Reviewerly/exHarmony huggingface dataset:
Crow Search based Scheduling Algorithm for Load Balancing in Cloud Environment

Task scheduling is a vital aspect in computer science, as it is the essence of how a computer executes various tasks and performs the related activities with accuracy and efficiency. In cloud computing, task scheduling is a typical NP-hard problem and scientists have been attempting to handle this problem for quite a long time. Although it is anything but difficult to accomplish a global optimum solution utilizing the ant colony algorithm and achieve promising outcomes. This research paper proposes a crow search based load balancing algorithm (CSLBA) for multi-objective task scheduling environment which concentrates on allocating best suitable resources for the task to be implemented with the consideration of various parameters like average makespan time (AMT), average waiting time (AWT) and average data center processing time (ADCPT). The present work provides a comparative analysis of proposed algorithm and Ant Colony Optimization based load balancing algorithm (ACOLBA). The experimentation is performed in steps and comparative analysis proved that proposed CSLBA is the optimal technique among the other scheduling technique considered in this research paper.
Global warming analysis for greenhouse gases impacts comparable to carbon-free nuclear energy using neuro-fuzzy algorithm

As one of energy characteristics, the importance of climate effects has been increasing due to the side-effect such as the drought, flood, heavy snow and so on. The nonlinear artificial intelligence can be reasonably applied in the analysis of the simulations, because the human-brain mimicking algorithm can show the practicable results. Basically, the quantifications in the study results are based on the randomly generated numbers where the Monte Carlo methods are applied. The Boolean numbers are generated in the variable constructions. Furthermore, there are multiplications in population which are decided by the expert judgments. The causes loops for CO2 and temperature are obtained. In addition, there is the result of variable albedo vs. normalised temperature with dimensionless values. Global collaboration can prepare and control the global warming as the geological scale aspect as well as the collaborated idea utilisation that can develop the carbon minimising technology and green energy development.
A Fast and Accurate Guessing Entropy Estimation Algorithm for Full-key Recovery

Guessing entropy (GE) is a widely adopted metric that measures the average computational cost needed for a successful side-channel analysis (SCA). However, with current estimation methods where the evaluator has to average the correct key rank over many independent side-channel leakage measurement sets, full-key GE estimation is impractical due to its prohibitive computing requirement. A recent estimation method based on posterior probabilities, although scalable, is not accurate.We propose a new guessing entropy estimation algorithm (GEEA) based on theoretical distributions of the ranking score vectors. By discovering the relationship of GE with pairwise success rates and utilizing it, GEEA uses a sum of many univariate Gaussian probabilities instead of multi-variate Gaussian probabilities, significantly improving the computation efficiency.We show that GEEA is more accurate and efficient than all current GE estimations. To the best of our knowledge, it is the only practical full-key GE evaluation on given experimental data sets which the evaluator has access to. Moreover, it can accurately predict the GE for larger sizes than the experimental data sets, providing comprehensive security evaluation.
An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt

Breast cancer has gradually become an important killer that endangers people’s health. How to diagnose breast cancer quickly and accurately has become a popular research direction. However, traditional testing by the doctor is time-consuming and laborious, and there is still the problem of accuracy. Deep learning becomes a tool of evidence-based medicine, which can effectively solve the above problems and realize the function of detecting breast cancer automatically and with high accuracy. In our study, we selected and applied the optimal CNN model named ResNet50 for breast cancer diagnosis. Due to the small size of images in our dataset, the 3*3 convolutional layer performed better than the 7*7 convolutional layer in our breast cancer classification task. Besides, our pre-trained ResNet50 achieved 94.698% accuracy on the WSI dataset, while un-pretrained ResNet50 only achieved 93.777%. The result presented pretraining in the ImageNet dataset can more effectively reduce the loss and improve accuracy. We also developed an application that integrated the CNN model with a chatbot implemented by NLTK and an interface constructed through PyQt.
A Decentralized E-Voting System Based on Blockchain Network

This paper describes a decentralized electronic voting system using blockchain technology with peer-to-peer network rather than the centralized voting system of server-client structure. In the proposed system, an Ethereum-based private blockchain network is configured and decentralized applications are implemented to store and distribute voting data to all nodes participating in the network to create secure and reliable electronic voting system. Smart contracts for electronic voting are implemented using the Solidity language and distributed to a configured network so that all users can view and vote on elections, and voting data are shared and contrasted by all users in the network, which makes it possible to build a safer and more reliable electronic voting system without third party involvement.
SalBiNet360: Saliency Prediction on 360° Images with Local-Global Bifurcated Deep Network

With the development of the virtual reality applications, predicting human visual attention on 360° images is valuable to content creators and encoding algorithms, and becomes essential to understand user behaviour. In this paper, we propose a local-global bifurcated deep network for saliency prediction on 360° images, which is named as SalBiNet360. In the global deep sub-network, multiple multi-scale contextual modules and a multilevel decoder are utilized to integrate the features from the middle and deep layers of the network. In the local deep sub-network, only one multi-scale contextual module and a single-level decoder are utilized to reduce the redundancy of local saliency maps. Finally, fused saliency maps are generated by linear combination of the global and local saliency maps. Experiments on two publicly available datasets illustrate that the proposed SalBiNet360 outperforms the tested state-of-the-art methods.
Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem

Cuckoo search (CS) is an efficient bio-inspired algorithm and has been studied on global optimisation problems extensively. It is expert in solving complicated functions but converges slowly. Another optimisation algorithm, covariance matrix adaption evolution strategy (CMA_ES) can speed up the convergence rate via the self-adaptative mutation distribution and the cumulative evolution path, whereas it performs badly in complex functions. Therefore, in this paper, we devise a hybridisation of CS and CMA_ES and name it CS_CMA, to improve performance for the different optimisation problems. An evolved population is initialised at the beginning of iteration, using the information of previous evolution. Self-adaptive parameter adjustments are employed through the successful parameter values. To validate the performance of CS_CMA, comparative experiments are conducted based on seven high-dimensional benchmark functions provided for CEC 2008 and an engineering optimisation problem chosen from CEC' 2011, and computational results demonstrate that CS_CMA outperforms other competitor algorithms.
A Smart Chatbot for Interactive Management in Beta Thalassemia Patients

Background. β-thalassemia is an inherited blood disorder that affects the production of hemoglobin molecules owing to the reduction or absence of beta chains. Transfusion therapy has had a key role in extending the lifespan of β-thalassemia patients. This life-saving therapy is linked to numerous assessments and complications that now comprise most thalassemia management considerations. Consequently, many patients do not receive adequate information about the required assessments, as indicated by evidence-based medical guidelines. Patients with β-thalassemia may benefit from chatbots that follow up on their condition and that provide the required assessment information. Self-management will hopefully have a positive impact on health outcomes. Objectives. This study aims to develop a chatbot that can assist in the management of β-thalassemia by providing the assessment information required to monitor patients’ statuses. Methods. The chatbot operated as a messaging system. A question/answer system was created based on knowledge pertaining to β-thalassemia assembled from experts, medical guidelines, and articles. Recommendations regarding the patient’s follow-up assessment are made based on the answers. Results. A prototype was implemented to demonstrate how the chatbots could dynamically and flexibly provide the assessment information required to follow up on and monitor patients. A small sample of adults with β-thalassemia used the chatbot to examine the system’s usability and perceived utility. A system usability scale and utility scale were implemented to complete a post-test survey. The chatbots were considered by 34 patients, of whom the majority (72%) found them easy to use, while more than 90% of patients considered their use beneficial. Most of the participants agreed that the chatbots could improve their knowledge about their β-thalassemia assessments. Conclusion. Our findings suggest that chatbots can be beneficial to the development of recommended tests and management related to the assessment of β-thalassemia.
Digital Competencies for Science Teaching: Adapting the DiKoLAN Framework to Teacher Education in Switzerland

Prospective teachers need both general and subject-specific digital competencies. However, available competency frameworks usually address only non-subject-specific general digital competencies. Although the joint framework curriculum of the German-speaking cantons of Switzerland not only provides a comprehensive definition of expected digital student competencies, but also suggestions for linking them to and implementing them in subject lessons, there is still no integral planning and structuring for subject-specific teacher training in the canton of Thurgau. For a curricular design of the promotion of subject-specific digital competencies, three competency frameworks for the study programs Primary level, Secondary level 1, and Secondary level 2 at the Thurgau University of Education were derived in expert interviews based on the DiKoLAN framework (Digital Competencies for Teaching in Science Education). While there are significant overlaps, the original reference framework clearly needs to be adapted. Furthermore, there are important commonalities between the newly formed individual competency frameworks. The methodological approach has proven to be fruitful and is recommended to follow-up studies and studies with similar research questions for imitation.
Human Instance Matting via Mutual Guidance and Multi-Instance Refinement

This paper introduces a new matting task called human instance matting (HIM), which requires the pertinent model to automatically predict a precise alpha matte for each human instance.Straightforward combination of closely related techniques, namely, instance segmentation, soft segmentation and human/conventional matting, will easily fail in complex cases requiring disentangling mingled colors belonging to multiple instances along hairy and thin boundary structures.To tackle these technical challenges, we propose a human instance matting framework, called Inst-Matt, where a novel mutual guidance strategy working in tandem with a multi-instance refinement module is used, for delineating multi-instance relationship among humans with complex and overlapping boundaries if present.A new instance matting metric called instance matting quality (IMQ) is proposed, which addresses the absence of a unified and fair means of evaluation emphasizing both instance recognition and matting quality.Finally, we construct a HIM benchmark for evaluation, which comprises of both synthetic and natural benchmark images.In addition to thorough experimental results on complex cases with multiple and overlapping human instances each has intricate boundaries, preliminary results are presented on general instance matting.Code and benchmark are available in https://github.com/nowsyn/InstMatt.
Applying Artificial Intelligence Privacy Technology in the Healthcare Domain

