Reviewerly/exHarmony huggingface dataset:
Optimisation of Cancer Status Prediction Pipelines using Bio-Inspired Computing

Cancer is one of the leading causes of death globally, and early detection is a fundamental factor in improving patient outcomes. The advent of high-throughput genetic profiling techniques in the last few decades has led to an explosion of genetic data related to cancer. Machine learning methods, and classification algorithms in particular, have been used to find underlying patterns in cancer data and make diagnostic predictions. The addition of feature selection to classification pipelines can lead to improvements in predictive capabilities, since the removal of non-important features benefits the construction of classification models. We developed a classification pipeline for cancer status prediction composed of a feature selection step with SelectKBest and an ensemble classifier system with five popular supervised learning algorithms. We used three bio-inspired optimization techniques to select the optimal sets of hyperparameters for the classification pipeline and compared these approaches on three cancer microarray datasets. The results indicate that the optimized pipelines have better predictive performance in all but one of the experiments compared to the ensemble alone.
Precise measurement of surface topography with microstructures based on differential confocal and spiral scanning

This paper proposes a new topography measurement method combining laser differential confocal and spiral-scanning measurements together (TM-DCSS), which can realize large-area, fast and high-accuracy measurement of microstructure topography. Firstly, the mapping relationship between probe centering deviation and reconstructed topography distortion is analyzed, and a wedge rotation alignment method is proposed to make the probe precisely aligned with the rotating stage. Secondly, a sampling strategy of spiral trajectory is designed to eliminate the sawtooth distortion. Thirdly, a threshold-tunable wavelet denoising algorithm based on translation-invariant and particle swarm optimization is proposed in order to effectively eliminate the noise disturbance. Finally, an experimental platform based on TM-DCSS principle is constructed. The measurement results show that the centralization deviation of the proposed system is within 1.94 μm, and the maximum relative error is less than 0.78% when compared with the commercial microscope. The proposed method shows great potentials in various non-contact precision measurement applications.
Influence of the Fuzzy Robust Gamma Rank Correlation, Fuzzy C-Means, and Fuzzy Cognitive Maps to Predict the Z Generation's Acceptance Attitudes Towards Internet Health Information

In this study, we propose an approach based on the advanced fuzzy techniques such as Fuzzy C-Means, Fuzzy Robust Gamma Rank Correlation and Fuzzy Cognitive Maps to predict the acceptance attitudes towards Internet health information. To improve the Fuzzy Cognitive Maps efficiency we introduce the setting the values of initial matrix with the use of fuzzy methods and to divide the concepts based on the clustering methods. This allows us to use maps as a tool for prediction the acceptance attitudes of the young people in the area of heath information management. Moreover, this work sheds the light on the novel application of both Fuzzy C-Means and Fuzzy Robust Gamma Rank Correlation as tools for settings the initial values of connections between concepts for Fuzzy Cognitive Maps.
A Low Rank Promoting Prior for Unsupervised Contrastive Learning

Unsupervised learning is just at a tipping point where it could really take off. Among these approaches, contrastive learning has led to state-of-the-art performance. In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC. In contrast to the existing conventional self-supervised approaches that only considers independent learning, our hypothesis explicitly requires that all the samples belonging to the same instance class lie on the same subspace with small dimension. This heuristic poses particular joint learning constraints to reduce the degree of freedom of the problem during the search of the optimal network parameterization. Most importantly, we argue that the low rank prior employed here is not unique, and many different priors can be invoked in a similar probabilistic way, corresponding to different hypotheses about underlying truth behind the contrastive features. Empirical evidences show that the proposed algorithm clearly surpasses the state-of-the-art approaches on multiple benchmarks, including image classification, object detection, instance segmentation and keypoint detection. Code is available: https://github.com/ssl-codelab/lorac.
A novel approach of privacy-preserving data sharing system through data-tagging with role-based access control

