FELICS - Fair Evaluation of Lightweight Cryptographic Systems

In this paper we introduce FELICS, a free and open-source benchmarking framework designed for fair and consistent evaluation of software implementations of lightweight cryptographic primitives for embedded devices. The framework is very flexible thanks to its modular structure, which allows for an easy integration of new metrics, target devices and evaluation scenarios. It consists of two modules that can currently asses the performance of lightweight block and stream ciphers on three widely used microcontrollers: 8-bit AVR, 16-bit MSP and 32-bit ARM. The metrics extracted are execution time, RAM consumption and binary code size. FELICS has a simple user interface and is intended to be used by cipher designers to compare new primitives with the state of the art. The extracted metrics are very detailed and assist embedded software engineers in selecting the best cipher to match the requirements of a particular application. The tool aims to increase the transparency and trust in benchmarking results of lightweight primitives and facilitates a fair comparison between different primitives using the same evaluation conditions.


Block Cipher Speed and Energy Efficiency Records on the MSP430: System Design Trade-Offs for 16-bit Embedded Applications

Embedded microcontroller applications often experience multiple limiting constraints: memory, speed, and for a wide range of portable devices, power. Applications requiring encrypted data must simultaneously optimize the block cipher algorithm and implementation choice against these limitations. To this end we investigate block cipher implementations that are optimized for speed and energy efficiency, the primary metrics of devices such as the MSP430 where constrained memory resources nevertheless allow a range of implementation choices. The results set speed and energy efficiency records for the MSP430 device at 132 cycles/byte and 2.18 μJ/block for AES-128 and 103 cycles/byte and 1.44 μJ/block for equivalent block and key sizes using the lightweight block cipher SPECK. We provide a comprehensive analysis of size, speed, and energy consumption for 24 different variations of AES and 20 different variations of SPECK, to aid system designers of microcontroller platforms optimize the memory and energy usage of secure applications.


The LED Block Cipher

We present a new block cipher LED. While dedicated to compact hardware implementation, and offering the smallest silicon footprint among comparable block ciphers, the cipher has been designed to simultaneously tackle three additional goals. First, we explore the role of an ultra-light (in fact non-existent) key schedule. Second, we consider the resistance of ciphers, and LED in particular, to related-key attacks: we are able to derive simple yet interesting AES-like security proofs for LED regarding relatedor single-key attacks. And third, while we provide a block cipher that is very compact in hardware, we aim to maintain a reasonable performance profile for software implementation.


Linear (Hull) and Algebraic Cryptanalysis of the Block Cipher PRESENT

The contributions of this paper include the first linear hull and a revisit of the algebraic cryptanalysis of reduced-round variants of the block cipher PRESENT, under known-plaintext and ciphertextonly settings. We introduce a pure algebraic cryptanalysis of 5-round PRESENT and in one of our attacks we recover half of the bits of the key in less than three minutes using an ordinary desktop PC. The PRESENT block cipher is a design by Bogdanov et al., announced in CHES 2007 and aimed at RFID tags and sensor networks. For our linear attacks, we can attack 25-round PRESENT with the whole code book, 2 25round PRESENT encryptions, 2 blocks of memory and 0.61 success rate. Further we can extend the linear attack to 26-round with small success rate. As a further contribution of this paper we computed linear hulls in practice for the original PRESENT cipher, which corroborated and even improved on the predicted bias (and the corresponding attack complexities) of conventional linear relations based on a single linear


Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.


Towards a Multimedia Knowledge-Based Agent with Social Competence and Human Interaction Capabilities

We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.


Temporal multi-hierarchy smoothing for estimating rates of rare events

We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).


Security in the Internet of Things: A Survey on Application Layer Protocols

The rapid development of technology nowadays led people to a new and revolutionary concept, named the Internet of Things. This model imposes that all "objects", such as personal objects (smartphones, notebooks, smart watches, tablets etc), electronic equipment embed with sensors and other environmental elements are always connected to a common network. Therefore, one can access any resource at any time, by using a device recognized in the network. While the IoT may be economically and socially beneficial, the implementation of such a system poses many difficulties, risks and security issues that must be taken into consideration. Nowadays, the architecture of the Internet must be updated and rethought in order to interconnect trillions of devices and to ensure interoperability between them. Nevertheless, the most important problem is the security requirements of the IoT, which is probably one of the main reasons of the relatively slow development of this field. This paper presents the most important application layer protocols that are currently used in the IoT context: CoAP, MQTT, XMPP. We discuss them both separately and by comparison, with focus on the security provided by these protocols. Finally, we provide some future research opportunities and conclusions.


Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints

Environmental stresses (salinity, drought, heat/cold, light and other hostile conditions) may trigger in plants oxidative stress, generating the formation of reactive oxygen species (ROS). These species are partially reduced or activated derivatives of oxygen, comprising both free radical $$ ( {\text{O}}_{2}^{\cdot - } ,{\text{OH}} \cdot , {\text{OH}}_{ 2} \cdot ) $$ and non-radical (H2O2) forms, leading to cellular damage, metabolic disorders and senescence processes. In order to overcome oxidative stress, plants have developed two main antioxidants defense mechanisms that can be classified as non-enzymatic and enzymatic systems. The first class (non-enzymatic) consists of small molecules such as vitamin (A, C and E), glutathione, carotenoids and phenolics that can react directly with the ROS by scavenging them. Second class is represented by enzymes among them superoxide dismutase, peroxidase and catalase which have the capacity to eliminate superoxide and hydrogen peroxide. In this review, we have tried to explore the related works, which have revealed the changes in the basic antioxidant metabolism of plants under various abiotic constraints.


Image analysis of blood microscopic images for acute leukemia detection

Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3–7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.


Beats: Tapping Gestures for Smart Watches

Interacting with smartwatches poses new challenges. Although capable of displaying complex content, their extremely small screens poorly match many of the touchscreen interaction techniques dominant on larger mobile devices. Addressing this problem, this paper presents beating gestures, a novel form of input based on pairs of simultaneous or rapidly sequential and overlapping screen taps made by the index and middle finger of one hand. Distinguished simply by their temporal sequence and relative left/right position these gestures are designed explicitly for the very small screens (approx. 40mm square) of smartwatches and to operate without interfering with regular single touch input. This paper presents the design of beating gestures and a rigorous empirical study that characterizes how users perform them -- in a mean of 355ms and with an error rate of 5.5%. We also derive thresholds for reliably distinguishing between simultaneous (under 30ms) and sequential (under 400ms) pairs of screen touches or releases. We then present five interface designs and evaluate them in a qualitative study in which users report valuing the speed and ready availability of beating gestures.


A Game Theoretic Framework for Analyzing Re-Identification Risk

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.


The hidden treasure in nursing leadership: informal leaders.

