title: A Wireless and Batteryless Intelligent Carbon Monoxide Sensor

abstract: Carbon monoxide (CO) poisoning from natural gas water heaters is a common household accident in Taiwan. We propose a wireless and batteryless intelligent CO sensor for improving the safety of operating natural gas water heaters. A micro-hydropower generator supplies power to a CO sensor without battery (COSWOB) (2.5 W at a flow rate of 4.2 L/min), and the power consumption of the COSWOB is only ~13 mW. The COSWOB monitors the CO concentration in ambient conditions around natural gas water heaters and transmits it to an intelligent gateway. When the CO level reaches a dangerous level, the COSWOB alarm sounds loudly. Meanwhile, the intelligent gateway also sends a trigger to activate Wi-Fi alarms and sends notifications to the mobile device through the Internet. Our strategy can warn people indoors and outdoors, thereby reducing CO poisoning accidents. We also believe that our technique not only can be used for home security but also can be used in industrial applications (for example, to monitor leak occurrence in a pipeline).


title: Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition

abstract: Despite tremendous variation in the appearance of visual objects, primates can recognize a multitude of objects, each in a fraction of a second, with no apparent effort. However, the brain mechanisms that enable this fundamental ability are not understood. Drawing on ideas from neurophysiology and computation, we present a graphical perspective on the key computational challenges of object recognition, and argue that the format of neuronal population representation and a property that we term ‘object tangling' are central. We use this perspective to show that the primate ventral visual processing stream achieves a particularly effective solution in which single-neuron invariance is not the goal. Finally, we speculate on the key neuronal mechanisms that could enable this solution, which, if understood, would have far-reaching implications for cognitive neuroscience.


title: Flow Coordination and Information Sharing in Supply Chains: Review, Implications, and Directions for Future Research

abstract: Advances in information technology, particularly in the e-business arena, are enabling firms to rethink their supply chain strategies and explore new avenues for inter-organizational cooperation. However, an incomplete understanding of the value of information sharing and physical flow coordination hinder these efforts. This research attempts to help fill these gaps by surveying prior research in the area, categorized in terms of information sharing and flow coordination. We conclude by highlighting gaps in the current body of knowledge and identifying promising areas for future research.


title: HuddleLamp: Spatially-Aware Mobile Displays for Ad-hoc Around-the-Table Collaboration

abstract: We present HuddleLamp, a desk lamp with an integrated RGB-D camera that precisely tracks the movements and positions of mobile displays and hands on a table. This enables a new breed of spatially-aware multi-user and multi-device applications for around-the-table collaboration without an interactive tabletop. At any time, users can add or remove displays and reconfigure them in space in an ad-hoc manner without the need of installing any software or attaching markers. Additionally, hands are tracked to detect interactions above and between displays, enabling fluent cross-device interactions. We contribute a novel hybrid sensing approach that uses RGB and depth data to increase tracking quality and a technical evaluation of its capabilities and limitations. For enabling installation-free ad-hoc collaboration, we also introduce a web-based architecture and JavaScript API for future HuddleLamp applications. Finally, we demonstrate the resulting design space using five examples of cross-device interaction techniques.


title: Code analysis of lightweight encryption algorithms using in RFID systems to improve cipher performance

abstract: This research has investigated the performance analysis of lightweight encryption algorithms, which are using in RFID applications. In the first part, the codes of the investigated ciphers are analyzed, and then, four encryption algorithms include TEA, HIGHT, KATAN, and KLEIN are implemented on AVR Microcontroller to evaluate the number of CPU clock cycle for each cipher. In the last part, the results of two previous parts are compared.


title: SQLIIDaaS: A SQL Injection Intrusion Detection Framework as a Service for SaaS Providers

abstract: Recently, we are attending to the proliferation of Cloud Computing (CC) as the new trending internet-based-Platform. Thanks to the outsourcing paradigm, CC is enabling many services. Software as a Service (SaaS) is one of those cloud-based-services. Indeed, SaaS model allows providers to reduce the cost of maintenance and management by transferring traditional on premise deployment to public Cloud. Clients can subscribe, in self-service, to SaaS services based on a pay-per-use model. However, since user data are outsourced to the Cloud, serious security breaches are rising and could harm the reputation of providers and slow down the subscription of clients. SQL injection attack (SQLIA) is one of the most critical SaaS vulnerabilities that allows attackers to violate the availability, confidentiality and integrity of user data. In this paper, we propose SQL injection intrusion detection framework as a service for SaaS providers, SQLIIDaaS, which allows a SaaS provider to detect SQLIAs targeting several SaaS applications without reading, analyzing or modifying the source code. To achieve SQL query/HTTP request mapping, we propose an event correlation based on the similarity between literals in SQL queries and parameters in HTTP requests. SQLIIDaaS is integrated and validated in Amazon Web Services (AWS). A SaaS provider can subscribe to this framework and launch its own set of virtual machines, which holds on-demand self-service, resource pooling, rapid elasticity, and measured service properties.


title: Crossmodal transfer of emotion by music

abstract: Music is one of the most powerful elicitors of subjective emotion, yet it is not clear whether emotions elicited by music are similar to emotions elicited by visual stimuli. This leads to an open question: can music-elicited emotion be transferred to and/or influence subsequent vision-elicited emotional processing? Here we addressed this question by investigating processing of emotional faces (neutral, happy and sad) primed by short excerpts of musical stimuli (happy and sad). Our behavioural experiment showed a significant effect of musical priming: prior listening to a happy (sad) music enhanced the perceived happiness (sadness) of a face irrespective of facial emotion. Further, this musical priming-induced effect was largest for neutral face. Our electrophysiological experiment showed that such crossmodal priming effects were manifested by event related brain potential components at a very early (within 100 ms post-stimulus) stages of neuronal information processing. Altogether, these results offer new insight into the crossmodal nature of music and its ability to transfer emotion to visual modality.


title: Multimodal Database of Emotional Speech, Video and Gestures

abstract: People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman’s emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.


title: PHCWT: A Persian Handwritten Characters, Words and Text dataset

abstract: Nowadays, different languages involve certain datasets to recognize optical characters in handwritten materials both online and offline. Although the valuable datasets in Persian language have been approved, there are several deficiencies such as insufficient characters, words and texts in a single database. This paper proposed a new dataset Persian Handwritten Characters Words Text (PHCWT). This unique dataset is the first extensive and integrated collection of Persian handwritten characters, words and texts, which can be used in various fields such as offline handwritten optical character recognition, word segmentation, text extraction, baseline detection, author detection, etc. This dataset contains a total of 51,200 character images, 3,600 word images and 400 texts collected by 400 contributors. One of the most important features of PHCWT is the adoption of skeleton and edge as two techniques for image extraction. Each contributor was handed 3 forms containing the characters in the Persian alphabet (in 4 modes: single, beginning, middle and ending), 9 words matching polymorph characters and an engineered text including all Persian characters. The PHCWT is available to researchers.


title: An Agile Development Team's Quest for CMMI® Maturity Level 5

abstract: Pragmatics performs agile development, has been rated at CMMI (Capability Maturity Model Integration) Maturity Level 4, and is striving to achieve CMMI Maturity Level (CML) 5. By maturing our agile disciplines, we feel we will not only improve the performance of our agile teams, which will ultimately benefit our agile development practices regardless of our appraisal rating, but will also lead to our being appraised at CML 5. This experience report describes the steps we are taking to improve our agile development disciplines, which we believe will lead to our being appraised at CML 5.


