Reviewerly/exHarmony huggingface dataset:
Dynamic Workflow Scheduling in Cloud using Neural Network based Multi-Objective Evolutionary Algorithm

Workflow is a series of jobs that are executed in order to complete a specific activity where the jobs are often dependent on each other. Data transfer that might occur between such jobs results into the creation of a workflow that aims at utilising resources for workflow tasks by optimising the use of cloud resources. Few of the existing single objectives workflows scheduling solutions have linearly combine multiple objectives to get a multi-objective solution, but it might not be able to model the real-world problem efficiently for certain conditions where the environment is dynamic in nature. Hence, a Neural Network based Multi-Objective Evolutionary Algorithm (NN-MOHEFT) that solves the multi-objective workflow scheduling issues in a dynamic environment was proposed in this article. The NN-MOHEFT learns the pattern behind changing Pareto optimal front for successive environment and tries to predict the Pareto optimal front for the next environment from the input Pareto optimal set of the current environment. The proposed NN-MOHEFT algorithm is at par with the original constructs when it comes to the hypervolume of objectives generated. It generates 10% more non-dominated solutions as compared to the original construct.
A COMPILATION OF FACTORS ASSOCIATED TO THE GOVERNANCE AND MANAGEMENT OF AGILE PROJECTS: A SYSTEMATIC LITERATURE REVIEW

The demand for project success is increasing, while the number of failed projects are still high. Whilst Project Management Offices help in the governance of Information Technology projects, the trending issues and challenges in Agile projects need a great deal of attention. More organisations are embracing the agile method due to its popularity and the benefits it yields. Literature discusses various issues and challenges, with an absence of a holistic review to determine its frequency, impact, and classification systematically. This study attempts to identify the factors addressed in literature over the last 2 decades, producing a list of factors and ranking them according to the most addressed and the frequency of reference. A systematic literature review was undertaken to identify the relevant literature using specific keywords search and filter, arriving at a final list of 175 selected studies for the analysis work, which was used to identify 37 prominent factors affecting agile project success. The highest number of studies and factors discussed were between years 2014 and 2016. Project Governance faired as the most addressed factor, while People related factors were widely discussed. Current trends are focused on attaining project governance with a sound understanding of the agile principles, by people with the right attitude, knowledge, skills, and the ability to work well with the team. Project governance with the careful implementation of the agile method and its processes in stages, with the right support tools and technology, is most important for project success. Competence of project stakeholders possessing the right attitude, with great collaboration and teamwork capabilities, and the ability to garner a close relationship with the customer and clearly articulate changing requirements is key to delivering successful agile projects. Future work will include the understanding and identification of critical challenges and crucial factors for ensuring agile project management success. It is proposed to expand the research further into the project governance and people-related area.
Hyperanalytic Wavelets Based Remote Sensing Images Despeckling

The acquisition of remote sensing images is affected by the presence of a multiplicative noise called speckle. This paper presents a denoising system based on the Hyperanalytic Wavelet Transform associated with a Maximum a Posteriori filter, named Adaptive Soft Thresholding filter. This filter represents the solution of an optimization problem. Some simulation results and comparisons made on remote sensing images show the performance of the proposed denoising algorithm.
First InvenioRDM Long-Term Support (LTS) version released today – and Front Matter is joining as a participating partner

The open source research data management platform InvenioRDM today announced the first Long-Term Support (LTS) release, usable on production services. And I am joining the effort as a participating partner via Front Matter, the organization I started this week.
The Implementation for Polishing Students’ Programming Skills Solving Problems

The key to programming education is to polish students' programming skills solving problems.The current programming courses in universities are mainly classroom-teaching models, and students' skill solving problems by programming can hardly be polished.Polishing students' programming skills solving problems is implemented by teaching material construction, curriculum construction and programming training system cross region.In teaching material construction, the book series "Collegiate Programming Contests and Education" focus on polishing students' programming skills solving problems in a systematic way, based on programming contests' problems.Experiments, consisting of programming contest problems and their analysis and solutions, are basic units for the book series.In curriculum construction, the case teaching is widely used based on experiments; and informatization technologies, such as online judge systems, and virtual online contests, are integrated into courses.A programming training system cross region, ICPC Asia Training League and ICPC International Joint Labs for Programming Technologies, is set up.It has been proved by practice that these works can polish students' programming skills solving problems efficiently.
Formation of the Culture of Working with Information in the Conditions of Distance Learning During the Covid-19 Pandemic

