Incoming Data Research Articles

Asynchronous Sampling-Based Hybrid Equalizer

An equalizer architecture based on asynchronous sampling is described. The circuit implementation is a hybrid feed-forward equalizer (FFE) using three digital postcursor taps realized via level-crossing samplers (LCSs) and inverter-based delays. Utilizing a current mode logic (CML)-based summer stage, an analog main tap and digital postcursor taps are combined into an inductively tuned resistor load. With a 0.67-mV-rms simulated output-referred noise, the FFE has better noise performance than a conventional continuous-time linear equalizer (CTLE) and a slicer/latch-based decision feedback equalizer (DFE) circuit. Moreover, it exhibits very good linearity and group delay performance suitable for an equalizer. Using inverter-based delays enables area efficiency as well as the capability to tune the delay across process voltage and temperature (PVT) variations. Since the FFE is an event-driven combinational logic based on the incoming data, it does not require synchronous sampling coming from a high-speed clock and data recovery (CDR), and it is advantageous for high-speed and multirate operation as its power consumption scales with the data rate. Fabricated in 40-nm CMOS technology, the hybrid FFE achieves energy per bit of 2.7 pJ/bit with a 20-dB Nyquist insertion loss link at the 28.57-Gb/s nonreturn to zero (NRZ) data rate.

A Federated Learning System for Histopathology Image Analysis with an Orchestral Stain-Normalization GAN.

Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized training on a large data set may be more reliable and more generalized, the slides to the examination are more often than not collected from many distributed medical institutes. This brings its own challenges, and the most important is the assurance of privacy and security of incoming data samples. In the discipline of histopathology image, the universal stain-variation issue adds to the difficulty of an automatic system as different clinical institutions provide distinct stain styles. To address these two important challenges in AI-based histopathology diagnoses, this work proposes a novel conditional Generative Adversarial Network (GAN) with one orchestration generator and multiple distributed discriminators, to cope with multiple-client based stain-style normalization. Implemented within a Federated Learning (FL) paradigm, this framework well preserves data privacy and security. Additionally, the training consistency and stability of the distributed system are further enhanced by a novel temporal self-distillation regularization scheme. Empirically, on large cohorts of histopathology datasets as a benchmark, the proposed model matches the performance of conventional centralized learning very closely. It also outperforms state-of-the-art stain-style transfer methods on the downstream Federated Learning image classification task, with an accuracy increase of over 20.0% in comparison to the baseline classification model.

Visualization of information in a digital educational environment

The development of a digital society as well as the digital transformation of education has a significant impact on the modern educational process. An increase in the volume of educational material involuntarily leads the student to the need to continuously process information flows both educational and extracurricular. Incoming data must be analyzed, comprehended, divided into useful and redundant, which in turn leads to the need to use innovative tools, digital content visualization technologies that would contribute to its perception, analysis, memorization, interpretation and reproduction. Infographics can become one of these tools, as well as improve the information and computer literacy of students as well as teachers in a digital educational environment. The skills of working with infographics in a digital educational environment can be useful for students in their future professional life and for social adaptation. The paper reveals the relationship between the development of digital literacy of students and the use of infographics and its elements for this purpose, which would allow students to master subject knowledge, skills and abilities as well as meta-subject competencies of the digital society. Infographics is a way of graphical representation of complex information, the purpose of which is to concisely, accurately and reliably represent large amounts of data without losing information. As a result of the analysis of the possibilities of using infographics as a way to visualize digital educational content and its elements in the classroom, for the development of digital literacy of students, it has been found that infographics can be used, for example, to explain the operation of various devices, when teaching programming, to visually illustrate facts or events, to create problematic situations, as reference materials in solving problems.

Intended Human Arm Movement Direction Prediction using Eye Tracking

ABSTRACT Collaborative robots are becoming increasingly popular in industries, providing flexibility and increased productivity for complex tasks. However, the robots are still not interactive enough since they cannot yet interpret humans and adapt to their behaviour, mainly due to limited sensory input. Prediction of human movement intentions could be one way to improve these robots. This paper presents a system that uses a recurrent neural network to predict the intended human arm movement direction, solely based on eye gaze, utilizing the notion of uncertainty to determine whether to trust a prediction or not. The network was trained with eye tracking data gathered using a virtual reality environment. The presented deep learning solution makes predictions on continuously incoming data and reaches an accuracy of 70.7%, for predictions with high certainty, and correctly classifies 67.89% of the movements at least once. The movements are, in 99% of the cases, correctly predicted the first time, before the hand reaches the target and more than 24% ahead of time in 75% of the cases. This means that a robot could receive warnings regarding in which direction an operator is likely to move and adjust its behaviour accordingly.

