Reviewerly/exHarmony huggingface dataset:
Research on Adaptive Evolution Model for Spacecraft Software

With the continuous expansion of space applications and the continuous increase of software complexity, spacecraft systems will face more uncertainties during the running time. However, the traditional control method “satellite-ground loop” is hard to make real-time decisions on the issues, and it greatly restricts the development of space explorations. Therefore, spacecraft system needs to have the ability of self-adaption. An adaptive evolution model is proposed in this paper with the definition of roles, behaviors and resources. System determines the roles and the behaviors through the perception of environment, and organizes the scheduling, allocation and operation of software and hardware resources through the mapping relationship between behaviors and resources to realize the dynamic adjustment for spacecraft software. The model simplifies the implementation of self-adaptation by the real-time parsing of the mapping relationship between each layer. And the feasibility of the model is proved in two experimental scenes at the end.
Heated Escape: A Virtual Reality Serious Game on the Urgency of the Climate Crisis

Climate change is widely considered to be the most defining crisis of our time. To address the challenge, education and engagement are key factors to change the global response to climate change. Virtual Reality (VR) serious games can provide immersive experiences, which can also serve educational purposes. In this paper, we introduce Heated Escape, a novel VR escape room game that aims to educate players about the urgency of the climate crisis. Here, we present the general game concepts, including several puzzle games.
An Improved Multi-task Approach to Pre-trained Model Based MT Quality Estimation

Machine translation (MT) quality estimation (QE) aims to automatically predict the quality of MT outputs without any references. State-of-the-art solutions are mostly fine-tuned with a pre-trained model in a multi-task framework (i.e., joint training sentence-level QE and word-level QE). In this paper, we propose an alternative multi-task framework in which post-editing results are utilized for sentence-level QE over an mBART-based encoder-decoder model. We show that the post-editing sub-task is much more in-formative and the mBART is superior to other pre-trained models. Experiments on WMT2021 English-German and English-Chinese QE datasets showed that the proposed method achieves 1.2%–2.1% improvements in the strong sentence-level QE baseline.
Judicial Evidence Storage Scheme Based on Smart Contract

In recent years, the security of evidence storage and the efficiency of case handling in the judicial field have gradually been regarded by the government as an important issue for optimizing functions and work levels. Such as the validity certification of evidence and the timeliness and convenience of the circulation of evidence documents. Blockchain provides an effective solution for judicial evidence storage due to the immutable, synchronous and traceable properties. At the same time, as an important component of the blockchain, smart contracts provide automatic execution functions. However, as a special software, the development and management of smart contracts need an effective domain-oriented framework. In this paper, a full-lifecycle smart contract design pattern that conforms to software engineering specifications is proposed, and a scheme and framework for the design and implementation of smart contracts is introduced in the judicial field.
Synchronic Representation of Spaces as a Way to Understanding their Diachronic Evolution

Abstract Starting from theoretical principles on the production of space and the role of perception, imagination, signification and their possible temporal overlap, the research addresses an innovative way of representing historical urban space. This, borrowing the sequentiality of the comic strip, intends to amplify its dynamics, transposing them beyond the pages and bringing them into a three-dimensional virtual environment for a synchronic narration of the transformations of the urban space over time.
Temporal Edge-Aware Hypergraph Convolutional Network for Dynamic Graph Embedding

Graph embedding is a critical aspect of network analysis that helps to advance various real-world applications such as social recommendation and protein structure prediction. Most of the existing graph embedding methods are designed for static graphs while many real-world graphs intrinsically behave as dynamic graphs. Recent works try to combine graph neural networks(GNN) with recurrent neural networks to address this issue. However, these methods can not independently utilize GNN models to cope with dynamic graphs and they ignore the inner edge-level correlations in dynamic graphs. To tackle these problems, we propose a novel dynamic graph embedding framework in this paper, called DynHyper. Specifically, we introduce a temporal hypergraph construction to capture the local structure information and temporal dynamics simultaneously. Then, we employ a hyperedge projection to obtain edge-level correlations. Further, we propose a temporal edge-aware hypergraph convolution to transmit and aggregate the messages in the temporal hypergraph. We conduct our experiments on seven real-world datasets to evaluate the effectiveness of DynHyper in both link prediction and node classification tasks. Experimental results show that DynHyper significantly outperforms all baselines, especially on the more complex datasets.
A Hybrid Video-to-Text Summarization Framework and Algorithm on Cascading Advanced Extractive- and Abstractive-based Approaches for Supporting Viewers' Video Navigation and Understanding