Purpose This paper aims to design a secure and seamless system that ensures quick sharing of health-care data to improve the privacy of sensitive health-care data, the efficiency of health-care infrastructure, effective treatment given to patients and encourage the development of new health-care technologies by researchers. These objectives are achieved through the proposed system, a “privacy-aware data tagging system using role-based access control for health-care data.” Design/methodology/approach Health-care data must be stored and shared in such a manner that the privacy of the patient is maintained. The method proposed, uses data tags to classify health-care data into various color codes which signify the sensitivity of data. It makes use of the ARX tool to anonymize raw health-care data and uses role-based access control as a means of ensuring only authenticated persons can access the data. Findings The system integrates the tagging and anonymizing of health-care data coupled with robust access control policies into one architecture. The paper discusses the proposed architecture, describes the algorithm used to tag health-care data, analyzes the metrics of the anonymized data against various attacks and devises a mathematical model for role-based access control. Originality/value The paper integrates three disparate topics – data tagging, anonymization and role-based access policies into one seamless architecture. Codifying health-care data into different tags based on International Classification of Diseases 10th Revision (ICD-10) codes and applying varying levels of anonymization for each data tag along with role-based access policies is unique to the system and also ensures the usability of data for research.
Automatic vocabulary and graph verification for accurate loop closure detection

Abstract Localizing previously visited places during long‐term localization and mapping, that is, loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. In common bag‐of‐words (BoW) model, a visual vocabulary is built to associate features for detecting loops. Currently, methods that build vocabularies off‐line determine scales of the vocabulary by trial‐and‐error, which results in unreasonable feature association. Moreover, the detection precision of the algorithm declines due to perceptual aliasing given that the BoW‐based method ignores the positions of visual features. To build the optimal vocabulary automatically and eliminate human heuristics, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors in vocabulary construction. Furthermore, a novel topological graph verification method is proposed for validating loop candidates, which can effectively distinguish visual ambiguities by involving geometrical position of features and thus improve the precision of LCD. Experiments on various public datasets verify the effectiveness of our proposed approach.
Sentiment Analysis of Ecommerce Product Review Data Based on Deep Learning

Aiming at the problem that traditional sentiment analysis methods are not effective in mining e-commerce product review data, we propose a sentiment analysis model based on deep learning for e-commerce product review data. In our model, comments are divided into positive and negative categories. The text is divided into words, and the word vector is combined with the word frequency to input into the neural network for training. Convolution neural network is used to mine the deep association between feature set and emotion tag, and train emotion classifier. The experimental results show that the model can achieve effective product feature extraction and high accuracy of sentiment classification, which is an effective model for online review analysis.
Autonomous flight through cluttered outdoor environments using a memoryless planner

This paper introduces a collision avoidance system for navigating a multicopter in cluttered outdoor environments based on the recent memory-less motion planner, rectangular pyramid partitioning using integrated depth sensors (RAP-PIDS). The RAPPIDS motion planner generates collision-free flight trajectories at high speed with low computational cost using only the latest depth image. In this work we extend it to improve the performance of the planner by taking the following issues into account. (a) Changes in the dynamic characteristics of the multicopter that occur during flight, such as changes in motor input/output characteristics due to battery voltage drop. (b) The noise of the flight sensor, which can cause unwanted control input components. (c) Planner utility function which may not be suitable for the cluttered environment. Therefore, in this paper we introduce solutions to each of the above problems and propose a system for the successful operation of the RAPPIDS planner in an outdoor cluttered flight environment. At the end of the paper, we validate the proposed method's effectiveness by presenting the flight experiment results in a forest environment. A video can be found at www.youtube.com/watch?v=3av5xEuKg2w&ab_channel=HiPeRLab
<scp>Context‐aware</scp>text classification system to improve the quality of text: A detailed investigation and techniques

Summary Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context‐aware text classification system to improve text quality. We use a content‐aware recommendation system to extract the data from well‐known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance.
Smart city development: Theft handling of public vehicles using image analysis and cloud network

Car theft is one of the most widespread crimes globally currently, and so protection of cars is important. Thieves generally gain entry to vehicles by smashing a car window. As a solution to this problem, a camera sensor is mounted within the car’s steering column and the driver needs to confirm his/her identity and before being able to use the key to unlock the vehicle or start the ignition. A city center has a cloud data center that holds all of a city’s cars’ details and information on their registered drivers. Once the camera in the car has taken an image of the driver, this photo will be compared with all existing drivers by checking the number of the particular car. This computation is done in the cloud network as cars have limited memory and generally have no processing device.
An Allreduce Algorithm and Network Co-design for Large-Scale Training of Distributed Deep Learning

