title: Tuning metaheuristics: A data mining based approach for particle swarm optimization

abstract: The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic's efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models.


title: Rogue access point detection using temporal traffic characteristics

abstract: As the cost of IEEE 802.11 hardware continues to fall, the appeal of inserting unauthorized wireless access into enterprise networks grows. These rogue access points (AP) expose the enterprise network to a barrage of security vulnerabilities in that they are typically connected to a network port behind the firewall. Most of the current approaches to detecting rogue AP are rudimentary and are easily evaded by hackers. We propose the use of temporal traffic characteristics to detect rogue AP at a central location. This detection is independent of the wireless technology (IEEE 802.11a, 802.11b, or 802.11g), is scalable, does not possess the inefficiencies of the current solutions, and is independent of the signal range of the rogue AP.


title: Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

abstract: We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used in these algorithms. While these biases might work well for some environments, they can also lead to sub-optimal behavior in others. Secondly, even though these algorithms can learn from few expert demonstrations, they require a prohibitively large number of interactions with the environment in order to imitate the expert for many real-world applications. In order to address these issues, we propose a new algorithm called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning to reduce policy-environment interaction sample complexity by an average factor of 10. Furthermore, since our reward function is designed to be unbiased, we can apply our algorithm to many problems without making any task-specific adjustments.


title: Business Process Modeling-A Comparative Analysis 1

abstract: Many business process modeling techniques have been proposed over the last decades, creating a demand for theory to assist in the comparison and evaluation of these techniques. A widely established way of determining the effectiveness and efficiency of modeling techniques is by way of representational analysis. This paper comparatively assesses representational analyses of 12 popular process modeling techniques in order to provide insights into the extent to which they differ from each other. We discuss several implications of our findings. Our analysis uncovers and explores representational root causes for a number of shortcomings that remain in process modeling practice, such as lack of process decomposition and integration of business rule specification. Our findings also serve as motivation and input to future research in areas such as context-aware business process design and conventions management.


title: Adaptive learning with covariate shift-detection for non-stationary environments

abstract: Learning with dataset shift is a major challenge in non-stationary environments wherein the input data distribution may shift over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series shifts its properties, is of utmost interest. Dataset shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptation in a timely manner. This paper presents an adaptive learning algorithm with dataset shift-detection using an exponential weighted moving average (EWMA) model based test in a non-stationary environment. The proposed method initiates the adaptation by reconfiguring the knowledge-base of the classifier. This algorithm is suitable for real-time learning in non-stationary environments. Its performance is evaluated through experiments using synthetic datasets. Results show that it reacts well to different covariate shifts.


title: Model-Coupled Autoencoder for Time Series Visualisation

abstract: We present an approach for the visualisation of a set of time series that combines an echo state network with an autoencoder. For each time series in the dataset we train an echo state network, using a common and fixed reservoir of hidden neurons, and use the optimised readout weights as the new representation. Dimensionality reduction is then performed via an autoencoder on the readout weight representations. The crux of the work is to equip the autoencoder with a loss function that correctly interprets the reconstructed readout weights by associating them with a reconstruction error measured in the data space of sequences. This essentially amounts to measuring the predictive performance that the reconstructed readout weights exhibit on their corresponding sequences when plugged back into the echo state network with the same fixed reservoir. We demonstrate that the proposed visualisation framework can deal both with real valued sequences as well as binary sequences. We derive magnification factors in order to analyse distance preservations and distortions in the visualisation space. The versatility and advantages of the proposed method are demonstrated on datasets of time series that originate from diverse domains.


title: Passive Nonlinear Observer Design for Ships Using Lyapunov Methods: Full-Scale Experiments with a Supply Vessel

abstract: Dynamic positioning (DP) and tracking systems for ships are usually designed under the assumption that the kinematic equations can be linearized about a set of predefined constant yaw angles, typically 36 operating points in steps of 10^o, to cover the whole heading envelope. This is necessary when applying linear (Kalman filter)theory and gain scheduling techniques. However, global exponential stability (GES) cannot be guaranteed if linear theory is used. In this paper a nonlinear observer is derived. The observer is proven to be passive and GES. The number of tuning parameters is reduced to a minimum by using passivity theory. This results in a simple and intuitive tuning procedure. The proposed observer includes features like estimation of both the low-frequency position and velocity of the ship from noisy position measurements, bias state estimation (environmental disturbances) and wave filtering. The nonlinear passive observer has been simulated on a computer model of a supply vessel and implemented on full-scale ships with excellent results.


title: Elderly people at home: technological help in everyday activities

abstract: The aim of this paper is to understand to what extent elderly people are likely to accept a technological aid in performing everyday activities. In this perspective, the present research focused on elderly people's strategies in performing everyday activities at home, in order to understand in what domestic domains technology can be considered an acceptable help. We administered a questionnaire focusing on preferred strategies in carrying out common domestic tasks, and on attitudes towards new technologies and home modification to a sample of 123 elderly people living in Rome. Results show that the adoption of a strategy, including the introduction of technological devices, is highly problem-specific, while personal factors are relevant only in particular situations. With increasing age, people are more inclined to give up, and higher educational levels correspond to more frequent technological solutions.


title: Bot or Not? Deciphering Time Maps for Tweet Interarrivals

abstract: This exploratory study used the R Statistical Software to perform Monte Carlo simulation of time maps, which characterize events based on the elapsed time since the last event and the time that will transpire until the next event, and compare them to time maps from real Twitter users. Time maps are used to explore differences in the interarrival patterns of Tweets between human users, humans who use scheduling services like TweetDeck and HootSuite, and non-human (bot) users. The results indicate that there are differences between the tweet interarrival patterns across these categories of users, and that time maps could potentially be used to automate the detection of bot accounts on Twitter. This could enhance social media intelligence capabilities, help bot developers build more human-like Twitter bots to avoid detection, or both.


title: The influence of chronotype and intelligence on academic achievement in primary school is mediated by conscientiousness, midpoint of sleep and motivation

abstract: Individuals differ in their timing of sleep (bed times, rise times) and in their preference for morning or evening hours. Previous work focused on the relationship between academic achievement and these variables in secondary school students. The main aim of the study is to investigate the relationship between chronotype and academic achievement in 10-year-old children (n = 1125) attending 4th grade of primary school. They filled a cognitive test (Culture Fair Intelligence Test, CFT 20-R) and questions about rise times and bed times, academic achievement, conscientiousness and motivation. We used the “scales for the assessment of learning and performance motivation” (SELLMO; Skalen zur Erfassung der Lern- und Leistungsmotivation for motivation), the short version of the Five-Factor Personality Inventory Children (FFPI-C) to measure conscientiousness, and the Composite Scale of Morningness (CSM) to assess morningness–eveningness. Mean CSM score was 37.84 ± 6.66, midpoint of sleep was 1:36 ± 00:25 and avera...


title: Micronutrient Needs of the Elderly

abstract: Older adults are becoming a significant percentage of the world’s population. A multitude of factors, from the normal aging process to the progression of chronic disease, influence the nutrition needs of this very diverse group of people. Appropriate micronutrient intake is of particular importance but is often suboptimal. Here we review the available data regarding micronutrient needs and the consequences of deficiencies in the ever growing aged population.


title: Classifying Short Text in Social Media: Twitter as Case Study

abstract: With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.


