Reviewerly/exHarmony huggingface dataset:
Systematic Scalability Modeling of QoS-aware Dynamic Service Composition

In Dynamic Service Composition (DSC), an application can be dynamically composed using web services to achieve its functional and Quality of Services (QoS) goals. DSC is a relatively mature area of research that crosscuts autonomous and services computing. Complex autonomous and self-adaptive computing paradigms (e.g., multi-tenant cloud services, mobile/smart services, services discovery and composition in intelligent environments such as smart cities) have been leveraging DSC to dynamically and adaptively maintain the desired QoS, cost and to stabilize long-lived software systems. While DSC is fundamentally known to be an NP-hard problem, systematic attempts to analyze its scalability have been limited, if not absent, though such analysis is of a paramount importance for their effective, efficient, and stable operations. This article reports on a new application of goal-modeling, providing a systematic technique that can support DSC designers and architects in identifying DSC-relevant characteristics and metrics that can potentially affect the scalability goals of a system. The article then applies the technique to two different approaches for QoS-aware dynamic services composition, where the article describes two detailed exemplars that exemplify its application. The exemplars hope to provide researchers and practitioners with guidance and transferable knowledge in situations where the scalability analysis may not be straightforward. The contributions provide architects and designers for QoS-aware dynamic service composition with the fundamentals for assessing the scalability of their own solutions, along with goal models and a list of application domain characteristics and metrics that might be relevant to other solutions. Our experience has shown that the technique was able to identify in both exemplars application domain characteristics and metrics that had been overlooked in previous scalability analyses of these DSC, some of which indeed limited their scalability. It has also shown that the experiences and knowledge can be transferable: The first exemplar was used as an example to inform and ease the work of applying the technique in the second one, reducing the time to create the model, even for a non-expert.
Corpus Statistics Empowered Document Classification

In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited. In this context, for long texts, we proposed Weighted Sparse Document Vector (WSDV), which performs clustering on the weighted data that emphasizes vital terms and moderates the soft clustering by removing outliers from the converged clusters. Besides the removal of outliers, WSDV utilizes corpus statistics in different steps for the vectorial representation of the document. For short texts, we proposed Weighted Compact Document Vector (WCDV), which captures better semantic insights in building document vectors by emphasizing potential terms and capturing uncertainty information while measuring the affinity between distributions of words. Using available corpus statistics, WCDV sufficiently handles the data sparsity of short texts without depending on external knowledge sources. To evaluate the proposed models, we performed a multiclass document classification using standard performance measures (precision, recall, f1-score, and accuracy) on three long- and two short-text benchmark datasets that outperform some state-of-the-art models. The experimental results demonstrate that in the long-text classification, WSDV reached 97.83% accuracy on the AgNews dataset, 86.05% accuracy on the 20Newsgroup dataset, and 98.67% accuracy on the R8 dataset. In the short-text classification, WCDV reached 72.7% accuracy on the SearchSnippets dataset and 89.4% accuracy on the Twitter dataset.
An Enhanced Proximal Policy Optimization-Based Reinforcement Learning Method with Random Forest for Hyperparameter Optimization

For most machine learning and deep learning models, the selection of hyperparameters has a significant impact on the performance of the model. Therefore, deep learning and data analysis experts have to spend a lot of time on hyperparameter tuning when building a model for accomplishing a task. Although there are many algorithms used to solve hyperparameter optimization (HPO), these methods require the results of the actual trials at each epoch to help perform the search. To reduce the number of trials, model-based reinforcement learning adopts multilayer perceptron (MLP) to capture the relationship between hyperparameter settings and model performance. However, MLP needs to be carefully designed because there is a risk of overfitting. Thus, we propose a random forest-enhanced proximal policy optimization (RFEPPO) reinforcement learning algorithm to solve the HPO problem. In addition, reinforcement learning as a solution to HPO will encounter the sparse reward problem, eventually leading to slow convergence. To address this problem, we employ the intrinsic reward, which introduces the prediction error as the reward signal. Experiments carried on nine tabular datasets and two image classification datasets demonstrate the effectiveness of our model.
BMFN3D: Bidirectional multilayer fusion network for indoor 3D object detection

