Reviewerly/exHarmony huggingface dataset:
Security-Aware GA Based Practical Byzantine Fault Tolerance for Permissioned Blockchain

Achieving good performance in terms of throughput and scalability, with high quality of security has always been a challenging problem in all sorts of scheduling. Permissioned blockchain allows multiple contracts of non-trusting peer organization to be deployed for working in consortium. It is the responsibility of the scheduler often called as an ordering service to form block of transactions to get committed through consensus. Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus algorithm used in current implementations of blockchain. Most consensus algorithms including PBFT follow FIFO scheduling strategy to create the block of transactions. With block chain being supported for multi-contract environment, scheduling should consider difference in service requirements specifically security requirement for heterogeneous contracts. The current work proposes SAGA-PBFT (Security-Aware Genetic Algorithm based Practical Byzantine fault Tolerance) scheduler, which uses multi objective genetic algorithm for balancing the security requirement and security overhead at the time of block creations. The algorithm assigns the security level to each transaction as per its security requirement but in an unpredictable manner making security attack difficult. It further uses security aware PBFT for consensus. Experimental simulation proves that SAGA-PBFT when compared with PBFT, achieves better commit time and scalability with best possible security.
Convolutional Neural Network with Dilated Anchors for Object Detection in Very High Resolution Satellite Images

Nowadays, object detection has acquired a great concentration either in ordinary images or satellite images. For satellite images, object detection is a challenging problem because objects have different scales and sparsity with very complicated background. Recent deep learning approaches have achieved breaking results for object detection than traditional ones. The ability of bounding boxes to catch existing objects with a complete and precise manner is still a challenging problem. We propose a dilated anchor method based on You Only Look Once version 3(YOLOv3) algorithm to make object detection more flexible and precise. The proposed method uses greater size anchor bounding boxes with about 30 % to 40 % larger than the traditional ones. This increase in anchor size increases the ability to catch more class objects with less influence on location detection. The experimental results using public NWPU VHR-10 dataset demonstrate the effectiveness of the proposed method in object detection of most classes and increase the overall accuracy with minimal effect on the precise location.
An Experimental Assessment of Random Forest Classification Performance Improvisation with Sampling and Stage Wise Success Rate Calculation

Imbalanced data classification with Random Forest Classification (RFC) technique has gained huge prominence in today's application era. Data imbalance between practical applications relates to either binary class imbalance or multiclass imbalance. Binary class imbalance constitutes one of the classes with majority data samples and other contains minor number of data samples. In case of multiclass there are two categories of multiclass imbalanced dataset as Multiclass Minority Imbalanced Class (MMinIC) and Multiclass Majority Imbalanced Class (MMajIC). Classification performance leans towards degradation for MMajIC than MMinIC due to major imbalance rate severity. In this paper, the study investigates the influence of RFC classification analysis method on binary and multiclass sample imbalanced datasets. The analytical study of RFC incorporates with measurement of classification accuracy with performance metrics as True Positive (TP) Rate, False Positive (FP) Rate, Precision (Pre), Recall (Rec) F-Measure, Operating Characteristics of Receiver (ROC) Area, Matthews Correlation Coefficient (MCC), Probabilistic Relevance Classification (PRC) area with respect to numerous classes in refereed dataset. This paper focuses the reduction of the negative influence of imbalanced data with the use of Synthetic Minority Oversampling Technique (SMOTE). Experimental analysis carried out with the use of Knowledge Extraction Evolutionary Learning (KEEL) imbalanced data learning repository incorporating RFC classification with SMOTE technique. It also deals with RFC model construction with stage wise success rate calculation in training and testing partition and its impact on accuracy. The incorporates with error analysis report of incorrectly classified instances. Experimental results of the study indicate that imbalanced data have significant impact on classification accuracy and RFC outperforms with SMOTE.
BookChain: Library-Free Book Sharing Based on Blockchain Technology

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.
Does bug report summarization help in enhancing the accuracy of bug severity classification?

The programmer cannot write a program without any bug. A large numbers of bugs are deposited into the bug tracking system through bug reports. To find the root cause of a bug, a meaningful and huge conversation happens between the developer and reporter. The developer (triager) reads the whole bug report and then classified according to severity. The previous researchers observed that the bug report summaries provide the more resourcefully investigate information in the bug repository to the developer as part of the severity classification task. To further investigate the relationship between bug report summary and bug severity classification. A novel approach is proposed by using swarm intelligence and machine learning approaches. Firstly the n-gram technique is used to extract the semantic features score. These features are fed into the Summary Subset Selection Phase to select the optimal summary subset. The selected subset features are fed into the feature scoring phase to provide a relative score to each feature. These optimized features are used to train the proposed model. At last Naive Bayes approach is used to classify the multiclass severity classification. The results are analyzed by using 10-fold cross-validation on three benchmark datasets showed better performance in terms of Precision, Recall and F-measure. It is observed that the performance depend on the bug report contents. If the bug report has larger data for summarization than the summarization increase the classification accuracy otherwise decrease.
Discretisation of conditions in decision rules induced for continuous data