title: Power loss comparison with different PWM methods for 3L-NPC inverter and 3L-T type inverter

abstract: As the increase of large-scale distributed generation system, the interest in the efficiency of the inverter has been higher. In this paper, the power losses are classified and calculated according to the switching states of the three level NPC inverter and three level T-type inverter. And they are compared with the results from the PSiM thermal module simulation. These power loss comparisons are carried out at the different switching frequencies and modulation index values with the different types of PWM methods. From the results, it is verified that the power loss from the T-type inverter is lower than that from the NPC inverter and it can be reduced by using the appropriate PWM method as like the DPWM method.


title: Optimal path planning for a mobile robot using cuckoo search algorithm

abstract: The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.


title: Autonomous Transportation and Deployment with Aerial Robots for Search and Rescue Missions

abstract: It is generally accepted that systems composed of multiple aerial robots with autonomous cooperation capabilities can assist responders in many search and rescue (SAR) scenarios. In most of the previous research work, the aerial robots are mainly considered as platforms for environmental sensing and have not been used to assist victims. In this paper, outdoor field experiments of transportation and accurate deployment of loads with single/multiple autonomous aerial vehicles are presented. This is a novel feature that opens the possibility to use aerial robots to assist victims during rescue phase operations. Accuracy in the deployment location is a critical issue in SAR scenarios in which injured people may have very limited mobility. The presented system is composed of up to three small-size helicopters and features cooperative sensing, using several different sensor types. The system supports several forms of cooperative actuation as well, ranging from the cooperative deployment of small sensors/objects to the coupled transportation of slung loads. The complete system is described, outlining the hardware and software framework used, as well as the approaches for modeling and control used. Additionally, the results of several flight field experiments are presented, including a description of the worldwide first successful autonomous load transportation experiment, using three coupled small-size helicopters (conducted in December 2007). During these experiments strong, steady winds and wind gusts were present. Various solutions and lessons learned from the design and operation of the system are also provided. © 2011 Wiley Periodicals, Inc.


title: Automatic LightBeam Controller for driver assistance

abstract: In this article, we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night-time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will turn low beams on when other vehicles are detected. In addition, low beams output will be selected when the vehicle is in a well lit or urban area. LightBeam Controller is used to assist drivers in controlling vehicle’s beams increasing its correct use, since normally drivers do not switch between high beams and low beams or vice versa when needed. Our system uses a B&W forward looking micro-camera mounted in the windshield area of a C4-Picasso prototype car. Image processing techniques are applied to analyse light sources and to detect vehicles in the images. Furthermore, the system is able to classify between vehicle lights and road signs reflections or nuisance artifacts by means of support vector machines. The algorithm is efficient and able to run in real time. The system has been tested with different video sequences (more than 7 h of video sequences) under real night driving conditions in different roads of Spain. Experimental results, a comparison with other representative state of the art methods and conclusions about the system performance are presented.


title: Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms

abstract: Human level recall performance in detecting breast cancer considering microcalcifications from mammograms has a recall value between 74.5% and 92.3%. In this research, we approach to breast microcalcification classification problem using convolutional neural networks along with various preprocessing methods such as contrast scaling, dilation, cropping etc. and decision fusion using ensemble of networks. Various experiments on Digital Database for Screening Mammography dataset showed that preprocessing poses great importance on the classification performance. The stand-alone models using the dilation and cropping preprocessing techniques achieved the highest recall value of 91.3%. The ensembles of the stand-alone models surpass this recall value and a 97.3% value of recall is achieved. The ensemble having the highest F1 Score (harmonic mean of precision and recall), which is 94.5%, has a recall value of 94.0% and a precision value of 95.0%. This recall is still above human level performance and the models achieve competitive results in terms of accuracy, precision, recall and F1 score measures.


title: $ N^4 $-Fields: Neural Network Nearest Neighbor Fields for Image Transforms

abstract: We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation. The architecture is based on a simple combination of convolutional neural networks with the nearest neighbor search.


title: Combining link and content for community detection: a discriminative approach

abstract: In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. These generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. To explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. First, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. Second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. These two models are unified seamlessly via the community memberships. We present efficient algorithms to solve the related optimization problems based on bound optimization and alternating projection. Extensive experiments with benchmark data sets show that the proposed framework significantly outperforms the state-of-the-art approaches for combining link and content analysis for community detection.


title: Poly A-linked non-isotopic microtiter plate reverse transcriptase assay for sensitive detection of clinical human immunodeficiency virus isolates.

abstract: A colorimetric reverse transcriptase assay (cRT assay) was developed for quantitative detection of HIV-1. In this format, reverse transcriptase incorporates biotin-labeled dUTP onto oligo-dT primers hybridized to poly A templates. The templates are covalently bound to the surface of microtiter wells. The amount of incorporated biotin-labeled dUTP is measured by binding horseradish peroxidase conjugated streptavidin, washing away unbound peroxidase, adding colorimetric substrate and then reading with a standard colorimetric reader. The sensitivity of the assay is very good. As little as 3 x 10(5) molecules of recombinant HIV-RT can be detected after 20 h of reaction time. Direct comparison using 3 cultured clinical isolates indicates that this level of detection is equivalent to the commercially available p24 antigen capture assay and the HIV-RNA assay based on branched DNA signal amplification. Other retroviruses, such as HIV-2 and feline immunodeficiency virus (FIV), can also be detected in this format. This non-isotopic assay is easy to perform and could provide a convenient and quantitative method for HIV study by monitoring reverse transcriptase, an essential activity in the infection process.


title: A novel approach to manage cloud security SLA incidents

abstract: Abstract Cloud computing is increasingly playing an important role in the service provisioning domain given the economic and technological benefits it offers. The popularity of cloud services is increasing but so are their customers’ concerns about security assurance and transparency of the Cloud Service Providers (CSPs). This is especially relevant in the case of critical services that are progressively moving to the cloud. Examples include the integrated European air traffic control system or public administrations through the governmental clouds. Recent efforts aim to specify security in cloud by using security service level agreements (secSLAs). However, the paucity of approaches to actually control the fulfillment of secSLAs and to react in case of security breaches, often results in distrust in cloud services. In this paper, we present a solution to monitor and enforce the fulfillment of secSLAs. Our framework is able to (a) detect occurrences that lead to unfulfillment of commitments, and (b) also provide mitigation to the harmful events that may or do compromise the validity of secSLAs.


title: Basic C Code Implementations for AVR Microcontrollers

abstract: The main purpose of this article is to summarize the possibilities of a C code implementation for the AVR microcontrollers. The process of implementation is presented in this paper and the basic differences between two famous compilers for AVRs: GCC and CodeVisionAVR are presented as well. Whole paper is composed as a useful tool for university students/teachers and brings the guideline how to use the microcontrollers' interrupt sources but also it brings the powerful combination of assembly routines called from the C functions without a time consuming study of huge manuals and datasheets. Short examples of concrete source codes for GCC avr-libe are presented in the text.


title: The Ins and Outs of Sexual Imaging

abstract: > Golf and sex are the only two things you don't have to be good at to enjoy. ::: > ::: > Kevin Costner in Tin Cup ::: What happens inside the human body during sexual intercourse has fascinated artists, scientists, and the public since the start of humanity. Leonardo da Vinci (1452–1519) drew the internal


title: Fixing Resource Leaks in Android Apps with Light-Weight Static Analysis and Low-Overhead Instrumentation

abstract: Fixing bugs according to bug reports is a labor-intensive work for developers and automatic techniques can effectively decrease the manual efforts. A feasible solution is to fix specific bugs by static analysis and code instrumentation. In this paper, we present a light-weight approach to fixing the resource leak bugs that exist widely in Android apps while guaranteeing the safety that the patches should not interrupt normal execution of the original program. This approach first performs a light-weight static analysis and then carefully designs the concise patch code that will be inserted into the byte-code. When the program is running, the patches will trace the state of leaked resources and release them in a proper place. Our experiments on dozens of real-world apps show that our approach can effectively fix resource leaks in the apps with negligible extra execution time and less than 4% extra code in a few seconds.


