Validity as a Measure of Data Quality in Internet of Things Systems

Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis. On analyzing the data quality of the two data sets on the basis of above-mentioned parameters. We show that validity for data set 1 was found to be 75% while it was found to be 67% only for data set 2. Availability and data freshness metrics performance were analyzed graphically. It was found that for data set 1, data freshness was better while availability metric was found better for data set 2. Usability obtained for data set 2 was 86% which was higher as compared to data set 1 whose usability metric was 69%. Thus, this work presents methods that can be leveraged for estimating data quality that can be beneficial in various IoT based industries which are essentially data centric and the decisions made by them depends upon the validity of data.

     

Modeling and simulation of temperature drift for ISFET-based pH sensor and its compensation through machine learning techniques

The paper presents modeling and simulation of ion-sensitive field-effect transistor (ISFET)-based pH sensor with temperature-dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal-oxide-semiconductor field-effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant-voltage, constant-current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state-of-the-art ML-based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature-independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.     

IMoS: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions

Can we make virtual characters in a scene interact with their surrounding objects through simple instructions? Is it possible to synthesize such motion plausibly with a diverse set of objects and instructions? Inspired by these questions, we present the first framework to synthesize the full-body motion of virtual human characters performing specified actions with 3D objects placed within their reach. Our system takes textual instructions specifying the objects and the associated ‘intentions’ of the virtual characters as input and outputs diverse sequences of full-body motions. This contrasts existing works, where full-body action synthesis methods generally do not consider object interactions, and human-object interaction methods focus mainly on synthesizing hand or finger movements for grasping objects. We accomplish our objective by designing an intent-driven full-body motion generator, which uses a pair of decoupled conditional variational auto-regressors to learn the motion of the body parts in an autoregressive manner. We also optimize the 6-DoF pose of the objects such that they plausibly fit within the hands of the synthesized characters. We compare our proposed method with the existing methods of motion synthesis and establish a new and stronger state-of-the-art for the task of intent-driven motion synthesis.     

A privacy-preserving model based on differential approach for sensitive data in cloud environment

A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.

     

Enhancement of Tensile and Microhardness of AA 6082-T6 Friction Stir Welds via Brass Interlayers

Russian Journal of Non-Ferrous Metals - This paper investigates the effects of brass interlayer on the microstructural and mechanical properties of friction stir welded AA 6082-T6. To analyze the...     

A review and recent developments on strategies to improve the photocatalytic elimination of organic dye pollutants by BiOX (X=Cl,Br, I,F) nanostructures

The main environmental problems associated with water body pollution are typically those caused by the discharge of untreated effluents released by various industries. Wastewater from the textile dye industry is itself a large contributor and contains a huge number of complex components, a wide spectrum of organic pollutants with high concentration of biochemical oxygen demand (BOD)/chemical oxygen demand (COD) and other toxic elements. One of several potential techniques to degrade such reactive dyes before being discharged to water bodies is photocatalysis, and bismuth-based photocatalysts are rapidly gaining popularity in this direction. Bismuth oxyhalides, BiOX (X=Cl, Br, I, F), as a group of ternary compound semiconductors (V-VI-VII), have been explored extensively for their photocatalytic activity due to their unique crystal lattice with special layered structure in pure as well as modified form. With suitable band gap and band edge positions, which are a required condition for efficient water breakup and high photon absorption, BiOCl scores over other oxyhalides. Photocatalytic activity depends on many factors such as synthesis method, morphology, size, illumination type, dye choice among others. This paper gives a critical review on bismuth oxyhalides as a family on various aspects of modifications such as doping (with unique and interesting metals as well), morphology and synthesis parameters, polymer and carbon assisted composites in order to further enhance the photocatalytic efficiency in UV/visible region of solar spectrum.     

