Reviewerly/exHarmony huggingface dataset:
BTDM: A QoS-based Trust Distribution Mechanism for Cloud Computing

Cloud computing makes the delivery of computing resources over the Internet as services. As there are many providers in the market, it is necessary to monitor their performance. Several mechanisms for monitoring service quality of providers have been reported in the literature. But, it is not possible to monitor the entire cloud system by a single monitor. Hence, there is a need for a mechanism to share the performance metrics across a large geographical area. In this paper, the authors propose a mechanism called Bayesian Trust Distribution Mechanism (BTDM) for sharing the performance metrics as trust scores across an extended geographical area. The proposed BTDM also checks the reliability of the received scores based on their previous experience and adjusts them based on the reliability of sender. BTDM was tested using simulations and the results show that it performs better than the other mechanisms reported in the literature.
Effective Teaching Management through Cooperative Online Learning Activies for Engineering Education

The research purpose aims 1) to manage cooperative learning activities in training of engineering students, 2) to evaluate the efficiency of the teaching and learning process for electrical engineering course. The research tool consists of training plan, work sheets, electronic slides, instructional media and achievement test. Sample group was 18 trainees who were bachelor students or interesting peoples at Thepsatri Rajabhat University, Lopburi, Thailand. The research results shown that 1) the efficiency of the teaching and learning process through cooperative learning activities agreed to the standard criteria of Meguigan's efficiency formula and the comparison of learning achievement between the pretest and posttest found that is different of statistically significant at the .05 level, 2) the trainees' satisfaction with the developed learning and teaching process was at a high level (the average equaled to 4.40). It can be seen that the cooperative learning activities implemented in training course can encourage the learners to have the knowledge and skills in the 21st century and can be used appropriately in the teaching of engineering curriculum.
Pedestrian Detection, Tracking, Counting, Waiting Time Calculation and Trajectory Detection for Pedestrian Crossings Traffic light systems

Road safety and traffic congestion are major concerns when it comes to Sri Lankan context. Pedestrian crossing plays a major role in this. Currently, in Sri Lanka different methods for control traffic lights in pedestrian crossings are available. Such as time base traffic lights and button push traffic lights. Even though these lights help up to some extent to increase safety and traffic flow it is not efficient. Therefore, in this paper it will be discussed how the image processing technologies can be used to pedestrian detection, tracking, counting, waiting time detection and trajectory detection can be done to improve traditional traffic light system. Haar-cascade classifier which used to use detect pedestrians has 50.47% of precision and 85.5% recall. Pedestrian counting module has 72% accuracy and the waiting time calculation model has an average error rate of 0.714. As well as the trajectory detection module has a 78.234% correct prediction rate.
Breast Cancer Diagnosis using Digitized Images of FNA Breast Biopsy and Optimized Neurofuzzy System

Breast cancer is the second highest cause of cancer death among women all around the world, especially between the ages of 40 and 65. Early detection of breast cancer tumor type, benign or malignant, has remained a big challenge for physicians and radiologists. By timely identifying the tumor and knowing its type, it can allow physicians to conduct the treatment in the right direction and to take appropriate medications for that type of tumor. Therefore, it is highly important to know the type of tumor accurately to save the patient's life. This paper proposes an intelligent diagnosis method for breast cancer tumor type detection and classification based on fine needle aspirate (FNA) of a breast mass and optimized fuzzy sets. The developed method applied image processing and feature selection techniques to analyze the digitized image of a FNA. In the developed method, nine features computed from a digitized image of a FNA of a breast mass are uses as the initial input of neuro-fuzzy system. In the proposed method, we applied the Association Rules (AR) to find the most informative and effective features of the extracted features. Moreover, in order to advance the act of neuro-fuzzy system, Chaotic Bat Optimization Algorithm (CBOA) is used as learning algorithm. The proposed method is tested on Breast Cancer Wisconsin (Diagnostic) data set. The obtained numerical analysis showed that the developed diagnostic system has excellent performance and high degree of precision.
Modified LSB on Audio Steganography using WAV Format

Steganography is a data security technique that used as an effort to secure data. Various techniques and media tested in the steganography technique. One of the classic methods that still today is the Least Significant Bit (LSB) method. As one of the classical methods, this method tested in many technical modification developments and tested on various types of cover media. Various LSB methods that have done always show changes in quality. Therefore, this study conducted tests to try to increase the capacity of the steganography and test the quality degradation that occurred using LSB and some modified LSB methods. The evaluations include calculating the PSNR value, drawing the visual spectrogram, and calculating the BER value. The PSNR evaluation shows that the larger the cover media size used, the smaller the risk of quality degradation, and the bigger the secret message, the greater the noise that will be generated. The evaluation of BER shows that any technique depends on the match between the cover media bits and the secret message bits. Evaluation of the calculation of the maximum steganographic capacity shows that the larger the size of the cover media, the greater the capacity of the secret message that the cover media has.
OOlong

We present OOlong, an object calculus with interface inheritance, structured concurrency and locks. The goal of the calculus is extensibility and reuse. The semantics are therefore available in a version for L A T E X typesetting (written in Ott), a mechanised version for doing rigorous proofs in Coq, and a prototype interpreter (written in OCaml) for typechecking an running OOlong programs.
EHR2CCAS: A framework for mapping EHR to disease knowledge presenting causal chain of disorders – chronic kidney disease example