title: Classifying EEG Recordings of Rhythm Perception

abstract: Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. In this paper, we present first classification results using deep learning techniques on EEG data recorded within a rhythm perception study in Kigali, Rwanda. We tested 13 adults, mean age 21, who performed three behavioral tasks using rhythmic tone sequences derived from either East African or Western music. For the EEG testing, 24 rhythms ‐ half East African and half Western with identical tempo and based on a 2-bar 12/8 scheme ‐ were each repeated for 32 seconds. During presentation, the participants’ brain waves were recorded via 14 EEG channels. We applied stacked denoising autoencoders and convolutional neural networks on the collected data to distinguish African and Western rhythms on a group and individual participant level. Furthermore, we investigated how far these techniques can be used to recognize the individual rhythms.


title: Vetting undesirable behaviors in android apps with permission use analysis

abstract: Android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system. This paper presents VetDroid, a dynamic analysis platform for reconstructing sensitive behaviors in Android apps from a novel permission use perspective. VetDroid features a systematic framework to effectively construct permission use behaviors, i.e., how applications use permissions to access (sensitive) system resources, and how these acquired permission-sensitive resources are further utilized by the application. With permission use behaviors, security analysts can easily examine the internal sensitive behaviors of an app. Using real-world Android malware, we show that VetDroid can clearly reconstruct fine-grained malicious behaviors to ease malware analysis. We further apply VetDroid to 1,249 top free apps in Google Play. VetDroid can assist in finding more information leaks than TaintDroid, a state-of-the-art technique. In addition, we show how we can use VetDroid to analyze fine-grained causes of information leaks that TaintDroid cannot reveal. Finally, we show that VetDroid can help identify subtle vulnerabilities in some (top free) applications otherwise hard to detect.


title: Spatially adaptive FLIP fluid simulations in bifrost

abstract: Spatial adaptivity has great promise for fluid simulation in visual effects. Given that a 2× increase in spatial resolution incurs an 8× increase in memory for 3D, using high resolution only where needed can offer a huge savings for many interesting scenarios. For example, figure 1 shows a large-scale simulation of a creature emerging from deep water: the depth is critical for the bulk motion, but the velocity field away from the creature and surface is relatively smooth and does not require as high resolution. Adaptive fluid simulation was proposed for graphics over a decade ago [Losasso et al. 2004], and the combination of FLIP and octrees seven years ago [Hong et al. 2009], yet it has been slow to catch on in the industry. Instead, FLIP with uniform resolution sparse voxel tiles has emerged as the standard approach. Sparse voxel tiles efficiently cover arbitrary domains, give excellent cache usage and low data structure overhead, and allow the use of popular voxel algorithms for level set computations etc. with barely any changes. By comparison, typical adaptive octree and/or tetrahedral mesh approaches incur sizeable overheads and may rule out many standard voxel algorithms. SPGrid [Setaluri et al. 2014] is one recent option for efficiently achieving octree-like adaptivity via multiple levels of sparse voxel tiles; in this talk we discuss a more aggressive approach using a wide-branching tile tree of voxels. We have fully implemented this in Bifrost in Autodesk Maya, demonstrating that even in uniform resolution we achieve the performance of optimized sparse voxel tile code, and that spatial adaptivity gives significant further improvements in both memory and runtime.


title: Multi-microgrid control systems (MMCS)

abstract: The objective of this paper is to present a new concept related to the revitalized microgrid concept and the paradigm of the smart grid. A combination of a rapidly aging North American power infrastructure, a clear trend towards distributed generation, and an emphasis on electrical reliability has spurred a shift toward a more distributed, decentralized power grid. The concept of the microgrid has dominated the discussion of power distribution on a localized level, while the smart grid model has been touted as the new macrogrid design paradigm. In order to bring together the microgrid and smart grid concepts, we propose the MMCS concept, which divide distribution systems into numerous microgrid-like regions, or MMCS regions, to facilitate the smart grid concepts of self-healing, intentional islanding, and anticipation for the entire distribution network while inheriting the dynamics of a microgrid. We implement the MMCS concept through the use of multi-agent systems.


title: Modeling and simulation of a PV pumping system under real climatic conditions

abstract: This article presents the modeling and simulation of a PV pumping system operating over the sun which has been developed under PSIM environment. The main objective is to optimize the PV pumping system through the introduction of two stages. The first one concerns the insertion of a DC-DC converter working with an MPPT in order to get the maximum power from the PV array. The second concerns the optimization of the DC-AC inverter which is controlled through a PWM control in order to get good voltage and current outputs spectrum with reduced losses and harmonics rate. The improvements brought by the MPPT and the PWM on the PV pumping system are shown through the example treated under real climatic conditions.


title: Maximum Complex Task Assignment: Towards Tasks Correlation in Spatial Crowdsourcing

abstract: Spatial crowdsourcing has gained emerging interest from both research communities and industries. Most of current spatial crowdsourcing frameworks assume independent and atomic tasks. However, there could be some cases that one needs to crowdsource a spatial complex task which consists of some spatial sub-tasks (i.e., tasks related to a specific location). The spatial complex task's assignment requires assignments of all of its sub-tasks. The currently available frameworks are inapplicable to such kind of tasks. In this paper, we introduce a novel approach to crowdsource spatial complex tasks. We first formally define the Maximum Complex Task Assignment (MCTA) problem and propose alternative solutions. Subsequently, we perform various experiments using both real and synthetic datasets to investigate and verify the usability of our proposed approach.


title: Adaptability of Neural Networks on Varying Granularity IR Tasks

abstract: Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks. Deep neural networks (DNN) are capable of learning ideal representations of data during the training process, removing the need for independently extracting features. However, the structures of these DNNs are often tailored to perform on specific datasets. In addition, IR tasks deal with text at varying levels of granularity from single factoids to documents containing thousands of words. In this paper, we examine the role of the granularity on the performance of common state of the art DNN structures in IR.


title: Visual Methods for Analyzing Time-Oriented Data

abstract: Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose, we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.


title: Approximate Newton methods for policy search in Markov decision processes

abstract: Approximate Newton methods are standard optimization tools which aim to maintain the benefits of Newton's method, such as a fast rate of convergence, while alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov decision processes (MDPs). We first analyse the structure of the Hessian of the total expected reward, which is a standard objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods drop certain terms in the Hessian. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to afine transformation of the parameter space and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EM-algorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.


title: Application of prebiotics and probiotics in poultry production

abstract: The intestinal microbiota, epithelium, and immune system provide resistance to enteric pathogens. Recent data suggest that resistance is not solely due to the sum of the components, but that cross-talk between these components is also involved in modulating this resistance. Inhibition of pathogens by the intestinal microbiota has been called bacterial antagonism, bacterial interference, barrier effect, colonization resistance, and competitive exclusion. Mechanisms by which the indigenous intestinal bacteria inhibit pathogens include competition for colonization sites, competition for nutrients, production of toxic compounds, or stimulation of the immune system. These mechanisms are not mutually exclusive, and inhibition may comprise one, several, or all of these mechanisms. Consumption of fermented foods has been associated with improved health, and lactic acid bacteria (lactobacilli and bifidobacteria) have been implicated as the causative agents for this improved health. Research over the last century has shown that lactic acid bacteria and certain other microorganisms can increase resistance to disease and that lactic acid bacteria can be enriched in the intestinal tract by feeding specific carbohydrates. Increased bacterial resistance to antibiotics in humans has caused an increase in public and governmental interest in eliminating sub-therapeutic use of antibiotics in livestock. An alternative approach to sub-therapeutic antibiotics in livestock is the use of probiotic microorganisms, prebiotic substrates that enrich certain bacterial populations, or synbiotic combinations of prebiotics and probiotics. Research is focused on identifying beneficial bacterial strains and substrates along with the conditions under which they are effective.