AIM
The goal of the present article was to generate awareness of characteristics of informal leaders in healthcare with the emphasis on nurses in acute care settings. There is limited research or literature regarding informal leaders in nursing and how they positively impact nursing management, the organization and, ultimately, patient care. Identification of nurses with leadership characteristics is important so that leadership development and mentoring can occur within the nursing profession.

BACKGROUND
More than ever, nursing needs energetic, committed and dedicated leaders to meet the challenges of the healthcare climate and the nursing shortage. This requires nurse leaders to consider all avenues to ensure the ongoing profitability and viability of their healthcare facility.

KEY ISSUES
This paper discusses clinical nurses as informal leaders; characteristics of the informal nurse leader, the role they play, how they impact their unit and how they shape the organization.

IMPLICATION FOR NURSING MANAGEMENT
  Informal nurse leaders are an underutilized asset in health care. If identified early, these nurses can be developed and empowered to impact unit performance, efficiency and environmental culture in a positive manner.


An Algorithm for image stitching and blending

In many clinical studies, including those of cancer, it is highly desirable to acquire images of whole tumour sections whilst retaining a microscopic resolution. A usual approach to this is to create a composite image by appropriately overlapping individual images acquired at high magnification under a microscope. A mosaic of these images can be accurately formed by applying image registration, overlap removal and blending techniques. We describe an optimised, automated, fast and reliable method for both image joining and blending. These algorithms can be applied to most types of light microscopy imaging. Examples from histology, from in vivo vascular imaging and from fluorescence applications are shown, both in 2D and 3D. The algorithms are robust to the varying image overlap of a manually moved stage, though examples of composite images acquired both with manually-driven and computer-controlled stages are presented. The overlap-removal algorithm is based on the cross-correlation method; this is used to determine and select the best correlation point between any new image and the previous composite image. A complementary image blending algorithm, based on a gradient method, is used to eliminate sharp intensity changes at the image joins, thus gradually blending one image onto the adjacent ‘composite’. The details of the algorithm to overcome both intensity discrepancies and geometric misalignments between the stitched images will be presented and illustrated with several examples.


INTELLIGENT SYSTEMS IN TOURISM A Social Science Perspective

Intelligent systems sense their environment and learn from the actions they implement to reach specific goals. They are increasingly used to support tourist information search and decision making as well as work processes. In order to model the tourism domain, these systems require a profound understanding of its nature. Looking at existing literature in tourism, this paper discusses critical gaps in the knowledge of the field to be filled so that intelligent system design can be informed and impacts understood. Specifically, it discusses the need to better conceptualize technology in tourism research and argues for a focus on uses and interactions. It challenges simplistic views of tourist information search and decisionmaking processes and calls for more research on potential impacts.


Broadband Capacitively Coupled Stacked Patch Antenna for GNSS Applications

A broadband stacked circular patch antenna is presented in this letter. The broadband characteristic is achieved by employing capacitively coupled feed structure. The antenna is fed by a wideband four-output-ports feed network with equal magnitude and consistent 90° phase shift. The final antenna provides very good circularly polarized radiation for Global Navigation Satellite System applications including GPS, GLONASS, Galileo, and Compass.


Secondary confessions: the influence (or lack thereof) of incentive size and scientific expert testimony on jurors' perceptions of informant testimony.

The goal of this research was to determine whether the size of the incentive (none, small, medium, or large, in terms of sentence reduction) a jailhouse informant receives for testifying, as well as scientific expert testimony regarding the fundamental attribution error, would influence mock juror decision-making in a criminal trial involving a secondary confession. Participants read a murder trial transcript involving informant testimony in which incentive size and expert testimony were manipulated and then provided verdict judgments, made attributions for the informant's decision to testify, and rated the informant and expert on a number of dimensions. Neither expert testimony nor size of incentive had a direct influence on verdicts. However, contrary to previous research on the influence of incentives on jurors' perceptions of secondary confessions, the presence of an incentive did influence verdict decisions, informant ratings, and attributional responses. Results imply that jury-eligible community members may be becoming aware of the issues with informant testimony as a function of incentive but that they are insensitive to the size of the incentive, and expert testimony may not sensitize them to the limitations of such testimony.


Variational Recurrent Neural Networks for Speech Separation

We present a new stochastic learning machine for speech separation based on the variational recurrent neural network (VRNN). This VRNN is constructed from the perspectives of generative stochastic network and variational auto-encoder. The idea is to faithfully characterize the randomness of hidden state of a recurrent neural network through variational learning. The neural parameters under this latent variable model are estimated by maximizing the variational lower bound of log marginal likelihood. An inference network driven by the variational distribution is trained from a set of mixed signals and the associated source targets. A novel supervised VRNN is developed for speech separation. The proposed VRNN provides a stochastic point of view which accommodates the uncertainty in hidden states and facilitates the analysis of model construction. The masking function is further employed in network outputs for speech separation. The benefit of using VRNN is demonstrated by the experiments on monaural speech separation.


Physiological-based emotion recognition with IRS model

A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.


Conceptual Integration of Enterprise Architecture Management and Security Risk Management

Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.


Particle Belief Propagation

The popularity of particle filtering for inference in Markov chain models defined over random variables with very large or continuous domains makes it natural to consider sample–based versions of belief propagation (BP) for more general (tree–structured or loopy) graphs. Already, several such algorithms have been proposed in the literature. However, many questions remain open about the behavior of particle–based BP algorithms, and little theory has been developed to analyze their performance. In this paper, we describe a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods. We prove that this algorithm is consistent, approaching the true BP messages as the number of samples grows large. We then use concentration bounds to analyze the finitesample behavior and give a convergence rate for the algorithm on tree–structured graphs. Our convergence rate is O(1/ √ n) where n is the number of samples, independent of the domain size of the variables.


A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation

In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarseto-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). Moreover, the worst case of DSC on the NIH dataset was improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.


Changes in excitability of the cortical projections to the human tibialis anterior after paired associative stimulation.

Paired associative stimulation (PAS) based on Hebb's law of association can induce plastic changes in the intact human. The optimal interstimulus interval (ISI) between the peripheral nerve and transcranial magnetic stimulus is not known for muscles of the lower leg. The aims of this study were to investigate the effect of PAS for a variety of ISIs and to explore the efficacy of PAS when applied during dynamic activation of the target muscle. PAS was applied at 0.2 Hz for 30 min with the tibialis anterior (TA) at rest. The ISI was varied randomly in seven sessions (n = 5). Subsequently, PAS was applied (n = 14, ISI = 55 ms) with the TA relaxed or dorsi-flexing. Finally, an optimized ISI based on the subject somatosensory evoked potential (SEP) latency plus a central processing delay (6 ms) was used (n = 13). Motor-evoked potentials (MEPs) were elicited in the TA before and after the intervention, and the size of the TA MEP was extracted. ISIs of 45, 50, and 55 ms increased and 40 ms decreased TA MEP significantly (P = 0.01). PAS during dorsi-flexion increased TA MEP size by 92% (P = 0.001). PAS delivered at rest resulted in a nonsignificant increase; however, when the ISI was optimized from SEP latency recordings, all subjects showed significant increases (P = 0.002). No changes in MEP size occurred in the antagonist. Results confirm that the excitability of the corticospinal projections to the TA but not the antagonist can be increased after PAS. This is strongly dependent on the individualized ISI and on the activation state of the muscle.


