title: Strata: Fine-Grained Acoustic-based Device-Free Tracking

abstract: Next generation devices, such as virtual reality (VR), augmented reality (AR), and smart appliances, demand a simple and intuitive way for users to interact with them. To address such needs, we develop a novel acoustic based device-free tracking system, called Strata, to enable a user to interact with a nearby device by simply moving his finger. In Strata, a mobile (e.g., smartphone) transmits known audio signals at inaudible frequency, and analyzes the received signal reflected by the moving finger to track the finger location. To explicitly take into account multipath propagation, the mobile estimates the channel impulse response (CIR), which characterizes signal traversal paths with different delays. Each channel tap corresponds to the multipath effects within a certain delay range. The mobile selects the channel tap corresponding to the finger movement and extracts the phase change of the selected tap to accurately estimate the distance change of a finger. Moreover, it estimates the absolute distance of the finger based on the change in CIR using a novel optimization framework. We then combine the absolute and relative distance estimates to accurately track the moving target. We implement our tracking system on Samsung Galaxy S4 mobile phone. Through micro-benchmarks and user studies, we show that our system achieves high tracking accuracy and low latency without extra hardware.


title: An Architecture of Dynamically Adaptive PHP-based Web Applications

abstract: Self-adaptive systems, changing their functional behavior at runtime, provide desired a level of flexibility. Although various runtime frameworks have been studied, they tend to rely on a particular architecture. It is inadequate to study the characteristics of self-adaptive systems. This paper presents an abstract, declarative framework for them and relates it to an adaptive PHP-based Web application architecture, which takes a model-based adaptation approach. The formalism can be a basis for understanding the distinctive roles of the model information and runtime mechanism.


title: Evaluating race and sex diversity in the world's largest companies using deep neural networks

abstract: Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep-learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks.


title: Guidelines for the Management of Dyspepsia

abstract: Dyspepsia is a chronic or recurrent pain or discomfort centered in the upper abdomen; patients with predominant or frequent (more than once a week) heartburn or acid regurgitation, should be considered to have gastroesophageal reflux disease (GERD) until proven otherwise. Dyspeptic patients over 55 yr of age, or those with alarm features should undergo prompt esophagogastroduodenoscopy (EGD). In all other patients, there are two approximately equivalent options: (i) test and treat for Helicobacter pylori (H. pylori) using a validated noninvasive test and a trial of acid suppression if eradication is successful but symptoms do not resolve or (ii) an empiric trial of acid suppression with a proton pump inhibitor (PPI) for 4–8 wk. The test-and-treat option is preferable in populations with a moderate to high prevalence of H. pylori infection (10%); empirical PPI is an initial option in low prevalence situations. If initial acid suppression fails after 2–4 wk, it is reasonable to consider changing drug class or dosing. If the patient fails to respond or relapses rapidly on stopping antisecretory therapy, then the test-and-treat strategy is best applied before consideration of referral for EGD. Prokinetics are not currently recommended as first-line therapy for uninvestigated dyspepsia. EGD is not mandatory in those who remain symptomatic as the yield is low; the decision to endoscope or not must be based on clinical judgement. In patients who do respond to initial therapy, stop treatment after 4–8 wk; if symptoms recur, another course of the same treatment is justified. The management of functional dyspepsia is challenging when initial antisecretory therapy and H. pylori eradication fails. There are very limited data to support the use of low-dose tricyclic antidepressants or psychological treatments in functional dyspepsia.


title: The Effect of Individual Needs, Trust and Identification in Explaining Participation Intentions in Virtual Communities

abstract: This paper studied individuals' intentions to participate in virtual communities. In order to assess what influences participation intentions, we studied individual-based factors (members' social needs and self expression needs), trust and identification. We found that although needs deficiency-based motivation theory is useful to explain participation intentions, trust and identification play an important moderating role


title: Why Do Firms Imitate Each Other?

abstract: Scholars from diverse disciplines have proposed numerous theories of business imitation. We organize these theories into two broad categories: (1) information-based theories, where firms follow others that are perceived as having superior information, and (2) rivalry-based theories, where firms imitate others to maintain competitive parity or limit rivalry. We describe conditions under which each type of imitation is most likely and offer guidance on identifying imitation in practice. Amplification effects and other performance implications of imitation are also addressed.


title: An adaptive multilevel wavelet collocation method for elliptic problems

abstract: An adaptive multilevel wavelet collocation method for solving multi-dimensional elliptic problems with localized structures is described. The method is based on multi-dimensional second generation wavelets, and is an extension of the dynamically adaptive second generation wavelet collocation method for evolution problems [Int. J. Comp. Fluid Dyn. 17 (2003) 151]. Wavelet decomposition is used for grid adaptation and interpolation, while a hierarchical finite difference scheme, which takes advantage of wavelet multilevel decomposition, is used for derivative calculations. The multilevel structure of the wavelet approximation provides a natural way to obtain the solution on a near optimal grid. In order to accelerate the convergence of the solver, an iterative procedure analogous to the multigrid algorithm is developed. The overall computational complexity of the solver is O(N), where N is the number of adapted grid points. The accuracy and computational efficiency of the method are demonstrated for the solution of two- and three-dimensional elliptic test problems.


title: Fast and scalable learning of neuro-symbolic representations of biomedical knowledge

abstract: In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neuro-symbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of $+0.28$ F-measure and $+0.22$ ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train ($\leq$ 1 minute vs. hours), much more economical in terms of embedding dimensions ($d=50$ vs. $d=512$), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.


title: Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices

abstract: Abstract Over the last few decades, the advance of new technologies in computer equipment, cameras and medical devices became a starting point for the shape of medical imaging systems. Since then, many new medical devices, e.g. the X-Ray machines, computed tomography scans, magnetic resonance imaging, etc., accompanied with operational algorithms inside has contributed greatly to successful diagnose of clinical cases. Enhancing the accuracy of segmentation, which plays an important role in the recognition of disease patterns, has been the focus of various researches in recent years. Segmentation using advanced fuzzy clustering to handle the problems of common boundaries between clusters would tackle many challenges in medical imaging. In this paper, we propose a new fuzzy clustering algorithm based on the neutrosophic orthogonal matrices for segmentation of dental X-Ray images. This algorithm transforms image data into a neutrosophic set and computes the inner products of the cutting matrix of input. Pixels are then segmented by the orthogonal principle to form clusters. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of clustering quality.


title: A duty cycle control circuit for high speed applications

abstract: An accurate and programmable CMOS duty-cycle control (DCC) circuit for high-speed applications is discussed. Proposed DCC circuit has a first order transfer function the accuracy of which is just limited by the on-chip device mismatch. Operating at 1GHz frequency, the duty-cycle of the output clock can be tuned between 45 to 60% by changing the charge and discharge currents of a charged-pump circuit (CPC). CPC's current is controlled through five controlling bits. The circuit is designed in a 0.18/spl mu/m CMOS technology and draws 160/spl mu/A from a 1.8V supply with less than 0.3/spl times/LSB error.


