Search

Background and ObjectiveQuestionnaires are essential tools in many scientific fields, including health and medicine. However, the analysis of paper-and-pencil questionnaires is time consuming, source of errors and expensive, limiting its use in large cohort studies. Computer-based questionnaires might be a valuable alternative but they may introduce bias, especially for sensitive questions, and they require programming skills. The aim of this study is to develop a reliable and adaptable open-source technique (i.e. LightQuest) to automatically analyse various types of scanned paper-and-pencil questionnaires with closed questions, including those with inverted scale.MethodsTo evaluate the usefulness of LightQuest, the time needed for 7 experimenters for manually code 10 sets of 4 frequently used questionnaires and the number of errors (i.e. reliability) were compared with the time and errors their made using LightQuest.ResultsLightQuest was twice as fast as the manual analysis, even though the time to create the reference model was taken into account (933s vs. 1935s, t(2)=8.81, p-1 for the manual technique versus 0.55s.question-1 for LightQuest (t(2)=22.5, pConclusionLightQuest demonstrated clear superiority both in terms of time and reliability. The script of this first open-source technique, which does not require programming skills, is downloadable in supplemental data and may become an asset for all studies using questionnaires.

Colorimetric analysis is being broadly applied in chemical sensing today; however, detection ranges and resolution limits are typically modest. In this paper, we introduce a methodology to quantify the colorimetric chemical response on a paper-based microfluidic device that enables high-resolution colorimetric detection over a broad pH range. We have achieved this by combining data from various indicators displaying sensitivity on partially overlapping small pH ranges and training machine learning classification models to the colorimetric output. The training dataset consists of images taken from the colorimetric response of three different pH indicators previously deposited on circular spots of a multilayer paper-based device, captured with a reference lab-grade camera. Instead of restricting the use of each pH indicator to their linear response regime within the RGB space, the models are trained against data spanning the entire range of pH values, from 3 to 9, in increments of 0.1, exploring the optimum combination of feature engineering and classification model to maximize the overall model accuracy. The combined analysis of image data captured simultaneously with the three indicators resulted in a pH detection accuracy above 85% with over the entire pH range with resolution down to 0.2 pH points. The demonstrated detection range and resolution are well-suited to support various applications in environmental and industrial analysis.

Colorimetric detection using microfluidic paper-based analytical devices (µPADs) and smartphones enable lowcost mobile chemical analysis solutions. However, variable illumination conditions and phone characteristics (i.e. camera hardware and software capabilities) limit the accurate interpretation and reproducibility of quantitative results. In this paper, we describe a method to automatically compensate an image captured by a smartphone camera under variable illumination conditions. By incorporating a two-step algorithm, we approximate the mobile camera picture color distribution to resemble a laboratory-grade measurement under reference illumination conditions. For every test image, the algorithm first applies a color mapping step that performs histogram matching of a set of color reference spots printed on the device to a laboratory reference measurement. After this initial correction step, a transformation matrix is computed via a least-square fit to minimize the differences between the device and the laboratory references. This matrix is then applied to the RGB channel values obtained from a µPAD to correct for illumination variations. The methodology was tested by correcting a test dataset captured using a smartphone to approximate to a calibration dataset acquired using a lab-grade camera. After correction, the relative error between the datasets fell to 10-20%, leading to an increase in classification accuracy between 12-33%. This approach enables colorimetric chemical analysis with smartphones outside the lab, removing the need to control external lighting conditions.

The growing consumption of illicit drugs in Brazil is becoming increasingly problematic for society. It is therefore critical to develop technologies to combat drug trafficking that allow for rapid, non-invasive evaluation of drug samples. Microfluidics is a technology that manipulates and studies small amounts of fluids, using structures with dimensions from ten to hundreds of micrometers (microdevices). The main advantages of microfluidic approaches are its low cost, speed, and ability to provide results in loco. Here, paper microfluidics were developed to perform the modified Scott test to calculate the cocaine hydrochloride content in seized samples of cocaine (n = 30) and crack (n = 30). A smartphone with the Photometrix® app was used to construct a model for quantifying the samples. A factorial model was developed to optimize microfluidic analytical parameters such as spot size (6, 8 and 10 mm), reagent content (50, 75, and 100% cobalt thiocyanate II), cocaine hydrochloride concentration (4, 6 and 8 mg mL-1) and response time (or analyte detection; t = 0, 0.5 1, 12 and 24 h). After experimental planning, a diameter of ΜPADs = 8 mm - [Co(SCN)2] = 100% and a 1 h response time were identified as the best conditions. We observed that the cocaine hydrochloride concentration did not influence the model. A sample concentration of 15 mg mL-1 was used to quantify cocaine hydrochloride in street samples apprehended by the Forensic Police of Espírito Santo state (with n = 60). The quantification curve constructed to determine the cocaine hydrochloride concentration showed a determination coefficient, R2, of 0.98246 and RMSEC (root mean squares error calibration - mean square error of the calibration) of 0.39480, with a LOD and LOQ of 0.09 and 0.30 mg mL-1, respectively. For the crack samples, the cocaine hydrochloride concentrations ranged from 2.5 to 60.8 wt% with an average purity content of 21.3 ± 13.3 wt%. For the seized cocaine samples, variation in hydrochloride content from 1.2 to 22.6 wt% was observed with a mean percentage of 14.19 ± 6.92 wt%. Finally, chemometric tools such as principal component analysis were used to assess the similarity among the samples.