title: Dataset Shift in Machine Learning

abstract: Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Mller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schlkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series


title: Young Romanian Scientists ’ Journal Reproducibility in computer-intensive sciences

abstract: Basic ethics in modern science leads to the conditioning of acceptance of the validity of research results by disclosure of enough detail on methods as to allow reproduction of research by another person than the original researcher. Of course, peer review does not go to the length of actually reproducing the research results; but the reviewer must have at least sensible reason to believe that the result can be reproduced. Besides its ethical value, reproducibility serves as a practical foundation for the advancement of science. Many times, new research proceeds by changing slightly an equation or the conditions of an experiment, and then by adding incremental changes while making sure that no errors have been introduced. The first step in this process is the actual reproduction of the previous research. Other times, validation of results obtained by new methods needs comparison with results obtained by already established methods. This again involves redoing some old work. During the last decades, technological advances have developed amazing computer power, and with it, the possibility of undertaking equally amazing virtual scientific experiments and calculations. This power has brought complexity too great to be fully described in the limited number of pages of readable scientific papers. The consequence is the slow death of effective reproducibility. A page full of equations and two figures can be the tip of an iceberg consisting in months of programming. Printing the code, which made so easy to understand and implement the algorithms in many scientific books and articles of the 70's, is unusual now, due to the large number of lines of code. Even if the code itself is known, the precise parameters and input data used to create the figures are missing. When the number of such input parameters is quite large, this alone can make the reproduction of the previous work intractable. Combining it with poor code documentation does not help either. The unknown value of an obscure numerical parameter without any physical meaning can make the difference between being or not being able to reproduce the results of the previous research.


Foreign Capital and Economic Performance of Pakistan


title: Static and dynamic 3D facial expression recognition: A comprehensive survey☆

abstract: Automatic facial expression recognition constitutes an active research field due to the latest advances in computing technology that make the user's experience a clear priority. The majority of work conducted in this area involves 2D imagery, despite the problems this presents due to inherent pose and illumination variations. In order to deal with these problems, 3D and 4D (dynamic 3D) recordings are increasingly used in expression analysis research. In this paper we survey the recent advances in 3D and 4D facial expression recognition. We discuss developments in 3D facial data acquisition and tracking, and present currently available 3D/4D face databases suitable for 3D/4D facial expressions analysis as well as the existing facial expression recognition systems that exploit either 3D or 4D data in detail. Finally, challenges that have to be addressed if 3D facial expression recognition systems are to become a part of future applications are extensively discussed.


title: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

abstract: Many years have passed since Baesens et al. published their benchmarking study of classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.]. The interest in prediction methods for scorecard development is unbroken. However, there have been several advancements including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. To close these research gaps, we update the study of Baesens et al. and compare several novel classification algorithms to the state-of-the-art in credit scoring. In addition, we examine the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy. Finally, we explore whether more accurate classifiers are managerial meaningful. Our study provides valuable insight for professionals and academics in credit scoring. It helps practitioners to stay abreast of technical advancements in predictive modeling. From an academic point of view, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.


title: Printed intelligence for consumer products

abstract: In the Ropas project VTT has developed printed humidity sensors on paper substrate. The goal has been to tailor the humidity sensor performance to be suitable for consumer product use such as on package labels. Printed sensors are thin and flexible, because they are directly printed on paper or plastic substrates. When printed on paper substrate disposability or recyclability is achieved thus making them environmental friendly products. The use of printing technologies also provides cost-effectiveness, because of the use of inexpensive materials, low material consumption and mass-scale fast manufacturing processes.


title: Murder-suicide by carbon dioxide (CO2) poisoning: a family case from Berlin, Germany

abstract: This report demonstrates how carbon dioxide (CO(2)) may be a potent weapon in murder-suicide, where the death scene offers virtually no clues as to the lethal modality and the autopsy findings are nonspecific. Four bodies were discovered in an apartment in midsummer 2012 in Berlin, Germany. The bodies were those of a father (a 69-year-old business consultant), his wife (aged 26-years), and two sons (aged 3 and 6 years, respectively). The police found the wife and two sons lying in their beds and the husband in a supine position on the floor with a plastic bag over his head tied loosely around his neck with a rope. A 500 g single-use CO(2) cylinder was standing on the floor. The container was almost empty and according to the label had been sold as a CO(2)-fertilizer for aquarium plants. Two synthetic inhalation face masks and tubing were also found, which tested positive for the DNA of all four deceased family members. It is hypothesized that the husband placed an inhalation mask over the mouths and noses of his wife and children while they were sleeping. Inhalation of pure CO(2) ensured their rapid unconsciousness due to hypercapnia and severe anoxia. The rapid increase in CO(2) concentration would render a victim helpless, with no time to wake and defend themselves, or others. The proximate cause of death in all cases was attributed to CO(2) intoxication, based on the scene findings, the reconstructed sequence of events, the autopsy, and results of toxicological studies.


title: MAFIA: a maximal frequent itemset algorithm for transactional databases

abstract: We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In a thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.


title: Brave and Cautious Reasoning in EL

abstract: Developing and maintaining ontologies is an expensive and error-prone task. After an error is detected, users may have to wait for a long time before a corrected version of the ontology is available. In the meantime, one might still want to derive meaningful knowledge from the ontology, while avoiding the known errors. We introduce brave and cautious reasoning and show that it is hard for EL. We then propose methods for improving the reasoning times by precompiling information about the known errors and using proof-theoretic techniques for computing justifications. A prototypical implementation shows that our approach is feasible for large ontologies used in practice.


title: Knowledge management as a catalyst for innovation within organizations: a qualitative study

abstract: The need for organizations to innovate and furthermore to ceaselessly innovate is stressed throughout the modern management literature on innovation. This need comes from increasing competition and customer demands and new market areas. Closely linked, but not synonymous, with innovation is the body of knowledge referred to collectively as knowledge management. Within this discourse knowledge is considered as a potential key competitive advantage, by helping to increase innovation within the organization. This paper focuses on the role of knowledge management in sustaining and enhancing innovation in organizations. In particular the paper seeks to establish a knowledge management model within which the principles of innovation can be incorporated. First, there is a brief review of the innovation and knowledge management literature and their respective synergies. From this literature a possible knowledge management model which incorporates innovation is suggested. Second, a research study is discussed which seeks to further examine and develop the model using an inductive grounded theory approach. The study involved socially constructed workshops representing 25 organizations, each of which constructed meanings in regard to innovation and the key areas of knowledge management as outlined in the model. Overall it was found that effective systematic knowledge management can incorporate innovation drivers in key areas which will result in both increased business and employee benefits. Copyright # 2000 John Wiley & Sons, Ltd.


title: Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

abstract: The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs.


