Reviewerly/exHarmony huggingface dataset:
Real-Time Big Data Processing and Analytics: Concepts, Technologies, and Domains

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.
Analytical Structure and Performance of Interval Type-2 Fuzzy Two-Term Controllers with Varying Footprint of Uncertainty

Abstract While type-2 fuzzy control has gained a lot of attention in the recent years, several challenges still remain with respect to controller design. The footprint of uncertainty (FoU) plays a very important role in designing type-2 fuzzy controllers, since the performance of type-2 fuzzy controllers largely depends on the choice of FoU. In this paper, we propose a simplified model of interval type-2 (IT2) fuzzy two-term controllers of Takagi–Sugeno (TS) type with only two rules in the rule base. The controller model is derived with varying FoUs based on only two type-2 input fuzzy sets (the simplest case). An extension to multiple input fuzzy sets is also presented. We investigate the variation in control surface and computational aspects of the IT2 fuzzy two-term controller as the FoU is varied. The performance of the proposed IT2 fuzzy controller with respect to varying FoUs is evaluated in the simulation study.
Forecast of photovoltaic generation in isolated rural areas of Ecuador using Holt-Winters and seasonal variation methods

Due to the shortage of electric power in isolated rural areas of Ecuador, implementing a photovoltaic power generation system is an optimal, viable, and sustainable alternative that can reduce the gap in electric power coverage in the nation. However, since the geographical location of a possible PV system implementation directly affects its optimal performance, it is essential to propose generation forecasting algorithms that use actual data from the place under study. For this reason, this paper compares Holt-Winters and Seasonal Variation forecasting methods applied to two rural locations in Ecuador. These methods use historical records of meteorological conditions such as solar irradiation and ambient temperature to achieve a short-term forecast of photovoltaic generation. Percentage errors obtained in the forecast power and the Pearson correlation coefficient are calculated as performance measures. The results show that although both algorithms have similar characteristics when getting the photovoltaic generation prediction, the Holt-Winters method presents improved behavior.
Steganography Techniques - An Overview

Cryptography, or the use of code, has a sinister relative called steganography. Steganography is supposed to offer confidentiality whereas encryption is said to provide privacy. A method of concealed communication is steganography. The process of steganography involves hiding a message in a suitable carrier, such as an image or audio file, which can then be transported to the receiver without anybody being aware that it contains a hidden message. This is a method that civil rights organizations in oppressive states, for instance, might utilize to propagate their message without the knowledge of their own government. This paper aims to discuss different techniques for implementing steganography to multimedia files (text, still images, audio, and video).
Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA).
Parallel Implementations of ARIA on ARM Processors and Graphics Processing Unit

The ARIA block cipher algorithm is Korean standard, IETF standard (RFC 5794), and part of the TLS/SSL protocol. In this paper, we present the parallel implementation of ARIA block cipher on ARMv8 processors and GPU. The ARMv8 processor is the latest 64-bit ARM architecture and supports ASIMD for parallel implementations. With this feature, 4 and 16 parallel encryption blocks are implemented to optimize the substitution layer of ARIA block cipher using four different Sboxes. Compared to previous works, the performance was improved by 2.76× and 8.73× at 4-plaintext and 16-plaintext cases, respectively. We also present optimal implementation on GPU architectures. GPUs are highly parallel programmable processors featuring maximum arithmetic and memory bandwidth. Optimal settings of ARIA block cipher implementation on GPU were analyzed using the Nsight Compute profiler provided by Nvidia. We found that using shared memory reduces the execution timing when performing substitution operations with Sbox tables. When using many threads with shared memory instead of global memory, it improves performance by about 1.08∼1.43×. Additionally, techniques using table expansion to minimize bank conflicts have been found to be inefficient when tables cannot be copied by the size of the bank. We measured the performance of ARIA block ciphers implemented with various settings. This represents an optimized GPU implementation of the ARIA block cipher.
Parallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments

