Reviewerly/exHarmony huggingface dataset:
Poster Abstract: Representation Learning from Multimodal Sensor Data with Maximally Correlated Autoencoders

With the development of sensing technology, multiple sensors are widely used in Internet of Things (IoT) devices. A key challenge is to learn feature representations from multimodal sensor data to combine the information of different sensors. Although progress has been made by previous works, the correlation between different sensors is still not well exploited, which may limit the performance of representation learning. To address this problem, we propose a deep learning approach to learn representations from multimodal sensor data with maximally correlated autoencoders (MCA). It can efficiently capture high dependence between different modalities at different feature levels. The learned representations are further used for the recognition task. Experimental results on the real-world RGB- D dataset demonstrate the high effectiveness of MCA.
Poster Abstract: Towards Shapley Value based Security Risk Attribution in Sensor Networks

Sensor Networks (SNs) are comprised of low-cost nodes deployed in an ad-hoc fashion over a large area and thus are susceptible to various security attacks. Overall SN's security metrics are often modeled by stochastic or Bayesian Attack Graphs which assume certain likelihoods of feasible attacks paths. This paper discusses a next step, security risk attribution to individual SN's components/vulnerabilities, which is an integral part of efficient risk miti-gation. The challenges are due to highly non-linear contribution of individual vulnerabilities to the overall system security risk and as-tronomically large number of mitigation scenarios in real systems. Motivated by (a) uniqueness of Shapley-value based attribution having some desirable properties, and (b) successful application of this attribution in various settings including financial risk attribution, we propose a Shapley value-based security risk attribution to individual SN's components/vulnerabilities.
Machine Learning Science Using Bioinformatics Leads To More Effective Treatments

Few machine learning algorithms have been developed to solve developing difficulties in biological or clinical investigation in past years, thanks to the rapid rise of biomedical information during the last several decades. Because they allow for the automated characteristic collection, selection, or construction of forecasting analytics, these approaches may be utilized in the research of diverse biological processes effectively. A wide range of bioinformatic methodologies, and also maintained resources or biologic networking, are regularly linked with machine learning approaches to improve training & verification, find the most interpretable characteristics, and allow featured or modeling exploration. The approaches discussed here include machine learning in a similar context as approaches from evolution, protein structural assessment, networks biology, or diseases genomics, and they are all described in detail elsewhere. We describe the difficulties that machine learning, and in particular deep learning, faces in biomedicine, but we argue that machine learning methods linked with proven bioinformatics methodologies may provide the distinct potential to solve most of these difficulties.
Applications of AI: Software Verifiability Point of View

Although Artificial Intelligence (AI) has turn out to be a saying for good quality commercial software development, its application to software program engineering has infrequently been examined at blueprint design level.In today's world, the demand for pleasant software programs continues to heighten because of the public's growing requirement on software programs and frequently overwhelming impact that a software program's mistakes may have in phrases of lifestyles, monetary damage or other time postponements. Additionally, software program mistakes are so customary as well as so destructive that they will feed to the countrywide financial prudence. Software delinquency and disasters are frequently precisely costly. Henceforth the significance of turning in pleasant software programs is an unavoidable element to software programs today. Software products need to guarantee steady besides mistakes unfastened process each time whilst they're used. It is an apparent truth that timely assessment of verifiability in improvement lifestyles cycle, completely at blueprint section, enhancing software program pleasantness, and will increase the probability of process automation in addition to verifiability. It may also show to be contributory to keep away from redecorate and reimplementation paintings at an advanced stage. Consequently, we are able to conclude that the strategies for assessing verifiability, which may be implemented throughout the outline section are maximum, probably most popular Software verifiability is a blueprint difficulty and it wishes to be talked about on the plan stage. Timely assessment of verifiability usually facilitates enhanced blueprint, and might eventually result in pleasant assurance. An extensive analysis of the applicable literature illustrates the truth that there may be no preferred method and established tenet to be had to compute verifiability at blueprint section. Verifiability is occupied as a prime element for gadget reliability in addition to maintenance. Additional complicated structures usually lower the verifiability of the Object-Oriented software program henceforth; an attempt has been positioned forth to govern software product verifiability via means of monitoring the object oriented properties.
In-situ and Real Time Spinner Processing Visualization and Control