Legal Protection for Automatic Exchange of Information Taxation in Indonesia: The Importance of Synergy with Cybersecurity Institutions

<span lang="EN-US">Every citizen who is the subject of taxes and already has a Taxpayer Identification Number (NPWP or NIK) must do tax reporting on his income. Such reporting ensures taxpayer property and income data validation while supporting good governance. Data automation support is needed from other agencies such as banks to find out the balance and ownership of deposits and bank securities, land bodies to know land ownership, stock exchanges to know share ownership and other institutions related to property ownership. Integration between these institutions will increase the validity of reporting property and income owned by government officials, ultimately improving integrity towards good governance. In reality, there is no automation of validation of annual notification letter (SPT) charging data on property ownership data in each institution due to the sectoral ego of each institution, and there is no real-time regulation of property validation. Automatic Exchange of Information (AEOI) enables the automated sharing of a set of information that has been previously defined by the tax authorities. AEOI is a plan of the G20 members and is initiated by the OECD. The AEOI system works through the exchange of financial data of foreign nationals living in a country. The exchange of financial data is carried out between the tax authorities in each country. Indonesia is prepared to put this into practice as a G20 member, as evidenced by the previous release of Minister of Finance Regulation Number 39/PMK.03/2017 (PMK 39), covering Procedures for Exchange of Information Based on International Agreements. Types of Documents and/or Additional Information Required to Be Retained by Taxpayers Conducting Transactions with Related Parties and Procedures for Management, Minister of Finance Regulation No. 213/PMK..03/2016 (PMK 213). As a result, in addition to the Automatic Exchange of Information (AEOI) regulation put out by the Organization for Economic Cooperation and Development, there needs to be legal protection (OECD). Furthermore, the author emphasizes the importance of cooperation and synergy with cybersecurity institutions to secure tax exchange data.</span>

Three-Part Composite Pareto Modelling for Income Distribution in Malaysia

Income distribution models can be useful for describing the economic properties of a population. In this study, three-part composite Pareto models are fitted to the income distribution in Malaysia for the years 2007, 2009, 2012, 2014, and 2016. The three-part composite Pareto models divide the population into three parts, each following a different distribution model. The lower part follows the inverse Pareto distribution, the upper part follows the Pareto distribution, and the middle part follows another unspecified distribution model. For application in income data, the use of Gaussian mixture distribution is proposed for the middle part, making the inverse Pareto–Gaussian mixture-Pareto distribution model semi-parametric. From the model, it is found that the levels of income inequality in the lower and upper income groups decrease over the period of study. Additionally, the proportion of data following the inverse Pareto distribution in the model is highly correlated with the official absolute poverty incidence.

Leveraging Old Knowledge to Continually Learn New Classes in Medical Images

Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned. This is especially needed in the medical domain where continually learning from new incoming data is required to classify an expanded set of diseases. In this work, we focus on how old knowledge can be leveraged to learn new classes without catastrophic forgetting. We propose a framework that comprises of two main components: (1) a dynamic architecture with expanding representations to preserve previously learned features and accommodate new features; and (2) a training procedure alternating between two objectives to balance the learning of new features while maintaining the model’s performance on old classes. Experiment results on multiple medical datasets show that our solution is able to achieve superior performance over state-of-the-art baselines in terms of class accuracy and forgetting.

Assessing socioeconomic impact of pathogen tested and conventional practice sweet potato production in the highlands of Papua New Guinea

Sweet potato (Ipomoea batatas) is a major staple food crop in Papua New Guinea (PNG), grown extensively in the high-altitude Highlands as a subsistence food crop by smallholder farmers and it is developing into commercial production for cash income. However, pests and diseases attack remain a major challenge. Most of the PNG farmers do not practice an active pests and diseases management, which has a significant impact on the yield and farmers' income. Few farmers have opted to use cultural practices, though these methods are ineffective and expensive. An alternative intervention technology through Pathogen Tested (PT) seeds as a part of Integrated Pest and Disease Management (IPDM) technology was trialed in three different zones in the Highlands of PNG and compared with the Conventional Practice (CP). The study was done using farmer participatory research approach (FPR). Pre-designed structured questionnaires were administered to collect data for yield, costs, income, and farmer's perception. The Benefit Cost Ratio (BCR) analysis was used to compare the costs, benefits, and net incomes. T-test statistics was used to compare the mean yield. The farmer perception was summarized using descriptive statistics. Results revealed that marketable sweet potato yield was significantly (P < 0.01) improved with implementation of the PT practice compared to the CP. The improvement in storage root yield had subsequent result in the net income doubled for the farmers. The yield and quality of storage roots had significant influence on the farmers' perception towards adoption of the IPDM technology.

