Query: Swarm: Hyper Awareness, Micro Coordination, and Smart Convergence through Mobile Group Text Messaging

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Interaction and outeraction: instant messaging in action <=SEP=> We discuss findings from an ethnographic study of instant messaging (IM) in the workplace and its implications for media theory. We describe how instant messaging supports a variety of informal communication tasks. We document the affordances of IM that support flexible, expressive communication. We describe some unexpected uses of IM that highlight aspects of communication which are not part of current media theorizing. They pertain to communicative processes people use to connect with each other and to manage communication, rather than to information exchange. We call these processes "outeraction". We discuss how outeractional aspects of communication affect media choice and patterns of media use.

Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns <=SEP=> Context -aware computing often involves tracking peoples’ location. Many studies and applications highlight the importance of keeping people’s location information private. We discuss two types of locationbased services; location-tracking services that are based on other parties tracking the user’s location and position-aware services that rely on the device’s knowledge of its own location. We present an experimental case study that examines people’s concern for location privacy and compare this to the use of location-based services. We find that even though the perceived usefulness of the two different types of services is the same, locationtracking services generate more concern for privacy than posit ion-aware services. We conclude that development emphasis should be given to position-aware services but that location-tracking services have a potential for success if users are given a simple option for turning the location-tracking off.

There is more to context than location <=SEP=> Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing research published the parameter location is most often used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations-a light sensitive display and an orientation aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further we explore fusion of sensors for acquisition of information on more sophisticated contexts. 1 Introduction Context is " that which surrounds, and gives meaning to something else " =. Various areas of computer science have been investigating this concept over the last 40 years, to relate information processing and communication to aspects of the situations in which such processing occurs. Most notably, context is a key concept in Natural Language Processing and more generally in Human-Computer Interaction. For instance, state of the art graphical user interfaces use context to adapt menus to contexts such as user preference and dialogue status. A new domain, in which context currently receives growing attention, is mobile computing. While a first wave of mobile computing was based on portable general-purpose computers and primarily focussed on location transparency, a second wave is now based on ultra-mobile devices and an interest in relating these to their surrounding situation of usage. Ultra-mobile devices are a new class of small mobile computer, defined as computing devices that are operational and operated while on the move, and characterized by a shift from general-purpose computing to task-specific support. Ultra-mobile devices comprise for instance Personal Digital Assistants (PDAs), mobile phones, and wearable computers. A primary concern of context-awareness in mobile computing is awareness of the physical environment surrounding a user and their ultra-mobile device. In recent work, this concern has been addressed by implementation of location-awareness, for instance based on global positioning, or the use of beacons. Location …

Cyberguide: A mobile context-aware tour guide <=SEP=> Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to o er greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of di erent hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.

The Active Badge Location System <=SEP=> A novel system for the location of people in an office environment is described. Members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors. The paper also examines alternative location techniques, system design issues and applications, particularly relating to telephone call routing. Location systems raise concerns about the privacy of an individual and these issues are also addressed.

Cross-domain transfer for reinforcement learning <=SEP=> A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer, a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.

Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities <=SEP=> The commensal, symbiotic, and pathogenic microbial community which resides inside our body and on our skin (the human microbiome) can perturb host energy metabolism and immunity, and thus significantly influence development of a variety of human diseases. Therefore, the field has attracted unprecedented attention in the last decade. Although a large amount of data has been generated, there are still many unanswered questions and no universal agreements on how microbiome affects human health have been agreed upon. Consequently, this review was written to provide an updated overview of the rapidly expanding field, with a focus on revealing knowledge gaps and research opportunities. Specifically, the review covered animal physiology, optimal microbiome standard, health intervention by manipulating microbiome, knowledge base building by text mining, microbiota community structure and its implications in human diseases and health monitoring by analyzing microbiome in the blood. The review should enhance interest in conducting novel microbiota investigations that will further improve health and therapy.

