous conduction mode (IISM-DCM). The maximum of OVR (MOVR) is investigated for a specified range of the input voltage and load resistance. This value of the MOVR is obtained for the minimum values of input voltage and load resistance. In this paper, the minimum values of the equivalent inductance and capacitance are calculated in obtaining the minimum value of the MOVR. One of the other performed studies in this paper is calculation of the switch peak current in CCM and DCM. In addition, the converter is designed based on the minimum values of the MOVR and stress of the switching current. Experimental and simulation results are used to prove the validity of the presented theoretical subjects.


title: Fingertip skin–inspired microstructured ferroelectric skins discriminate static/dynamic pressure and temperature stimuli

abstract: In human fingertips, the fingerprint patterns and interlocked epidermal-dermal microridges play a critical role in amplifying and transferring tactile signals to various mechanoreceptors, enabling spatiotemporal perception of various static and dynamic tactile signals. Inspired by the structure and functions of the human fingertip, we fabricated fingerprint-like patterns and interlocked microstructures in ferroelectric films, which can enhance the piezoelectric, pyroelectric, and piezoresistive sensing of static and dynamic mechanothermal signals. Our flexible and microstructured ferroelectric skins can detect and discriminate between multiple spatiotemporal tactile stimuli including static and dynamic pressure, vibration, and temperature with high sensitivities. As proof-of-concept demonstration, the sensors have been used for the simultaneous monitoring of pulse pressure and temperature of artery vessels, precise detection of acoustic sounds, and discrimination of various surface textures. Our microstructured ferroelectric skins may find applications in robotic skins, wearable sensors, and medical diagnostic devices.


title: Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior

abstract: In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.


title: Layout and configuration design for a satellite with variable mass using hybrid optimization method

abstract: This article deals with configuration design and layout optimization of a GEO communication satellite. Layout design optimization of satellite objects are performed based on two-stage algorithm. Finite Circle Method (FCM) is used to express geometric constraints mathematically, and provide a smooth design space. For mathematical expression of performance constraints such as electromagnetic and thermal requirements, the distance constraints between objects have been developed. Then a hybrid method is proposed to optimize layout design which is a combination of simulated annealing optimization and quasi-Newton methods. This approach provides global and deterministic optimal solutions. Developed hybrid method validation is performed on a sample test problem. Finally, the proposed algorithm has been implemented for configuration and optimal layout design of a communication satellite. The results show that considered constraints of communication satellite are satisfied.


title: Cortical Reorganization of Sensorimotor Systems and the Role of Intracortical Circuits After Spinal Cord Injury

abstract: The plasticity of sensorimotor systems in mammals underlies the capacity for motor learning as well as the ability to relearn following injury. Spinal cord injury, which both deprives afferent input and interrupts efferent output, results in a disruption of cortical somatotopy. While changes in corticospinal axons proximal to the lesion are proposed to support the reorganization of cortical motor maps after spinal cord injury, intracortical horizontal connections are also likely to be critical substrates for rehabilitation-mediated recovery. Intrinsic connections have been shown to dictate the reorganization of cortical maps that occurs in response to skilled motor learning as well as after peripheral injury. Cortical networks incorporate changes in motor and sensory circuits at subcortical or spinal levels to induce map remodeling in the neocortex. This review focuses on the reorganization of cortical networks observed after injury and posits a role of intracortical circuits in recovery.


title: Exploiting tf-idf in deep Convolutional Neural Networks for Content Based Image Retrieval

abstract: In this paper, a novel term frequency-inverse document frequency (tf-idf) based method that utilizes deep Convolutional Neural Networks (CNN) for Content Based Image Retrieval (CBIR) is proposed. That is, we treat the learned filters of the convolutional layers of a CNN model as detectors of visual words. Each of these filters has been trained to be activated in different visual patterns. Thus, since the activations of each filter provide information about the degree of presence of the visual pattern that the filter has learned during the training procedure, we consider the activations of these filters as the tf part. Subsequently, we propose three approaches of computing the idf part. Finally, we propose a query expansion technique on top of the formulated descriptors. The proposed approach interconnects the standard tf-idf method with the modern CNN analysis for visual content, providing a very powerful image retrieval technique with improved results as it is highlighted by extensive experiments in four challenging image datasets.


