Reviewerly/exHarmony huggingface dataset:
A Personalized Video Synopsis Framework for Spherical Surveillance Video

Video synopsis is an effective way to easily summarize long-recorded surveillance videos.The omnidirectional view allows the observer to select the desired fields of view (FoV) from the different FoV available for spherical surveillance video.By choosing to watch one portion, the observer misses out on the events occurring somewhere else in the spherical scene.This causes the observer to experience fear of missing out (FOMO).Hence, a novel personalized video synopsis approach for the generation of non-spherical videos has been introduced to address this issue.It also includes an action recognition module that makes it easy to display necessary actions by prioritizing them.This work minimizes and maximizes multiple goals such as loss of activity, collision, temporal consistency, length, show, and important action cost respectively.The performance of the proposed framework is evaluated through extensive simulation and compared with the state-of-art video synopsis optimization algorithms.Experimental results suggest that some constraints are better optimized by using the latest metaheuristic optimization algorithms to generate compact personalized synopsis videos from spherical surveillance videos.
An Innovative multi-sensor data fusion method based on thrice deeply-fusion architecture for multi-object tracking

For the purpose of track maintenance in a cluttered environment with some false alarms, false dismissals and measurement disturbances, this paper presents a new multi-object tracking procedure with uncertainty of the source of the measurement data returned from multi-sensor. The tracking method is a thrice deeply-fusion approach constructed by a primary fusion based on an improved probabilistic data association filter (IPDAF), a secondary fusion with historical motion trajectories, and a thrice fusion with road-markings. It incorporates the existence probabilities of the individual tracks and the variable number of objects based on the Bayesian estimation theory, which can improve the tracking performances effectively in an environment with high clutter density. A binary 2-D assignment is adopted for the optimal data association, which is established as a nonlinear optimization problem. In the motion modeling, it introduces multiple measurement models for different sensors into the method. Then, the estimation could be performed with greater reliability. The computational efficiency is satisfying and it can be used for real-time application, which is verified by two real test scenarios.
Industry practice of configuration auto-tuning for cloud applications and services

Auto-tuning attracts increasing attention in industry practice to optimize the performance of a system with many configurable parameters. It is particularly useful for cloud applications and services since they have complex system hierarchies and intricate knob correlations. However, existing tools and algorithms rarely consider practical problems such as workload pressure control, the support for distributed deployment, and expensive time costs, etc., which are utterly important for enterprise cloud applications and services. In this work, we significantly extend an open source tuning tool – KeenTune to optimize several typical enterprise cloud applications and services. Our practice is in collaboration with enterprise users and tuning tool developers to address the aforementioned problems. Specifically, we highlight five key challenges from our experiences and provide a set of solutions accordingly. Through applying the improved tuning tool to different application scenarios, we achieve 2%-14% improvements for the performance of MySQL, OceanBase, nginx, ingress-nginx, and 5%-70% improvements for the performance of ACK cloud container service.
Auxiliary Modeling of Online Intelligent System of Hotel Management Apprenticeship by SaaS-based Technical Skills Platform

Auxiliary modeling of online intelligent system of hotel management apprenticeship by SaaS -based technical skills platform is presented in the paper. The process of clustering the relevance matrix ARM using the BEA algorithm is to gradually aggregate the attributes with high relevance through continuous row and column transformation, and finally maximize the value of the variable ME. The algorithm uses the core multi-tenant sharing virtual machine method to process the requests of all tenants, which can efficiently use the virtual machine and save the underlying resources. With the implementations of the model, the systematic performance is validated.
Breast Cancer Prediction using Standard Machine Learning Algorithms

Lots of women are suffering from Breast Cancer all over the world. Various data set about breast cancer is available online. Machine Learning algorithms are very useful in predicting breast cancer. In this paper six standard machine learning algorithms are applied on a data set for breast cancer prediction. These algorithms are giving better results while applied diferent data sets for different diseases. After testing these algorithms, it is observed that Logistic Regression is best with 98.5% accuracy for breast cancer prediction.
A Machine Learning Method for Early Detection of Breast Masses on Screening Mammography

Breast malignancy is the most frequent cancer among women and the second most prevalent cancer in the general population. With millions of breast screening tests performed every day throughout the world, radiologists are faced with a time-consuming chore of analysing and classifying them all, with the majority of exams proving to be normal instances.
Optimizing Feature Representation via A Nested Network for Object Segmentation