A Real–time Global Illumination Approach for High Resolution Reflective Shadow Maps in Open World Scenes


title: New Word Detection in Ancient Chinese Literature

abstract: Mining Ancient Chinese corpus is not as convenient as modern Chinese, because there is no complete dictionary of ancient Chinese words which leads to the bad performance of tokenizers. So finding new words in ancient Chinese texts is significant. In this paper, the Apriori algorithm is improved and used to produce candidate character sequences. And a long short-term memory (LSTM) neural network is used to identify the boundaries of the word. Furthermore, we design word confidence feature to measure the confidence score of new words. The experimental results demonstrate that the improved Apriori-like algorithm can greatly improve the recall rate of valid candidate character sequences, and the average accuracy of our method on new word detection raise to 89.7%.


title: DEMO: An IDS framework for internet of things empowered by 6LoWPAN

abstract: The Internet of Things (IoT) is an emerging paradigm where smart objects are seamlessly connected to the overall Internet and can potentially cooperate to achieve common objectives such as supporting innovative home automation services. With reference to such a scenario, this paper presents an Intrusion Detection System (IDS) framework for IoT empowered by IPv6 over low-power personal area network (6LoWPAN) devices. In fact, 6LoWPAN is an interesting protocol supporting the realization of IoT in a resource constrained environment. 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. The proposed IDS framework which includes a monitoring system and a detection engine has been integrated into the network framework developed within the EU FP7 project `ebbits'. A penetration testing (PenTest) system had been used to evaluate the performance of the implemented IDS framework. Preliminary tests revealed that the proposed framework represents a promising solution for ensuring better security in 6LoWPANs.


title: RRC and MAC call flow procedures for D2D communication through sidelink in LTE-A

abstract: Device-to-Device (D2D) communication is one of the most popular topic in the upcoming device communication. D2D provides access to services through direct or indirect connection path with neighboring devices with or/and without ENodeB (eNB) assistance. Some of the problems faced in this case are discovering process of neighbor user equipment (UE) and services, as well as designing suitable and secure protocols for D2D communication. In this paper, we propose a sidelink based D2D communication, wherein a separate unlicensed band apart from uplink and downlink is considered to be allocated for sidelink and is functional without the presence of eNB. In this paper we present a detailed study of operational procedures at Radio Resource Control (RRC) and Media Access Control (MAC) layers for sidelink based D2D communication along with this various messages handled for D2D communication for both scenarios using sidelink has been discussed. Two scenarios of D2D communications have been considered-for both UE's in-coverage and both UE's' out-of-coverage. The client side architecture of D2D communication with respect to RRC and MAC layers is also presented.


title: Design Considerations for Very High Frequency dc-dc Converters

abstract: This document describes several aspects relating to the design of dc-dc converters operating at frequencies in the VHF range (30–300 MHz). Design considerations are treated in the context of a dc-dc converter operating at a switching frequency of 100 MHz. Gate drive, rectifier and control designs are explored in detail, and experimental measurements of the complete converter are presented that verify the design approach. The gate drive, a self-oscillating multi-resonant circuit, dramatically reduces the gating power while ensuring fast on-off transitions of the semiconductor switch. The rectifier is a resonant topology that absorbs diode parasitic capacitance and is designed to appear resistive at the switching frequency. The small sizes of the energy storage elements (inductors and capacitors) in this circuit permit rapid start-up and shut-down and a correspondingly high control bandwidth. These characteristics are exploited in a high bandwidth hysteretic control scheme that modulates the converter on and off at frequencies as high as 200 kHz.


title: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

abstract: We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.


title: QoS-aware cloud service composition using eagle strategy

abstract: Abstract In recent years, several cloud services have proliferated that conspicuously result in providing similar services having same functionality by multiple service providers, but varying in Quality of Service (QoS) properties. Thus, providing a cloud service composition with optimal QoS values that satisfy the requirements of an user becomes complex and challenging in a cloud environment. Several metaheuristics proposed in solving this problem. However, many of them fail to maintain a suitable balance between exploration and exploitation. We propose a novel Eagle Strategy with Whale Optimization Algorithm (ESWOA) that ensures the proper balance between exploration and exploitation.


title: Characterizing the dynamics of mental representations: the temporal generalization method

abstract: Parsing a cognitive task into a sequence of operations is a central problem in cognitive neuroscience. We argue that a major advance is now possible owing to the application of pattern classifiers to time-resolved recordings of brain activity [electroencephalography (EEG), magnetoencephalography (MEG), or intracranial recordings]. By testing at which moment a specific mental content becomes decodable in brain activity, we can characterize the time course of cognitive codes. Most importantly, the manner in which the trained classifiers generalize across time, and from one experimental condition to another, sheds light on the temporal organization of information-processing stages. A repertoire of canonical dynamical patterns is observed across various experiments and brain regions. This method thus provides a novel way to understand how mental representations are manipulated and transformed.


title: Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation

abstract: Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity assumption and thus L2 loss. To allow the generation of high quality images by VAE, we increase the capacity of decoder network by employing residual blocks and skip connections, which also enable efficient optimization. To overcome the limitation of L2 loss, we propose to generate images in a multi-stage manner from coarse to fine. In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component. Since the second component is independent of the VAE model, it can employ other loss functions beyond the L2 loss and different model architectures. The proposed framework can be easily generalized to contain more than two components. Experiment results on the MNIST and CelebA datasets demonstrate that the proposed multi-stage VAE can generate sharper images as compared to those from the original VAE.


title: FgDetector: Fine-Grained Android Malware Detection

abstract: Smartphones are rapidly becoming a necessity in our lives and Android is one of the most popular mobile operating systems. However, a large number of Android malicious applications hidden behind the benign applications pose a serious threat to the Android platform. In this paper, we have proposed and implemented FgDetector, an automatically malware detection tool, based on machine learning models. FgDetector can extract features from Android applications and convert it into a low-dimensional feature vector for training the detection model to detect whether an application is malicious or not. Furthermore, FgDetector can classify the fine-grained malware families when identifying malicious applications. To better assess, we have conducted an evaluation with a public data set with 123, 453 benign applications and 5560 malware to prove the feasibility of FgDetector. Our experimental results show that FgDetector outperforms several related traditional machine learning models whether in binary classification or multiclass classification. FgDetector can detect 99.20% of the malware with a high F1-score and can achieve 93.77% precision when detecting the detailed malware family of a malicious application.


title: Authenticated Hash Tables Based on Cryptographic Accumulators

abstract: Suppose a client stores $$n$$n elements in a hash table that is outsourced to an untrusted server. We address the problem of authenticating the hash table operations, where the goal is to design protocols capable of verifying the correctness of queries and updates performed by the server, thus ensuring the integrity of the remotely stored data across its entire update history. Solutions to this authentication problem allow the client to gain trust in the operations performed by a faulty or even malicious server that lies outside the administrative control of the client. We present two novel schemes that implement an authenticated hash table. An authenticated hash table exports the basic hash-table functionality for maintaining a dynamic set of elements, coupled with the ability to provide short cryptographic proofs that a given element is a member or not of the current set. By employing efficient algorithmic constructs and cryptographic accumulators as the core security primitive, our schemes provide constant proof size, constant verification time and sublinear query or update time, strictly improving upon previous approaches. Specifically, in our first scheme which is based on the RSA accumulator, the server is able to construct a (non-)membership proof in constant time and perform updates in $$O\left( n^{\epsilon }\log n\right) $$On∈logn time for any fixed constant $$0<\epsilon <1$$0<∈<1. A variation of this scheme achieves a different trade-off, offering constant update time and $$O(n^{\epsilon })$$O(n∈) query time. Our second scheme uses an accumulator based on bilinear pairings to achieve $$O(n^{\epsilon })$$O(n∈) update time at the server while keeping all other complexities constant. A variation of this scheme achieves $$O(n^{\epsilon }\log n)$$O(n∈logn) time for queries and constant update time. An experimental evaluation of both solutions shows their practicality.


Giving credit where its due: The operation of micro-credit models in an Indigenous Australian context


title: Fast collapsed gibbs sampling for latent dirichlet allocation

abstract: In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text corpora. Conventional Gibbs sampling schemes for LDA require O(K) operations per sample where K is the number of topics in the model. Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample. On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA. No approximations are necessary, and we show that our fast sampling scheme produces exactly the same results as the standard (but slower) sampling scheme. Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes. For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA, our speedup of 5.7 can save 5 CPU months of computation.


title: Graph Isomorphism in Quasipolynomial Time

abstract: We show that the Graph Isomorphism (GI) problem and the related problems of String Isomorphism (under group action) (SI) and Coset Intersection (CI) can be solved in quasipolynomial (exp (logn) O(1) � ) time. The best previous bound for GI was exp(O( √ nlogn)), where n is the number of vertices (Luks, 1983); for the other two problems, the bound was similar, exp( e O( √ n)), where n is the size of the permutation domain (Babai, 1983). The algorithm builds on Luks’s SI framework and attacks the barrier configurations for Luks’s algorithm by group theoretic “local certificates” and combinatorial canonical partitioning techniques. We show that in a well-defined sense, Johnson graphs are the only obstructions to effective canonical partitioning.


