Modular Audio Recognition Framework (MARF) is an open-source research platform and a collection of voice, sound, speech, text and natural language processing (NLP) algorithms written in Java and arranged into a modular and extensible framework that attempts to facilitate addition of new algorithms. MARF may act as a library in applications or be used as a source for learning and extension. A few example applications are provided to show how to use the framework. There is also a detailed manual and the API reference in the javadoc format as the project tends to be well documented. MARF, its applications, and the corresponding source code and documentation are released under the BSD-style license.
Two-phase locking
In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released. Shrinking phase: locks are released and no locks are acquired. Two types of locks are used by the basic protocol: Shared and Exclusive locks. Refinements of the basic protocol may use more lock types. Using locks that block processes, 2PL, S2PL, and SS2PL may be subject to deadlocks that result from the mutual blocking of two or more transactions. == Read and write locks == Locks are used to guarantee serializability. A transaction is holding a lock on an object if that transaction has acquired a lock on that object which has not yet been released. For 2PL, the only used data-access locks are read-locks (shared locks) and write-locks (exclusive locks). Below are the rules for read-locks and write-locks: A transaction is allowed to read an object if and only if it is holding a read-lock or write-lock on that object. A transaction is allowed to write an object if and only if it is holding a write-lock on that object. A schedule (i.e., a set of transactions) is allowed to hold multiple locks on the same object simultaneously if and only if none of those locks are write-locks. If a disallowed lock attempts on being held simultaneously, it will be blocked. == Variants == Note that all conflict serializable schedules are also view serializable (but not vice-versa). === Two-phase locking === According to the two-phase locking protocol, each transaction handles its locks in two distinct, consecutive phases during the transaction's execution: Expanding phase (aka Growing phase): locks are acquired and no locks are released (the number of locks can only increase). Shrinking phase (aka Contracting phase): locks are released and no locks are acquired. The two phase locking rules can be summarized as: each transaction must never acquire a lock after it has released a lock. The serializability property is guaranteed for a schedule with transactions that obey this rule. Typically, without explicit knowledge in a transaction on end of phase 1, the rule is safely determined only when a transaction has completed processing and requested commit. In this case, all the locks can be released at once (phase 2). === Conservative two-phase locking === Conservative two-phase locking (C2PL) differs from 2PL in that transactions obtain all the locks they need before the actual execution begins. This is to ensure that a transaction that already holds some locks will not block waiting for other locks. C2PL prevents deadlocks. In cases of heavy lock contention, C2PL reduces the time locks are held on average, relative to 2PL and Strict 2PL, because transactions that hold locks are never blocked. In light lock contention, C2PL holds more locks than is necessary, because it is difficult to predict which locks will be needed in the future, thus leading to higher overhead. A C2PL transaction will not obtain any locks if it cannot obtain all the locks it needs in its initial request. Furthermore, each transaction needs to declare its read and write set (the data items that will be read/written), which is not always possible. Because of these limitations, C2PL is not used very frequently. === Strict two-phase locking === To comply with the strict two-phase locking (S2PL) protocol, a transaction needs to comply with 2PL, and release its write (exclusive) locks only after the transaction has ended (i.e., either committed or aborted). On the other hand, read (shared) locks are released regularly during the shrinking phase. Unlike 2PL, S2PL provides strictness (a special case of cascade-less recoverability). This protocol is not appropriate in B-trees because it causes Bottleneck (while B-trees always starts searching from the parent root). === Strong strict two-phase locking === or Rigorousness, or Rigorous scheduling, or Rigorous two-phase locking To comply with strong strict two-phase locking (SS2PL), a transaction's read and write locks are released only after that transaction has ended (i.e., either committed or aborted). A transaction obeying SS2PL has only a phase 1 and lacks a phase 2 until the transaction has completed. Every SS2PL schedule is also an S2PL schedule, but not vice versa.