Inequalities in dental service utilization among Thai adults from 2000 to 2017.

The Universal Coverage Health Scheme (UCS) was implemented in Thailand in 2002 to increase access to health services among the Thai population. This study aimed to evaluate socioeconomic inequalities in dental service utilization (DU) before and after UCS implementation and to assess factors associated with DU among Thai adults. This study is based on secondary data from four Thai national oral health surveys in 2000-2001, 2006-2007, 2012 and 2017. Whether an individual from a nationally representative sample of Thai adults aged 35-44 went to see the dentist during the past year was used to define DU. The slope index of inequality (SII) and relative index of inequality (RII) were used to assess and compare education and income-related inequalities in DU. Poisson regression was used to assess factors associated with DU. Income inequalities were not reported for the year 2001 (Income data not available). Although DU increased after UCS implementation, socioeconomic inequalities persisted. DU was concentrated among high-education and income groups. Absolute educational inequalities were 0.16 (95% CI: 0.09-0.22), 0.21 (95% CI: 0.10-0.32), 0.26 (95% CI: 0.14-0.38) and 0.25 (95% CI: 0.18-0.32) in 2001, 2007, 2012 and 2017, respectively. Absolute income inequalities were 0.15 (95% CI: 0.04-0.26), 0.07 (95% CI: -0.04-0.18) and 0.12 (95% CI: 0.05-0.19) in 2007, 2012 and 2017, respectively. Occupation, type of health insurance scheme, sex and oral health-related behaviour were associated with DU. UCS has improved accessibility to dental services, but UCS alone might not narrow the inequalities gap for Thai adults.

Predicting potential underreporting of firearm ownership in a nationally representative sample.

This study utilized demographic and intrapersonal variables to identify individuals who may have falsely denied firearm ownership and determined if individuals can be divided into meaningful subgroups. Participants were United States residents (N = 3500) recruited from January to June 2020. matched to the 2010 census data for age, race, sex, income, and education level. A Zero-Inflated Negative Binomial (ZINB) regression was utilized to determine potential underreporting of firearm ownership, and a latent class analysis was utilized to determine unique subgroups of those who were identified as underreporting firearm ownership in the ZINB. Participants (N = 1306) were identified as underreporting firearm ownership (excess zeros) based on a model that included demographic and intrapersonal variables. A latent class analysis indicated that among excess zeros, three unique subgroups exist. Determining who may be underreporting firearm ownership will allow for a more comprehensive understanding of firearm ownership in the US and more targeted safe storage messages that may reach those who own firearms and are at risk for firearm-related injury and death.

An investigation of the performance of parametric functional forms for the Lorenz curve.

Given that an excellent performance of any parametric functional form for the Lorenz curve that is based on a single country case study and a limited range of distribution must be treated with great caution, this study investigates the performance of a single-parameter functional form proposed by Paul and Shankar (2020) who use income data of Australia to show that their functional form is superior to the other existing widely used functional forms considered in their study. By using both mathematical proof and empirical data of 40 countries around the world, this study demonstrates that Paul and Shankar (2020)'s functional form not only fails to fit the actual observations well but also is generally outperformed by the other popular functional forms considered in their study. Moreover, to overcome the limitation of the performance of a single-parameter functional form on the criterion of the estimated Gini index, this study employs a functional form that has more than one parameter in order to show that, by and large, it performs better than all popular single-parameter functional forms considered in Paul and Shankar (2020)'s study. Thus, before applying any functional form to estimate the Lorenz curve, policymakers should check if it could describe the shape of income distributions of different countries through the changes in parameter values and yield the values of the estimated Gini index that are close to their observed data. Using a functional form that does not fit the actual observations could adversely affect inequality measures and income distribution policies.

Incentives, globalization, and redistribution

We offer a new explanation for why taxes have become less redistributive in many countries while the concentration of incomes has increased. Our argument is based on the prevalence of incentive contracts in modern economies, in conjunction with increased product market integration. Globalization widens the spread of project returns and makes contract choices more responsive to tax changes. This can result in a lower optimal income tax rate while simultaneously increasing the income share of top earners. These results are confirmed in a calibrated version of our model based on U.S. income data.