The main purpose of the study predetermines the main issue of the article: to show the importance of formation of a culture of working with information among students during distance learning, which means the ability to evaluate, perceive information, prevent manipulation, distinguish truth from falsehood. The main task is to assess the existing and desired level of this competence, to develop an organizational model of its formation which consists of the following components: goals and objectives, pedagogical conditions and evaluation criteria. The model was implemented simultaneously at the Drahomanov National Pedagogical University and National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” by teachers of humanities in experimental and control groups (16 people) in three stages: ascertaining, formative and final. A questionnaire and a self-assessment map of the formation of the culture of working with information were developed for the ascertaining stage; purposeful work with students was conducted (special tasks, discussion conversations, discussions) at the formative stage the results before and after the experiment were compared at the final stage in particular. Positive dynamics in the experimental group was noted, the average rate of formation increased. The proposed evaluation criteria – motivational, cognitive and activity ones have demonstrated practical value. It is advisable to recommend their use at other faculties. It is concluded that the organizational model is effective. The prospect of the study is the scientific substantiation of the implementation of the model among a wider range of students and the development of a distance specialized course.
Ensemble Distillation for BERT-Based Ranking Models

Over the past two years, large pretrained language models such as BERT have been applied to text ranking problems and showed superior performance on multiple public benchmark data sets. Prior work demonstrated that an ensemble of multiple BERT-based ranking models can not only boost the performance, but also reduce the performance variance. However, an ensemble of models is more costly because it needs computing resource and/or inference time proportional to the number of models. In this paper, we study how to retain the performance of an ensemble of models at the inference cost of a single model by distilling the ensemble into a single BERT-based student ranking model. Specifically, we study different designs of teacher labels, various distillation strategies, as well as multiple distillation losses tailored for ranking problems. We conduct experiments on the MS MARCO passage ranking and the TREC-COVID data set. Our results show that even with these simple distillation techniques, the distilled model can effectively retain the performance gain of the ensemble of multiple models. More interestingly, the performances of distilled models are also more stable than models fine-tuned on original labeled data. The results reveal a promising direction to capitalize on the gains achieved by an ensemble of BERT-based ranking models.
Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.
Estimation of a Human-Maneuvered Target Incorporating Human Intention

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.
Robust Zero-Change Self-Configuration of the Rectangular Mesh

We propose an algorithm to self-configure the rectangular mesh [Clements et al. , Optica , vol. 3, p. 1460, 2016] using only external sources and detectors. This procedure is robust to hardware imperfections and leads to significant accuracy improvements for optical neural networks on realistic hardware.
High Anti-Interception Orbital Angular Momentum Spread Spectrum Communications Systems

With the same transmission rate and Bit Error Rate (BER) as the traditional Direct Sequence Spread Spectrum (DSSS) system, the stronger anti-interception performance can be obtained by Orbital Angular Momentum (OAM). In other words, the contradiction between the transmission rate and the security can be solved by the double-channel OAM Spread Spectrum (SS) system. In this paper, the performance of the low probability of intercept and the low probability of detection are evaluated by Computational Complexity (CC) and Channel Exposure Time (CET), respectively. In addition, the case of trans-mitting the generator polynomial coefficients by OAM, as well as the cases with encryption algorithms, are proposed and verified. As a result, these proposed schemes greatly increase the CC and reduce the CET, which means that the great improvement of the anti-interception performance can be achieved.
Visual Information Features and Machine Learning for Wushu Arts Tracking

Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts' body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method.
A Text Category Detection and Information Extraction Algorithm with Deep Learning

Abstract In order to solve the problem that the text classification model based on neural network is easy to over-fit and ignore the key words in sentences in the training process, a Bi-GRU Chinese text classification model based on hierarchical Attention mechanism is proposed. The model introduces the idea of layering, uses bi-directional gated cyclic neural network to learn the text representation at word level and sentence level, uses Self-Attention hierarchical model to obtain the information of the influence of words and sentences on text classification, shares the weight between embedded layer and softmax layer by binding, and uses AMSBound optimization method to obtain the optimal weight matrix quickly and effectively while reducing the parameters in the model. Two commonly used Chinese data sets, FudanSet and THUCNews, are tested on the long Chinese text classification data set FudanSet. The experimental results show that the accuracy, recall rate and F-score of this model are better than Text-CNN model, Attention-BiLSTM model and Bi-GRU_CNN model, and the accuracy, recall rate and F-score index are improved by 5.9%, 5.8% and 4.6%, respectively.
Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

Facial expression recognition is a human emotion classification problem attracting much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are still the issues when extracting features of human emotions. To cope with these challenges, the authors propose an automatic system that provides users with a well-adopted classifier for recognizing facial expressions in a more accurate manner. The system is based on two fundamental machine learning stages, namely feature selection and feature classification. Feature selection is realized by active shape model (ASM) composed of landmarks while the feature classification algorithm is based on seven well-known classifiers. The authors have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. The experimental results show that quadratic classifier (DA) provides excellent performance, and it outperforms the other classifiers with the highest recognition rate of 100% for the same dataset.
Comprehensive evaluation model of abnormal production data based on Entropy Weight Method and TOPSIS Method

The paper is to build a mathematical model of the time series data recorded by the equipment in the production area from 00:00:00-22:59:59 on a certain day and the data used in the experiment is from the May Day Mathematical Contest in Modeling.According to the two characteristics of risky data sustainability and linkage, the Risky Data Detection Model based on partition and statistical analysis is established, and then dividing the abnormal group into non-risky data and risky data.After normalization, establishing a Comprehensive Evaluation Model based on Entropy Weight Method and TOPSIS Method, scoring the degree of abnormal on the data among each moment.
Bento