In this work, we propose the development of a hybrid video-to-text summarization (VTS) framework on cascading the advanced and code-accessible extractive and abstractive (EA) approaches for supporting viewers' video navigation and understanding. More precisely, the contributions of this paper are three-fold. First, we devise an automated and unified hybrid VTS framework that takes an arbitrary video as an input, generates the text transcripts from its human dialogues, and then summarizes the text transcripts into one short video synopsis. Second, we advance the binary merge-sort approach and expand its use to develop an intuitive and heuristic abstractive-based algorithm, with the time complexity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(T_{L}logT_{L})$</tex> and the space complexity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(T_{L})$</tex> , where T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> is the total number of word tokens on a text, to dynamically and successively split and merge a long piece of text transcripts, which exceeds the input text size limitation of an abstractive model, to generate one final semantic video synopsis. At the end, we test the feasibility of applying this proposed framework and algorithm in conducting the preliminarily experimental evaluations on three different videos, as a pilot study, in genres, contents, and lengths. We show that our approach outperforms and/or levels most of the individual EA methods stated above by 75% in terms of the ROUGE F1-Score measurement.
Shifting Reward Assignment for Learning Coordinated Behavior in Time-Limited Ordered Tasks

We propose a variable reward scheme in decentralized multi-agent deep reinforcement learning for a sequential task consisting of a number of subtasks which can be completed when all subtasks are executed in a certain order before a deadline by agents with different capabilities. Developments in computer science and robotics are drawing attention to multi-agent systems for complex tasks. However, coordinated behavior among agents requires sophistication and is highly dependent on the structures of tasks and environments; thus, it is preferable to individually learn appropriate coordination depending on specific tasks. This study focuses on the learning of a sequential task by cooperative agents from a practical perspective. In such tasks, agents must learn both efficiency for their own subtasks and coordinated behavior for other agents because the former provides more chances for the subsequent agents to learn, while the latter facilitates the execution of subsequent subtasks. Our proposed reward scheme enables agents to learn these behaviors in a balanced manner. We then experimentally show that agents in the proposed reward scheme can achieve more efficient task execution compared to baseline methods based on static reward schemes. We also analyzed the learned coordinated behavior to see the reasons of efficiency.
Piping Circuit Development using K-Prototype Clustering

The oil and gas industry is one of Indonesia's vital industries, and it contributes the most to the country's foreign exchange. Piping is an important piece of equipment in the oil and gas production facilities; therefore, the piping inspection plan should be well prepared. An integral part of inspection plan development is a piping circuit; it allows an inspector to manage the necessary inspections, calculations, and better recordkeeping. A problem faced in piping circuit development is the need for relatively many working hours and variability results. Although this problem is often encountered, piping circuit development generated by manual work is still common in practice. To overcome the issues in the piping circuit development, therefore a k-prototype algorithm was introduced. A k-prototype algorithm was used to accommodate the shortcomings in grouping objects with features comprised of mixed categorical and numerical data. This study concludes that the k-prototype algorithm is a promising clustering technique that can reduce the time spent developing the piping circuit and eliminating the resulting variability.
YOLO-Head: An Input Adaptive Neural Network Preprocessor

Over the past decade, object detectors have demonstrated remarkable performance in various applications, such as traffic monitoring, customer tracking, and surveillance. Although advanced lightweight models have been proved to have ultra real-time speed on GPU, in edge-based video analytics system, edge servers are usually embedded devices with NPU which support general neural network operators. When we implemented deep learning models on embedded devices, images usually need to be preprocessed to the network input size. This leads to the common target detectors not being end-to-end. Image preprocessing is not the key problem of real-time inferencing on devices with high-performance CPU, but the same algorithm will bring noticeable delay on embedded devices. To overcome this, we designed YOLO-Head, a module that can handle the input of arbitrarily size according to general neural network operators. Experiment results show that YOLO-Head achieves significant (60.89%) speed improvement when 1080p image zooms to 640 $$\,\times \,$$ 640. Furthermore, YOLOv5-S detector with adaptive head can effectively solve the delay problem due to the image resize. The frame rate improves to 35.2 FPS, approximately 6 times faster than the convectional method in video stream processing on RK3588.
KLAttack: Towards Adversarial Attack and Defense on Neural Dependency Parsing Models

