Reviewerly/exHarmony huggingface dataset:
Autonomous runtime consumer interface generation and deployment for service interoperability

The new Industry 4.0 approach contributes to addressing evolving industrial requirements, which are continuously fueled by changing market demands. This situation leads to growing complexity and considerable increases in development and maintenance costs. A significant portion of engineering time is dedicated to the integration and interconnection of heterogeneous components. The solution for interoperability issues and the reduction in the associated engineering time are thus key tasks for increasing productivity and efficiency. Therefore, this paper provides an engineering approach to create interoperability among heterogeneous systems in Service Oriented Architecture (SOA) based environments by means of generating an autonomous consumer interface code at runtime. This paper aims to present a novel interoperability solution. The proposed approach makes use of service interface descriptions to dynamically instantiate a new autonomously generated interface that solves service mismatches between a provider and a consumer. This paper includes the definition of the consumer interface generator system, as well as the benefits and challenges associated with the autonomous generation and deployment of a consumer interface code at runtime. To illustrate the potential of this approach, a prototype of the system, which shows positive results, is implemented and tested.
<i>Metered Boot</i>: Trusted Framework for Application Usage Rights Management in Virtualized Ecosystems

The adoption of virtualization and cloud computing technologies have revolutionized how services and applications can be developed, deployed, and operated to achieve better elasticity, flexibility, and scalability. Multiple stakeholders can be involved for providing online services; each of them plays one or more roles (i.e., service operator, application vendor, and infrastructure provider) to create a customized operating model based on the business requirements. The operating model changes from one business to another, and it may even change at different stages of the same business. A trusted relationship among stakeholders for secure information exchange is the key to enable such flexibility. However, traditional usage compliance methods (e.g., in-person audit, dynamic licensing, and subscription) lack explicit trust among involved parties and the flexibility and scalability to support dynamic sizing of services and applications with low overhead. In this work, we argue the need for a new trust framework to manage application usage rights and propose Metered Boot to provide trusted, capacity/usage-based usage rights management for services and applications deployed in virtualized environments. Metered Boot decouples application workload instantiation for service operators, usage rights governance for application vendors, and resource provisioning for infrastructure providers. We leverage cryptoprocessors (e.g., Trusted Platform Module (TPM)) on commodity servers to generate trusted proofs which are managed by efficient cryptographic construction, Merkle hash tree, for usage rights compliance. We integrated our framework with OpenStack and demonstrate that Metered Boot is able to achieve high scalability and low overhead for instantiating virtual network functions (VNFs).
A Multi-modal System for Video Semantic Understanding

This paper proposes a video semantic understanding system based on multi-modal data fusion. The system includes two sub-models, the video classification tag model (VCT) and the video semantic tag model (VST), to generate classification tags and semantic tags for videos respectively. The VCT model uses bidirectional LSTM model and Attention mechanism to integrate the video features, which can effectively improve the model result than other methods. The VST model directly extracts semantic tags from text data with the combined model of ROBERTA and CRF. We implemented the system in the CCKS 2021 Task 14 and achieved an F1 score of 0.5054, ranking second among 187 teams.
Prototype Design of a Multi-modal AI-Based Web Application for Hateful Content Detection in Social Media Posts

Hate-Speech Detection and filtering of hateful content is an important aspect of any social media post. The ever increasing amount of content posted daily on social media has led to an excessive amount of digital hate being spread in the form of posts, images and comments. The proposed system is developed in order to act as a tool for determining if a particular social media post is hateful and is aimed to aid any benign social media user who has been affected by hate speech and wants to report it. The proposed system uses a multimodal artificial intelligence based approach by classifying different formats of posts, i.e., images and comments or captions separately. An ensemble convolutional neural network architecture is used for this classification, thus, proving to be a strong tool for finding evidence of any prevalent hate speech. This system is tested using the Likert scale for its user interface, accuracy and utility. Based on the result this paper proposes a prototype design of a web application which can be used for hateful content detection.
Cross Domain Authentication Based on Blockchain for Mobile Terminals in Edge Computing Environment