SURF-based Detection of Copy-Move Forgery in Flat Region


title: A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data

abstract: This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k-NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.


title: A Comparison of Feature Combination Strategies for Saliency-Based Visual Attention Systems

abstract: Bottom-up or saliency-based visual attention allows primates to detect nonspecific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable “spotlight.” We use a model that reproduces the attentional scan paths of this spotlight. Simple multi-scale “feature maps” detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique “master” or “saliency” map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a threefold to eightfold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to fourfold improvement, while preserving generality.


title: Occlusion and Disocclusion Time Changes in Single Unit Crowns Designed by Functional Generated Path Technique: A Randomised Clinical Trial

abstract: Although it is believed that implementation of the functional generated path (FGP) technique can facilitate occlusal surface design for restorations, it has not been objectively compared in situ with the conventional fabrication yet. Therefore, in the present study, a single-blind crossover clinical trial was conducted using T-scan to compare changes in occlusion time (OT) and disocclusion time (DT) of single posterior artificial crowns designed differently using FGP technique (FGP), average-value FGP technique (AVR) and conventional fabrication (CON). Each of the 10 participants took part in the study tried three artificial crowns in different sequences according to a computer generated randomization list. The results objectively revealed that changes in OT and DT were significantly smaller for FGP than CON (P < 0.05) and considerably smaller for AVR than CON, respectively. The subjective feedback and the occlusal adjusting time were better and shorter for FGP and AVR than CON (P < 0.05). No harm to the participants occurred. Overall, FGP is an efficient technique showing more physiological harmonious relationship with the articulating system.


title: Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

abstract: The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.


title: Challenging the five-stage model for e-learning: a new approach

abstract: The five-stage approach to e-moderating has provided a coherent model upon which to base online learning design in higher education. However, despite its growing popularity, there are concerns that the model is becoming a dominant discourse, being adapted as a template for the design of all online teaching and learning, to the exclusion of other ideas. It is suggested that the five-stage model may not be the panacea it appears and alternative models of e-learning cannot be ignored. This paper reviews the five-stage model and contrasts it with a new conceptual model, ‘the e-learning ladder’, conceived as part of research with healthcare students in the higher education setting. DOI: 10.1080/09687760601129588


title: Complementary Switching in Oxide-Based Bipolar Resistive-Switching Random Memory

abstract: Resistive-switching random access memory (RRAM) devices utilizing a crossbar architecture represent a promising alternative for Flash replacement in high-density data storage applications. However, RRAM crossbar arrays require the adoption of diodelike select devices with high on-off -current ratio and with sufficient endurance. To avoid the use of select devices, one should develop passive arrays where the nonlinear characteristic of the RRAM device itself provides self-selection during read and write. This paper discusses the complementary switching (CS) in hafnium oxide RRAM, where the logic bit can be encoded in two high-resistance levels, thus being immune from leakage currents and related sneak-through effects in the crossbar array. The CS physical mechanism is described through simulation results by an ion-migration model for bipolar switching. Results from pulsed-regime characterization are shown, demonstrating that CS can be operated at least in the 10-ns time scale. The minimization of the reset current is finally discussed.


title: A 1-V 140-/spl mu/W 88-dB audio sigma-delta modulator in 90-nm CMOS

abstract: A single-loop third-order switched-capacitor /spl Sigma/-/spl Delta/ modulator in 90-nm standard digital CMOS technology is presented. The design is intended to minimize the power consumption in a low-voltage environment. A load-compensated OTA with rail-to-rail output swing and gain enhancement is chosen in this design, which provides higher power efficiency than the two-stage OTA. To lower the power consumption further, class-AB operation is also adapted in the OTA design. Due to the relatively low threshold voltage of the advanced technology, no clock bootstrapping circuits are needed to drive the switches and the power consumption of the digital circuits is reduced. All the capacitors are implemented using multilayer metal-wall structure, which can provide high-density capacitance. The modulator achieves 88-dB dynamic range in 20-kHz signal bandwidth with an oversampling ratio of 100. The power consumption is 140 /spl mu/W under 1-V supply voltage and the chip core size is 0.18 mm/sup 2/.


title: A Bidirectional LSTM Approach with Word Embeddings for Sentence Boundary Detection

abstract: Recovering sentence boundaries from speech and its transcripts is essential for readability and downstream speech and language processing tasks. In this paper, we propose to use deep recurrent neural network to detect sentence boundaries in broadcast news by modeling rich prosodic and lexical features extracted at each inter-word position. We introduce an unsupervised word embedding to represent word identity, learned from the Continuous Bag-of-Words (CBOW) model, into sentence boundary detection task as an effective feature. The word embedding contains syntactic information that is essential for this detection task. In addition, we propose another two low-dimensional word embeddings derived from a neural network that includes class and context information to represent words by supervised learning: one is extracted from the projection layer, the other one comes from the last hidden layer. Furthermore, we propose a deep bidirectional Long Short Term Memory (LSTM) based architecture with Viterbi decoding for sentence boundary detection. Under this framework, the long-range dependencies of prosodic and lexical information in temporal sequences are modeled effectively. Compared with previous state-of-the-art DNN-CRF method, the proposed LSTM approach reduces 24.8% and 9.8% relative NIST SU error in reference and recognition transcripts, respectively.


title: Personality Neuroscience and the Biology of Traits

abstract: Personality neuroscience involves the use of neuroscience methods to study individual differences in behavior, motivation, emotion, and cognition. Personality psychology has contributed much to identifying the important dimensions of personality, but relatively little to understanding the biological sources of those dimensions. However, the rapidly expanding field of personality neuroscience is increasingly shedding light on this topic. This article provides a survey of progress in the use of neuroscience to study personality traits, organized using a hierarchical model of traits based on the Big Five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄Intellect. Evidence is reviewed for hypotheses about the biological systems involved in each trait.


title: A committee of neural networks for traffic sign classification

abstract: We describe the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate of 98.98%.We obtain an even better recognition rate of 99.15% by further training the nets. Our fast, fully parameterizable GPU implementation of a Convolutional Neural Network does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. A CNN/MLP committee further boosts recognition performance.


Punctuation Prediction for Unsegmented Transcript Based on Word Vector.


title: Big-data security management issues

abstract: Big data phenomenon arises from the increasing number of data collected from various sources, including the internet. Big data is not only about the size or volume. Big data posses specific characteristics (volume, variety, velocity, and value - 4V) that make it difficult to manage from security point of view. The evolution of data to become big data rises another important issues about data security and its management. NIST defines guide for conducting risk assessments on data, including risk management process and risk assessment. This paper looks at NIST risk management guidance and determines whether the approach of this standard is applicable to big data by generally define the threat source, threat events, vulnerabilities, likelihood of occurence and impact. The result of this study will be a general framework defining security management on Big data.


title: Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

abstract: The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.


title: Effect of Low Level Laser Therapy on Chronic Compression of the Dorsal Root Ganglion

abstract: Dorsal root ganglia (DRG) are vulnerable to physical injury of the intervertebral foramen, and chronic compression of the DRG (CCD) an result in nerve root damage with persistent morbidity. The purpose of this study was to evaluate the effects of low level laser therapy (LLLT) on the DRG in a CCD model and to determine the mechanisms underlying these effects. CCD rats had L-shaped stainless-steel rods inserted into the fourth and fifth lumbar intervertebral foramen, and the rats were then subjected to 0 or 8 J/cm2 LLLT for 8 consecutive days following CCD surgery. Pain and heat stimuli were applied to test for hyperalgesia following CCD. The levels of TNF-α, IL-1β and growth-associated protein-43 (GAP-43) messenger RNA (mRNA) expression were measured via real-time PCR, and protein expression levels were analyzed through immunohistochemical analyses. Our data indicate that LLLT significantly decreased the tolerable sensitivity to pain and heat stimuli in the CCD groups. The expression levels of the pro-inflammatory cytokines TNF-α and IL-1β were increased following CCD, and we found that these increases could be reduced by the application of LLLT. Furthermore, the expression of GAP-43 was enhanced by LLLT. In conclusion, LLLT was able to enhance neural regeneration in rats following CCD and improve rat ambulatory behavior. The therapeutic effects of LLLT on the DRG during CCD may be exerted through suppression of the inflammatory response and induction of neuronal repair genes. These results suggest potential clinical applications for LLLT in the treatment of compression-induced neuronal disorders.


title: Survey on Vision-based Path Prediction

abstract: Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.