Discrimination and racial disparities in health: evidence and needed research <=SEP=> This paper provides a review and critique of empirical research on perceived discrimination and health. The patterns of racial disparities in health suggest that there are multiple ways by which racism can affect health. Perceived discrimination is one such pathway and the paper reviews the published research on discrimination and health that appeared in PubMed between 2005 and 2007. This recent research continues to document an inverse association between discrimination and health. This pattern is now evident in a wider range of contexts and for a broader array of outcomes. Advancing our understanding of the relationship between perceived discrimination and health will require more attention to situating discrimination within the context of other health-relevant aspects of racism, measuring it comprehensively and accurately, assessing its stressful dimensions, and identifying the mechanisms that link discrimination to health.

Augmented Virtual Environments (AVE): Dynamic Fusion of Imagery and 3D Models <=SEP=> An Augmented Virtual Environment (AVE) fuses dynamic imagery with 3D models. The AVE provides a unique approach to visualize and comprehend multiple streams of temporal data or images. Models are used as a 3D substrate for the visualization of temporal imagery, providing improved comprehension of scene activities. The core elements of AVE systems include model construction, sensor tracking, real-time video/image acquisition, and dynamic texture projection for 3D visualization. This paper focuses on the integration of these components and the results that illustrate the utility and benefits of the resulting augmented virtual environment.

Real time automation of agricultural environment <=SEP=> The paper, “Real time automation of agricultural environment”, using PIC16F877A and GSM SIM300 modem is focused on automating the irrigation system for social welfare of Indian agricultural system. This system will be useful for monitoring the soil moisture condition of the farm as well as controlling the soil moisture by monitoring the level of water in the water source and accordingly switching the motor ON/OFF for irrigation purposes. The system proposes a soil moisture sensor at each place where the moisture has to be monitored. Once the moisture reaches a particular level, the system takes appropriate steps to regulate or even stop the water flow. The circuit also monitors the water in the water source so that if the water level becomes very low, it switches off the motor to prevent damage to the motor due to dry run. The system also consists of a GSM modem through which the farmer can easily be notified about the critical conditions occurring during irrigation process.

Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms <=SEP=> The mobile platform of a parallel cable-driven mechanism is connected in parallel to a base by lightweight links, such as cables. Since the cables can only work in tension, the set of poses of the mobile platform for which the cables can balance any external wrench, i.e., for which the platform of the mechanism is fully constrained, is often limited or even nonexistent. Thus, the study and determination of this set of poses, called the wrench-closure workspace (WCW), is an important issue for parallel cable-driven mechanisms. In this paper, the case of planar parallel cable-driven mechanisms is addressed. Theorems that characterize the poses of the WCW are proposed. Then, these theorems are used to disclose the parts of the reachable workspace which belong to the WCW. Finally, an efficient algorithm that determines the constant-orientation cross-sections of these parts is introduced.

Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder <=SEP=> The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and having large amplitude pressure oscillations with varying spatial scales of periodic coherent vortex shedding. Early detection and close monitoring of combustion instability are the keys to extending the remaining useful life (RUL) of any gas turbine engine. However, such impending instability to a stable combustion is extremely difficult to detect only from pressure data due to its sudden (bifurcationtype) nature. Toolchains that are able to detect early instability occurrence have transformative impacts on the safety and performance of modern engines. This paper proposes an endto-end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. Performance comparison is done with a wellknown image processing tool, conditional random field that is trained to be selective as well. In this context, an informationtheoretic threshold value is derived. The proposed framework is validated on a set of real data collected from a laboratory scale combustor over varied operating conditions where it is shown to effectively detect subtle instability features as a combustion process makes transition from stable to unstable region. ∗corresponding author Adedotun Akintayo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Marginal structural models for estimating effect modification. <=SEP=> PURPOSE
The use of marginal structural models (MSMs) to adjust for measured confounding factors is becoming increasingly common in observational studies. Here, we propose MSMs for estimating effect modification in observational cohort and case-control studies.


