Reviewerly/exHarmony huggingface dataset:
Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Determining the best parameters of the membership functions (MFs) in developing interval type-2 fuzzy logic controller (IT2FLC) is always difficult and complex because of the presence of high level of uncertainty and imprecision. Flower Pollination Algorithm (FPA) is a nature-inspired global optimization technique that imitates the pollination behavior of flowering plants, which avoids trapping in local optimum solution. This paper describes the optimization of IT2FLC using FPA for telemedical problem and applies to predict shock level in cardiac patients. The optimized IT2FLC is used to handle the uncertainties inherent in the membership functions (MFs) of the type-2 using cardiac patients’ data for remote vital signs monitoring. The parameters of the MFs of the IT2FLC had not been previously optimized as this study considers applying Flower Pollination Algorithm (FPA) to optimize interval Footprint of Uncertainty (FOU) MFs of IT2FLS. We use the FPA to find the best MFs parameters of the IT2FL and the choice of FPA is because of the linear nature of the algorithm which gives attractive performances when compared to state-of-the-art algorithms. The empirical comparison is made on the designed system using cardiac patients’ health datasets obtained both from real-time and clinical scenarios. Simulation results indicate that FPA-based interval type-2 fuzzy algorithm outperforms those results of a single IT2FL algorithm.
The Effect of an Adaptive Simulated Inner Voice on User’s Eye-gaze Behaviour, Ownership Perception and Plausibility Judgement in Virtual Reality

Abstract Virtual cognitions (VCs) are a stream of simulated thoughts people hear while emerged in a virtual environment, e.g. by hearing a simulated inner voice presented as a voice over. They can enhance people’s self-efficacy and knowledge about, for example, social interactions as previous studies have shown. Ownership and plausibility of these VCs are regarded as important for their effect, and enhancing both might, therefore, be beneficial. A potential strategy for achieving this is the synchronization of the VCs with people’s eye fixation using eye-tracking technology embedded in a head-mounted display. Hence, this paper tests this idea in the context of a pre-therapy for spider and snake phobia to examine the ability to guide people’s eye fixation. An experiment with 24 participants was conducted using a within-subjects design. Each participant was exposed to two conditions: one where the VCs were adapted to eye gaze of the participant and the other where they were not adapted, i.e. the control condition. The findings of a Bayesian analysis suggest that credibly more ownership was reported and more eye-gaze shift behaviour was observed in the eye-gaze-adapted condition than in the control condition. Compared to the alternative of no or negative mediation, the findings also give some more credibility to the hypothesis that ownership, at least partly, positively mediates the effect eye-gaze-adapted VCs have on eye-gaze shift behaviour. Only weak support was found for plausibility as a mediator. These findings help improve insight into how VCs affect people.
Design of automatic generation system of equipment protection common sense pocket book content based on power big data

Abstract In order to improve the power system and process, strengthen personnel training and guidance, this paper proposes a new automatic generation system of equipment protection knowledge pocket book content based on power big data. Firstly, the architecture of each level of the system is designed. Secondly, according to the architecture design results, the main hardware and software of the system are designed. The hardware mainly includes pocket book content naming module, user management module and pocket book content automatic generation module. In the software part, K-mean clustering algorithm is used to cluster the power big data, and the main content of equipment protection knowledge in power big data is extracted. The above hardware and software are combined to complete the design of automatic generation system for the contents of power equipment protection pocket book. The experimental results show that the performance of the system is greatly improved.
Adaptive Selecting and Learning Network and a New Benchmark for Imbalanced Fine-Grained Ship Classification

Ship classification is one of the most essential tasks in ship surveillance, which is an important but challenging problem. Most existing methods are designed for remote sensing images and there are few works processing natural ship images captured by camera. In this paper, we design a new framework called Adaptive Selecting and Learning Network (ASL), to solve the problem of fine-grained classification of ship natural images in the real world. Our method has two key contributions to enable more effective learning from the imbalanced ship data. First, we present a memory network equipped with an adaptive selecting learning strategy to selectively memorize the hard samples that are difficult to classify. The presented learning strategy can re-balance the data distribution of different classes in the training procedure, which achieves more effective learning. Second, we design an inference network with an attention mechanism, to capture the structural similarities between new samples and hard samples. The attention mechanism can enhance the training of hard samples and yields better learning performance. Moreover, our framework can be easily combined with existing fine-grained methods. In addition, we propose a new Dachan Island Ship (DIS) dataset, which is collected in the real-world scenarios. The DIS dataset has a significant imbalanced distribution between classes that aligns with the real situation. Extensive experimental results on the proposed DIS show that our model outperforms most of the existing fine-grained classification methods.
Machine learning based approval prediction for enhancement reports