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

Lately, Deep Learning has indicated its capacity by adequately tackling complex learning issues which were not possible previously. Specifically, Convolutional Neural Networks are most generally utilized and have indicated their viability in image detection and recognition problems. Convolutional Neural Networks(CNN) can be used to perform different kinds of tasks. However, to recognize even a single image, billions of calculations are required. For example, ResNet 50 takes 8 billion calculations just to figure out what's in a single image. So we need to find out ways to be able to run that superfast and that's why we need hardware accelerators. In this paper the basic information about Convolutional Neural Networks is provided along with the key operations involved and the brief idea about Field Programmable Gate Arrays(FPGA) is given which enable them to be used for accelerating the inference process of Convolutional Neural Networks. Various techniques which were employed previously for accelerating the Convolutional Neural Networks are discussed. We are focussing on designing a energy efficient Field Programmable Gate Arrays accelerator for accelerating the inference process of Convolutional Neural Networks with reduced latency and increased throughput.

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people’s images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person’s body, possible carried objects, and background. Then the most frequent colors of person’s body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.

Over 100 fatalities and more than 8000 injuries are reported on average every day in the US caused by motor vehicle accidents. In order to provide drivers a safer travel plan, we present a machine learning powered risk profiler for road segments using geo-spatial data. We built an end-to-end pipeline to extract static road features from map data and combined them with other data such as weather and traffic patterns. Our approach proposes novel methods for data pre-processing and feature engineering using statistical and clustering methods. Our model achieves significant performance improvement for risk prediction using hyper-parameter optimization (HPO) and the open source AutoGluon library to optimize the ML model. Finally, an enduser visualization interface is developed in the form of interactive maps. The results indicate 31% improvement in model performance compared to baseline when model is applied to a new geo location. We tested this approach on six major cities in the US. The findings of this research will provide users a tool to quantitatively assess accident risk at road segment level.

Attention to the environment and its problems has undergone unprecedented growth in recent years [...]

The aim of this study was to examine the effects that reading on screens (using digital devices with different screen sizes) and on paper have on reading time and comprehension. The study group was...

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing. Under consideration for acceptance in Theory and Practice of Logic Programming., Comment: 16 pages. Under consideration for acceptance in TPLP

Cardiovascular diseases (CVDs) are reported to be the leading cause of death worldwide by World Health Organization (WHO) with an approximate of 31% of global deaths. Consequently, it has been noted that the largest sector of population which experiences coagulation or hemostatic imbalances or undergo oral anticoagulation therapy (OAT) are the major sector susceptible to the risk of mortality due to cardiovascular ailments such as atherosclerosis, deep vein thrombosis (DVT), stroke etc. These candidates are prescribed or administered using oral anticoagulation therapy (OAT) and are advised to take the Prothrombin Time (PT) test respectively. Prothrombin Time (PT) analyzers are the type of haemostasis analyzer mainly catering to the coagulation following the extrinsic pathways. Additionally, the candidates under OAT requires routine administration of the coagulation status for proper dosage of warfarin or its derivatives which shares a very narrow therapeutic window. A slight amount of imbalance in the coagulation chemistry can disrupt the cardiovascular functioning, hence a routine monitoring is strongly mandated by cardiac surgeons. It is important to realize a simplified hand held, low cost point of care solution for diagnosing hemostasis or coagulation status to cater both the rural and urban sectors resulting in lower casualties due to coagulation imbalance. The respective work proposes a handheld point of care PT sensing paper sensor using very inexpensive pencil and correction pen and Whattman filter paper. In addition the sensor has enhanced capability of carrying our sensitive detection of coagulation status compared to the existing complex and expensive sensors developed through a variety of methods like screen printing, lithography, flexography printing, wax printing, PDMS printing, stamping etc. The proposed device consists of two pencil marks as electrodes. The device is illustrated in Figure 1. The sensor monitors a change in impedance (resistance) w.r.t time. Blood being an ionic fluid whose chemistry changes with the ionic interactions during the process of coagulation results in a direct correlation of the coagulation process to the respective impedimetric signatures. The synthetic coagulation behavior and the response of the device has been measure using nail paint as an analyte. The points of maximum changes in resistance (first and second) denotes the application of the analyte to the sensor and initiation of coagulation or solidification respectively (shown in Figure 2) from which the coagulation time is further derived. Lab View has been used as an interfacing platform for processing the respective resistive data and generating corresponding coagulation status. Thus, the particular PDE-PAD (pencil drawn electrodes- paper-based analytical devices) can be considered as a promising future device for low cost sensitive detection platform for a point of care PT diagnosis. Figure 1

