Reviewerly/exHarmony huggingface dataset:
Dual feature enhanced video super-resolution network based on low-light scenarios

Video super-resolution (VSR) reconstruction technique aims to improve the spatiotemporal resolution of consecutive low resolution (LR) frames. Most of previous methods use the correlation of inter-frame to restore pixels. It is still a challenge to exploit intra-frame and inter-frame correlations to recover high-resolution (HR) frames, especially reconstruction of video in low-light conditions for sharp imaging results has been a bottleneck in current industrial environments. Therefore, this paper proposes a novel VSR network. Specifically, we first generate the hidden information of the current frame by using the appropriate combination of the front and rear frames. Then, a re-fusion block (RFB) is designed, which utilizes the hidden information to re-fuse with corresponding LR frame. After that, we integrate an improved dual attention mechanism (DAM) into network to extract more accurate feature of intra-frame without increasing the number of parameters. The technology has important practical significance in low-light and dim scenes, so we collect some industrial video sequences and make datasets to complete VSR task. Experimental results show that our model significantly outperforms state-of-the-art methods in performance.
Two‐stage approach for cooperative multi‐vehicle localization using integrated measurements

Abstract In this article, the distributed filtering of absolute and relative measurements for the cooperative localisation of multiple vehicles is investigated. A novel two‐stage approach that uses two unbiased cooperative filters for the sequential processing of integrated measurements is proposed. The first filter sequentially estimates each vehicle state by replacing extra neighbouring states with corresponding estimates obtained only from absolute measurements and by adding relative measurements. The second filter considers extra neighbouring states as auxiliary coloured noise. The proposed filters have low communication loads and computational complexity because of the sequential processing of the absolute and relative measurements. Unlike existing cooperative filters, the proposed two‐stage structure makes the filters robust against the presence of unreliable links between neighbouring vehicles. We present simulation results demonstrating the effectiveness and accuracy of the proposed filters when applied to vehicles performing two‐dimensional manoeuvres in three network topologies: a ring, line, and mesh.
Trend-based forecast of cryptocurrency returns

Cryptocurrencies are widely known for their limited publicly available information, making it challenging to predict market returns. Technical analysis has emerged as an essential tool in this context, but its effectiveness in the cryptocurrency market remains an open question. Using data from nearly 3,000 cryptocurrencies at daily, weekly, and monthly horizons from 2013 to 2022, we systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns and find that price-based signals are more effective in predicting short-term horizons, while volume-based signals are more powerful in predicting long-term horizons. Further analysis shows that machine learning techniques can significantly improve the performance of technical indicators, and technical indicators based on different information respond differently to the COVID-19 outbreak. These results provide direct evidence that volume imparts information to technical analysis independently of price.
Research on cloud-edge AC Power Monitoring Systems based on MODBUS

Abstract With the development of the Internet of Things and automation technology in the industry, the types of equipment in the industrial field are increasing, and the data parameters of the industrial process are gradually becoming complex. It puts forward higher requirements for the system perception layer’s real-time performance, reliability, and maintainability. The original data acquisition architecture is not easy to ensure the reliable transmission and real-time processing of massive industrial data. In order to solve these problems, this paper designs an AC Power Monitoring System. It proposes a cloud edge computing framework based on MODBUS: First, the data is collected and sent to the edge server by MODBUS through the RS485 bus. Second, combined with the advantages of the powerful service capability of cloud computing resources and the low transmission delay of edge computing, the edge side and cloud side will work together. Finally, the functions of data reading, uploading and sending, correction, configuration, and fault alarm are realized. The field operation shows that using a cloud edge computing framework can reduce the processing pressure of cloud computing, reduce network latency and lower requirements for network bandwidth and make the system efficient and safe for collecting and analyzing big data. The cooperation of MODBUS protocol enhances the reliability and practicability of communication. The cloud edge computing system based on MODBUS is easy to design and maintain and is suitable for many monitoring systems, which have high promotion value.
Research on the Form of Energy-saving Building under the Influence of Natural Ventilation Technology

Abstract With the development of science and technology and the progress of society, humanity is confronting a huge energy crisis. The energy consumption of buildings and the energy consumed to produce that energy have greatly changed the environment in which we live, causing serious energy and environmental consequences. Taking the natural ventilation design method as the main line and the excellent energy-saving building cases as the breakthrough point, this paper focuses on the three dimensions of building groups, building units and building components, and compares and studies the effects of various natural ventilation technologies, the shape of the building. These buildings have strong climate adaptability and climate regulation ability, which can effectively improve and create comfortable indoor microclimates, so that the buildings do not need or reduce the dependence on building equipment, so as to achieve the purpose of reducing building energy consumption.
Dynamic Learning Rate of Template Update for Visual Target Tracking

