Reviewerly/exHarmony huggingface dataset:
Model-Based Clustering of Mixed Data With Sparse Dependence

Mixed data refers to a mixture of continuous and categorical variables. The clustering problem with mixed data is a long-standing statistical problem. The latent Gaussian mixture model, a model-based approach for such a problem, has received attention owing to its simplicity and interpretability. However, these approaches are prone to dimensionality problems. Specifically, parameters must be estimated for each group, and the number of covariance parameters is quadratic in the number of variables. To address this, we propose “regClustMD,” a novel model-based clustering method that can address sparse dependence among variables. We consider a sparse latent Gaussian mixture model, assuming that the precision matrix between variables has sparse nonzero elements. We propose maximizing a penalized complete log-likelihood using the Monte Carlo expectation-maximization (MCEM) algorithm. We demonstrate our method through a simulation study and real-world data examples.
Attack Detection for Intelligent Vehicles via CAN- Bus: A Lightweight Image Network Approach

This article investigates the security detection mechanism of intelligent vehicles under DoS attack. A lightweight CanNet-based attack detection mechanism is developed, which is suitable for both periodic and aperiodic DoS attacks. Different from the existing attack detection mechanisms, the proposed one utilizes a designed CAN image generation scheme to convert CAN traffic data into CAN images, which makes the attack visible and traceable. And the lightweight CanNet image classification network is constructed to detect the abnormalities in the generated CAN images. In order to exploit the maximum correlation of data attributes among the acquired CAN traffic data, copula entropy, the equivalent form of mutual information, is introduced into the CAN image generation scheme. In addition, an extended CAN image color coding scheme is also designed to cope with the secure detection of traffic data beyond the 12-bit CAN ID. Finally, the effectiveness of the designed detection mechanism is validated by the comparative experiments employing the CAN traffic data from YF Sonata (the type of car).
A Token-Based Access Control Mechanism for the Internet of Things Using Blockchain

The traditional access control model faces the problems of coarse granularity and poor management, and its centralized implementation architecture also leads to the emergence of security problems such as single point of failure and information leakage. In response to this problem, this paper proposes a token-based access control mechanism and uses blockchain technology for distributed implementation. In our scheme, the access control process consists of three steps: Policy upload, Token request and Resource request. It takes the token as the credentials of user access permissions as well as realizes fine-grained resource allocation and permission management through token control. In addition, we use blockchain technology for distributed implementation, which alleviates the security risks brought by the centralized architecture. The performance evaluation results show that the model can achieve reliable permission allocation and management and control the access request processing delay within 1 s.
Bitcoin and Corporate Balance Sheets: Strategic Reserve Asset or a New Business Model?

Bitcoin and other cryptocurrencies entered the balance sheets of pioneering corporations primarily during the COVID-19 period. While most were implementing traditional finance industry business models, there were also companies that declared its Bitcoin holdings as their strategic reserve asset. The goal of this paper is to examine the true purpose of Bitcoin holdings of these latter mentioned companies. Our findings show that Bitcoin does not meet the conditions of a non-risk asset that is used as a reserve asset, but to the contrary, it belongs to risk assets that provide risk premia for undertaking risk. It cannot be used as a reserve asset in any of the three use cases of strategic reserves, when the firm is under financial constraints, accumulates funds for future investment projects or when outside funds become expensive in risk-off shocks. Another finding shows, that selected companies buy and keep disproportionate Bitcoin holdings compared to their core business balance sheet or revenues. Purchases are often financed by loans or corporate bond issuance, so we argue these companies deploy a new business model which could be called “Bitcoin investment bank”. In our qualitative research, we apply the praxeological approach of the Austrian school of economics with a priori approach, axiom, and deductive logic.
Message from Conference Chair

I am very happy to present this IEEE proceedings of an International Conference on Industry 4.0-Nascent Technologies and Sustainability for “Make in India” Initiative - SPICON � 2022 held on December 22 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and 23 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> , 2022.
Action Recognition with Neural Networks

With the increasing usage of monitoring applications such as environments, entertainment, and healthcare systems, “Activity Recognition” has gained a lot of attention in video analytics technology. If we consider large-scale surveillance systems, such as finding suspicious activity in an airport, it is also a challenging task for trained human resources. If we can't find the culprit, it results in adverse situations. Considering the healthcare system, observing patients all day long is one such task. If we detect suspicious activity, it means we can respond spontaneously. This is how activity recognition can be applied in real-world scenarios. Gesture recognition and prediction are closely related to most computer vision problems, such as human gesture prediction and recognition, gait recognition, and future event recognition. Enterprise products like Microsoft's Kinect are notable examples of action recognition techniques. Most surveillance systems attempt to solve the problem of video stream/file input-based detection, prediction, and activity recognition of humans and vehicles for AI-based surveillance of social environments such as apartments and public environments such as busy road crossings, railway stations, and airports.
Action Recognition on American Sign Language using Deep Learning