Moving object detection
Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected. Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border, etc. == Definition == Moving object detection is to recognize the physical movement of an object in a given place or region. By acting segmentation among moving objects and stationary area or region, the moving objects' motion can be tracked and thus analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object detection is to find the foreground moving target(s), either in each video frame or only when the moving target shows the first appearance in the video. == Traditional methods == Among all the traditional moving object detection methods, we could categorize them into four major approaches: Background subtraction, Frame differencing, Temporal Differencing, and Optical Flow. === Frame differencing === Instead of using traditional approach, to use image subtraction operator by subtracting second and images afterwards, the frame differencing method makes comparisons between two successive frames to detect moving targets. === Temporal differencing === The temporal differencing method identifies the moving object by applying pixel-wise difference method with two or three consecutive frames.
Irwin Sobel
Irwin Sobel (born September 12, 1940) is a scientist and researcher in digital image processing. == Biography == Irwin Sobel was born in New York City. He graduated from MIT in 1961 and completed his Ph.D. research at the Stanford Artificial Intelligence Project (SAIL) with thesis Camera Models and Machine Perception. His Ph.D. advisor was Jerome A. Feldman. Starting in 1973, he spent nine years doing postdoctoral research at Columbia University. After 1982, he worked as a Senior Researcher at HP Labs. == Sobel operator == In 1968, Sobel gave a talk entitled "An Isotropic 3x3 Image Gradient Operator" at SAIL; this method became known as the Sobel operator. It was developed jointly with a colleague, Gary Feldman, also at SAIL.
Enterprise mobile application
The term enterprise mobile application is used in the context of mobile apps created/brought by individual organizations for their workers to carry out the functions required to run the organization. It is the process of building a mobile application for the requirements of an enterprise. An enterprise mobile application belonging to an organization is expected to be used by only the workers of that organization. The definition of enterprise mobile application does not include the mobile apps that an organization create for its customers or consumers of the products or services generated by the organization. == Example == An organization, whether for-profit or non-profit, may create a mobile app for its members to track inventory levels of supplies they distribute to their target communities or materials used in product manufacturing. Such a mobile app comes under the definition of enterprise mobile application. However, the same organization may also create another mobile app to sell their products to end users or spread awareness of their services to various communities, and that mobile app would not come under definition of enterprise mobile application. == Enterprise mobile solution providers == Enterprise Mobile solution providers create and develop apps for individual organizations that can buy instead of creating the apps themselves. Reasons for Organizations buying the apps include time and cost savings, technical expertise. Today Enterprise Mobility is playing track role for enterprise transformation. Today, enterprises needs productivity is a fast way. Enterprise mobility helps business owners to build their work in a progressive way by assisting enterprise mobility solutions.
Deep learning in photoacoustic imaging
Photoacoustic imaging (PA) is based on the photoacoustic effect, in which optical absorption causes a rise in temperature, which causes a subsequent rise in pressure via thermo-elastic expansion. This pressure rise propagates through the tissue and is sensed via ultrasonic transducers. Due to the proportionality between the optical absorption, the rise in temperature, and the rise in pressure, the ultrasound pressure wave signal can be used to quantify the original optical energy deposition within the tissue. Photoacoustic imaging has applications of deep learning in both photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM). PACT utilizes wide-field optical excitation and an array of unfocused ultrasound transducers. Similar to other computed tomography methods, the sample is imaged at multiple view angles, which are then used to perform an inverse reconstruction algorithm based on the detection geometry (typically through universal backprojection, modified delay-and-sum, or time reversal ) to elicit the initial pressure distribution within the tissue. PAM on the other hand uses focused ultrasound detection combined with weakly focused optical excitation (acoustic resolution PAM or AR-PAM) or tightly focused optical excitation (optical resolution PAM or OR-PAM). PAM typically captures images point-by-point via a mechanical raster scanning pattern. At each scanned point, the acoustic time-of-flight provides axial resolution while the acoustic focusing yields lateral resolution. == Applications of deep learning in PACT == The first application of deep learning in PACT was by Reiter et