title: EFFICIENT METHOD OF AUDIO STEGANOGRAPHY BY MODIFIED LSB ALGORITHM AND STRONG ENCRYPTION KEY WITH ENHANCED SECURITY

abstract: In the current internet community, secure data transfer is limited due to its attack made on data communication. So more robust methods are chosen so that they ensure secured data transfer. One of the solutions which came to the rescue is the audio Steganography. But existing audio steganographic systems have poor interface, very low level implementation, difficult to understand and valid only for certain audio formats with restricted message size. Enhanced Audio Steganography (EAS) is one proposed system which is based on audio Steganography and cryptography, ensures secure data transfer between the source and destination. EAS uses most powerful encryption algorithm in the first level of security, which is very complex to break. In the second level it uses a more powerful modified LSB (Least Significant Bit) Algorithm to encode the message into audio. It performs bit level manipulation to encode the message. The basic idea behind this paper is to provide a good, efficient method for hiding the data from hackers and sent to the destination in a safer manner. Though it is well modulated software it has been limited to certain restrictions. The quality of sound depends on the size of the audio which the user selects and length of the message. Though it shows bit level deviations in the frequency chart, as a whole the change in the audio cannot be determined.


title: Production of first and second generation biofuels: A comprehensive review

abstract: Sustainable economic and industrial growth requires safe, sustainable resources of energy. For the future re-arrangement of a sustainable economy to biological raw materials, completely new approaches in research and development, production, and economy are necessary. The 'first-generation' biofuels appear unsustainable because of the potential stress that their production places on food commodities. For organic chemicals and materials these needs to follow a biorefinery model under environmentally sustainable conditions. Where these operate at present, their product range is largely limited to simple materials (i.e. cellulose, ethanol, and biofuels). Second generation biorefineries need to build on the need for sustainable chemical products through modern and proven green chemical technologies such as bioprocessing including pyrolysis, Fisher Tropsch, and other catalytic processes in order to make more complex molecules and materials on which a future sustainable society will be based. This review focus on cost effective technologies and the processes to convert biomass into useful liquid biofuels and bioproducts, with particular focus on some biorefinery concepts based on different feedstocks aiming at the integral utilization of these feedstocks for the production of value added chemicals.


title: The SECI model of knowledge creation: some empirical shortcomings

abstract: This paper presents a critique of key empirical aspects of Nonaka’s model of knowledge creation. If, as is now widely accepted, it is important to manage knowledge for a variety of reasons, then it is equally important that we have good models to assist this process. Nonaka and his colleagues’ model, in particular the SECI matrix of knowledge conversion, is increasingly being cited by authors in a widening set of disciplines, and has evidently achieved something like a paradigmatic status. However, as I shall argue below, it is not supported by empirical evidence, and some of the conversion modes are not coherent. The paradigmatic status of this theory may thus be unwarranted.


title: Salient Object Detection: A Discriminative Regional Feature Integration Approach

abstract: Feature integration provides a computational framework for saliency detection, and a lot of hand-crafted integration rules have been developed. In this paper, we present a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation. In addition to contrast features, we introduce regional object-sensitive descriptors: the objectness descriptor characterizing the common spatial and appearance property of the salient object, and the image-specific backgroundness descriptor characterizing the appearance of the background of a specific image, which are shown more important for estimating the saliency. To the best of our knowledge, our supervised feature integration framework is the first successful approach to perform the integration over the saliency features for salient object detection, and outperforms the integration approach over the saliency maps. Together with fusing the multi-level regional saliency maps to impose the spatial saliency consistency, our approach significantly outperforms state-of-the-art methods on seven benchmark datasets. We also discuss several followup works which jointly learn the representation and the saliency map using deep learning.


title: Differentiation of Chara intermedia and C. baltica compared to C. hispida based on morphology and amplified fragment length polymorphism

abstract: Charophytes are macrophytic green algae, occurring in standing and running waters throughout the world. Species descriptions of charophytes are contradictive and different determination keys use various morphologic characters for species discrimination. Chara intermedia Braun, C. baltica Bruzelius and C. hispida Hartman are treated as three species by most existing determination keys, though their morphologic differentiation is based on different characteristics. Amplified fragment length polymorphism (AFLP) was used to detect genetically homogenous groups within the C. intermedia-C. baltica-C. hispida-cluster, by the analysis of 122 C. intermedia, C. baltica and C. hispida individuals from central and northern Europe. C. hispida clustered in a distinct genetic group in the AFLP analysis and could be determined morphologically by its aulacanthous cortification. However, for C. intermedia and C. baltica no single morphologic character was found that differentiated the two genetic groups, thus C. intermedia and C. baltica are considered as cryptic species. All C. intermedia specimen examined came from freshwater habitats, whereas the second group, C. baltica, grew in brackish water. We conclude that the species differentiation between C. intermedia and C. baltica, which is assumed to be reflected by the genetic discrimination groups, corresponds more with ecological (salinity preference) than morphologic characteristics.


title: Integrated DC-DC converter design for Electric Vehicle powertrains

abstract: In this paper, an integrated, reconfigurable DC-DC converter for plugin and hybrid Electric Vehicles (EV) is proposed. The converter integrates functionality for both EV powertrain and charging operation into a single unit. During charging, the proposed converter functions as a DAB converter, providing galvanic isolation. For powertrain operation, the converter functions as an interleaved boost converter. During light load powertrain operation, the efficiency of the converter can be further improved by employing the integrated DAB. The proposed integrated converter does not require any extra relays or contactors for charging and powertrain operation. By using such integration, the overall volume and weight of the power electronics circuits, passives and associated cooling system can be improved. In addition, the power flow efficiency from EV battery to the high voltage DC bus for the motor inverter can be improved. The experimental results of the prototype are presented to verify the functionality of the proposed converter.


title: Robust control of uncertain Markov Decision Processes with temporal logic specifications

abstract: We present a method for designing a robust control policy for an uncertain system subject to temporal logic specifications. The system is modeled as a finite Markov Decision Process (MDP) whose transition probabilities are not exactly known but are known to belong to a given uncertainty set. A robust control policy is generated for the MDP that maximizes the worst-case probability of satisfying the specification over all transition probabilities in this uncertainty set. To this end, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming solves for a e-suboptimal robust control policy with time complexity O(log1/e) times that for the non-robust case.


title: Magnetic resonance imaging in pediatric appendicitis: a systematic review

abstract: Magnetic resonance imaging for the evaluation of appendicitis in children has rapidly increased recently. This change has been primarily driven by the desire to avoid CT radiation dose. This meta-analysis reviews the diagnostic performance of MRI for pediatric appendicitis and discusses current knowledge of cost-effectiveness. We used a conservative Haldane correction statistical method and found pooled diagnostic parameters including a sensitivity of 96.5% (95% confidence interval [CI]: 94.3–97.8%), specificity of 96.1% (95% CI: 93.5–97.7%), positive predictive value of 92.0% (95% CI: 89.3–94.0%) and negative predictive value of 98.3% (95% CI: 97.3–99.0%), based on 11 studies. Assessment of patient outcomes associated with MRI use at two institutions indicates that time to antibiotics was 4.7 h and 8.2 h, time to appendectomy was 9.1 h and 13.9 h, and negative appendectomy rate was 3.1% and 1.4%, respectively. Alternative diagnoses were present in ~20% of cases, most commonly adnexal cysts and enteritis/colitis. Regarding technique, half-acquisition single-shot fast spin-echo (SSFSE) pulse sequences are crucial. While gadolinium-enhanced T1-weighted pulse sequences might be helpful, any benefit beyond non-contrast MRI has not been confirmed. Balanced steady-state free precession (SSFP) sequences are generally noncontributory. Protocols do not need to exceed five sequences; four-sequence protocols are commonly utilized. Sedation generally is not indicated; patients younger than 5 years might be attempted based on the child’s ability to cooperate. A comprehensive pediatric cost-effectiveness analysis that includes both direct and indirect costs is needed.


title: Adaptive control of Aerial Manipulation Vehicle

abstract: Adaptive Control of an Aerial Manipulation Vehicle is discussed here. The aerial manipulation vehicle consisting of a quadrotor and a robotic arm has a highly coupled dynamics. The nonlinear coupling introduces additional forces and moments on the quadrotor which prevents it from precisely hovering at a position and tracking of reference trajectory. A decentralized control of robotic arm and quadrotor is considered. The robotic arm is controlled by a PID approach with acceleration feedback, and the quadrotor is controlled by PD method in the inner loop and adaptive position control in the outer loop. The proposed method successfully handles the problem of hover stabilization and trajectory tracking.