People who are hard of hearing use sign language to reinforce knowledge they acquire in everyday interactions and to convey their thoughts and feelings. Even though they represent a large portion of any population, the majority of people are unable to communicate with them because they have little to no understanding of sign languages. The goal of sign language recognition is to recognize, evaluate, and comprehend the important hand and body movements. By bridging the deaf community's current communication gap with the rest of society, automatic sign language recognition offers better services to the deaf community. Such devices might save the lives of hearing-impaired individuals who are in life-threatening circumstances like cardiac arrest, crashes, accidents, etc. In this work, deep learning-based models for sign language gesture recognition are created to precisely anticipate the American Sign Language emergency signs (ASL). The collection includes the movies for eight distinct emergency scenarios glosses. The different models used for the study are CNN, LSTM, CNN with LSTM, BiLSTM and LCRN.
Joint Learning of Topology and Invertible Nonlinearities from Multiple Time Series

Discovery of causal dependencies among time series has been tackled in the past either by using linear models, or using kernel- or deep learning-based nonlinear models, the latter ones entailing great complexity. This paper proposes a nonlinear modelling technique for multiple time series that has a complexity similar to that of linear vector autoregressive (VAR), but it can account for nonlinear interactions for each sensor variable. The modelling assumption is that the time series are generated in two steps: i) a VAR process in a latent space, and ii) a set of invertible nonlinear mappings applied component-wise, mapping each sensor variable into a latent space. Successful identification of the support of the VAR coefficients reveals the topology of the interconnected system. The proposed method enforces sparsity on the VAR coefficients and models the component-wise nonlinearities using invertible neural networks. To solve the estimation problem, a technique combining proximal gradient descent (PGD) and projected gradient descent is designed. Experiments conducted on real and synthetic data sets show that the proposed algorithm provides an improved identification of the support of the VAR coefficients, while improving also the prediction capabilities.
Design of Smartdoor for Live Face Detection Based on Image Processing Using Physiological Motion Detection

In the current era, technology is developing very rapidly, especially in the field of image processing, technological developments can help and facilitate human work. The purpose of image processing is to learn how to process images to detect objects. This project has the aim of implementing real face detection based on image processing in the smart door design. The physiological motion detection system can recognize the difference between real faces and photo imitations based on facial reflexes in the eyes and mouth. The methods used for face detection are Histogram Oriented Gradient and Haar-Cascade, motion detection of facial reflexes using the Support Vector Machine. The result of this project concludes that the smart door system with physiological motion detection that has been designed successfully performs real face detection, the average accuracy rate is 93.5% and photo imitation faces have an average of 90.7% based on eye reflex motion detection. and mouth.
Real-Time Indian Sign Language Detection using SSD-Mobilenet

Humans communicate with each other through natural language, such as speech and written words. However for the deaf people, the sign language is the only means of conveying information. Without an interpreter, they are unable to converged with each other. Because of this, the implementation of a technology that understands sign language would be of significant help to the social lives of deaf people. Indian sign language (ISL) uses both hands to make gestures instead of one hand unlike ASL. The suggested approach is to develop an ISL recognition system which converts the sign language into readable text. This can be done by various approaches like smart gloves, object detection and pose detection. The proposed method uses a tensorflow object detection for detecting hand moments. Mobile-net is used yo extract feature with the help of and further detection by using single shot multi-box detection (SSD). The system can able to detect 26 of 26 ISL signs in real time with mAP score 96.1 %
Coordination PID-PSS Control Based on Ant Colony optimization In Sulselrabar System

The stability of a generator has an important function in the continuity of electricity production. A multimachine electric power system has many generators connected. The Sulselrabar system consists of several interconnected power plants. Proper coordination between generating centres can support the performance of the electric power system, especially when disturbances can disrupt system stability. Sudden load changes are one of the electric power system’s disturbances, which can impact the generator’s stability. In generator operation, the controller is assigned to the generator excitation equipment. However, the dynamics of the electric power system continue to evolve, causing the generator excitation equipment to reach its limit when a large disturbance occurs. Control equipment such as PID and Power System Stabilizer (PSS) produce good performance on the system. The use of these controls requires optimal coordination in finding the right parameters and locations. In this study, an approach is proposed in coordinating PID and PSS controllers for multi-engine generators in the Sulselrabar system. The Ant Colony optimization (ACO) algorithm is a smart algorithm that adopts the behavior of ants in finding food sources. ACO is used for precise PID-PSS parameter optimization. A case study was used in Sengkang generators that were subjected to load change disturbances. From the simulation results, it is obtained that the performance of the Sengkang generator is optimal in terms of speed overshoot response and minimum rotor angle. The application of PID-PSS also increases the damping system so that the oscillations generated due to disturbances can be properly attenuated.
Amigo : My Virtual Friend - Digital Diary Using Sentiment Analysis