Characterization of Polymeric Microneedle Arrays for Transdermal Drug Delivery

Microfabrication of dissolvable, swellable, and biodegradable polymeric microneedle arrays (MNs) were extensively investigated based in a nano sensitive fabrication style known as micromilling that is then combined with conventional micromolding technique. The aim of this study was to describe the polymer selection, and optimize formulation compounding parameters for various polymeric MNs. Inverse replication of micromilled master MNs reproduced with polydimethylsiloxane (PDMS), where solid out of plane polymeric MNs were subsequently assembled, and physicochemically characterized. Dissolvable, swellable, and biodegradable MNs were constructed to depth of less than 1 mm with an aspect ratio of 3.6, and 1/2 mm of both inter needle tip and base spacing. Micromolding step also enabled to replicate the MNs very precisely and accurate. Polymeric microneedles (MN) precision was ranging from ± 0.18 to ± 1.82% for microneedle height, ± 0.45 to ± 1.42% for base diameter, and ± 0.22 to ± 0.95% for interbase spacing. Although dissolvable sodium alginate MN showed less physical robustness than biodegradable polylactic-co-glycolic acid MN, their thermogravimetric analysis is of promise for constructing these polymeric types of matrix devices.


Mobile robot navigation based on lidar

A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.


Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control

This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30~70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.


SystemT: a system for declarative information extraction

As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.


A Fractional-N Sub-Sampling PLL using a Pipelined Phase-Interpolator With an FoM of -250 dB

A fractional-N sub-sampling PLL architecture based on pipelined phase-interpolator and Digital-to-Time-Converter (DTC) is presented in this paper. The combination of pipelined phase-interpolator and DTC enables efficient design of the multi-phase generation mechanism required for the fractional operation. This technique can be used for designing a fractional-N PLL with low in-band phase noise and low spurious tones with low power consumption. The short-current-free pipelined phase-interpolator used in this work is capable of achieving high-linearity with low-power while minimizing the intrinsic jitter. A number of other circuit techniques and layout techniques are also employed in this design for ensuring high-performance operation with minimal chip area and power consumption. The proposed fractional-N PLL is implemented in standard 65 nm CMOS technology. The PLL has an operating range of 600 MHz from 4.34 GHz to 4.94 GHz. In fractional-N mode, the proposed PLL achieves -249.5 dB FoM and less than -59 dBc fractional spurs.


Structural Design of Convolutional Neural Networks for Steganalysis

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.


Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection

In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.


A Low power and area efficient CLA adder design using Full swing GDI technique

The low power VLSI design has an important role in designing of many electronic systems. While designing any combinational or sequential circuits, the important parameters like power consumption, implementation area, voltage leakage and performance of the circuit are to be considered. Design of area, high speed and powerefficient data path logic systems forms the largest areas of research in VLSI system design. This paper presents a low power Carry look ahead adder design using Full swing Gate diffusion (FS-GDI) technique. The proposed CLA implementation utilizes improved full-swing GDI F1 and F2 gates, which are the counterparts of standard CMOS NAND and NOR gates. The basic Gate Diffusion Input (GDI) logic style suffers from some practical limitations like swing degradation, fabrication complexity in standard CMOS process and bulk connections. These limitations can be overcome by Full swing GDI technique. The proposed technique utilizes a single swing restoration (SR) transistor to improve the output swing of F1 and F2 GDI gates. A 16-bit CLA is designed and Simulations are performed by Mentor graphics 130nm CMOS technology ELDO simulator. Simulation results have shown a greater reduction in delay, power dissipation and area.


A 300 nW , 15 ppm / C , 20 ppm / V CMOS Voltage Reference Circuit Consisting of Subthreshold MOSFETs

A low-power CMOS voltage reference was developed using a 0.35 m standard CMOS process technology. The device consists of MOSFET circuits operated in the subthreshold region and uses no resistors. It generates two voltages having opposite temperature coefficients and adds them to produce an output voltage with a near-zero temperature coefficient. The resulting voltage is equal to the extrapolated threshold voltage of a MOSFET at absolute zero temperature, which was about 745 mV for the MOSFETs we used. The temperature coefficient of the voltage was 7 ppm/ C at best and 15 ppm/ C on average, in a range from 20 to 80 C. The line sensitivity was 20 ppm/V in a supply voltage range of 1.4–3 V, and the power supply rejection ratio (PSRR) was 45 dB at 100 Hz. The power dissipation was 0.3 W at 80 C. The chip area was 0.05 mm . Our device would be suitable for use in subthreshold-operated, power-aware LSIs.


Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

In this paper, we present a unied end-to-end approach to build a large scale Visual Search and Recommendation system for ecommerce. Previous works have targeted these problems in isolation. We believe a more eective and elegant solution could be obtained by tackling them together. We propose a unied Deep Convolutional Neural Network architecture, called VisNet 1, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop [14] dataset. We then share the design decisions and trade-os made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India’s largest e-commerce company. e deployment of our solution has yielded a signicant business impact, as measured by the conversion-rate.


CMOS Amperometric Instrumentation and Packaging for Biosensor Array Applications

An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.


Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator

Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.


A five level cascaded H-bridge inverter based on space vector pulse width modulation technique

Multilevel inverters brought a tremendous revolution in high power superior performance industrial drive applications. Minimizing harmonic distortion is a challenging issue for power electronic drive. This problem can be solved by developing suitable modulation technique. There are number of modulation techniques established in the last decade. Among them, the space vector pulse width modulation (SVPWM) is extensively used because of their easier digital realization, better DC bus utilization and lower THD. Conventional SVPWM requires different number of logical computations of on time calculations and different number of lookup table for each triangle due to having their different number of switching states. In this paper, a simple SVPWM is presented where proposed only four logical computations of on time calculation, four active switching states and four lookup table for each triangle. Five level cascaded h-bridge inverter (CHBI) based on SVPWM technique are modeled and analyzed by using MATLAB/Simulink and origin 6.1 with a passive R-L load that can be extended to any level. These results are compared with other lower levels of inverter and with previous established results. From these results, five level CHBI based on SVPWM shows better performance compared to others in terms of THD.