Regulations set out strict restrictions on processing personal data. ML models must also adhere to these restrictions, as it may be possible to infer personal information from trained models. In this paper, we demonstrate the use of two novel AI Privacy tools in a real-world healthcare application.
A State Space Modeling Method for Aero-Engine Based on AFOS-ELM

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement.
Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders

To reap the advantages of many continual growing cloud services, cloud industries should adopt smart and holistic resources scheduling strategies. By deploying efficient deep learning technologies, many chaotic cloud traffics’ potential issues may be solved. Toward efficient cloud instances rightsizing and scheduling, we adopt in this paper a new Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked Denoising Autoencoders to forecast simultaneously future hourly virtual CPU, memory, and storage utilizations. Using various data ranges of resources under three AWS instances families, the best forecasting results achieved so far [1,83,30,78,331,11] of mean RMSE values and [1,37,21,63,245,13] of mean MAE values while predicting respectively future vCPU, memory, and storage utilizations. In addition, the proposed model also proved its precision stability and outperformance over the three considered SDAE-GRU, SDAE-LSTM and BiGRU benchmark models. Given the neglected power consumption measurement noticed in most related studies, we eventually validated the proposed predictor’s power efficiency by measuring in addition its real time consumed power in watt and temperature throughout the training process duration. The proposed predictor decreased the average consumed power by 5% compared to a classical sparse AE-BiGRU.
Smart Academic and Professional Education

The evolution of technology brings closer the endless possibilities of education, allowing a human to learn something new anywhere and anytime. With the crisis created by the pandemic situation for the last two years, new ways of education have taken form to maintain the flow of learning and qualification; thus, the term “distance learning” has been implemented in all types of learning, from primary education all the way to tertiary education. This paper covers the image of tertiary education, mostly at the level of universities. Many changes took form at this level, such as developing new ways for the distance learning implementation by creating new programs dedicated for this new method of education. We will present to you how these changes took form and how they can evolve with the help of various technologies such as Blockchain and XR, and other strategic learning methods such as Massive Open Online Courses (MOOCs) and gamification. Universities start to create new programs based on their unique crypto coin, which help students pay for their studies, such as articles, new disciplines, and exchange programs. The gamification of these programs raises the interactivity that students have during class hours, thus motivating them and creating an optimal curve of learning, combined with the implementation of XR technology.
Keywords-aware dynamic graph neural network for multi-hop reading comprehension

The multi-hop reading comprehension (RC) is challenging for machine reading comprehension. It is crucial for multi-hop RC to comprehend complex questions and contents between multiple paragraphs. In this paper, we propose a strategy of keywords-aware dynamic graph neural network (KA-DGN) to improve the performance of multi-hop reading comprehension. First of all, KA-DGN focuses on the salient information in the text, extracts keywords from the question and context. A window is specifically designed to frame the interrogative pronoun/adverb and its nearby words in the question, which encourages the model to focus on the answer. Next, the token-level answer span is predicted under the guidance of the keywords. And the boundary loss function is also invented to enhance the boundary awareness of the model on extracting the answer, which maximizes the probability of answer span bound while minimizing that of the noise. Finally, the model builds a dynamic reasoning graph combining explicit keywords and implicit semantic information among sentences. Graph neural network is applied to predict the sentence-level supporting facts. While evaluating on HotpotQA, the proposed KA-DGN achieves competitive performance in distractor setting.
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation

We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance.It consists of two procedures: bidirectional pretraining and unidirectional finetuning.Both procedures utilize SimCut, a simple regularization method that forces the consistency between the output distributions of the original and the cutoff sentence pairs.Without leveraging extra dataset via back-translation or integrating large-scale pretrained model, Bi-SimCut achieves strong translation performance across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU scores of 31.16 for en → de and 38.37 for de → en on the IWSLT14 dataset, 30.78 for en → de and 35.15 for de → en on the WMT14 dataset, and 27.17 for zh → en on the WMT17 dataset.Sim-Cut is not a new method, but a version of Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be considered as a perturbation-based method.Given the universality and simplicity of SimCut and Bi-SimCut, we believe they can serve as strong baselines for future NMT research.
Inertial Data-Based AI Approaches for ADL and Fall Recognition

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset's lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation

Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.Existing works mostly focus on contrastive learning on the instance-level without discriminating the contribution of each word, while keywords are the gist of the text and dominant the constrained mapping relationships.Hence, in this work, we propose a hierarchical contrastive learning mechanism, which can unify hybrid granularities semantic meaning in the input text.Concretely, we first propose a keyword graph via contrastive correlations of positive-negative pairs to iteratively polish the keyword representations.Then, we construct intra-contrasts within instance-level and keyword-level, where we assume words are sampled nodes from a sentence distribution.Finally, to bridge the gap between independent contrast levels and tackle the common contrast vanishing problem, we propose an inter-contrast mechanism that measures the discrepancy between contrastive keyword nodes respectively to the instance distribution.Experiments demonstrate that our model outperforms competitive baselines on paraphrasing, dialogue generation, and storytelling tasks.