Reviewerly/exHarmony huggingface dataset:
Hyper-Parameter Optimization by Using the Genetic Algorithm for Upper Limb Activities Recognition Based on Neural Networks

A method of Upper Limb Activities Recognition (UPLA) based on Neural Networks is presented. The accuracy of activity recognition will be influenced by the size of sliding window, the overlapping of adjacent sequences and the number of neurons for neural networks. Whereas, there is less work in hyper parameters optimization of neural networks automatically. It is very time-consuming to optimize hyper parameters by experts through an experience and error approach. In the paper, Genetic algorithm is used to find the best hyper parameters automatically: the size of sliding window, the overlapping of adjacent sequences and the number of neurons for neural networks. The basic genetic algorithm has a slow convergence problem and it is very easy to fall into a local optimum. To solve the problem, the population selection mechanism is improved. A comparison is made for the improving method with seven traditional classification algorithms and convolutional neural network, an accuracy of 97.9% is reached by using the new method. Finally, an App is developed that can collect and recognize upper limb activity in real time.
Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial Network

Future optical network is developing towards highly heterogeneity and flexibility, which means that the various signals will be transmitted in the network and the optical performance monitor is more likely to encounter the signal beyond its monitoring range. When the signal beyond monitoring range (abnormal data) is input, the conventional optical performance monitoring (OPM) framework without the ability of data filtering will produce completely wrong results. Although the serial OPM framework has the ability of data filtering, it increases the processing time cost. We propose a novel parallel OPM framework, in which the judgement and analysis modules process the input data simultaneously to reduce the time cost. Moreover, the light-weight and high-performance skip connected generative adversarial network (GAN) trained only on the normal data (within the monitoring range) is proposed in the judgement module to filter the abnormal data in a fast-speed way (~9 ms). In the simulation, eight common signals are used to test the performance of the skip connected GAN in the judgement module. The optimal area under the curve (AUC) value of 0.952 is obtained when the abnormal data is defined as 60 Gbps 64QAM signal. Besides, the impact of the latent vector length, the task weights, the weight of abnormal score, shifted K values and training data size on the model performance are studied.
Nontarget-Based Measurement of 6-DOF Structural Displacement Using Combined RGB Color and Depth Information

Structural displacement is an important physical quantity that provides essential information regarding the structural conditions and the safety of a structure. To date, a wide variety of displacement measurement methods have been developed. As a noncontact type of measurement, the computer-vision-based approach receives significant attention for its cost effectiveness, measurement convenience, and great performance. However, most existing methods are limited to one- or two-degrees-of-freedom (DOF) displacement measurement and require known dimensions of structural members or predefined target markers attached to the target structure, which limits real-world application. This article proposes a nontarget-based 6-DOF displacement measurement method using the combined RGB and depth information. The proposed method utilizes three-dimensional information of measurement points on the target structure obtained using the RGB-D camera, and suggests a coordinate transform scheme to determine the 6-DOF displacement in the physical domain. The proposed method is validated in two laboratory experiments using a shear building model and a pan-tilt with a measurement plate, in which both translational and rotational displacements are measured.
Tunable Optical Bistability, Tristability and Multistability in Arrays of Graphene

The optical bistability, tristability and multistability are explored in arrays of graphene. The arrays are periodically arranged spatially by single sheets of graphene. Optical bistability could be achieved with a strong enough incident intensity of light wave. The thresholds of optical bistability and the intervals between the upper and lower thresholds change with the surface conductivity of graphene and the incident wavelength. By increasing the intensity of incident light, tristability and multistability can be induced as well. Furthermore, the thresholds of bistability, tristability and multistability can be regulated via the chemical potential of graphene. This study may have potential applications in optical logic gates, all-optical switches and photomemory.
Conditional Introspective Variational Autoencoder for Image Synthesis