Simulation of duplicate tooling consumption at aircraft manufacturing enterprises

To simulate the consumption of duplicate technological tooling (hereinafter referred to as TT), it is proposed to use the apparatus of multivariate regression analysis, which provides the main patterns formalization of consumption formation value in the form of linear regression dependencies. An important difference of the proposed approach to the development of regression models is an increased degree of detail due to the use of the number of detail operations performed using specific TT sizes as independent variables, which makes it possible to eliminate the calculation error associated with the presence of passing positions in the planning period (batches of parts and assembly units ). As a result, a quantitative assessment of the constructed dependencies based on the calculation of multiple correlation coefficients, the Fisher dispersion ratio, the Theil’s coefficient of irrelevance allowed us to conclude that they are adequate to the conditions of consumption TT at the aviation enterprise. To improve the adequacy of displaying real processes of TT consumption, the developed models are equipped with a parametric adaptation mechanism. The use of the steepest descent method makes it possible to adjust the regression parameters based on incoming data, taking into account the main trends in the development of the simulated process and providing for smoothing its random fluctuations.

Stream Aggregation with Compressed Sliding Windows

High performance stream aggregation is critical for many emerging applications that analyze massive volumes of data. Incoming data needs to be stored in a sliding window during processing, in case the aggregation functions cannot be computed incrementally. Updating the window with new incoming values and reading it to feed the aggregation functions are the two primary steps in stream aggregation. Although window updates can be supported efficiently using multi-level queues, frequent window aggregations remain a performance bottleneck as they put tremendous pressure on the memory bandwidth and capacity. This article addresses this problem by enhancing StreamZip, a dataflow stream aggregation engine that is able to compress the sliding windows. StreamZip deals with a number of data and control dependency challenges to integrate a compressor in the stream aggregation pipeline and alleviate the memory pressure posed by frequent aggregations. In addition, StreamZip incorporates a caching mechanism for dealing with skewed-key distributions in the incoming data stream. In doing so, StreamZip offers higher throughput as well as larger effective window capacity to support larger problems. StreamZip supports diverse compression algorithms offering both lossless and lossy compression to integers as well as floating-point numbers. Compared to designs without compression, StreamZip lossless and lossy designs achieve up to 7.5× and 22× higher throughput, while improving the effective memory capacity by up to 5× and 23×, respectively.

County augmented transformer for COVID-19 state hospitalizations prediction

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation.

An adaptive constrained clustering approach for real-time fault detection of industrial systems

Thanks to the pervasive deployment of sensors in Industry 4.0, data-driven methods are recently playing an important role in the fault diagnosis and prognosis of industrial systems. In this paper, a novel Adaptive Constrained Clustering algorithm is defined to support real-time fault detection of an industrial machine, by clustering the incoming monitoring data into two clusters over time, representing the nominal and non-nominal work conditions, respectively. To this aim, the proposed algorithm relies on a two-stage procedure: micro-clustering and constrained macro-clustering. The former stage is responsible for grouping the batches of work-cycle data into micro-clusters, while the data stream continuously arrives from the data acquisition system. Then, after condensing the micro-clusters into vectors of cluster features, and leveraging on additional knowledge on the nominal and non-nominal working conditions (i.e., constraints on some samples), the second stage aims at offline grouping the micro-clusters features into macro-clusters. Experimental results on a real-world industrial case study show that the proposed real time framework achieves the same results of offline baseline methods (e.g., Constrained K-means) with a higher responsiveness and processing speed; in comparison to stream baseline methods (e.g., Stream K-means), the proposed approach obtains more accurate and easily interpretable results.

Self-organized and fully service-automated monitoring approach at the cloud-network slice granularity

The cloud-network slicing concept has been established to deal with the advent of the Fifth Generation (5G) of mobile networks and its enabling technologies, thus promoting softwarization and cloudification. The Novel Enablers for Cloud Slicing (NECOS) ecosystem distinguishes itself over state of the art through the definition of slicing at both cloud and network levels and by promoting a Management and Orchestration (MANO) platform that provisions features with a self-organized and full-service automation approach across multiple federated domains. In the NECOS architecture, the Infrastructure and Monitoring Abstraction (IMA) component fetches Key Performance Indicators (KPIs) associated with the constituent parts of the active cloud-network slice instances managed by the NECOS platform. However, the design of the IMA monitoring component follows a centralized approach running at the core-cloud domain. Thus, the IMA concentrates on the monitoring data fetching function, done through interaction with measurement applications. It books all of them into a database and then forwards the incoming data for targeting management applications. Our findings in the slice monitoring state of the art study and assessments in IMA central cloud monitoring suggest that the centralized cloud approach cannot make distinct monitoring technologies compatible, besides not presenting a monitoring-as-a-service perspective that allows a self-organized and fully-service automated monitoring management scheme. In this regard, this work proposes the Distributed Infrastructure and Monitoring Abstraction (DIMA) multilevel monitoring plan. DIMA can promote monitoring as a service across an edge-cloud continuum inside the NECOS domain, enabling cloud/edge-centric and distributed monitoring schemes at the granularity of cloud-network slices' constituent parts.