Distributed training of Deep Neural Networks (DNNs) on High-Performance Computing (HPC) systems is becoming increasingly common. HPC systems dedicated entirely or mainly to Deep Learning (DL) workloads are becoming a reality. The collective communication overhead for calculating the average of weight gradients, e.g., an Allreduce operations, is one of the main factors limiting the scaling of data parallelism training. Several active efforts across different layers of the training stack including the training algorithms, parallelism strategy, communication algorithms, and system design have been proposed to cope with this communication challenge when scaling distributed training of DNNs. However, even with those methods, communication still becomes a bottleneck with the steady increase in model sizes, e.g.,100-10,000s MB, and the number of compute nodes, e.g., 1,000-10,000s of GPUs. In this work, we investigate the benefits of co-design of Allreduce algorithms and the network system. We propose to replace the Fat-tree network topology with a variant of Distributed Loop Network topology that guarantees a fixed routing paths length between any pairs of computing nodes for the communication pattern of halving-doubling Allreduce algorithm. We also propose a technique to eliminate/mitigate the network contention.
Instance-Level Image Translation With a Local Discriminator

Instance-level image translation aims to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">only</i> translate instance of interest and can be operated more finely and flexibly than object-level and holistic-level image translation. However, current algorithms are not suitable to do it since they employ a holistic or object level’s discriminator that tends to change the whole image or all instances. To address the issue, we propose a simple yet effective local discriminator, in which the input image is split into two parts, region of interest (ROI) and background. Instance mask is employed to align the ROI and the background is design to be random in a prior distribution to mitigate a divergence between the ROI and the background. In this way, we obtain translated instance with decent margins without artifacts as current algorithms get. Moreover we propose a new architecture to simultaneously realize versatile instance-level image translation. Experimental results prove that our proposed algorithm outperforms the state-of-the-art in position accuracy and background retainment by a clear margin.
A Block-chain Oriented Model Driven Framework for handling Inconsistent Requirements in Global Software Development

Requirements engineering (RE) is one of the most important phases in software development process. Therefore, the handling of inconsistent/ conflicting requirements is considered as a major issue in the requirements phase. Due to the evolving practices of Global Software Development (GSD), where the development team is geographically spread, the phase of RE is more prone to inconsistencies. Many approaches have been suggested to overcome the problem, however, these solutions are very limited in their scope and does not fit the peculiarities of GSD configurations. On the other hand, Block Chain methodology has shown promising results in various domains and has very sophisticated features like transparency and decentralization to support the management of inconsistent requirements. Similarly, Model Driven Software Engineering (MDSE) is marked with abstraction and reducing complexity which may be benefited to handle inconsistent requirements. Consequently, this article introduces a Block chain Oriented Model Driven (BOMO) framework by integrating the concepts of RE and Block Chains in the context of MDSE. This allows the effective management of inconsistent requirements through block chain technique with simplicity as offered by MDSE. As part of the framework, a meta-model is proposed which has been subsequently evolved into a Sirius graphical modelling tool. The applicability of the proposed framework has been demonstrated via a case study. Experimental results prove that the proposed framework may be used with sufficient reliability and can be further evolved to handle inconsistent requirements in a promising manner.
Shriek: A Role Playing Game Using Unreal Engine 4 and Behaviour Trees

Shriek is a Role Playing Game which offers an immersive experience to players in a medieval fantasy environment. Here, the player can see stunning magical landscapes and levels created using variety of lighting, meshes and materials. It has a choice based story line, which adds a depth to the choices made and affects the story line further into the game. Player can play with any of the four characters who have their own independent but intertwining stories. It has an attractive GUI which helps the player to navigate through the menus and other options. Shriek combines additionally gives dynamic difficulty and adaptive AI, implemented using Unreal Engine 4's Behaviour Trees, which assesses how well the player is playing, and based on that dynamically adjusts the difficulty of the game and the AI.
Alignment-Based Graph Network for Judicial Examination Task