Design and Experiment of a Conformal Monopulse Antenna for Passive Radar Applications


title: Coverage and adoption of altmetrics sources in the bibliometric community

abstract: Altmetrics, indices based on social media platforms and tools, have recently emerged as alternative means of measuring scholarly impact. Such indices assume that scholars in fact populate online social environments, and interact with scholarly products in the social web. We tested this assumption by examining the use and coverage of social media environments amongst a sample of bibliometricians examining both their own use of online platforms and the use of their papers on social reference managers. As expected, coverage varied: 82 % of articles published by sampled bibliometricians were included in Mendeley libraries, while only 28 % were included in CiteULike. Mendeley bookmarking was moderately correlated (.45) with Scopus citation counts. We conducted a survey among the participants of the STI2012 participants. Over half of respondents asserted that social media tools were affecting their professional lives, although uptake of online tools varied widely. 68 % of those surveyed had LinkedIn accounts, while Academia.edu, Mendeley, and ResearchGate each claimed a fifth of respondents. Nearly half of those responding had Twitter accounts, which they used both personally and professionally. Surveyed bibliometricians had mixed opinions on altmetrics' potential; 72 % valued download counts, while a third saw potential in tracking articles' influence in blogs, Wikipedia, reference managers, and social media. Altogether, these findings suggest that some online tools are seeing substantial use by bibliometricians, and that they present a potentially valuable source of impact data.


title: Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

abstract: Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two recently published methods for nonlinear projection: Isomap and Curvilinear Distance Analysis (CDA). Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in the data space. However, they di6er from the classical linear MDS by the metrics they use and by the way they build the mapping (algebraic or neural). While Isomap relies directly on the traditional MDS, CDA is based on a nonlinear variant of MDS, called Curvilinear Component Analysis (CCA). Although Isomap and CDA share the same metric, the comparison highlights their respective strengths and weaknesses.


title: Do personality disorders exist? On the validity of the concept and its cognitive‐behavioral formulation and treatment

abstract: Since the introduction of personality disorders as important diagnostic categories in the DSM-III [APA (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: American Psychiatric Association] the concept of personality disorder (PD) has met both skepticism and enthusiasm. This paper addresses criticisms, reviews theoretical and empirical evidence for the concept of personality disorders (PD), critically discusses widespread clinical ideas about PDs, and reviews cognitive-behavioral models and treatments of PDs. It is concluded that there is enough evidence for the usefulness of PDs as diagnostic categories. A confirmatory factor analysis for instance yielded very reasonable evidence for the categories hypothesized by the DSM. Contradicting widespread ideas, research indicates that PDs are not a contraindication for cognitive-behavioral treatments of axis-I problems, and that there are even indications that PD pathology itself can be successfully treated using modified CBT methods. In stark contrast to the generally rich empirical history of CBT, research on cognitive models and treatment of PDs is virtually absent. However, the sparse attempts indicate that cognitive models may be fruitful in this area. Future research should be directed at further developing and testing cognitive conceptualizations and treatment models.


title: Tracking the Digital Traces of Russian Trolls: Distinguishing the Roles and Strategy of Trolls On Twitter

abstract: Online trolling has raised serious concerns about manipulating public opinion and exacerbating political divides among social media users. In this work, we analyse the role and behaviour of Russian trolls on Twitter through the lens of the social theory inspired by Tarde's ancient theory of monadology and its further development in Actor-Network Theory. Based on what the theory suggests, the social role or identity of individuals should be defined based on the traces they leave behind, which we define as a succession of timestamped items (e.g. tweet texts). To operationalise this, we develop a novel variant of text distance metric, time-sensitive semantic edit distance, accounting for temporal context across multiple traces. The novel metric allows us to classify roles of trolls based on their traces, in this case tweets, against a reference set. Through experiments aiming to identify the role of trolls into one of left-leaning, right-leaning, and news feed categories, we show the effectiveness of the proposed metric to measure differences between tweets in a temporal context. Through the qualitative analysis of tweet visualisation using the semantic edit distance, we find: (1) cooperation between different types of trolls despite having different and distinguishable roles; (2) complex and orchestrated interplay between left- and right-leaning trolls; (3) multiple agendas of news feed trolls to promote social disturbance and make Western democracy more fractious; and (4) a distinct shift of trolling strategy from before to after the 2016 US election.


title: Stochastic, distributed and federated optimization for machine learning

abstract: We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that enables linear convergence for strongly convex objectives. Second, we study distributed setting, in which the data describing the optimization problem does not fit into a single computing node. In this case, traditional methods are inefficient, as the communication costs inherent in distributed optimization become the bottleneck. We propose a communication-efficient framework which iteratively forms local subproblems that can be solved with arbitrary local optimization algorithms. Finally, we introduce the concept of Federated Optimization/Learning, where we try to solve the machine learning problems without having data stored in any centralized manner. The main motivation comes from industry when handling user-generated data. The current prevalent practice is that companies collect vast amounts of user data and store them in datacenters. An alternative we propose is not to collect the data in first place, and instead occasionally use the computational power of users' devices to solve the very same optimization problems, while alleviating privacy concerns at the same time. In such setting, minimization of communication rounds is the primary goal, and we demonstrate that solving the optimization problems in such circumstances is conceptually tractable.


title: General Sales Forecast Models for Automobile Markets and their Analysis

abstract: In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to ex- plain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.


title: GfxDoctor: A Holistic Graphics Energy Profiler for Mobile Devices

abstract: Graphics is one of the major energy drain sources in smartphone apps. To optimize the app graphics energy, however, developers face the challenge of highly complex graphics rendering process, which involves multiple system layers including the app, the framework, the GPU, and the asynchronous interactions among them. Current diagnostic tools can profile the resource usage from certain layers, but fall short in stitching together profiling information across all the layers which is needed to provide developers with the visual effect-energy tradeoff at the app source-code level. In this paper, we design and implement a holistic graphics energy diagnosis tool, GfxDoctor1, that helps developers to systematically diagnose energy inefficiencies in app graphics at the app source-code level, by precisely quantifying (1) the visual effect of each UI update, and (2) the aggregate energy drain spent in traversing the entire frame rendering stack due to each UI update. GfxDoctor overcomes three challenges faced in deriving per-UI-update visual effect and energy accounting, asynchrony across system layers, UI update batching, and "black-box" GPU, with two key techniques -- lightweight view-frame-ID-based information flow tracking, and OpenGL record-and-replay plus frame diffing. We show the effectiveness of GfxDoctor by profiling a randomly sampled set of 30 popular Android apps which reveals three types of graphics energy bugs happening in 8 out of the 30 apps. Removing these bugs reduces the app energy drain by 46% to 90%.


title: The Involvement of Pineal Gland and Melatonin in Immunity and Aging: II. Thyrotropin-Releasing Hormone and Melatonin Forestall Involution and Promote Reconstitution of the Thymus in Anterior Hypothalamic Area (Aha)-Lesioned Mice

abstract: A stereotactic electrolytic lesion of the anterior hypothalamic area in mice produces a rapid involution of the thymus and a reduction of lymphocytes in the peripheral blood. This effect on the thymus and blood lymphoid compartment can be prevented by postoperational administration of thyrotropin-releasing hormone (TRH) or melatonin. These activities of TRH or melatonin are antagonized by the opioid receptor blocker naltrexone. They do not seem to depend on stimulation of the thyroid gland or of the endogenous opioid system but rather on a direct activity of TRH on thymic targets or binding sites on lymphocytes.


title: An Efficient Industrial Big-Data Engine

abstract: Current trends in industrial systems opt for the use of different big-data engines as a means to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, this paper explores the development of a medium size big-data engine (i.e., implementation) able to improve the performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases the performance of current clusters improving I/O operations remarkably as empirical results revealed.


title: When attention is not scarce - detecting boredom from mobile phone usage

abstract: Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom --leveraging features related to recency of communication, usage intensity, time of day, and demographics-- can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation.