In modern times, the maintenance of the software application plays a vital role in its success. Software applications obtain enhancement requests on a large scale to fulfil user requirements through different Issue Tracking Systems. Issue tracking system provides an effective way for keeping the bugs records in the software development system. Conventionally, developers used to manually check these requests themselves. However, manual inspection of these requests turns out to be a boring, hectic and time-consuming activity. Therefore, there is dire need of developing an automatically prediction system, that can help in decision making for further improvement. In this work, we propose a Support Vector Machine-based classifier to automatically approve or reject an enhancement report. Our approach can be divided into different steps. Firstly, we perform the pre-processing on each enhancement report using natural language processing (NLTK) techniques. Secondly, we generate a feature vector for each pre-processed enhancement report. Finally, we train a Support Vector Machine-based classifier that automatically predicts the rejection or approval of the enhancement report. In order to have a thorough analysis, we also evaluate and compare other well-known machine learning algorithms e.g. Multinomial Naïve Bayes and Logistic Regression. We use a well-known open-source dataset extracted from the Bugzilla software application for our experiments. Our experiments suggest that Support Vector Machine-based classifier outperforms other approaches and achieves high accuracy on 35 different open-source applications which include 40,000 enhancement reports. The evaluated results of tenfold cross-validation show that the proposed approach can increase the accuracy as compared to the state-of-the-art accuracy. We believe that our approach will help developers save time and address user-requirements in a more efficient manner.
Machine Learning and Counter-Terrorism: Ethics, Efficacy, and Meaningful Human Control

Machine Learning (ML) is reaching the peak of a hype cycle. If you can think of a personal or grand societal challenge – then ML is being proposed to solve it. For example, ML is purported to be able to assist in the current global pandemic by predicting COVID-19 outbreaks and identifying carriers (see, e.g., Ardabili et al. 2020). ML can make our buildings and energy grids more efficient – helping to tackle climate change (see, e.g., Rolnick et al. 2019). ML is even used to tackle the very problem of ethics itself – creating an algorithm to solve ethical dilemmas. Humans, it is argued, are simply not smart enough to solve ethical dilemmas; however, ML can use its mass processing power to tell us the answers regarding how to be ‘good’, in the same way it is better at Chess or Go (Metz 2016). States have taken notice of this new power and are attempting to use ML to solve their problems, including their security problems and, of particular importance in this thesis, the problem of countering terrorism. Counterterrorism procedures including border checks, intelligence collection, waging war against terrorist armed forces, etc. These practices are all being ‘enhanced’ with ML-powered tools (Saunders et al. 2016; Kendrick 2019; Ganor 2019), including: bulk data collection and analysis, mass surveillance, and autonomous weapons among others. This is concerning. Not because the state should not be able to use such power to enhance the services it provides. Not because AI is in principle unethical to use – like land mines or chemical weapons. This is concerning because little has been worked out regarding how to use this tool in a way that is compatible with liberal democratic values. States are in the dark about what these tools can and should do.
Research on Symbol Color of Automotive Augmented Reality Head-up Display

Abstract Complex environmental information and lighting conditions will affect the usability of automotive augmented reality display in the outdoor environment. In this way, users’ recognition and semantic understanding of AR-HUD information may be affected. The driver’s cognitive performance directly affects the driving safety and driving experience, so the color-coding of AR-HUD is very important for the driver to identify information easily, quickly, and accurately. Taken the automotive AR-HUD display as the object, this paper compared the visibility and significance of foreground object colors in different backgrounds in the way of combining both subjective and objective experiment methods. The experimental results show that the color of white, cyan, and green are recommended to be used in automotive AR-HUD display, which has a positive influence to improving the automobile AR-HUD color design.
Batch Signature-Based Verification of Data Computation in Cloud Applications