title: Web Service Recommendation With Reconstructed Profile From Mashup Descriptions

abstract: Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods. Note to Practitioners —Service recommendation accuracy for mashup creation is often limited due to poor quality of service descriptions. Mashup descriptions contain valuable information about functions and features of its component services, which can be leveraged to enhance descriptive quality of original service profiles. Based on the assumption, this paper proposes a novel two-phase service recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel service recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.


title: D-CAD: Deep and Crowded Anomaly Detection

abstract: In this digital era, the complexity-efficient anomaly concentration becomes a major concern for the surveillance systems. This paper addresses the issue with an efficient and rapid technique for anomaly concentration and detection in the crowded scenes. Our model is trained by a concept of the deep learning (i.e. convolutional neural networks) framework, which makes use of an advanced feature-learning technique. Initially, a deep neural network is used for prematurely segmenting many normal frames. Our network works on the small frame being the initial stage, before cautiously rescaling the remaining features of interest, and assessing those at the following step using a more complex and deeper CNN. Superficial layers of the deep network (constructed as Gaussian classifiers, acting as weak classifiers) detect simple normal frames, such as background frames, and more complex normal patches are detected at deeper layers. It is shown that the proposed original technique (combination of deep neural networks and Kalman filter) will churn out comparable to today's top-performing localization and detection techniques on standard touchstones, but outshine those in general with respect to required computation time.


title: Learning customized and optimized lists of rules with mathematical programming

abstract: We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://doi.org/10.5281/zenodo.1344142.


title: High performance scalable image compression with EBCOT

abstract: A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR scalability together with a random access property. The algorithm has modest complexity and is extremely well suited to applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.


title: On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing

abstract: This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamic-programming algorithms as oracle solvers for sub-problems, together with a simple method for forcing agreement between the different oracles. The approach provably solves a linear programming (LP) relaxation of the global inference problem. It leads to algorithms that are simple, in that they use existing decoding algorithms; efficient, that they avoid exact algorithms for the full model; and often exact, in that empirically they often recover the correct solution in spite of using an LP relaxation. We give experimental results on two problems: 1) the combination of two lexicalized parsing models; and 2) the combination of a lexicalized parsing model and a trigram part-of-speech tagger.


title: Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks

abstract: Learning users' preferences is critical to enable personalized recommendation services in various online applications such as e-commerce, entertainment and many others. In this paper, we study on how to learn users' preferences from abundant online activities, e.g., browsing and examination, which are usually called implicit feedbacks since they cannot be interpreted as users' likes or dislikes on the corresponding products directly. Pairwise preference learning algorithms are the state-of-the-art methods for this important problem, but they have two major limitations of low accuracy and low efficiency caused by noise in observed feedbacks and non-optimal learning steps in update rules. As a response, we propose a novel adaptive pairwise preference learning algorithm, which addresses the above two limitations in a single algorithm with a concise and general learning scheme. Specifically, in the proposed learning scheme, we design an adaptive utility function and an adaptive learning step for the aforementioned two problems, respectively. Empirical studies show that our algorithm achieves significantly better results than the state-of-the-art method on two real-world data sets.


title: Optimized Assistive Human–Robot Interaction Using Reinforcement Learning

abstract: An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.


A comparison of classifiers and document representations for the routing problem


title: [Healthcare workers' hand hygiene: compliance of the recommendations].

abstract: BACKGROUND ::: Handwashing is the most effective method of preventing the spread of nosocomial infection. Despite its simplicity, handwashing is frequently omitted by health care personnel. In order to promote hand hygiene at Farhat Hached hospital (Sousse-Tunisia), the team of hygiene sets up a programme of hand hygiene programme.This work enters within the framework of the evaluation of the program's impact relatively on the healthcare worker's hand decontamination practices (physicians and nurses). ::: ::: ::: AIM ::: Our aim is to determine the evolution of hand washing rates, specific compliance (per unit and category of healthcare workers) and adapted compliance. ::: ::: ::: METHODS ::: Our study is experimental with comparison of the results of 2 audits, the first carried out before the installation of the program (winter 2003) and the second after (winter 2006). The study was conducted in 4 units: two medical units (internal medicine and pneumology), a surgical unit (general surgery) and intensive care unit. ::: ::: ::: RESULTS ::: On the whole, 208 and 319 acts of care were observed respectively during the first and the second audit. The rate of total observance and that of correct observance of handwashing doubled and passed respectively from 28% to 46.4% (p = 2 x 10(-5)) and from 13.9% to 23.5% (p = 4 x 10(-5)). Failures causes observed at washing hands time (2006) are mainly related to a later recontamination of the hands. The observance of handwashing increased only for nurses, from 34% to 47.5% (p < 10(-2)). ::: ::: ::: CONCLUSION ::: With the aim to improve hand washing compliance, hand hygiene programme must target even more the physicians and provide alcohol hand rubs.


title: TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources

abstract: With the rapid growth of the cyber attacks, sharing of cyber threat intelligence (CTI) becomes essential to identify and respond to cyber attack in timely and cost-effective manner. However, with the lack of standard languages and automated analytics of cyber threat information, analyzing complex and unstructured text of CTI reports is extremely time- and labor-consuming. Without addressing this challenge, CTI sharing will be highly impractical, and attack uncertainty and time-to-defend will continue to increase. Considering the high volume and speed of CTI sharing, our aim in this paper is to develop automated and context-aware analytics of cyber threat intelligence to accurately learn attack pattern (TTPs) from commonly available CTI sources in order to timely implement cyber defense actions. Our paper has three key contributions. First, it presents a novel threat-action ontology that is sufficiently rich to understand the specifications and context of malicious actions. Second, we developed a novel text mining approach that combines enhanced techniques of Natural Language Processing (NLP) and Information retrieval (IR) to extract threat actions based on semantic (rather than syntactic) relationship. Third, our CTI analysis can construct a complete attack pattern by mapping each threat action to the appropriate techniques, tactics and kill chain phases, and translating it any threat sharing standards, such as STIX 2.1. Our CTI analytic techniques were implemented in a tool, called TTPDrill, and evaluated using a randomly selected set of Symantec Threat Reports. Our evaluation tests show that TTPDrill achieves more than 82% of precision and recall in a variety of measures, very reasonable for this problem domain.


title: Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

abstract: Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from different view points. The main challenge is the disambiguation of the camera motion from scene motion, which becomes more difficult as the amount of rigidity observed decreases, even with successful estimation of 2D image correspondences. Compared to other state-of-the-art 3D scene flow estimation methods, in this paper we propose to \emph{learn} the rigidity of a scene in a supervised manner from a large collection of dynamic scene data, and directly infer a rigidity mask from two sequential images with depths. With the learned network, we show how we can effectively estimate camera motion and projected scene flow using computed 2D optical flow and the inferred rigidity mask. For training and testing the rigidity network, we also provide a new semi-synthetic dynamic scene dataset (synthetic foreground objects with a real background) and an evaluation split that accounts for the percentage of observed non-rigid pixels. Through our evaluation we show the proposed framework outperforms current state-of-the-art scene flow estimation methods in challenging dynamic scenes.


title: DIRT @SBT@discovery of inference rules from text

abstract: In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y a X wrote Y", "X solved Y a X found a solution to Y", and "X caused Y a Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.


title: Knitty : 3 D Modeling of Knitted Animals with a Production Assistant Interface

abstract: Knitty is an interactive design system for creating knitted animals. The user designs a 3D surface model using a sketching interface. The system automatically generates a knitting pattern and then visualizes the shape of the resulting 3D animal model by applying a simple physics simulation. The user can see the resulting shape before beginning the actual knitting. The system also provides a production assistant interface for novices. The user can easily understand how to knit each stitch and what to do in each step. In a workshop for novices, we observed that even children can design their own knitted animals using our system.