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor—it is largely relegated to proxies and hidden services—or to nimbly react to new forms of attack. Conversely, “non-anonymous” Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes.
Model Pengembangan Sistem Informasi Akademik Berbasis User Centered Design Menerapkan Framework Flask Python

Along with the rapid development of the times, one of which is in the field of information technology, this is increasingly demanding that we have to work faster. The ineffective technological development at SMK Negeri 1 Bitung makes students and teachers still using the manual system in academic activities, such as inputting grades, in providing information on school activities and so on. SMK 1 Negeri Bitung needs technology development solutions as school infrastructure. In this study, an Academic Information System was designed by implementing the Flask Python Framework technology. This study shows the performance of the system to help the process of academic activities to be better
A high-efficient and low-cost secure AMBA framework utilizing configurable data encryption modeling against probe attacks

In this paper, a high-efficient and low-cost secure AMBA framework utilizing the bus data encryption modeling is proposed to resist the probe attacks. By encrypting the confidential data flowing through the bus, the proposed configurable encryption model meets the security requirement of total SoC. Further, a data encryption pipeline with the third-level branch predictor is proposed to accelerate the encryption process. Finally, an SoC with the 32-bit proposed AMBA framework is established and validated with 55nm technology. Experimental results show that the proposed framework achieves 6152Mbps throughput, consumes 39547um2 area, and provides a stronger resistance compared to the other countermeasures.
Up to 50 dB Extinction in Broadband Single-Stage Thermo- Optic Mach-Zehnder Interferometers for Programmable Low-Loss Silicon Nitride Photonic Circuits

We demonstrate single-stage thermo-optic Si 3 N 4 interferometers with (46.1±2.5) dB average extinction ratio over 1460-1640 nm (≈50× of typical devices). We analyze performance requirements for creating reconfigurable high-quality-factor resonators in programmable circuits using such interferometers.
Secure High Dimensional Quantum Key Distribution Based on Wavelength-Multiplexed Time-Bin Encoding

We experimentally demonstrated high-dimensional quantum key distribution with wavelength-multiplexed time-bin encoding, with photon information efficiency up to 4.082 secure-key bits/photon and secure-key rate up to 237-kbit/s. Security will be verified with dual-basis Franson interferometers.
Forensic Artifact Finder (ForensicAF): An Approach &amp; Tool for Leveraging Crowd-Sourced Curated Forensic Artifacts

Current methods for artifact analysis and understanding depend on investigator expertise. Experienced and technically savvy examiners spend a lot of time reverse engineering applications while attempting to find crumbs they leave behind on systems. This takes away valuable time from the investigative process, and slows down forensic examination. Furthermore, when specific artifact knowledge is gained, it stays within the respective forensic units. To combat these challenges, we present ForensicAF, an approach for leveraging curated, crowd-sourced artifacts from the Artifact Genome Project (AGP). The approach has the overarching goal of uncovering forensically relevant artifacts from storage media. We explain our approach and construct it as an Autopsy Ingest Module. Our implementation focused on both File and Registry artifacts. We evaluated ForensicAF using systematic and random sampling experiments. While ForensicAF showed consistent results with registry artifacts across all experiments, it also revealed that deeper folder traversal yields more File Artifacts during data source ingestion. When experiments were conducted on case scenario disk images without apriori knowledge, ForensicAF uncovered artifacts of forensic relevance that help in solving those scenarios. We contend that ForensicAF is a promising approach for artifact extraction from storage media, and its utility will advance as more artifacts are crowd-sourced by AGP.
Improved Efficient Convolutional Neural Networks for Complex Scene Mask-Wearing Detection

<p indent=0mm>To solve the problem about low accuracy of mask wear detection under complex lighting and face lean conditions, a method of mask wear detection under intricate environment using efficient convolutional neural network is proposed, which uses pre-training such as hard negative mining to learn more samples of face feature, utilize multi-task convolutional neural networks (MTCNN) to estimate the possibility of face information, and get accurate face location. With attention mechanism in feature pyramid network, enhancing the weight of key points on human face, employing efficient neural network detection will be wore on mask-wearing detection as a simple binary classification problem. Under the environment of TensorFlow platform, not only data training, data preprocessing, but also the contrast experiment with AIZOO method are completed. A data set containing with 816 images is collected, marked and trained. During the data preprocessing, images are set as fixed size to reduce the amount of computation and promote the detection speed. Then, image enhancement algorithm is used to conduct distortion processing to improve the robustness of this model. On this basis, MTCNN is used to detect the face information in pictures, modify and normalize all data, then put them into neural network and the trained model to detection. The experimental results show that under complex conditions such as complex lighting and face tilt, the accuracy can reach 83% and 91% respectively, which means can accurately detect whether wearing a mask.