Reviewerly/exHarmony huggingface dataset:
Collaborative Framework of Cloud Transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming

With the rapid development of high-speed Internet access and popularization of high-performance smart devices, past decade has witnessed the great development of crowdsourced live streaming (CLS) service. Transcoding and video distribution are essential in CLS service to guarantee viewer engagement. Large CLS systems gradually migrate their services to multi-cloud platforms. However, highly dynamic viewers' requests influence transcoding and CDN distribution decisions, eventually lead to fluctuation in QoE and increase in operational cost. It is challenging for the CLS system to serve viewer's requests in multi-cloud platforms with fluctuation in cloud transcoding and distribution performance. In this paper, we propose a collaborative framework of cloud transcoding and distribution supporting CLS service. First, we define cost model and QoE model in multi-cloud platforms, comprehensively considering cloud transcoding and distribution. Second, we propose a collaborative cost-efficient approach based on multi-agent decision model. We use a G-Greedy exploration approach to learn what actions to take by exploration and exploitation based on the state of current environment. The trace-driven experiments demonstrate that our proposed approach is cost-efficient and QoE-maintained and can reduce operational cost compared with alternatives (5.37%-21.21%) while maintaining QoE of viewers.
Efficient 2D Keypoint-based Hand Pose Estimation

Human hands are regarded as important tools for communicating with others owing to their variety and flexibility. As the development of convolutional neural networks (CNNs) in image recognition improves, many researchers have begun to apply CNNs to the research of hand pose estimation, and they have made remarkable breakthroughs. Despite this, with the volume of mobile phones and other portable devices decreasing, if we want to apply the estimation model to such devices, we need to be able to use the least amount of model parameters and calculation cost to achieve the purpose of hand pose estimation.In this paper, we aim to achieve the purpose of hand pose estimation by using fewer parameters than the state-of-the-art model. To use the least number of possible parameters, we use a 2D CNN as the backbone. In our model, a variety of operations to reduce the number of parameters are used, finally resulting in the number of parameters being reduced to 1.5M. This is 21% less than the state-of-the-art model [1]. In addition, our model can achieve 48 frames per second easily in real time. Finally, we test our model on the common hand pose estimation dataset RHD. From the experimental results, considering the accuracy and model parameters, we have found that our method can reach 0.9713 on PCK@0.2, and outperform the current model with parameters less than 10M.
TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively.
Investigation of SLA Management in Cloud Computing and Future Directions

Cloud computing is an on-demand service provisioning technology in which computer and networking resources like software applications, computation power, storage space and software based network devices are provided to the customer under a service level agreement (SLA). In dynamic environment of cloud computing service level agreement and its management is very crucial component from customer&#x0027;s trust and long term business opportunity point of view. The SLA management process includes many activities such as SLA Negotiation, SLA Pricing and SLA Monitoring etc. Many SLA management approaches have been proposed by researchers. This paper systematically investigates different research publications branching form SLA management into different directions and assesses the relative extent of research done so far in each direction. The analysis reveals that latest trends in SLA related research are SLA violations, Auditing, and development of SLA frameworks.
Assessing Cognitive Load for Junior Software Engineers: A Mixed-Method Study

The rising complexity of software systems, combined with the proliferation of new tools and techniques, has challenged Software Engineering educators to refine pedagogical approaches to preparing junior developers for the practice. Despite a growing body of literature devoted to computer science education in general, there exists only scant studies from the software engineering perspective. In this paper, we examine this concern through the lens of perceived cognitive load. A mixed mode study was conducted within a senior level capstone course. Our results suggest that while significant cognitive load was experienced throughout the entire agile process (design, project management and implementation), participants found the latter most demanding. There were indicators that mitigating strategies mindful of working memory could be effective, and results indicated a significant requisite to scaffold the skill sets applied in navigating documentation, and determining appropriateness for task requirements.
SA_3AM: A Novel Sentiment Analysis Approach Integrating Three-Level Attention Mechanism