title: LoRa Transmission Parameter Selection

abstract: Low-Power Wide-Area Network (LPWAN) technologies such as Long Range (LoRa) are emerging that enable power efficient wireless communication over very long distances. LPWAN devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. However, to ensure efficient and reliable communication LPWAN devices often provide a large number of transmission parameters. For example, a LoRa device can be configured to use different spreading factors, bandwidth settings, coding rates and transmission powers, resulting in over 6720 possible settings. It is a challenge to determine the setting that minimises transmission energy cost while meeting the required communication performance. This paper is the first to present a thorough analysis of the impact of LoRa transmission parameter selection on communication performance. We study in detail the impact of parameter settings on energy consumption and communication reliability. Using this study we develop a link probing regime which enables us to quickly determine transmission settings that satisfy performance requirements. The presented work is a first step towards an automated mechanism for LoRa transmission parameter selection that a deployed LoRa network requires, but is not yet specified within the Long Range Wide Area Network (LoRaWAN) framework.


title: Semiautonomous industrial mobile manipulation for industrial applications

abstract: The mobile industrial manipulator(MIM) which combines the advantages of both the mobile robot and industrial robot and owns powerful mobility, flexibility and functionality will be the next generation of robots. However fully autonomous MIM performing various tasks in unstructured or semi-structured environment is very challenging in autonomous localization & navigation, object identification, control and coordination. However, there is a demand in industry for MIMs to be deployed in hazardous, dangerous and/or remotely environments. Therefore, a semi-autonomous MIM is investigated to perform industrial operations. An MIM prototype using a mobile platform and an industrial robot is developed. Manual and autonomous methods are proposed and implemented to control the prototype manually and autonomously. Experiments using the prototype were performed. Experimental results demonstrate that the robot can run autonomously if there is no ambiguity and the operators can interrupt the autonomous mode at any time if needed. The developed prototype and proposed methods have big potential in industrial applications.


title: A scenario is a behavioral view - Orchestrating services by scenario integration

abstract: The construction of a complex service orchestration is a tedious and error-prone tasks as multiple service interactions with a single orchestrating service must be specified and combined. We suggest to specify a service orchestration in terms of behavioral scenarios that capture a specific aspect of service interaction, a behavioral view in isolation. By synchronizing the different scenarios, the views get integrated and define the behavior of a complex service orchestration. Our formal model for scenarios and their integration is a class of Petri nets called oclets.


title: Mechanical properties of composite resin blocks for CAD/CAM

abstract: This study compared commercial composite resin blocks with one ceramic block for use in computer-aided design/computer aided manufacturing (CAD/CAM). Four composite resins, one composite ceramic, and one feldspar-ceramic block were investigated. Flexural strength (FS), flexural modulus (FM), and Vickers hardness (VH) were determined under three conditions: dry storage; immersion in water at 37°C for 7 days; and immersion in water at 37°C for 7 days followed by 10,000 thermocycles. After dry storage, FS ranged from 127 to 242 MPa, FM from 9.6 to 51.5 GPa, and VH from 64 to 455. Two-way ANOVA was performed for FS, FM and VH followed by Tukey's multiple comparison (α<0.05). Results demonstrated that the materials degraded after water immersion and thermocycling, but their properties were within the acceptable range for fabrication of single restorations according to the ISO standard for ceramics (ISO 6872:2008).


title: A novel bridgeless buck PFC converter with half dead zones

abstract: A novel bridgeless Buck power factor correction (PFC) converter with only half dead zones is proposed in this paper. The operation of the proposed PFC converter can be divided into two different modes including Buck mode in positive line cycles and Cuk mode in negative line cycles. Compared with the bridge or bridgeless Buck PFC converter, only half dead zones occur in this proposed PFC converter under the same input and output voltage leading to a higher power factor (PF). This bridgeless PFC converter is designed to work in discontinues conduction mode (DCM). The comparison of performance between the conventional Buck PFC converter and this novel PFC converter has been presented by PSIM simulations and experiments.


title: Shared design thinking process model for supporting collaborative design

abstract: Shared understanding about both the design content and the design process has significant meaning for collaborative design, which influences the quality of final products. Existing approaches have difficulties in supporting designers to structure and share their design thinking while they are performing real tasks. In this paper we propose Shared Design Thinking Process Model (S-DTPM) to support collaborative design, which is based on co-evolutionary design theory and existing design rationale frameworks. Two principles for S-DTPM have been considered. The elements and representation schema of S-DTPM have been discussed in detail. S-DTPM can help designers to create shared understanding about the design as well as record shared design thinking process for future use such as design review, product maintenance and redesign.


title: Mining Brand Perceptions from Twitter Social Networks

abstract: Consumer perceptions are important components of brand equity and therefore marketing strategy. Segmenting these perceptions into attributes such as eco-friendliness, nutrition, and luxury enable a fine-grained understanding of the brand’s strengths and weaknesses. Traditional approaches towards monitoring such perceptions (e.g., surveys) are costly and time consuming, and their results may quickly become outdated. Extant data mining methods are unsuitable for this goal, and generally require extensive hand-annotated data or context customization, which leads to many of the same limitations as direct elicitation. Here, we investigate a novel, general, and fully automated method for inferring attribute-specific brand perception ratings by mining the brand’s social connections on Twitter. Using a set of over 200 brands and three perceptual attributes, we compare the method’s automatic ratings estimates with directly-elicited survey data, finding a consistently strong correlation. The approach provides a reliable, flexible, and scalable method for monitoring brand perceptions, and offers a foundation for future advances in understanding brand-consumer social media relationships.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0968 .


title: Experimenting with Arabic text visualizing

abstract: The amount of unstructured textual data on the Internet has been increased dramatically. Text visualization becomes a significant tool that facilitates knowledge discovery and insightful presentation of large amounts of data. In this paper we present a technique of the visual exploration of Arabic text documents. We apply Latent Semantic Indexing (LSI) as a dimensionality reduction technique that helps in extracting some knowledge from text documents. We present an Arabic Visualization System. The experiments were carried on several datasets extracted from an Arabic corpus. The system can create views from different perspectives based on the user's needs. It is successfully used for visualizing different kinds of document corpora. The system is very helpful for data analysis offering quick insight into the structure of the visualized corpus.


title: A Journey through the Land of Model-View-Design Patterns

abstract: Every software program that interacts with a user requires a user interface. Model-View-Controller (MVC) is a common design pattern to integrate a user interface with the application domain logic. MVC separates the representation of the application domain (Model) from the display of the application's state (View) and user interaction control (Controller). However, studying the literature reveals that a variety of other related patterns exists, which we denote with Model-View- (MV) design patterns. This paper discusses existing MV patterns classified in three main families: Model-View-Controller (MVC), Model-View-View Model (MVVM), and Model-View-Presenter (MVP). We take a practitioners' point of view and emphasize the essentials of each family as well as the differences. The study shows that the selection of patterns should take into account the use cases and quality requirements at hand, and chosen technology. We illustrate the selection of a pattern with an example of our practice. The study results aim to bring more clarity in the variety of MV design patterns and help practitioners to make better grounded decisions when selecting patterns.


title: Constructing Structured Information Networks from Massive Text Corpora

abstract: In today's computerized and information-based society, text data is rich but messy. People are soaked with vast amounts of natural-language text data, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). To turn such massive unstructured text data into actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations, events) in the text. In this tutorial, we introduce data-driven methods to construct structured information networks (where nodes are different types of entities attached with attributes, and edges are different relations between entities) for text corpora of different kinds (especially for massive, domain-specific text corpora) to rep- resent their factual information. We focus on methods that are minimally-supervised, domain-independent, and language-independent for fast network construction across various application domains (news, web, biomedical, reviews). We demonstrate on real datasets including news articles, scientific publications, tweets and reviews how these constructed networks aid in text analytics and knowledge discovery at a large scale.


