Reviewerly/exHarmony huggingface dataset:
Breast Cancer Screening Using Digital Breast Tomosynthesis

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Oncology HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Oncology journal
Medical diagnosis using NIR and THz tissue imaging and machine learning methods

The problem of extracting useful information for medical diagnosis from 2D and 3D optical imaging experimental data is of great importance. We are discussing challenges and perspectives of medical diagnosis using machine learning analysis of NIR and THz tissue imaging. The peculiarities of tissue optical clearing for tissue imaging in NIR and THz spectral ranges aiming the improvement of content data analysis, methods of extracting of informative features from experimental data and creating of prognostic models for medical diagnosis using machine learning methods are discussed.
Universal voice-enabled user interfaces using JavaScript

Countless voice-enabled user interfaces rely on keyword spotting (KWS) systems for wake word detection and simple command recognition. As a practical matter, these applications run on "edge" devices, where dozens of different platforms exist; typically, platform-dependent implementation are required whenever keyword spotting capabilities are needed. This impedes the rapid deployment of voice-enabled interfaces. Fortunately, with the development of several recent frameworks, JavaScript enables us to deploy neural networks for keyword spotting to support a wide range of speech-based user interfaces. We present three voice-enabled applications that use a unified, JavaScript-based KWS system: an in-browser game, a desktop virtual assistant, and a smart lightbulb controller. We are, to the best of our knowledge, the first to demonstrate the feasibility of JavaScript-based keyword spotting for universal voice-enabled user interfaces.
APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science

In this paper, we propose an application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates application metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.
Dial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e753" altimg="si7.gif"><mml:mover accent="true"><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mo>ˆ</mml:mo></mml:mrow></mml:mover></mml:math> for monotonic: A kernel-based Bayesian approach to state-trace analysis

A monotonic state-trace implies that a single latent factor is sufficient to explain the joint variation between two outcome variables across a set of conditions. There are, however, few methods available for assessing how much evidence a sample of data provides about whether the variables are truly monotonically related or not. We present a model that estimates the statistic Mˆ which reflects the amount of evidence a dataset provides about whether two or more outcome variables are jointly monotonically related at the group level. This statistic is based on modeling the covariation between outcome measures in terms of a kernel function, which allows for computation of the latent derivatives of each outcome variable with respect to the other. We then compare the prior and posterior probabilities that these derivatives are all of the same sign (and are thus monotonic) to obtain Mˆ. Simulations show that Mˆ discriminates between monotonic and non-monotonic state traces and an example illustrates how the model can be applied to both continuous and binomial data from between-subjects, within-subjects, or mixed designs.
Three-D Wide Faces (3DWF): Facial Landmark Detection and 3D Reconstruction over a New RGB–D Multi-Camera Dataset

Latest advances of deep learning paradigm and 3D imaging systems have raised the necessity for more complete datasets that allow exploitation of facial features such as pose, gender or age. In our work, we propose a new facial dataset collected with an innovative RGB⁻D multi-camera setup whose optimization is presented and validated. 3DWF includes 3D raw and registered data collection for 92 persons from low-cost RGB⁻D sensing devices to commercial scanners with great accuracy. 3DWF provides a complete dataset with relevant and accurate visual information for different tasks related to facial properties such as face tracking or 3D face reconstruction by means of annotated density normalized 2K clouds and RGB⁻D streams. In addition, we validate the reliability of our proposal by an original data augmentation method from a massive set of face meshes for facial landmark detection in 2D domain, and by head pose classification through common Machine Learning techniques directed towards proving alignment of collected data.
From time theft to time stamps: mapping the development of digital forensics from law enforcement to archival authority

The field of digital forensics seems at first glance quite separate from archival work and digital preservation. However, professionals in both fields are trusted to attest to the identity and integrity of digital documents and traces – they are regarded as experts in the acquisition, interpretation, description and presentation of that material. Archival science and digital forensics evolved out of practice and grew into established professional disciplines by developing theoretical foundations, which then returned to inform and standardize that practice. They have their roots in legal requirements and law enforcement. A significant challenge to both fields, therefore, is the identification of records (archival focus) and evidence (digital forensics focus) in digital systems, establishing their contexts, provenance, relationships, and meaning. This paper traces the development of digital forensics from practice to theory and presents the parallels with archival science.
Self-orthogonal codes from equitable partitions of association schemes

We give a method of constructing self-orthogonal codes from equitable partitions of association schemes. By applying this method, we construct self-orthogonal codes from some distance-regular graphs. Some of the obtained codes are optimal. Further, we introduce a notion of self-orthogonal subspace codes. We show that under some conditions equitable partitions of association schemes yield such self-orthogonal subspace codes and we give some examples from distance-regular graphs.
A New Weighted k-Nearest Neighbor Algorithm Based on Newton’s Gravitational Force