We explored the paper augmentation technique where a tablet computer is placed under the paper to modify its content appearance and create the illusion of a static object appearing perceptually dynamic. Our approach is based on the Deformation Lamps (DL) technique commonly used with front projection. However, we applied it to rear projection, which removes the need for a fixed hardware setup or handling and wearing dedicated hardware. The key challenge was to ensure the appropriate mixing of light reflecting from the paper (to see what is printed on the paper) and the light passing though the paper (to see what the tablet screen is projecting) since (i) a tablet screen is a relatively weak light source when being used in rear projection and (ii) commonly, paper has high opacity so the content from one side is not visible on the other side. In order to achieve the appropriate mixing of light, we reduced the opacity of the paper by perforating it (thus allowing more light to pass through), and modified the algorithm for generating dynamic luminance animation by adding gamma and brightness correction. The outcomes provide a guide on how to modify the paper and the algorithm in order to achieve the DL effect with rear projection using a tablet computer.

Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [...]

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called “ NaDev” (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called “NaDevEx” (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39–73%); however, precision is better (75–97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.

The age of a relatively old document is one of the pending issues to be resolved in the field of forensic documentary examination. Although nowadays there are a variety of analytical methodologies focused in the analysis of inks for dating documents, the paper analysis has attained little attention. This work aims to develop two complementary approaches for estimating the age of documents based on paper analysis employing the pyrolysis technique coupled to gas chromatography with detection by mass spectrometry (Py-GC/MS): (i) a direct approach using the pyrolytic fingerprints and multivariate regression with artificially aged samples, and (ii) an indirect approach based on the identification of compounds characteristic of the document period. The direct approach has successfully allowed the age estimation of relatively old documents under police custody (up to 30 years of age) and the determination of a relation between the natural and the accelerated aging of paper under the used conditions. This approach is applicable to papers that have the same (or similar) composition and have been stored under comparable storage conditions. Additionally, the indirect approach is presented as an interesting perspective to ratify valuable information of the document age.

At present, under the current quality education, the examination is still one of the main measure of teachers’ teaching ability and student achievement. At the same time, different levels of examination are different to the test paper. Aiming at the multi-combination of constraints in the test paper, an improved genetic algorithm is proposed, which combines the constraints of the papers effectively, so that the test papers can be maximized to meet the needs of the users.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