Department of Defense Password Management Guidelines


title: A CSRR Loaded MIMO Antenna System for ISM Band Operation

abstract: A 2 × 2 (four-element) multiple-input multiple-output (MIMO) patch antenna system is designed and fabricated for a 2.45-GHz ISM band operation. It uses complementary split-ring resonator (CSRR) loading on its ground plane for antenna miniaturization. This reduces the single-element antenna size by 76%. The total board size of the proposed MIMO antenna system, including the GND plane is 100 × 50 × 0.8 mm3, while the single-patch antenna element has a size of 14 × 18 mm2. The antenna is fabricated and tested. Measured results are in good agreement with simulations. A minimum measured isolation of 10 dB is obtained given the close interelement spacing of 0.17λ. The maximum measured gain for a single operating element is -0.8 dBi.


title: Design of Broadband Linear and Efficient Power Amplifier for Long-Term Evolution Applications

abstract: In this letter, a broadband linear and efficient power amplifier for base-stations in long-term evolution (LTE) applications is designed, fabricated, and experimentally verified, employing Cree's CGH40010F GaN HEMT. A novel method of baseband, fundamental and harmonic impedances control is used to provide high linearity and efficiency across a broad bandwidth. When producing a two-tone carrier-to-intermodulation ratio (C/I) of 30 dBc, the circuit has demonstrated a two-tone power added efficiency (PAE) between 45%-60% across the frequency range from 1.6 to 2.6 GHz while delivering 36.0-38.5 dBm average output power. For a single-carrier 20 MHz LTE signal with a peak-to-average ratio (PAR) of 6.5 dB, a measured high PAE of 40%-55% can be achieved at an average output power of 35.3-37.5 dBm with an adjacent channel leakage ratio (ACLR) of about -30 dBc from 1.6 to 2.6 GHz.


title: A New Sentence Similarity Method Based on a Three-Layer Sentence Representation

abstract: Sentence similarity methods are used to assess the degree of likelihood between phrases. Many natural language applications such as text summarization, information retrieval, text categorization, and machine translation employ measures of sentence similarity. The existing approaches for this problem represent sentences as vectors of bag of words or the syntactic information of the words in the phrase. The likelihood between phrases is calculated by composing the similarity between the words in the sentences. Such schemes do not address two important concerns in the area, however: the semantic problem and the word order. This paper proposes a new sentence similarity measure that attempts to address such problems by taking into account the lexical, syntactic, and semantic analysis of sentences. The new similarity measure proposed outperforms the state of the art systems in around 6%, when tested using a standard and publically available dataset.


title: Entity-Based System Dynamics

abstract: We describe a new platform for system dynamics modeling that supports detailed and object oriented modeling while preserving attractive features of existing tools, including a completely declarative language with a graphical representation. New concepts supporting this platform include collections of entities, attributes, relationships, aggregation and allocation functions, and actions, which are presented with examples. The design facilitates modularity and collaboration, provides a more natural description of detail than arrays, and solves sparse matrix problems. It has application to both traditional system dynamics, with modular sectors, and to agent based modeling.


title: Design: The Evolution of Information Punctuated by Decisions

abstract: As design theory matures, it is becoming more decision-centric supporting the definition of design as the evolution of information punctuated by decisions. Current methods to support design decision-making are not sufficient in their ability to help teams make the best possible decisions. This turns out to be as much a cognitive psychology issue as it is an engineering problem. Understanding engineering design, human cognition and team decision-making can lead to tools to better support the new reality. The paper ends with a list of needs for design decision support systems.


title: Learning API usages from bytecode: a statistical approach

abstract: Mobile app developers rely heavily on standard API frameworks and libraries. However, learning API usages is often challenging due to the fast-changing nature of API frameworks for mobile systems and the insufficiency of API documentation and source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include HAPI, a statistical model of API usages and three algorithms to extract method call sequences from apps' bytecode, to train HAPI based on those sequences, and to recommend method calls in code completion using the trained HAPIs. Our empirical evaluation shows that our prototype tool can effectively learn API usages from 200 thousand apps containing 350 million method sequences. It recommends next method calls with top-3 accuracy of 90% and outperforms baseline approaches on average 10--20%.


title: On Covert Acoustical Mesh Networks in Air

abstract: Covert channels can be used to circumvent system and network policies by establishing communications that have not been considered in the design of the computing system. We construct a covert channel between different computing systems that utilizes audio modulation/demodulation to exchange data between the computing systems over the air medium. The underlying network stack is based on a communication system that was originally designed for robust underwater communication. We adapt the communication system to implement covert and stealthy communications by utilizing the ultrasonic frequency range. We further demonstrate how the scenario of covert acoustical communication over the air medium can be extended to multi-hop communications and even to wireless mesh networks. A covert acoustical mesh network can be conceived as a meshed botnet or malnet that is accessible via inaudible audio transmissions. Different applications of covert acoustical mesh networks are presented, including the use for remote keylogging over multiple hops. It is shown that the concept of a covert acoustical mesh network renders many conventional security concepts useless, as acoustical communications are usually not considered. Finally, countermeasures against covert acoustical mesh networks are discussed, including the use of lowpass filtering in computing systems and a host-based intrusion detection system for analyzing audio input and output in order to detect any irregularities.


title: Advanced Applications of Neural Networks and Artificial Intelligence: A Review

abstract: Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN"s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN"s are used and discusses the critical role of AI & NN played in different areas.


title: A rapid and sensitive procedure for the combined assay of noradrenaline, dopamine, and serotonin in a single brain sample

abstract: Abstract A method for the assay of noradrenaline, dopamine, and serotonin in brain is described. Acid-solvent extraction and adsorption on alumina, two established techniques for the extraction of the catecholamines, are combined and followed by a fluorescence development of the hydroxyindole type, after the method of Chang. The assay of serotonin by extraction with alkaline butanol and subsequent reaction with ninhydrin is integrated with this procedure in a way that retains the advantages of each step and the sensitivity of each assay. It also permits assay of all three amines from the same tissue sample. The procedure is rapid and more sensitive than established procedures; an accurate assay can be performed on a single tissue sample containing 20 ng of noradrenaline, 30 ng of serotonin, and 100 ng of dopamine.


title: Comparing lexical, acoustic/prosodic, structural and discourse features for speech summarization

abstract: We present results of an empirical study of the usefulness of different types of features in selecting extractive summaries of news broadcasts for our Broadcast News Summarization System. We evaluate lexical, prosodic, structural and discourse features as predictors of those news segments which should be included in a summary. We show that a summarization system that uses a combination of these feature sets produces the most accurate summaries, and that a combination of acoustic/prosodic and structural features are enough to build a ‘good’ summarizer when speech transcription is not available.


title: Image fusion by a ration of low-pass pyramid

abstract: Abstract This paper introduces a hierarchial image merging scheme based on a multiresolution contrast decomposition (the ratio of low-pass pyramid). The composite images produced by this scheme preserve those details from the input images that are most relevant to visual perception. Some applications of the method are indicated.


Chaotic Autonomous Mobile Robot for Military Missions


title: On the Mechanisms and Effects of Calibrating RSSI Measurements for 802.15.4 Radios

abstract: Wireless sensor network protocols and applications, including those used for localization, topology control, link scheduling, and link quality estimation, make extensive use of Received Signal Strength Indication (RSSI) measurements. In this paper we show that inaccuracies in the RSSI values reported by widely used 802.15.4 radios, such as the CC2420 and the AT86RF230, have profound impact on these protocols and applications. Furthermore, we experimentally derive the response curves which translate actual RSSI values to the raw RSSI readings that the radios report and show that they contain non-linear and even non-injective regions. Fortunately, these curves are consistent across radios of the same model, making RSSI calibration practical. We present a calibration mechanism that removes the artifacts in the raw RSSI measurements, including ambiguities created by the non-injective regions in the response curves, and generates calibrated RSSI readings that are linear. This calibration removes many of the outliers generated when raw RSSI readings are used to estimate Signal to Noise (and Interference) ratios, estimate radio model parameters, and perform RF-based localization.


title: Face Matching and Retrieval Using Soft Biometrics

abstract: Soft biometric traits embedded in a face (e.g., gender and facial marks) are ancillary information and are not fully distinctive by themselves in face-recognition tasks. However, this information can be explicitly combined with face matching score to improve the overall face-recognition accuracy. Moreover, in certain application domains, e.g., visual surveillance, where a face image is occluded or is captured in off-frontal pose, soft biometric traits can provide even more valuable information for face matching or retrieval. Facial marks can also be useful to differentiate identical twins whose global facial appearances are very similar. The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher. We propose to utilize demographic information (e.g., gender and ethnicity) and facial marks (e.g., scars, moles, and freckles) for improving face image matching and retrieval performance. An automatic facial mark detection method has been developed that uses (1) the active appearance model for locating primary facial features (e.g., eyes, nose, and mouth), (2) the Laplacian-of-Gaussian blob detection, and (3) morphological operators. Experimental results based on the FERET database (426 images of 213 subjects) and two mugshot databases from the forensic domain (1225 images of 671 subjects and 10 000 images of 10 000 subjects, respectively) show that the use of soft biometric traits is able to improve the face-recognition performance of a state-of-the-art commercial matcher.