We present a variational autoencoder (VAE) learning framework with introspective training for conditional image synthesis, and explore conditional capsule encoder by class-wise mask label insertion for this framework. Our model only consists of encoder (E), generator (G) and classifier (C), where E and G can be adversarially optimized, and C helps to boost conditional generation, improve authenticity and provide generation measures for E and G. Discriminator is not necessary in our framework and its absence makes our model more concise with fewer artifacts and pattern collapse problems. To compensate for the blurry weakness of VAE-like models, feature matching is introduced into loss functions by means of C to offer more reasonable measures between real and synthesized images. Moreover, in consideration of the key role of E in autoencoders as well as the interesting characteristics of capsule structure, conditional capsule encoder is preliminary explored in the image synthesis model. Class labels participate conditional encoding by masking high-level capsules of other categories, and capsule loss for the encoder is added to facilitate conditional synthesis. Experiments on MNIST and Fashion-MNIST data sets show that our model achieves real conditional synthesis performances with better diversity and fewer artifacts. And conditional capsule encoder also reveals interesting synthesis effects.
Unsupervised k-means combined with SOFM structure adaptive radar signal sorting algorithm

With the overlapping of signal parameters, signal sorting faces great challenges. K-means clustering algorithm and self-organizing Feature Mapping (SOFM) neural network algorithm are widely used in radar signal sorting. However, the cluster number of k-means algorithm needs to be determined in advance, and the initial cluster center also needs to be randomly selected, so it is easy to fall into local optimal. The accuracy of SOFM neural network sorting results is greatly affected by the preset structure. Aiming at the above two problems, this paper introduces the density dynamic clustering into the traditional k-means clustering algorithm and combines it with SOFM neural network to put forward an unsupervised structural adaptive radar signal sorting algorithm. The simulation results show that the algorithm can effectively solve the problem of signal sorting in the case of parameter space overlap and the computation is small.
Analysis of State Estimation Drift on a MAV Using PX4 Autopilot and MEMS IMU During Dead-reckoning

In perceptually degraded situations, state estimation drift is a major source of failure for autonomous operations of Micro Aerial Vehicles (MAV). This paper serves as a guide for understanding and characterizing estimation drift during dead-reckoning navigation with systems using onboard MEMS inertial measurement units. The analysis is conducted with the commercial-off-the-shelf Pixhawk flight controller, running the commonly-used PX4 autopilot. The performance of the Extended Kalman Filter (EKF2) and Local Position Estimator (LPE+Q) were characterized through a two-step experiment. First, the onboard IMU is manually excited while the estimators receive state updates from a motion capture system. In a second step, a dead-reckoning scenario is created removing the state updates in the estimators. Heat control was also added to analyze temperature effects on the IMU biases and hence estimation drift. Our analysis shows how the estimation drift depends on the quality of the IMU, excitation of the IMU, the estimation algorithm, and temperature control.
Alphabet city: orthographic differentiation and branding in late capitalist cities

This article examines the emergence and design of letter-based city logos in order to discuss what they reveal about writing and cities, and the dynamics between them. Developing a political economic and social semiotic analysis, I suggest that these letter-based logos respond to an increased competition for attention in society—strongly linked to new media technology and patterns of mobility in late capitalist economies. I further argue that they illustrate globalizing linguistic processes such as the truncation, commodification and adaptation of language to global audiences and technologies, and thereby contribute to mask the social and material reality of the branded cities. The article contributes to the broader examination of the ways in which cities and writing interact by drawing attention to the continued spread of abstract and standardized register forms for representing institutional power and distinction within globalized markets, and by discussing their local socio-economic embedding.
Data Integrity and Recovery Management in Cloud Systems

Data integrity and recovery management is a more important issue in cloud computing because data is located in everywhere. There is a big challenge in backup recovery and security. It is required to provide an efficient and more reliable system in data storage. In this paper, a new methodology is focused and proposed data recovery and data management to assure high-level scalability and high order reliability to provide fault recognition and fault tolerance cloud-based systems. We propose a methodology of segmenting data and generating tokens for the data split-up by adding the address of the cloud or locations of the cloud storage using the tailing method. Thus the missing segment of any faulty node is easily recognized within a short range of limits and will get the data backup from the neighboring nodes.
Development of web-based e-Tendering system for Nigerian public procuring entities