title: Surface light fields for 3D photography

abstract: A surface light field is a function that assigns a color to each ray originating on a surface. Surface light fields are well suited to constructing virtual images of shiny objects under complex lighting conditions. This paper presents a framework for construction, compression, interactive rendering, and rudimentary editing of surface light fields of real objects. Generalization of vector quantization and principal component analysis are used to construct a compressed representation of an object's surface light field from photographs and range scans. A new rendering algorithm achieves interactive rendering of images from the compressed representation, incorporating view-dependent geometric level-of-detail control. The surface light field representation can also be directly edited to yield plausible surface light fields for small changes in surface geometry and reflectance properties.


title: Joint entity recognition and relation extraction as a multi-head selection problem

abstract: Abstract State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.


title: Parsing English with a Link Grammar

abstract: We develop a formal grammatical system called a link grammar, show how English grammar can be encoded in such a system, and give algorithms for efficiently parsing with a link grammar. Although the expressive power of link grammars is equivalent to that of context free grammars, encoding natural language grammars appears to be much easier with the new system. We have written a program for general link parsing and written a link grammar for the English language. The performance of this preliminary system ‐ both in the breadth of English phenomena that it captures and in the computational resources used ‐ indicates that the approach may have practical uses as well as linguistic significance. Our program is written in C and may be obtained through the internet.


title: The Effects of Early Maternal Employment on Child Development in the UK

abstract: This paper uses data from the ALSPAC cohort of 12000 births to explore the effects of early maternal employment on child cognitive and behavioural outcomes. The results indicate that full time maternal employment begun in the 18 months after childbirth has small negative effects on later child outcomes. Part-time work and work begun later than 18 months, however, do not seem to have any adverse consequences. We explore the issue of whether our results are biased by unobserved heterogeneity but find no evidence that our results are sensitive to the inclusion of controls for a wide range of background factors. We conduct sub-group analyses to investigate whether certain groups may be more vulnerable to the effects of early full time maternal employment than others. This paper also explores the mechanisms linking maternal employment to children's development. The mechanisms examined relate to the parenting behaviours of the mother and father, breastfeeding behaviour, maternal tiredness and stress, household income and the use of non-maternal childcare. We find that a number of factors work to minimise the effect of mothers' labour market participation on their children. Fathers are significantly more involved in child rearing in households where mothers return to work early and this more equal division of parenting has strongly beneficial effects on later child outcomes. Negative employment effects are concentrated in those families where mothers work full time and also rely on unpaid care by a friend or relative. The use of paid childcare protects children from these negative effects and attendance at a centre-based provider may actually lead to better cognitive outcomes than if the child were at home with a non-working mother.


title: REVIEW OF PROVEN TECHNOLOGIES AVAILABLE FOR THE REDUCTION OF RAW SUGAR COLOUR

abstract: Colour removal is the chief cost centre of sugar re fineries, hence, by reducing the colour of the raw sugar to be processed, the level of process ing and costs incurred by a refinery are also reduced. A lower colour raw sugar is thus highly ma rketable. There are a number of proven technologies that can be applied in raw sugar mills to control and reduce raw sugar colour. These mechanisms are r eviewed in order to identify effective and economically viable means of reducing the colou r of South African raw sugars to a reasonable level, ca 1 000 ICUMSA units. The technologies include: • syrup flotation clarification • juice and syrup sulphitation • alternative boiling schemes • double curing of C-massecuites • additional washing in centrifugals • the use of specialised chemicals and flocculants du ring clarification • hydrogen peroxide in wash water during centrifugati on.


title: Exploring the Impact of Gambling Advertising: An Interview Study of Problem Gamblers

abstract: This study qualitatively explored the impact of gambling advertising on problem gambling by interviewing twenty-five people with current or past gambling problems. Interviews were relatively long and involved the participants’ viewing numerous examples of gambling advertising. A quarter of the participants reported that gambling advertising had no impact on their problems, slightly over half of them reported that advertising had a marginal impact, and one fifth reported a tangible impact. However, none considered advertising to be a main cause of their gambling problems. The negative self-perceived impact was primarily that advertising triggered impulses to gamble. Advertising thus increased already high involvement in gambling and/or made it harder to stick to a decision to gamble less or not at all.


title: State of the Art and Research Challenges of new services architecture technologies

abstract: The telecommunication world is evolving towards networks and different services. It is necessary to ensure interoperability between different networks to provide seamless and on-demand services. As the number of services and users in Internet Protocol Multimedia Subsystem (IMS) keeps increasing, network virtualization and cloud computing technologies seem to be a good alternative for Mobile Virtual Network Operators (MVNOs) in order to provide better services to customers and save cost and time. Cloud computing known as an IT environment that includes all elements of the IT and network stack, enabling the development, delivery, and consumption of Cloud Services. In this paper, we will present the challenges and issues of these emerging technologies. The first part of this paper describes Cloud computing as the networks of the future. It presents an overview of some works in this area. Some concepts like cloud services and Service oriented Architecture designed to facilitate rapid prototyping and deployment of on demand services that enhance flexibility, communication performance, robustness, and scalability are detailed. The second part exposes SOA and its concept, the third one deals with virtualization.


title: On the Limits of Recursively Self-Improving AGI

abstract: Self-improving software has been a goal of computer scientists since the founding of the field of Artificial Intelligence. In this work we analyze limits on computation which might restrict recursive self-improvement. We also introduce Convergence Theory which aims to predict general behavior of RSI systems.


title: A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel

abstract: The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively handle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed kernel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.


title: A high-speed/power laser transmitter for single photon imaging applications

abstract: A compact laser pulser emitting ∼100 ps, ∼10 W pulses at >100 kHz is presented. The high pulsing frequency is achieved using a MOSFET-based current driver, whereas the high pulse power is a merit of the used laser diode with an asymmetric waveguide structure leading to enhanced gain switching. The pulsing frequency is higher than with avalanche transistor based current pulsing circuits due to lower heating, and the current pulse width is shown to be even shorter than with avalanche transistor based circuits. The laser diode transmitter was developed especially for the pulsed time-of-flight laser radar application utilizing a single photon avalanche diode (SPAD) matrix as the detector element. A demonstration measurement is done enabling centimeter-precision distance measurement to 50 m in a measurement time of ∼5 ms outdoors in sunny weather.


title: CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

abstract: We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling a massive amount of noisy labels and data imbalance effectively. We design a new learning curriculum by measuring the complexity of data using its distribution density in a feature space, and rank the complexity in an unsupervised manner. This allows for an efficient implementation of curriculum learning on large-scale web images, resulting in a high-performance CNN model, where the negative impact of noisy labels is reduced substantially. Importantly, we show by experiments that those images with highly noisy labels can surprisingly improve the generalization capability of the model, by serving as a manner of regularization. Our approaches obtain state-of-the-art performance on four benchmarks: WebVision, ImageNet, Clothing-1M and Food-101. With an ensemble of multiple models, we achieved a top-5 error rate of 5.2% on the WebVision challenge for 1000-category classification. This result was the top performance by a wide margin, outperforming second place by a nearly 50% relative error rate. Code and models are available at: https://github.com/MalongTech/CurriculumNet .