title: Application of Fascial Manipulation technique in chronic shoulder pain--anatomical basis and clinical implications.

abstract: Classical anatomy still relegates muscular fascia to a role of contention. Nonetheless, different hypotheses concerning the function of this resilient tissue have led to the formulation of numerous soft tissue techniques for the treatment of musculoskeletal pain. This paper presents a pilot study concerning the application of one such manual technique, Fascial Manipulation, in 28 subjects suffering from chronic posterior brachial pain. This method involves a deep kneading of muscular fascia at specific points, termed centres of coordination (cc) and centres of fusion (cf), along myofascial sequences, diagonals, and spirals. Visual Analogue Scale (VAS) measurement of pain administered prior to the first session, and after the third session was compared with a follow-up evaluation at 3 months. Results suggest that the application of Fascial Manipulation technique may be effective in reducing pain in chronic shoulder dysfunctions. The anatomical substratum of the myofascial continuity has been documented by dissections and the biomechanical model is discussed.


title: PE-PUC: A Graph Based PU-Learning Approach for Text Classification

abstract: This paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed with positive and negative documents. This problem is also called PU-Learning. The key feature of PU-Learning is that there is no negative document for training. Recently, several approaches have been proposed for solving this problem. Most of them are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. Generally speaking, these existing approaches do not perform well when the size of P is small. In this paper, we propose a new approach aiming at improving the system when the size of P is small. This approach combines the graph-based semi-supervised learning method with the two-step method. Experiments indicate that our proposed method performs well especially when the size of P is small.


Limited effect of rituximab on thrombocytopaenia and anticardiolipin antibodies in a patient with primary antiphospholipid syndrome


title: Toxicity Prediction using Deep Learning

abstract: Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines — and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA and FDA launched the Tox21 Data Challenge within the “Toxicology in the 21st Century” (Tox21) initiative. The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds. State of the art toxicity prediction methods build upon specifically-designed chemical descriptors developed over decades. Though Deep Learning is new to the field and was never applied to toxicity prediction before, it clearly outperformed all other participating methods. In this application paper we show that deep nets automatically learn features resembling well-established toxicophores. In total, our Deep Learning approach won both of the panel-challenges (nuclear receptors and stress response) as well as the overall Grand Challenge, and thereby sets a new standard in tox prediction.


title: Conditioning effect on dentin, resin tags and hybrid layer of different acidity self-etch adhesives applied to thick and thin smear layer

abstract: Abstract Objectives to assess the conditioning effect (CE) of self-etch adhesives of different acidity applied to thick and thin smear layer (SL), the corresponding resin tags (RT) and hybrid layer (HL). Methods Twenty-seven molars had their occlusal dentin exposed and were sectioned into two halves. Each of them was ground with a 60 or 600-grit SiC paper, respectively for thick and thin SL production. Three self-etch adhesives: a mild (Clearfil SE Bond), an intermediary (Optibond Solo SE and Solo Plus) and a strong (Tyrian Self Priming Etchant + One Step Plus) and an etch-&-rinse system (Scotchbond Multi Purpose Plus) were used. For CE evaluation, the self-etch primers were applied and rinsed off with acetone and alcohol prior to SEM preparation. For RT and HL specimens self-etch adhesives were applied and restored with Z-250. For RT specimens, dentin was removed with HCl (6N) and NaClO (1%) baths. The HL specimens were fixed, dehydrated, dried with HMDS, embedded, polished and slightly demineralized (6N HCl) and deproteinized (1% NaClO). After gold sputtering they were observed by SEM. Results The thick smear layer was clearly not totally removed by the mild self-etch primer. RT varied in density and shape among the self-etch adhesives. Thicker HL was observed for the strong self-etch and etch-&-rinse adhesives. Conclusions The etch-&-rinse adhesive presented the thickest HL and was the only adhesive to produce RT in high density and uniform distribution along the whole dentin surface, independently of the SL thickness.


title: Governance by algorithms: reality construction by algorithmic selection on the Internet

abstract: This article explores the governance by algorithms in information societies. Theoretically, it builds on (co-)evolutionary innovation studies in order to adequately grasp the interplay of technolog...


Initial Progress in Transfer for Deep Reinforcement Learning Algorithms


title: An Examination of Mindfulness-Based Programs in US Medical Schools

abstract: Some medical schools have begun introducing mindfulness concepts to their students, and academic mindfulness centers associated with medical schools (AMCAMS) have started to emerge. However, knowledge of the expansion of mindfulness-based interventions within academic medical institutions is primarily anecdotal. The study objective was to evaluate the scope of mindfulness-related activities across medical schools in the USA and to identify AMCAMS and their programmatic characteristics. First, we conducted a systematic website content search to evaluate mindfulness activities related to wellness, research, clinical use, and education among the 140 accredited US medical schools. Second, we surveyed AMCAMS directors to query the type of programs offered, the type of professionals participating at AMCAMS, and approaches for financial sustainability. Website content analysis showed that mindfulness-related activity was present at 79 % of US medical schools in 2014. Activities for wellness (87/140; 62 %) and research (69/140; 49 %) were most common. A potential AMCAMS was identified at 27 % (38/140) of medical schools, and 33 of 38 directors at those centers completed our survey (87 % response rate). AMCAMS directors reported that the most commonly offered program was Mindfulness-Based Stress Reduction (MBSR) followed by Mindful Movement and Mindfulness-Based Cognitive Therapy (MBCT). Private-pay MBI groups (21/33; 64 %), private donations (17/33; 52 %), and fee-for-service billing (16/33; 48 %) were the most common contributors to financial sustainability. This is the first effort to collect data on all mindfulness-based programming within US medical schools and the first study to describe and identify AMCAMS characteristics. We discuss next steps for research on AMCAMS and dissemination of mindfulness in medicine.


title: New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory?

abstract: The integration of adult-born neurons into the circuitry of the adult hippocampus suggests an important role for adult hippocampal neurogenesis in learning and memory, but its specific function in these processes has remained elusive. In this article, we summarize recent progress in this area, including advances based on behavioural studies and insights provided by computational modelling. Increasingly, evidence suggests that newborn neurons might be involved in hippocampal functions that are particularly dependent on the dentate gyrus, such as pattern separation. Furthermore, newborn neurons at different maturation stages may make distinct contributions to learning and memory. In particular, computational studies suggest that, before newborn neurons are fully mature, they might function as a pattern integrator by introducing a degree of similarity to the encoding of events that occur closely in time.


title: High-dynamic-range image recovery from flash and non-flash image pairs

abstract: In this paper, we propose a novel method for creating HDR images from only two images--flash and non-flash images. Our method consists of two main steps, namely brightness gamma correction and bi-local chromatic adaptation transform (CAT). The brightness gamma correction performs series of increases and decreases of the non-flash brightness and yields multiple images with various exposure values. The bi-local CAT enhances the quality of each computed image by recovering missing details, using information from the flash image. The final multi-exposure images are then merged together to compute an HDR image. An evaluation shows that our HDR images, obtained by using only two LDR images, are close to HDR images, obtained by combining five manually taken multi-exposure images. Our method does not require the usage of a tripod and it is suitable for images of non-still objects, such as people, candle flames.


title: Behavior Analysis for Elderly

abstract: Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.


title: Actuator fault detection and isolation system for an hexacopter

abstract: The problem of detection and isolation of actuator faults in hexacopter vehicles is address in this article. The aim is to detect and isolate possible faults which can occur on the vehicle actuation system (i.e. rotors and propellers). The dynamic nonlinear model of an hexarotor vehicle is first derived to build a model-based diagnosis system. Then a Thau observer is developed to estimate the states of the hexarotor and, on the usage of this information, to generate the needed set of residuals. The developed fault detection and isolation system is finally tested in a high fidelity hexacopter simulator in which different fault situations are considered.


