Web-Services can be referred as a family of technologies that standardized the communication of applications through world wide web in a cost-effective manner. Few of the major software vendors like IBM, Microsoft, Oracle, SAP are all embracing the web service standards into the newer versions of their applications which are web services enabled. All the constrains like cost, time, space for discovering, and e-business transactions can be solved easily by using web-services. With the introduction of these Web services design of the business application to provide service, integrating with other business entities, and conducting business transactions completely changed the working of a business organization. These web-services became a viable component in distributed E-Commerce platforms. In determining server response time, high speed communication systems along with computing capacity and network latency have become important. In web technologies a new model of architecture for distributed E-Commerce applications has been proposed which aims at integration and inter-operation between different platforms. In this paper we have proposed a model for developing an E-Commerce application using the Spring framework which helps in easy integration with other frameworks and solves most difficulties in an enterprise application development.

Advancements in mobile phones and technologies, have forced the mobile software community to develop smart applications which can help users in general purposes. During the pandemic, people have shifted their focus onto Video chatting and conferencing applications for conducting business, schools, and universities. The current software's available provide several useful functions like live transcripts, allow users to create notes and providing third party application support. But there are certain areas where, video conferencing applications can improve upon primarily in interns of language support. This application will provide a new video conferencing feature which is live translation of speech which could help conduct business and conducting lectures.

The pervasiveness of online mis/disinformation escalated during the COVID-19 pandemic. To address the proliferation of online mis/disinformation, it is critical to build reliability into the tools older adults use to seek health information. On average, older adult populations demonstrate disproportionate susceptibility to false messages spread under the guise of accuracy and were the most engaged with false information about COVID-19 across online platforms when compared to other age-groups. In a design-thinking challenge posed by AARP to graduate students in a Digital Health course at Tufts University School of Medicine, students leveraged existing solutions to design a web browser extension that is responsive to both passive and active health information-seeking methods utilized by older adults in the United States. This paper details the design-thinking process employed, insights gained from primary research, an overview of the prototyped solution, and insights relating to the design of effective health information-seeking platforms for older adults.

A document by Edern Ollivier . Click on the document to view its contents.

A non-derivative direct search approach called Generating Set Search (GSS) algorithm with varying bind tolerance to solve non-convex Economic Load Dispatch problem of the thermal stations in Nigeria is presented. A complete poll was carried out with initial mesh size of 1.0, expansion factor of 2.0 and contraction factor of 0.5. The binding tolerance was varied from 100 – 2200 with an increment of 100. The stopping criteria were based on the following: mesh tolerance of 0.000001, maximum iteration of 1500 and maximum function evaluation of 30000. The Economic Load Dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW produced optimal solutions at binding tolerances of 500, 600, 1100, and 1600 respectively. The economic cost (measured in quantity of fuel) obtained for the dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW were 83577.6936190168 MMBTU/hr, 83577.6936667599 MMBTU/hr, 83577.6937160183 MMBTU/hr and 83577.694264612 MMBTU/hr respectively. The evaluations carried out on the function in order to obtain the best solution were 1484, 5709, 6895 and 7556 for 2500 MW, 3000 MW, 3500 MW and 4000 MW of load respectively. Although the optimal bind tolerances had more iterations and evaluations, these can be traded off for the best solutions offered.

We present a hybrid numerical method that combines full-wave analysis with ray-tracing to efficiently model propagation in reconfigurable intelligent surface (RIS)-enabled communication channels. Full-wave simulation is used to obtain the radiation pattern of the transmitter and the complex radar cross-section (RCS) of the RIS. Then, the RIS is imported into a ray-tracer as a secondary transmitter. That enables the modeling of the interaction of the RIS with complex environments. We present the formulation of the proposed technique and its validation against full-wave (finite element) results in representative scenarios of indoor propagation in the presence of an RIS.

Decentralized ledger technology (DLT) is becoming ubiquitous in today’s society. However, organizations need to connect their existing systems and processes to blockchains (centralized, decentralized) securely and reliably, sometimes also implying that they need to connect blockchains (decentralized, decentralized). This challenge is known as blockchain interoperability. We put the case forward that academia and industry must propose evaluation frameworks for blockchain interoperability solutions that address the three interoperability modes. Those are data transfers, asset transfers, and asset exchanges. In this position paper, we illustrate the remaining challenges of interoperability, focusing on the systematic evaluation of interoperability mechanisms based on the state of the art and our own experience. Our evaluation is a systematic online survey of 17 items targeting blockchain specialists. Our quantitative analysis shows that several interesting metrics can show promising directions for systematically evaluating integration solutions.

We have measured surface EMG during swallowing of liquid and saliva.