The advent of the Internet of everything era has led to a dramatic increase in the number of terminal devices. With frequent interactions between devices and information service entities in different communication domains, identity authentication faces a series of security issues and challenges. For the cross-domain authentication problem of heterogeneous terminal devices accessing network services in different communication domains in IoT, a blockchain-based cross-domain authentication scheme is proposed in the edge computing environment. The identity of the terminal device is generated by the smart contract in the blockchain, and the Hash value of its identity is stored in the decentralized blockchain, and the terminal device identity can roam randomly in different communication domains with one registration, which can realize effective identity authentication.
Relation Extraction with Sentence Simplification Process and Entity Information

Graph-based Knowledge Bases (KBs) are composed of relational facts that can be perceived as two entities, called head and tail, linked via a relation. Processes of constructing KBs, i.e., populating them with such facts, as well as revising and updating them are of special interest. These should be performed automatically, especially in the case when the main sources of facts are textual documents. For this reason, a task of Relation Extraction (RE), i.e., predicting a relation that links two entities mentioned in a sentence, is one of the most important activities. Using RE processes, new relational facts can be extracted, and KBs can be built and updated using unstructured information. In this paper, we propose a novel procedure for RE. It is based on a sentence distilling technique that works on dependency trees and removes noisy tokens from sentences while preserving the most relevant and useful ones. In addition, the proposed procedure utilizes information about types of linked entities, it means types of relations’ heads and tails. Our neural network model using processed and new input information is evaluated on the widely used NYT dataset and compared to other state-of-the-art RE methods. Experimental results show the effectiveness of the proposed procedure against other methods.
Improvement and evaluation of daily global solar radiation decomposition models using meteorological parameters: A case study for Turkey

In this study, some of the commonly used daily global solar radiation decomposition models are improved by considering ambient temperature, relative humidity, and air pressure to estimate hourly global solar irradiance from measured daily global solar radiation in Eskişehir, Turkey. Accordingly, the identified functions of the considered parameters are integrated into the selected daily global solar radiation decomposition models. The accuracy of the improved models is evaluated based on the measured monthly average hourly global solar irradiance values. In addition, the monthly average power values provided by the original and improved models are compared with the measured power output values. The results indicate that the improved models have significantly higher performance in the accuracy of both global solar radiation estimation and power output forecasting in a specific time interval. Therefore, the proposed methodology can be used to improve any global solar radiation decomposition model based on locally measured data for different regions.
Deletion and Insertion Tampering Detection of Digital Audio Based on Enf Fluctuating Super Vector

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Reinforcement Learning at the Cognitive Level in a Belief, Desire, Intention UAS Agent

We have proposed a lane-based approach to handle large-scale Unmanned Aircraft Systems (UAS) traffic management [1]. Given the number of UAS in the airways simultaneously, it is necessary to imbue each UAS with a learning capability at the cognitive level so that it can optimize its performance in the face of weather, airway congestion and other contingencies. Here we describe a Belief, Desire, Intention (BDI) architecture for the representation and reasoning over cognitive states, where these beliefs include goals for the UAS such as staying in its lane, on heading and at the desired speed. Such goals are represented as logical propositions, and if false, they may be selected as goals (intentions). We apply reinforcement learning to optimize the selection of a plan to achieve the goal of the UAS. That is, a policy is determined which given the cognitive state of the UAS, including for example weather conditions, a plan is selected which achieves the goal with minimal cost and maximal effectiveness.
Attribute attack graph generation method based on attack stage

Aiming at the problem that the current attack graph generation method cannot cope with complex attack types and the data is missing, an attribute attack graph generation method based on the attack stage is proposed. Multi-stage analysis of the vulnerability of a single node can be used to simulate penetration more accurately. process. First, the vulnerability attack model technology and attack matrix division technology are used to infer the attack stage of the vulnerability; then, an attribute attack graph generation framework based on the attack stage is proposed; finally, simulation experiments are carried out through intranet data to verify the effectiveness of the method.
Design of natural language model based on BiGRU and attention mechanism

This paper implements a natural language model design based on bidirectional GRU (BiGRU) and attention mechanism. Natural language model is an important part of natural language processing and a typical prediction problem. BiGRU is a bidirectional GRU, an improved LSTM network. It can not only make effective use of context information, but also reduce training parameters and improve efficiency. The attention mechanism focuses on the most relevant information according to the weight distribution. The algorithm is implemented based on Tensorflow platform. Words are expressed as dense vectors by word2vec method. In order to prevent over fitting, Dropout mechanism is superimposed in the model. Finally, Perplexity is used to evaluate the performance of the model. Then the BiLSTM model is also tested. The results show that BiGRU model has better performance in Perplexity and learning efficiency.
Quantum Fisher information of N-qubit W and GHZ superposition state under nonlocal operation