The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper we propose a weighting based on the Newton’s gravitational force, so that a mass (or relevance) has to be assigned to each instance. We evaluated this idea in the kNN context over 13 benchmark data sets used for binary and multi-class classification experiments. Results in $$\mathrm {F}_1$$ score, statistically validated, suggest that our proposal outperforms the original version of kNN and is statistically competitive with the distance weighted kNN version as well.
Bangla and English Text Cryptography Based on Modified Blowfish and Lempel-Ziv-Welch Algorithm to Minimize Execution Time

The goal of this research is to provide data security without sacrificing data transfer speed especially for Bangla language in digital platforms such as e-commerce portals, online marketing, electronic banking, social media networks, mobile telecommunication etc. Different cryptography algorithms have been reviewed, and Blowfish and Lempel-Ziv-Welch (LZW) have been found suitable owing to various advantages. Blowfish, a symmetric block cipher algorithm, uses a variable-length key from 32 bits to 448 bits. It is unpatented, requires less memory to execute, fast, and simple to operate. The LZW algorithm is also simple to implement and works by building a dictionary adaptively. It is effective particularly against data loss. The implemented modified Blowfish algorithm is observed to be faster than LZW algorithm in terms of total time to encrypt and decrypt Bangla and English text. It is observed that modified Blowfish algorithm is the fastest (0.16ms) to encrypt Bangla text while LZW algorithm is the fastest (0.07ms) to decrypt Bangla text. It is also observed from inputting text of different packet size that on average Bangla text decryption is considerably slower than Bangla text encryption by modified Blowfish algorithm.
Online Handwritten Gujarati Word Recognition

In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.
On the computation and verification of $$\pi $$ using BBP-type formulas

In this paper, we propose two Bailey–Borwein–Plouffe (BBP)-type formulas for $$\pi $$. We show that computation and verification of $$\pi $$ using the two different BBP-type formulas require 20% fewer terms than verification by shifting the starting position of a few hexadecimal digits of $$\pi $$ using Huvent’s formula, which is known as the BBP-type formula with the least number of terms.
Data Augmentation via Variational Auto-Encoders

Data augmentation is a widely considered technique to improve the performance of Convolutional Neural Networks during training. This step consists in synthetically generate new labeled data by perturbing the samples of the training set, which is expected to provide more robustness to the learning process. The problem is that the augmentation procedure has to be adjusted manually because the perturbations considered must make sense for the task at issue. In this paper we propose the use of Variational Auto-Encoders (VAEs) to generate new synthetic samples, instead of resorting to heuristic strategies. VAEs are powerful generative models that learn a parametric latent space of the input domain from which new samples can be generated. In our experiments over the well-known MNIST dataset, the data augmentation by VAEs improves the base results, yet to a lesser extent of that obtained by a well-adjusted conventional data augmentation. However, the combination of both conventional and VAE-guided data augmentations outperforms all the results, thereby demonstrating the goodness of our proposal.
Interaction with Collaborative Robot Using 2D and TOF Camera

With increasing count of applications with collaborative robots it is important to be able to set up conditions for controlling the robot and its interaction with human operator. This article describes a quick response system detecting hands of the operator using 2D and TOF camera in the shared operator-robot workspace. The technology provides data about position of hands and gestures which is used to instant reactions of the robot on presence of the operator and to control the robot. Interaction with robot with gestures is for the operator more intuitive with no need of expertise knowledge. Operator can use both hands, so the operation is more efficient and faster. This system was tested on prototype workplace and results are evaluated in this article.
A blotch detection method for archive video restoration using a neural network

Due to an impressive progress in digital imaging reached over past decades, the importance of analog video systems as primary instruments of receiving, transmitting and storing data has been greatly reduced. However, there still exist large amount of data stored in analog formats on media affected by aging. In this paper, a new three-stage method for detecting and restoring blotches on a video sequence has been developed. The new method including the motion compensation, LBP calculations and data classification using the neural network is shown to have higher efficiency than commonly used ROD, SROD and SDI methods.
Non‐alternating stochastic <i>K</i> ‐means based on probabilistic representation of solution space

A non-alternating (NA) form of K-means is proposed to improve the performance for the most fundamental, yet highly non-convex clustering problem. The motivation of this Letter is that the non-convex nature of K-means can be better handled by stochastic optimisation. However, the alternating update of the Lloyd's algorithm prohibits an effective stochastic optimisation. In order to fully realise the idea, a probabilistic representation is provided for the solution space of K-means, which leads to a simple yet efficient NA update. Experiments show that the proposed method outperforms the existing variants, especially for large numbers of clusters, with reasonable time complexity.
Pythia