Nowadays, social networks have become the primary ways for people to communicate in their daily lives. Due to the great value of short texts generated by social networks, sentiment analysis on them has been attracting more attention from academics and business. However, emojis, which have taken the world by storm, have been challenging sentiment analysis because of their flexible ability to convey emotions. Therefore, this paper proposes a novel sentiment analysis approach by integrating three-level attention mechanism, namely SA_3AM. Particularly, SA_3AM can be divided into three phases: <tex>$i$</tex>) attention-oriented word sentiment semantics learning, where SA_3AM utilizes Bi-LSTM to capture bidirectional semantic dependencies within various dimensions conveyed in pure words; ii) attention-oriented emoji sentiment seantics mining, where SA_3AM employs column max-pooling to obtain rich sentiment semantics of emojis by fusing the sentiment dependencies of words; and iii) attention-oriented microblog sentiment semantics calculating, where SA_3AM uses attention mechanism to mine implicit intentions conveyed by microblogs. A real-world dataset Weibo_SL containing 6,436 Chinese microblogs from January 2017 to July 2020 was built to evaluate our SA_3AM. Our experimental results not only show that SA_3AM outperforms the peers, but also demonstrate the feasibility of the dataset Weibo SL.
A method for identifying customer business application scenarios of cloud dedicated access

In order to reasonably allocate bandwidth in different service application scenarios to meet customer requirements, this paper proposes a method for identifying customer business application scenarios. First, multiple traffic features and time features are constructed to analyze service application scenarios of cloud dedicated access better. Then, feature set is selected based on the method of Pearson correlation coefficient. Finally, Tri-training model is used to identify customer service application scenarios of cloud dedicated access. Compared with other models, the accuracy, precision and recall of Tri-training model are all higher than those of the other two models, which proves the reliability of the Tri-training model.
Enabling Conflict-free Collaborations with Cloud Storage Services

Cloud storage services (e.g., Dropbox) have become pervasive in not only simple file sharing but also advanced collaborative file editing (collaboration for short). Using Dropbox for collaboration is much easier than SVN and Git, thus greatly facilitating common users. In practice, however, many Dropbox users are perplexed by unexpected collaboration conflicts, which severely impair their experiences. Through various benchmark experiments, we unveil the two root causes of collaboration conflicts: 1) Dropbox never locks an edited file during collaboration; 2) Dropbox only guarantees eventual data consistency among the collaborators, significantly aggravating the probability of conflicts. In this paper, we attempt to enable conflict-free collaborations with Dropbox-like cloud storage services. This attempt is empowered by three key findings and measures. First, although the end-to-end sync delay is unpredictable due to eventual consistency, we can always track the latest version of an edited file by actively resorting to the cloud via certain web APIs. Second, although all application-level data is encrypted in Dropbox, we can roughly deduce the sync status from traffic statistics. Third, applying a couple of useful mechanisms (e.g., distributed architecture and data lock) learned from Git, we can effectively and efficiently avoid collaboration conflicts-of course, this requires re-implementing Git mechanisms in cloud storage services with minimum overhead and user interference. Integrating above efforts, we build the ConflictReaper system capable of helping users automatically avoid almost all collaboration conflicts with affordable network and computation overhead.
Sentiment Analysis Method Based on Deep Learning in Adversarial Environment

With the continuous increase of SaaS (Software as a service) market demand, the competition of SaaS platforms is also increasing. Using deep learning to conduct sentiment analysis on users&#x0027; reviews can provide a better reference for users to choose SaaS services. But some recent studies have pointed out that deep learning models are easily affected by attackers. When an attacker adds a very small disturbance to the input sample, the deep learning model will give wrong classification results. In order to further study the vulnerability of deep learning models in the field of sentiment analysis, we designed a model of review intention recognition based on DIKW Pyramid, which can recognize the intention of the review, and we also propose an attack algorithm based on synonym substitution of evaluation words and evaluation reasons, which can generate usable and efficient adversarial samples under black box attack. We designed and implemented adversarial attacks on three deep learning models of LSTM, Bi-LSTM and CNN, and combined with Random adversarial attack algorithm, population based adversarial attack algorithm and DeepWordBug adversarial attack algorithm. The experimental results proved that the adversarial attack algorithm based on synonym substitution of evaluation words and evaluation reasons is better than the other three attack algorithms in terms of attack success rate and word replacement rate.
Comparative Analysis of Classifier Methods for Effort Estimation