Among various indoor 3D object detection methods, one of which is based on 3D convolution. However, in current 3D convolutional method for indoor 3D object detection, feature fusion is only implemented in a single direction and adjacent layer, which leads to the feature loss during feature propagation and weakens the detection performance. To solve the above problem, a bidirectional multilayer fusion network (BMFN) is proposed to enrich features for prediction. Furthermore, we propose a new network based on BMFN for 3D object detection, BMFN3D. In BMFN3D, the channel and spatial attention mechanisms are introduced into the backbone and head of detection network, so as to enhance the selection of effective feature information. Finally, a new calculation method for regression loss is proposed with the consideration of the 3D spatial information. We validate our method with two indoor datasets of SUN RGB-D and ScanNet, and achieve state-of-the-art results.
Social Media Information Credibility Based on User Perception and Cloud Computing System

While people enjoy the convenience brought by more and more technological developments, its importance is also more widely known. Big data and cloud computing are the two most typical representatives of many new information technologies, and their combination is also the general trend. But big data will also have privacy and security issues in cloud computing. Displayed security mechanisms and implicit security mechanisms are the two main directions for discussion, so that user privacy and security can be in the big data that it is well guaranteed when processing the calculations and storage. The rapid development of social media has been widely used, but it has also brought new social problems, such as the increasing difficulty of verifying the authenticity of information. This article will discuss the influence of the credibility of social media on the credibility of social media from the perspectives of the credibility of the communicator and the information media, the quality of the information, the reason for the transmission, and the form of expression. The influence on information credibility and information quality is the relationship between information credibility and other influencing factors when trust tendency and information involvement have become moderating variables.
Semantic Analysis Technology of English Translation Based on Deep Neural Network

English translation plays an important role in the development of science and technology and cultural exchanges. With the increase in translation volume, intelligent translation has become inevitable, but there is no effective solution for semantic translation in English translation. To provide an effective translation improvement scheme in English translation, this paper studies and analyzes the application of deep neural network in English translation semantic analysis. Based on a brief analysis of the research progress of English translation analysis and the current situation of neural network, a neural network translation architecture is established, and a deep neural network model for English translation analysis is proposed. Aiming at the problem of gradient disappearance in RNN model, the ability of Gru neural network to deal with long-distance translation is enhanced, and the computational complexity of Gru neural network is reduced. At the same time, a bi-directional Gru model is designed to translate according to the context. For some nonlinear translation, a deep neural network model based on part of speech sequence information is proposed to realize semantic analysis, and experiments are designed to test the translation effect of the neural network model. The simulation results show that the English translation based on deep neural network can improve the translation effect, reduce errors and improve the accuracy of semantic analysis of English translation, which has certain reference significance for improving the level of English translation.
Application of an Internet of Things Oriented Network Education Platform in English Language Teaching

China’s information technology development is rapid; the organic combination of science and technology and education has promoted the reform of the education system; the education platform supplemented by mobile devices provides students with a new learning mode; student learning is no longer limited by the traditional education system; and learners can use mobile terminals for online learning, self-control learning steps, arrange learning time, and be able to combine their own deficiencies to strengthen learning content. Therefore, this paper discusses the design of English education teaching system in the Internet of Things technology environment, in order to provide students with a good learning environment and improve students’ English integration strength. This paper analyzes the research status of mobile learning at home and abroad, expounds the construction principles of mobile English learning system, and designs and studies the overall structure and functional modules of the system. The application of the system can replace the traditional English learning method and can enable students to improve their English ability through mobile devices. In view of the problems of poor teaching effect and high energy consumption in the traditional Internet of Things education platform, this paper puts forward a teaching effect evaluation method of Internet of Things education platform based on long-term memory network. By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. Finally, through the study of the model, the teaching effect of the Internet of Things education platform is evaluated. The experimental results show that when the method is used to evaluate the teaching effect, the operating energy consumption accounts for 84% of the total energy consumption of the system, which proves the effectiveness of the method.
An Improved Beetle Antennae Search Algorithm Based on Inertia Weight and Attenuation Factor