Estimating the distance to objects is crucial for autonomous vehicles, but cost, weight or power constraints sometimes prevent the use of dedicated depth sensors. In this case, the distance has to be estimated from on-board mounted RGB cameras, which is a complex task especially for environments such as natural outdoor landscapes. In this paper, we present a new depth estimation method suitable for use in such landscapes. First, we establish a bijective relationship between depth and the visual parallax of two consecutive frames and show how to exploit it to perform motion-invariant pixel-wise depth estimation. Then, we detail our architecture which is based on a pyramidal convolutional neural network where each level refines an input parallax map estimate by using two customized cost volumes. We use these cost volumes to leverage the visual spatio-temporal constraints imposed by motion and make the network robust for varied scenes. We benchmarked our approach both in test and generalization modes on public datasets featuring synthetic camera trajectories recorded in a wide variety of outdoor scenes. Results show that our network outperforms the state of the art on these datasets, while also performing well on a standard depth estimation benchmark.
Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
Designing smart pulse flow meters using diversion analysis

&lt;span lang="EN-US"&gt;The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.&lt;/span&gt;
Cascading Alignment for Unsupervised Domain-Adaptive DETR with Improved DeNoising Anchor Boxes

Transformer-based object detection has recently attracted increasing interest and shown promising results. As one of the DETR-like models, DETR with improved denoising anchor boxes (DINO) produced superior performance on COCO val2017 and achieved a new state of the art. However, it often encounters challenges when applied to new scenarios where no annotated data is available, and the imaging conditions differ significantly. To alleviate this problem of domain shift, in this paper, unsupervised domain adaptive DINO via cascading alignment (CA-DINO) was proposed, which consists of attention-enhanced double discriminators (AEDD) and weak-restraints on category-level token (WROT). Specifically, AEDD is used to aggregate and align the local-global context from the feature representations of both domains while reducing the domain discrepancy before entering the transformer encoder and decoder. WROT extends Deep CORAL loss to adapt class tokens after embedding, minimizing the difference in second-order statistics between the source and target domain. Our approach is trained end to end, and experiments on two challenging benchmarks demonstrate the effectiveness of our method, which yields 41% relative improvement compared to baseline on the benchmark dataset Foggy Cityscapes, in particular.
Multi-Subject Image Retrieval by Fusing Object and Scene-Level Feature Embeddings

Most existing image retrieval methods separately retrieve single images, such as a scene, content, or object, from a single database. However, for general purposes, target databases for image retrieval can include multiple subjects because it is not easy to predict which subject is entered. In this paper, we propose that image retrieval can be performed in practical applications by combining multiple databases. To deal with multi-subject image retrieval (MSIR), image embedding is generated through the fusion of scene- and object-level features, which are based on Detection Transformer (DETR) and a random patch generator with a deep-learning network, respectively. To utilize these feature vectors for image retrieval, two bags-of-visual-words (BoVWs) were used as feature embeddings because they are simply integrated with preservation of the characteristics of both features. A fusion strategy between the two BoVWs was proposed in three stages. Experiments were conducted to compare the proposed method with previous methods on conventional single-subject datasets and multi-subject datasets. The results validated that the proposed fused feature embeddings are effective for MSIR.
Healthcare Monitoring System Using IoT

Abstract: Today, the Internet of Things (IoT) is playing important role in health sector for the development of India. It has many applications in the medical field. Due to the increase of many diseases, problems related to human health are raised. The population is also increasing day by day. Therefore, it is not possible to get a treatment that is right for each person anytime soon. Internet of Things offers a lot of sensors for temperature checking. In this project, we made patient monitoring system using IoT that is easy to handle and less cost. The sensor used are LM35 temperature and pulse rate sensor for measuring body temperature and pulse rate. The pulse sensor gives digital output by placing finger on it. These sensors are connected to the Arduino UNO. IoT Health Monitoring System is an atomization system that is use for measuring body temperature and pulse rate
Experiences of Parents and Teachers with Virtual Classrooms during the COVID-19 Restrictions: A Study Focusing on Inclusive Education in Malaysia

The COVID-19 pandemic has resulted in emergency remote teaching in many countries. In Malaysia, not all households were prepared for remote teaching. This has caused some groups of students to be left out. Therefore, in this study we aim to investigate the experiences of parents and teachers concerning inclusiveness of the education delivered via virtual classrooms during the pandemic time in Malaysia. Questionnaires were distributed online to gather feedback from parents, teachers and anyone having both roles. 379 respondents completed the questionnaire. The findings indicate that stable Internet access, adequate information and communications technology (ICT) competencies among teachers and parents, and a more holistic role of the Ministry of Education could contribute to implementing a more inclusive remote teaching. Statistical data showing significant associations suggest that parents and teachers having master’s degree and above tended to be more concerned with providing inclusive remote teaching to the students. Readiness in making virtual classrooms more inclusive was higher among those teaching science and mathematics subjects than those teaching art and humanities subjects. An implication of this study is to guide the implementation of digital teaching and learning in the future, considering the involvement of policy makers, researchers, teachers, and parents.
Unified Cross-Layer Cluster-Node Scheduling for Heterogeneous Datacenters