Die Biointelligenz zählt derzeit zu den am meisten beachteten Innovationspfaden in Deutschland und Europa. Maßgeblich für die Entwicklung der Vision der Biointelligenz sind die jüngsten Erfolge der Bio- und Informationstechnologie. So ist es nicht verwunderlich, dass die wissenschaftliche Gemeinschaft, politische Entscheidungsträger wie auch Vertreter der Industrie dem Thema Biointelligenz geradezu enthusiastisch gegenüberstehen. In der Region Stuttgart-Tübingen haben sich beispielsweise über 40 Wissenschaftler im Kompetenzzentrum Biointelligenz zusammengeschlossen, um das Thema gemeinsam, interdisziplinär voranzubringen. Nicht nur die Bundesregierung, sondern auch verschiedene Landesregierungen, planen umfangreiche Fördermaßnahmen. Auf Ebene der EU-Kommission wird hinter vorgehaltener Hand teilweise sogar der Begriff Industrie 5.0 verwendet. Fakt ist, dass die Biointelligenz enormes Potenzial für Sprunginnovationen und Standortvorteile in nahezu allen industriellen Branchen eröffnet. Vielfach wird der Begriff jedoch missverstanden, was in letzter Konsequenz dazu führt, dass die Bedeutung ausgehöhlt und damit das Potenzial für den Wirtschaftsstandort Deutschland unterminiert wird. Für Wissenschaft, Industrie und Politik ist es entscheidend einen Konsens zu finden. Dazu soll dieser Artikel einen Beitrag leisten, indem er die Begriffsherkunft zusammenfasst, einen detaillierten Definitionsvorschlag präsentiert und eine Kategorisierungssystematik erarbeitet. Biointelligence is currently one of the most noted innovation paths in Germany and Europe. Crucial to the development of the Biointelligence vision are recent biotech and information technology breakthroughs. It is therefore not surprising that the scientific community, political decision-makers as well as industry representatives are enthusiastic about the subject of biointelligence. In the Stuttgart-Tübingen region, for example, more than 40 scientists have joined forces in the Competence Center Biointelligence to promote the topic in an interdisciplinary fashion. Not only the federal government, but also various state governments are planning extensive support measures. At the level of the European Commission, the term Industry 5.0 is sometimes even used behind closed doors. The fact is that biointelligence opens up enormous potential for break through innovations, leap frogging and competitive advantages in almost all industrial sectors. In many cases, however, the term is misunderstood, which ultimately leads to undermining the meaning and thus undermining the potential for Germany as a business location. For science, industry and politics it is crucial to find a consensus. This article thus aims to contribute by summarizing the term background, presenting a detailed definition and developing a categorization system.

The written exam is a universal tool for evaluating student performance in the field of education. The written exam provides a mechanism by which instructors and organizations ensure the consistency of the assessment process. Human effort required for the assessment is very high and it depends on several factors such as knowledge of the teacher, application level understanding of the teacher, criteria of the marking and time allotted. However, traditional evaluation processes consume very costly efforts and take huge time for the completion of the complete evaluation, verification and publishing of the result process. This research introduces the design and implementation of Handwritten Answer Evaluation (HAES) system for student exam papers. The HAES is an automatic response assessment system that enables the identification of text in answer sheets and can evaluate the grade of each answer based on the previous knowledge of the model. In this study, Optical Character Recognition (OCR) tool is used to extract the text from human written scanned answer script and machine learning/natural language processing (NLP) techniques are used for grading the answer sheets. The scores are based on cosine set similarity measures, where each sentence in the evaluated answer paper carries their corresponding mark. The developed model can be used to evaluate the marks of the unscored descriptive answers.

In this paper, a method has been proposed for user profiling based on tweeter data. The sentiments of the tweets are retrieved programmatically with the help of WordNet and News Paper Archive. In this experiment, the English WordNet 2.1 has been used as an online semantic dictionary and machine readable version of the "Times of India" news paper has been used to generate a news paper archive. The algorithm is tested on a data set of 1000 tweets from four different categories which are initially tagged by their innate senses for validation of the derived result.First of all, the data set is evaluated with the help of newspaper archive by using lexical overlap and the accuracy in sense retrieval task is 48.7%. The reason behind this scenario is the varieties of representations of a single statement in natural language which creates a mare similarity between the lexical entities of the statements. To overcome this problem, the contexts of the tweets are expanded with the help of WordNet by considering the synonyms of every meaningful word of the tweets and after that the senses of these tweets are evaluated. As the contexts of the statements are expanded in this approach, semantic relatedness between the statements is resolved in an efficient way which leads the system towards a better performance.

Color 3D printing allows for 3D-printed parts to represent 3D objects more realistically, but its surface color quality evaluation lacks comprehensive objective verification considering printing materials. In this study, a unique test model was designed and printed using eco-friendly and vivid paper-based full-color 3D printing as an example. By measuring the chromaticity, roughness, glossiness, and whiteness properties of 3D-printed surfaces and by acquiring images of their main viewing surfaces, this work skillfully explores the correlation between the color representation of a paper-based 3D-printed coloring layer and its attached underneath blank layer. Quantitative analysis was performed using ΔE*ab, feature similarity index measure of color image (FSIMc), and improved color-image-difference (iCID) values. The experimental results show that a color difference on color-printed surfaces exhibits a high linear correlation trend with its FSIMc metric and iCID metric. The qualitative analysis of microscopic imaging and the quantitative analysis of the above three surface properties corroborate the prediction of the linear correlation between color difference and image-based metrics. This study can provide inspiration for the development of computational coloring materials for additive manufacturing.