The beetle antennae search algorithm is an effective bio-inspired algorithm. However, the algorithm is easy to fall into local optimal solution when dealing with high-dimensional and multimodal problems. An improved beetle antennae search algorithm based on inertia weight and attenuation factor was proposed in order to solve the problems. The inertia weights of normal distribution, versoria distribution, and random distribution are introduced into the weight to improve the search strategy, which was introduced to control the proportion of global search and local search so that the algorithm will escape the local optimum. Meanwhile, the randomness is introduced in the process of beetle’s step-size update, which can better help beetle to escape from the local optimum. Experiments on some benchmarks show that the algorithm has obvious performance improvement in dealing with high-dimensional and multimodal problems.
After the fall: Bitcoin’s true legacy may be blockchain technology

Bitcoin and its peers have set off a technological revolution that will transform money, finance, and society. However, the future of cryptocurrencies as financial assets is far from certain – as can be seen from Bitcoin’s halving in value in six months since November 2021; the total value of all cryptocurrencies fell from $3 trillion to $1.3 trillion over this period. Rather, it is the underlying technology that enables cryptocurrency – the blockchain – that is likely to prove its true legacy.
Blockchain beyond cryptocurrency: A revolution in information management and international security

Public attention on blockchain is currently centered on the erratic fluctuation of cryptocurrency, overshadowing other potential use-cases that can have significant impact on global security, including the tracking, accounting, and securing of sensitive assets such as nuclear material and facilities.
An end-to-end heterogeneous network for graph similarity learning

At present, the similarity learning methods used in many deep models can only capture the local context relations among samples in small cliques (e.g. pairs and triplets), so it is difficult to construct and leverage their latent global correlations to further boost the learning of discriminant features. To this end, we propose a novel end-to-end heterogeneous network, in which a global correlation inference model is adopted to induce the learning of a feature embedding model. Specifically, we design an intersection graph structure to build local context similarity for each sample. Then, taking the learned feature embeddings as nodes, the connection relationship between all nodes is computed through the global correlation inference model. Meanwhile, by combining two loss functions it can feedback the global connection information to push the discriminant learning of the feature embedding model. Consequently, both parts of the proposed heterogeneous network can be optimized from each other during deep similarity learning. The extensive ablation studies and comparative experimental results demonstrate that each component is effective and the whole network is competitive.
Multivariate multi-layer classifier

The variance-ratio binary multi-layer classifier (VRBMLC) has been recently proposed and shown to outperform conventional binary decision trees (BDTs). Though effective with better interpretability, the VRBMLC generates deep layers of tree nodes as it employs a one-feature-at-a-time binary split at each layer. To further condense the tree depth and enhance the classification performance, this research proposes a multivariate multi-layer classifier that applies a variance-ratio criterion to enable ternary splits of each tree node and that integrates the oblique discriminant hyperplane in the tree node. We benchmark 16 state-of-the-art univariate and multivariate classifiers on 43 publicly available datasets. The results show that the proposed methods greatly simplify the tree structure and yield a significantly higher average accuracy.
Issues and Tasks of Lecture Pairing Design and Management through Analysis of Student Lecture Pairing Reports

This paper analyzes a total of 25 lecture pairing reports to better plan a learner-oriented lecture pairing education and to draw implications for its operation. The reports were from the winners from the 1st and 2nd semesters of a 2021 contest conducted in the A subject of University D, which is implemented as a part of learner-centered convergence education, First, I classified the types of convergence shown in the lecture pairing reports into three types according to the contribution of the two subjects in the process of topic derivation in convergence, and then looked at their distribution. In addition, while analyzing the lecture pairing reports, the contribution of subject A to deriving the convergence theme was examined by classifying the contribution and role of subject A in the process of drawing conclusions of the report into upper, middle, and lower reports.&lt;br/&gt; As a result of the analysis of the reports of the contest winners, Type II, which raises a problem in subject A and seeks to find an answer in other paired subjects, occupied the highest proportion (56%) of students. Also, subject A had the lowest proportion and accounted for more than half (52%) of the students. Therefore, in the design of subject A in the future, first, instead of narrowing the scope of the theme covered by the subject, it is necessary that we deepen its contents. Through a meeting with professors in charge of major subjects and student presentations used in the award-winning lecture pairing reports, both learners and professors are given an opportunity to give feedback on the results of the report, giving professors &lt;i&gt;an proposed to have a feedback process&lt;/i&gt; for self-directed convergence subject design. (I’m not sure if I understood this part, but did you mean: “This will give professors the necessary feedback which will help them in their process of designing a self-directed convergence subject).
The Study on AI Curriculum for Non-Computer Majors