Hierarchical Convolutional Attention Networks for Text Classification


title: Deep Learning in Physical Layer Communications

abstract: DL has shown great potential to revolutionize communication systems. This article provides an overview of the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with the block structure, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing DL-based end-to-end communication systems. Finally, potential research directions are identified to boost intelligent physical layer communications. Introduction


FACTORS ASSOCIATED WITH PROXY KNOWLEDGE OF EMPLOYMENT-RELATED INFORMATION


title: Feed-Forward Scheme for an Electrolytic Capacitor-Less AC/DC LED Driver to Reduce Output Current Ripple

abstract: In order to achieve high-efficiency, high-power-factor, high-reliability, and low-cost, a flicker-free electrolytic capacitor-less single-phase ac/dc light emitting diode (LED) driver is investigated in this paper. This driver is composed of a power-factor-correction (PFC) converter and a bidirectional converter. The bidirectional converter is used to absorb the second harmonic component in the output current of the PFC converter, thus producing a pure dc output to drive the LEDs. The spectrum of the output capacitor voltage of the bidirectional converter is analyzed, indicating that the output capacitor voltage contains harmonic components at multiples of twice the line frequency apart from the dc component and second harmonic component. A feed-forward control scheme with a series of calculation operation is proposed to obtain the desired modulation signal, which contains the corresponding harmonic components, to ensure the bidirectional converter fully absorb the second harmonic current in the output of the PFC converter. Finally, a 33.6 W prototype is fabricated and tested in the lab, and the experiment results show that the proposed control scheme greatly reduces the ripple of the LED driving current.


title: Glycolysis and oxidative phosphorylation as a function of cytosolic phosphorylation state and power output of the muscle cell

abstract: A mathematical description of the regulation of ATP production in muscle cells is presented whereby the activity of OxP can be calculated as a function of (1) free [ADP] as the substrate and (2) a second driving force PhiDelta G (kilojoules per mole) resulting from the difference of free energy Delta G(ox,ap) (kilojoules per mole)-Delta G(ATP,cyt) (kilojoules per mole). In turn, the term Delta G(ox,ap) results from the proton motive force and the generation of ATP in the matrix space including the ATP-ADP exchange, whereas the phosphorylation state of the CHEP-sytem is described by Delta G(ATP,cyt). Regulation of glycolysis is calculated as a function of free [ADP] and [AMP] at the level of PFK. The PFK is inhibited by a decreasing pH resulting from lactate accumulation. The ATP/PCr equilibrium of the CHEP-system is calculated by algebraic equations. The dynamic behaviour of the metabolic control of ATP production as a function of ATP consumption is calculated by a system of two 1st-order non-linear differential equations, including a time delay considering oxygen transport. Lactate distribution and elimination is calculated using a two-compartment model with an active lactate producing, and a passive, space including lactate elimination by combustion. The simulation of the dynamics of energy metabolism of muscle cells is performed by the stepwise solution of the differential equations with a 5th-order Runge-Kutta-Fehlberg-routine. Examples of various applications of the simulation of the dynamics of energy supply demonstrate the qualitative and quantitative congruence to the behaviour of metabolic processes in experiments during rest, exercise and recovery.


title: The Operational Analysis of Queueing Network Models

abstract: Queueing network models have proved to be cost effectwe tools for analyzing modern computer systems. This tutorial paper presents the basic results using the operational approach, a framework which allows the analyst to test whether each assumption is met in a given system. The early sections describe the nature of queueing network models and their apphcations for calculating and predicting performance quantitms The basic performance quantities--such as utilizations, mean queue lengths, and mean response tunes--are defined, and operatmnal relationships among them are derwed Following this, the concept of job flow balance is introduced and used to study asymptotic throughputs and response tunes. The concepts of state transition balance, one-step behavior, and homogeneity are then used to relate the proportions of time that each system state is occupied to the parameters of job demand and to dewce charactenstms Efficmnt methods for computing basic performance quantities are also described. Finally the concept of decomposition is used to stmphfy analyses by replacing subsystems with equivalent devices. All concepts are illustrated liberally with examples


title: Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey

abstract: Vehicle-to-anything (V2X) communications refer to information exchange between a vehicle and various elements of the intelligent transportation system (ITS), including other vehicles, pedestrians, Internet gateways, and transport infrastructure (such as traffic lights and signs). The technology has a great potential of enabling a variety of novel applications for road safety, passenger infotainment, car manufacturer services, and vehicle traffic optimization. Today, V2X communications is based on one of two main technologies: dedicated short-range communications (DSRC) and cellular networks. However, in the near future, it is not expected that a single technology can support such a variety of expected V2X applications for a large number of vehicles. Hence, interworking between DSRC and cellular network technologies for efficient V2X communications is proposed. This paper surveys potential DSRC and cellular interworking solutions for efficient V2X communications. First, we highlight the limitations of each technology in supporting V2X applications. Then, we review potential DSRC-cellular hybrid architectures, together with the main interworking challenges resulting from vehicle mobility, such as vertical handover and network selection issues. In addition, we provide an overview of the global DSRC standards, the existing V2X research and development platforms, and the V2X products already adopted and deployed in vehicles by car manufactures, as an attempt to align academic research with automotive industrial activities. Finally, we suggest some open research issues for future V2X communications based on the interworking of DSRC and cellular network technologies.


title: Predicting Experiential Qualities of Architecture by Its Spatial Properties

abstract: While experiential qualities of rectangular architectural spaces can be effectively predicted from their proportions and area (Franz, von der Heyde, & Bulthoff, 2003), these factors obviously cannot be directly transferred on open-plan indoor spaces. We introduce an approach that relates experiential qualities of arbitrarily shaped architectural spaces to their spatial form using isovists (Benedikt, 1979) that allow to generically describe spatial properties. In an exploratory psychological experiment, 33 characteristic values derived from the isovists were tested on their predictive power on experiential qualities by correlating them with averaged affective appraisals. Thirty-four virtual reality simulated indoor scenes were rated by 2x8 participants using the semantic differential. Particularly measurands capturing the qualities spaciousness, openness, complexity, and order turned out to be effective predictor variables. The findings are discussed in terms of evolutionary and information rate related theories of environmental preferences.


title: Diagnosis and Management of Filler Adverse Effects: An Algorithm

abstract: Fillers are frequently used in beautifying procedures. Despite major advancements of the chemical and biological features of injected materials, filler-related adverse events may occur, and can substantially impact the clinical outcome. Filler granulomas become manifest as visible grains, nodules, or papules around the site of the primary injection. Early recognition and proper treatment of filler-related complications is important because effective treatment options are available. In this report, we provide a comprehensive overview of the differential diagnosis and diagnostics and develop an algorithm of successful therapy regimens.


title: Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?