Multifunctional temperature-responsive polymers as advanced biomaterials and beyond

The versatility and applicability of thermoresponsive polymeric systems have led to great interest and a multitude of publications. Of particular significance, multifunctional poly(N-isopropylacrylamide) (PNIPAAm) systems based on PNIPAAm copolymerized with various functional comonomers or based on PNIPAAm combined with nanomaterials exhibiting unique properties. These multifunctional PNIPAAm systems have revolutionized several biomedical fields such as controlled drug delivery, tissue engineering, self-healing materials, and beyond (e.g., environmental treatment applications). Here, we review these multifunctional PNIPAAm-based systems with various cofunctionalities, as well as highlight their unique applications. For instance, addition of hydrophilic or hydrophobic comonomers can allow for polymer lower critical solution temperature modification, which is especially helpful for physiological applications. Natural comonomers with desirable functionalities have also drawn significant attention as pressure surmounts to develop greener, more sustainable materials. Typically, these systems also tend to be more biocompatible and biodegradable and can be advantageous for use in biopharmaceutical and environmental applications. PNIPAAm-based polymeric nanocomposites are reviewed as well, where incorporation of inorganic or carbon nanomaterials creates synergistic systems that tend to be more robust and widely applicable than the individual components. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48770.     

LAG-3, TIM-3 and VISTA Expression on Tumor-Infiltrating Lymphocytes in Oropharyngeal Squamous Cell Carcinoma—Potential Biomarkers for Targeted Therapy Concepts

Tumor growth and survival requires a particularly effective immunosuppressant tumor microenvironment (TME) to escape destruction by the immune system. While immunosuppressive checkpoint markers like programmed cell death 1 ligand (PD-L1) are already being targeted in clinical practice, lymphocyte-activation-protein 3 (LAG-3), T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) and V-domain Ig suppressor of T cell activation (VISTA) inhibitors are currently under investigation in clinical trials. Reliable findings on the expression status of those immune checkpoint inhibitors on tumor-infiltrating lymphocytes (TILs) in the TME of oropharyngeal squamous cell carcinoma (OPSCC) are lacking. This work aims to describe the expression of LAG-3, TIM-3, and VISTA expression in the TME of OPSCC. We created a tissue microarray of paraffin-embedded tumor tissue of 241 OPSCC. Expression of the immune checkpoint protein LAG-3, TIM-3, and VISTA in OPSCC was evaluated using immunohistochemistry and results were correlated with CD8+ T-cell inflammation and human papillomavirus (HPV)-status. 73 OPSCC stained positive for LAG-3 (31%; HPV+:44%; HPV-:26%, p = 0.006), 122 OPSCC stained positive for TIM-3 (51%; HPV+:70%; HPV-:44%, p < 0.001) and 168 OPSCC (70%; HPV+:75%; HPV-:68%, p = 0.313) for VISTA. CD8+ T-cells were significantly associated with LAG-3, TIM-3 and VISTA expression (p < 0.001, p < 0.001, p = 0.007). Immune checkpoint therapy targeting LAG-3, TIM-3, and/or VISTA could be a promising treatment strategy especially in HPV-related OPSCC. Future clinical trials investigating the efficacy of a checkpoint blockade in consideration of LAG-3, TIM-3, and VISTA expression are required.     

Towards Environment-to-Environment (E2E) multimedia communication systems

We present an approach to connect multiple remote environments over web for natural interaction among people and objects. Focus of current communication and telepresence systems severely restrict user affordances in terms of movement, interaction, peripheral vision, spatio-semantic integrity and even information flow. These systems allow information transfer rather than experiential interaction. We propose Environment-to-Environment (E2E) as a new paradigm for communication which allows users to interact in natural manner using text, audio, and video by connecting environments. Each Environment is instrumented using as many different types of sensors as may be required to detect presence and activity of objects. This object position and activity information is used by a scalable event-based multimodal information system called EventServer to share the appropriate experiential information with other environments as well as to present incoming multimedia information on right displays and speakers. This paper describes the design principles for E2E communication, discusses system architecture, and gives our experience in implementing prototypes of such systems in telemedicine and office collaboration applications. We also discuss the research challenges and a road-map for creating more sophisticated E2E applications in near future.