Automatic object segmentation based on artificial neural networks is a critical task in an array of real-world applications. Localizing and region segmentation is of particular interest, although typical approaches rely on complex networks and/or human interactions. Therefore, various complex networks suffer from suboptimal segmentation due to inaccurate feature extraction. This paper introduces a Multi-Gated Nested Network (MGN-net) that provides precise segmentation performance by capturing relevant contextual information via a channel gating mechanism. Results utilize challenging biomedical image databases, featuring MRI Brain and Chest X-ray images, are presented. The results show that the MGN-net approach subjectively and objectively performs favorably compared to multiple state-of-the-art methods, such as the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -net and U-net networks.
An Evolutionary Multitasking Method for Multiclass Classification [Research Frontier]

As an important research topic of machine learning, multiclass classification has wide applications ranging from computer vision to bioinformatics. A variety of multiclass classification algorithms with promising performance have been proposed. Among them, the decomposition-based algorithms have shown their competitiveness, since they transform the original problem into several easily solved binary classification sub-problems. Unlike existing decomposition-based algorithms which tackle each sub-problem independently, this paper suggests an evolutionary multitasking method, named EMT-MC, for multiclass classification, where the concept of multitasking is introduced to achieve the multiclass classifier with better quality. To be specific, in EMT-MC, each binary classification sub-problem is firstly viewed as a task. Then, during the evolution, the tasks with low performance (termed “ill-solved” tasks) are aided by some well-selected “assisting” tasks by using the evolutionary multitasking learning. This not only ensures that the useful information in “assisting” tasks can be transferred into those “ill-solved” tasks, but also helps them to achieve classifiers with higher accuracy. Numerical experiments on different multiclass classification datasets demonstrate the superiority of the proposed method over the state-of-the-art algorithms.
Breast Cancer Prediction using Deep Network Model with Multi-Modal Data Fusion

Breast cancer is an illness that significantly influences women’s health. It is regarded as the leading cancer mortality. Accurate identification and pharmacological intervention are critical for lowering the mortality rate from breast cancer. In the latest days, deep learning methods have been widely used for reliable illness detection, with multi-model fusion being one of the most commonly used.Nevertheless, decision trees with lower categorization quality and superior similarities are created during training process affecting the model’s overall classification efficiency. A hierarchical multi-model is made in this research. The hierarchical clustering approach applies the concept to decision trees by assessing the resemblance among all the decision trees.Sample trees are chosen from separated groups to build the hierarchical randomization with lesser commonality and good accuracy.This report presents the use of the Wisconsin Diagnosis Breast Cancer (WDBC) database and samples from UCIrepository. The suggested method’s performance was measured using accuracy, precision, sensitivity, selectivity, and AUC (Area Under ROC Curve).The experimental results demonstrate that the segmentation depends on the multi-modelas a subset of features approach achieves the best accuracy of 97% and 97% when compared to others. This study’s strategy is a valuable tool for identifying prostate cancer.
FeatureExtractor: A tool for extracting key input features of code intelligence models

<h2>Abstract</h2> Although becoming successful in modeling source code, deep neural models are challenging to interpret. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety–critical applications. Therefore, we propose a tool, referred to as FeatureExtractor, that aims at improving the transparency and explainability of opaque models. Given an input program and a code intelligence model, the tool applies a program reduction technique to reduce the size of the input program while preserving the prediction of the model. The goal is to identify key input features from reduced programs for explaining the model's prediction.
Perturbed Gradients Updating within Unit Space for Deep Learning

In deep learning, optimization plays a vital role. By focusing on image classification, this work investigates the pros and cons of the widely used optimizers and proposes a new optimizer: Perturbed Unit Gradient Descent (PUGD) algorithm with extending normalized gradient operation in tensor within perturbation to update in unit space. Via a set of experiments and analyses, we show that PUGD is locally bounded updating, which means the updating from time to time is controlled. On the other hand, PUGD can push models to a flat minimum, where the error remains approximately constant, not only because of the nature of avoiding stationary points in gradient normalization but also by scanning sharpness in a unit ball. From a series of rigorous experiments, PUGD helps models to gain a state-of-the-art Top-1 accuracy in Tiny ImageNet and competitive performances in CIFAR- {10, 100}. We open-source our code at link: https://github.com/hanktseng131415go/PUGD.
What have we learned from OpenReview?

Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 11,915 submissions and their 41,276 reviews from the OpenReview platform. We also collect these submissions’ citation data from Google Scholar and their non-peer-reviewed versions from arXiv.org. By acquiring deep insights into these data, we have several interesting findings that could help understand the effectiveness of the public-accessible double-blind peer review process. Our results can potentially help writing a paper, reviewing it, and deciding on its acceptance.
IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.
Sentiment Analysis to Find Sentence Polarity on Tweet Data

In this present age, everyone uses social media. Whilst ideas spread through these mediums can be colourful, there are certain views of people which are shared with a tone of negativity, mischief and offensiveness to them as well. One of the most used of such social media platforms include Twitter, which is a platform where messages with limited word limit are sent by the people. This present work, thus, is about analysing the polarity of a tweet by using millions of data achieved through Natural Language Processing (NLP). We collected a large dataset of tweets which are marked by levels of polarity from negative to positive. We use Machine Learning algorithms on this dataset to detect the polarity of the tweet. Here we have used Multinomial Naïve-Bayes, Complement Naïve-Bayes and Logistic Regression classifier to find the polarity of tweets. The dataset size is 1.6 million and we got the best result using Logistic Regression. The highest accuracy is 78.05%.
Content-Based Ranking of Web Documents by Normalizing Link Score

As the precious and worthy information associate several domains are obtainable in the form of hypertext in different Web resources. Search engine provides a medium to retrieve this information according to the need of the user. An interface to access the Web resources listing the search result according to the most relevant of the user query is a major issue. In this paper, a new a set of techniques proposed for giving better performance of Google search engines based on the content of the Web pages and the links associated with them. The primary goal is to design and upgrade the technique of ranking based on the searched link by content analysis of Web. This technique for providing robustness of search in the context of Web contents-based approach tried to rearrange the search results in increasing order according to the relevancy with the search query by using normalization term frequency.
Energy Efficient Model for Balancing Energy in Cloud Datacenters Using Dynamic Voltage Frequency Scaling (DVFS) Technique

Background and Purpose: Nowadays energy awareness embodies a great experience for cloud infrastructures as the implementation of cloud computing is becoming more in demand. Our main aim is to reduce power consumption for computation purposes. Method and Material Used: Now a day's cloud computing have attained surprising outcomes in power saving. Our work is based on dynamic voltage frequency scaling (DVFS) methods with different parameters. Finally; we have performed the DVFS method on the cloud data center using cloudsim simulator to reduce power consumption by the data center. Result: We have used the data center available on the amazon web services (AWS) cloud platform for the experiment. We have calculated total power consumption without applying the Dynamic Voltage Frequency Scaling method that is 751.20 Wh and total power consumption with the Dynamic Voltage Frequency Scaling method that is 589.20 Wh. Which is 21.57% lesser than without the Dynamic Voltage Frequency Scaling method. Conclusion: Storage arrangement embodies a vital factor of power use in the data centers. So this paper, in the cloud storage environment, reviews the technologies, and methods utilize in power-aware storage procedures arrangements in the data center. This paper presents a method named Dynamic Voltage Frequency Scaling (DVFS) model for cloud optimization strategy.
Blockchain-Based Efficient and Secured Framework for Logistic Management

Logistic operation involves the process of managing the flow of goods (household items, gadgets, food, and so on) from one point to another which is globally expanding due to the industry 4.0 revolution. Despite the benefits of the new logistic services, they also pose a lot of risks for personal information disclosure as their information (customer address, name, and credit card number) is shared or handled by the logistic company. As multiple organizations continue to join the supply chain in logistic operation, more complication arises, and the company finds it difficult to handle operation due to lack of end-to-end visibility, thereby leading to delay in decision-making. To address these challenges, Blockchain as an emerging technology with its proven security and efficiency capabilities will be used to provide the solution to privacy and efficient issues in logistic management operations. We have highlighted the Blockchain use case in logistic management and have done some literature surveys on the use of Blockchain in the management of logistic and supply chains. Blockchain can enhance the efficiency of logistic management by engaging with its decentralized network which will help enhance end-to-end visibility and with its Hyperledger Fabrics technology which will guarantee optimal security against unauthorized access or alteration of customer information and provide security to logistic company information security.
Producing and Consuming Instructional Material in Manufacturing Contexts: Evaluation of an AR-based Cyber-Physical Production System for Supporting Knowledge and Expertise Sharing