Medical Professionalism in the New Millennium : A Physician Charter


title: Tracking Cyber Adversaries with Adaptive Indicators of Compromise

abstract: A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. ::: In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.


title: Path planning of automated guided vehicles based on improved A-Star algorithm

abstract: With the development of automated logistics systems, flexible manufacture systems (FMS) and unmanned automated factories, the application of automated guided vehicle (AGV) gradually become more important to improve production efficiency and logistics automatism for enterprises. The development of the AGV systems play an important role in reducing labor cost, improving working conditions, unifying information flow and logistics. Path planning has been a key issue in AGV control system. In this paper, two key problems, shortest time path planning and collision in multi AGV have been solved. An improved A-Star (A*) algorithm is proposed, which introduces factors of turning, and edge removal based on the improved A* algorithm is adopted to solve k shortest path problem. Meanwhile, a dynamic path planning method based on A* algorithm which searches effectively the shortest-time path and avoids collision has been presented. Finally, simulation and experiment have been conducted to prove the feasibility of the algorithm.


Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks


title: SRAM voltage scaling for energy-efficient convolutional neural networks

abstract: State-of-the-art convolutional neural networks (ConvNets) are now able to achieve near human performance on a wide range of classification tasks. Unfortunately, current hardware implementations of ConvNets are memory power intensive, prohibiting deployment in low-power embedded systems and IoE platforms. One method of reducing memory power is to exploit the error resilience of ConvNets and accept bit errors under reduced supply voltages. In this paper, we extensively study the effectiveness of this idea and show that further savings are possible by injecting bit errors during ConvNet training. Measurements on an 8KB SRAM in 28nm UTBB FD-SOI CMOS demonstrate supply voltage reduction of 310mV, which results in up to 5.4× leakage power reduction and up to 2.9× memory access power reduction at 99% of floating-point classification accuracy, with no additional hardware cost. To our knowledge, this is the first silicon-validated study on the effect of bit errors in ConvNets.


A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat


title: Unsupervised Camera Motion Estimation and Moving Object Detection in Videos

abstract: In this article, we consider the robust estimation of a location parameter using Mestimators. We propose here to couple this estimation with the robust scale estimate proposed in [Dahyot and Wilson, 2006]. The resulting procedure is then completely unsupervised. It is applied to camera motion estimation and moving object detection in videos. Experimental results on different video materials show the adaptability and the accuracy of this new robust approach.


title: Database replication techniques: a three parameter classification

abstract: Data replication is an increasingly important topic as databases are more and more deployed over clusters of workstations. One of the challenges in database replication is to introduce replication without severely affecting performance. Because of this difficulty, current database products use lazy replication, which is very efficient but can compromise consistency. As an alternative, eager replication guarantees consistency but most existing protocols have a prohibitive cost. In order to clarify the current state of the art and open up new avenues for research, this paper analyses existing eager techniques using three key parameters (server architecture, server interaction and transaction termination). In our analysis, we distinguish eight classes of eager replication protocols and, for each category, discuss its requirements, capabilities and cost. The contribution lies in showing when eager replication is feasible and in spelling out the different aspects a database replication protocol must account for.


title: LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

abstract: Deep convolutional Neural Networks (CNN) are the state-of-the-art performers for object detection task. It is well known that object detection requires more computation and memory than image classification. Thus the consolidation of a CNN-based object detection for an embedded system is more challenging. In this work, we propose LCDet, a fully-convolutional neural network for generic object detection that aims to work in embedded systems. We design and develop an end-to-end TensorFlow(TF)-based model. Additionally, we employ 8-bit quantization on the learned weights. We use face detection as a use case. Our TF-Slim based network can predict different faces of different shapes and sizes in a single forward pass. Our experimental results show that the proposed method achieves comparative accuracy comparing with state-of-the-art CNN-based face detection methods, while reducing the model size by 3x and memory-BW by ~4x comparing with one of the best real-time CNN-based object detector such as YOLO. TF 8-bit quantized model provides additional 4x memory reduction while keeping the accuracy as good as the floating point model. The proposed model thus becomes amenable for embedded implementations.