From the perspective of nonlocal operation, we investigate the quantum Fisher information of N-qubit W and GHZ superposition state. Taking advantage of the general Ising model and Lipkin–Meshkov–Glick model, we analytically present the QFI of N-qubit superposition state. Evidently the traditional fixed QFI is changed and largely increased. As an paradigm, we numerically study the QFI of 3-qubit superposition state with respect to ratio $$\alpha $$ and interaction strength $$\varepsilon $$ , and find the trends of QFI in both models are similar. There always exists a turning point for QFI, and the $$\alpha $$ is around 0.7. Interestingly, for the interaction strength $$\varepsilon =1$$ in the Ising model, the well-known Heisenberg limit is almost achieved for all $$\alpha $$ . Moreover, under the same interaction strength, we compare the QFI of N-qubit superposition state in both models. With the increasing qubits involved, the $$\alpha $$ for maximal QFI in the Ising model is changed from close to 1 to the middle region, while it is different in Lipkin–Meshkov–Glick model and the $$\alpha $$ is close to 1.
The Work of Infrastructural Bricoleurs in Building Civic Data Dashboards

The Historic Westside Universities Alliance Data Dashboard was created to meet the data equity needs of a group of resource-constrained communities in the Westside neighborhoods of Atlanta. We observed the development of this dashboard by participating as ethnographers and developers of its public safety module. Our ethnography and subsequent situational analysis of the dashboard's infrastructuring process led us to the work that individual stakeholders do in bringing such a dashboard to fruition. These stakeholders, whom we call infrastructural bricoleurs, operate through principles of situated knowledge, partial perspectives, limited power, and located accountability. Our analysis, which builds on concepts of infrastructures, bricolage, and feminist principles of technology design, helps us add further specificity to the work that infrastructural bricoleurs need to do when building civic data dashboards with resource constrained communities. Our findings benefit other researchers studying data infrastructures as well as administrators and practitioners within programs like DSSG (Data Science for Social Good) who are interested in building data infrastructures under similar constraints.
“Wow” Experience with Immersive Reality: Gamification in the Tec Virtual Campus

Crisis means opportunity, and during the Covid-19 self-isolation contingency, many universities experienced the engagement problem in higher education. Suddenly, teachers had the best opportunity in the world to reinvent themselves and use creativity to redesign their classes so that, despite being remote, they were motivating and high-quality in academic content. Many professors gamified their classes, employing virtual and augmented reality platforms to engage students in class topics and make content easier to assimilate. Gamification motivates a desire to win and allows students to repeat the experience as many times as necessary until their learning is meaningful. This research aimed to share the lessons of an academic activity designed with gamification and using an immersive reality platform called “Tec Virtual Campus.” [1] To achieve meaningful learning through the gamification of complex and theoretical class topics was a big challenge. Designing an academic activity in immersive reality is not just for fun; it is a means to acquire knowledge in a fun way, contributing to students' emotional balance with entertaining activities and high-level academic content.
CWoT-Share: Context-Based Web of Things Resource Sharing in Blockchain Environment

Web of Things (WoT) resources are not only numerous, but also have a wide range of applications and deployments.The centralized WoT resource sharing mechanism lacks flexibility and scalability, and hence cannot satisfy requirement of distributed resource sharing in large-scale environment.In response to this problem, a trusted and secure mechanism for WoT resources sharing based on context and blockchain (CWoT-Share) was proposed.Firstly, the mechanism can respond quickly to the changes of the application environment by dynamically determining resource access control rules according to the context.Then, the flexible resource charging strategies, which reduced the fees paid by the users who shared more resources and increased the fees paid by users who frequently used resources maliciously, were used to fulfill efficient sharing of WoT resources.Meanwhile, the charging strategies also achieve load balancing by dynamic selection of WoT resources.Finally, the open source blockchain platform Ethereum was used for the simulation and the simulation results show that CWoT-Share can flexibly adapt to the application environment and dynamically adjust strategies of resource access control and resource charging.
A Survey on Content-Based Encrypted Image Retrieval in Cloud Computing