Intelligent reflecting surface (IRS)-enabled communication systems provide higher system capacity and spectral efficiency by reflecting the incident signals from transmitters in a low-cost passive reflecting way. However, it poses new challenges in resource allocation due to surrounding interference and phase shift, especially when IRS is employed in heterogeneous networks (HetNets). In this paper, a joint power allocation and phase shift optimization problem is studied for the downlink IRS-enabled HetNet, in which the IRS is deployed to enhance the communications between small cell users (SCUs) and associated base station (BS). The signal-to-interference-plus-noise ratio (SINR) received at the SCU is maximized by jointly optimizing the transmit power of the small-cell BS and the phase shift of the IRS, subject to the constraints on the minimum SINR requirement of the macro-cell user (MCU) and the phase shift. Although the formulated problem is non-convex, we develop an optimal power allocation and the IRS’s passive array coefficient solution for the single-user scenario. For the multi-user scenario, we propose an iterative algorithm to maximize the total rates of SCUs for obtaining a suboptimal solution by an alternating iteration manner, where the sum of multiple-ratio fractional programming problem is converted into a convex semidefinite program (SDP) problem. Simulation results show that the proposed algorithm significantly improves the achieved transmission rates of SCUs compared to the case without the IRS.

It is a new grid scheduling algorithm that uses ratio formula for task scheduling and equal makes optimum use of available research base on criteria such as resource size, speed of communication link and so on

This is a review-article which selectively reviews key concepts adopted in team performance evaluation literature. This review intends to promote future innovative methods for objective quantification and analysis of team performance. The review summarizes methods, experimental frameworks, sensors for physiological and behavioral recordings, data processing to derive team level objective measures, in the light of team performance evaluation. Observing the advancements in sensor technologies and computation power, towards advancing team performance evaluation, this review summarizes some of the current multimodal based team research. Finally, the review provides suggestions on aspects that the future research focus to overcome some of existing limitations and drawbacks.

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.

Several computational models provide varying approaches to emotional modeling, e.g., event appraisal, motivational states, embodied robots, and data-driven. However, no model has wholly represented the complex emotional process, which calls for searching alternative ways. This paper proposes a simple hybrid computational model called RIO: Robotic Intuition Operation applicable in autonomous agents. Every event or object capable of emotional influence on an agent is classified as a stimulus having independent properties (including emotions and personality). An agent’s emotional state is defined as a set of six coexisting basic emotions (Ekman’s). Each perceived stimulus in a cycle independently deflects this emotional state towards its own (basic) emotions while considering the stimuli, other users, and the agent’s personality, trust, and likeness. The agent makes emotional decisions to support its primary goal and perform specific skills. Experiments and limitations follow the model’s novelty and discussion.

A detailed description of how to set up an AI Laboratory.

A document by Hari T S Narayanan . Click on the document to view its contents.

We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of subjects that evolve and deform over time. This problem is challenging since surfaces come with arbitrary parameterizations and thus, they need to be spatially registered onto each others. Also, different deforming subjects, hereinafter referred to as 4D surfaces, evolve at different speeds and thus, they need to be temporally aligned onto each others. We solve this spatiotemporal registration problem using a Riemannian approach. We treat a 3D surface as a point in a shape space equipped with an elastic Riemmanian metric that measures the amount of bending and stretching that the surfaces undergo. A 4D surface can then be seen as a trajectory in this space. With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories, or 1D curves, embedded in a nonlinear Riemannian manifold. However, performing the spatiotemporal registration, and subsequently computing statistics, on such nonlinear spaces is not straightforward as they rely on complex nonlinear optimizations. Our core contribution is the mapping of the surfaces to the space of Square-Root Normal Fields (SRNF) where the L2 metric is equivalent to the partial elastic metric in the space of surfaces. Thus, by solving the spatial registration in the SRNF space, the problem of analyzing 4D surfaces becomes the problem of analyzing trajectories embedded in the SRNF space, which has a Euclidean structure. In this paper, we develop the building blocks that enable such analysis. These include: (1) the spatiotemporal registration of arbitrarily parameterized 4D surfaces even in the presence of large elastic deformations and large variations in their execution rates, (2) the computation of geodesics between 4D surfaces, (3) the computation of statistical summaries, such as means and modes of variation, of collections of 4D surfaces, and (4) the synthesis of random 4D surfaces. We demonstrate the utility and performance of the proposed framework using 4D facial surfaces and 4D human body shapes.

CypherCab: A Blockchain enabled Transportation ApplicationAditya Raj1, Akansha Mittal2, Ayush Rajniwal3 and Naman Crist Vatsa4

Edge intelligence (EI), as an emerging technology, has been attracting significant attention. It pushes the frontier of artificial intelligence (AI) from the cloud to the network edge, aiming to embrace and support the next-generation communications while unleashing AI services. However, it faces challenges in its decentralized management and security, which limit its capabilities to support services with numerous requirements. On the other side, blockchain (BC), as a promising decentralized technology, is beneficial to tackle the above issues. However, there exist some technical challenges for BC, such as transaction capacity, scalability, and fault tolerance. Motivated by significant current interest around EI and BC, this survey examines whether the synergy of EI and BC can make a powerful network with combined functionalities of both cutting-edge technologies. Accordingly, we develop EI-chain and Chain-intelligence to realize reliable computing-power management, data administration, and model optimization at the edges, while improving the functions of BC by leveraging complementary characteristics of EI and BC, further making up for their own limitations. In this survey, a wide spectrum of literature is carefully reviewed to enable the EI-chain and Chain-intelligence. Moreover, we cover the technological aspects of EI-chain and Chain-intelligence: overview, motivations, and frameworks. Finally, some challenges and future directions are explored. We believe this survey will provide developers and researchers a comprehensive view on the synergy of EI and BC, while accelerating the design of a powerful integrated network.