title: Human activity recognition in pervasive single resident smart homes: State of art

abstract: The increasing number of elderly persons, in addition to the lack of infrastructures designed to manage them brings an awareness of the importance of maintaining them at home by developing assistive technologies. Recent research on the latter focused on Human Activity Recognition (HAR). HAR aims to recognize the sequence of actions by a specific resident at home using sensor readings. In eldercare applications, HAR allows for a continuous evaluation of older people physical and cognitive capabilities by monitoring the completion of their Activities of Daily Living (ADLs). After the recognition step, assistive technologies may intervene to provide them with the necessary support to live independently at home. Most of the work in single resident HAR has been done in computer vision. Because, HAR is generally performed in a private setting (i.e. smart homes), the camera based solution would not be suitable due to human privacy concerns. Recently, many researchers are interested in the use of pervasive sensors to recognize single resident ADLs. This paper reviews recent approaches applied for HAR in pervasive single resident smart homes. It presents the latest developments and highlights the open issues in this field.


title: Directional asymmetries in human smooth pursuit eye movements.

abstract: PURPOSE ::: Humans make smooth pursuit eye movements to bring the image of a moving object onto the fovea. Although pursuit accuracy is critical to prevent motion blur, the eye often falls behind the target. Previous studies suggest that pursuit accuracy differs between motion directions. Here, we systematically assess asymmetries in smooth pursuit. ::: ::: ::: METHODS ::: In experiment 1, binocular eye movements were recorded while observers (n = 20) tracked a small spot of light moving along one of four cardinal or diagonal axes across a featureless background. We analyzed pursuit latency, acceleration, peak velocity, gain, and catch-up saccade latency, number, and amplitude. In experiment 2 (n = 22), we examined the effects of spatial location and constrained stimulus motion within the upper or lower visual field. ::: ::: ::: RESULTS ::: Pursuit was significantly faster (higher acceleration, peak velocity, and gain) and smoother (fewer and later catch-up saccades) in response to downward versus upward motion in both the upper and the lower visual fields. Pursuit was also more accurate and smoother in response to horizontal versus vertical motion. CONCLUSIONS. Our study is the first to report a consistent up-down asymmetry in human adults, regardless of visual field. Our findings suggest that pursuit asymmetries are adaptive responses to the requirements of the visual context: preferred motion directions (horizontal and downward) are more critical to our survival than nonpreferred ones.


title: Preserving the Archival Bond in Distributed Ledgers: A Data Model and Syntax

abstract: Distributed cryptographic ledgers, such as the blockchain, are now being used in recordkeeping. However, they lack a key feature of more traditional recordkeeping systems needed to establish the authenticity of records and enable reliance on them for trustworthy recordkeeping. The missing feature is known in archival science as the archival bond -- the mutual relationship that exists among documents by virtue of the actions in which they participate. In this paper, we propose a novel data model and syntax using core web principles that can be used to address this shortcoming in distributed ledgers as recordkeeping systems.


Local Interpretable Model-Agnostic Explanations for Music Content Analysis


title: Collaborative Sequence Prediction for Sequential Recommender

abstract: With the surge of deep learning, more and more attention has been put on the sequential recommender. It can be casted as sequence prediction problem, where we will predict the next item given the previous items. RNN approaches are able to capture the global sequential features from the data compared with the local features derived in Markov Chain methods. However, both approaches rely on the independence of users' sequences, which are not true in practice. We propose to formulate the sequential recommendation problem as collaborative sequence prediction problem to take the dependency of users' sequences into account. In order to solve the collaborative sequence prediction problem, we define the dynamic neighborhood relationship between users and introduce manifold regularization to RNN on the basis of the multi-facets of collaborative filtering, referred to as MrRNN. Experimental results on benchmark datasets show that our approach outperforms the state-of-the-art baselines.