abstract: Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the sensor and coupling it with binarized mixed-signal image sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22nm SOI technology shows a silicon area of 2.61mm2 for implementing a combinational BNN with 32x32 binary input sensor receptive field and weight parameters fixed at design time. This is 2.2x smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-sensor processing, our approach features a 10x higher energy efficiency.


title: A coverage-preserving node scheduling scheme for large wireless sensor networks

abstract: In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime, as well as maintain sufficient sensing coverage and reliability. In this paper, we propose a node-scheduling scheme, which can reduce system overall energy consumption, therefore increasing system lifetime, by turning off some redundant nodes. Our coverage-based off-duty eligibility rule and backoff-based node-scheduling scheme guarantees that the original sensing coverage is maintained after turning off redundant nodes. We implement our proposed scheme in NS-2 as an extension of the LEACH protocol. We compare the energy consumption of LEACH with and without the extension and analyze the effectiveness of our scheme in terms of energy saving. Simulation results show that our scheme can preserve the system coverage to the maximum extent. In addition, after the node-scheduling scheme turns off some nodes, certain redundancy is still guaranteed, which we believe can provide enough sensing reliability in many applications.


title: BENEFITS FROM USING CONTINUOUS RATING SCALES IN ONLINE SURVEY RESEARCH

abstract: Likert-type scales require individuals to choose between a limited number of choices, and have been criticized in the scholarly literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but are rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. We use a real-world sample and a simulation to illustrate how noise impacts our data set. A noise level of 10% has only a small effect on both classical and robust estimates, but when 20% of noise is added, the classical estimators become severely distorted.


title: Construction heuristics for two-dimensional irregular shape bin packing with guillotine constraints

abstract: The paper examines a new problem in the irregular packing literature that has many applications in industry: two-dimensional irregular (convex) bin packing with guillotine constraints. Due to the cutting process of certain materials, cuts are restricted to extend from one edge of the stock-sheet to another, called guillotine cutting. This constraint is common place in glass cutting and is an important constraint in two-dimensional cutting and packing problems. In the literature, various exact and approximate algorithms exist for finding the two dimensional cutting patterns that satisfy the guillotine cutting constraint. However, to the best of our knowledge, all of the algorithms are designed for solving rectangular cutting where cuts are orthogonal with the edges of the stock-sheet. In order to satisfy the guillotine cutting constraint using these approaches, when the pieces are non-rectangular, practitioners implement a two stage approach. First, pieces are enclosed within rectangle shapes and then the rectangles are packed. Clearly, imposing this condition is likely to lead to additional waste. This paper aims to generate guillotine-cutting layouts of irregular shapes using a number of strategies. The investigation compares three two-stage approaches: one approximates pieces by rectangles, the other two approximate pairs of pieces by rectangles using a cluster heuristic or phi-functions for optimal clustering. All three approaches use a competitive algorithm for rectangle bin packing with guillotine constraints. Further, we design and implement a one-stage approach using an adaptive forest search algorithm. Experimental results show the one-stage strategy produces good solutions in less time over the two-stage approach.


title: Specifying access control policies on data streams

abstract: Many data stream processing systems are increasingly being used to support applications that handle sensitive information, such as credit card numbers and locations of soldiers in battleground [1,2,3,6]. These data have to be protected from unauthorized accesses. However, existing access control models and mechanisms cannot be adequately adopted on data streams. In this paper, we propose a novel access control model for data streams based on the Aurora data model [2]. Our access control model is role-based and has the following components. Objects to be protected are essentially views (or rather queries) over data streams. We also define two types of privileges - Read privilege for operations such as Filter, Map, BSort, and a set of aggregate privileges for operations such as Min, Max, Count, Avg and Sum. The model also allows the specification of temporal constraints either to limit access to data during a given time bound or to constraint aggregate operations over the data within a specified time window. In the paper, we present the access control model and its formal semantics.


title: Specific Impairment of Smiling Increases the Severity of Depressive Symptoms in Patients with Facial Neuromuscular Disorders

abstract: Depressive symptoms and related emotional distress are prevalent among patients with facial neuromuscular disorders, and the psychological distress impacts the functional disabilities associated with the facial impairment. A specific impairment in the ability to smile may elevate the risk for depression, with patients experiencing a reduced physiological feedback associated with smiling as well as the social consequences of the inability to communicate positive emotion. We tested the hypothesis that specific impairments in the ability to smile increase the severity of depressive symptoms in patients with facial neuromuscular disorders. Twenty-nine consecutive patients (mean age, 50.2 years; SD, 17.0 years; range, 18-81 years) with a facial neuromuscular disorder, who volunteered and completed all of the assessment measures participated. Facial neuromuscular impairments were assessed using multiple measures of facial motility and dysfunction, and emotional functioning was assessed using self-report measures of depression, anxiety, and positive and negative affect. Severity of global facial impairment was statistically controlled in evaluating the association between specific impairment in smiling and the degree of depressive symptoms. Separate hierarchical linear regression analyses indicated the specific impairment of smiling contributed to the prediction of depression (R(2) =.41, df = 3,25, p =.00) and anxiety (R(2) =.35, df = 3,25, p =.00), controlling first for the contribution of global impairment and facial physical disability. The specific impairment of smiling did not contribute to the prediction of positive emotional experience. Specific impairment of smiling and physical disability, but not global impairment of facial motion, were key predictors of depression in patients with facial neuromuscular disorders. The results emphasize the need to assess and treat depression and anxiety in patients with a facial neuromuscular disorder.


title: EXPLOITATION, EXPLORATION, AND PROCESS MANAGEMENT: THE PRODUCTIVITY DILEMMA REVISITED.

abstract: We develop a contingency view of process management's influence on both technological innovation and organizational adaptation. We argue that while process management activities are beneficial for organizations in stable contexts, they are fundamentally inconsistent with all but incremental innovation and change. But dynamic capabilities are rooted in both exploitative and exploratory activities. We argue that process management activities must be buffered from exploratory activities and that ambidextrous organizational forms provide the complex contexts for these inconsistent activities to coexist.


title: A Planar Balanced Power Divider With Tunable Power-Dividing Ratio

abstract: This paper reports on, for the first time, the design and implementation of planar balanced tunable power dividers. The wide power-dividing tuning range is obtained by using two tuning capacitors and a single control voltage. The differential-mode operation, common-mode rejection, and cross-mode conversion suppression are realized for all tuning states. Closed-form equations for design parameters are derived. For verification, a power divider operating at 1 GHz with power-dividing tuning ratio of 0.01–3.61 is designed based on the given closed-form design equations. Finally, a microstrip tunable power divider with two varactor diodes MA46H202 is fabricated and measured. The measured results are in good agreement with the simulated ones.


title: A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey

abstract: This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R 2 ). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions. © 2007 Elsevier B.V. All rights reserved.


title: Household Food Waste: Multivariate Regression and Principal Components Analyses of Awareness and Attitudes among U.S. Consumers

abstract: We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents’ food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits.


title: Erectile Dysfunction and Depression: Screening and Treatment

abstract: The comorbid conditions erectile dysfunction (ED) and depression are highly prevalent in men. Multiple regression analysis to control for all other predictors of ED indicate that men with high depression scores are nearly twice as likely to report ED than nondepressed men. Depression continues to be among the most common comorbid problems in men with ED, both in the community and in clinical samples. This article reviews the current knowledge about the relationship between ED and depression, the effect of treatments for depression on ED, ways to improve screening for depression, and treatment of ED in patients with this comorbidity.