In chip fabrication, spinner equipment is used for many steps such as creating a uniform layer of photo-resist or etching solvent over wafer. Spinner equipment works by rotating wafer at high speed (∼ 0.1 - 1 kHz) after depositing liquid solvent over wafer. The rotation causes solvent to distribute uniformly over wafer under ideal conditions. Due to impurities and rough terrain (chip layout), the spread is non-uniform. This results in spatially inconsistent chemical processing even though same pattern is printed throughout wafer. Process defects arise due to this non-uniformity. Currently, wafer inspection is done post the spinner process completion. Even though the spinner step is crucial, efficient real time quality tracking and in-line process control methods do not exist. In this paper, we present a novel hardware setup and corresponding software algorithm which makes the spinner track its process and dynamically adjust processing parameters (e.g. speed of rotation, tilt etc.) in order to improve the spread uniformity. We perform theoretical modelling of proposed novel hardware setup and present simulation result to prove its feasibility.
A Critical Investigation in the Approach Suggested to Maintain the Quality of Service in Wireless Ad Hoc Network Solutions

Quality of service essentially refers to the set of technologies that essentially connects a network of high-priority applications by controlling network traffic. Effective integration of quality of service effectively enhances the quality of network distribution which is considered to be an essential element for network distribution and data flow both. Thus ensuring effective maintenance of quality of service plays an essential role in improving user experience. On the other hand, ad hoc networks essentially allow connecting multiple devices at a time without having a single central point of the network. This essentially helps connect devices easily. The concerned research used positivism research philosophy, deductive approach and descriptive research design to research necessary research outcome. In addition, secondary data collection is used to collect proper information related to research topic. Upon considering the discussion based on data analysis, the need for quality of service emerged to be essential for wireless ad hoc networks in terms of facilitating network distribution and maintaining data flow to the devices. On the other hand, effective integration of “best effort” and resource reservation can be the significant ways to maintain quality of service for wireless ad hoc networks.
Towards Low-Overhead Resilience for Data Parallel Deep Learning

Data parallel techniques have been widely adopted both in academia and industry as a tool to enable scalable training of deep learning models. At scale, DL training jobs can fail due to software or hardware bugs, may need to be preempted or terminated due to unexpected events, or may perform suboptimally because they were misconfigured. Under such circumstances, there is a need to recover and/or reconfigure data-parallel DL training jobs on-the-fly, while minimizing the impact on the accuracy of the DNN model and the runtime overhead. In this regard, state-of-art techniques adopted by the HPC community mostly rely on checkpoint-restart, which inevitably leads to loss of progress, thus increasing the runtime overhead. In this paper we explore alternative techniques that exploit the properties of modern deep learning frameworks (overlapping of gradient averaging and weight updates with local gradient computations through pipeline parallelism) to reduce the overhead of resilience/elasticity. To this end we introduce a failure simulation framework and two resilience strategies (immediate mini-batch rollback and lossy forward recovery), which we study compared with checkpoint-restart approaches in a variety of settings in order to understand the trade-offs between the accuracy loss of the DNN model and the runtime overhead.
Sentiment Analysis using NLP and Machine Learning Techniques on Social Media Data

Internet usage has made social media an integral part of our everyday lives. With the aid of Natural Language Tool Kit (NLTK), sentiment analysis refers to the process of identifying and analyzing a piece of writing in order to determine whether its sentiment, opinions, views, and emotions are positive, negative, or neutral towards a specific issue, item, etc. People today depend on social media to stay connected. Users are allowed to put their ideologies on Twitter, a widely used communication site. People can write short messages and leave comments. An organization can analyze Twitter sentiments to find out how its image is discussed by individuals. With numerous applications for different spaces, there are numerous methods of sentiment analysis. The two main strategies for analyzing opinions are knowledge base and machine learning. In this study, the Twitter data was collected from tweets that were tagged in voting systems. Text mining was used to pre-process Tweets. Then, using the inverse document frequency and term frequency, a vector space model was constructed, and then sentiment analysis was carried out with Random Forest Classifier, Decision Tree Classifier, and Logistic Regression algorithms. Experiments are discussed and conclusions are drawn.
Circumstantial Discussion on Security and Privacy Protection using Cloud Computing Technology