title: FingerPad: private and subtle interaction using fingertips

abstract: We present FingerPad, a nail-mounted device that turns the tip of the index finger into a touchpad, allowing private and subtle interaction while on the move. FingerPad enables touch input using magnetic tracking, by adding a Hall sensor grid on the index fingernail, and a magnet on the thumbnail. Since it permits input through the pinch gesture, FingerPad is suitable for private use because the movements of the fingers in a pinch are subtle and are naturally hidden by the hand. Functionally, FingerPad resembles a touchpad, and also allows for eyes-free use. Additionally, since the necessary devices are attached to the nails, FingerPad preserves natural haptic feedback without affecting the native function of the fingertips. Through user study, we analyze the three design factors, namely posture, commitment method and target size, to assess the design of the FingerPad. Though the results show some trade-off among the factors, generally participants achieve 93% accuracy for very small targets (1.2mm-width) in the seated condition, and 92% accuracy for 2.5mm-width targets in the walking condition.


title: Learning Topic Models by Neighborhood Aggregation

abstract: Topic models are one of the most frequently used models in machine learning due to its high interpretability and modular structure. However extending the model to include supervisory signal, incorporate pre-trained word embedding vectors and add nonlinear output function to the model is not an easy task because one has to resort to highly intricate approximate inference procedure. In this paper, we show that topic models could be viewed as performing a neighborhood aggregation algorithm where the messages are passed through a network defined over words. Under the network view of topic models, nodes corresponds to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and add nonlinear output function to the model in a simple manner. Moreover, we describe a simple way to train the model that is well suited in a semi-supervised setting where we only have supervisory signals for some portion of the corpus and the goal is to improve prediction performance in the held-out data. Through careful experiments we show that our approach outperforms state-of-the-art supervised Latent Dirichlet Allocation implementation in both held-out document classification tasks and topic coherence.


title: Convolutional Neural Network-Based Regression for Depth Prediction in Digital Holography

abstract: Digital holography enables us to reconstruct objects in three-dimensional space from holograms captured by an imaging device. For the reconstruction, we need to know the depth position of the recoded object in advance. In this study, we propose depth prediction using convolutional neural network (CNN)-based regression. In the previous researches, the depth of an object was estimated through reconstructed images at different depth positions from a hologram using a certain metric that indicates the most focused depth position; however, such a depth search is time-consuming. The CNN of the proposed method can directly predict the depth position with millimeter precision from holograms.


title: Completely Derandomized Self-Adaptation in Evolution Strategies

abstract: This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.


title: Pest Identification using Image Processing Techniques in Detecting Image Pattern through Neural Network

abstract: In rice production, pest invasion is considered as the most challenging task for crop technicians and farmers. Pest invasion can cause serious losses and affect the income of farmers. It is then important to assess their density for pests forecasting decision making. Existing identification techniques of these species comprise of using different traps to detect their presence. However, these traditional methods are labor-intensive and sometimes experts on this field are not available. Another problem is that multiple site and frequent monitoring of rice pests is time consuming and tedious for a crop technician. This can lead to low accuracy and delays in obtaining accurate count of these species. In this study, an identification system was developed to automatically identify the insect pests in the paddy field. Sticky trap was used to capture the insect which continuously monitored by a wireless camera to record the video. Different image processing techniques was utilized to detect and extract the captured insect from the image, and Kohonen Self Organizing Maps neural network was used to identify the extracted insect pests. The results indicate that the proposed automated identification system is capable of accurately identifying the insect pests even though there are lots of lighting variations. The new automated identification system developed in this study provides a reliable identification and was found to be faster than a human expert in identifying the insect pests caught by a sticky trap.