Typically discretisation procedures are implemented as a part of initial pre-processing of data, before knowledge mining is employed. It means that conclusions and observations are based on reduced data, as usually by discretisation some information is discarded. The paper presents a different approach, with taking advantage of discretisation executed after data mining. In the described study firstly decision rules were induced from real-valued features. Secondly, data sets were discretised. Using categories found for attributes, in the third step conditions included in inferred rules were translated into discrete domain. The properties and performance of rule classifiers were tested in the domain of stylometric analysis of texts, where writing styles were defined through quantitative attributes of continuous nature. The performed experiments show that the proposed processing leads to sets of rules with significantly reduced sizes while maintaining quality of predictions, and allows to test many data discretisation methods at the acceptable computational costs.
Vision-based Human Action Recognition on Pre-trained AlexNet

The Deep learning analysis has been extensively carried out in the context of object/ pattern recognition due to its excellence in feature extraction and classification. However, the superior performance just can be guaranteed with the availability of huge amounts of training data and also high-specification data processing unit to process the data deeper at high speeds. Hence, another alternative is by applying transfer learning. In transfer learning, a neural network model is first trained on a data similar to the targeted data. With that, the knowledge such as features, weights etc. could be leveraged from the trained model to train the new model. In this project, a vision-based human action recognition via a transfer learning is conducted. Specifically, in the proposed approach, the earlier layers of a pre-trained AlexNet is preserved since those extracted low-level features are characterizing generic features which are common to most data. However, the pre-train network is fine-tuned based on our interested data, that is human action data. Since AlexNet requires input data of size 227*227*3, the frames of each video are processed into 3 different templates. The three computed templates are: (1) Motion History Image carrying spatio-temporal information, (2) Binary Motion Energy Image incorporating motion region information and (3) optical flow template holding accumulative motion speed information. The proposed approach is validated on two publicly available databases, which are Weizmann database and KTH database. From the empirical results, a promising performance is obtained with about 90% accuracy from the databases.
Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network

In the diagnosis of hydronephrosis, there are cases where pathological symptoms and causes are difficult to judge. In order to improve the ultrasound diagnosis of hydronephrosis, based on CNN neural network technology, this paper analyzes the traditional image resolution processing algorithm by contrast analysis method, and proposes a fast image super-resolution reconstruction algorithm. The method improves the image processing efficiency and combines the CNN neural network to effectively treat the hydronephrosis ultrasound image. In addition, this study analyzes the clinical effects of the algorithm by establishing a controlled trial. The analysis shows that the results of this study can obtain effective diagnosis results, that is, the diagnosis results of this study can provide an effective reference for the diagnosis of hydrocephalus ultrasound images, which has certain clinical significance.
New logical operator algorithms for mapping of hydrothermally altered rocks using ASTER data: A case study from central Turkey

This study uses logical operator algorithms along with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral reflectance and emissivity data to map hydrothermally altered igneous rocks associated with mineralization around the towns of Hamurcu and Başdere in central Anatolia, Turkey. In addition to band ratios used in previous studies, the logical operator algorithms developed in this work perform multiple mineral indices and directed principal components (DPC) analysis, thereby providing better constraints on the spatial extent of hydrothermally altered rocks around the Hamurcu and Başdere areas. We designed the unique mineral indices and DPC analysis based on the effective use of ASTER bands corresponding to diagnostic spectral absorption features of alteration minerals. The calculation of threshold values of logical operator algorithms is based on a two-stepped statistical approach. The novel logical operator algorithms successfully mapped hydrothermal alteration zones in the study area that include mineral assemblages, such as kaolinite, alunite, sericite, smectite, and ferric iron oxides together with various forms of silica. The Hamurcu area contains altered rocks that display a characteristic alteration pattern of a high-sulfidation epithermal system. On the other hand, alteration minerals are primarily located along the faults in the southern part of the Başdere area. The results of field investigations in conjunction with laboratory spectral reflectance measurements, microscopic examination of thin sections and X-ray diffraction analysis of the samples are quite consistent with ASTER mineral maps derived from the logical operator algorithms from this work. As a result, the new logical operator algorithms implemented to ASTER data of the study area prove to be a powerful tool for generating preliminary, quick, and reliable alteration mineral maps at low cost in similar terrains of different geographic locations.
Situated Visualization in Augmented Reality: Exploring Information Seeking Strategies