TA-COS 2018 : 2 nd Workshop on Text Analytics for Cybersecurity and Online Safety

In this study, we present toxicity annotation for a Thai Twitter Corpus as a preliminary exploration for toxicity analysis in the Thai language. We construct a Thai toxic word dictionary and select 3,300 tweets for annotation using the 44 keywords from our dictionary. We obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing target, and word sense ambiguity. Finally, we conducted supervised classification using our corpus as a dataset and obtained an accuracy of 0.80, which is comparable with the inter-annotator agreement of this dataset. Our dataset is available on GitHub.


Correlation Hashing Network for Efficient Cross-Modal Retrieval

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which improves the quality of hash coding by exploiting the semantic correlation across different modalities, has received increasing attention recently. For most existing cross-modal hashing methods, an object is first represented as of vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Correlation Hashing Network (CHN) architecture for cross-modal hashing, in which we jointly learn good data representation tailored to hash coding and formally control the quantization error. The CHN model is a hybrid deep architecture constituting four key components: (1) an image network with multiple convolution-pooling layers to extract good image representations, and a text network with several fully-connected layers to extract good text representations; (2) a fully-connected hashing layer to generate modality-specific compact hash codes; (3) a squared cosine loss layer for capturing both cross-modal correlation and within-modal correlation; and (4) a new cosine quantization loss for controlling the quality of the binarized hash codes. Extensive experiments on standard cross-modal retrieval datasets show the proposed CHN model yields substantial boosts over latest state-of-the-art hashing methods.


ontoAGA: Ontology to Support Educational Systems Interoperability

Looking forward to promote the exchange and integration of information in order to build a unique reference model for the universities it is essencial to assure commom concepts of higher education institutions in Brazil. This paper proposes an University Support Management Ontology, named ontoAGA, to help the operation of legacy data in Semantic Web in a standardized manner and to allow the integration of databases with different technologies.To evaluate the research questions two usages cenarios were defined and the results point to the proposal viability. Resumo. Para promover o intercâmbio e a integração de informações de forma a se construir um modelo de referência para as universidades há necessidade de se estruturar conceitos comuns às instituições de ensino superior no Brasil. Este artigo propõe uma ontologia, no domínio da gestão acadêmica nas universidades, denominada ontoAGA, para ajudar a exploração de dados legados na web Semântica de forma padronizada e para possibilitar a integração de bases de dados de diferentes tecnologias. Para avaliação das questões de pesquisa foram definidos dois cenários de uso, cujos resultados apontam para a viabilidade da proposta.


Safer smart contracts through type-driven development

We show how dependent and polymorphic types can make smart contract development safer. This is demonstrated by using the functional language Idris to describe smart contracts on the Ethereum platform. In particular, we show how one class of common errors can be captured at compile time using dependent types and algebraic side effects. We also bring type annotations to the realm of smart contracts, helping developers to circumvent another class of common errors. To demonstrate the feasibility of our solutions, we have extended the Idris compiler with a backend for the Ethereum Virtual Machine. While we find that the functional paradigm might not be the most suitable for the domain, our approach solves the identified problems and provides advantages over the languages in current use.


Complexities in Internet peering: Understanding the “Black” in the “Black Art”

Peering in the Internet interdomain network has long been considered a “black art”, understood in-depth only by a select few peering experts while the majority of the network operator community only scratches the surface employing conventional rules-of-thumb to form peering links through ad hoc personal interactions. Why is peering considered a black art? What are the main sources of complexity in identifying potential peers, negotiating a stable peering relationship, and utility optimization through peering? How do contemporary operational practices approach these problems? In this work we address these questions for Tier-2 Network Service Providers. We identify and explore three major sources of complexity in peering: (a) inability to predict traffic flows prior to link formation (b) inability to predict economic utility owing to a complex transit and peering pricing structure (c) computational infeasibility of identifying the optimal set of peers because of the network structure. We show that framing optimal peer selection as a formal optimization problem and solving it is rendered infeasible by the nature of these problems. Our results for traffic complexity show that 15% NSPs lose some fraction of customer traffic after peering. Additionally, our results for economic complexity show that 15% NSPs lose utility after peering, approximately, 50% NSPs end up with higher cumulative costs with peering than transit only, and only 10% NSPs get paid-peering customers.


Learning from mistakes: towards a correctable learning algorithm

Many learning algorithms generate complex models that are difficult for a human to interpret, debug, and extend. In this paper, we address this challenge by proposing a new learning paradigm called correctable learning, where the learning algorithm receives external feedback about which data examples are incorrectly learned. We define a set of metrics which measure the correctability of a learning algorithm. We then propose a simple and efficient correctable learning algorithm which learns local models for different regions of the data space. Given an incorrect example, our method samples data in the neighborhood of that example and learns a new, more correct local model over that region. Experiments over multiple classification and ranking datasets show that our correctable learning algorithm offers significant improvements over the state-of-the-art techniques.


Wrapper Induction for Information Extraction

Many Internet information resources present relational data|telephone directories, product catalogs, etc. Because these sites are formatted for people, mechanically extracting their content is di cult. Systems using such resources typically use hand-coded wrappers, procedures to extract data from information resources. We introduce wrapper induction, a method for automatically constructing wrappers, and identify hlrt, a wrapper class that is e ciently learnable, yet expressive enough to handle 48% of a recently surveyed sample of Internet resources. We use PAC analysis to bound the problem's sample complexity, and show that the system degrades gracefully with imperfect labeling knowledge.


Achieving Secure, Universal, and Fine-Grained Query Results Verification for Secure Search Scheme over Encrypted Cloud Data

Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object. Performance evaluation shows that the proposed schemes are practical and efficient.


Collective Opinion Target Extraction in Chinese Microblogs

Microblog messages pose severe challenges for current sentiment analysis techniques due to some inherent characteristics such as the length limit and informal writing style. In this paper, we study the problem of extracting opinion targets of Chinese microblog messages. Such fine-grained word-level task has not been well investigated in microblogs yet. We propose an unsupervised label propagation algorithm to address the problem. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. Topics in microblogs are identified by hashtags or using clustering algorithms. Experimental results on Chinese microblogs show the effectiveness of our framework and algorithms.


Voting models in random networks

A crucial problem of Social Sciences is under what conditions agreement, or disagreement, emerge in a network of interacting agents. This topic has application in many contexts, including business and marketing decisions, with potential impact on information and technological networks. In this paper we consider a particular model of interaction between a group of individuals connected through a network of acquaintances. In the first model, a node waits an exponentially time with parameter one, and when it expires it chooses one of its neighbors' at random and adopts its decision. In the second one, the node chooses the opinion which is the most adopted by its neighbors (hence, majority rule). We show how different updating rule of the agent' state lead to different emerging patterns, namely, agreement and disagreement. In addition, in the case of agreement, we provide bounds on the time to convergence for various types of graphs.