Software effort estimation is a process to predict effort for the development of quality software. Development of software is required to do a correct estimation. While implementing software it creates a problem if it is not estimated correctly. To predict a cost it is important to predict effort first. It is done on the requirement based complexity of the software which is yet to be matured. In this paper, classification technique is used to estimate effort on four datasets. The datasets taken into consideration are cost and effort estimation datasets. Such datasets are- Albercht, china, Cocomonasa, and kitchenham. A comparative study of classification techniques is presented in this work. Broadly WEKA tool has four classification categories such as functions, lazy, meta, and tree. To compare these categories and its classification techniques five performance measures are used such as correlation coefficient, mean absolute error, root mean square error, relative absolute error and relative root squared error. Out of all categories classification models, SMO gives best results on all the datasets. An analysis of distinct classifier algorithms is used for the task of effort estimation.
Uncertainty Estimation for Twitter Inference

Twitter provides a platform for users to express their opinions in the form of Twitter messages called tweets. Analyzing tweets from a specific city or demographical group requires geographical or demographical information. Unfortunately, most Twitter users do not provide these details in their profiles. To overcome this challenge, tools have been developed for inferring users' geographical and demographical information from Twitter data. Using inference results is risky due to the lack of uncertainty estimation of these results. Here, we present a framework to estimate uncertainties of Twitter inference results. The effectiveness of this framework is verified in experiments.
Player Modeling using Behavioral Signals in Competitive Online Games

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player&#x2019;s behavior such as goals, strategy, and expertise when making assignments.
Evaluating the Effectiveness of Various IR Models for Requirements Traceability

A frequently asked question during software development life cycle is: &#x201C;Which source code document(s) implement requirement X?&#x201D; Requirements traceability (RT) involves uncovering links between source code entities and requirement specification documents. Information Retrieval (IR) techniques are used to perform requirements traceability. There are not many studies that determine the effectiveness of various IR techniques in terms of precision, recall and ranking on big data. In this paper, we compare standard vector space model, probabilistic approach based Jensen-Shannon similarity model and dimensionality reduction based Latent Semantic Indexing model on 10 open source software systems in terms of precision, recall and ranking.
Triple Extraction of Knowledge Graphs with DGWPN

Information extraction aims to provide required high-quality RDF triples for knowledge graph which is the basis of knowledge construction. The current two mainstream methods include a joint model concentrates on using deep learning to extract entities and relationships and a pipeline model based on sequential extraction. The joint method will be limited by the number of parameters and the types of parameters, this will lead to the loss of calculation results. In the pipeline model, the result of entity extraction needs to be substituted into the relationship classification, and this usually causes the interference of redundant entities to the relationship classification. For the joint training model, in the training process, entities and relationships are extracted at the same time. The results of extractions are continuously transmitted to each other during training and influence each other. If a certain parameter limit is deleted during the extraction of one type of result, the extraction result of the other side will also be deleted of the same type, which will eventually cause some results to be lost. For the pipeline model, if entities are first extracted, then relations are classified. In the process of entity extraction, invalid entity pairs with no matching relationship will be generated. This causes the convolution training speed to slow down. In this paper, in order to solve such problems in entity and relationship extraction, we propose an entity relationship extraction model includes Dilated Gate unit convolutional layers with Weight Pointer Networks, named this structure as DGWPN. This is helpful to obtain more receptive fields and multiple triples models. The advantages of our model are demonstrated through the comparison of multiple groups of experiments. The experimental results show that our method outperforms other current different baseline models on the SKE and CHIP data sets, about 5.3% higher than the current mainstream Bi-LSTM with CRF model on precision, and about 5.6% higher on F1 value, which indicates that our model can extract more high-quality triples for the construction of knowledge graphs.
Evaluation of distance learning and students’ achievement in Saudi Higher Education Institutions (HEIs)