Income Segregation Analysis in Limited‐Data Contexts: A Methodology Based on Iterative Proportional Fitting

Since the 1950s, researchers in Urban Geography have created multiple instruments for measuring income segregation. However, the computation of such indexes requires the availability of income data and population distribution for small areal units. This approach is problematic for countries and cities where a government's decennial census does not collect or report income data for small‐enough areal units to capture income variability within a neighborhood. To address this gap, we use Iterative Proportional Fitting (IPF) to combine neighborhood‐level census data with an individual‐level income survey data and then estimate small area discrete and continuous income distributions for each small area. We show that it is possible to compute segregation indices based solely on estimated probability distributions without the need to generate a full synthetic population or to obtain integer population counts. We test our empirical method with the case of Mexican cities, for which global and local indexes of segregation are computed with bootstrapped confidence intervals. The major contributions of this article are twofold. First, it uses a method for income‐data generation to measure income segregation. Secondly, it demonstrates a linkage between the computation of segregation measures based on probability distributions and the feasibility of computing them directly from the same IPF estimated distributions of income.

Real-time defect detection using online learning for laser metal deposition

Defect prevention and detection are very crucial for the quality improvement of additive manufacturing (AM) processes. Timely identification of imperfections and flaws in the manufactured products will allow effective and early implementation of corrective actions and thus impede the spread of defects to the whole industrial value chain. In laser metal deposition (LMD) AM processes, defects are imperfections which typically include “porosity” and “cracks formation”. This study presents novel data-driven anomaly detection techniques that use both transfer learning and online learning to assess the quality of melt pool images taken during the LMD process of a part. The proposed methods incorporate the characteristics and dynamics of the manufactured part during the printing process along with previous knowledge acquired from historical data. Continual online learning models (K-means and self-organizing maps) are developed whose parameters are adapted to the incoming data collected in real-time as a new part is being manufactured. The proposed models significantly outperformed the performance of their batch-learning counterparts in detecting anomalous melt pool images in an additively manufactured Ti–6Al–4V thin-walled part. Both models required an average of ∼0.07 s to process each incoming melt pool image, update their parameters and give a prediction on the image's health. This shows the potential of continual online learning for real-time anomaly detection.

Explaining the Link Between Paternal Socioeconomic Position and Small for Gestational Age Birth: The Effect of Maternal Unhealthy Behaviors.

To determine the proportion of small for gestational age (weight < 10th percentile, SGA) births among fathers with lifelong low (compared to high) socioeconomic position (SEP) attributable to white and African-American women's unhealthy pregnancy-related behaviors. Oaxaca-Blinder decomposition methods were conducted on the Illinois transgenerational dataset of infants (1989-1991) and their Chicago-born parents (1956-1976) with appended US census income data. The neighborhood income of father's residence at the time of his birth and at the time of his infant's birth were used to estimate his lifetime SEP. Maternal unhealthy pregnancy-related behaviors were defined as cigarette smoking, inadequate prenatal care, and/or low weight gain during pregnancy. Among African-American women, births (n = 4426) to fathers with lifetime low SEP had an SGA rate of 14.8% compared to 12.1% for those (n = 365) born to fathers with lifetime high SEP (p < 0.0001). Among white women, births (n = 1430) to fathers with lifetime low SEP had an SGA rate of 9.8% compared to 6.2% for those (n = 9141) born to fathers with lifetime high SEP (p < 0.0001). Adjusting for maternal age, marital status, education, and parity, African-American and white women's unhealthy pregnancy behaviors accounted for 25% and 33%, respectively, of the disparity in SGA rates among infants of lifetime low (compared to high) SEP fathers. A significant proportion of the disparity in SGA rates between fathers with lifelong low (compared to high) SEP is explained in both races by maternal unhealthy pregnancy behaviors.