Reviewerly/exHarmony huggingface dataset:
Harmony: A Dynamic Heterogeneity−Aware Resource Provisioning In the Cloud Environment

HARMONY, a resource management system for heterogeneity-aware settings that dynamically provide capacity.The workload is divided into different task classes with comparable properties in terms of resource and performance needs using the K-means clustering technique.The DCP is an optimization problem that takes into account the heterogeneity of machines and workloads.It is more adaptable and may offer a very effective DCP solution for any workload composition.The heterogeneous characteristics will produce insufficient energy savings and protracted scheduling delays as a result of a mismatch between workload requirements and the resources offered by the supplied machines.a special technique for dynamically adjusting the number of machines of each kind to lower total energy consumption and performance costs with regard to scheduling delays.These findings imply the need for more effective provisioning and scheduling techniques to shorten the scheduling lag for these challenging jobs.
CrossVision: Real-time On-Camera Video Analysis via Common RoI Load Balancing

Smart cameras with on-device deep learning inference capabilities are enabling distributed video analytics at the data source without sending raw video data over the often unreliable and congested wireless network. However, how to unleash the full potential of the computing power of the camera network requires careful coordination among the distributed cameras, catering to the uneven workload distribution and the heterogeneous computing capabilities. This paper presents CrossVision, a distributed framework for real-time video analytics, that retains all video data on cameras while achieving low inference delay and high inference accuracy. The key idea behind CrossVision is that there is a significant information redundancy in the video content captured by cameras with overlapped Field-of-Views (FoVs), which can be exploited to reduce inference workload as well as improve inference accuracy between correlated cameras. CrossVision consists of three main components to realize its function: a Region-of-Interest (RoI) Matcher that discovers video content correlation based on a segmented FoV transformation scheme; a Workload Balancer that implements a randomized workload balancing strategy based on a bulk-queuing analysis, taking into account the cameras' predicted future workload arrivals; an Accuracy Guard that ensures that the inference accuracy is not sacrificed as redundant information is discarded. We evaluate CrossVision in a hardware-augmented simulator and on real-world cross-camera datasets, and the results show that CrossVision is able to significantly reduce inference delay while improving the inference accuracy compared to a variety of baseline approaches.
A Minimal Solution for Sphere-Based Camera-Projector Pair Calibration

We propose a minimal solution for sphere-based camera-projector pair (CPP) calibration. Previous works often treated the camera and projector calibration as two independent problems, which exploit only intra-view information from geometric properties of sphere dual image formation and hence require at least three spheres for CPP calibration. However, other than intra-view information, we observe that inter-view information between camera and projector provides additional constraints. Combining these two kinds of information yields a minimal solution for CPP calibration, where only a single sphere is required. Extensive experiments have verified the effectiveness of proposed minimal solver, which demonstrates higher flexibility and comparable accuracy to the state-of-the-art methods. Moreover, the achieved flexibility allows high-quality 3D reconstruction with an uncalibrated CPP, given only a single sphere in the scene.
Document Recommendation In Conversations using Keyword Extraction And Clustering Method

In order to satisfy the information demands of discussion participants without diverting them, it is necessary to create lists of papers that are succinct, varied, and relevant.This study tackles this challenge.These lists are regularly fetched by sending several implicit requests generated from the spoken phrases.Each query is sent to a search engine over the English Wikipedia and is connected to one of the subjects mentioned in the conversation fragment before the recommendation.Utilizing a submodular reward function that rewards both the thematic closeness of documents to the conversation terms and their diversity, we offer an algorithm in this study for the diverse merging of these lists.Via crowdsourcing, we assess the suggested approach.The outcomes demonstrate the effectiveness of the diverse merging strategy over a number of alternatives that do not uphold subject diversity.In this study, we provide a unique method for extracting keywords from the ASR output that increases the coverage of users' prospective information demands while minimising the usage of irrelevant terms.After a collection of keywords has been extracted, they are clustered to create a number of topically separated searches that may be performed individually and provide more accuracy than a single, larger topically mixed query.Before providing the results as suggestions to users, the results are eventually combined into a ranked set.
Pixel Bleach Network for Detecting Face Forgery under Compression