METHODS
MSMs for estimating effect modification were derived by the use of the potential outcome model. The proposed methods were applied to a cohort study and a case-control study.


RESULTS
In cohort studies, effect modification can be estimated by the application of a logistic MSM to individuals who experienced the event in question. In case-control studies, effect modification can be estimated by the ratio between the estimate from the model applied to case data and that applied to control data. The application of the model to real data from a cohort study indicated that the estimate from the proposed method was close to that from standard regression analysis. In a case-control study, the estimate from the proposed method may be biased.


CONCLUSIONS
Epidemiological researchers can use MSMs to estimate effect modification. In case-control studies, it should be determined whether the estimated effect modification is biased by applying a logistic MSM of control data.

Vectorization for SIMD architectures with alignment constraints <=SEP=> When vectorizing for SIMD architectures that are commonly employed by today's multimedia extensions, one of the new challenges that arise is the handling of memory alignment. Prior research has focused primarily on vectorizing loops where all memory references are properly aligned. An important aspect of this problem, namely, how to vectorize misaligned memory references, still remains unaddressed.This paper presents a compilation scheme that systematically vectorizes loops in the presence of misaligned memory references. The core of our technique is to automatically reorganize data in registers to satisfy the alignment requirement imposed by the hardware. To reduce the data reorganization overhead, we propose several techniques to minimize the number of data reorganization operations generated. During the code generation, our algorithm also exploits temporal reuse when aligning references that access contiguous memory across loop iterations. Our code generation scheme guarantees to never load the same data associated with a single static access twice. Experimental results indicate near peak speedup factors, e.g., 3.71 for 4 data per vector and 6.06 for 8 data per vector, respectively, for a set of loops where 75% or more of the static memory references are misaligned.

RISC I: A Reduced Instruction Set VLSI Computer <=SEP=> The Reduced Instruction Set Computer (RISC) Project investigates an alternatrve to the general trend toward computers wrth increasingly complex instruction sets: With a proper set of instructions and a corresponding architectural design, a machine wrth a high effective throughput can be achieved. The simplicity of the instruction set and addressing modes allows most Instructions to execute in a single machine cycle, and the srmplicity of each instruction guarantees a short cycle time. In addition, such a machine should have a much shorter design trme. This paper presents the architecture of RISC I and its novel hardware support scheme for procedure call/return. Overlapprng sets of regrster banks that can pass parameters directly to subrouttnes are largely responsible for the excellent performance of RISC I. Static and dynamtc comparisons between this new architecture and more traditional machines are given. Although instructions are simpler, the average length of programs was found not to exceed programs for DEC VAX 11 by more than a factor of 2. Preliminary benchmarks demonstrate the performance advantages of RISC. It appears possible to build a single chip computer faster than VAX 11/780.

A Reinforcement Learning Approach to Automated GUI Robustness Testing <=SEP=> Graphical User Interfaces (GUIs) can be found in almost all modern desktop, tablet and smartphone applications. Since they are the glue between an application’s components, they lend themselves to system level testing. Unfortunately, although many tools promise automation, GUI testing is still an inherently difficult task and involves great manual labor. However, tests that aim at critical faults, like crashes and excessive response times, are completely automatable and can be very effective. These robustness tests often apply random algorithms to select the actions to be executed on the GUI. This paper proposes a new approach to fully automated robustness testing of complex GUI applications with the goal to enhance the fault finding capabilities. The approach uses a well-known machine learning algorithm called Q-Learning in order to combine the advantages of random and coverage-based testing. We will explain how it operates, how we plan to implement it and provide arguments for its usefulness.