title: A Novel Anti phishing framework based on visual cryptography

abstract: With the advent of internet, various online attacks has been increased and among them the most popular attack is phishing. Phishing is an attempt by an individual or a group to get personal confidential information such as passwords, credit card information from unsuspecting victims for identity theft, financial gain and other fraudulent activities. Fake websites which appear very similar to the original ones are being hosted to achieve this. In this paper we have proposed a new approach named as "A Novel Anti-phishing framework based on visual cryptography "to solve the problem of phishing. Here an image based authentication using Visual Cryptography is implemented. The use of visual cryptography is explored to preserve the privacy of an image captcha by decomposing the original image captcha into two shares (known as sheets) that are stored in separate database servers(one with user and one with server) such that the original image captcha can be revealed only when both are simultaneously available; the individual sheet images do not reveal the identity of the original image captcha. Once the original image captcha is revealed to the user it can be used as the password. Using this website cross verifies its identity and proves that it is a genuine website before the end users.


title: Wizard-of-Oz prototyping for co-operative interaction design of graphical user interfaces

abstract: It is often difficult for users to specify requirements on interactivity in multimedia products. These properties are often designed by designers or software engineers and the users run the risk of playing a passive role having low influence on important decisions made. This paper presents an attempt to improve the interactive design process by using graphical Wizard-of-Oz prototyping. The study shows that graphical Wizard-of-Oz prototyping allow co-operative design and test of computer interaction early in the design process ahead of traditional programming. It also demonstrates that this type of prototyping can promote collaboration and constructive dialogue between users and designers, thus strengthening the user as a stakeholder in the development process.


Explicit Image Quality Detection Rules for Functional Safety in Computer Vision


title: Fibre-Optic Chemical Sensor Approaches Based on Nanoassembled Thin Films: A Challenge to Future Sensor Technology

abstract: Optical phenomena have been employed extensively by human civilization throughout the centuries for lighting, communication, calculations, observations, etc. and have played a crucial role in industrial development. The applications of the optics increased significantly after the first demonstration of the light guiding phenomenon based on total internal reflection in the 1840s, which was the precursor for the development of modern optical fibres. In modern life, optical fibres found their niche in telecommunications and, more recently, as sensors.


title: A survey on security and privacy issues in big data

abstract: Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost every industry is trying to cope with this huge data. Big data phenomenon has begun to gain importance. However, it is not only very difficult to store big data and analyse them with traditional applications, but also it has challenging privacy and security problems. For this reason, this paper discusses the big data, its ecosystem, concerns on big data and presents comparative view of big data privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications an overall perspective of security and privacy issues in big data is suggested.


title: Adaptive Skinning for Interactive Hair-Solid Simulation

abstract: Reduced hair models have proven successful for interactively simulating a full head of hair strands, building upon a fundamental assumption that only a small set of guide hairs are needed for explicit simulation, and the rest of the hair move coherently and thus can be interpolated using guide hairs. Unfortunately, hair-solid interactions is a pathological case for traditional reduced hair models, as the motion coherence between hair strands can be arbitrarily broken by interacting with solids. In this paper, we propose an adaptive hair skinning method for interactive hair simulation with hair-solid collisions. We precompute many eligible sets of guide hairs and the corresponding interpolation relationships that are represented using a compact strand-based hair skinning model. At runtime, we simulate only guide hairs; for interpolating every other hair, we adaptively choose its guide hairs, taking into account motion coherence and potential hair-solid collisions. Further, we introduce a two-way collision correction algorithm to allow sparsely sampled guide hairs to resolve collisions with solids that can have small geometric features. Our method enables interactive simulation of more than 150 K hair strands interacting with complex solid objects, using 400 guide hairs. We demonstrate the efficiency and robustness of the method with various hairstyles and user-controlled arbitrary hair-solid interactions.


title: Hebbian plasticity requires compensatory processes on multiple timescales

abstract: We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.


title: Dynamic nature of trust in virtual teams

abstract: We empirically examine the dynamic nature of trust and the differences between high- and low-performing virtual teams in the changing patterns in cognition- and affect-based trust over time (early, middle, and late stages of project). Using data from 36, four-person MBA student teams from six universities competing in a web-based business simulation game over an 8-week period, we found that both high- and low-performing teams started with similar levels of trust in both cognitive and affective dimensions. However, high-performing teams were better at developing and maintaining the trust level throughout the project life. Moreover, virtual teams relied more on a cognitive than an affective element of trust. These findings provide a preliminary step toward understanding the dynamic nature and relative importance of cognition- and affect-based trust over time.


title: Topical drug classification

abstract: Current definitions of lotions, gels, creams and ointments vary depending on literature source, market history or traditional use. This often leads to confusion when deciding which dosage form to prescribe and/or purchase. The existing classification of topical dosage forms needs to be re-examined to ensure that definitions for different dosage forms are based on consistent scientific principles and that dosage forms can be distinguished from one another. The purpose of this study is to obtain a scientifically based, systematic classification of dosage forms for topical drugs. A variety of prescription and over-the-counter topical products currently marketed as lotions, gels, creams, and ointments are evaluated using different techniques including rheology (viscosity and shear rate versus shear stress), loss on drying (LOD), specific gravity, surface tension, thermogravimetric analysis (TGA), water absorption, dilution properties, microscopic evaluation, transmittance of visible light, appearance and composition. Rheology is the most discriminating property separating creams and lotions. Water plus volatiles (as measured by LOD) and composition separate ointments and creams. Composition and thermal behavior separate gels from the other dosage forms. Based on these findings, new definitions and a decision tree are presented to assist in the determination of the appropriate nomenclature for a topical dosage form.


title: Second-Order Belief Hidden Markov Models

abstract: Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model.


title: Log clustering based problem identification for online service systems

abstract: Logs play an important role in the maintenance of large-scale online service systems. When an online service fails, engineers need to examine recorded logs to gain insights into the failure and identify the potential problems. Traditionally, engineers perform simple keyword search (such as "error" and "exception") of logs that may be associated with the failures. Such an approach is often time consuming and error prone. Through our collaboration with Microsoft service product teams, we propose LogCluster, an approach that clusters the logs to ease log-based problem identification. LogCluster also utilizes a knowledge base to check if the log sequences occurred before. Engineers only need to examine a small number of previously unseen, representative log sequences extracted from the clusters to identify a problem, thus significantly reducing the number of logs that should be examined, meanwhile improving the identification accuracy. Through experiments on two Hadoop-based applications and two large-scale Microsoft online service systems, we show that our approach is effective and outperforms the state-of-the-art work proposed by Shang et al. in ICSE 2013. We have successfully applied LogCluster to the maintenance of many actual Microsoft online service systems. In this paper, we also share our success stories and lessons learned.


title: Random Johnson-Mehl tessellations

abstract: A unified exposition of random Johnson-Mehl tessellations in d-dimensional Euclidean space is presented. In particular, Johnson-Mehl tessellations generated by time-inhomogeneous Poisson processes and nucleation-exclusion models are studied. The «practical» cases d = 2 and d = 3 are discussed in detail. Several new results are established, including first- and second-order moments of various characteristics for both Johnson-Mehl tesselations and sectional Johnson-Mehl tessellations


title: Functional Virtual Prototyping Environment for a Family of Map-Reduce Embedded Accelerators

abstract: This emergence of the heterogeneous computing is based mainly on various forms of parallel accelerators. We present a family of accelerators for embedded computation with a map-reduce architecture based on the partial recursive functions computation model introduced by Stephen Kleene. A three-level virtual prototyping environment is provided to support the development of embedded applications. The first level is written in a Lisp-like functional language. The second is a C-like environment which segregates the intense part of the computation from the complex part. The last one is a low level simulator able to provide support for advanced optimizations. The environment is designed for developing applications by tuning the architecture of a family of many-core machines which provide high performance per Watt and cm2. The energy efficiency of processors backing our architectural approach is in the range of 10 pJ/flop evaluated for the standard cell 28nm technology.