Cloud computing is becoming a demanding technology due to its flexibility, sensibility and remote accessibility. Apart from these applications of cloud computing, privacy and security are two terms that pose a circumstantial discussion. Various authors have argued on this topic that cloud computing is more secure than other data sharing and storing methods. The conventional data storing system is a computer system or smartphone storage. The argument debate also states that cloud computing is vulnerable to enormous types of attacks which make it a more concerning technology. This current study has also tried to draw the circumstantial and controversial debate on the security and privacy system of cloud computing. Primary research has been conducted with 65 cloud computing experts to understand whether a cloud computing security technique is highly secure or not. An online survey has been conducted with them where they provided their opinions based on the security and privacy system of cloud computing. Findings showed that no particular technology is available which can provide maximum security. Although the respondents agreed that blockchain is a more secure cloud computing technology; however, the blockchain also has certain threats which need to be addressed. The study has found essential encryption systems that can be integrated to strengthen security; however, continuous improvement is required.
An Enhanced Rapid and Effective Algorithm to Remove Various Noise in Digital Images

The Rapid and Effective (RE) technique that can remove various types of noise with minimal computing cost. From the noise-corrupted image, the algorithm calculates the extent of noise corruption. In another step, the mean value of some of the nearby pixels is used to replace the centre pixel, which is based on a threshold value. Two opposing parameters are noise removal with edge preservation and computational complexity. For these requirements, the recommended approach is the best option. The algorithm's performance is compared to that of conventional mean filters (MF), wiener filters (WF), alpha trimmed mean filters (ATMF), K- means filters (KMF), bilateral filters (BF), and the recently suggested trilateral filter (TF). In terms of equally individual and objective calculations, new results expose that the proposed filtering algorithm outperforms existing common algorithms. The suggested solution minimises different noise and improves edge preservation while requiring fewer computational complexity, making it simple to implement in hardware.
Deep Learning in Object Oriented Software Engineering: A Case Of Stability

The software engineering stages assisted by means of deep learning to know due to the variety of SE responsibilities, it is vital to perceive the present SE duties assisted through deep learning, given that it supports practitioners locate the plausible to leverage deep learning knowledge of in their personal glitches. A Stability estimation framework for OO blueprint maintainability angle has been deliberate on this paper using deep learning. The evolved framework correlates the item orientated blueprint traits with maintainability and additionally correlates maintainability with Stability. No such framework has been current in the literature overview that quantifies software program Stability of OO blueprint through taking maintainability into attention using deep learning. The framework bridges the gap amongst item orientated blueprint traits, maintainability and Stability. Framework measures and improves the maintainability of software program blueprint on the blueprint degree of software program improvement lifestyles cycle, maximum massive to a dependable cease product. Stability and maintainability estimation fashions were proposed through the use of the evolved framework. Stability estimation version has been formulated which takes item orientated blueprint traits into attention and proposed Stability estimation fashions take maintainability in attention for enumerating stability of object oriented blueprint.
A New Switching-Mode Liner Filtering Scheme and Algorithm for Noise Removal in Medical Images

Ultrasound imaging has been presented to deliver a non-invasive and non-destructive method either in manufacturing or medicinal field. Ultrasound is commonly used in medicine to diagnose prenatal and malignant sickness. This is connected to the creation of speckles in ultrasound images, which type them difficult to analyse quickly. A new switching-Mode linear filtering (NSMLF) approach is proposed aimed at restoring images that have been heavily contaminated by salt and pepper noise. The method is used to create a Method. Prior to estimate, the new technique incorporates the idea of replacing corrupted values with linear predictions. For this, a new simple linear predictor is being created. The purpose of the method and technique is to eliminate high-density salt and pepper noise (HD-SPN) from photographs. The proposed technique progresses image quality by having a higher peak to signal ratio (PSNR), a lower Mean Square Error (MSE), greater edge retention, and less streaking. With less computing complexity, good presentation is attained. In terms of graphic and quantifiable outcomes, the presentation is related to that of numerous existing systems. The proposed scheme and algorithm's performance is established.
The Implementation of Big Data With Cloud and Edge Computing in Enhancing the Smart Grid Information Processes Through Sem Model

It is not possible to handle and huge amounts of data using present software programmes and personal computers owing to a lack of appropriate computing power. Manufacturing organisations must either employ Cloud computing technology or engage in more current techniques including Edge Computing technology if they are to compete in the global market. The SEM model is used to examine the Smart Grid Information Processes in this article. Furthermore, it is proposed that the research contributes to the expansion of experience in the discipline of smart meters and Internet of Things acceptability, despite the fact that this is a very rare topic.
Face Age Estimation: A Survey

Over the year's researchers have been attempting to evolve ways for the computers to discern and acknowledge the visual data. Age Estimation became a pivotal field in the Computer Vision and has gained enormous acclamation owing to its scores of applications. Many researches and study have been devoted in the field of estimation of age but still there is a significant gap to fill in. This paper highlights a comprehensive review of the previous works in the domain of Face Age Estimation.
Integration Of Ai With Mobile Application Development