Thanks to the advances made in the field of artificial intelligence, which is a key technology of the 4th Industrial Revolution, society is changing into one that is driven by intelligent information. In an intelligent information society, talent with digital literacy and AI literacy capabilities is needed to utilize artificial intelligence technology. Digital literacy refers to the ability to solve problems using digital technology. In this type of society, AI literacy supported by the right ethical consciousness should be cultivated in applying and utilizing AI technology. Talent with digital literacy and AI literacy capabilities is required regardless of computer-related majors, and education regarding this sort of content is required in all academic fields.&lt;br/&gt; Currently, at domestic SW-centered universities, non-computer major students are also being provided with essential basic education to cultivate digital literacy and AI literacy. In this paper, digital literacy and AI literacy capabilities were proposed through related research analysis and analysis of the current status of basic domestic liberal arts courses conducted for non-computer majors. In addition, through the analysis of the current curriculum, the basic curriculum that should be aimed at in the future for non-computer majors was discussed.&lt;br/&gt; The results of this study show that basic data for designing a curriculum for AI literacy education for non-computer majors is necessary.
Cartoonized Anonymization of Sign Language Videos

In this paper, we propose a novel method for the anonymization of sign language footage: given a source RGB video of a sign language user, we conceal the identity of the original signer by reproducing the video using animated cartoon characters. Our method pays particular attention to the cues that are important for sign language communication, transferring the motion and articulation of the hands, upper body and head of the real signer to the cartoon character in a faithful manner. To effectively capture these cues, we build upon an effective combination of the most robust and reliable deep learning methods for body, hand and face tracking that have been introduced lately. Our system first extracts the skeleton pose sequence from the input video as well as the cartoon’s skeleton from its reference figure. The extracted skeletons are then fed into our skeleton retargeting algorithm, which combines the bone lengths from the cartoon character with the pose information from the human signer. The recombined parameters are then used as input to a recursive kinematic tree-based algorithm, which retargets the input skeleton pose sequence to the cartoon’s skeleton. Finally, the reproduced frames of the signing cartoon are generated from the retargeted skeleton pose sequence. To the best of our knowledge, our method is the first to implement video reproduction using cartoon characters as a solution to the challenging task of sign language video anonymization. We conduct qualitative evaluations to demonstrate the effectiveness of our approach and the promising results that we obtain.
Novel View Synthesis for Sparse RGB-D camera networks

This paper presents a novel method for view synthesis from sparse neighboring views based on RGB-D images. However, distortion and misalignment can occur in the warped images from neighboring viewpoints to the target viewpoint due to errors in the estimated depth map and camera calibration. This degrades the quality of the novel view images, especially when the cameras are far apart. We propose a novel network to estimate the offsets needed to compensate for the misalignment. The experimental results on the ScanNet dataset show that the proposed method outperforms the others. The experimental results on the ScanNet dataset show that the proposed method outperforms the state-of-the-art methods.
Virtual validation of a multi-object tracker with intercamera tracking for automotive fisheye based surround view systems

Surround view systems are becoming more and more important in Autonomous Driving and Advanced Driver Assistance Systems where multiple perception tasks have been developed in order to solve the needs in this field. However, there is a lack of literature for multi-object trackers that deals with multiple cameras in automotive setups. In this paper, we propose a system to tackle multi-object tracking with inter-camera object identification using fisheye cameras covering a 360º view in real time. We provide results from a custom ground truth annotated sequence using CARLA simulator.
Does Self-Supervised Pretraining Really Match ImageNet Weights?

Self-supervised pretraining methods for computer vision learn transferable representations given a large number of unlabeled images. Several methods from the field match or even outperform ImageNet weights when finetuning on downstream tasks, creating the impression that self-supervised weights are superior. We challenge this believe and show that state-of-the-art self-supervised methods match ImageNet weights either in classification or object detection but not in both. Furthermore, we demonstrate in experiments on image classification, object detection, instance segmentation and keypoint detection that using a more sophisticated supervised training protocol can greatly improve upon ImageNet weights and at least match and usually outperform state-of-the-art self-supervised methods.