title: Design and simulation of 10-bit SAR ADC for low power applications using 180nm technology

abstract: Analog-to-Digital converters plays vital role in medical and signal processing applications. Normally low power ADC's were required for long term and battery operated applications. SAR ADC is best suited for low power, medium resolution and moderate speed applications. This paper presents a 10-bit low power SAR ADC which is simulated in 180nm CMOS technology. Based on literature survey, low power consumption is attained by using Capacitive DAC. Capacitive DAC also incorporate Sample-and-Hold circuit in it. Dynamic latch comparator is used to increase in speed of operation and to get lower power consumption.


title: PARIS, an optogenetic method for functionally mapping gap junctions

abstract: For the tissues and organs of our bodies to work properly, the cells within them need to communicate with each other. One important part of cellular communication is the movement of signals – usually small molecules or ions – directly from one cell to another. This happens via structures called gap junctions, a type of sealed ‘channel’ that connects two cells. Gap junctions are found throughout the body, but investigating their precise roles in health and disease has been difficult. This is due to problems with the tools available to detect and monitor gap junctions. Some are simply harmful to cells, while others cannot be restricted to specific cell populations within a tissue. This lack of specificity makes it difficult to study gap junctions in the brain, where it is important to understand the connectivity patterns between distinct types of nerve cells. Wu et al. wanted to develop a new, non-harmful method to track gap junctions in distinct groups of cells within living tissues. To do this, Wu et al. devised PARIS, a two-part, genetically encoded system. The first part comprises a light-sensitive molecular ‘pump’, which can only be turned on by shining a laser onto the cell of interest. When the pump is active, it transports hydrogen ions out of the cell. The second part of the system is a fluorescent sensor, present inside ‘receiving’ cells, which responds to the outcoming hydrogen ions (small enough to pass through gap junctions). If an illuminated ‘signaling’ cell is connected via gap junctions to cells containing the fluorescent sensor, they will light up within seconds, but other cells not connected through gap junctions will not. The researchers first tested PARIS in cultured human and rat cells that had been genetically engineered to produce both components of the system. The experiments confirmed that PARIS could both detect networks of gap junctions in healthy cells and reveal when these networks had been disrupted, for instance by drugs or genetic mutations. Experiments using fruit flies demonstrated that PARIS was stable in living tissue and could also map the gap junctions connecting specific groups of nerve cells. PARIS is a valuable addition to the toolbox available to study cell communication. In the future, it could help increase our understanding of diseases characterized by defective gap junctions, such as seizures, cardiac irregularities, and even some cancers.


title: Mechanisms of Communication in the Mammalian Circadian Timing System

abstract: 24-h rhythms in physiology and behaviour are organized by a body-wide network of endogenous circadian clocks. In mammals, a central pacemaker in the hypothalamic suprachiasmatic nucleus (SCN) integrates external light information to adapt cellular clocks in all tissues and organs to the external light-dark cycle. Together, central and peripheral clocks co-regulate physiological rhythms and functions. In this review, we outline the current knowledge about the routes of communication between the environment, the main pacemakers and the downstream clocks in the body, focusing on what we currently know and what we still need to understand about the communication mechanisms by which centrally and peripherally controlled timing signals coordinate physiological functions and behaviour. We highlight recent findings that shed new light on the internal organization and function of the SCN and neuroendocrine mechanisms mediating clock-to-clock coupling. These findings have implications for our understanding of circadian network entrainment and for potential manipulations of the circadian clock system in therapeutic settings.


title: Internet of Things for Smart Cities

abstract: The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.


title: Semantic Textual Similarity Methods, Tools, and Applications: A Survey

abstract: Measuring Semantic Textual Similarity (STS), between words/ terms, sentences, paragraph and document plays an important role in computer science and computational linguistic. It also has many application sover several fields such as Biomedical Informatics and Geoinformation. In this paper, we present a survey on different methods of textual similarity and we also reported about the availability of different software and tools those are useful for STS. In natural language processing (NLP), STS is a important component formany tasks such as document summarization, word sense disambiguation, short answer grading, information retrieval and extraction. We split out the measures for semantic similarity into three broad categories such as (i) Topological/Knowledge-based (ii) Statistical/Corpus Based (iii) String based. More emphasisi s given to the methods related to the WordNet taxonomy. Because topological methods, plays an important role to understand intended meaning of an ambiguous word, which is very difficult to process computationally. We also propose a new method for measuring semantic similarity between sentences. This proposed method, uses the advantages of taxonomy methods and merge these information to a language model. It considers the WordNet synsets for lexical relationships between nodes/words and a uni-gram language model is implemented over a large corpus to assign the information content value between the two nodes of different classes.


title: Steel Plates Faults Diagnosis with Data Mining Models

abstract: Problem statement: Over the last two decades, Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. FD may be considered as a pattern recognition problem. It has been gaining more and more attention to develop methods for improving the accuracy and efficiency of pattern recognition. Many computational tools and algorithms that have been recently developed could be used. Approach: This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and Other_Faults. The models include C5.0 decision tree (C5.0 DT) with boosting, Multi Perception Neural Network (MLPNN) with pruning and Logistic Regression (LR) with step forward. The steel plates fault dataset investigated in this study is taken from the University of California at Irvine (UCI) machine learning repository. Results: Given a training set of such patterns, the individual model learned how to differentiate a new case in the domain. The diagnosis performances of the proposed models are presented using statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the C5.0 decision tree with boosting algorithm has achieved a remarkable performance with 97.25 and 98.09% accuracy on training and test subset. C5.0 has outperformed the other two models. Conclusion: Experimental results showed that data mining algorithms in general and decision trees in particular have the great impact of on the problem of steel plates fault diagnosis.


title: Building retinal connectomes

abstract: Understanding vertebrate vision depends on knowing, in part, the complete network graph of at least one representative retina. Acquiring such graphs is the business of synaptic connectomics, emerging as a practical technology due to improvements in electron imaging platform control, management software for large-scale datasets, and availability of data storage. The optimal strategy for building complete connectomes uses transmission electron imaging with 2 nm or better resolution, molecular tags for cell identification, open-access data volumes for navigation, and annotation with open-source tools to build 3D cell libraries, complete network diagrams and connectivity databases. The first forays into retinal connectomics have shown that even nominally well-studied cells have much richer connection graphs than expected.


title: Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

abstract: The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.


title: A geometric alternative to Nesterov's accelerated gradient descent

abstract: We propose a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov's accelerated gradient descent. The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method. We provide some numerical evidence that the new method can be superior to Nesterov's accelerated gradient descent.