The judicial examination task is to select the correct options for a given question, which is a challenging task and is helpful for legal assistant systems. We argue that by leveraging the potential semantic information between question and option, we can enhance the model's ability to understand the task's content and, therefore, propose an Alignment-Based Graph Network (ABGN). Given a question-option pair, ABGN first constructs semantic relation between question and option with an alignment network and then uses a gated graph attention network to utilise all question-option pairs' global information. The experimental results show that our model achieves competitive performance with standard methods on judicial examination task. Besides, with multi-dimension analyses, we show the effectiveness of some components in our full model.
Not the Same Everywhere: Comparing the Scope and Definition of User Experience between the Brazilian and International Communities

The Human-Computer Interaction community worked over the last years on scoping and defining User Experience (UX). As reported in the literature, UX’s understanding changes according to the socio-cultural context and between professional positions, making a global consensus unattainable. Nevertheless, tracking and investigating the most accepted UX definitions and statements may be advantageous for visualizing multiple contexts’ needs. Such an investigation is still scarce and even more significant in developing countries since there are structural differences from developed countries in the software development industry that directly impact user research practices. To fill in this gap, we replicated in Brazil a survey distributed before, mostly among professionals from developed countries. We compared 252 responses from academics and practitioners and identified differences and similarities among Brazilian and international communities. The definitions of UX chosen by the academic and industrial domains explain differences in UX design and evaluation. When comparing Brazilians’ responses with previous studies, Brazilians give greater importance to qualitative measures and to different UX time spans.
A Blockchain based Privacy-Preserving Reputation Scheme for Cloud Service

Nowadays, the quality of cloud services offered by different service providers varies greatly. Reputation mechanism, as a better service evaluation method, can help standardize and regulate the cloud service market. However, existing reputation systems either rely on a trusted third party with security and privacy issues, or lack reliable evaluation. To address the above issues, we propose a blockchain based privacy-preserving dynamic reputation mechanism for cloud service and a reputation management smart contract (RM) is designed to implement trusted reputation computation. The reputation integrates conformance trust in the subjective view and recommendation trust in the objective view together to provide a comprehensive evaluation. Besides, a miner selection algorithm is designed to prevent miners from launching a collusion attack. Moreover, the Paillier homomorphic encryption algorithm (PHE) is introduced to encrypt the sensitive data of customers, ensuring the security of data stored on the blockchain. Experiment results reveal that the proposed model is feasible and the performance of encryption is acceptable.
Research on PAD Blended Teaching in the field of English Education Based on Artificial Intelligence and Big data

In recent years, the further development of information technologies has effectively accelerated the reform and innovation of teaching mode and promoted the deep integration of information technology and education. Through the screening and statistical research of related research, this paper analyzes the origin and development of blended teaching and clarifies the concept of blended teaching from different perspectives. From the aspects of theoretical guidance, teaching structure, teaching environment and teaching design, this paper probes into the ways and methods of implementing the blended teaching mode in the practical teaching practice. Furthermore, the creation of PDA Blended teaching model based on artificial intelligence and big data is analyzed and the effect of the model is clarified on the basis of the data analysis. Finally, the paper stresses the significance of blended teaching and some advantages of the integration of modern technologies and traditional teaching mode. Meanwhile, this paper explores some existing problems in the process of implementing blended teaching mode as well as puts forward some suggestions on the further development of blended teaching mode in the field of English education.
Intuitive Cognition-Based Method for Generating Speech Using Hand Gestures

Muteness at its various levels is a common disability. Most of the technological solutions to the problem creates vocal speech through the transition from mute languages to vocal acoustic sounds. We present a new approach for creating speech: a technology that does not require prior knowledge of sign language. This technology is based on the most basic level of speech according to the phonetic division into vowels and consonants. The speech itself is expected to be expressed through sensing of the hand movements, as the movements are divided into three rotations: yaw, pitch, and roll. The proposed algorithm converts these rotations through programming to vowels and consonants. For the hand movement sensing, we used a depth camera and standard speakers in order to produce the sounds. The combination of the programmed depth camera and the speakers, together with the cognitive activity of the brain, is integrated into a unique speech interface. Using this interface, the user can develop speech through an intuitive cognitive process in accordance with the ongoing brain activity, similar to the natural use of the vocal cords. Based on the performance of the presented speech interface prototype, it is substantiated that the proposed device could be a solution for those suffering from speech disabilities.