Web frameworks that allow developers to create applications based on design patterns such as the Model View Controller (MVC), provide by default a number of security checks. Nevertheless, by using specific constructs, developers may disable these checks thus re-introducing classic application vulnerabilities such as Cross-site Scripting (XSS) and Cross-Site Request Forgery (CSRF). Framework-specific elements including (1) the complex nature of these applications, (2) the different features that they involve (e.g. templates), and (3) the inheritance mechanisms that governs them, make the identification of such issues very difficult.
The MOR Cryptosystem in Classical Groups with a Gaussian Elimination Algorithm for Symplectic and Orthogonal Groups

In this chapter, we study the MOR cryptosystem with symplectic and orthogonal groups over finite fields of odd characteristics. There are four infinite families of finite classical Chevalley groups. These are special linear groups SL(d, q), orthogonal groups O(d, q), and symplectic groups Sp(d, q). The family O(d, q) splits into two different families of Chevalley groups depending on the parity of d. The MOR cryptosystem over SL(d, q) was studied by the second author. In that case, the hardness of the MOR cryptosystem was found to be equivalent to the discrete logarithm problem in F q d . In this chapter, we show that the MOR cryptosystem over Sp(d, q) has the security of the discrete logarithm problem in F q d . However, it seems likely that the security of the MOR cryptosystem for the family of orthogonal groups is F q d 2 . We also develop an analog of row-column operations in symplectic and orthogonal groups which is of independent interest as an appendix.
Automated system of audio components analysis and synthesis

The work is aimed to research the process of synthesis and analysis of musical compositions. The main goal is to increase speed and quality of this process. This is done by combining sound analysis algorithms with music synthesis technologies. By this method music taken by the device microphone is processed by the mobile application and then transformed to the note sequence which could be edited by the user.
MOTIVATION OF EDUCATIONAL ACTIVITY: BASIC CONCEPTS AND PROBLEMS

The requirements for the outcomes of professional education in federal state educational standards are high. However, we observe many students who have low motivation for study, as they do not want to master their majors and be good specialists in the future. The reality is that only 15 % of the total number of students are motivated to study. Therefore, the instructor should understand the factors that positively influence the students’ motivation. This article clarifies the definition of student’s motivation and its constituent components, analyzes various approaches, concepts, assesses the initial level of student’s motivation and highlights the main motivating factors. These factors can contribute to resolving the contradiction between the low student’s motivational-value attitude to learning and the modern requirements of regulatory documents for the training of professionals. To solve these problems the questionnaire, the retrospective analysis, tests developed by T.I. Iljina, and diagnostics created by A.A. Reana and V.A. Yakunin were used.
Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

The increasement of software complexity directly results in the augment of software fault and costs a lot in the process of software development and maintenance. The complex network model is used to study the accumulation and accumulation of faults in complex software as a whole. Then key nodes with high fault probability and powerful fault propagation capability can be found, and the faults can be discovered as soon as possible and the severity of the damage to the system can be reduced effectively. In this paper, the algorithm MFS_AN (mining fault severity of all nodes) is proposed to mine the key nodes from software network. A weighted software network model is built by using functions as nodes, call relationships as edges, and call times as weight. Exploiting recursive method, a fault probability metric FP of a function, is defined according to the fault accumulation characteristic, and a fault propagation capability metric FPC of a function is proposed according to the fault propagation characteristic. Based on the FP and FPC, the fault severity metric FS is put forward to obtain the function nodes with larger fault severity in software network. Experimental results on two real software networks show that the algorithm MFS_AN can discover the key function nodes correctly and effectively.
Complicated intermittent scratches detection research on surface of optical components based on adaptive sector scanning algorithm cascading mean variance threshold algorithm

In inertial confinement fusion system, the intermittent scratches on the polished surface of single-sided polished and bottom surface frosted optical components are complex, and it’s of great difficulty to extract them completely. In order to solve this problem, established in the light-field surface detection system, this paper brings forward a novel intermittent scratch detection method based on adaptive sector scanning algorithm (ASC) cascading mean variance threshold algorithm (MVTH). In the preprocessing step, dividing the original image into subimages with a number of integer multiple of cpu cores so as to fully compress image processing time utilizing parallel processing, using mean filter to balance background and then obtaining binary subimages utilizing morphology and threshold operations, finally, utilizing Two-pass algorithm to label the connected domains of binary subimages. In the detection step, considering the complexity of the pattern of intermittent scratches, ASC is first used for routine intermittent scratches stitching and then supplemented by MVTH. In the verification step, in order to prove that the detected intermittent scratches satisfy the criteria for scratches in human eyes, the method of support vector machine (SVM) pattern recognition is utilized to compare the detected results with the continuous scratch samples detected by human eyes. This algorithm has high degree of parallelism, high speed and strong robustness. The experimental results illustrate that the complete extraction rate of intermittent scratches is 93.59% , the average processing time of single image is merely 0.029 second and the accuracy rate of detection is up to 98.72% by SVM verification.