title: Dynamic mixed membership blockmodel for evolving networks

abstract: In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.


title: Digital Multiplierless Realization of Two Coupled Biological Morris-Lecar Neuron Model

abstract: Modeling and implementation of biological neural networks are significant objectives of the neuromorphic research field. In this field, neuronal synchronization plays a significant role in the processing of biological information. This paper presents a set of piecewise linear (MLPWL1) and multiplierless piecewise linear (MLPWL2) neuron models, which mimic behaviors of different types of neurons, similar to the biological behavior of conductance-based neurons. Both simulations and a low-cost digital implementation are carried out to compare the proposed models to a single ML neuron and two coupled ML neurons, demonstrating the required range of dynamics with a more efficient implementation. Hardware implementations on a field-programmable gate array (FPGA) show that the modified models mimic the biological behavior of different types of neurons with higher performance and significantly lower implementation costs compared to the previous realizations of the ML model. The mean normalized root mean square errors (NRMSEs) of the MLPWL1 and MLPWL2 models are 3.70% and 4.89%, respectively, as compared to the original ML model.


title: Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks

abstract: In this note, we revisit the problem of scheduling stabilizing control tasks on embedded processors. We start from the paradigm that a real-time scheduler could be regarded as a feedback controller that decides which task is executed at any given instant. This controller has for objective guaranteeing that (control unrelated) software tasks meet their deadlines and that stabilizing control tasks asymptotically stabilize the plant. We investigate a simple event-triggered scheduler based on this feedback paradigm and show how it leads to guaranteed performance thus relaxing the more traditional periodic execution requirements.


Applications of Operator Theory to Time-Frequency Analysis and Classification


title: Localization of Shipping Containers in Ports and Terminals Using Wireless Sensor Networks

abstract: The most advanced logistics solutions that are currently adopted in ports and terminals use RFID- and GPS-based technologies to identify and localize shipping containers in the yard. Nevertheless, because of the limits of these techniques, the position of containers is still affected by some errors or it cannot be determined in real-time. We propose a non-conventional approach where the position of containers can be continuously determined by means of a wireless sensor network. Each container is equipped with a number of nodes that use wireless communication to detect neighbor containers. At the base station, geometrical constraints and proximity data are combined together to determine the relative positions of containers.


title: Design and implementation of autonomous surface vehicle

abstract: This paper explores the design and implementation of an autonomous surface vehicle (ASV) at Qatar University which includes mechanical design, image processing, control and navigation, and power management. A PID controller is designed for the heading control and waypoint control of the ASV and both simulation and experimental results are presented.


title: Attentional convolutional neural networks for object tracking

abstract: As low-altitude airspace opens up, aeronautical surveillance based Unmanned Aerial Vehicle (UAV) has started to be widely used in the transportation system. Visual object tracking plays an important role in aeronautical surveillance for its accuracy and timeliness. Although traditional trackers have made great progress, they still tend to fail in complex scenes, such as occlusions, illumination variations, background clutter, and etc. In order to make use of appearance features to distinguish the object and surroundings, we propose a novel architecture called attentional convolutional neural networks (ACNN) in conjunction with offline training and online learning for object tracking. ACNN consists of a trunk equipped with attention blocks that highlight the interesting object, and several branches, which are respectively responsible for specific training sequences. In the tracking stage, all branches are removed and a new fully-connected (fc) layer is added to accomplish binary classification. We regard the candidate with the highest probability as current target. Extensive experimental results on public benchmark show that our method performs outstandingly against state-of-the-art methods. In addition, we have also investigated the relationship between the number of network layers and tracking performance for its practical use.


title: PCT: Partial Co-Alignment of Social Networks

abstract: People nowadays usually participate in multiple online social networks simultaneously to enjoy more social network services. Besides the common users, social networks providing similar services can also share many other kinds of information entities, e.g., locations, videos and products. However, these shared information entities in different networks are mostly isolated without any known corresponding connections. In this paper, we aim at inferring such potential corresponding connections linking multiple kinds of shared entities across networks simultaneously. Formally, the problem is referred to as the network "Partial Co-alignmenT" (PCT) problem. PCT is an important problem and can be the prerequisite for many concrete cross-network applications, like social network fusion, mutual information exchange and transfer. Meanwhile, the PCT problem is also very challenging to address due to various reasons, like (1) the heterogeneity of social networks, (2) lack of training instances to build models, and (3) one-to-one constraint on the correspondence connections. To resolve these challenges, a novel unsupervised network alignment framework, UNICOAT (UNsupervIsed COncurrent AlignmenT)), is introduced in this paper. Based on the heterogeneous information, UNICOAT transforms the PCT problem into a joint optimization problem. To solve the objective function, the one-to-one constraint on the corresponding relationships is relaxed, and the redundant non-existing corresponding connections introduced by such a relaxation will be pruned with a novel network co-matching algorithm proposed in this paper. Extensive experiments conducted on real-world co-aligned social network datasets demonstrate the effectiveness of UNICOAT in addressing the PCT problem.


title: Ultrasound evaluation of the ulnar collateral ligament of the elbow: Which method is most reproducible?

abstract: INTRODUCTION ::: The ulnar collateral ligament (UCL) is an important medial stabilizer of the elbow, particularly in overhead-throwing athletes. However, there is no universally accepted method for evaluating UCL thickness with ultrasound (US). ::: ::: ::: OBJECTIVE ::: To assess reproducibility of previously published methods, as well as a modified technique, for evaluating the UCL via US. We hypothesize that a modified technique would show greater reproducibility. ::: ::: ::: MATERIAL AND METHODS ::: Using US, the thickness of the UCL in 50 volunteers was measured by two musculoskeletal trained radiologists using two different measurement techniques. The techniques utilized were as described by Nazarian and Jacobson/Ward (JW). Technique measurements were evaluated using interclass correlation coefficients (ICC) to determine the reproducibility of each method. Twenty-eight of the subjects also underwent measurement via a modified JW technique, measured perpendicular to the ligament rather than the frame of imaging. This technique was also evaluated with ICC values. ::: ::: ::: RESULTS ::: The ICC value for the Nazarian technique was 0.82 (very good) and 0.51 (moderate) for the JW technique. When using the modified JW technique, we found an ICC value of 0.84 (very good). Mean ligament thickness was greatest with the Nazarian technique, 6.41 mm, with the JW technique measuring 1.86 mm and the modified technique measuring 1.38 mm. ::: ::: ::: CONCLUSION ::: US assessment of UCL thickness by all three measurement techniques are reproducible. The JW technique had less interobserver agreement when compared to the Nazarian method, whereas the modified JW technique had greater reproducibility compared to the JW technique and similar to the Nazarian technique.


title: Risk Assessment in Social Networks Based on User Anomalous Behaviors

abstract: Although the dramatic increase in Online Social Network (OSN) usage, there are still a lot of security and privacy concerns. In such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each OSN user. For this reason, in this paper, we propose a risk assessment based on the idea that the more a user behavior diverges from what it can be considered as a ‘normal behavior’, the more it should be considered risky. In doing this, we have taken into account that OSN population is really heterogeneous in observed behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN users’ behaviors. However, we expect that similar people tend to follow similar rules with the results of similar behavioral models. For this reason, we propose a risk assessment approach organized into two phases: similar users are first grouped together, then, for each identified group, we build one or more models for normal behavior. The carried out experiments on a real Facebook dataset show that the proposed model outperforms a simplified behavioral-based risk assessment where behavioral models are built over the whole OSN population, without a group identification phase.


title: On Identification of Distribution Grids

abstract: Large-scale integration of distributed energy resources into distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created ample opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of admittance parameters and topology of a polyphase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low-rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real and synthetic household demands.


title: Optimisation of a sensor for onsite detection of partial discharges in power transformers by the UHF method

abstract: Among the different solutions which allow onsite partial discharge measurement in energized power transformers, the UHF technique is gaining general interest. In order to apply this method in existing transformers, it is considered advantageous to design a UHF sensor to be fitted inside the transformer tank. This paper describes the constraints affecting the development of such sensor and the process followed to optimize its design. During this process, different solutions for broadband UHF antennas were analysed. The selection was based on computer simulation and experimental results. Computer simulation was used to evaluate differences in radiation pattern, antenna impedance, gain and effective area. Measurements of the antenna response to different partial discharge sources in oil were performed using an oil dielectric breakdown test set in an electromagnetic shielded laboratory. A group of selected antennas were then tested in a power transformer simulating the broadband signals generated by partial discharges by injecting controlled voltage pulses in the transformer. Finally, a sensor based on a conical monopole antenna structure was built and attached to a specially designed housing to be fitted inside the transformer tank through the drain valve. This final prototype was benchmarked during a high voltage factory test of a power transformer which showed a significant partial discharge activity.