As Machine Learning (ML) becomes pervasive in the era of artificial intelligence, ML specific tools and frameworks are required for architectural research. This paper introduces NeuroMeter, an integrated power, area, and timing modeling framework for ML accelerators. NeuroMeter models the detailed architecture of ML accelerators and generates a fast and accurate estimation on power, area, and chip timing. Meanwhile, it also enables the runtime analysis of system-level performance and efficiency when the runtime activity factors are provided. NeuroMeter’s micro-architecture model includes fundamental components of ML accelerators, including systolic array based tensor units (TU), reduction trees (RT), and 1D vector units (VU). NeuroMeter has accurate modeling results, with the average power and area estimation errors below 10% and 17% respectively when validated against TPU-v1, TPU-v2, and Eyeriss.Leveraging the NeuroMeter’s new capabilities on architecting manycore ML accelerators, this paper presents the first in-depth study on the design space and tradeoffs of “Brawny and Wimpy” inference accelerators in datacenter scenarios with the insights that are otherwise difficult to discover without NeuroMeter. Our study shows that brawny designs with 64x64 systolic arrays are the most performant and efficient for inference tasks in the 28nm datacenter architectural space with a 500mm2 die area budget. Our study also reveals important tradeoffs between performance and efficiency. For datacenter accelerators with low batch inference, a small $(\sim 16$%) sacrifice of system performance (in achieved Tera OPerations per Second, aka TOPS) can lead to more than a 2x efficiency improvement (in achieved TOPS/TCO). To showcase NeuroMeter’s capability to model a wide range of diverse ML accelerator architectures, we also conduct a followon mini-case study on implications of sparsity on different ML accelerators, demonstrating wimpier accelerator architectures benefit more readily from sparsity processing despite their lower achievable raw energy efficiency.

In a paper manufacturing system, it is substantially important to detect machine failure before it occurs and take necessary maintenance actions to prevent an unexpected breakdown of the system. Multiple sensor data collected from a machine provides useful information on the system's health condition. However, it is hard to predict the system condition ahead of time due to the lack of clear ominous signs for future failures, a rare occurrence of failure events, and a wide range of sensor signals which might be correlated with each other. We present two versions of feature extraction techniques based on the nearest neighbor combined with machine learning algorithms to detect a failure of the paper manufacturing machinery earlier than its occurrence from the multistream system monitoring data. First, for each sensor stream, the time series data is transformed into the binary form by extracting the class label of the nearest neighbor. We feed these transformed features into the decision tree classifier for the failure classification. Second, expanding the idea, the relative distance to the local nearest neighbor has been measured, results in the real‐valued feature, and the support vector machine is used as a classifier. Our proposed algorithms are applied to the dataset provided by Institute of Industrial and Systems Engineers 2019 data competition, and the results show better performance than other state‐of‐the‐art machine learning techniques.

BACKGROUND: Digital models are considered the new standard in the clinical practice of orthodontics. However, they are difficult to use in the absence of a true scale for chairside use during clinical appointments. Therefore, an easy standardized method with true-scale paper print images would be of great utility. The aim of this study was to correlate and compare measurements obtained manually in true-scale paper printed images of digital models with those obtained digitally in the computer. METHODS: Sixty desktop scanned digital models (30 upper and 30 lower) were analyzed. Ortho Insight 3D software was used for generating the printed images on paper and for measuring the digital models. Intercanine width, interpremolar width (first and second), intermolar width (first), and arch length measures were performed with both methods and were correlated and compared. RESULTS: There was a high correlation between measurements performed with both methods. Values obtained from paper print images were lower and showed statistically significant differences (

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

Uniqueness is inherited in every individual. Each individual links with another individual forming some social organization. Social organization in common places some trends or norms that influence each of its members. Thus, each individual ascribes common liking or behavior. Analysis of this behavioral pattern leads to the development of a recommender system, which helps to predict and determine the preference an individual ought to opt. Recommender system proves significant in e-commerce marketing strategies. Besides, recommender system finds its application in academics, health, entertainment, etc. This paper proposes a probabilistic model research paper recommender system using naive Bayes algorithm. The originality this paper incorporates is to classify the instances of the target paper into attributes such as title, reference, citation, calculated conditional probability and label them into recommendation or not. Deploying naive Bayes classifier, the evaluation of the current research is done generating the confusion matrix and hence calculated accuracy, precision, recall and F-measure. We have achieved an accuracy of 90.47%.