al. in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selected test images were 0.28 mm and 0.37 mm respectively. After this initial implementation, the applications of deep learning in PACT have branched out primarily into removing artifacts from acoustic reflections, sparse sampling, limited-view, and limited-bandwidth. There has also been some recent work in PACT toward using deep learning for wavefront localization. There have been networks based on fusion of information from two different reconstructions to improve the reconstruction using deep learning fusion based networks. === Using deep learning to locate photoacoustic point sources === Traditional photoacoustic beamforming techniques modeled photoacoustic wave propagation by using detector array geometry and the time-of-flight to account for differences in the PA signal arrival time. However, this technique failed to account for reverberant acoustic signals caused by acoustic reflection, resulting in acoustic reflection artifacts that corrupt the true photoacoustic point source location information. In Reiter et al., a convolutional neural network (similar to a simple VGG-16 style architecture) was used that took pre-beamformed photoacoustic data as input and outputted a classification result specifying the 2-D point source location. ==== Deep learning for PA wavefront localization ==== Johnstonbaugh et al. was able to localize the source of photoacoustic wavefronts with a deep neural network. The network used was an encoder-decoder style convolutional neural network. The encoder-decoder network was made of residual convolution, upsampling, and high field-of-view convolution modules. A Nyquist convolution layer and differentiable spatial-to-numerical transform layer were also used within the architecture. Simulated PA wavefronts served as the input for training the model. To create the wavefronts, the forward simulation of light propagation was done with the NIRFast toolbox and the light-diffusion approximation, while the forward simulation of sound propagation was done with the K-Wave toolbox. The simulated wavefronts were subjected to different scattering mediums and Gaussian noise. The output for the network was an artifact free heat map of the targets axial and lateral position. The network had a mean error rate of less than 30 microns when localizing target below 40 mm and had a mean error rate of 1.06 mm for localizing targets between 40 mm and 60 mm. With a slight modification to the network, the model was able to accommodate multi target localization. A validation experiment was performed in which pencil lead was submerged into an intralipid solution at a depth of 32 mm. The network was able to localize the lead's position when the solution had a reduced scattering coefficient of 0, 5, 10, and 15 cm−1. The results of the network show improvements over standard delay-and-sum or frequency-domain beamforming algorithms and Johnstonbaugh proposes that this technology could be used for optical wavefront shaping, circulating melanoma cell detection, and real-time vascular surgeries. === Removing acoustic reflection artifacts (in the presence of multiple sources and channel noise) === Building on the work of Reiter et al., Allman et al. utilized a full VGG-16 architecture to locate point sources and remove reflection artifacts within raw photoacoustic channel data (in the presence of multiple sources and channel noise). This utilization of deep learning trained on simulated data produced in the MATLAB k-wave library, and then later reaffirmed their results on experimental data. === Ill-posed PACT reconstruction === In PACT, tomographic reconstruction is performed, in which the projections from multiple solid angles are combined to form an image. When reconstruction methods like filtered backprojection or time reversal, are ill-posed inverse problems due to sampling under the Nyquist-Shannon's sampling requirement or with limited-bandwidth/view, the resulting reconstruction contains image artifacts. Traditionally these artifacts were removed with slow iterative methods like total variation minimization, but the advent of deep learning approaches has opened a new avenue that utilizes a priori knowledge from network training to remove artifacts. In the deep learning methods that seek to remove these sparse sampling, limited-bandwidth, and limited-view artifacts, the typical workflow involves first performing the ill-posed reconstruction technique to transform the pre-beamformed data into a 2-D representation of the initial pressure distribution that contains artifacts. Then, a convolutional neural network (CNN) is trained to remove the artifacts, in order to produce an artifact-free representation of the ground truth initial pressure distribution. ==== Using deep learning to remove sparse sampling artifacts ==== When the density of uniform tomographic view angles is under what is prescribed by the Nyquist-Shannon's sampling theorem, it is said that the imaging system is performing sparse sampling. Sparse sampling typically occurs as a way of keeping production costs low and improving image acquisition speed. The typical network architectures used to remove these sparse sampling artifacts are U-net and Fully Dense (FD) U-net. Both of these architectures contain a compression and decompression phase. The compression phase learns to compress the image