Design and Evaluation of a VR Training Simulation for Pump Maintenance


title: Video fire detection - Review

abstract: This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor ''volumes'' and do not have transport delay that the traditional ''point'' sensors suffer from. It is possible to cover an area of 100 km^2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.


title: Is There a Cost to Privacy Breaches? An Event Study

abstract: While the literature on information security economics has begun to investigate the stock market impact of security breaches and vulnerability announcements, little more than anecdotal evidence exists on the effects of privacy breaches. In this paper we present the first comprehensive analysis of the impact of a company’s privacy incidents on its market value. We compile a broad data set of instances of exposure of personal information due to failures of some security mechanism (hacking, stolen or lost equipment, poor data handling processes, and others) and we present the results of various empirical analyses, including event study analysis. We show that there exists a negative and statistically significant impact of data breaches on a company’s market value on the announcement day for the breach. The cumulative effect increases in magnitudes over the day following the breach announcement, but then decreases and loses statistical significance. We also present regression analyses that aim at disentangling the eff ects of a number of factors on abnormal stock returns due to reported breaches. Finally, we comment on the differences between the impact of the security breaches already described in the literature, and the privacy breaches described here.


title: Buy one get one free: Distant annotation of Chinese tense, event type and modality

abstract: We describe a “distant annotation” method where we mark up the semantic tense, event type, and modality of Chinese events via a word-aligned parallel corpus. We first map Chinese verbs to their English counterparts via word alignment, and then annotate the resulting English text spans with coarse-grained categories for semantic tense, event type, and modality that we believe apply to both English and Chinese. Because English has richer morpho-syntactic indicators for semantic tense, event type and modality than Chinese, our intuition is that this distant annotation approach will yield more consistent annotation than if we annotate the Chinese side directly. We report experimental results that show stable annotation agreement statistics and that event type and modality have significant influence on tense prediction. We also report the size of the annotated corpus that we have obtained, and how different domains impact annotation consistency.


title: Group skyline computation

abstract: Given a multi-dimensional dataset of tuples, skyline computation returns a subset of tuples that are not dominated by any other tuples when all dimensions are considered together. Conventional skyline computation, however, is inadequate to answer various queries that need to analyze not just individual tuples of a dataset but also their combinations. In this paper, we study group skyline computation which is based on the notion of dominance relation between groups of the same number of tuples. It determines the dominance relation between two groups by comparing their aggregate values such as sums or averages of elements of individual dimensions, and identifies a set of skyline groups that are not dominated by any other groups. We investigate properties of group skyline computation and develop a group skyline algorithm GDynamic which is equivalent to a dynamic algorithm that fills a table of skyline groups. Experimental results show that GDynamic is a practical group skyline algorithm.


title: Eudragit RL and RS Pseudolatices: Properties and Performance in Pharmaceutical Coating as a Controlled Release Membrane for Theophylline Pellets

abstract: AbstractTheophylline Active pellets were coated with Eudragit RL and RS pseudolatices in a fluidized bed. The effects of polymer ratio, additional oven drying, addition of dispersed solids, and addition of water miscible organic solvents on sustained drug release through the lates film were determined by using a modified U.S.P. Paddle dissolution method.The release rate of theophylline can be varied by changing the polymer ratio. permeability to the drug increases with an increase in the content of Eudragit RL. Additional oven drying at 60°C for 10 hours caused no significant change in the dissolution profiles. The addition of dispersed solids such as talcum and silica resulted in an increase in drug release rate. There is no significant change in dissolution profiles when 50% methanol or acetone was added to the Eudragit RS pseudolatex.


title: Developing Mindfulness in College Students Through Movement-Based Courses: Effects on Self-Regulatory Self-Efficacy, Mood, Stress, and Sleep Quality

abstract: Abstract Objective: This study examined whether mindfulness increased through participation in movement-based courses and whether changes in self-regulatory self-efficacy, mood, and perceived stress mediated the relationship between increased mindfulness and better sleep. Participants: 166 college students enrolled in the 2007–2008 academic year in 15 week classes in Pilates, Taiji quan, or GYROKINESIS. Methods: At beginning, middle, and end of the semester, participants completed measures of mindfulness, self-regulatory self-efficacy, mood, perceived stress, and sleep quality. Results: Total mindfulness scores and mindfulness subscales increased overall. Greater changes in mindfulness were directly related to better sleep quality at the end of the semester after adjusting for sleep disturbance at the beginning. Tiredness, Negative Arousal, Relaxation, and Perceived Stress mediated the effect of increased mindfulness on improved sleep. Conclusions: Movement-based courses can increase mindfulness. Increase...


title: Can Infants Map Meaning to Newly Segmented Words? Statistical Segmentation and Word Learning

abstract: The present experiments investigated how the process of statistically segmenting words from fluent speech is linked to the process of mapping meanings to words. Seventeen-month-old infants first participated in a statistical word segmentation task, which was immediately followed by an object-label-learning task. Infants presented with labels that were words in the fluent speech used in the segmentation task were able to learn the object labels. However, infants presented with labels consisting of novel syllable sequences (nonwords; Experiment 1) or familiar sequences with low internal probabilities (part-words; Experiment 2) did not learn the labels. Thus, prior segmentation opportunities, but not mere frequency of exposure, facilitated infants∗ learning of object labels. This work provides the first demonstration that exposure to word forms in a statistical word segmentation task facilitates subsequent word learning.


title: Determinants of Enterprise 2.0 adoption: A value-based adoption model approach

abstract: Enterprise 2.0 technology has become popular in business, facilitated by Web 2.0 applications. Many researches on Enterprise 2.0 and related practitioners have recognized the importance of benefits and costs for individuals using Web 2.0. However, the effects of such benefits on enterprise adoption intentions are not discussed. To investigate this issue, this paper proposed a model to examine and understand the intention to adopt Enterprise 2.0. From the view of top management, three factors of importance were identified through a value-based adoption model (VAM), namely perceived benefits, perceived costs, and perceived value. An empirical survey was conducted to collect data, with 500 samples collected within 18 days, of which 150 were valid; a response rate of 30%. Partial Least Squares (PLS) was used to analyze the data and verify the model. The results showed that perceived value plays an important role in mediating the relationship between perceived benefits, costs, and adoption intentions, as well as exerting a significant influence on intention to adopt Enterprise 2.0. Moreover, this study identified major factors that may affect perceived value. It was also found that both perceived benefits and perceived costs have significant impacts on perceived value, and increase intentions to adopt Enterprise 2.0. Lastly, the implications of such findings were discussed.


title: Neural correlates of motor learning, transfer of learning, and learning to learn.

abstract: Recent studies on the neural bases of sensorimotor adaptation demonstrate that the cerebellar and striatal thalamocortical pathways contribute to early learning. Transfer of learning involves a reduction in the contribution of early learning networks and increased reliance on the cerebellum. The neural correlates of learning to learn remain to be determined but likely involve enhanced functioning of the general aspects of early learning


title: Wideband FSS superstrate for millimeter-wave antenna gain enhancement

abstract: A wideband Frequency Selective Surfaces (FSS) which is used as a superstrate for a patch antenna to form a high-gain resonant cavity antenna is presented. This FSS consists of two metal structures separated by a substrate in the middle. It exhibits a wide stop-band in the 60GHz band. Further a panel of this FSS is implemented as a superstrate for antenna gain enhancement. It is observed that a broadband gain enhancement of 5.9dB with variations less than 2dB throughout the operating band is obtained.


title: Modular structured multilevel inverter (MSMI) for high power AC power supply applications

abstract: Another main application of inverter systems in the industry is as AC power supplies, which include UPS systems and power conditioners for alternate/renewable sources of energy. The rapid development in information related industries and the inclination towards alternate/renewable sources of energy as future energy supply by power utilities have made an impact on the AC power supplies design which is towards higher power ratings and efficiency. Incorporation of a modular structured multilevel inverter (MSMI) with a digital signal processor (DSP) based on online optimal PWM switching strategies as part of its control scheme are proposed for high voltage applications. This is to ensure fixed voltage with minimum specified total harmonic distortion (THD) at a fixed frequency is maintained on the MSMI output regardless of the loading conditions. This paper focuses on the development of this control scheme, which is supported by some results based on simulation and experimental work.


title: Diffusion of hydrogen in iron

abstract: A mass spectrometer was used to study hydrogen diffusion and trapping phenomena in fullyannealed and slightly cold-worked pure iron specimens which were in contact with distilled water or dilute acidic Buffer solutions. In the case of fully-annealed iron and slightly coldworked iron, hydrogen can diffuse into iron only when the iron contacts water directly. This diffusion phenomenon of hydrogen increased markedly with temperature and was accelerated by abrasion and hydrogen ion concentration in dilute acid. Abrasion and hydrogen ions in a dilute acidic Buffer solution did not affect the diffusion coefficient of hydrogen,D, but increased the hydrogen concentration at the iron surface contacting water or Buffer solution,C s . The permeability of hydrogen in fully-annealed iron in contact with distilled water and the diffusion coefficient of hydrogen in fully-annealed and slightly cold-worked iron for the temperature range 10° to 100°C were measured. Trapping parameters in the slightly cold-worked iron were calculated.