Evolving Graphs and Networks with Edge Encoding: Preliminary Report

We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental investigation of the relative merits of these encoding schemes is currently in progress.


EMD-L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors

We propose a fast algorithm, EMD-L1, for computing the Earth Mover’s Distance (EMD) between a pair of histograms. Compared to the original formulation, EMD-L1 has a largely simplified structure. The number of unknown variables in EMD-L1 is O(N) that is significantly less than O(N) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function is also simplified. We prove that the EMD-L1 is formally equivalent to the original EMD with L1 ground distance without approximation. Exploiting the L1 metric structure, an efficient tree-based algorithm is designed to solve the EMD-L1 computation. An empirical study demonstrates that the new algorithm has the time complexity of O(N), which is much faster than previously reported algorithms with super-cubic complexities. The proposed algorithm thus allows the EMD to be applied for comparing histogram-based features, which is practically impossible with previous algorithms. We conducted experiments for shape recognition and interest point matching. EMD-L1 is applied to compare shape contexts on the widely tested MPEG7 shape dataset and SIFT image descriptors on a set of images with large deformation, illumination change and heavy noise. The results show that our EMD-L1based solutions outperform previously reported state-of-the-art features and distance measures in solving the two tasks.


Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks

Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justi es some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then di erentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite e ectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.


Robust Real-time Object Detection

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second.


Modeling of a Vertical Axis Wind Turbine with Permanent Magnet Synchronous Generator for Nigeria

The performances of a vertical axis wind turbine (VAWT) with permanent magnet synchronous generator (PMSG) under low and unsteady wind speed is investigated and Modelled in this paper. The ever increasing demand for electrical energy has led to the continuous search for the most readily available means of providing electricity. Wind turbine is an alternative source of electricity. To implement a wind turbine, careful and extensive investigation is required since different environments or geographical locations call for different wind speed. Nigeria is a typical example of locations with low and unsteady wind speed. The wind turbine system consists of three main parts; the wind speed, the turbine and the generator. A three phase PMSG is chosen in this work due to its higher efficiency and less need for maintenance when compared to other types of generators. It does not require rotor windings which imply a reduction of weight and cost. These elements and entire idea of this work have been modelled and simulated using MATLAB/ SIMULINK. Results obtained showed good system performance. The methodology used in this work, if fully implemented, will not only reduce the cost of design but will also reduce the cost of maintenance, making it economical and affordable.


PUF-FSM: A Controlled Strong PUF

Existing strong controlled physical unclonable function (PUF) designs are built to resist modeling attacks and they deal with noisy PUF responses by exploiting error correction logic. These designs are burdened by the costs of the error correction logic and information shown to leak through the associated helper data for assisting error corrections; leaving the design vulnerable to fault attacks or reliability-based attacks. We present a hybrid PUF–finite state machine (PUF-FSM) construction to realize a controlled strong PUF. The PUF-FSM design removes the need for error correction logic and related computation, storage of the helper data and loading it on-chip by only employing error-free responses judiciously determined on demand in the absence of the underlying PUF—an Arbiter PUF—with a large challenge response pair space. The PUF-FSM demonstrates improved security, especially to reliability-based attacks and is able to support a range of applications from authentication to more advanced cryptographic applications built upon shared keys. We experimentally validate the practicability of the PUF-FSM.


A Machine Learning Based Intrusion Detection System for Software Defined 5G Network

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing centralized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function modules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance.


Medusa: Simplified Graph Processing on GPUs

Graphs are common data structures for many applications, and efficient graph processing is a must for application performance. Recently, the graphics processing unit (GPU) has been adopted to accelerate various graph processing algorithms such as BFS and shortest paths. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To simplify graph processing on GPUs, we propose a programming framework called Medusa which enables developers to leverage the capabilities of GPUs by writing sequential C/C++ code. Medusa offers a small set of user-defined APIs and embraces a runtime system to automatically execute those APIs in parallel on the GPU. We develop a series of graph-centric optimizations based on the architecture features of GPUs for efficiency. Additionally, Medusa is extended to execute on multiple GPUs within a machine. Our experiments show that 1) Medusa greatly simplifies implementation of GPGPU programs for graph processing, with many fewer lines of source code written by developers and 2) the optimization techniques significantly improve the performance of the runtime system, making its performance comparable with or better than manually tuned GPU graph operations.


Efficacy of Topical Combination of 0.25% Finasteride and 3% Minoxidil Versus 3% Minoxidil Solution in Female Pattern Hair Loss: A Randomized, Double-Blind, Controlled Study.

BACKGROUND
The relationship between female pattern hair loss (FPHL) and androgenic hormones is not well established, but some evidence indicates oral finasteride may be efficacious in FPHL. Use of a topical formulation has been proposed to minimize unwanted effects.

OBJECTIVES
Our objective was to compare the efficacy and safety of topical 0.25% finasteride combined with 3% minoxidil solution and 3% minoxidil solution as monotherapy in the treatment of FPHL.

METHODS
This was a prospective, randomized, double-blind study in 30 postmenopausal women with FPHL. Each participant was randomized to receive either topical 0.25% finasteride combined with topical 3% minoxidil or topical 3% minoxidil solution as monotherapy for 24 weeks. To determine efficacy, the hair density and diameter was measured and global photographic assessment was conducted at baseline and 8, 16, and 24 weeks. Side effects and serum dihydrotestosterone levels were also evaluated.

RESULTS
By 24 weeks, hair density and diameter had increased in both groups, and finasteride/minoxidil was significantly superior to minoxidil solution in terms of hair diameter (p = 0.039). No systemic side effects were reported. However, serum dihydrotestosterone levels in the finasteride/minoxidil group significantly decreased from baseline (p = 0.016).

CONCLUSION
A topical combination of 0.25% finasteride and 3% minoxidil may be a promising option in the treatment of FPHL with an additional benefit of increasing hair diameter. Nevertheless, as it may be absorbed percutaneously, it should be reserved for postmenopausal women.

TRIAL REGISTRATION
clinicaltrials.in.th; identifier TCTR20160912002.


Exercise treatment for major depression: maintenance of therapeutic benefit at 10 months.

OBJECTIVE
The purpose of this study was to assess the status of 156 adult volunteers with major depressive disorder (MDD) 6 months after completion of a study in which they were randomly assigned to a 4-month course of aerobic exercise, sertraline therapy, or a combination of exercise and sertraline.

METHODS
The presence and severity of depression were assessed by clinical interview using the Diagnostic Interview Schedule and the Hamilton Rating Scale for Depression (HRSD) and by self-report using the Beck Depression Inventory. Assessments were performed at baseline, after 4 months of treatment, and 6 months after treatment was concluded (ie, after 10 months).