title: Insight, but not self-reflection, is related to subjective well-being

abstract: Abstract This investigation tested the hypotheses that (1) individuals with higher levels of insight would be both happier and more satisfied with their lives than those with lower levels and (2) that an interaction between self-reflection and insight would affect both happiness and life satisfaction. Participants ( N = 208) were community members who completed four self-report instruments. They were divided into three groups according to their self-reflection (SRE) and insight scores. Then a 3 (SRE) × 3 (insight) MANCOVA was performed on satisfaction with life (SWLS) and subjective happiness (SHS) after controlling for age and psychological distress. Results indicated that insight was significantly positively associated with SWLS and SHS, but SRE was not. The interaction between SRE and insight was also not significant. Univariate analyses indicated that participants with the highest levels of insight were both significantly more satisfied with their lives and happier than participants with medium or low levels of insight, but the medium and low insight groups did not differ significantly from each other on either SWLS or SHS. Implications for future research are discussed.


title: Effects of illumination during anodization of porous silicon

abstract: The effects of illumination during anodic etching of porous Si have been studied. For a fixed current density and anodization time, it has been observed that below a critical irradiance level, increasing the radiant flux density during anodization results in higher photoluminescence and a blue shift of the photoluminescence spectra. For irradiance above the critical value, the photoluminescence intensity decreases. Transmission Fourier-transform infrared spectroscopy, x-ray photoelectron spectroscopy and atomic force microscopy have been employed to investigate the effects of illumination on the characteristics of porous Si.


title: Compiling a high-level language for GPUs: (via language support for architectures and compilers)

abstract: Languages such as OpenCL and CUDA offer a standard interface for general-purpose programming of GPUs. However, with these languages, programmers must explicitly manage numerous low-level details involving communication and synchronization. This burden makes programming GPUs difficult and error-prone, rendering these powerful devices inaccessible to most programmers. We desire a higher-level programming model that makes GPUs more accessible while also effectively exploiting their computational power. This paper presents features of Lime, a new Java-compatible language targeting heterogeneous systems, that allow an optimizing compiler to generate high quality GPU code. The key insight is that the language type system enforces isolation and immutability invariants that allow the compiler to optimize for a GPU without heroic compiler analysis. Our compiler attains GPU speedups between 75% and 140% of the performance of native OpenCL code.


title: Self-optimizing AST interpreters

abstract: An abstract syntax tree (AST) interpreter is a simple and natural way to implement a programming language. However, it is also considered the slowest approach because of the high overhead of virtual method dispatch. Language implementers therefore define bytecodes to speed up interpretation, at the cost of introducing inflexible and hard to maintain bytecode formats. We present a novel approach to implementing AST interpreters in which the AST is modified during interpretation to incorporate type feedback. This tree rewriting is a general and powerful mechanism to optimize many constructs common in dynamic programming languages. Our system is implemented in Java and uses the static typing and primitive data types of Java elegantly to avoid the cost of boxed representations of primitive values in dynamic programming languages.


title: Real-time mobile recipe recommendation system using food ingredient recognition

abstract: In this paper, we propose a mobile cooking recipe recom mendation system employing object recognition for food ingredients such as vegetables and meats. The proposed system carries out object recognition on food ingredients in a real-time way on an Android-based smartphone, and recommends cooking recipes related to the recognized food ingredients. By only pointing a built-in camera on a mobile device to food ingredients, the user can obtain a recipe list instantly. As an object recognition method, we adopt bag-of-features with SURF and color histogram extracted from multiple images as image features and linear SVM with the one-vs-rest strategy as a classifier. We built 30 kinds of food ingredient short video database for experiments. With this database, we achieved the 83.93% recognition rate within the top six candidates. In the experiment, we made user study by comparing mobile recipe recommendation systems with/without ingredient recognition.


title: Hypothalamic Expression of KiSS1 and RFamide-related Peptide-3 mRNAs during The Estrous Cycle of Rats

abstract: Kisspeptin and RFamide-related peptide-3 (RFRP-3) are known to affect GnRH/luteinizing hormone (LH) in several species, including the rat. It has been hypothesized that GnRH/LH changes during the rat estrous cycle may result from changes in the expression of KiSS1 and RFRP-3 genes. Therefore, the present study investigates KiSS1 and RFRP-3 gene expression at the transcriptional level in the rat hypothalamus during the estrous cycle. In the present experimental study, 36 adult female Sprague-Dawley rats (3-4 months old) were used to study the expression of KiSS1 and RFRP-3 mRNA in the hypothalamus during the estrous cycle. Four rats were ovariectomized, whereas the remainder were allotted to four different phases of the estrous cycle (n=8 per estrus phase). Rats were decapitated, and the hypothalami were immediately dissected and frozen in liquid nitrogen. Expressions of KiSS1 and RFRP-3 mRNAs were analyzed by real-time PCR. The expression of KiSS1 mRNA during estrus was lower than other phases of the cycle (p<0.01). Expression of KiSS1 mRNA during the metestrus phase was lower than the proestrus phase (p<0.01). The expression of RFRP-3 mRNA during proestrus was lower than the diestrus phase (p<0.01). Results of the present study showed the role of coordinated expression of KiSS1 and RFRP-3 mRNA in the hypothalamus in the control of the rat estrous cycle.


title: Aerial shepherds: Coordination among uavs and swarms of robots

abstract: We address the problem of deploying groups of tens or hundreds of unmanned ground vehicles (UGVs) in urban environments where a group of aerial vehicles (UAVs) can be used to coordinate the ground vehicles. We envision a hierarchy in which UAVs with aerial cameras can be used to monitor and command a swarm of UGVs, controlling the splitting and merging of the swarm into groups and the shape (distribution) and motion of each group. We call these UAVs Aerial Shepherds. We show a probabilistic approach using the EM algorithm for the initial assignment of shepherds to groups and present behaviors that allow an efficient hierarchical decomposition. We illustrate the framework through simulation examples, with applications to deployment in an urban environment.


title: Group music therapy for patients with persistent post-traumatic stress disorder - an exploratory randomized controlled trial with mixed methods evaluation

abstract: Objectives. Not all patients with post-traumatic stress disorder (PTSD) respond to cognitive behavioural therapy (CBT). Literature suggests group music therapy might be beneficial in treating PTSD. However, feasibility and effectiveness have not been assessed. The study objectives were to assess whether group music therapy was feasible for patients who did not respond to CBT, and whether it has an effect on PTSD symptoms and depression. ::: ::: Design. The study employed mixed methods comprising of an exploratory randomized controlled trial, qualitative content analysis of therapy, and patient interviews. ::: ::: Method. Patients with significant PTSD symptoms (n= 17) following completion of CBT were randomly assigned to treatment (n= 9) or control groups (n= 8). The treatment group received 10 weeks of group music therapy after which exit interviews were conducted. Control group patients were offered the intervention at the end of the study. Symptoms were assessed on the Impact of Events Scale-Revised and Beck Depression Inventory II at the beginning and end of treatment. ::: ::: Results. Treatment-group patients experienced a significant reduction in severity of PTSD symptoms (−20.18; 95% confidence interval [CI]: [−31.23, −9.12]) and a marginally significant reduction in depression (−11.92; 95%CI: [−24.05, 0.21]) at 10 weeks from baseline compared to the control. Patients viewed music therapy as helpful and reported experiences concur with current literature. ::: ::: Conclusions. Group music therapy appears feasible and effective for PTSD patients who have not sufficiently responded to CBT. Limitations include the small sample size and lack of blinding. Further research should address these limitations, test sustainability, and identify specific factors that address symptoms in treatment.