India is one of the countries in the world with the highest rates of mental illness. As a consequence of the COVID-19 pandemic, 20 percent of the population is likely to be affected by some kind of mental health disorder. Around 56 million Indians struggle with depression, and 38 million have an anxiety illness of some kind. Although there is a clear need of assistance with such issues, there are only 3 psychiatrists and psychologists for every 100000 people in India. In the age of Artificial Intelligence, attempts are being made to develop smart systems that can aid medical professionals to cater to the increasing number of patients requiring assistance with mental health issues. The automatic extraction of positive or negative attitude expressions from text, known as sentiment analysis, has drawn a lot of attention from researchers in the last ten years. This technique is currently being used by social networking companies like Twitter, Instagram, Facebook, etc. to identify how their users are feeling based on their comments and posts in order to present them with targeted content in the form of advertisements and promotional posts. The same techniques can be extended to predict moods of individuals on a daily basis and use the records to depict the alterations over different periods of time. This can be of great use to individuals and healthcare professionals to keep track of behavioral changes and maintain a routine of self check-ins. In this paper, we have made use of the Long Short Term Memory model, which is a deep learning approach, to carry out sentiment analysis and have integrated it into a web application which is an artificially intelligent digital diary called’ Amigo: My Virtual Friend.’
Enhancement of Underwater Images using White-Balanced EMD and Convolution

When light travels through water, it gets attenuated due to the scattering of photons. This results in the addition of noise, low contrast, and blurring of images when the image is captured by the camera. This paper focuses on the enhancement of such underwater images with the help of the Empirical Mode Decomposition (EMD) algorithm. To improve the performance of the EMD algorithm, the images are white balanced before applying the EMD algorithm. The Gray World Algorithm is used to white balance the images. After applying the algorithm, the images are convoluted so that the output obtained has sharp images. With this approach, there was an approximately 93% to 98% reduction in the Mean Squared Error and a 31% gain in the Peak Signal to Noise Ratio when compared to the existing studies.
Last Mile and Blockchain: Opportunities and Challenges

The last mile (LM) has become one of the main problems to be solved in large cities. The growing population, the rise of e-commerce and the sustainability aspects demanded by smart cities have brought the urban distribution of goods to the brink of collapse. In this context, where accurate and fast information is critical for correct performance, blockchain technology can offer opportunities to improve urban logistics in cities. This paper conducts a systematic literature review (SLR) of the areas of implementation of blockchain technology in urban logistics. In order to check the degree of maturity, the leading real projects currently being developed are also analysed. This study aims to help practitioners and researchers better understand the issues related to blockchain application in urban logistics, considering the potential benefits and possible limitations of this technology.
Classification of Ornamental Plants with Convolutional Neural Networks and MobileNetV2 Approach

Indonesia has two seasons, and the potential as a producer of superior products in the plantation sector is tremendous. Coverage in the plantation sector has ornamental plant species. Ornamental plants are plants that can be used as decorations indoors or outdoors. Each form of the plant is diverse and has its charm. Some Indonesian people still do not know the types of ornamental plants, so one of the efforts is to introduce ornamental plants to the public. In this case, with conditions that are currently digital, computer applications can be used to introduce ornamental plants. Therefore, there is a technology with the Deep Learning method using Convolutional Neural Networks. Using the dataset obtained, there are 1554 images with five categories of ornamental plants divided by a ratio of 80% train data and 20% test data. Then using the Pareto principle, the train data will be divided into 80% train data and 20% data validation. After the training and testing, the accuracy results are 75% for train data and 67% for data validation. Several experiments were conducted to find the parameters that get the model with the best accuracy, namely by experimenting with the MobilenetV2 model.
User stories as a tool for the specification of requirements: institutional projects and skills development