RESULTS
After 4 months patients in all three groups exhibited significant improvement; the proportion of remitted participants (ie, those who no longer met diagnostic criteria for MDD and had an HRSD score <8) was comparable across the three treatment conditions. After 10 months, however, remitted subjects in the exercise group had significantly lower relapse rates (p = .01) than subjects in the medication group. Exercising on one's own during the follow-up period was associated with a reduced probability of depression diagnosis at the end of that period (odds ratio = 0.49, p = .0009).

CONCLUSIONS
Among individuals with MDD, exercise therapy is feasible and is associated with significant therapeutic benefit, especially if exercise is continued over time.


Hardware Intrinsic Security from Physically Unclonable Functions

Counterfeiting of goods in general and of electronic goods in particular is a growing concern with a huge impact on the global economy, the society, and the security of its critical infrastructure. Various examples are known where companies suffer from economic and brand damage due to competition with counterfeit goods. In some cases the use of counterfeit components has even led to tragic accidents in which lives were lost. It has also recently become clear that counterfeit products can penetrate the critical and security infrastructure of our modern societies and hence cause a threat to national security. One of the difficulties to deal with this problem stems from the fact that counterfeit goods can originate from sources that are able to make copies that are very hard to distinguish from their legitimate counterpart. A first well-known aspect of counterfeiting is product cloning. A second much less known but increasingly dangerous aspect consists of overproduction of goods. A special, but modern, case of counterfeiting is theft of Intellectual Property such as software and designs. The attractive part from the attackers’ point of view is that it is relatively easy to steal and has a high value without having to do huge investments in research and development. From a high-level point of view one can state that the attack can be thwarted by using encryption and authentication techniques. Device configuration data or embedded software can, for example, be encrypted such that it will only run on the device possessing the correct cryptographic key. Since encrypted data is still easy to copy, it now becomes essential that the secret key is well protected against copying or cloning. In order to deal with these two aspects of counterfeiting, a secret unclonable identifier is required together with strong cryptographic protocols. In this chapter we focus on a new way to address these problems: Hardware Intrinsic Security. It is based on the implementation and generation of secret physically unclonable identifiers used in conjunction with cryptographic techniques such as encryption and


Development and Applications of CRISPR-Cas9 for Genome Engineering

Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of mammalian genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review, we describe the development and applications of Cas9 for a variety of research or translational applications while highlighting challenges as well as future directions. Derived from a remarkable microbial defense system, Cas9 is driving innovative applications from basic biology to biotechnology and medicine.


A waveguide slot array antenna with integrated T-shaped filters in the corporate-feed circuit

In this paper, we demonstrate a waveguide slot array antenna by integration of T-shaped filters using the diffusion bonding of laminated thin metal plates. The excellent performance of the T-shaped filter has been experimentally verified by the in-band transmission and the out-of-band rejection of the fabricated antenna.


Information overload: why some people seem to suffer more than others

We studied information overload among senior managers in an industrial company. We used the critical incident collection technique to gather specific examples of information overload and coping strategies. We then used textual interpretation and the affinity diagram technique to interpret the interviews and to categorize our respondents, the critical incidents they described, and the coping strategies they mentioned. Our results show that the extent to which people suffer from information overload is closely related to the strategies they use to deal with it.


Do you care if a computer says sorry?: user experience design through affective messages

While traditional HCI research emphasizes usability based on models of cognition, user experience (UX) focuses on affect and emotion through the provision of positive interactive experiences. Providing affective cues, such as apologetic on-screen display messages, appears to be a way to influence users' affective states as well as their perceptions toward an information retrieval system. A study was designed to determine whether users' affect and perceptions differ between three types of systems: neutral, apologetic, and non-apologetic. Our results revealed that the users perceived the apologetic system as more aesthetically appealing and usable than the neutral or non-apologetic system. The result also showed that users' frustration was the lowest when using the apologetic system. We discuss the implications of these results in designing a more experience-centered system.


Parsing sewing patterns into 3D garments

We present techniques for automatically parsing existing sewing patterns and converting them into 3D garment models. Our parser takes a sewing pattern in PDF format as input and starts by extracting the set of panels and styling elements (e.g. darts, pleats and hemlines) contained in the pattern. It then applies a combination of machine learning and integer programming to infer how the panels must be stitched together to form the garment. Our system includes an interactive garment simulator that takes the parsed result and generates the corresponding 3D model. Our fully automatic approach correctly parses 68% of the sewing patterns in our collection. Most of the remaining patterns contain only a few errors that can be quickly corrected within the garment simulator. Finally we present two applications that take advantage of our collection of parsed sewing patterns. Our garment hybrids application lets users smoothly interpolate multiple garments in the 2D space of patterns. Our sketch-based search application allows users to navigate the pattern collection by drawing the shape of panels.


Easy Questions First? A Case Study on Curriculum Learning for Question Answering

Cognitive science researchers have emphasized the importance of ordering a complex task into a sequence of easy to hard problems. Such an ordering provides an easier path to learning and increases the speed of acquisition of the task compared to conventional learning. Recent works in machine learning have explored a curriculum learning approach called selfpaced learning which orders data samples on the easiness scale so that easy samples can be introduced to the learning algorithm first and harder samples can be introduced successively. We introduce a number of heuristics that improve upon selfpaced learning. Then, we argue that incorporating easy, yet, a diverse set of samples can further improve learning. We compare these curriculum learning proposals in the context of four non-convex models for QA and show that they lead to real improvements in each of them.


Improving additive manufacturing by image processing and robotic milling

This paper describes an improvement of Additive Manufacturing (AM) to process products with complex inner and outer geometries such as turbine blades by using a combination of AM, optical measuring techniques and robotics. One problem of AM is the rough surface caused by the layer-by-layer 3D printing with granular material, so the geometries need to be post-processed with milling techniques. To overcome this problem, we implement an inline quality control management to post-process several manufactured layers by a integrated imaging and robotic techniques, especially while the geometries are still accessible. The produced surfaces are measured by an inline stereo vision system and are compared with the real 3D CAD model data. Detected differences are translated to movements of a robot arm which then follows the contours to smoothen the surfaces with a milling tool. We show our current state on the 3D imaging, on developing the driver for the deployed robot arm and on a 3D simulation using the Robot Operating System (ROS).