title: VIEWPOINT Lipoprotein(a) as a Potential Causal Genetic Risk Factor of Cardiovascular Disease A Rationale for Increased Efforts to Understand its Pathophysiology and Develop Targeted Therapies

abstract: Recent published studies have provided increasing evidence that lipoprotein(a) [Lp(a)] may be a potential causal, genetic, independent risk factor for cardiovascular disease (CVD). Lp(a) levels >25 mg/dl are present in ∼30% of Caucasians and 60% to 70% of Blacks. Lp(a) is composed of apolipoprotein B-100 and apolipoprotein (a) [(apo(a)]. Circulating Lp(a) levels are primarily influenced by the LPA gene without significant dietary or environmental effects, mediating CVD risk throughout the patient's lifetime. Recent clinical outcomes studies, meta-analyses, and Mendelian randomization studies, in which randomization of Lp(a) levels is achieved through the random assortment of LPA gene variants thereby removing confounders, have shown that genetically determined Lp(a) levels are continuously and linearly related to risk of CVD. Currently, Lp(a) pathophysiology is not fully understood, and specifically targeted therapies to lower Lp(a) are not available. We provide a rationale for increased basic and clinical investigational efforts to further understand Lp(a) pathophysiology and assess whether reducing Lp(a) levels minimizes CVD risk. First, a detailed understanding of Lp(a) synthesis and clearance has not been realized. Second, several mechanisms of atherogenicity are known to varying extent, but the relative contributions of each are not known. Lp(a) may be atherothrombotic through its low-density lipoprotein moiety, but also through apo(a), including its ability to be retained in the vessel wall and mediate pro-inflammatory and proapoptotic effects including those potentiated by its content of oxidized phospholipids, and antifibrinolytic effects. Finally, development of specific Lp(a)-lowering agents to potently lower Lp(a) will allow testing of mechanistic hypotheses in animal models and the design of randomized clinical trials to assess reduction in CVD. A convergence of academic, scientific, pharmaceutical, and National Institutes of Health priorities and efforts can make this a reality in the next decade.


title: Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results

abstract: Industry 4.0 combines the strengths of traditional industries with cutting edge internet technologies. It embraces a set of technologies enabling smart products integrated into intertwined digital and physical processes. Therefore, many companies face the challenge to assess the diversity of developments and concepts summarized under the term industry 4.0. The paper presents the result of a study on the potential of industry 4.0. The use of current technologies like Big Data or cloud-computing are drivers for the individual potential of use of Industry 4.0. Furthermore mass customization as well as the use of idle data and production time improvement are strong influence factors to the potential of Industry 4.0. On the other hand business process complexity has a negative influence.


title: Impulsive Buying Behaviour: The Role of Feelings When Shopping for

abstract: This paper presents an in-depth study of young Swedish consumers and their impulsive online buying behaviour for clothing. The aim of th e study is to develop the understanding of what factors affect impulse buying of clothing online and what feelings emerge when buying online. The study carri ed out was exploratory in nature, aiming to develop an understanding of impulse buyin g behaviour online before, under and after the actual purchase. The empirical data w as collected through personal interviews. In the study, a pattern of the consumer s recurrent feelings are identified through the impulse buying process; escapism, pleas ure, reward, scarcity, security and anticipation. The escapism is particularly occurrin g since the study revealed that the consumers often carried out impulse purchases when they initially were bored, as opposed to previous studies.


title: Development of robot mechanism for inspection of live-line suspension insulator string in 345kV power lines

abstract: A new inspection robot mechanism for live-line suspension insulator strings was developed to prevent an insulator failure in power transmission lines. By adopting a wheel-leg moving mechanism, the robot structure becomes very simple, small-sized and lightweight. In addition, this mechanism is superior in insulation to the existing ones, because the number of the insulators contacted by the mechanism is only one. Through experiments, we confirmed its effectiveness.


title: Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

abstract: Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.


title: Exercise promotes BCAA catabolism: effects of BCAA supplementation on skeletal muscle during exercise.

abstract: Branched-chain amino acids (BCAAs) are essential amino acids that can be oxidized in skeletal muscle. It is known that BCAA oxidation is promoted by exercise. The mechanism responsible for this phenomenon is attributed to activation of the branched-chain alpha-keto acid dehydrogenase (BCKDH) complex, which catalyzes the second-step reaction of the BCAA catabolic pathway and is the rate-limiting enzyme in the pathway. This enzyme complex is regulated by a phosphorylation-dephosphorylation cycle. The BCKDH kinase is responsible for inactivation of the complex by phosphorylation, and the activity of the kinase is inversely correlated with the activity state of the BCKDH complex, which suggests that the kinase is the primary regulator of the complex. We found recently that administration of ligands for peroxisome proliferator-activated receptor-alpha (PPARalpha) in rats caused activation of the hepatic BCKDH complex in association with a decrease in the kinase activity, which suggests that promotion of fatty acid oxidation upregulates the BCAA catabolism. Long-chain fatty acids are ligands for PPARalpha, and the fatty acid oxidation is promoted by several physiological conditions including exercise. These findings suggest that fatty acids may be one of the regulators of BCAA catabolism and that the BCAA requirement is increased by exercise. Furthermore, BCAA supplementation before and after exercise has beneficial effects for decreasing exercise-induced muscle damage and promoting muscle-protein synthesis; this suggests the possibility that BCAAs are a useful supplement in relation to exercise and sports.


title: Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks

abstract: The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features, such as clear organ boundaries. Convolutional neural networks provide a powerful framework for post-processing such convolved direct reconstructions. In this paper, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems.


title: Meta-metadata: a metadata semantics language for collection representation applications

abstract: Collecting, organizing, and thinking about diverse information resources is the keystone of meaningful digital information experiences, from research to education to leisure. Metadata semantics are crucial for organizing collections, yet their structural diversity exacerbates problems of obtaining and manipulating them, strewing end users and application developers amidst the shadows of a proverbial tower of Babel. We introduce meta-metadata, a language and software architecture addressing a metadata semantics lifecycle: (1) data structures for representation of metadata in programs; (2) metadata extraction from information resources; (3) semantic actions that connect metadata to collection representation applications; and (4) rules for presentation to users. The language enables power users to author metadata semantics wrappers that generalize template-based information sources. The architecture supports development of independent collection representation applications that reuse wrappers. The initial meta-metadata repository of information source wrappers includes Google, Flickr, Yahoo, IMDb, Wikipedia, and the ACM Portal. Case studies validate the approach.


title: Kineticons: using iconographic motion in graphical user interface design

abstract: Icons in graphical user interfaces convey information in a mostly universal fashion that allows users to immediately interact with new applications, systems and devices. In this paper, we define Kineticons - an iconographic scheme based on motion. By motion, we mean geometric manipulations applied to a graphical element over time (e.g., scale, rotation, deformation). In contrast to static graphical icons and icons with animated graphics, kineticons do not alter the visual content or "pixel-space" of an element. Although kineticons are not new - indeed, they are seen in several popular systems - we formalize their scope and utility. One powerful quality is their ability to be applied to GUI elements of varying size and shape from a something as small as a close button, to something as large as dialog box or even the entire desktop. This allows a suite of system-wide kinetic behaviors to be reused for a variety of uses. Part of our contribution is an initial kineticon vocabulary, which we evaluated in a 200 participant study. We conclude with discussion of our results and design recommendations.


title: Sensor Fusion of Camera, GPS and IMU using Fuzzy Adaptive Multiple Motion Models

abstract: A tracking system that will be used for augmented reality applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay, occurring because of the delay in estimating the motion of the user and displaying images based on this estimate). This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS–IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context running at modest frame rates due to the design of the fusion algorithm.


title: Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

abstract: In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset--based on the city of Porto--, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.


title: A framework for exploring categorical data

abstract: In this paper, we present a framework for categorical data analysis which allows such data sets to be explored using a rich set of techniques that are only applicable to continuous data sets. We introduce the concept of separability statistics in the context of exploratory categorical data analysis. We show how these statistics can be used as a way to map categorical data to continuous space given a labeled reference data set. This mapping enables visualization of categorical data using techniques that are applicable to continuous data. We show that in the transformed continuous space, the performance of the standard k-nn based outlier detection technique is comparable to the performance of the k-nn based outlier detection technique using the best of the similarity measures designed for categorical data. The proposed framework can also be used to devise similarity measures best suited for a particular type of data set.


title: Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs

abstract: In this paper, we present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing consistency among their global features (e.g., GIST) using GMCP. In this context, we argue that using a robust distance function for finding the similarity between the global features is essential for the cases where the query matches multiple reference images with dissimilar global features. Towards this end, we propose a robust distance function based on the Gaussian Radial Basis Function (G-RBF). We evaluated the proposed framework on a new data set of 102k street view images; the experiments show it outperforms the state of the art by 10 percent.