title: Cloud ERP System Customization Challenges

abstract: Customization is one of the known challenges in traditional ERP systems. With the advent of Cloud ERP systems, a question of determining the state of such systems regarding customization and configuration ability arises. As there are only a few literature sources partially covering this topic, a more comprehensive and systematic literature review is needed. Thus, this paper presents a literature review performed in order to give an overview of reported research on "Cloud ERP Customization" topic performed in the last 5 years. In two search iterations, a total of 32 relevant papers are identified and analyzed. The results show that several dominant research trends are identified along with 12 challenges and issues. Additionally, based on the results, the possible future researches are proposed.


title: VeriUI: attested login for mobile devices

abstract: Mobile apps increasingly require users to login to remote services such as Facebook and Twitter. Unfortunately, today's mobile platforms provide weak protection for login credentials such as passwords. To address this problem, we introduce the idea of an attested login and an embodiment of this idea called VeriUI. Attested login augments user credentials with a certificate describing the software and hardware that handled the credentials. Experiments with a VeriUI prototype found that it avoids the sluggish responsiveness of a thin-client approach, while a small app study indicates that VeriUI would require minor changes to existing apps.


title: Surface fairing and featuring by mean curvature motions

abstract: The aim of this paper is smoothing triangular surface meshes using a diffusion process while preserving or enhancing several important features. These features include sharp feature, detail structure, homogeneity of the mesh, interpolation of some vertices and approximation of the initial mesh. We realize these goals using a unified partial differential equation model, so that the numerical solving process of the equation is significantly simplified.


title: Chainsaw: Chained Automated Workflow-based Exploit Generation

abstract: We tackle the problem of automated exploit generation for web applications. In this regard, we present an approach that significantly improves the state-of-art in web injection vulnerability identification and exploit generation. Our approach for exploit generation tackles various challenges associated with typical web application characteristics: their multi-module nature, interposed user input, and multi-tier architectures using a database backend. Our approach develops precise models of application workflows, database schemas, and native functions to achieve high quality exploit generation. We implemented our approach in a tool called Chainsaw. Chainsaw was used to analyze 9 open source applications and generated over 199 first- and second-order injection exploits combined, significantly outperforming several related approaches.


Thinking Aloud : Reconciling Theory and Practice —


title: Key factors for adopting inner source

abstract: A number of organizations have adopted Open Source Software (OSS) development practices to support or augment their software development processes, a phenomenon frequently referred to as Inner Source. However the adoption of Inner Source is not a straightforward issue. Many organizations are struggling with the question of whether Inner Source is an appropriate approach to software development for them in the first place. This article presents a framework derived from the literature on Inner Source, which identifies nine important factors that need to be considered when implementing Inner Source. The framework can be used as a probing instrument to assess an organization on these nine factors so as to gain an understanding of whether or not Inner Source is suitable. We applied the framework in three case studies at Philips Healthcare, Neopost Technologies, and Rolls-Royce, which are all large organizations that have either adopted Inner Source or were planning to do so. Based on the results presented in this article, we outline directions for future research.


title: Real time detection of phishing websites

abstract: Web Spoofing lures the user to interact with the fake websites rather than the real ones. The main objective of this attack is to steal the sensitive information from the users. The attacker creates a ‘shadow’ website that looks similar to the legitimate website. This fraudulent act allows the attacker to observe and modify any information from the user. This paper proposes a detection technique of phishing websites based on checking Uniform Resources Locators (URLs) of web pages. The proposed solution is able to distinguish between the legitimate web page and fake web page by checking the Uniform Resources Locators (URLs) of suspected web pages. URLs are inspected based on particular characteristics to check the phishing web pages. The detected attacks are reported for prevention. The performance of the proposed solution is evaluated using Phistank and Yahoo directory datasets. The obtained results show that the detection mechanism is deployable and capable to detect various types of phishing attacks maintaining a low rate of false alarms.


title: Normative Data for the Rey–Osterrieth and the Taylor Complex Figure Tests in Quebec-French People

abstract: The Rey-Osterrieth (ROCF) and Taylor (TCF) complex figure tests are widely used to assess visuospatial and constructional abilities as well as visual/non-verbal memory. Normative data adjusted to the cultural and linguistic reality of older Quebec-French individuals is still nonexistent for these tests. In this article, we report the results of two studies that aimed to establish normative data for Quebec-French people (aged at least 50 years) for the copy, immediate recall, and delayed recall trials of the ROCF (Study 1) and the TCF (Study 2). For both studies, the impact of age, education, and sex on test performance was examined. Moreover, the impact of copy time on test performance, the impact of copy score on immediate and delayed recall score, and the impact of immediate recall score on delayed recall performance were examined. Based on regression models, equations to calculate Z scores for copy and recall scores are provided for both tests.


title: Correction of faulty pattern using cuckoo search algorithm and symmetrical element failure technique along with distance adjustment between the antenna array

abstract: In this work, we propose a cuckoo search algorithm (CSA) based on symmetrical element failure technique along with distance adjustment between the array element for the correction of faulty beams. Our assumption is that, the faulty element location is known. Due to element fails, the whole radiation pattern is disturbed in terms of raise in sidelobe levels and dislocation of nulls from their required positions. In this approach, the CSA is used to find the locations d n , of the active elements with minimum sidelobes level and null position in failed array antenna. The meta-heuristic cuckoo search algorithm (CSA) is used for the suppression of sidelobes level and steering of nulls at their required positions. The CSA is based on the normal oblige offspring bloodsucking behavior of the cuckoo species in combination with the Levy flight behavior of some birds. The fitness function is used to minimize the error between the desired and estimated pattern along with null constraints. Simulation results for a Chebyshev pattern of 21 elements have been given to display the validity and performance of the proposed technique.


title: Magneto-dielectrics in electromagnetics: concept and applications

abstract: In this paper, the unique features of periodic magneto-dielectric meta-materials in electromagnetics are addressed. These materials, which are arranged in periodic configurations, are applied for the design of novel EM structures with applications in the VHF-UHF bands. The utility of these materials is demonstrated by considering two challenging problems, namely, design of miniaturized electromagnetic band-gap (EBG) structures and antennas in the VHF-UHF bands. A woodpile EBG made up of magneto-dielectric material is proposed. It is shown that the magneto-dielectric woodpile not only exhibits band-gap rejection values much higher than the ordinary dielectric woodpile, but also for the same physical dimensions it shows a rejection band at a much lower frequency. The higher rejection is a result of higher effective impedance contrasts between consecutive layers of the magneto-dielectric woodpile structure. Composite magneto-dielectrics are also shown to provide certain advantages when used as substrates for planar antennas. These substrates are used to miniaturize antennas while maintaining a relatively high bandwidth and efficiency. An artificial anisotropic meta-substrate having /spl mu//sub r/>/spl epsiv//sub r/, made up of layered magneto-dielectric and dielectric materials is designed to maximize the bandwidth of a miniaturized patch antenna. Analytical and numerical approaches, based on the anisotropic effective medium theory (AEMT) and the finite-difference time-domain (FDTD) technique, are applied to carry out the analyzes and fully characterize the performance of finite and infinite periodic magneto-dielectric meta-materials integrated into the EBG and antenna designs.


title: Sentiment analysis and visualisation in a backchannel system

abstract: Digital backchannel systems have been proven useful to help a lecturer gather real-time online feedback from students in a lecture environment. However, the large number of posts made during a lecture creates a major hurdle for the lecturer to promptly analyse them and take actions accordingly in time. To tackle this problem, we propose a solution that analyses the sentiment of students' feedback and visualises the morale trend of the student population to the lecturer in real time. In this paper, we present the user interface for morale visualisation and playback of ranked posts as well as the techniques for sentiment analysis and morale computation.


title: Event Detection and Factuality Assessment with Non-Expert Supervision

abstract: Events are communicated in natural language with varying degrees of certainty. For example, if you are “hoping for a raise,” it may be somewhat less likely than if you are “expecting” one. To study these distinctions, we present scalable, highquality annotation schemes for event detection and fine-grained factuality assessment. We find that non-experts, with very little training, can reliably provide judgments about what events are mentioned and the extent to which the author thinks they actually happened. We also show how such data enables the development of regression models for fine-grained scalar factuality predictions that outperform strong baselines.