title: Executive Functions in Students With Depression, Anxiety, and Stress Symptoms

abstract: INTRODUCTION ::: This study aimed to investigate and compare the executive functions of students with depression, anxiety, and stress symptoms with those functions in healthy ones. ::: ::: ::: METHODS ::: This study was a comparative and non-clinical analysis. The study population comprised all students of Shahid Beheshti University, Tehran, Iran. A total of 448 students were recruited using convenience sampling method. They were also screened using the Depression Anxiety Stress Scales (DASS) test comprising 21 items. Of study participants, 30 people were depressed, 27 had anxiety, and 15 suffered from stress. Then, 50 control people were matched with them. Next, both groups were compared using the Stroop test, Wisconsin card sorting, and cognitive ability test. ::: ::: ::: RESULTS ::: Using MANOVA test, data analysis revealed no significant differences among 4 groups with regard to selective attention and shifting attention. Depressed group reacted rapidly as opposed to the anxiety group with regard to measures of shifting attention and cognitive abilities; it was observed that the memory, inhibition control, planning, and flexibility of the healthy group were better than those of the 3 other groups. ::: ::: ::: CONCLUSION ::: The findings of this research raised specific issues in relation to the role of depression, anxiety, and stress in the disruption of the executive functions of sufferers. Selective and shifting attention and cognitive abilities are specifically affected in this regard. Meanwhile, the role of stress in impairing decision making and the major role of anxiety in impairing sustained attention was shown to be considerable.


title: Understanding Z: A Specification Language and its Formal Semantics

abstract: By providing a formal semantics for Z, this book justifies the claim that Z is a precise specification language, and provides a standard framework for understanding Z specifications. It makes a detailed theoretical comparison between schemas, the Z construct for breaking specifications into modules, and the analogous facilities in other languages such as CLEAR and ASL. The final chapter contains a number of studies in Z style, showing that Z can be used for a wide variety of specification tasks.


title: Construction and Block Error Rate Analysis of Polar Codes Over AWGN Channel Based on Gaussian Approximation

abstract: Polar codes are usually constructed to minimize the upper bound of a block error ratio (BLER). In this paper, we discuss the estimation of the exact BLERs of polar codes as well as the construction of polar codes. Assuming that successive cancellation (SC) decoding is employed, we present a method for estimating the exact BLER of polar codes with the help of Gaussian approximation (GA). A new algorithm is proposed to construct polar codes, aimed at minimizing the exact BLER, instead of the upper bound of the BLER. Analysis indicates that the new method is less complex than the existing methods. It is also shown that the estimation results match the simulations well.


title: MARVEL: A Large-Scale Image Dataset for Maritime Vessels

abstract: Fine-grained visual categorization has recently received great attention as the volumes of the labelled datasets for classification of specific objects, such as cars, bird species, and aircrafts, have been increasing. The collection of large datasets has helped vision based classification approaches and led to significant improvements in performances of the state-of-the-art methods. Visual classification of maritime vessels is another important task assisting naval security and surveillance applications. In this work, we introduce a large-scale image dataset for maritime vessels, consisting of 2 million user uploaded images and their attributes including vessel identity, type, photograph category and year of built, collected from a community website. We categorize the images into 109 vessel type classes and construct 26 superclasses by combining heavily populated classes with a semi-automatic clustering scheme. For the analysis of our dataset, extensive experiments have been performed, involving four potentially useful applications; vessel classification, verification, retrieval, and recognition. We report encouraging results for each application. The introduced dataset is publicly available.


title: Proteomic analysis of hybrid poplar xylem sap

abstract: Abstract Xylem sap collected from Populus trichocarpa × Populus deltoides using root pressure was estimated to contain more than 100 proteins. Ninety-seven of these proteins were identified using liquid chromatography–tandem mass spectrometry (LC–MS/MS). These proteins were classified into 10 functional categories including metabolism, signaling, stress response and cell wall functions. The majority of xylem sap proteins were metabolic enzymes involved in processes including translation, proteolysis, and glycolysis. Stress-related proteins were also prevalent. In contrast to xylem sap proteins collected from annual plants, the majority of poplar xylem sap proteins do not appear to be classically secreted since only 33 proteins were predicted to have an N-terminal signal peptide targeting them to the secretory pathway. Of the remaining 64 proteins, 27 were predicted to be secreted non-classically. While a number of proteins identified here have been previously reported in xylem sap proteomes of annual plants, many xylem sap proteins were identified in poplar which may reflect functions specific to perennial plants.


title: How to use the SOINN software: user's guide (version 1.0)

abstract: The Self-Organizing Neural Network (SOINN) is an unsupervised classifier that is capable of online incremental learning. Studies have been performed not only for improving the SOINN, but also for applying it to various problems. Furthermore, using the SOINN, more intelligent functions are achieved, such as association, reasoning, and so on. In this paper, we show how to use the SOINN software and to apply it to the above problems.


title: Stock market prediction exploiting microblog sentiment analysis

abstract: This paper proposes a stock market prediction method exploiting sentiment analysis using financial microblogs (Sina Weibo). We analyze the microblog texts to find the financial sentiments, then combine the sentiments and the historical data of the Shanghai Composite Index (SH000001) to predict the stock market movements. Our framework includes three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). The MF module is based on LDA to get the financial microblogs. The SA module first sets up a financial lexicon, then gets the sentiments of the microblogs obtained from the MF module. The SP module proposes a user-group model which adjusts the importance of different people, and combines it with stock historical data to predict the movement of the SH000001. We use about 6.1 million microblogs to test our method, and the result demonstrates our method is effective.


title: Launching Extreme Programming at a Process-Intensive Company

abstract: The author describes a project that was started using many extreme programming practices in a company that has a traditional formal development process. He discusses how XP was proposed to management, how the project seed was started and grown, what the team faced during its first six months, and what worked.


title: Non-chaotic dynamics in generalrelativistic and scalar-tensor

abstract: In the context of scalar?tensor models of dark energy and inflation, the dynamics of vacuum scalar?tensor cosmology are analysed without specifying the coupling function or the scalar field potential. A conformal transformation to the Einstein frame is used and the dynamics of general relativity with a minimally coupled scalar field are derived for a generic potential. It is shown that the dynamics are non-chaotic, thus settling an existing debate.


title: Measurement of Quality of Experience of Video-on-Demand Services: A Survey

abstract: Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services.


title: To give or to receive? Factors influencing members' knowledge sharing and community promotion in professional virtual communities

abstract: Professional virtual communities (PVCs) bring together geographically dispersed, like-minded people to form a network for knowledge exchange. To promote knowledge sharing, it is important to know why individuals choose to give or to receive knowledge with other community members. We identified factors that were considered influential in increasing community knowledge transfer and examined their impact in PVCs. Data collected from 323 members of two communities were used in our structural equation modeling (SEM). The results suggested that norm of reciprocity, interpersonal trust, knowledge sharing self-efficacy, and perceived relative advantage were significant in affecting knowledge sharing behaviors in PVCs. The knowledge contributing and collecting behaviors were positively related to knowledge utilization. Furthermore, while the collecting behavior had a significant effect on community promotion, the influence of contributing behavior on community promotion was limited.


title: Real time streaming data storage and processing using storm and analytics with Hive

abstract: In big data world, Hadoop Distributed File System (HDFS) is one of the famous file system to store huge data. HDFS will take care about managing and maintaining the data in distributed way. Based on research we did to discuss that how the real time streaming data can be processed and stored into Mongo DB and Hive. Big data analytics can be performed on data stored on Hadoop distributed file system using Apache Hive, Tez and Apache Presto. Hive is an ecosystem which is on top of Hadoop (Map Reduce), and provides higher-level language to use Hadoop's core component Map Reduce to process the data. The key benefits of this approach are it can able to store and process the large amount of data. It can also handle the millions of user requests concurrently. It can provide the scalability for the system is enhanced by adding new nodes. Integrating the Visualization tools with Big Data applications will give the big picture to the users to view the insights of the Big data. It can provide the analytic reports for giving the big picture about the system.


title: A General Method for Amortizing Variational Filtering

abstract: We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves performance across several deep dynamical latent variable models.