In this paper, we present a two-level hierarchical scheduler for datacenters called Qin. The goal of the proposed scheduler is to exploit increased server heterogeneity. It combines in a unified approach cluster and node level scheduling algorithms, and it can consider specific optimization objectives including job completion time, energy usage, and energy delay product (EDP). Its novelty lies in the unified approach and in modeling interference and heterogeneity. Experiments on a real cluster demonstrate the proposed approach outperforms state-of-the-art schedulers by 10.2 % in completion time, 38.65 % in energy usage, and 41.98% in EDP.
Review on Human Activity Recognition for Military Restricted Areas

Abstract: Human Activity Recognition(HAR) is an active field of research and scientific development in which various models have been proposed using different methods for identification and categorization of activities using Machine Learning. HAR has reached a remarkable milestone in the area of computer vision. Except for applications in human-computer interactions, surveillance systems and robotics, lately it has extended its applicability in the fields of healthcare, multimedia retrieval, social networking, and education as well. It aims to bring latest technologies together to develop complex assistive system with adaptive capability and learning behaviour. HAR interprets human motion using computer and machine vision technologies to identify and detect simple and complex actions in real-world. This paper presents research made for surveillance of restricted military areas. Our scope is to develop a live monitoring system for tracking the illegal activities done in the restricted area for border security, which is an issue of concern since decades. In this we have introduced a deep learning model that learns to classify human actions without having prior knowledge. The features of image or video set are extracted and detected for detected for classifying whether the activity is illegal or not. Many harmful actions can be avoided or at least have their negative effects reduced as a result of the adoption of this concept. Finally, the activity recognition rate showed a good performance as a result of these findings
Effect of Collector Tilt Angle on the Performance of Solar Water Heating System in Erbil of Iraq

Solar energy is a fascinating green energy source.the most basic use of solar energy is to convert it into usable heat.Domestic hot water is the second-largest source of household electricity after lighting.The subject of this research is the thermosiphon theory of normal water circulation.An experimental setup and a computer program (POLYSUN) of a solar thermal system are presented in this work in Erbil city of Iraq.A four-person single-family house with a flat-plate solar thermosiphon water heating system and a water capacity of 200 L and also had a total surface area of 2 m 2 .All simulations are conducted with the collector tilt angles of 0°, 10°, 20°, 30°, 40°, 50°, 60°, 70°, 80°, and 90° for the whole year.In January 2021, the experimental work was also taken out in Erbil.The optimum tilt angle of the solar collector is estimated to be close to 40o. the overall energy savings, solar fraction, and collector efficiency are 1284 kWh, 29.6%, and 37.9%, respectively, for the entire year.Polysun Software and the experimental setting of the solar thermal system were compared on the basis of electricity consumption.
Social Capital and Performance of Manufacturing MSMEs in Kenya

Sustained growth and development of economies is mainly attributed to the manufacturing sector, and is often the differentiator between developed and developing countries.In Kenya, the sector has played a crucial role, with a contribution of 10% to gross domestic product for the duration from 2008 to 2014.Statistics show that in 2017, 2018 and 2019, the contribution deteriorated to 8.4%, 7.7% and 7.54% respectively, which implies possible deindustrialization.The government's goal of achieving a robust manufacturing sector through Big Four Agenda may not be fruitful if this trend continues.Informed by these trends, the study sought to establish the connection between social capital and firm performance among the micro, small and medium manufacturing enterprises.Keys among the existing theories applied in the study are social capital theory and behavioral theory of the firm.The sample size was 384 licensed manufacturing businesses operating within Nairobi City County, derived from a population of 61,931.The study applied descriptive and explanatory research designs.Primary data was collected, and analysis was done using descriptive and inferential statistics.The outcomes showed that relational and cognitive social capital had a positive and significant effect on performance of micro, small and medium manufacturing ventures, whereas structural social capital was noted to have a significant effect.This study recommends that the firms' management taps into additional and diverse networks to drive innovation, and subsequently create a competitive edge for their firms.