This article presents a study that aims to promote the success of software development projects using the agile approach efficiently in the requirements specification stage. The objective of the research is to implement as a strategy the definition of User Stories proposed by the agile approach. The case study presents the academic project "Acanmul", a comprehensive solution developed with emerging technological tools that lead to easy access and availability of tourist information from the state of Campeche, Mexico. During the development of the Acanmul software product, the implementation and start-up of the roles, artifacts, events, tools and techniques provided by the Scrum framework that ensure the agile management of the project during its execution and, in addition, favor the development of generic and disciplinary competencies in the area of project management. In this way, it is ensured that the development of the Acanmul software can collect and determine the specific requirements of the own needs for the reactivation of social, economic and commercial processes in the state and, therefore, in the country.
A coupled geostatistical and machine learning approach to address spatial prediction of trace metals and pollution indices in sediments of the abandoned gold mining site of Bekao, Adamawa, Cameroon

Trace metals present in high amounts in aquatic systems are a perpetual concern. This study applied geostatistical and machine learning models namely Ordinary Kriging (OK), Ordinary Cokriging (OCK) and Artificial Neural Network (ANN) to assess the spatial variability of trace metals and pollution indices in surface sediments along the Lom River in an abandoned gold mining site at Bekao (Adamawa Cameroon). For this purpose, thirty-one (31) surface sediment samples are collected in order to determine the total concentrations of As, Cr, Cu, Fe, Mn, Ni, Pb, Sn and Zn. These trace metals are used to compute pollution indices as the sediment pollution index (SPI), the Nemerow index (NI), the modified contamination degree (mCD), and the potential ecological risk assessment (RI). OK, OCK and ANN models are compared to determine the best model performance. The best models are selected based on the values of the root mean square error (RMSE), the coefficient of determination (R2), the scatter index (SI) and the BIAS. Results showed that the sequence of trace metal mean concentrations in the sediments is Fe > Mn > Cu > Ni > Sn > Cr > Zn > Pb > As. The mean concentrations of Ni, Cu, Zn and Sn are above the average shale values (ASV) and the pollution status is globally moderate to significant with a low potential ecological risk. The spatial dependency obtained with semivariogram models are moderate to weak for Mn, Fe, Ni, Pb, SPI, NI, mCD, RI As, Cr, and Sn and strong for Cu and Zn. According to cross-validation parameters, ANN model is the best method for the prediction on trace metal concentrations and pollution indices in surface sediments along the Lom River in the abandoned gold mining site of Bekao.
Assessment of image ratio technique: Targeting structural features and mineralization characteristics in the southwestern part of the Sokoto Basin in Nigeria using Landsat 8 imagery

Landsat 8 used multispectral images to map surface structures and validate the existence of suspected mineral alteration zones in the southwestern part of the Sokoto Basin in Nigeria. The area falls within contrasting sedimentary, metasediment, metamorphic, and older granitoid lithologies. The processing and analysis of the Landsat 8 imagery involving false color composite (FCC), band ratio, and principal component analysis (PCA) were implemented in the visible near-infrared and short-wave infrared bands of Landsat 8. PCA highlighted areas with high concentrations of clay minerals and iron oxides using RGB composite bands 7, 5, and 3. Moreover, PCA enhanced iron oxide, hydroxyl-bearing minerals, and likely prospective zones mainly constrained in amphibolite, phyllite, quartzite, and alluvial deposits that have undergone metamorphism and metasedimentation within Yegiwa and Kurege. It identified iron ore and clay mineral alteration, suggesting the occurrence of manganese, goethite, and carbonate minerals around Maburo and Derrena. The observed alteration zones and their associated quartz veins are usually consistent with the main NE–SW provincial structural trend dipping to the northwest zone. However, field verification validates rock units typically consisting of muscovite, hornblende, manganite, tourmaline, quartz vein, and granitic rocks. Accordingly, remote-sensing and field verification served as a guide for future subsurface structural investigation and in-depth geophysical exploration.
Data Centre Efficiency Enhancement by Metrics Oriented Approach to Revamp Green Cloud Computing Concept

Cloud computing inherits sharing of data from pool of resources existing in data centres when ever demanded. The imminent requirement for this purpose is proficiency of the data centre for fulfilment of this coveted objective. The pursuit of energy-efficient peak performance level is challenged by a simultaneous hike of energy consumption. The energy-efficient metrics contribute a major role for attainment of desired objective of safeguarding the environment. These metrics address the enhancement of the system’s proficiency. An increased energy-efficiency results into reduced consumption of energy resources since these energy resources are mostly non-renewable in nature and are the main source of carbon and heat emissions from operational data centres. As a matter of fact, any individual metric is not capable of achieving enhanced energy-efficient performance in a data centre. Therefore a collective utilization of selected metrics pertaining to power, performance and network traffic can improve the energy-efficient capability of data centre communication systems. The testing platform for such metrics is based on certain architectures which include D Cell, B Cube, Hyper Cube and Fat tree three-tier architectures.