The existing face forgery algorithms have achieved remarkable progress in how to generate reasonable facial images and can even successfully deceive human beings. Considering public security, face forgery detection is of vital importance, making it essential to design face forgery detection algorithms to detect forgery images over the Internet. Despite the great success achieved by the existing Deepfake detection algorithms, they usually failed to achieve satisfactory Deepfake detection performance when deployed to handle the forgery videos in practice. One significant reason is compression. The videos over the Internet are inevitably compressed considering the transmission efficiency. To address this issue, in this paper, we propose a generic, simple yet effective “bleaching” pre-processing module based on the generative model and the high-level feature representations to produce a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bleached image, which shares a similar appearance with the compressed images. The bleached images with recovered information can be identified accurately by the optimized Deepfake detection models without retraining. The proposed method has utilized a redesigned feature representation, which serves as a navigator to effectively and sufficiently alter the feature distribution in the high-dimensional space to remedy the difference between real facial images and forgery counterparts. Thus, the proposed method can successfully avoid misclassification. Comprehensive and extensive experiments are carried out on four low-quality Faceforensics++ datasets, demonstrating the effectiveness of our method in recovering the information loss caused by the compression artifacts across various backbones and compression.</b>
Secure and Efficient Data Processing for Cloud Computing with Fine-Grained Access Control

Nowadays, with rapid development of cloud computing, many information systems are running on the cloud platform. However, the cloud servers are not fully trustworthy, for the purpose of privacy preserving, the users need to encrypt their data before uploading it to the cloud. However, this also brings challenges in utilizing the data. Generally speaking, several desirable properties should be achieved for data processing on the cloud platform. First, the cloud servers should be able to perform computations on the encrypted data without learning users’ sensitive information. Second, fine-grained access control should be enforced on the computed results. Third, flexibility requires that the identities who can access the computed results should be unknown when these results are generated. Fourth, the scheme should have low overheads on computation and communication. To the best of our knowledge, most of the existing schemes cannot satisfy these requirements simultaneously. In order to address this issue, we propose a secure and efficient privacy preserving data processing scheme for cloud computing with fine-grained access control, using a homomorphic proxy re-encryption scheme and an efficient attribute-based encryption scheme. Security analyses prove that it satisfies all the desirable security properties, and performance evaluation demonstrates that it is more efficient than the state-of-the-art schemes targeting similar problems. In particular, the size of ciphertexts and the decryption time for the computed results are constant in our scheme, regardless the access structure. Therefore, our scheme contributes to a more practical data processing scheme for the cloud platform with fine-grained access control.
A Novel IoT Based Multi-modal Edge Computing Optimization Method

Extracting information from the rich multi-source data of the power system is one of the keys to playing the role of the core production factors of the power grid. On the one hand, multi-source data fusion will facilitate an accurate and comprehensive analysis of various scenarios in all source, network, and load storage aspects. The fusion of multi-source information can complement and enhance each other, thereby effectively improving the accuracy of target perception. To this end, we propose a method based on the power Internet of Things and deep learning for feature extraction of multi-source information. Since traditional methods reveal hidden correlations between different morphologies of learned features through linear connections, it is difficult to obtain desirable results. Therefore, this paper proposes a deep learning computing model of DCCM, which further improves the feature extraction and fusion capabilities of rich multi-source data.
Research on Risk Prevention and Control of Distribution Network Based on Knowledge Graphs

With the application of high proportion of power electronic equipment and the access of high proportion of renewable energy, the fault patterns of distribution network are increasingly complex, and the risk associated data are also more complex and diverse. To effectively improve the data perception ability and risk response ability of distribution network, this paper combines the traditional risk prevention and control of distribution network with the knowledge graph technology with interconnected, structured, and visualized knowledge, and proposes a risk prevention and control method of distribution network based on knowledge graph. The top-down and bottom-up graph construction method is adopted to construct the graph from two aspects: pattern layer and data layer, and it is divided into distribution network entity graph, the auxiliary decision-making graph, and case graph. The critical construction technologies of knowledge graph are analyzed, and ideas are provided for the realization of each link of the risk control process. Finally, combined with the actual digital twin case of distribution network, the auxiliary decision-making graph in the actual fault situation is demonstrated. The consequence show that this method can swiftly and efficiently realize the power loss recovery of the distribution network, and realize multi-dimensional data perception and human-computer interaction.
Temporal Analysis of Privacy Enhancing Technology Traffic Using Deep Learning