E-Tendering is gradually gaining wide acceptance by public sector organisations. Many public procuring entities in both the developed and developing countries have been transitioning from manual-based to digital-based tendering processes, primarily to address the numerous problems associated with the manual-based system such as lack of efficiency, transparency and manipulation of the tendering processes. In Nigeria, the tendering process is largely manual-based and there are no existing e-Tendering systems that support the entire tendering lifecycle. This research addresses that by developing a web-based system that supports key steps in the tendering process, i.e. tender notification, submission, opening, evaluation, approval and awarding. The system has been validated and found to have the potential to significantly improve the proficiency of the current manual-based tendering system used by the Nigerian public procuring entities. The system was developed using object-oriented methodologies. Specifically, Ripple and Unified Process along with Unified Modelling Language were used to guide the system development.
Lumos

Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We presentLumos, a Python library built using the principles of A/B testing to systematically diagnose metric regressions to automate such analysis.Lumos has been deployed across the component teams in Microsoft's Real-Time Communication (RTC) applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application ofLumos has resulted in freeing up as much as $95%$ of the time allocated to metric-based investigations. In this work, we open sourceLumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.
Artificial Intelligence to Support Independent Assessment of Screening Mammograms—The Time Has Come

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
A comparison of methods for 3D scene shape retrieval

3D scene shape retrieval is a brand new but important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on 2D scene sketch-based and image-based 3D scene model retrieval have been organized by us in 2018 and 2019, respectively. In 2018, we built the first benchmark for each track which contains 2D and 3D scene data for ten (10) categories, while they share the same 3D scene target dataset. Four and five distinct 3D scene shape retrieval methods have competed with each other in these two contests, respectively. In 2019, to measure and compare the scalability performance of the participating and other promising Query-by-Sketch or Query-by-Image 3D scene shape retrieval methods, we built a much larger extended benchmark for each type of retrieval which has thirty (30) classes and organized two extended tracks. Again, two and three different 3D scene shape retrieval methods have contended in these two tracks, separately. To solicit state-of-the-art approaches, we perform a comprehensive comparison of all the above methods and an additional new retrieval methods by evaluating them on the two benchmarks. The benchmarks, evaluation results and tools are publicly available at our track websites (Yuan et al., 2019 [1]; Abdul-Rashid et al., 2019 [2]; Yuan et al., 2019 [3]; Abdul-Rashid et al., 2019 [4]), while code for the evaluated methods are also available: http://github.com/3DSceneRetrieval.
Alibi: A Flaw in Cuckoo-Hashing Based Hierarchical ORAM Schemes and a Solution

There once was a table of hashes That held extra items in stashes It all seemed like bliss But things went amiss When the stashes were stored in the caches The first Oblivious RAM protocols introduced the "hierarchical solution," (STOC '90) where the server stores a series of hash tables of geometrically increasing capacities. Each ORAM query would read a small number of locations from each level of the hierarchy, and each level of the hierarchy would be reshuffled and rebuilt at geometrically increasing intervals to ensure that no single query was ever repeated twice at the same level. This yielded an ORAM protocol with polylogarithmic overhead. Future works extended and improved the hierarchical solution, replacing traditional hashing with cuckoo hashing (ICALP '11) and cuckoo hashing with a combined stash (Goodrich et al. SODA '12). In this work, we identify a subtle flaw in the protocol of Goodrich et al. (SODA '12) that uses cuckoo hashing with a stash in the hierarchical ORAM solution. We give a concrete distinguishing attack against this type of hierarchical ORAM that uses cuckoo hashing with a combined stash. This security flaw has propagated to at least 5 subsequent hierarchical ORAM protocols, including the recent optimal ORAM scheme, OptORAMa (Eurocrypt '20). In addition to our attack, we identify a simple fix that does not increase the asymptotic complexity. We note, however, that our attack only affects more recent hierarchical ORAMs, but does not affect the early protocols that predate the use of cuckoo hashing, or other types of ORAM solutions (e.g. Path ORAM or Circuit ORAM).
A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are considered to demonstrate the efficiency of the proposed approach compared with original extreme learning machine, Tikhonov regularization optimally pruned extreme learning machine, and backpropagation algorithms. The results show that the proposed method is fast and produces better accuracy and generalization performances.
Auto-abstracting of texts in the Kazakh language