The COVID-19 pandemic has forced the world to adopt online platforms and distance learning in most educational institutions. Multiple controversies have been discussed concerning the advantages and disadvantages of distance learning. This paper explores the distance learning process in Saudi Higher Education Institutions (HEIs) compared to in-person learning. Additionally, this paper investigates the impact of the COVID-19 pandemic on Saudi HEIs. This study utilized an online survey tool to examine the relationship between the distance learning process and students&#x2019; achievement. The participants were 189 faculty members recruited from different Saudi HEIs. The questionnaire was composed of two sections; 1) section one where the demographic data of the participants were collected, and 2) section two where the participants&#x2019; perspectives concerning distance learning contributions, in-person learning contributions, feelings about distance learning, and experience after distance learning were reported. Based on their viewpoints, there is no important difference between distance learning and in-person sessions based on students&#x2019; achievement levels. Additionally, there was no difference in faculty members&#x2019; experiences when they used online platforms during the distance learning process. Our results demonstrate that academic institutions may consider training the faculty members to use technology and online platforms in lectures, communication with students, and other academic activities.
An Approach on an Autonomous Grading System using Adaptive Booster Gradient Technique

Due to the rapid up-gradation in the technology, there is a voluminous diversification in computation, size and framework of interconnected devices made a requirement of an autonomous computing system. The purpose of managing such uprising problems without the intervention of humans can be achieved with the usage of autonomic computing. In this paper the adaptive boosting technique is applied on the grading system to get the desired and applied specifications. It is done after applying the infinite restricted Boltzmann machine using adaptive boosting technique to achieve the grades. The performance measures used are recall, precision and accuracy
Research into machine learning algorithms for the construction of mathematical models of multimodal data classification problems

Currently, machine learning algorithms (ML) are increasingly integrated into everyday life. There are many areas of modern life where classification methods are already used. Methods taking into account previous predictions and errors that are calculated as a result of data integration to obtain forecasts for obtaining the classification result are investigated. A general overview of classification methods is conducted. Experiments on machine learning algorithms for multimodal data are performed. It is important to consider all the characteristics of metrics and features when using ML algorithms to predict multimodal data. The main advantages and disadvantages of Gradient Boosting, Random Forest, Logistic Regression and XGBoost algorithms are analyzed in the work.
Pedagogical models and their application to pedagogical strategies for citizenship education

The following article aims to carry out a documentary review of the pedagogical models in order to establish one that adapts to the needs of current education, specifically the citizenship training of individuals. With a qualitative methodology, the article has first a theoretical part that focuses on the different pedagogical models, followed by an argumentative part in which we propose the experimental constructivist model as the basis of strategies for the formation of current citizens. It concludes with the analysis of the model mentioned as the proper way of educating to encourage more the focus of human beings on themselves and their community
Chaotic Map-based Image Encryption for Vehicle Registration Certificate Application: A Backend Subsystem Design

This paper proposed a backend API subsystem for image encryption to provide chaos maps and storage services to securely upload and store documents for the Vehicle Registration Certificate extension by using a mobile app. The proposed system helped to make the Vehicle Registration Certificate extension more effectively done because it enabled vehicle owners to do it from their homes. This paper tried to approach the problem by using Henon's map and Arnold's cat map algorithm to encrypt image documents and using a database service to store the documents. The result showed that for image size between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$50\ \text{pixels} \times 50\ \text{pixels}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1000\ \text{pixels} \times 1000\ \text{pixels}$</tex> , Arnold's cat map algorithm's API response time grew exponentially to image size, while the Henon's map's response time grew significantly slower with a coefficient factor of 0.368 (weak correlation). Arnold's cat map's response time also grew proportionally to the number of iterations that were used as the secret key, so there was a trade-off between security and response time. This paper concluded that Henon's map algorithm could be used to encrypt image-based documents and also served as proof of concept, while Arnold's cat map algorithm, which had a slow response time, was not feasible to be implemented in a mobile app environment.