title: A dual-frequency Wilkinson power divider: for a frequency and its first harmonic

abstract: A Wilkinson power divider operating not only at one frequency f/sub 0/, but also at its first harmonic 2f/sub 0/ is presented. This power divider consists of two branches of impedance transformer, each of which consists of two sections of 1/6-wave transmission-line with different characteristic impedance. The two outputs are connected through a resistor, an inductor, and a capacitor. All the features of a conventional Wilkinson power divider, such as an equal power split, impedance matching at all ports, and a good isolation between the two output ports, can be fulfilled at f/sub 0/ and 2f/sub 0/, simultaneously.


title: A review of genome-wide association studies for multiple sclerosis: classical and hypothesis-driven approaches

abstract: Multiple sclerosis (MS) is a common complex neurodegenerative disease of the central nervous system. It develops with autoimmune inflammation and demyelination. Genome-wide association studies (GWASs) serve as a powerful tool for investigating the genetic architecture of MS and are generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. This review considers the main achievements and challenges of using GWAS to identify the genes involved in MS. It also describes hypothesis-driven studies with extensive genome coverage of the selected regions, complementary to GWASs. To date, over 100 MS risk loci have been identified by the combination of both approaches; 40 of them were found in at least two GWASs and meet genome-wide significance threshold (p ≤ 5 × 10−8) in at least one GWAS, whereas the threshold for the rest of GWASs was set in our review at p < 1 × 10−5. Yet, MS risk loci identified to date explain only a part of the total heritability, and the reasons of “missing heritability” are discussed. The functions of MS-associated genes are described briefly; the majority of them encode immune-response proteins involved in the main stages of MS pathogenesis.


title: Robust Entropy-based Endpoint Detection for Speech Recognition in Noisy Environments

abstract: This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recognition performance under noisy environments, with an average error rate reduction of more than 16%.


title: Finger Vein Authentication Technology and Its Future

abstract: In this paper, finger vein authentication, a new biometric method utilizing the vein patterns inside one's fingers for personal identification, is introduced. Vein patterns are different for each finger and for each person, and as they are hidden underneath the skin's surface, forgery is extremely difficult. These unique aspects of finger vein pattern recognition set it apart from previous forms of biometrics and have led to its adoption by the major Japanese financial institutions as their newest security technology. This paper discusses the technology and applications of finger vein authentication, as well as the importance of semiconductor devices in its future development


title: Simulating Creativity in Jazz Performance

abstract: This paper considers the problem of simulating creativity in the domain of Jazz improvisation and accompaniment. Unlike most current approaches, we try to model the musicians' behavior by taking into account their experience and how they use it with respect to the evolving contexts of live performance. To represent this experience we introduce the notion of Musical Memory, which explores the principles of Case-Based Reasoning (Slade 1991). To produce live music using this Musical Memory we propose a problem solving method based on the notion of PACTs (Potential ACTions) that are activated according to the context and then combined in order to produce notes. We show that our model supports two of the main features of creativity: non-determinism and absence of well-defined goals (Johnson-Laird 1992).


title: Transdermal monitoring of glucose and other analytes using ultrasound

abstract: 1, including implantable sensors 2‐4 , minimally invasive skin microporation approaches involving laser or miniaturized lancets 5 and noninvasive technologies such as near-infrared spectroscopy 5 , transdermal permeation enhancers 6 or reverse iontophoresis 7,8 . However, none are used in routine clinical practice 5 . One of the fundamental problems in noninvasive transdermal diagnostics is obtaining sufficient quantities of analyte for detection. Ultrasound, particularly at low frequencies, enhances transdermal delivery of drugs (sonophoresis) 9‐11 . Here, we sought to determine whether such ultrasound facilitates the outward transport of analytes present in the interstitial fluid, thereby allowing noninvasive extraction of clinically useful analytes 12


A Multi-task Learning Approach for Image Captioning


title: IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

abstract: We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples. Experiments demonstrate that our method produces high-resolution photo-realistic images (e.g., CELEBA images at \(1024^{2}\)), which are comparable to or better than the state-of-the-art GANs.


title: Meta-Learning with Latent Embedding Optimization

abstract: Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.


title: Smart urban parking detection system

abstract: Nowadays in an urban area, looking for available parking spots, is not only a waste of time and energy but also it causes more traffic and air pollution. Therefore finding available parking spots is becoming more challenging on daily life. This paper proposes a novel solution by providing motorists with real-time information about the availability of spots in parking spaces through a smartphone application. Ultrasonic sensors and raspberry pi were used at the gates of parking zones to detect cars and calculate the number of available parking spots. Client web server was used to accommodate multiple users in an android platform. Drivers can see available parking spots in real time on a map, they also can get navigate to nearest available parking spots using the mobile application. The parking system is designed for different types of parking zone (such as open space car parking and multilevel car parking); the cost of deploying the system is inexpensive due to the low demand for Raspberry pi single board computer as a local server and ultrasonic sensors for each entrance and exit gates compared to those that require a sensor for each parking spot.


title: Language independent big-data system for the prediction of user location on Twitter

abstract: Social media interactions have become increasingly important in today's world. A survey conducted in 2014 among adult Americans found that a majority of those surveyed use at least one social media site. Twitter, in particular, serves 310 million active users on a monthly basis, and thousands of tweets are published every second. The public nature of this data makes it a prime candidate for data mining. Twitter users publish 140-character long messages and have the ability to geo-tag these tweets using a variety of methods: GPS coordinates, IP geolocation and user-declared location. However, few users disclose their location, only between 1% and 3% of users provide location data, according to our empirical findings. In this article, we aim to aggregate information from different sources to provide an estimation on the location of any Twitter user. We use an hybrid approach, using techniques in the fields of Natural Language Processing and network theory. Tests have been conducted on two datasets, inferring the location of each individual user and then comparing it against the actual known location of users with geolocation information. The estimation error is the distance in kilometers between the estimation and the actual location. Furthermore, there is a comparison of the relative average error per country, to account for difference in country sizes. Our results improve those presented in different researches in the literature. Our research has as feature to be independent of the language used by the user, while most of works in the literature use just one language or a reduced set of languages. The article also showcases the evolution of our estimation approach and the impact that the modifications had on the results.


title: Automated Optic Nerve Disc Parameterization

abstract: New information technologies provide a possibility of collecting a large amount of fundus images into databases. It allows us to use automated processing and classification of images for clinical decisions. Automated localization and parameterization of the optic nerve disc is particularly important in making a diagnosis of glaucoma, because the main symptoms in these cases are relations between the optic nerve and cupping parameters. This article describes the automated algorithm for the optic nerve disc localization and parameterization by an ellipse within colour retinal images. The testing results are discussed as well.


title: Locality Sim: Cloud Simulator with Data Locality

abstract: Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment, and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the three-tire data center as a case study with considering the data locality feature.


title: Learning management systems and cloud file hosting services: A study on students' acceptance

abstract: The aim of this paper is to investigate the motivations that lead higher education students to replace several Learning Management Systems (LMS) services with cloud file hosting services for information sharing and collaboration among them. The research approach is based on the Technology Acceptance Model (TAM). More specifically, the model is devoted to identifying barriers and enablers to the acceptance of these technologies. A questionnaire comprising three factors (Attitude toward using technology, Perceived ease of use and Perceived usefulness) was applied to a sample consisting of 121 higher education students. Results show that the perceived ease of use of cloud file hosting services is above that of LMS tools and services and that cloud file hosting services presented higher levels of perceived usefulness than standard learning management tools. In addition, attitude toward using cloud file hosting services is well above that of using LMS tools.