Submission to the NIPS 2016 Workshop on Machine Learning for Education Estimating student proficiency : Deep learning is not the panacea


title: Ensuring Safety, Security, and Sustainability of Mission-Critical Cyber–Physical Systems

abstract: Cyber-physical systems (CPSs) couple their cyber and physical parts to provide mission-critical services, including automated pervasive health care, smart electricity grid, green cloud computing, and surveillance with unmanned aerial vehicles (UAVs). CPSs can use the information available from the physical environment to provide such ubiquitous, energy-efficient and low-cost functionalities. Their operation needs to ensure three key properties, collectively referred to as S3: 1) safety: avoidance of hazards; 2) security: assurance of integrity, authenticity, and confidentiality of information; and 3) sustainability: maintenance of long-term operation of CPSs using green sources of energy. Ensuring S3 properties in a CPS is a challenging task given the spatio-temporal dynamics of the underlying physical environment. In this paper, the formal underpinnings of recent CPS S3 solutions are aligned together in a theoretical framework for cyber-physical interactions, empowering CPS researchers to systematically design solutions for ensuring safety, security, or sustainability. The general applicability of this framework is demonstrated with various exemplar solutions for S3 in diverse CPS domains. Further, insights are provided on some of the open research problems for ensuring S3 in CPSs.


title: The Falling Factorial Basis and Its Statistical Applications

abstract: We study a novel spline-like basis, which we name the falling factorial basis, bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factorial functions are not actually splines, but are close enough to splines that they provably retain some of the favorable properties of the latter functions. We examine their application in two problems: trend filtering over arbitrary input points, and a higher-order variant of the two-sample Kolmogorov-Smirnov test.


title: Active query sensing for mobile location search

abstract: While much exciting progress is being made in mobile visual search, one important question has been left unexplored in all current systems. When the first query fails to find the right target (up to 50% likelihood), how should the user form his/her search strategy in the subsequent interaction? In this paper, we propose a novel Active Query Sensing system to suggest the best way for sensing the surrounding scenes while forming the second query for location search. We accomplish the goal by developing several unique components -- an offline process for analyzing the saliency of the views associated with each geographical location based on score distribution modeling, predicting the visual search precision of individual views and locations, estimating the view of an unseen query, and suggesting the best subsequent view change. Using a scalable visual search system implemented over a NYC street view data set (0.3 million images), we show a performance gain as high as two folds, reducing the failure rate of mobile location search to only 12% after the second query. This work may open up an exciting new direction for developing interactive mobile media applications through innovative exploitation of active sensing and query formulation.


title: FORECASTING FOREIGN EXCHANGE RATES USING RECURRENT NEURAL NETWORKS

abstract: This article proposes the use of recurrent neural networks in order to forecast foreign exchange rates. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series. In particular, recurrent networks, in which activity patterns pass through the network more than once before they generate an output pattern, can learn extremely complex temporal sequences. Three recurrent architectures are compared in terms of prediction accuracy of futures forecast for Deutsche mark currency. A trading strategy is then devised and optimized The profitability of the trading strategy, taking into account transaction costs, is shown for the different architectures. The methods described here, which have obtained promising results in real-time trading, are applicable to other markets.


title: Isolation and genetic characterization of human coronavirus NL63 in primary human renal proximal tubular epithelial cells obtained from a commercial supplier, and confirmation of its replication in two different types of human primary kidney cells

abstract: Background ::: Cryopreserved primary human renal proximal tubule epithelial cells (RPTEC) were obtained from a commercial supplier for studies of Simian virus 40 (SV40). Within twelve hrs after cell cultures were initiated, cytoplasmic vacuoles appeared in many of the RPTEC. The RPTEC henceforth deteriorated rapidly. Since SV40 induces the formation of cytoplasmic vacuoles, this batch of RPTEC was rejected for the SV40 study. Nevertheless, we sought the likely cause(s) of the deterioration of the RPTEC as part of our technology development efforts.


title: Binaural Auditory Beats Affect Vigilance Performance and Mood

abstract: LANE, J. D., S. J. KASIAN, J. E. OWENS AND G. R. MARSH.Binaural auditory beats affect vigilance performance and mood. PHYSIOL BEHAV63(2) 249 ‐252, 1998.—When two tones of slightly different frequency are presented separately to the left and right ears the listener perceives a single tone that varies in amplitude at a frequency equal to the frequency difference between the two tones, a perceptual phenomenon known as the binaural auditory beat. Anecdotal reports suggest that binaural auditory beats within the electroencephalograph frequency range can entrain EEG activity and may affect states of consciousness, although few scientific studies have been published. This study compared the effects of binaural auditory beats in the EEG beta and EEG theta/delta frequency ranges on mood and on performance of a vigilance task to investigate their effects on subjective and objective measures of arousal. Participants (n 5 29) performed a 30-min visual vigilance task on three different days while listening to pink noise containing simple tones or binaural beats either in the beta range (16 and 24 Hz) or the theta/delta range (1.5 and 4 Hz). However, participants were kept blind to the presence of binaural beats to control expectation effects. Presentation of beta-frequency binaural beats yielded more correct target detections and fewer false alarms than presentation of theta/delta frequency binaural beats. In addition, the beta-frequency beats were associated with less negative mood. Results suggest that the presentation of binaural auditory beats can affect psychomotor performance and mood. This technology may have applications for the control of attention and arousal and the enhancement of human performance. © 1998 Elsevier Science Inc.


title: Vivaldi Antennas for Microwave Imaging: Theoretical Analysis and Design Considerations

abstract: The geometrical and electrical features of the Vivaldi antenna are studied in the light of the frequency-independent antenna theory. A scaling principle is derived for the exponential tapering of the antenna, and a closed-form model for the current distribution is provided. Such theoretical results are in good agreement with several numerical simulations performed by using the NEC2 code. Furthermore, a practical feeding system, based on a double-Y balun, is developed and tested to obtain a more systematic approach to the design of the aforesaid antennas


title: The gender similarities hypothesis

abstract: The differences model, which argues that males and females are vastly different psychologically, dominates the popular media. Here, the author advances a very different view, the gender similarities hypothesis, which holds that males and females are similar on most, but not all, psychological variables. Results from a review of 46 meta-analyses support the gender similarities hypothesis. Gender differences can vary substantially in magnitude at different ages and depend on the context in which measurement occurs. Overinflated claims of gender differences carry substantial costs in areas such as the workplace and relationships.


Automatic Road Anomaly Detection Using Smart Mobile Device


Character-Based Embedding Models and Reranking Strategies for Understanding Natural Language Meal Descriptions


A Comprehensive Survey of Multi-Agent Reinforcement Learning


title: Survivable key compromise in software update systems

abstract: Today's software update systems have little or no defense against key compromise. As a result, key compromises have put millions of software update clients at risk. Here we identify three classes of information whose authenticity and integrity are critical for secure software updates. Analyzing existing software update systems with our framework, we find their ability to communicate this information securely in the event of a key compromise to be weak or nonexistent. We also find that the security problems in current software update systems are compounded by inadequate trust revocation mechanisms. We identify core security principles that allow software update systems to survive key compromise. Using these ideas, we design and implement TUF, a software update framework that increases resilience to key compromise.