Intelligent Detection System for e-banking Phishing websites using Fuzzy Data Mining

Detecting and identifying e-banking Phishing websites is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining Techniques can be an effective tool in assessing and identifying e-banking phishing websites since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the „fuzziness‟ in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria‟s with a layer structure. A Case study was applied to illustrate and simulate the phishing process. Our experimental results showed the significance and importance of the e-banking phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final e-banking phishing website rate. KeywordsPhishing, Fuzzy Logic, Data Mining, Classification, association, apriori, e-banking risk assessment


Watermarking, Tamper-Proofing, and Obfuscation-Tools for Software Protection

We identify three types of attack on the intellectual property contained in software, and three corresponding technical defenses. A potent defense against reverse engineering is obfuscation, a process that renders software unintelligible but still functional. A defense against software piracy is watermarking, a process that makes it possible to determine the origin of software. A defense against tampering is tamper-proofing, so that unauthorized modifications to software (for example to remove a watermark) will result in non-functional code. We briefly survey the available technology for each type of defense.


A multidisciplinary approach to 3D survey and reconstruction of historical buildings

The aim of this research is to suggest a methodology based on 3D survey and reconstructive modeling, suitable to increase the actual knowledge of an historical building and supporting its historical interpretation. The case study used for testing the proposed methodology is the huge Chartreuse of Pavia, with a special focus on a relatively unexplored portion of the monument. The survey, based on 3D laser scanning and orthoimages, integrated by historical studies and other complementary information (thermoluminescence dating, IR imaging, hystorical analysis), allowed to read all the architectural aspects hidden in this highly architecturally stratified monument, improving in this way the comprehension of the building's transformations in time. A 3D reconstruction approach was then suggested, merging several information of different nature, from the actual geometry of the building to the interpretation of historical documents, suggesting a sequence of diachronic models as virtual narration of the historical evolution. On other hand the 3D models were used to obtain a cross-validation of the historical evolution hypotheses developed by experts in the various disciplines involved in the project. The data collected were exploited through a web portal in order to enhance the readability of tangible and intangible heritage associated to that Chartreuse portion, nowadays not accessible to common public.


Design and Validation of Computer Protocols

This report aims at giving a short overview on rank function analysis of authentication protocols concentrating on the results of Schneider in [4] and Heather in [2]. Therefore it shows a standard form of protocol implementation in CSP, shows how authentication properties can be captured as trace specifications, gives basic definitions necessary to reason about the unbounded set of messages, reformulates the central rank function theorem and restates it proof. Additionally it gives hints on applying this approach to verify secrecy protocols as well, a short overview on tool support available and some hints on interesting properties of rank functions. This report will use notations introduced in earlier reports [11-16] throughout and only reintroduce notations where they are considered vital.


Entry into Platform-Based Markets ∗

We develop a theoretical model to examine the relative importance of platform quality, indirect network effects, and consumer expectations on the success of entrants in platform-based markets. We find that an entrant’s success depends on the strength of indirect network effects and on the consumers’ discount factor for future applications. We illustrate the model’s applicability by examining Xbox’s entry into the video game industry. We find that Xbox had a small quality advantage over the incumbent, PlayStation 2, and the strength of indirect network effects and the consumers’ discount factor, while statistically significant, fall in the region where PlayStation 2’s position is unsustainable.


Learning Styles and Learning Spaces : A Review of the Multidisciplinary Application of Experiential Learning Theory in Higher Education

Using the concept of learning style and learning space to describe the interface between learners and the learning environment in higher education, in this chapter we review studies addressing how learning style information and the experiential learning model have been applied to improve teaching and learning in sixteen different academic fields and professions. Studies suggest that experiential learning affords educators a way to design and implement teaching and learning strategies conducive to creating a learning environment beneficial for both faculty and students with possibilities for institutional wide dissemination of its core principles and practices. Drawing from the findings generated from the studies, we propose guiding principles for creating growth promoting learning spaces throughout higher education.


A First Look at Music Composition using LSTM Recurrent Neural Networks

In general music composed by recurrent neural networks (RNNs) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, attempts at learning an entire musical form and using that knowledge to guide composition have been unsuccessful. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing & counting and CSL learning. In the current study I show that LSTM is also a good mechanism for learning to compose music. I compare this approach to previous attempts, with particular focus on issues of data representation. I present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and I believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen.


Stock Trend Prediction Using Regression Analysis – A Data Mining Approach

Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.


Dual-Band Loop-Dipole Composite Unidirectional Antenna for Broadband Wireless Communications

The design of a novel dual-band loop-dipole composite unidirectional antenna for broadband wireless applications is proposed. First, design guidelines of the antenna are presented, and then, a step-by-step derivation of the antenna geometry is drawn. The antenna's operation principle is analyzed by carrying out parametric studies of surface current distribution, impedance and radiation patterns. The reflection coefficient, radiation patterns and gain of the antenna are numerically and experimentally studied to verify the operation principle and the design approach. It is demonstrated that two pass-bands, from 1.54 to 3.24 and from 4.88 to 6.80 GHz, of reflection coefficient smaller than -10 dB are obtained. The antenna has stable and unidirectional radiation pattern with low cross polarization within its pass-bands. The average gain of the antenna is 6.7 dBi at low frequency band and 6.5 dBi at high frequency band.


Fundamental Limitations of Small Antennas

A capacitor or inductor operating as a small antenna is theoretically capable of intercepting a certain amount of power, independent of its size, on the assumption of tuning without circuit loss. The practical efficiency relative to this ideal is limited by the "radiation power factor" of the antenna as compared with the power factor and bandwidth of the antenna tuning. The radiation power factor of either kind of antenna is somewhat greater than (1/6π) (Ab/l2) in which Ab is the cylindrical volume occupied by the antenna, and l is the radianlength (defined as 1/2π wavelength) at the operating frequency. The efficiency is further limited by the closeness of coupling of the antenna with its tuner. Other simple formulas are given for the more fundamental properties of small antennas and their behavior in a simple circuit. Examples for 1-Mc. operation in typical circuits indicate a loss of about 35 db for the I.R.E. standard capacitive antenna, 43 db for a large loop occupying a volume of 1 meter square by 0.5 meter axial length, and 64 db for a loop of 1/5 these dimensions.


Design of a Compact Dual-Band Annular-Ring Slot Antenna

This letter aims at miniaturizing an annular-ring slot antenna that is suitable for the 2.4/5-GHz dual-band operations. The miniaturization purpose was achieved by embedding in the center patch of the antenna a pair of slots to excite three resonant modes. The resonant band of the first excited resonant mode was lowered from that of the unperturbed annular-ring slot antenna, whereas those of the second and third excited resonant modes were combined to form a wide upper operating band by appropriately adjusting the dimensions of the embedded slots. The proposed antenna proves to have very similar copolarization radiation patterns in its two operating bands and have enough antenna gains for practical applications.