title: Effect of the Bobath Technique, Conductive Education and Education to Parents in Activities of Daily Living in Children with Cerebral Palsy in Iran

abstract: Objective: In children with cerebral palsy, one of the most important roles of occupational therapists is to promote the children’s independence in functional performance. The goal of this study was to compare the effect of the Bobath technique, conductive education (CE), and education to parents in activities of daily living (ADLs) in children with cerebral palsy aged 4–8 years in Iran. Methods: This study was a quasi-experimental clinical trial with pre/post design. Forty-five children with cerebral palsy were selected by convenience sampling in a rehabilitation centre and allocated to three groups by matching closely their sex, age, and IQ; 15 children were in the Bobath group, 15 were in the CE group, and 15 were in the education-to-parents group. Outcome measures were the Goodenough test and the Client Development Evaluation Report. Results: There were no significant differences in mean age (p = .261) and IQ (p = .449) at baseline among the three groups. There were significant differences in ADLs after treatment within each group (p = .001). Significant differences were also found in the average total ADLs scores between the three groups after treatment (p < .0001). We found that the most effective approach was CE, followed by education to parents and the Bobath technique. There were significant differences between 12 sub-skills out of 16 in ADLs in the three groups, and children in the CE group achieved higher scores in comparison with other groups. Conclusion: The Bobath technique, CE, and education to parents improved ADL skills in children with cerebral palsy. In the CE approach, group education promoted better social communication and more mobility in familiar environments. On the other hand, practical education and treatment programmes improved ADLs skills.


title: PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study

abstract: Purpose ::: To identify optimal pulsed gradient spin-echo (PGSE) and oscillating gradient spin-echo (OGSE) sequence settings for maximizing sensitivity to axon diameter in idealized and practical conditions. ::: Methods ::: Simulations on a simple two-compartment white matter model (with nonpermeable cylinders) are used to investigate a wide space of clinically plausible PGSE and OGSE sequence parameters with trapezoidal diffusion gradient waveforms. Signal sensitivity is measured as a derivative of the signal with respect to axon diameter. Models of parallel and dispersed fibers are investigated separately to represent idealized and practical conditions. ::: Results ::: Simulations show that, for the simple case of gradients perfectly perpendicular to straight parallel fibers, PGSE always gives maximum sensitivity. However, in real-world scenarios where fibers have unknown and dispersed orientation, low-frequency OGSE provides higher sensitivity. Maximum sensitivity results show that on current clinical scanners (Gmax = 60 mT/m, signal to noise ratio (SNR) = 20) axon diameters below 6 µm are indistinguishable from zero. Scanners with stronger gradient systems such as the Massachusetts General Hospital (MGH) Connectom scanner (Gmax = 300 mT/m) can extend this sensitivity limit down to 2–3 µm, probing a much greater proportion of the underlying axon diameter distribution. ::: Conclusion ::: Low-frequency OGSE provides additional sensitivity to PGSE in practical situations. OGSE is particularly advantageous for systems with high performance gradients.


title: Competitive Advantage Achievement through Innovation and Knowledge

abstract: In today's highly competitive environment the goal of each organisation is to defeat competition and win new customers. Individuals who are holders of knowledge represent a tool for the gen- eration of innovations. Thanks to their personal creativity, their knowledge, skills and abilities it is possible to generate new innovative ideas that will help organisations to achieve a competitive advantage. The aim of the article is to present the findings of a survey targeted at innovations and to identify knowledge as an important element in the process of innovations. Primary data were obtained through a questionnaire survey carried out in organisations in the Czech Repub- lic and evaluated using the tools of descriptive statistics and also the methods of comparison, induction, deduction and synthesis were applied. One of the conclusions of the article is that organisations find it important to innovate and support an innovative culture. Knowledge too is very significant in the innovation process since it represents not only important input, but also output of the transformation process.


title: Dynamic modeling and inverse dynamic control of mobile robot

abstract: This paper presents a dynamic modeling and control studies of mobile robot. The robot is mainly built all around three wheels and platform. A mathematical model of unicycle mobile robot is determined by kinematic and dynamic model. The dynamic modelling equations are based on Lagrangian formulation. The motion control strategy is based on the inverse of dynamic control. This leads to accurate tracking of trajectory. The goal to reach in this paper is to improve the performances of inverse dynamic control of mobile robot. The validity of the proposed controller is demonstrated by the simulation of two wheels mobile robots case.


title: Convolutional Neural Networks for Figure Extraction in Historical Technical Documents

abstract: We present a method of extracting figures and images from the pages of scanned documents, especially from technical research articles. Our approach is novel in two key ways. First, we treat this as a computer vision problem, and train convolutional neural networks to recognize figures in scanned pages. Second, we generate our training data from 'born-digital' structured documents, allowing us to automatically produce labels for our training set using PDF figure extractors. This avoids the otherwise tedious task of hand-labelling thousands of document pages. Our convolutional neural networks achieve precision and recall of close to 85% in identifying figures from a test set consisting of modern journal papers and conference proceedings, and obtain precision and recall above 80% on an application data set comprised of historical technical documents scanned from the Bell Labs Records. Our results show that models trained on digital documents transfer very well to historical scans. Finally, it is easy to extend our models to identify other document elements such as tables and captions.


title: RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian with Application to Material Recognition

abstract: Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) description of infinite dimensional Gaussian is difficult due to its non-linear Riemannian geometric structure and the infinite dimensional setting, hence effective approximation is necessary, (2) traditional maximum likelihood estimation (MLE) is not robust to high (even infinite) dimensional covariance matrix in Gaussian setting. To address these challenges, explicit feature mapping (EFM) is first introduced for effective approximation of infinite dimensional Gaussian induced by additive kernel function, and then a new regularized MLE method based on von Neumann divergence is proposed for robust estimation of covariance matrix. The EFM and proposed regularized MLE allow a closed-form of RAID-G, which is very efficient and effective for high dimensional features. We extend RAID-G by using the outputs of deep convolutional neural networks as original features, and apply it to material recognition. Our approach is evaluated on five material benchmarks and one fine-grained benchmark. It achieves 84.9% accuracy on FMD and 86.3% accuracy on UIUC material database, which are much higher than state-of-the-arts.


title: Ultra-low-Voltage high-performance CMOS VCOs using transformer feedback

abstract: A transformer-feedback voltage-controlled oscillator (TF-VCO) is proposed to achieve low-phase-noise and low-power designs even at a supply below the threshold voltage. The advantages of the proposed TF-VCO are described together with its detailed analysis and its cyclo-stationary characteristic. Two prototypes using the proposed TF-VCO techniques are demonstrated in a standard 0.18-/spl mu/m CMOS process. The first design using two single-ended transformers is operated at 1.4 GHz at a 0.35-V supply using PMOS transistors whose threshold voltage is around 0.52 V. The power consumption is 1.46 mW while the measured phase noise is -128.6 dBc/Hz at 1-MHz frequency offset. Using an optimum differential transformer to maximize quality factor and to minimize the chip area, the second design is operated at 3.8 GHz at a 0.5-V supply with power consumption of 570 /spl mu/W and a measured phase noise of -119 dBc/Hz at 1-MHz frequency offset. The figures of merits are comparable or better to that of other state-of-the-art VCO designs operating at much higher supply voltage.


title: Analysis of the mutual inductance of a precise Rogowski coil

abstract: A Rogowski coil is well-known current-to-voltage transducer, and in order to use it for the high-accuracy measurement of AC current (at power supply frequency) all influencing quantities and their contribution should be recognized and analyzed. Therefore, in this paper the analysis of the partial influence due to the deviation from the central position of the primary conductor for a real sensor, with the nonhomogeneous density of secondary turns is analyzed. The measured deviation of the mutual inductance in this case showed very good agreement with the theoretical prediction, which is promising toward the expected application of such sensor.