Android application development over the years have seen massive improvement. It has gotten to the point where Android users aim to see certain features upgraded, it is destined for any industry in the mobile app development to fail if certain things aren&#x0027;t included. The awareness of a versatile application depends on the view, feel and substantial participation of a client using it. The field of Android Application development is furiously competitive and in the event that your app does not, this moreover incorporates execution and solidness.
The Critical Role of Implementing Blockchain Technology in Enhancing Financial Risk Management in the Cryptocurrency Market

The purpose of this research work is to understand the ways and benefits of block chain in mitigating the risks associated with crypto currencies. The literature review has demonstrated that risks associated with it are related to regulations and cyber attacks. The findings of this research have explained that algorithm-based learning and efficiency peer to peer framework has helped to mitigate these risks.
The Emerging Role of Integrating Artificial Intelligence (Ai) in the Software Defined Network for Effective Traffic Prediction using Correlation Analysis

Traffic technology can be improved using SDN as it enhances overall performance, SDN provides flow control capabilities, distributed access restrictions, traffic analysis and conditions and more. SDN offers many benefits over the past. Cultural network design, which provides better planning and control from a global network perspective, is also important for analyzing network status and performance requirements. The next thing is that SDN provides better flexibility as data quality is significantly improved, in order to increase the efficient allocation of network resources…. Flow control provides the ability to address issues related to transportation costs and data quality, but tolerance limits refers to the ability to detect the cause of network failures and respond quickly, and traffic analysis plays an important role in identifying network failures. The TE supports in monitoring the network parameters including the utilisation of the bandwidth, however the critical element is to generate the data related to the utilisation, this also enable in monitoring and traffic analysis in an effective manner. It also warns of congestion or problems in the system. There are two main ways that OFF flow meter data can be collected, the first is to collect the associated controls and then distribute the necessary equipment. Nowadays, various technologies and monitoring technologies have been used to understand the overall performance of the device. The application of AI techniques tends to offer better tacking the aggregation issue and provide better and flexible approach which can be used to manage the network traffic in an effective manner.
An Effective Analysis of Cloud Related Platform using Big Data Approaches for Effective Implementation of High-Performance Computing through Structural Equation Modeling (SEM) Approach

The recent technologies are focusing on High- performance computing to fasten the complex calculations and market procedures in industries. Therefore, analysis of the big- data and cloud-computing is necessary to implement the &#x201C;High- performance computing&#x201D; (HPC) in practice. Based <sup>on</sup> the advantages, the research aimed to identify the advantages and disadvantages of cloud computing as well as HPCs. The research used SEM approach to justify the procedure for implementing HPC effectively. In this research, SPSS software was used to conduct multiple regression analysis. A significance value of p &#x003C; 0.05 is considered &#x2018;significant&#x2019; which describes a strong relationship between the dependent and independent variable. The independent variables selected are HPC in industry and year; and the dependent variable is &#x2018;Profit Gain&#x2019;. Findings showed an insignificant relationship between the dependent and independent variable. According to the result of the SPSS data, the selected variables are not significant and the regression data is not fitting as per the requirements as well.
A Critical Review Analysis of the Opportunities and Potential of Implementing Cloud Computing System for Large Scale Ad Hoc Network

The increase in the usage of various computing and mobile devices has resulted in implementing large scale ad hoc networks as the user demand is on the rise and companies&#x0027; find it difficult to invest more in the IT infrastructure to meet the surging demand. The traditional model of networking enables the mobile devices to face various issues like lower bandwidth, mobility, security and storage et. Hence, in order to meet the overall service requirement and to enhance the overall efficiency of the network, cloud computing was introduced. The implementation of these devices tends to support in every node, it enhances better communication in a better range towards another nodes. There is a critical administration and support devices from everywhere in an effective manner. Hence the new paradigm of implementing cloud computing support the computing and medical devices in the neighbouring network. The ad hoc network supports in covering larger area base and hence cloud computing supports them in enhancing the effectiveness of operations. This study covers in addressing the critical determinants of analysing the overall opportunities and potential in implementing cloud computing for ad hoc networks. The major factors considered are enhancing the processing capabilities using cloud computing, scalability of network and autonomous and independent networking support. These aspects promote enhanced ad hoc networking for enhancing communication between the devices, enable the organisation to spend less on the IT infrastructure