Fast-paced knowledge and expertise sharing (KES) is a typical demand in contemporary workplaces due to dynamic markets and ever-changing work practices. Past and current computer supported cooperative work (CSCW) research has long been investigating how computer technologies can support people with KES. Recent claims have asserted that augmented reality- (AR-)based cyber-physical production systems (CPPS) are poised to bring significant changes in the ways that KES unfolds in manufacturing contexts. This paper scrutinises such claims by implementing a short-term evaluation of an AR-based CPPS and assessing how it can potentially support (1) the generation of AR content by experienced production workers and (2) the visualisation and processing of such content by novice workers. We, therefore, contribute a user study to the CSCW community that sheds light on the use of a particular type of AR-based CPPS for KES in industrial contexts.?
Handoff and Deposit: Designing Temporal Coordination in Cross-Device Transfer Techniques for Mixed-Focus Collaboration

When working together, people frequently share information with each other to enable division of labour, assistance, and delegation of responsibility. The literature has explored both synchronous and asynchronous transfer techniques, known as Handoff and Deposit, respectively. However, current cross-device environments tend to only provide a single mechanism. Moreover, we have little understanding of the impact of different techniques on collaborative process. To understand how Handoff and Deposit may be designed to support complex sensemaking tasks, we followed a Research through Design process to iteratively design Handoff and Deposit techniques using paper and digital sketches and high-fidelity prototypes. We consulted the HCI literature to corroborate our findings with studies and descriptions of existing cross-device transfer designs and to understand the potential impact of those designs on mixed-focus collaboration. We learned that as we move away from a restricted physical workspace and leverage the flexibility of digital personal devices, there is a large design space for realizing cross-device transfer. To inform these designs, we provide five design considerations for cross-device transfer techniques: Transfer Acceptance, Action Dependencies, Immediate Usability, Interruption Potential, and Connection Actions.
How Should the Agent Communicate to the Group? Communication Strategies of a Conversational Agent in Group Chat Discussions

In online group discussions, balanced participation can improve the quality of discussion, members' satisfaction, and positive group dynamics. One approach to achieve balanced participation is to deploy a conversational agent (CA) that encourages participation of under-contributing members, and it is important to design communication strategies of the CA in a way that is supportive to the group. We implemented five communication strategies that a CA can use during a decision-making task in a small group synchronous chat discussion. The five strategies include messages sent to two types of recipients (@username vs. @everyone) crossed by two separate channels (public vs. private), and a peer-mediated strategy where the CA asks a peer to address the under-contributing member. Through an online study with 42 groups, we measured the balance of participation and perceptions about the CA by analyzing chat logs and survey responses. We found that the CA sending messages specifying an individual through a private channel is the most effective and preferred way to increase participation of under-contributing members. Participants also expressed that the peer-mediated strategy is a less intrusive and less embarrassing way of receiving the CA's messages compared to the conventional approach where the CA directly sends a message to the under-contributing member. Based on our findings, we discuss trade-offs of various communication strategies and explain design considerations for building an effective CA that adapts to different group dynamics and situations.
When Does it Become Harassment?

Calling out, a phenomenon where people publicly broadcast their critiques of someone to a larger audience using, has become increasingly common on social media. However, there has been concerns that it could develop into harassment, deteriorating the quality of public discourse by over-punishing individuals for minor transgressions. To investigate this phenomenon, we interviewed 32 Twitter users who had been called out, had called out, or had witnessed a calling out on Twitter. We found that a key determining factor that distinguishes criticism from harassment was the callee's ability to respond to or engage with the criticism, and that different stakeholders hold different perspectives toward how online harassment is defined. We also discovered that the distinction between callers and callees was not absolute, and that there was high interchangeability of roles both within and across events. Through these findings, we discuss design implications for the platform in promoting healthy discourse while preventing toxic behavior on social media.