Although neural language models achieve great performance on many Natural Language Processing tasks, they suffer from various adversarial attacks. Previous works mainly focus on semantic adversarial examples, which have similar semantics to the original sentences, while syntactic adversarial attacks against the dependency parsing task are still in an early stage of research. In this paper, we propose a novel method KLAttack, crafting word-level adversarial examples to attack neural-network-based dependency parsing models. Specifically, we retrieve the class probabilities from the victim dependency parsing model and compute the KL divergence by masking every word in a sentence. Then we use pre-trained language models and reference parsers to generate candidates for substitution. Experiments on the English Penn Treebank (PTB) dataset show that our method improves the attack success rate against Deep Biaffine Parser by up to 13.04% compared with previous related studies. Based on KLAttack, we further propose Syntax-Aware Transformer for Input Reconstruction, a denoiser to recover the original sentences from the adversarial examples. Trained adversarially with successfully attacked sentences from KLAttack, we enhance the robustness of the dependency parsing models by concatenating the denoiser ahead of the victim models.
The Cost of Virtualizing Time in Linux Containers

Containerization has enabled applications to be deployed in ever-changing environments, restarted, updated, migrated, and frequently rolled back to earlier versions. Because host placement and scheduling are not guaranteed, an application may be restarted in a different host or at a later time, losing its sense of time and refusing service owing to incongruent states or network timeouts. Until now, process time was determined by the host. The most recent Linux time namespace allows for per-service timelines, regardless of the host. Because container engines do not yet support the time namespace, we offer a workflow for creating time-aware containers, as well as the first performance analysis of virtualizing time in Linux containers using this namespace. We consider 11 time-related system calls and their vDSO variants, making this one of the most comprehensive studies on the overhead of time virtualization in the literature. Our findings show that time virtualization adds 2-4% overhead, in line with current containerization overhead.
Knowledge service technology of soil testing formula based on the integration of knowledge map and location service

CCS CONCEPTS • Information systems∼Information systems applications∼Decision support systems∼Expert systems
Caching HTTP 404 Responses Eliminates Unnecessary Archival Replay Requests

Upon replay, JavaScript on archived web pages can generate recurring HTTP requests that lead to unnecessary traffic to the web archive. In one example, an archived page averaged more than 1000 requests per minute. These requests are not visible to the user, so if a user leaves such an archived page open in a browser tab, they would be unaware that their browser is continuing to generate traffic to the web archive. We found that web pages that require regular updates (e.g., radio playlists, updates for sports scores, image carousels) are more likely to make such recurring requests. If the resources requested by the web page are not archived, some web archives may attempt to patch the archive by requesting the resources from the live web. If the requested resources are not available on the live web, the resources cannot be archived, and the responses remain HTTP 404. Some archived pages continue to poll the server as frequently as they did on the live web, while some pages poll the server even more frequently if their requests return HTTP 404 responses, creating a high amount of unnecessary traffic. On a large scale, such web pages are effectively a denial of service attack on the web archive. Significant computational, network, and storage resources are required for web archives to archive and then successfully replay pages as they were on the live web, and these resources should not be spent on unnecessary HTTP traffic. Our proposed solution is to optimize archival replay using Cache-Control HTTP response headers. We implemented this approach in a test environment and cached HTTP 404 responses that prevented the browser’s requests from reaching the web archive server.
Development and Integration Model of Online Music Education Resources Based on Collaborative filtering Algorithm

With the continuous popularization and development of network technology in recent years, the growth of information on the Internet presents an explosive speed. Such huge information brings retrieval difficulties to users, and the traditional search engine technology is difficult to meet the needs of users. Under the background that the existing technology can not meet the needs, Collaborative filtering algorithm(CFA) system has emerged, and then developed into a research hotspot of recommendation leading city. With the development of science and technology, online music appears in everyone’s vision. In order to improve the existing management mode of music resources, we should integrate and develop online music resources according to the characteristics of educational resources. In order to make efficient use of music resources, it is necessary to change the traditional management mode of music resources. The common ways are: designing a more efficient and fast way to search music resources; Upgrade the original music database; Improve the existing management mode of music database. In this paper, by importing weighting factors, the project-based filtering and user based filtering are integrated to build a new intelligent filtering algorithm for music recommendation, and the performance of the recommendation algorithm is verified by key indicators. The conclusion shows that this method can provide users with high accuracy music education.
Environment Emulation in 3D Graphics Software for Fringe Projection Profilometry