FACTORS INFLUENCING CITIZENS ’ ADOPTION OF AN E-GOVERNMENT SYSTEM : VALIDATION OF THE DECOMPOSED THEORY OF PLANNED BEHAVIOR ( DTPB ) <=SEP=> The study explores the adoption of an electronic government (e-government) system called electronic district (e-District) system in context of India. The study validates the decomposed theory of planned behaviour (DTPB) to understand the impact of its factors on the potential adopter’s intention to adopt this system. The proposed research model is validated using the data collected from 304 respondents from a state called Bihar of India where this e-government was supposed to be implemented. The data is analysed through regression analysis technique. The empirical findings of the proposed research model indicated the significant relationships of all proposed hypotheses. The study also provides its limitations, future research directions, and implications for theory and practice toward the end.

The Measurement of Dielectric Properties of Liquids at Microwave Frequencies Using Open-ended Coaxial Probes <=SEP=> Significant progress has been made in recent years in the development of microwave tomographic imaging systems for medical applications. In order to design an appropriate microwave imaging system for industrial applications, and to interpret the images produced, the materials under imaging need to be characterised. In this paper, we describe the use of open-ended coaxial probes for the measurement of dielectric properties of liquids at frequencies between 400MHz and 20GHz. The results obtained using the Misra-Blackham model for a number of liquids including water of different salinity are compared with those published in the literature showing a good agreement. For saline water, in particular, the frequency of the minimum loss depends on the salinity. It may change from 1.5GHz for the inclusion of 0.2% NaCl to 7GHz for the inclusion of 3.5% NaCl. The real part of the permittivity may also change by approximately 50% from 400MHz to 20GHz.

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. <=SEP=> The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.

Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification <=SEP=> High-resolution satellite imagery has been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Despite the high availability, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this data set is used to train deep-learning-based models in order to accurately classify satellite images that contain or not contain zebra crossings. A novel data set with more than 240000 images from 3 continents, 9 countries, and more than 20 cities was used in the experiments. The experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.

Single carrier FDMA for uplink wireless transmission <=SEP=> Single carrier frequency division multiple access (SC FDMA), a modified form of orthogonal FDMA (OFDMA), is a promising technique for high data rate uplink communications in future cellular systems. SC-FDMA has similar throughput performance and essentially the same overall complexity as OFDMA. A principal advantage of SC-FDMA is the peak-to-average power ratio (PARR), which is lower than that of OFDMA. SC FDMA is currently a strong candidate for the uplink multiple access scheme in the long term evolution of cellular systems under consideration by the third generation partnership project (3GPP). In this paper, we give an overview of SC-FDMA. We also analyze the effects of subcarrier mapping on throughput and PARR. Among the possible subcarrier mapping approaches, we find that localized FDMA (LFDMA) with channel-dependent scheduling (CDS) results in higher throughput than interleaved FDMA (JFDMA). However, the PARR performance of IFDMA is better than that of LFDMA. As in other communications systems there are complex tradeoffs between design parameters and performance in an SC-FDMA system

A Linear Programming Approach for Multiple Object Tracking <=SEP=> We propose a linear programming relaxation scheme for the class of multiple object tracking problems where the inter-object interaction metric is convex and the intra-object term quantifying object state continuity may use any metric. The proposed scheme models object tracking as a multi-path searching problem. It explicitly models track interaction, such as object spatial layout consistency or mutual occlusion, and optimizes multiple object tracks simultaneously. The proposed scheme does not rely on track initialization and complex heuristics. It has much less average complexity than previous efficient exhaustive search methods such as extended dynamic programming and is found to be able to find the global optimum with high probability. We have successfully applied the proposed method to multiple object tracking in video streams.