The goal of this work was to capture diseases in patients by comprehending the fine-grained medical conditions and disease progression manifested by transitions in medical conditions. We realize this by introducing our earlier work on a state-of-the-art knowledge presentation, which defines a disease as a causal chain of abnormal states (CCAS). Here, we propose a framework, EHR2CCAS, for constructing a system to map electronic health record (EHR) data to CCAS. EHR2CCAS is a framework consisting of modules that access heterogeneous EHR to estimate the presence of abnormal states in a CCAS for a patient in a given time window. EHR2CCAS applies expert-driven (rule-based) and data-driven (machine learning) methods to identify abnormal states from structured and unstructured EHR data. It features data-driven approaches for unlocking clinical texts and imputations based on the EHR temporal properties and the causal CCAS structure. This study presents the CCAS of chronic kidney disease as an example. A mapping system between the EHR from the University of Tokyo Hospital and CCAS of chronic kidney disease was constructed and evaluated against expert annotation. The system achieved high prediction performance in identifying abnormal states that had strong agreement among annotators. Our handling of narrative varieties in texts and our imputation of the presence of an abnormal state markedly improved the prediction performance. EHR2CCAS presents patient data describing the temporal presence of abnormal states in CCAS, which is useful in individual disease progression management. Further analysis of the differentiation of transition among abnormal states outputted by EHR2CCAS can contribute to detecting disease subtypes. This work represents the first step toward combining disease knowledge and EHR to extract abnormality related to a disease defined as fine-grained abnormal states and transitions among them. This can aid in disease progression management and deep phenotyping.
An Application of SVD++ Method in Collaborative Filtering

Collaborative filtering algorithms have important applications in the implementation of recommendation systems. Collaborative filtering generally focuses on the user's evaluation scores for commodity items, and these evaluation data imply some specific relevance. Therefore, this type of collaborative filtering Model research has attracted widespread attention. SVD is currently one of the most classic and practical collaborative filtering algorithm models. It can deal with the problem of data sparseness in the recommendation system and derive different types of SVD models on this basis, but its recommendation results It has never been significantly improved. In response to such problems, this article proposes a new variant of the SVD++ algorithm that incorporates a special timing mechanism for dynamic adjustment, and uses the average absolute error, root mean square error and standard average absolute error for the recommended results. After evaluation, we got better results than the current classic SVD++ model.
Interval Type-2 Fuzzy Clustering Based Association Rule Mining Method

Searching interesting rules in transactional databases is considered as one of the most important data mining problems in many practical applications. Clustering based fuzzy association rule mining algorithms usually deal with data sets by clustering numerical values into boolean ones then using boolean method. However, the numerical data will have various disturbances in the practical application, as a result, the obtained association rules will change greatly. Therefore, the uncertainty in the association rule mining should be considered. In this paper, a type-2 fuzzy clustering based association rule mining algorithm (IT2FARM) is proposed, which mines the association rules among multi-dimensional data without prior knowledge. Meanwhile, we also design a method to test the number of correct fuzzy rules. Experimental results have shown that our proposed algorithm is superior in the number of association rules and the anti-noise capability compared with general clustering based methods.
Deep Neural Network of Earthquake Signal Identification using Stridenet

Earthquake is one of the main causes of worldwide destruction. A seismogram is a time function recording of ground motion. In the next few years, there will be a massive increase in the volume of seismogram data generated by earthquake events sourced from the IRIS, USGS, and GEOFON seismic networks. From this twenty-four-hour record of data, we must draw a distinction between earthquake and earthquake noise. One of the applications of Artificial Intelligence can be used to identify between seismic events and noise. In this paper, we studied seismograms to identify an earthquake. Our main objective is to identify earthquake signals and earthquake noises with the combination of CNN, LSTM, and fully connected layer using established datasets and achieve accuracy even better than pretrain CNN architectures. Due to the limitation of hardware, we only trained a part of earthquake datasets. The average validation accuracy of the model when reaching saturation was 98,08% and validation loss was 14,76%.
Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection

Situational awareness as a necessity in connected and autonomous vehicles (CAV) domain is the subject of significant number of researches in recent years. The driver's safety is directly dependent on robustness, reliability and, scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. The notion of feature sharing, proposed in our previous work, aims to address these challenges by maintaining a balance between computation and communication load. In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance. The performance of the proposed framework is verified through experiments on Volony dataset. The results confirm that our proposed framework outperforms our previous cooperative object detection method (FS-COD) in terms of average precision.
Altitude Measurement-Based Optimization of the Landing Process of UAVs

The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.
Research on the Application of Data Middle Platform Technology in Integrated Energy Business System