The lack of a public data set for specific applications has been a problem for the development of deep learning techniques in recent years. There is limited information for non-contact optical measurement applications such as fringe projection profilometry. The complexity and cost of generating a large amount of data to train the models to present two problems: time and personnel required aren’t easy or possible for most developers. Taking advantage of the advanced technology of current graphic simulators, we have generated an emulated environment for the projection of fringes that allows us to simulate projections on objects and perform phase changes according to N-Step. Briefly explaining the unwrapping algorithm and the pre-configuration of the software simulation environment, we determine the most important factors that could affect the quality of our data for the application of the different phase unwrapping techniques. Performing a qualitative evaluation of the reconstructions of the virtual objects by projecting the fringes to obtain the synthetic images throughout the work, we analyze the most important factors to develop a database with the best quality and similarity to the process carried out in the real world to obtain this data.
PELATIHAN DESIGN BUILDING INFORMATION MODELLING (BIM) DENGAN SKETCHUP DI SMK NEGERI 6 KOTA BEKASI

Field Work Lecture (KKN) is one of the activities carried out by all students of the 45 Bekasi Islamic University to apply the knowledge gained while sitting in lectures as well as a real form of community service. To help support KKN (Field Work Lectures) activities, a training program related to the 2021/2022 period KKN (Field work lectures) activity program is needed. The purpose of this training program is to improve the knowledge, attitudes and skills of students in SMK about building drawing technology using 3D (three-dimensional) SketchUp. This program discusses the stages in drawing a one-story residential building technique using the 3D (three-dimensional) digital method. There are many kinds of computer applications that are used to draw in 3D (three dimensions). Although there are many, the SketchUp application was chosen as an interesting solution for drawing 3D (three- dimensional) buildings, because using this application the process is not too difficult. From the results of the training carried out, it can be said that building design can be more effective and efficient if it is implemented into a three-dimensional design, so that the final results can be presented.
DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction

Due to hardware constraints, standard off-the-shelf digital cameras suffers from low dynamic range (LDR) and low frame per second (FPS) outputs. Previous works in high dynamic range (HDR) video reconstruction uses sequence of alternating exposure LDR frames as input, and align the neighbouring frames using optical flow based networks. However, these methods often result in motion artifacts in challenging situations. This is because, the alternate exposure frames have to be exposure matched in order to apply alignment using optical flow. Hence, over-saturation and noise in the LDR frames results in inaccurate alignment. To this end, we propose to align the input LDR frames using a pre-trained video frame interpolation network. This results in better alignment of LDR frames, since we circumvent the error-prone exposure matching step, and directly generate intermediate missing frames from the same exposure inputs. Furthermore, it allows us to generate high FPS HDR videos by recursively interpolating the intermediate frames. Through this work, we propose to use video frame interpolation for HDR video reconstruction, and present the first method to generate high FPS HDR videos. Experimental results demonstrate the efficacy of the proposed framework against optical flow based alignment methods, with an absolute improvement of 2.4 PSNR value on standard HDR video datasets [1], [2] and further benchmark our method for high FPS HDR video generation.
Skeleton-Based Action Quality Assessment via Partially Connected LSTM with Triplet Losses

Human action quality assessment (AQA) recently has attracted increasing attentions in computer vision for its practical applications, such as skill training, physical rehabilitation and scoring sports events. In this paper, we propose a partially connected LSTM with triplet losses to evaluate different skill levels. Compared to human action recognition (HAR), we explain and discuss two characteristics and countermeasures of AQA. To ignore the negative influence of complex joint movements in actions, the skeleton is not regarded as a single graph. The fully connected layer in the LSTM model is replaced by the partially connected layer, using a diagonal matrix which activates the corresponding weights, to explore hierarchical relations in the skeleton graph. Furthermore, to improve the generalization ability of models, we introduce additional functions of triplet loss to the loss function, which make samples with similar skill levels close to each other. We carry out experiments to test our model and compare it with seven LSTM architectures and three GNN architectures on the UMONS-TAICHI dataset and walking gait dataset. Experimental results demonstrate that our model achieves outstanding performance.
TextSMatch: Safe Semi-supervised Text Classification with Domain Adaption

The performance of many efficient Deep semi-supervised learning(SSL) is severely degraded when the distribution of unlabeled and labeled data does not match. Some recent approaches have chosen to weaken or even remove out-of-distribution (OOD) data, which can lose the potential value of OOD data. We propose TextSMatch to solve this issue, a simple, safe and effective SSL method for text classification, which recycles the OOD data near the labeled domain to make full use of the information in OOD data. Specifically, adversarial domain adaptation is applied to the OOD data to project it into the space of ID and labeled data, and its recoverability is assessed using the use of migration probabilities. Moreover, TextSMatch unifies the mainstream methods. In addition to consistency regularization training of class probabilities for unlabeled data and its augmented data, we also normalized the structure of embedding with contrastive learning based on pseudo-labeled. TextSMatch performs significantly better than other baseline methods on AG News and Yelp datasets in scenarios such as class mismatch and different amounts of labeled data.