to a latent representation that lacks the imaging artifacts and other details. The decompression phase then combines with information passed by the residual connections in order to add back image details without adding in the details associated with the artifacts. FD U-net modifies the original U-net architecture by including dense blocks that allow layers to utilize information learned by previous layers within the dense block. Another technique was proposed using a simple CNN based architecture for removal of artifacts and improving the k-wave image reconstruction. ==== Removing limited-view artifacts with deep learning ==== When a region of partial solid angles are not captured, generally due to geometric limitations, the image acquisition is said to have limited-view. As illustrated by the experiments of Davoudi et al., limited-view corruptions can be directly observed as missing information in the frequency domain of the reconstructed image. Limited-view, similar to sparse sampling, makes the initial reconstruction algorithm ill-posed. Prior to deep learning, the limited-view problem was addressed with complex hardware such as acoustic deflectors and full ring-shaped transducer arrays, as well as solutions like compressed sensing, weighted factor, and iterative filtered backprojection. The result of this ill-posed reconstruction is imaging artifacts that can be removed by CNNs. The deep learning algorithms used to remove limited-view artifacts include U-net and FD U-net, as well as generative adversarial networks (GANs) and volumetric versions of U-net. One GAN implementation of note improved upon U-net by using U-net as a generator and VGG as a discriminator, with the Wasserstein metric and gradient penalty to stabilize training (WGAN-GP). ==== Pixel-wise interpolation
BabyCenter
BabyCenter is an online media company based in San Francisco, New York City, Chicago, and Los Angeles that provides information on conception, pregnancy, birth, and early childhood development for parents and expecting parents. BabyCenter operates 8 country and region specific properties including websites, apps, emails, print publications, and an online community where parents can connect on a variety of topics. The visitors of website and the users of the app can sign up for free weekly email newsletters that guide them through pregnancy and their child's development. In addition to publishing detailed, medically reviewed information about pregnancy and parenting, BabyCenter, under its Mission Motherhood initiative, ran numerous social programs and has participated in public health initiatives in partnership with hospitals, healthcare agencies, nonprofits, NGOs, and government agencies to provide pregnancy and parenting advice. It also annually publishes the most popular baby names. BabyCenter LLC is part of the Everyday Health Group, a division of Ziff Davis. == History == BabyCenter was founded in October 1997 by Stanford University MBA graduates Matt Glickman and Mark Selcow, who recognized a need for information about pregnancy and parenting on the internet. BabyCenter was initially funded through $13.5 million in startup capital funding from venture capital firms, including Bessemer Venture Partners, Intel, and Trinity Ventures. The funds were used to open the BabyCenter Store in October 1998. In the early years of its operation, BabyCenter offered multiple resources and services for parents, including a website that provided medically reviewed information and guidance to new and expectant parents on such topics as fertility, labor, and childcare; a weekly email for pregnant women tailored to their week of pregnancy (based on their pregnancy due date); and community groups and chat rooms for pregnant couples and parents to discuss pregnancy and child-rearing strategies. The site grew quickly, and by early 1999 had 175 employees and an annual revenue of $35 million. In April of that year, the two founders sold BabyCenter to another website, eToys.com, for $190 million in stock. Twenty-three months later, in 2001, shortly before declaring bankruptcy, eToys sold the site to Johnson & Johnson for $10 million. During the eToys ownership, BabyCenter launched its first international E-commerce site in the UK during the spring of 2000. Starting in 2005, BabyCenter launched an expansion plan, extending its global network to Australia, Canada and other countries, staffing each outpost with local editors. In 2007, BabyCenter debuted a Mandarin-language site in China, initiated operations in India, launched a Spanish language website, and introduced its first mobile site. BabyCenter released My Pregnancy Today, its first mobile app, to Apple's App Store in August 2010 and to the Android market in April 2011. The app provided daily information, nutrition tips, advice relevant to the user's week of pregnancy, and 3-D animated videos showcasing a baby's development in utero. The My Pregnancy app was joined by a My Baby Today app in October 2011. In 2015, BabyCenter released Mom Feed, its first mobile app for parents of toddlers and older children (ages 1 to 8). Mom Feed offered personalized, stage-based information as well as content from the BabyCenter Community and Blog in a real-time stream. In 2016, BabyCenter launched its web-based Baby Names Finder. In 2018, Mom Feed was discontinued and BabyCenter replaced that experience with