The Magnetoelectric Dipole—A Wideband Antenna for Base Stations in Mobile Communications

In this paper, stringent requirements imposed on the design of base station antennas for mobile communications are summarized. Conventional techniques for implementing base station antennas are reviewed. The complementary antenna concept of combining an electric dipole with a magnetic dipole is reconsidered. Recently, this kind of antenna has been commonly called a “Huygen's source.” The purpose is to develop wideband unidirectional antennas with stable frequency characteristics and low back radiation. Based on this concept, the magnetoelectric dipole was invented by integrating an electric dipole with an -probe fed shorted quarter-wave patch antenna. A number of magnetoelectric dipoles with different radiation patterns and different polarizations have been developed in recent years. An overview of the characteristics of this new class of complementary antennas is presented. Major design challenges are explained. Finally, a new magnetoelectric dipole that is low in profile and robust in structure is presented. The magnetic dipole part of this antenna is realized by a triangular-shaped loop antenna. The antenna is inherently direct current (dc) grounded, which satisfies the requirement for outdoor applications.


A UWB Unidirectional Antenna With Dual-Polarization

A novel ±45° dual-polarized unidirectional antenna element is presented, consisting of two cross center-fed tapered mono-loops and two cross electric dipoles located against a reflector for ultrawideband applications. The operation principle of the antenna including the use of elliptically tapered transmission line for transiting the unbalanced energy to the balanced energy is described. Designs with different reflectors-planar or conical-are investigated. A measured overlapped impedance bandwidth of 126% (SWR <; 2) is demonstrated. Due to the complementary nature of the structure, the antenna has a relatively stable broadside radiation pattern with low cross polarization and low back lobe radiation over the operating band. The measured gain of the proposed antenna varies from 4 to 13 dBi and 7 to 14.5 dBi for port 1 and port 2, respectively, over the operating band, when mounted against a conical backed reflector. The measured coupling between the two ports is below -25 dB over the operating band.


Digital Ordering System for Restaurant Using Android

Nowadays web services technology is widely used to integrate heterogeneous systems and develop new applications. Here an application of integration of hotel management systems by web services technology is presented. Digital Hotel Management integrates lots of systems of hotel industry such as Ordering System Kitchen Order Ticket (KOT), Billing System, Customer Relationship Management system (CRM) together. This integration solution can add or expand hotel software system in any size of hotel chains environment. This system increases quality and speed of service. This system also increases attraction of place for large range of customers. Implementing this system gives a cost-efficient opportunity to give your customers a personalized service experience where they are in control choosing what they want, when they want it – from dining to ordering to payment and feedback. We are implementing this system using android application for Tablet PC’s. The front end will be developed using JAVA Android and the backend will work on MySQL database. Index TermsDFD: Data Flow Diagram. DOSRUA: Digital Ordering System for Restaurant Using KOT: Kitchen Order Ticket Android UML: Unified Modeling Language.


Acute Effects of Cocaine on Human Brain Activity and Emotion

We investigated brain circuitry mediating cocaine-induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.


Interaction in the segmentation of medical images: A survey

Segmentation of the object of interest is a difficult step in the analysis of digital images. Fully automatic methods sometimes fail, producing incorrect results and requiring the intervention of a human operator. This is often true in medical applications, where image segmentation is particularly difficult due to restrictions imposed by image acquisition, pathology and biological variation. In this paper we present an early review of the largely unknown territory of human-computer interaction in image segmentation. The purpose is to identify patterns in the use of interaction and to develop qualitative criteria to evaluate interactive segmentation methods. We discuss existing interactive methods with respect to the following aspects: the type of information provided by the user, how this information affects the computational part, and the purpose of interaction in the segmentation process. The discussion is based on the potential impact of each strategy on the accuracy, repeatability and interaction efficiency. Among others, these are important aspects to characterise and understand the implications of interaction to the results generated by an interactive segmentation method. This survey is focused on medical imaging, however similar patterns are expected to hold for other applications as well.


Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes

Internet memes are a pervasive phenomenon on the social Web. They typically consist of viral catch phrases, images, or videos that spread through instant messaging, (micro) blogs, forums, and social networking sites. Due to their popularity and proliferation, Internet memes attract interest in areas as diverse as marketing, sociology, or computer science and have been dubbed a new form of communication or artistic expression. In this paper, we examine the merits of such claims and analyze how collective attention into Internet memes evolves over time. We introduce and discuss statistical models of the dynamics of fads and fit them to meme related time series obtained from Google Trends. Given data as to more than 200 memes, we find that our models provide more accurate descriptions of the dynamics of growth and decline of collective attention to individual Internet memes than previous approaches from the literature. In short, our results suggest that Internet memes are nothing but fads.


Targeting Horror via Level and Soundscape Generation

Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player’s tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them. Procedural content generation (PCG) is an extensive area of game research, and is often used as an effective method to reduce content creation costs and increase game longevity (Togelius et al. 2011). However, research in procedural audio is uncommon in the field (Collins 2013), likely due to the additional demands that sound often requires. Digital games are multi-faceted creative domains (Liapis, Yannakakis, and Togelius 2014), where facets such as audio, visuals, levels and game mechanics work in conjunction to create interactive digital experiences (Lopes and Yannakakis 2014). This paper investigates the interplay between level design and sound, where designers define the player experience while the system generates levels and their respective soundscapes to accommodate the designer’s intentions. The survival horror genre is unique in its heavy reliance on sound to convey negative affective states such as shock, disgust, ecstasy, fear and relief (Ekman and Lankoski 2009). It also focuses on exploration and hiding as players have limited combat ability (e.g. no weapons or limited ammunition). These complex characteristics of player affect raise important challenges for the generation of levels and soundscapes, where the focus is in evoking these types of emotions. For instance, an interesting question is how a level generator can anticipate and influence the affective state of a player, while consistently balancing feelings such as stress and relief; or how players navigate through a level under the effects of stress caused by previously encountered monsters. This paper tackles some of these challenges by exploring the generation of levels and soundscapes in the survival horror genre Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. while also operating on a simplified player model, simulating horror gameplay during level traversal. This paper presents an extension to the Sonancia prototype (Lopes, Liapis, and Yannakakis 2015), a system capable of generating multiple facets of horror games. Levels consist of rooms in a haunted mansion that are procedurally generated via genetic search, while audio snippets are distributed throughout the rooms (i.e. level sonification). Earlier work focused on generating the architecture of the level autonomously based on the distance of a singular path. This paper focuses on the generation of levels which include added gameplay elements (i.e. monsters, quest items), and on the generation of their soundscapes, where notions of tension and suspense are used to drive the level and soundscape generative process respectively. Sonancia allows designers to define the flow of relaxation and tension through the generated level. To do this, the system requires two tension curves: the desired (designerspecified) tension curve (DTC) and the actual (level-based) tension curve (LTC). The system searches, via a genetic algorithm, for room setups where the actual tension curve more closely matches the desired one, so that generated levels and their respective soundscapes follow the designer specifications as closely as possible, while still maintaining a degree of variability.