Developing a frame of reference for ex-ante IT/IS investment evaluation <=SEP=> Investment appraisal techniques are an integral part of many traditional capital budgeting processes. However, the adoption of Information Systems (IS) and the development of resulting infrastructures are being increasingly viewed on the basis of consumption. Consequently, decision-makers are now moving away from the confines of rigid capital budgeting processes, which have traditionally compared IS with non-ISrelated investments. With this in mind, the authors seek to dissect investment appraisal from the broader capital budgeting process to allow a deeper understanding of the mechanics involved with IS justification. This analysis presents conflicting perspectives surrounding the scope and sensitivity of traditional appraisal methods. In contributing to this debate, the authors present taxonomies of IS benefit types and associated natures, and discuss the resulting implications of using traditional appraisal techniques during the IS planning and decision-making process. A frame of reference that can be used to navigate through the variety of appraisal methods available to decision-makers is presented and discussed. Taxonomies of appraisal techniques that are classified by their respective characteristics are also presented. Perspectives surrounding the degree of involvement that financial appraisal should play during decision making and the limitations surrounding investment appraisal techniques are identified. European Journal of Information Systems (2002) 11, 74–82. DOI: 10.1057/palgrave/ejis/3000411

Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data <=SEP=> Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.

Player AnalysisInput Frame Depth EstimationDepth Estimation Scene ReconstructionScene <=SEP=> We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.

Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid <=SEP=> Wind energy is a prominent area of application of variable-speed generators operating on the constant grid frequency. This paper describes the operation and control of one of these variable-speed wind generators: the direct driven permanent magnet synchronous generator (PMSG). This generator is connected to the power network by means of a fully controlled frequency converter, which consists of a pulsewidth-modulation (PWM) rectifier, an intermediate dc circuit, and a PWM inverter. The generator is controlled to obtain maximum power from the incident wind with maximum efficiency under different load conditions. Vector control of the grid-side inverter allows power factor regulation of the windmill. This paper shows the dynamic performance of the complete system. Different experimental tests in a 3-kW prototype have been carried out to verify the benefits of the proposed system.

Autonomous Agents and Ethical Decision-Making <=SEP=> Machine ethics, also known as artificial morality, is a newly emerging field concerned with ensuring appropriate behavior of machines toward humans and other machines. In this article, we discuss the importance of machine ethics and present a computational model of ethical decision-making for autonomous agents. The proposed model implements a mechanism for integrating the results of diverse assessments into a unique cue, and takes into account the agent’s preferences, good and bad past experiences, ethical rules, and current emotional state as the main factors involved in choosing the most appropriate option. The design of the model is based on theories and models developed in fields such as neuroscience, psychology, artificial intelligence, and cognitive informatics. In particular, the model attempts to emulate neural mechanisms of the human brain involved in ethical decision-making.

Design and prototype of monolithic compliant grippers for adaptive grasping <=SEP=> This study presents the designs of monolithic compliant grippers made by silicon rubber for adaptive grasping applications. The proposed grippers are symmetric two-finger designs actuated by one linear actuator. An optimal design procedure including topology and size optimization methods is used to synthesize the rubber grippers with the objective to maximize mechanical advantage, which is defined as the ratio of output force to input force. Three grippers synthesized with different design parameters are prototyped. The best design is identified through the adaptability test, and a robotic gripper assembly is developed to demonstrate the effectiveness of the design through the test of grasping irregular objects. The maximum payload for the gripper assembly is identified as 2.5kg.

Biology and therapy of fibromyalgia. New therapies in fibromyalgia <=SEP=> Fibromyalgia is a chronic, musculoskeletal pain condition that predominately affects women. Although fibromyalgia is common and associated with substantial morbidity and disability, there are no US Food and Drug Administration-approved treatments. However, progress has been made in identifying pharmacological and non-pharmacological treatments for fibromyalgia. Recent pharmacological treatment studies have focused on selective serotonin and norepinephrine reuptake inhibitors, which enhance serotonin and norepinephrine neurotransmission in the descending pain pathways and lack many of the adverse side effects associated with tricyclic medications. Promising results have also been reported for medications that bind to the alpha2delta subunit of voltage-gated calcium channels, resulting in decreased calcium influx at nerve terminals and subsequent reduction in the release of several neurotransmitters thought to play a role in pain processing. There is also evidence to support exercise, cognitive behavioral therapy, education, and social support in the management of fibromyalgia. It is likely that many patients would benefit from combinations of pharmacological and non-pharmacological treatments, but more study is needed.

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format <=SEP=> Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.