In recent years augmented reality applications have been increasingly demonstrating the requirement for an interaction with information related to and directly shown in the surrounding environment. Situated information is visualized in its semantic and spatial context, building up an environment enhanced by an information level that dynamically adapts to the production of the information and to the actions of the user. The exploration and manipulation of this type of data through see-through augmented reality devices still represents a challenging task. The development of specific interaction strategies capable to mitigating the current limitations of augmented reality devices is essential. In this context, our contribution has been to design possible solutions to address some of these challenges allowing a dynamic interaction with situated information. Following the visual "information-seeking mantra" proposed by Shneiderman and introducing some "superpowers" for the users, in this work we present different strategies aimed at obtaining an overview and filtering, and acquiring details of a collection of situated data.
Cervical Cancer Identification Based Texture Analysis Using GLCM-KELM on Colposcopy Data

Early identification of cervical cancer is still being carried out intensively by the World Health Organization (WHO). Some programs for early identification of cervical cancer are carried out in several ways such as pap smears, IVA test, and colposcopy. Examinations such as pap smears require laboratories to identify cancer from a network of cervical cells. IVA test is done using acetic acid fluid, while colposcopy is done by identifying the condition of the vulva in the vagina and recorded into colposcopy photo data. From the colposcopy photos can be identified automatically using Computer Aided Diagnosis (CAD), by utilizing image processing and classifying them using artificial intelligence methods. In this research, early identification of cervical cancer based on cancer stage using texture information on colposcopy images looks at pixel neighbor information using the Gray Level Co-occurrence Matrix (GLCM) method and classifies it using the Kernel Extreme Learning Machine (KELM) method which is a development of the method ELM by adding a kernel to the system. The results showed that using a linear kernel resulted in an error of 78.5%, a polynomial kernel of 87.5% and the best accuracy achievement of 95% using a gaussian kernel with the best neighborhood angle was 45°. This shows that the data is more likely to have a Gaussian distribution with the best reading of the GLCM, using diagonal pixel readings.
A multi-context representation approach with multi-task learning for object counting

Object counting is a fundamental while challenging computer vision task, as it requires the object appearance information as well as semantic understanding of the object. In this paper, we propose an end-to-end multi-context embedding deep network for object counting(MCENet), which observes the object counting task from the three different perspectives to count the number of vehicles in the traffic video frame, or to estimate the number of the pedestrian in the largely congested scene. The first sub-network of MCENet extracts the potential features for the appearance context and the semantic context from different-level layers. The two different-level features from the first sub-network are transferred into the two parallel and complementary sub-networks, which are used to model the appearance context and semantic context for final counting. And thus the multiple contexts are represented and embedded to assist the counting task. Extensive experimental evaluations are reported in this paper, using up to three different object counting benchmarks, which show the proposed approach achieves a competitive performance in all these heterogeneous scenarios.
The role of feedback and instruction on the cross-situational learning of vocabulary and morphosyntax: Mixed effects models reveal local and global effects on acquisition

First language acquisition is implicit, in that explicit information about the language structure to be learned is not provided to children. Instead, they must acquire both vocabulary and grammar incrementally, by generalizing across multiple situations that eventually enable links between words in utterances and referents in the environment to be established. However, this raises a problem of how vocabulary can be acquired without first knowing the role of the word within the syntax of a sentence. It also raises practical issues about the extent to which different instructional conditions – about grammar in advance of learning or feedback about correct decisions during learning – might influence second language acquisition of implicitly experienced information about the language. In an artificial language learning study, we studied participants learning language from inductive exposure, but under different instructional conditions. Language learners were exposed to complex utterances and complex scenes and had to determine the meaning and the grammar of the language from these co-occurrences with environmental scenes. We found that learning was boosted by explicit feedback, but not by explicit instruction about the grammar of the language, compared to an implicit learning condition. However, the effect of feedback was not general across all aspects of the language. Feedback improved vocabulary, but did not affect syntax learning. We further investigated the local, contextual effects on learning, and found that previous knowledge of vocabulary within an utterance improved learning but that this was driven only by certain grammatical categories in the language. The results have implications for theories of second language learning informed by our understanding of first language acquisition as well as practical implications for learning instruction and optimal, contingent adjustment of learners’ environment during their learning.
A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems

This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.
Reconstruction algorithm for archaeological fragments using slope features

The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.
Calculation and Evaluation of Network Reliability using ANN Approach

The network is an arrangement where minimum of two nodes are connected and communicated via different independent paths. The swift expansion of communication network causes the problem of reliability in the computer network. Reliability is the capacity of a system to do an ideal task, such as communication. In network communication, designing and consistency of a network are used to identify QOS, as the networks are becoming more wide and complex. The previous research in areas of wired reliability is not competent to deal with issues of the wireless network. There are several strategies helps us to increase the reliability of the network. The exact calculation of reliability in the network is calculated by our new algorithm and effective solution can be attained by using the artificial neural network (ANN). This paper more concentrates on reliability calculation with network flexibility.