title: An Efficient Intrusion Detection System Design

abstract: Intrusion detection systems have proved to be an effective instrument for protecting computer and network resources. In addition to preventive security mechanisms (e.g. authentication, encryption, or access control) they provide an automatic detection of security violations. Some systems are able to reduce arising damage by the automatic execution of intrusion response actions. For host-based systems, the most effective detection approach is audit data analysis with signature detection methods. Because of the character of audit records, these approaches are post-mortem techniques. Thus, the success of an intrusion response activity essentially depends on the time difference between the real appearance and the detection of the particular security violation. At the Brandenburg University of Technology Cottbus we are currently working on HEIDI (High-Efficient Intrusion Detection Infrastructure), which is a new approach for solving this intrusion detection efficiency problem. HEIDI consists of modules and mechanisms, which are aimed to maximize the detection speed in distributed environments. The main types of modules are sensors for fast local audit record capturing and preprocessing, and agents for performing the detection of local and distributed attacks. Unlike any other known system, HEIDI applies a combined signature evaluation scheme with maximal local concentration of analysis functionality. This leads to a minimal need for inter-agent network traffic and delay. Additionally, for assuring a continuous operation, HEIDI uses an adaptive mechanism to compensate temporary overload situations, e.g. audit bursts. To avoid a stop of execution, the affected agents are able to delegate their analysis functionality to other agents temporarily. By combining sensors and agents, it is possible to get tailored hierarchical intrusion detection architectures for given target environments. The HEIDI prototype implementation is currently in progress.


title: PhotoOCR: Reading Text in Uncontrolled Conditions

abstract: We describe Photo OCR, a system for text extraction from images. Our particular focus is reliable text extraction from smartphone imagery, with the goal of text recognition as a user input modality similar to speech recognition. Commercially available OCR performs poorly on this task. Recent progress in machine learning has substantially improved isolated character classification, we build on this progress by demonstrating a complete OCR system using these techniques. We also incorporate modern data center-scale distributed language modelling. Our approach is capable of recognizing text in a variety of challenging imaging conditions where traditional OCR systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise and other distortions. It also operates with low latency, mean processing time is 600 ms per image. We evaluate our system on public benchmark datasets for text extraction and outperform all previously reported results, more than halving the error rate on multiple benchmarks. The system is currently in use in many applications at Google, and is available as a user input modality in Google Translate for Android.


title: Patients with hippocampal amnesia cannot imagine new experiences

abstract: Amnesic patients have a well established deficit in remembering their past experiences. Surprisingly, however, the question as to whether such patients can imagine new experiences has not been formally addressed to our knowledge. We tested whether a group of amnesic patients with primary damage to the hippocampus bilaterally could construct new imagined experiences in response to short verbal cues that outlined a range of simple commonplace scenarios. Our results revealed that patients were markedly impaired relative to matched control subjects at imagining new experiences. Moreover, we identified a possible source for this deficit. The patients' imagined experiences lacked spatial coherence, consisting instead of fragmented images in the absence of a holistic representation of the environmental setting. The hippocampus, therefore, may make a critical contribution to the creation of new experiences by providing the spatial context into which the disparate elements of an experience can be bound. Given how closely imagined experiences match episodic memories, the absence of this function mediated by the hippocampus, may also fundamentally affect the ability to vividly re-experience the past.


Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays


title: Frog: A Framework for Context-Based File Systems

abstract: This article presents a framework, Frog, for Context-Based File Systems (CBFSs) that aim at simplifying the development of context-based file systems and applications. Unlike existing informed-based context-aware systems, Frog is a unifying informed-based framework that abstracts context-specific solutions as views, allowing applications to make view selections according to application behaviors. The framework can not only eliminate overheads induced by traditional context analysis, but also simplify the interactions between the context-based file systems and applications. Rather than propagating data through solution-specific interfaces, views in Frog can be selected by inserting their names in file path strings. With Frog in place, programmers can migrate an application from one solution to another by switching among views rather than changing programming interfaces. Since the data consistency issues are automatically enforced by the framework, file-system developers can focus their attention on context-specific solutions. We implement two prototypes to demonstrate the strengths and overheads of our design. Inspired by an observation that there are more than 50p of small files (