Ensuring security in a cloud environment is an uphill task. In this paper, a scheme for ensuring cloud security called Batch Signature-based Enhanced Data Verification Technique (BSEDVT) is presented for identifying its significance in verifying the amount of application data stored on the cloud. BSEDVT supports faster batch verification comprising of multiple digital signatures when compared to the verification time of the benchmarking schemes in the literature. It utilizes the process of signature tree-based approach for dynamic integrity checking with the inclusion setup, challenge-prove and update phases. It uses component-characterized relations derived from cloud applications that are to be highly processed for constructing the component graph. It also uses a component ranking algorithm for estimating the significant values of cloud components. The proposed BSEVT is used for identifying most of the significant parameters as they represent the potentiality of each component.
Algorithm Design of Translation Accuracy Correction for English Translation Softwares

Abstract With the development of economic globalization, English is needed in more and more different cases in people’s daily life, and so is English translation. Traditional English software translation mainly depends on machine, which on the one hand facilitates people’s life, while on the other hand it also has some disadvantages. For example, translation errors are characterized by iterative transmission, weak logic and low accuracy. Traditional machine translation has been unable to meet people’s demand for both speed and quality, so it can no longer meet people’s needs. Therefore, this paper puts forward the design of translation accuracy correction algorithm for English translation softwares. First of all, an in-depth research on the traditional English translation methods, such as lexical semantic translation and phrase translation, are conducted with the method of documentation. Then, a dependency tree to string model and log linear model are established. Finally, the BLEU value and NIST value of the three translation softwares as well as the translation accuracy before and after algorithm correction is compared and analyzed. The conclusion shows that the highest accuracy of English translation before correction is only 75.6%, while the lowest accuracy is as high as 98.7% after the algorithm is adopted in this paper. The difference of accuracy between the two shows that the effectiveness of the correction system in this paper makes an outstanding contribution.
IDENTIFICATION OF PARAMETERS OF MATHEMATICAL NON-LINEAR MODEL OF VESSEL LONGITUDINAL MOTION

The article considers the important task of modern control theory to construct an adequate mathematical model of a specific dynamic object or vessel movement, that is, its identifica-tion (structural or parametric). It is proposed to construct a nonlinear model of the longitudinal motion of the ship. Practical data were obtained experimentally, a brief description of the experimental platform for an unmanned boat and equipment used in the study of the object dynamic characteristics is given. It is noted that the boat is characterized by three modes of movement: displacement, transitional and planning. The relationship between the thrust of the propeller and the speed of the boat is determined. To solve the identification problem in the work, it is proposed to use the apparatus of power series. The power series coefficients are determined experimentally. The problem of selecting the coefficients of a power series can be solved using the least squares method. In order to avoid the cumbersome calculations there were used numerical optimization methods applying the capabilities of the MS Excel computing environment. The solution to the problem was obtained by using a different number of members of the power series (N = 1, 3, 5). The efficiency of the proposed approach is justified.
A Proof of a Dodecahedron Conjecture for Distance Sets

A finite subset of a Euclidean space is called an s-distance set if there exist exactly s values of the Euclidean distances between two distinct points in the set. In this paper, we prove that the maximum cardinality among all 5-distance sets in $$\mathbb {R}^3$$ is 20, and every 5-distance set in $$\mathbb {R}^3$$ with 20 points is similar to the vertex set of a regular dodecahedron.
Research and application of hyperledger fabric based on VRF on electronic order

With the rapid development of information digitization, a large number of electronic orders have also brought a series of problems such as data security and trust. In the face of the ever-increasing demand for electronic data storage, the traditional and conventional storage methods have gradually exposed many problems such as extremely high cost, low efficiency, and difficulty in acceptance. It has become an urgent need to ensure the data security and credibility of electronic orders. solved problem. Aiming at how to ensure the authority and security of electronic documents, through the study of the Hyperledger Fabric consensus mechanism, and at the same time, in order to solve the security risks and processing bottlenecks caused by the use of fixed endorsement nodes in the hyperledger structure to handle all exchanges, the use of A non-interactive, verifiable random endorsement scheme. On the basis of the consensus mechanism of "endorsement-sorting-verification", by introducing a candidate set of endorsing nodes, using a verifiable random function to randomly select endorsing nodes and endorsing each order, a non-interactive verifiable random selection of endorsing nodes is realized And parallel endorsement process. Analysis and experiments show that the optimized consensus mechanism has higher security and faster transaction processing speed.
A Performance Evaluation of Machine Learning Models on Human Activity Identification (HAI)