Gas Turbine Axial Compressor Fouling And Washing.


title: Low-Profile Printed Octa-Band LTE/WWAN Mobile Phone Antenna Using Embedded Parallel Resonant Structure

abstract: A simple low-profile antenna for octa-band LTE/WWAN operation in the internal mobile phone application is proposed and studied in this article. Consisting of a feeding strip and a coupling strip mainly, the presented antenna can be easily printed on the no-ground area of the top of a FR4 substrate, yet it occupies only a size of 15 × 40 mm2 and has a height of 3.5 mm. In this scheme, a chip inductor (L1 = 20 nH) is loaded in the long inductive strip, which can form a parallel resonant structure together with the coupling strip section close to the feeding strip. With these presences, a double-resonance mode (λ/4 resonant mode) is successfully generated at about 720 and 900 MHz to cover the lower band of LTE700/GSM850/900 (704-960 MHz). While the desired upper band of DCS1800/PCS1900/UMTS2100/LTE2300/2500 (1710-2690 MHz) can be obtained with the help of second two resonant modes, including a high-order λ/2 resonant mode at 1800 MHz and a λ/4 resonant mode at 2550 MHz. That is to say that those two wide operating bandwidths are achieved to cover all the octa-band LTE/WWAN operation. Good radiation efficiency and antenna gain for frequencies over the desired operating bands is obtained. Detailed design considerations of the proposed antenna are described, and both experimental and simulation results are also presented and discussed.


title: Kernel-Mapping Recommender system algorithms

abstract: Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose a new algorithm that we call the Kernel-Mapping Recommender (KMR), which uses a novel structure learning technique. This paper makes the following contributions: we show how (1) user-based and item-based versions of the KMR algorithm can be built; (2) user-based and item-based versions can be combined; (3) more information-features, genre, etc.-can be employed using kernels and how this affects the final results; and (4) to make reliable recommendations under sparse, cold-start, and long tail scenarios. By extensive experimental results on five different datasets, we show that the proposed algorithms outperform or give comparable results to other state-of-the-art algorithms.


title: FearNot! - An Experiment in Emergent Narrative

abstract: We discuss the experience of constructing the application FearNot! (Fun with Empathic Agents Reaching Novel Outcomes in Teaching), an application of virtual drama to anti-bullying education inspired by Forum Theatre. An appraisal-driven agent architecture is presented as a mechanism for generating an emergent, that is, unscripted, narrative. A small-scale evaluation is discussed and the lessons learned are described.


title: A Key Recovery Attack on Discrete Log-based Schemes Using a Prime Order Subgroup

abstract: Consider the well-known oracle attack: somehow one gets a certain computation result as a function of a secret key from the secret key owner and tries to extract some information on the secret key. This attacking scenario is well understood in the cryptographic community. However, there are many protocols based on the discrete logarithm problem that turn out to leak many of the secret key bits from this oracle attack, unless suitable checkings are carried out. In this paper we present a key recovery attack on various discrete log-based schemes working in a prime order subgroup. Our attack may reveal part of, or the whole secret key in most Diffie-Hellman-type key exchange protocols and some applications of ElGamal encryption and signature schemes.


title: CRUSH: controlled, scalable, decentralized placement of replicated data

abstract: Emerging large-scale distributed storage systems are faced with the task of distributing petabytes of data among tens or hundreds of thousands of storage devices. Such systems must evenly distribute data and workload to efficiently utilize available resources and maximize system performance, while facilitating system growth and managing hardware failures. We have developed CRUSH, a scalable pseudorandom data distribution function designed for distributed object-based storage systems that efficiently maps data objects to storage devices without relying on a central directory. Because large systems are inherently dynamic, CRUSH is designed to facilitate the addition and removal of storage while minimizing unnecessary data movement. The algorithm accommodates a wide variety of data replication and reliability mechanisms and distributes data in terms of user-defined policies that enforce separation of replicas across failure domains.


title: A Face-to-Face Neural Conversation Model

abstract: Neural networks have recently become good at engaging in dialog. However, current approaches are based solely on verbal text, lacking the richness of a real face-to-face conversation. We propose a neural conversation model that aims to read and generate facial gestures alongside with text. This allows our model to adapt its response based on the "mood" of the conversation. In particular, we introduce an RNN encoder-decoder that exploits the movement of facial muscles, as well as the verbal conversation. The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural. We train our neural network by having it "watch" 250 movies. We showcase our joint face-text model in generating more natural conversations through automatic metrics and a human study. We demonstrate an example application with a face-to-face chatting avatar.


title: An improved ultra-low-phase noise tunable YIG oscillator operating in the 6–12 GHz range

abstract: This paper presents the improvement of a conventionally designed state-of-the-art yttrium-iron-garnet tuned oscillator with the main focus on the minimization of the phase noise. The employment of modern CAD tools allows for accurate oscillator optimization resulting in a phase noise performance below −133 dBc/Hz at a frequency offset of 100 kHz over the entire range of 6–12 GHz. The presented oscillator is modeled as a one-port oscillator employing a SiGe hetero bipolar transistor as a reflection amplifier and a loop-coupled YIG sphere resonator, modeled and simulated in a commercial FEM 3D-fullwave simulator considering all coupling effects and respective parasitics. The achieved phase noise is compared to a number of state of the art YIG oscillators around the frequency offset of 10 kHz, 100 kHz and 1 MHz. Finally some further phase noise improvements over the frequency range of 6–12 GHz are shown.


title: High altitude UAV navigation using IMU, GPS and camera

abstract: This paper deals with the integration of measurements provided by inertial sensors (gyroscopes and accelerometers), GPS (Global Positioning System) and a video system in order to estimate position and attitude of an high altitude UAV (Unmanned Aerial Vehicle). In such a case, the vision algorithms present ambiguities due to the plane degeneracy. This ambiguity can be avoided fusing the video information with inertial sensors measurements. On the other hand, inertial sensors are widely used for aircraft navigation because they represent a low cost and compact solution, but their measurements suffer of several errors which cause a rapid divergence of position and attitude estimates. To avoid divergence, inertial sensors are usually coupled with other systems as for example GPS. A camera presents several advantages with respect to GPS as for example great accuracy and higher data rate. Moreover, it can be used in urban area or, more in general, where no useful GPS signal is present. On the contrary, it has lower data rate than inertial sensors and its measurements have latencies which can prejudice the performances and the effectiveness of the flight control system. The integration of inertial sensors with a camera allows exploiting the better features of both the systems, providing better performances in position and attitude estimation. The data fusion is performed via a multirate Unscented Kalman Filter (UKF) because of the nonlinear dynamic system equation. Experimental results show the effectiveness of the proposed method.


title: Algorithm-Dependent Generalization Bounds for Multi-Task Learning

abstract: Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order $\mathcal {O}(1/n)$ , where $n$ is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order $\mathcal {O}(1/T)$ , where $T$ is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.