At present, the information of digital and Internet is pushing the social form and infrastructure changes unceasingly, in this highly digital information age, the original data from every industry and in the field of business functions constantly emerge, however the primitive data does not bring value to the enterprise, must be efficiently planning from the Angle of business data to the original data into valuable data assets, thus brings to the enterprise productivity. By comparing the characteristics of data middle platform, data warehouse and data platform, the capability and function of data middle platform are expounded. Combined with the construction of ubiquitous electric power internet of things, it points out the key indicators to be concerned about in the construction of data middle platform, and provides a reference for the construction of enterprise data middle platform.
Collaborative Ontologies in Social Tagging Tools: A Literature Review of Natural Folksonomy

Web 2.0 had a great influence on the Web creating a collaborative environment where everyone can share his input resulting in an increase in invaluable information for web users. Social tagging tools are examples of the applications that enable users to build shared tagged content that works as a natural classification of relevant information. This paper highlights the difference between tags in different systems such as folksonomy, taxonomy. Further, the paper presents a brief survey of different techniques for creating natural folksonomies thus building precision in search and navigation. The paper explores the types of tags and their components with a detailed highlight of tag navigation and tag clouds.
A distributed cache system based on Redis for high-speed railway catenary monitoring system

The catenary is one of the most critical subsystems for high-speed railway. By analyzing the online monitoring data of the catenary, aiming at the depot-level monitoring data of 1000 kilometers and 160000 monitoring points, this paper proposes a distributed cache system based on Redis. In the preprocessing stage, to reduce the load on the data center, this system caches the online monitoring data received from each sensor module of the catenary and redundantly stores the data in the distributed cluster for further data center processing. To verify the performance, this paper sets up a cache system experiment platform with eight local servers to test the functionality. The experiments show that the system excels in high concurrency, availability, read/write speeds for the online status monitoring data of the high-speed railway catenary.
Usability Testing in Kanban Agile Process for Club Management System

A Club management system is a centralized management and communications platform for clubs and societies. It is developed as a social innovation which aims to combat the limitations of current systems utilized by clubs, whereby there is no central repository for information, and each club utilizes their own methods for management, member communication, and event organization. A greater attention had been considered to Kanban as an agile software development method in developing such a management system. However, the adoption of this methodology is quite challenging to incorporate user experience and usability in an agile process. Hence, this study utilized the Kanban approach which is an implementation of Lean methodology to manage the software life cycle in developing a university club management system. The user requirements have been identified and documented in a Software Requirements Specification (SRS) along with the use case diagram and entity relationship diagram for the database definition. The software was developed using the Laravel PHP Framework. Functional testing and usability testing have been carried out to validate the user requirement. The result of usability testing shows that the system satisfies five usability factors. This will spark some new insights in the use of its technologies and methods for future research.
Optimization method for linear constraint problems

There are various optimization problems that involve different constraints. In this study, we propose an optimization method, called the error feedback method (EFM), for linear constraint problems. This method is used to calculate the errors that violate the constraints and then allocates the errors in proportion to each parameter, such that the new offspring satisfies the constraints. EFM has the advantages of saving computing resources and converging faster than the penalty function method. To verify the performance of the EFM, we use a typical benchmark function and three engineering models as examples. The models include a complex linear inequality constraint, a Markov prediction model with linear equality constraints, and two mathematical planar four-bar linkage models with inequality constraints. To optimize each model, we select three popular algorithms, namely, particle swarm optimization, teaching-learning-based optimization, and differential evolution algorithm. We compare the EFM with the penalty function method and other methods for handling linear constraint problems. The experimental results show that the EFM has significantly better stability and faster convergence than the compared methods.
Logistic Regression with Variable Fractional Gradient Descent Method

Logistic regression is a classic classification method in machine learning. Classical logistic regression uses a general gradient descent method to solve the best parameters of the loss function, but it is easy to fall into the dilemma of local extremes. The fractional step descent method cannot converge to the exact extreme point, and there is always a deviation, so we use a variable fractional step descent method to simulate a new type of iteration to ensure the global convergence of the optimization algorithm. In this paper, the new variable fractional gradient descent method is used to solve the optimal parameters of the loss function, which overcomes the limitation of the gradient function on the step size of the loss function. In this paper, logistic regression and variable fractional step descent methods are used to deal with the problem of data dimensionality reduction to verify the effectiveness of the algorithm after optimization.
Adaptive Hybrid Composition Based Super-Resolution Network via Fine-Grained Channel Pruning

In recent years, remarkable progress has been made in single image super-resolution due to the powerful representation capabilities of deep neural networks. However, the superior performance is at the expense of excessive computation costs, limiting the SR application in resource-constrained devices. To address this problem, we firstly propose a hybrid composition block (HCB), which contains asymmetric and shrinked spatial convolution in parallel. Secondly, we build our baseline model based on cascaded HCB with a progressive upsampling method. Besides, feature fusion method is developed which concatenates all of the previous feature maps of HCB. Thirdly, to solve the misalignment problem in pruning residual networks, we propose a fine-grained channel pruning that allows adaptive connections to fully skip the residual block, and any unimportant channel between convolutions can be pruned independently. Finally, we present an adaptive hybrid composition based super-resolution network (AHCSRN) by pruning the baseline model. Extensive experiments demonstrate that the proposed method can achieve better performance than state-of-the-art SR models with ultra-low parameters and Flops.
Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference

In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, “Learn and Vote,” for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.