Human activity identification (HAI) is presently a promising field for artificial intelligence researchers. HAI can be applied in various areas in our daily life such as surveillance, health care, etc. There are several machine learning and deep learning techniques that are applied to recognize the human activity. Various multi-dimensional sensors from smartphones, smart tablets, smartwatches can record different types of human activity. Some extensive sensors in smartphones such as accelerometer, gyroscope, microphone, GPS, and camera which can respond to record human gestures. These gestures can be sitting, lying, standing, walking, etc. In this study, the authors analyze the performance of traditional machine learning techniques as well as 1D-CNN architecture to recognize human activity using the UCI human activity identification dataset. PCA has been enforced to reduce the dimension and feature selection to get better results. 1D-CNN performs better in this study with an accuracy of 97.30%. Where Logistic regression achieves 96.00%, Linear Support Vector Machine achieves 93.71%, Kernel-Support Vector Machine achieves 94.85%, Random Forest achieves 90.59% and Decision Tree achieves 84.46% accuracy. All the accuracies are considered by taking the average of individual class accuracy. However, the 1D-CNN model is proposed by the authors to implement in less computational powered devices such as smartphones, smartwatches, etc.
Urine red blood cell classification based on Siamese Network

Abstract Urine examination is an important examination commonly used in medical in vitro examination. The morphological classification of red blood cells in urine plays an important role in the diagnosis of hematuria and renal diseases. In order to improve the accuracy and efficiency of urine red blood cell classification, a urinary red blood cell classification algorithm based on Siamese Network of dual-mode contrastive loss function was proposed. In the dual-mode contrastive loss function, the Cosine Similarity measure and a weight factor were added as the constraint of Euclidean distance in the Contrastive Loss function, which improves the similarity judgment ability of the Contrastive Loss function for the two input eigenvectors. The experimental results show that the Cosine Similarity measure has a positive effect on the Contrastive Loss function, and can effectively improve the accuracy of the whole classification model.
Real-time Object Detection with FPGA Using CenterNet

the paper proposes methodology for transferring architecture of modern neural network CenterNet to FPGA. CenterNet is a OneStage object detector that is used to detect and locate objects in images. Although this neural network has simple decoder, it shows good performance in terms of accuracy. Very high operation speed of the neural network hardware is achieved due to the choice of suitable encoder and efficient hardware implementation of both the decoder and the last layer with object filtering, and also transition to fixed-point arithmetic. At this, quality of the obtained predictions remains high.
A mixed image segmentation method based on intelligent equipment

Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of “preprocessing + feature extraction + classifier”, this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition.
Analytical Study of Blockchain Enabled Security Enhancement Methods for Healthcare Data

Abstract Healthcare data protection is very much required in the present time due to the rate of growth in data volume and sensitivity of the data. The data is collected through EHR, diagnostic image like scan or x-ray or IoT wearable devices which has sensors and shared with stakeholders who can analyse the data and use it for betterment of the processes. Benefits of medical data sharing among the entities makes it even more vulnerable. In this paper we have studied and analysed few blockchain based methods of securing healthcare data which is stored in cloud. The methods studied is successful in keeping the data safe from unauthorized access.
WWW3E8: 259,000 Relevance Labels for Studying the Effect of Document Presentation Order for Relevance Assessors

In IR evaluation based on depth-k pooling, there are several strategies to order the pooled documents for relevance assessors. Among them, the simplest approach is to completely randomise the order "so assessors cannot tell if a document was highly ranked by some system or how many systems (or which systems) retrieved the document." An approach that is in sharp contrast to the above is the prioritisation approach taken by NTCIRPOOL, a tool widely used at NTCIR. NTCIRPOOL sorts the pooled documents by "pseudorelevance," a statistic that reflects the popularity of each document within the depth-k pools. Although these two strategies have coexisted for over two decades, the IR research community has yet to reach a consensus as to what advantages each of these two strategies actually offer. To help researchers directly address this question using their favourite methods of analysis, we have released a large-scale data set called WWW3E8. It comprises eight independent sets of qrels for the 160 English topics of the NTCIR-15 WWW-3 task: four qrels files constructed using the randomisation approach, and another four constructed using the prioritisation approach of NTCIRPOOL. Each qrels file covers 32,375 topic-document pairs; hence, WWW3E8 contains a total of 259,000 relevance labels. Moreover, the data set contains the raw English subtask run files from the WWW-3 task, the randomised and prioritised pool files, and topic-by-run score matrices of the official measures used in the task. Hence, researchers interested in the above research question regarding document ordering can utilise WWW3E8 as a common ground to directly compare the two strategies.