title: Semantics-Based Machine Translation with Hyperedge Replacement Grammars

abstract: We present an approach to semantics-based statistical machine translation that uses synchronous hyperedge replacement grammars to translate into and from graph-shaped intermediate meaning representations, to our knowledge the first work in NLP to make use of synchronous context free graph grammars. We present algorithms for each step of the semantics-based translation pipeline, including a novel graph-to-word alignment algorithm and two algorithms for synchronous grammar rule extraction. We investigate the influence of syntactic annotations on semantics-based translation by presenting two alternative rule extraction algorithms, one that requires only semantic annotations and another that additionally relies on syntactic annotations, and explore the effect of syntax and language bias in meaning representation structures by running experiments with two different meaning representations, one biased toward an English syntax-like structure and another that is language neutral. While preliminary work, these experiments show promise for semantically-informed machine translation.


title: A Convolutional Click Prediction Model

abstract: The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network. CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on two public large-scale datasets indicate that CCPM is effective on click prediction.


title: Learning to Grasp Without Seeing

abstract: Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9% over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6%. We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.


title: Anderson acceleration for geometry optimization and physics simulation

abstract: Many computer graphics problems require computing geometric shapes subject to certain constraints. This often results in non-linear and non-convex optimization problems with globally coupled variables, which pose great challenge for interactive applications. Local-global solvers developed in recent years can quickly compute an approximate solution to such problems, making them an attractive choice for applications that prioritize efficiency over accuracy. However, these solvers suffer from lower convergence rate, and may take a long time to compute an accurate result. In this paper, we propose a simple and effective technique to accelerate the convergence of such solvers. By treating each local-global step as a fixed-point iteration, we apply Anderson acceleration, a well-established technique for fixed-point solvers, to speed up the convergence of a local-global solver. To address the stability issue of classical Anderson acceleration, we propose a simple strategy to guarantee the decrease of target energy and ensure its global convergence. In addition, we analyze the connection between Anderson acceleration and quasi-Newton methods, and show that the canonical choice of its mixing parameter is suitable for accelerating local-global solvers. Moreover, our technique is effective beyond classical local-global solvers, and can be applied to iterative methods with a common structure. We evaluate the performance of our technique on a variety of geometry optimization and physics simulation problems. Our approach significantly reduces the number of iterations required to compute an accurate result, with only a slight increase of computational cost per iteration. Its simplicity and effectiveness makes it a promising tool for accelerating existing algorithms as well as designing efficient new algorithms.


title: Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization

abstract: Robust facial landmark localization remains a challenging task when faces are partially occluded. Recently, the cascaded pose regression has attracted increasing attentions, due to it's superior performance in facial landmark localization and occlusion detection. However, such an approach is sensitive to initialization, where an improper initialization can severly degrade the performance. In this paper, we propose a Robust Initialization for Cascaded Pose Regression (RICPR) by providing texture and pose correlated initial shapes for the testing face. By examining the correlation of local binary patterns histograms between the testing face and the training faces, the shapes of the training faces that are most correlated with the testing face are selected as the texture correlated initialization. To make the initialization more robust to various poses, we estimate the rough pose of the testing face according to five fiducial landmarks located by multitask cascaded convolutional networks. Then the pose correlated initial shapes are constructed by the mean face's shape and the rough testing face pose. Finally, the texture correlated and the pose correlated initial shapes are joined together as the robust initialization. We evaluate RICPR on the challenging dataset of COFW. The experimental results demonstrate that the proposed scheme achieves better performances than the state-of-the-art methods in facial landmark localization and occlusion detection.


title: PCPs and the Hardness of Generating Private Synthetic Data

abstract: Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm A that takes a database D ∈ ({0, 1}d)n and outputs a "synthetic database" D all of whose two-way marginals are approximately equal to those of D. (A two-way marginal is the fraction of database rows x ∈ {0, 1}d with a given pair of values in a given pair of columns). This answers a question of Barak et al. (PODS '07), who gave an algorithm running in time poly(n, 2d). ::: ::: Our proof combines a construction of hard-to-sanitize databases based on digital signatures (by Dwork et al., STOC '09) with encodings based on probabilistically checkable proofs. ::: ::: We also present both negative and positive results for generating "relaxed" synthetic data, where the fraction of rows in D satisfying a predicate c are estimated by applying c to each row of D and aggregating the results in some way.


Advancing the field of 3 D biomaterial printing


abstract: A hybrid cascaded multilevel inverter application for renewable energy resources including a reconfiguration technique is developed. The objective of this research is to propose an alternative topology of hybrid cascaded multilevel inverter applied to a low voltage dc microgrid in telecommunication buildings. The modified PWM technique is also developed to reduce switching losses. Also, the proposed topology can reduce the number of required power switches compared to a traditional cascaded multilevel inverter. A possible reconfiguration technique after faulty condition is also discussed. PSIM (PowerSim) and Simulink/MATLAB are used to simulate the circuit operation and control signal. A 3-kW prototype is developed. The switching losses of the proposed multilevel inverter are also investigated. By using the modified PWM technique and reconfiguration method, the proposed hybrid inverter can improve system efficiency and reliability. The proposed inverter efficiency is 97% under tested condition. The results show that proposed hybrid inverter topology is a promising method for a low voltage dc microgrid interfacing with renewable energy resources.


Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework


title: Mechanisms of Polymorphic and Metamorphic Viruses

abstract: Malware has been generally accepted as one of the top security threats to computer systems around the globe. As malware evolves at a tremendous pace and demonstrates new ways to exploit, infect and victimize the computer systems of enterprises and businesses, remaining economically viable is becoming increasingly difficult. The new trends of malware development are focused on the use of complex and sophisticated code to obstruct analysis as well as spoofing contemporary anti-virus scanners. Polymorphic and metamorphic viruses use the obfuscation techniques to obstruct deep static analysis and defeat dynamic emulators. Malware may also employ metamorphism-based methods, including encryption and decryption engines, multi-packer, garbage code insertion, instruction permutation, code transformation, anti-debugging and virtual machine, registry modification and polymorphic engines. The structural mechanisms of both polymorphic and metamorphic viruses will be presented and discussed in this paper. Finally, the new complex computer viruses such as W32/Fujacks and W32/Vundo were researched as well.


A Fundamental Approach to Cyber Risk Analysis


title: A 70–80-GHz SiGe Amplifier With Peak Output Power of 27.3 dBm

abstract: This paper presents a fully integrated 16-way power-combining amplifier for 67–92-GHz applications in an advanced 90-nm silicon germanium HBT technology. The 16-way amplifier is implemented using three-stage common-emitter single-ended power amplifiers (PAs) as building blocks, and reactive $\lambda $ /4 impedance transformation networks are used for power combining. The three-stage single PA breakout has a small-signal gain of 22 dB at 74 GHz, and saturation output power ( $P_{\mathrm{ sat}}$ ) of 14.3–16.4 dBm at 68–99 GHz. The power-combining PA achieves a small-signal gain of 19.3 dB at 74 GHz, and $P_{\mathrm{ sat}}$ of 25.3–27.3 dBm at 68–88 GHz with a maximum power added efficiency of 12.4%. The 16-way amplifier occupies 6.48 mm2 (including pads) and consumes a maximum current of 2.1 A from a 1.8 V supply. To the best of our knowledge, this is the highest power silicon-based $E$ -band amplifier to date.


title: Dynamic learning rate using Mutual Information

abstract: This paper demonstrates dynamic hyper-parameter setting, for deep neural network training, using Mutual Information (MI). The specific hyper-parameter studied in this paper is the learning rate. MI between the output layer and true outcomes is used to dynamically set the learning rate of the network through the training cycle; the idea is also extended to layer-wise setting of learning rate. Two approaches are demonstrated - tracking relative change in mutual information and, additionally tracking its value relative to a reference measure. The paper does not attempt to recommend a specific learning rate policy. Experiments demonstrate that mutual information may be effectively used to dynamically set learning rate and achieve competitive to better outcomes in competitive to better time.