title: Mapping brain asymmetry

abstract: Brain asymmetry has been observed in animals and humans in terms of structure, function and behaviour. This lateralization is thought to reflect evolutionary, hereditary, developmental, experiential and pathological factors. Here, we review the diverse literature describing brain asymmetries, focusing primarily on anatomical differences between the hemispheres and the methods that have been used to detect them. Brain-mapping approaches, in particular, can identify and visualize patterns of asymmetry in whole populations, including subtle alterations that occur in disease, with age and during development. These and other tools show great promise for assessing factors that modulate cognitive specialization in the brain, including the ontogeny, phylogeny and genetic determinants of brain asymmetry.


A Survey on Parts of Speech Tagging for Indian Languages


title: SURVEY OF TEXTURE MAPPING

abstract: Texture mapping is one of the most successful new techniques in high-quality image synthesis. It can enchance the visual richness of raster-scan images immensely while entailing only a relatively smann increase in computation. The technique has been applied to a number of surface attributes: surface color, surface normal, specularity, transparency, illumination, and surface displacement?to name a few. Although the list is potentially endless, the techniques of texture mapping are essentially the same in all cases. This article surveys the fundamentals of texture mapping, which can be spilt into two topics: the geometric mapping that warps a texture onto a surface, and the filtering necessary to avoid aliasing. An extensive bibliography is included.


title: Demystifying Numenta anomaly benchmark

abstract: Detecting anomalies in large-scale, streaming datasets has wide applicability in a myriad of domains like network intrusion detection for cyber-security, fraud detection for credit cards, system health monitoring, and fault detection in safety critical systems. Due to its wide applicability, the problem of anomaly detection has been well-studied by industry and academia alike, and many algorithms have been proposed for detecting anomalies in different problem settings. But until recently, there was no openly available, systematic dataset and/or framework using which the proposed anomaly detection algorithms could be compared and evaluated on a common ground. Numenta Anomaly Benchmark (NAB), made available by Numenta1 in 2015, addressed this gap by providing a set of openly-available, labeled data files and a common scoring system, using which different anomaly detection algorithms could be fairly evaluated and compared. In this paper, we provide an in-depth analysis of the key aspects of the NAB framework, and highlight inherent challenges therein, with the objective to provide insights about the gaps in the current framework that must be addressed so as to make it more robust and easy-to-use. Furthermore, we also provide additional evaluation of five state-of-the-art anomaly detection algorithms (including the ones proposed by Numenta) using the NAB datasets, and based on the evaluation results, we argue that the performance of these algorithms is not sufficient for practical, industry-scale applications, and must be improved upon so as to make them suitable for large-scale anomaly detection problems.


title: Feature Grouping-Based Outlier Detection Upon Streaming Trajectories

abstract: Outlier detection acts as one of the most important analysis tasks for trajectory stream. In stream scenarios, such properties as unlimitedness, time-varying evolutionary, sparsity, and skewness distribution of trajectories pose new challenges to outlier detection technique. Trajectory outlier detection techniques mainly focus on finding trajectory that is dissimilar to the majority of the others, which is based on the hypothesis that they are probably generated by a different mechanism. Most distance-based methods tend to utilize a function (e.g., weighted linear sum) to measure the similarity of two arbitrary objects provided that representative features have been extracted in advance. However, this kind of method is not tailored to identify the outlier which is close to its neighbors according to some features, but behaves significantly different from its neighbors in terms of the other features. To address this issue, we propose a feature grouping-based mechanism that divides all the features into two groups, where the first group ( Similarity Feature ) is used to find close neighbors and the second group ( Difference Feature ) is used to find outliers within the similar neighborhood. According to the feature differences among local adjacent objects in one or more time intervals, we present two outlier definitions, including local anomaly trajectory fragment ( TF-outlier ) and evolutionary anomaly moving object ( MO-outlier ). We devise a basic solution and then an optimized algorithm to detect both types of outliers. Experimental results show that our proposal is both effective and efficient to detect outliers upon trajectory data streams.