Horizontal Well Performance Flow Simulation CFD-Application


title: Automated test data generation using an iterative relaxation method

abstract: This dissertation addresses the problem of automated test data generation for commonly used coverage based criteria. A new program execution based approach to automated test data generation using iterative relaxation methods has been developed. In this approach, test data generation is initiated with an arbitrarily chosen input, which is iteratively refined using a relaxation technique to generate the desired input. New algorithms to generate test data for statement, branch, data flow and path coverage have been developed. They are general in their applicability as they can generate test data for programs with branch predicates that compute linear and nonlinear functions of input. They can detect linear infeasible paths and can handle programs using arrays, records, pointers and loops. A new code transformation method to handle test data generation for functions with pointer inputs has been developed. A new method called Unified Numerical Approach has also been developed to compute a real and an integer feasible solution of a set of linear constraints. It enables the test data generation algorithms developed in this dissertation to handle programs that have both integer and floating point inputs. The experimental evaluation reveals that these methods automatically generated test data that covers a large percentage of coverage elements. The average time taken to generate a test case was of the order of a few seconds. The experiments also revealed their power in detecting infeasible linear paths as well as in indicating potentially infeasible nonlinear paths.


title: Automated parking lot management system using embedded robot type smart car based on wireless sensors

abstract: As the population of vehicles worldwide continues to increase, more and more parking space is required. And efficient parking management is becoming an important issue. A lot of researches have been done to detect parking lot status, but they usually have limitations including extra sensor devices and hence high cost. In addition, most system is for indoor parking lot because of the management issue. We suggest a fully automated parking lot management system using an embedded robot type smart car that is applicable to any type of parking lots. Smart car intelligently explores the parking lot along parking block lines, judges the route to be taken for itself, classifies the vehicles after detecting them into three types: normally parked vehicles, illegally parked vehicles, and poorly parked vehicles. To implement the system, we used infrared and ultrasonic sensors installed in the smart car. Proposed system makes it possible to build a location-independent, fully automated parking lot management system with low cost.


title: Discriminative acoustic model for improving mispronunciation detection and diagnosis in computer-aided pronunciation training (CAPT).

abstract: In this study, we propose a discriminative training algorithm to jointly minimize mispronunciation detection errors (i.e., false rejections and false acceptances) and diagnosis errors (i.e., correctly pinpointing mispronunciations but incorrectly stating how they are wrong). An optimization procedure, similar to Minimum Word Error (MWE) discriminative training, is developed to refine the ML-trained HMMs. The errors to be minimized are obtained by comparing transcribed training utterances (including mispronunciations) with Extended Recognition Networks [3] which contain both canonical pronunciations and explicitly modeled mispronunciations. The ERN is compiled by handcrafted rules, or data-driven rules. Several conclusions can be drawn from the experiments: (1) data-driven rules are more effective than hand-crafted ones in capturing mispronunciations; (2) compared with the ML training baseline, discriminative training can reduce false rejections and diagnostic errors, though false acceptances increase slightly due to a small number of false-acceptance samples in the training set.


title: Mining Sequential Alarm Patterns in a Telecommunication Database

abstract: A telecommunication system produces daily a large amount of alarm data which contains hidden valuable information about the system behavior. The knowledge discovered from alarm data can be used in finding problems in networks and possibly in predicting severe faults. In this paper, we devise a solution procedure for mining sequential alarm patterns from the alarm data of a GSM system. First, by observing the features of tile alarm data, we develop operations for data cleaning. Then, we transform the alarm data into a set of alarm sequences. Note that the consecutive alarm events exist in the alarm sequences, and it is complicated to count the occurrence counts of events and extract patterns. Hence, we devise a new procedure to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. By utilizing time constraints to restrict tile time difference between two alarm events, we devise a mining algorithm to discover useful sequential alarm patterns. The proposed mining algorithm is implemented and applied to test against a set of real alarm data provided by a cellular phone company. The quality of knowledge discovered is evaluated. The experimental results show that the proposed operations of data cleaning are able to improve tile execution of our mining algorithm significantly and tile knowledge obtained from the alarm data is very useful from tile perspective of network operators for alarm prediction and alarm control.


title: Agreeing to disagree: active learning with noisy labels without crowdsourcing

abstract: We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label \(y = h(x)\)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled.


Stereotypes in Video Games and How They Perpetuate Prejudice


title: Chemical Perturbation of Mcl-1 Pre-mRNA Splicing to Induce Apoptosis in Cancer Cells

abstract: The myeloid cell leukemia-1 (MCL1) gene encodes antiapoptotic Mcl-1(L) and proapoptotic Mcl-1(S) proteins. In cancer, the Mcl-1(L)/Mcl-1(S) ratio is very high, accounting for the antiapoptotic nature of cancer cells. As such, reducing this ratio can render the cancer cells prone to apoptosis. The Mcl-1(L)/Mcl-1(S) ratio is determined in the alternative pre-mRNA splicing step that is regulated by splicing factor 3B1 (SF3B1). Here, we report that meayamycin B, a potent inhibitor of SF3B1, reversed the dominant isoform from Mcl-1(L) to Mcl-1(S) at the mRNA and protein levels. The resulting proapoptotic cellular environment was further exploited; when meayamycin B was combined with Bcl-x(L) inhibitor ABT-737, the combination treatment triggered apoptosis in nonsmall cell lung cancer A549 and H1299 cells that were otherwise resistant to ABT-737. These results demonstrate that perturbation of the MCL1 splicing with small molecule inhibitors of SF3B1 provides a means to sensitize cancer cells toward Bcl-x(L) inhibitors.


title: Efficiency of Accounting Information System and Performance Measures – Literature Review

abstract: Accounting information system had been widely used by many organizations to automate and integrate their business operations .The main objective s of many businesses to adopt this system are to improve their business efficiency and increase competitiveness. The qualitative characteristic of any Accounting Information System can be maintained if there is a sound internal control system. Internal control is run to ensure the achievement of operational goals and performance. Therefore the purpose of this study is to examine the efficiency of Accounting Information System on performance measures using the secondary data in which it was found that accounting information system is of great importance to both businesses and organization in which it helps in facilitating management decision making, internal controls ,quality of the financial report ,and it facilitates the company’s transaction and it also plays an important role i n economic system, and the study recommends that businesses, firms and organization should adopt the use of AIS because adequate accounting information is essential for every effective decision making process and adequate information is possible if accounting information systems are run efficiently also, efficient Accounting Information Systems ensures that all levels of management get sufficient, adequate, relevant and true information for planning and controlling activities of the business organization.


Media Richness and Social Norms in the Choice to Attend Lectures or to Watch them Online


Forensics Analysis On Smart Phones Using Mobile Forensics Tools


title: Linguistic Style Matching In Social Interaction

abstract: Three experiments were conducted to determine the psychometric properties of language in dyadic interactions. Using text-analysis, it was possible to assess the degree to which people coordinate their word use in natural conversations. In Experiments 1 (n = 130) and 2( n = 32), college students interacted in dyadic conversations in laboratory-based private Internet chat rooms. Experiment 3 analyzed the official transcripts of the Watergate tapes involving the dyadic interactions between President Richard Nixon and his aids H. R. Haldeman, John Erlichman, and John Dean. The results of the three studies offer sub- stantial evidence that individuals in dyadic interactions exhibit linguistic style matching (LSM) on both the conversation level as well as on a turn-by-turn level. Furthermore, LSM is unrelated to ratings of the quality of the interaction by both participants and judges. We propose that a coordination-engagement hypothesis is a better description of linguistic behaviors than the coordination-rapport hypothesis that has been proposed in the nonver- bal literature. For years social psychologists have exalted the power of the situa- tion. We comfortably acknowledge that across different situations, with different people, we may act in a range of ways, or even talk using a variety of styles. Aware of this tendency, Gergen (1972) began his explorations of our shifting masks of identity. In writing letters to close friends, he realized that he came across as a "completely different per- son" in each letter. "In one, I was morose, pouring out a philosophy of existential sorrow; in another I was a lusty realist: in a third I was a lighthearted jokester" (p. 32). Based merely on his word choices, Gergen inadvertently varied his style to adapt to the recipients of his letters. This is a prime demonstration of our inherent knowledge of the mutability of our language with respect to varying social contexts.


title: A business-driven IT services improvement model

abstract: Under current approaches, service performance is generally evaluated using technical metrics, for the following two basic reasons: 1) They are easily understood by technical people and 2) They are typically easy to measure directly. The actual impact on the business of low value and quality are not apparent. Service metrics are objectively measured, however, subjective criteria - those that depend on the subject (person) being polled - are not easily brought into the picture. Uncertainty is not typically considered in the measurement process. Service metrics are gathered strictly on an operational level without any effective information available at a strategic level. Within this scope of work, we propose a model in order to quantify an IT service aggregated value and a hierarchical quality indicator within a time snapshot, which can be used to help managers in activities relating to continual service improvement. We carried out a case-study in a real environment of a Brazilian Bank whereby the results indicate that our non-intrusive model is useful and can be adopted by corporations in addition to their actual continual service improvement rules.


An Android Application Protection Scheme against Dynamic Reverse Engineering Attacks