With the wide popularization of intelligent devices and digital imaging technology, the types and volumes of images are growing rapidly. The resource constrained data owners outsource the local data to cloud servers, where data can be stored, shared and retrieved. However, the image data may contain the user's sensitive information, which may be exposed to the semi-trusted cloud providers and external attackers, resulting in the privacy leakage of users. Among numerous methods to solve the content-based image retrieval (CBIR) with privacy protection in cloud computing (e.g., access control, digital watermark, encryption), we mainly study the content-based encrypted image retrieval (CBEIR), which is more effective because it operates directly on the data itself. In CBEIR, some operations (e.g., feature extraction, index building) consume quite a few computing resources. In this paper, according to the trade-off of user resources, we divide the existing schemes into two categories: feature-based CBEIR schemes and image-based CBEIR schemes. For each category, its system model and common encrypted algorithms are introduced in detail. And other factors affecting these two schemes are summarized, including feature selection, feature similarity measurement methods and appropriate index structure. Finally, the future research directions are discussed and prospected.
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net
Security Analysis of Aadhaar Authentication Process and Way Forward

Aadhaar, India’s indigenous digital identity program, has already covered more than 90% of the residents of India by issuing more than 1.30 billion Aadhaar numbers by 15th Oct 2021 and is set to achieve the global strategic goal (SDG 16.9) as defined in the UN Sustainable Development Goals of providing legal identification for all by 2030 in near future. Aadhaar is the world’s largest identity project which aims to provide unique identity to all the residents of India. A number of government social welfare schemes are using Aadhaar as a means of identification of beneficiaries for disbursement of services, and increasingly, a host of private sector entities such as banks etc. are also using Aadhaar. The number of Aadhaar based transactions is more than 40 million per day and is likely to increase exponentially, however many cases of frauds taking place in Aadhaar authentication are also being reported in media. This technology assessment focuses on analyzing the security problems associated with Aadhaar authentication process and various mitigation steps taken to minimize frauds. This paper concludes by providing recommendations to minimize the security risks and frauds in Aadhaar authentication.
Construction of English Teaching Ability Evaluation Model Based on Fuzzy K-means Clustering

With the continuous development of national economy and socialist higher education with Chinese characteristics, the demand for traditional “one-sided” and homogeneous talents has plummeted, and talents with distinctive professional background and diversified professional skills are favored. The teaching quality of college English is directly related to the quality of personnel training and the development of colleges and universities themselves. How to improve the teaching quality of college English teachers and promote the long-term development of teachers is one of the focuses of every university. The change of talent demand has attracted attention and support for the development of application-oriented colleges and universities with professional characteristics. Based on k-means algorithm, the index parameters of post competency are clustered and integrated to realize the scientific evaluation of CET post competency. And integrate mega data information fusion and k-means algorithm to cluster and integrate the index parameters of CET ability, and compile the corresponding teaching resource allocation scheme to realize the evaluation of CET ability. In order to ensure the accuracy of the designed CET ability evaluation method based on feedback model, the simulation comparison experiment is carried out in the simulated classroom teaching environment. The effectiveness of the designed evaluation method can be verified through the effective analysis of experimental data.
Detection of Image Forgery for Forensic Analytics

Due to the technical revolution in digital image processing, different advanced image manipulation software has been used in recent years to produce new unrealistic images without leaving evidence of what is happening in the world, so it would be difficult to detect tampering visually. Digital image forgeries have many techniques, but it is still very difficult to identify copy-move forgery. Therefore, we use a robust algorithm in this paper to detect copy-move forgery based on the descriptor speed-up robust feature (SURF) as a key-point detection, high-pass filtering as a matching feature, nearest neighbor used as a clustering algorithm to divide the entire image. By swapping the matched feature points with the corresponding super pixel blocks, the doubtful regions are identified, and then, the corresponding blocks are combined on the basis of similar local color features (LCF). Finally, to obtain the suspected forged areas, morphological close operation was applied. The results of the study indicate that the proposed method achieves considerable output based on key-point detection compared to other forgery detection methods used in the current method in order to address the research challenges.