Nature of small tank cascade systems and a framework for rehabilitation of tanks within them


title: Effects of caffeine, sleep loss, and stress on cognitive performance and mood during U.S. Navy SEAL training

abstract: Rationale: When humans are acutely exposed to multiple stressors, cognitive performance is substantially degraded. Few practical strategies are available to sustain performance under such conditions. Objective: This study examined whether moderate doses of caffeine would reduce adverse effects of sleep deprivation and exposure to severe environmental and operational stress on cogni- tive performance. Methods: Volunteers were 68 U.S. Navy Sea-Air-Land (SEAL) trainees, randomly assigned to receive either 100, 200, or 300 mg caffeine or placebo in capsule form after 72 h of sleep deprivation and continuous exposure to other stressors. Cognitive tests administered included scanning visual vigilance, four- choice visual reaction time, a matching-to-sample work- ing memory task and a repeated acquisition test of motor learning and memory. Mood state, marksmanship, and saliva caffeine were also assessed. Testing was conducted 1 and 8 h after treatment. Results: Sleep deprivation and environmental stress adversely affected performance and mood. Caffeine, in a dose-dependent manner, mitigated many adverse effects of exposure to multiple stressors. Caffeine (200 and 300 mg) significantly improved visual vigilance, choice reaction time, repeated acquisition, self- reported fatigue and sleepiness with the greatest effects on tests of vigilance, reaction time, and alertness. Marks- manship, a task that requires fine motor coordination and steadiness, was not affected by caffeine. The greatest effects of caffeine were present 1 h post-administration, but significant effects persisted for 8 h. Conclusions: Even in the most adverse circumstances, moderate doses of caffeine can improve cognitive function, including vigi- lance, learning, memory, and mood state. When cognitive performance is critical and must be maintained during exposure to severe stress, administration of caffeine may provide a significant advantage. A dose of 200 mg appears to be optimal under such conditions.


title: Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

abstract: A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of data poisoning, i.e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process. To date, these attacks have been devised only against a limited class of binary learning algorithms, due to the inherent complexity of the gradient-based procedure used to optimize the poisoning points (a.k.a. adversarial training examples). In this work, we rst extend the de nition of poisoning attacks to multiclass problems. We then propose a novel poisoning algorithm based on the idea of back-gradient optimization, i.e., to compute the gradient of interest through automatic di erentiation, while also reversing the learning procedure to drastically reduce the attack complexity. Compared to current poisoning strategies, our approach is able to target a wider class of learning algorithms, trained with gradient- based procedures, including neural networks and deep learning architectures. We empirically evaluate its e ectiveness on several application examples, including spam ltering, malware detection, and handwritten digit recognition. We nally show that, similarly to adversarial test examples, adversarial training examples can also be transferred across di erent learning algorithms.


Negation Handling in Sentiment Analysis at Sentence Level


title: Wideband Single-Layer DC-Decoupled Substrate Integrated Waveguide (SIW) - to - Microstrip Transition Using an Interdigital Configuration

abstract: A transition from the substrate integrated waveguide (SIW) to the microstrip line with built-in dc-decoupling is presented. The proposed transition has an interdigital configuration on a single layer. It features a 25% relative bandwidth with less than 0.7 dB of insertion loss and more than 15 dB of return loss.


title: Channel estimation methods for preamble‐based OFDM/OQAM modulations

abstract: In this paper, OFDM/OQAM is proposed as an alternative to conventional OFDM with cyclic prefix (CP) for transmission over multi-path fading channels. Two typical features of the OFDM/OQAM modulation are the absence of a guard interval (GI) and the fact that the orthogonality property only holds in the real field and for a distortion-free channel. Thus, the classical channel estimation (CE) methods used for OFDM cannot be directly applied to OFDM/OQAM. Therefore, we propose an analysis of the transmission of an OFDM/OQAM signal through a time-varying multi-path channel and we derive two new CE methods. The first proposed method only requires the use of pair of real pilots (POP). In a second method, named interference approximation method (IAM), we show how the imaginary interference can be used to improve the CE quality. Several preamble variants of the IAM are compared with respect to the resulting instantaneous power. Finally, the performance results obtained for the transmission of an OFDM/OQAM signal through an IEEE 802.22 channel using the POP method and three variants of IAM are compared to those obtained with CP-OFDM. Copyright © 2008 John Wiley & Sons, Ltd.


title: McSimA+: A manycore simulator with application-level+ simulation and detailed microarchitecture modeling

abstract: With their significant performance and energy advantages, emerging manycore processors have also brought new challenges to the architecture research community. Manycore processors are highly integrated complex system-on-chips with complicated core and uncore subsystems. The core subsystems can consist of a large number of traditional and asymmetric cores. The uncore subsystems have also become unprecedentedly powerful and complex with deeper cache hierarchies, advanced on-chip interconnects, and high-performance memory controllers. In order to conduct research for emerging manycore processor systems, a microarchitecture-level and cycle-level manycore simulation infrastructure is needed. This paper introduces McSimA+, a new timing simulation infrastructure, to meet these needs. McSimA+ models x86based asymmetric manycore microarchitectures in detail for both core and uncore subsystems, including a full spectrum of asymmetric cores from single-threaded to multithreaded and from in-order to out-of-order, sophisticated cache hierarchies, coherence hardware, on-chip interconnects, memory controllers, and main memory. McSimA+ is an application-level+ simulator, offering a middle ground between a full-system simulator and an application-level simulator. Therefore, it enjoys the light weight of an application-level simulator and the full control of threads and processes as in a full-system simulator. This paper also explores an asymmetric clustered manycore architecture that can reduce the thread migration cost to achieve a noticeable performance improvement compared to a state-of-the-art asymmetric manycore architecture.


title: Behavior Matching in Multimodal Communication Is Synchronized

abstract: A variety of theoretical frameworks predict the resemblance of behaviors between two people engaged in communication, in the form of coordination, mimicry, or alignment. However, little is known about the time course of the behavior matching, even though there is evidence that dyads synchronize oscillatory motions (e.g., postural sway). This study examined the temporal structure of nonoscillatory actions—language, facial, and gestural behaviors—produced during a route communication task. The focus was the temporal relationship between matching behaviors in the interlocutors (e.g., facial behavior in one interlocutor vs. the same facial behavior in the other interlocutor). Cross-recurrence analysis revealed that within each category tested (language, facial, gestural), interlocutors synchronized matching behaviors, at temporal lags short enough to provide imitation of one interlocutor by the other, from one conversational turn to the next. Both social and cognitive variables predicted the degree of temporal organization. These findings suggest that the temporal structure of matching behaviors provides low-level and low-cost resources for human interaction.


title: Deep Joint Face Hallucination and Recognition

abstract: Deep models have achieved impressive performance for face hallucination tasks. However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality. In this paper, we address this problem by jointly learning a deep model for two tasks, i.e. face hallucination and recognition. In particular, we design an end-to-end deep convolution network with hallucination sub-network cascaded by recognition sub-network. The recognition sub- network are responsible for producing discriminative feature representations using the hallucinated images as inputs generated by hallucination sub-network. During training, we feed LR facial images into the network and optimize the parameters by minimizing two loss items, i.e. 1) face hallucination loss measured by the pixel wise difference between the ground truth HR images and network-generated images; and 2) verification loss which is measured by the classification error and intra-class distance. We extensively evaluate our method on LFW and YTF datasets. The experimental results show that our method can achieve recognition accuracy 97.95% on 4x down-sampled LFW testing set, outperforming the accuracy 96.35% of conventional face recognition model. And on the more challenging YTF dataset, we achieve recognition accuracy 90.65%, a margin over the recognition accuracy 89.45% obtained by conventional face recognition model on the 4x down-sampled version.


title: Semantic Mapping Using Mobile Robots

abstract: Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.


title: Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm

abstract: Synthesizing images or texts automatically is a useful research area in the artificial intelligence nowadays. Generative adversarial networks (GANs), which are proposed by Goodfellow in 2014, make this task to be done more efficiently by using deep neural networks. We consider generating corresponding images from an input text description using a GAN. In this paper, we analyze the GAN-CLS algorithm, which is a kind of advanced method of GAN proposed by Scott Reed in 2016. First, we find the problem with this algorithm through inference. Then we correct the GAN-CLS algorithm according to the inference by modifying the objective function of the model. Finally, we do the experiments on the Oxford-102 dataset and the CUB dataset. As a result, our modified algorithm can generate images which are more plausible than the GAN-CLS algorithm in some cases. Also, some of the generated images match the input texts better.


title: Using General Adversarial Networks for Marketing: A Case Study of Airbnb

abstract: In this paper, we examine the use case of general adversarial networks (GANs) in the field of marketing. In particular, we analyze how GAN models can replicate text patterns from successful product listings on Airbnb, a peer-to-peer online market for short-term apartment rentals. To do so, we define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of functions that forces the model's generated output to include a set of user-defined keywords. This allows the general adversarial network to recommend a way of rewording the phrasing of a listing description to increase the likelihood that it is booked. Although we tailor our analysis to Airbnb data, we believe this framework establishes a more general model for how generative algorithms can be used to produce text samples for the purposes of marketing.