Tor is an open-source communications software program that enables anonymity on the Internet. Tor’s ability to hide its users’ identity means it is incredibly popular with criminals, who use it to keep their online activities secret from law enforcement authorities. Tor uses layers of encryption to hide its users’ data on the Web. However, most encryption techniques implemented till date do not provide full anonymity. We can use classification algorithms based on machine learning and deep learning to extract information about the users from network traffic. In this paper, we show that by performing a temporal analysis of Tor network traffic flowing between the user node and guard node, one can classify the Tor network traffic into various application types such as browsing, chat, email, P2P, FTP, audio, video, VoIP, and file-transfer. We apply many standard and popular machine learning and deep learning algorithms to categorize traffic by application and achieved an accuracy of 95.75% for Random Forest which outperforms the best work done till date on the ISCXTor2016 dataset.
Edge Local Differential Privacy for Dynamic Graphs

Abstract Huge amounts of data are generated and shared in social networks and other network topologies. This raises privacy concerns when such data is not protected from leaking sensitive or personal information. Network topologies are commonly modeled through static graphs. Nevertheless, dynamic graphs better capture the temporal evolution and properties of such networks. Several differentially private mechanisms have been proposed for static graph data mining, but at the moment there are no such algorithms for dynamic data protection and mining. So, we propose two locally $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -differentially private methods for dynamic graph protection based on edge addition and deletion through the application of the noise-graph mechanism. We apply these methods to real-life datasets and show promising results preserving graph statistics for applications in community detection in time-varying networks. The main contributions of this work are: extending the definition of local differential privacy for edges to the dynamic graph domain, and showing that the community structure of the protected graphs is well preserved for suitable privacy parameters.
Virtual Reality Technology in Computer Hardware Repair and Maintenance

The formation of skills is achieved through continuous practical activities, but some practical activities in computer education also cause failure or even damage to computer software and hardware. This paper aims to study the application of virtual reality technology in computer hardware repair and maintenance. Firstly, this paper introduces the concept and research significance of virtual experiment platform, analyzes the research results of virtual experiment platform at home and abroad, and summarizes the advantages and disadvantages of the existing virtual hardware experiment platform. And then put forward a framework of virtual experiment platform composed of DOS virtual machine, virtual driver and working circuit demonstration system. Through analyze the functions of each module in the framework, enumerate the technologies that can realize each function, and finally confirm the specific technology to realize each module. Experiments have shown that the students who learn computer hardware repair and maintenance through virtual reality technology can effectively improve their computer repair and maintenance technology.
Linear-time algorithms for computing a maximal increasing subsequence

The longest increasing subsequence is a fundamental problem which has been studied for a long time in computer science. In this paper, we consider the maximal increasing subsequence problem where the constraint is released from the longest to the maximal. For two kinds of increasing (monotone increasing and strictly increasing), we propose linear-time algorithms computing a maximal increasing subsequence of an input sequence from an alphabet . Our algorithm for computing a maximal monotone increasing subsequence requires space and our algorithm for computing a maximal strictly increasing subsequence requires space.
Improved topography measurement with a high dynamic range using phase difference sensing technology

Phase difference sensing technology (PDST) is employed for topography measurement, and two interference structures are proposed to achieve upper-limit adjustment and high resolution in the measurement range: a dual-wavelength system with a single Fabry–Perot (FP) cavity and a single-wavelength system with dual FP cavities. The phase difference between the two interference signals is determined by an elliptic fitting algorithm (EFA), and this change in phase difference is utilized to characterize the step height. Experimental results indicate that the measurement upper-limit can be adjusted to either 410 µm, 187 µm, or 108 µm by varying the wavelength difference in the dual-wavelength system, which gives a measurement error of 2.96%. In contrast, while offering a measurement resolution of 3.47 nm, the single-wavelength system exhibits a measurement error of 5.38%. The proposed method is capable of satisfying the measurement requirements during micro-electromechanical system (MEMS) processing with proficiency.
Improvements in Rogue Scholar: tags and images

The Rogue Scholar science blog archive adds important functionality to existing blogs, namely long-term archiving, full-text search, and DOI registration. In this week's update, I focussed on improving functionality that is specific for blogs and not really found regularly with other formats for scholarly content. Tags Tags are a common way to categorize blog posts and help find content of interest.
Heterogeneous Graph Interaction based Event Extraction with Attentional Position Embeddings