In this article, the authors propose an approach for abstracting text resources and documents in the Kazakh language. Using software solutions to normalize texts in the Kazakh language, the text data developed by the scientific team of the authors of this work was prepared for further processing. Reviewing is based on keywords and phrases. To extract keywords and phrases, an algorithm is used TF-IDF algorithm to extract keywords and phrases from texts in the Kazakh language. To solve the problem, an approach based on machine learning was applied. To determine the similarity of the sentence, the cosine similarities of the data of the sentence are calculated, and thus the semantic content of the text is determined. When outputting text annotations, the volume of text is taken into account, that is, the amount of annotation depends on the volume of the document. Abstracting of texts in the Kazakh language is an urgent task of classification, clustering of text and information retrieval. The paper presents the results of experimental calculations for various approaches. The results of the study show that the presented approach is the best solution for extracting annotations from texts in the Kazakh language.
Machine Learning And Neural Network Methodologies of Analyzing Social Media

The rapid development of the Internet has led to a significant increase in the number of news sites and social networks that describe various events in the world and society. People actively share their opinions about various events in the world. Manually tracking and analyzing such a volume of information is not possible. So, in this way, the use of algorithms for automatic analysis of texts and user comments is an important feature. Published articles and user comments in most cases are of a certain emotional aspect. This article analyzes texts and user comments of Kazakhstan media space. Sentiment classification is done using machine learning algorithms and convolutional and recurrent neural networks (CNN and RNN). A comparative review of the obtained results was performed after the classification.
GGraph: An Efficient Structure-Aware Approach for Iterative Graph Processing

Many iterative graph processing systems have recently been developed to analyze graphs. Although they are effective from different aspects, there is an important issue that has not been addressed yet. A real-world graph follows the power-law property, in which a small number of vertices have high degrees (i.e., are connected to most other vertices in the graph). These vertices are called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hot-vertices</i> and usually require more iterations to converge. In the existing solutions, these hot-vertices may be allocated to many or even all graph partitions along with other vertices that are easy to converge. As the result, the partitions with hot-vertices have to be loaded repeatedly (and consequently the system suffers from high data access cost), although perhaps only a few vertices in these partitions are active. To cope with this issue, we develop an efficient open source graph partition manager, called GGraph, which can be integrated into the existing graph processing systems to efficiently support iterative graph processing, by taking into account the power-law property of the graph structure. It uses a novel graph repartitioning scheme with low overhead to dynamically partition the hot-vertices together, so as to avoid loading the inactive vertices in the same partition as the repeatedly processed hot-vertices. By such means, it not only enables less data access cost, but also enables the privileged processing of the hot-vertices. In order to further increase the convergence speed, a scheduling algorithm is further proposed in this work to prioritize the processing of the hot-vertices with low overhead. To demonstrate the efficiency of GGraph, we plug it into four state-of-the-art graph processing systems, i.e., Gemini, GraphChi, Chaos, and GridGraph, and experimental results show that GGraph improves their performance by up to 3.2 times, 3.8 times, 3.9 times, 3.5 times, respectively.
Effect of Sense of Embodiment on Curvature Redirected Walking Thresholds

Redirected walking (RDW) allows users to explore a virtual environment larger than the physical tracking space by real walking. When RDW is applied within certain thresholds, users do not notice it and remain immersed in the virtual environment. There are many factors that affect these thresholds such as walking speed, gender, and field of view. However, redirected walking thresholds (RDTs) were never studied when an avatar is present, and it is not known how the sense of embodiment over this avatar affects users’ RDTs.