a separate Child Health content area on its website. Also in 2018, BabyCenter launched its mobile baby name generator, the Baby Names app, which, like the web-based Baby Names Finder, leverages data from hundreds of thousands of parents that culminates in its annual most popular Baby Names Report. In 2019, Johnson & Johnson sold Baby Center to Everyday Health Group, a division of New York-based parent company of Ziff Davis, Inc. Neither side disclosed terms of the deal. == Popular research == BabyCenter's most popular baby names is released annually and often cited by the media. In March 2024, BabyCenter did a review of the app Temu and said that the website has found products that have been recalled, could be counterfeit or circumvent U.S. safety standards and features that are important in preventing issues like choking. In 2025, BabyCenter released a report about the cost of raising a newborn baby in the first year. == Content and products == === Websites === BabyCenter has 8 country and region-specific websites around the world, including sites for the United States, Canada, Australia, Brazil, India, Germany, the United Kingdom, and Latin America. Users can find parenting and pregnancy advice in seven languages: English, Spanish, Portuguese, Arabic, French, German, and Hindi BabyCenter content for each country- or region-specific site is written by an editorial team based in that country or region. Medical and health content for each site is reviewed by a medical advisory board based there and adheres to that country or region's medical standards. For example, the U.S. site works with and follows the recommendations of such U.S. medical authorities as the American Academy of Pediatrics, the American Congress of Obstetrics & Gynecology and the Society for Maternal-Fetal Medicine. BabyCenter regularly conducts research and provides thought leadership on pregnancy and parenting topics, popularly cited by major media outlets including The Wall Street Journal, Forbes, The Washington Post, BuzzFeed, Insider, MarketWatch, Axios. === Community, blogs and social === From its earliest days, BabyCenter has had a community area that allows people to join a group of parents with children born in the same month, known as a Birth Club. BabyCenter launched a blog called Momformation in 2007. Eventually, the name was changed to BabyCenter Blog. In April 2021, the BabyCenter Community was identified in a research article within the journal PLOS Computational Biology as facilitating "unobstructed communication" between parents, which avoids the "strong echo chamber phenomena" that can foster and perpetuate vaccine misinformation. === My Pregnancy and Baby Today App === The app is available in six languages, although not all features are supported for every market. Initially the apps only featured pregnancy articles that could be found on the BabyCenter website, but over the years the feature set has expanded to include a growing list of app-specific tools such as weekly fetal development information, a kick tracker, a birth plan worksheet, a contraction timer, a baby growth tracker, a photo journal for pregnant women to record their pregnancy bellies, and a photo journal for documenting a baby's first year. === Mission Motherhood™ === BabyCenter was a cofounder of the Mobile Alliance for Maternal Action (MAMA), a public-private partnership between USAID, Johnson & Johnson, the UN Foundation, and BabyCenter from 2011 to-to 2015. The MAMA program sparked the creation of MomConnect, an initiative of the South African Department of Health for which BabyCenter developed SMS messages with health information about pregnancy and a child's first year of life. BabyCenter helped develop similar messages for mMitra, a voice messaging program in India. A research article in the Maternal and Child Health Journal stated the mMitra program offered strong evidence "that tailored mobile phone voice messages can improve key infant care knowledge and practices that lead to improved infant health outcomes in low-resource settings. BabyCenter's Mission Motherhood Messages were available to qualifying organizations on the BabyCenter website. BabyCenter contributed websites for Free Basics. These websites featured age and stage-based pregnancy and baby articles targeted to low-income, lower-education women who would not otherwise have access to health information. Content developed for this program was also used to support a UNICEF SMS program during the 2016 Zika outbreak. == Awards and recognition == In 1998, BabyCenter won a Webby Award for Best Home Site. Since then, it has been nominated for a Webby Award 19 times and won either a Webby or a People's Choice Webby Award 12 times – including a People's Voice win in 2021 for Lifestyle websites and mobile sites. In 2002, it won Service Journalism award from Online Journalism Awards (OJA). In 2015, BabyCenter won five Digital Health Awards for content about autism in children. In 2016, BabyCenter won seven Digital Health Awards: four for videos about the aches and pains of pregnancy, baby sleep, and the walking milestone in child development; two for articles about baby sleep training and sleep apnea in babies; and one for the BabyCenter mobile app My Pregnancy & Baby Today. In 2021, Forbes Health chose My Pregnancy & Baby Today as the best pregnancy app of 2021, and Women's Health identified it