Document-level event extraction has become one of the important research directions in natural language processing, which aims to extract the complete event arguments from the whole document. However, existing models cannot fully utilize contextual semantic relations and sequential information in complex document-level scenarios, failing to mine the relational facts between multiple sentences and arguments. To address the above problems, we propose a heterogeneous graph interaction event extraction model fusing positional embedding and attention matrix called GPAIT. Firstly, contextual semantic relationships are enhanced between entities by constructing entity attention relationship matrix; Secondly, the attention matrix is combined to rectify the representation of heterogeneous graphs; Finally, positional embedding is fused into graphs to build a graph convolutional neural network that can better capture semantic sequential relationships. Experiments on ChFinAnn and COVID-19 News show that GPAIT outperforms other document-level event extraction methods in terms of F1 by 1.2% and 3.6% respectively.
Linking Field Decomposition and Coverage Path Planning: A Coevolution Approach

Coverage path planning (CPP) for complex fields in precision farming is often solved by splitting the problem: Finding a good field decomposition and optimizing the paths for each individual field. While various techniques have been proposed for both steps, they are performed sequentially, ignoring the knowledge gain from performing CPP for field decomposition. To overcome this limitation, an approach to link theses two steps and optimize them in parallel is proposed. A proof of concept is presented using coevolution to demonstrate the feasibility.
A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess

The rules of the two phases of Tibetan Jiu chess, layout and battle, are very different, and using the same UCT search algorithm globally will result in a large overhead of time and storage space in the search process, so a phased game algorithm for Tibetan Jiu chess is proposed, with different strategies designed for the layout and battle phases, respectively. First, the layout phase uses a combination of Gaussian distribution and fast online estimation to improve the UCT algorithm, thus generating the optimal action selection scheme. Second, in order to take full advantage of reinforcement learning and deep learning, a neural network model with residual network structure is used in the battle phase to guide the search of Monte Carlo trees, and the default strategy is improved by "pruning" in the expansion step to improve the quality of the expanded nodes. The dataset is generated by self-play and used to train the neural network model to obtain the optimal model. It is verified through experiments that the phased gaming algorithm proposed in this study effectively reduces the process of blindly exploring the board state during the layout and battle phases of the UCT search algorithm, and improves the quality of the layout and the self-learning efficiency of the neural network model.
Integrated System of Augmented and Virtual Reality for Ruins Tourism

For ruins tourism, we employ indirect augmented reality (AR) to virtually reproduce and show the buildings of the past that do not exist now. In addition, by taking advantage of indirect AR, we develop an integrated system that seamlessly transitions the AR experience to the VR experience. We evaluated the system through an actual local government event.
An Assessment of The Use of Quality 4.0 Trends in Higher Education Institutions in South Africa

The fourth industrial revolution (4IR) has brought many changes in our lives, and we must adapt in different ways.Quality 4.0 is also part of the change.4IR affected how organizations perceive quality; how has it affected the higher education sector in South Africa?This is the question this study attempts to answer.The study aimed to assess the use of quality 4.0 trends in higher education institutions in South Africa.To best achieve the study, The following objectives were listed: (1) To identify the most used quality 4.0 trends in higher education institutions in South Africa;(2) To highlight the respondents' perspective on quality 4.0 and finally; (3) To recommend ways to a successful implementation of quality 4.0 in higher education institutions.The study followed a quantitative approach using a questionnaire to collect data from 300 respondents mixed between staff and students from higher education institutions.Descriptive and inferential statistics were used to analyze data.To assess the reliability of the questionnaire, Cronbach's alpha was used.The study revealed that quality 4.0 is a relatively new field in South Africa, both on the business and academic levels.In addition, most of the respondents are not familiar with the quality 4.0 concept but are familiar with its trends.From the most used trend in higher education institutions, the top five were: Online social platforms (YouTube, WhatsApp, Zoom, etc.), IoT, Simulation, AR and Big data analytics.
Scalable Missing Data Imputation With Graph Neural Networks

Missing features in tabular and graph structured data are common: a company may not want to disclose all of their accounting, and users online do not always engage in social platforms in the same way as their peers. Recently, models such as the GRAPE architecture have achieved state-of-the-art results in the task of feature imputation. We present an extension of GRAPE that performs mini-batch learning on datasets which do not fit in the GPU. Moreover, we add preprocessing and post-processing steps that allow the model to be used with graph structured data. We experimentally show the behaviour of the model on an academic citation network under different regimes of missing data. We observe that the model performance starts decreasing when we have less than 1% of observed edges. We additionally perform an ablation study of key elements of the model, such as its capacity, the batch size and the number of layers.