The trackers based on discriminative correlation filter (DCF) have achieved remarkable performance in visual target tracking in recent years. Since the targets are usually affected by various factors such as deformation, rotation, motion blur and so on, the trackers have to update the templates for tracking online. The purpose of template update is to adapt to the target changes, the magnitude of which is closely related to the motion state of the target. Actually, the learning rate of template update indicates the weight of the historical sample, and its value is fixed in most existing trackers, which will decrease the precision of the tracker or make the tracker unstable. In this study, a new dynamic learning rate method for template update is proposed for visual target tracking. The motion state of the target is defined by the difference in target center position between the frames. Then, the learning rate is adjusted dynamically according to the motion state of the target instead of the fixed value, which could achieve better performance. Experiments on the popular datasets OTB100 and UAV123 show that with the proposed dynamic learning rate for template update, the DCF-based trackers can improve tracking accuracy and obtain better tracking stability in scenarios such as fast movement and motion blur.
Feature pre-inpainting enhanced transformer for video inpainting

Transformer-based video inpainting methods aggregate coherent contents into missing regions by learning dependencies spatial–temporally. However, existing methods suffer from the inaccurate self-attention calculation and excessive quadratic computational complexity, due to uninformative representations of missing regions and inefficient global self-attention mechanisms, respectively. To mitigate these problems, we propose a Feature pre-Inpainting enhanced Transformer (FITer) video inpainting method, in which the feature pre-inpainting network (FPNet) and local–global interleaving Transformer are designed. The FPNet pre-inpaints missing features before the Transformer by exploiting spatial context, and the representations of missing regions are thus enhanced with more informative content. Therefore, the interleaving Transformer can calculate more accurate self-attention weights and learns more effective dependencies between missing and valid regions. Since the interleaving Transformer involves both global and window-based local self-attention mechanisms, the proposed FITer method can effectively aggregate spatial–temporal features into missing regions while improving efficiency. Experiments on YouTube-VOS and DAVIS datasets demonstrate that the FITer method outperforms previous methods qualitatively and quantitatively.
The Relationship Between Chinese International Students' Library Usage, Satisfaction and Academic Achievement: Focusing on Universities in P Metropolitan City

Based on previous studies, this study analyzed the structural relationship between the academic information data usage for foreign students, services usage, learning facilities usage, the satisfaction of library usage, and academic achievement. The summary of the research results is as follows. First, First, according to self-professional satisfaction (F=7.694, p=.001), college life satisfaction (F=5.837, p=.001) and library use frequency (F=3.624, p=.001) Differences in the impact on academic achievement were found. Second, academic information data usage(β=0.164, p=.007), library service usage(β=0.257, p.001), and learning facilities usage(β=0.233, p.001) are all related to library use satisfaction. It was confirmed that it had a positive (+) effect. Third, satisfaction with major(β=0.251, p.001), satisfaction with college life(β=0.255, p.001), frequency of library use(β=0.182, p=.004), satisfaction with library use(β=0.846, p.001) all had a positive (+) effect on academic achievement. The implications of this study are as follows. First, the library purchases additional multilingual books, such as Western and Chinese books, so that international students can acquire knowledge better by reading native books when they are in a situation of poor knowledge due to language acquisition. Second, by increasing the library
Privacy Protection Scheme for Personal Health Record System Using Blockchain Based on Homomorphic Encryption

With the advancement and popularization of science and technology, much research explores the provision of health care services or health management with the assistance of information technology in addition to traditional clinical diagnosis. Personal Health Records (PHR) are available for personalized health record information in the autonomous management system. The personal health record system is to improve disease management or strengthen personal health management. However, users are concerned about the safety and confidentiality of PHR in healthcare systems. In 2008, the blockchain architecture was proposed by Satoshi as a peer-to-peer network architecture that contains a Distributed Ledger Technology (DLT). In this study, we proposed a blockchain-based PHR system using the homomorphic encryption to improve the privacy and security of the users. It allows a third party to perform operations on the ciphertext which can be retrieved correctly later, while the privacy and security of the nodes on the chain are ensured and provided for multiple users to protect the security of their information.
Enhanced Differential-Linear Attacks on Reduced Round ChaCha

We present numerous refinements to the previous differential-linear attacks on ChaCha in this study. Beierle et al. discovered a 3.5-round differential at CRYPTO 2020, which was based on the condition that suitable key-IV pairs are picked, which they termed as 'right pair'. They were able to refine their approach by doing so, but they also observed that the acquisition of a right pair requires an average of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> iterations. In our work, we propose a method for achieving the right pairs with the help of listing, so that the extra multiplication of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> in the overall complexity can be avoided. In addition, we present a tactical enhancement in 'Probabilistic Neutral Bit'- searching algorithm, a change in complexity computation and a novel attack strategy based on two input-output pairs. We employ them to lower the attack complexity from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">230.86</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">218.95</sup> for the 7-round ChaCha256. Furthermore, after almost ten years, we enhance the complexity of a 6-round 128-bit version of ChaCha (Shi et al: ICISC 2012) by more than 78 million times and for the first time, propose attacks on 7.25-round ChaCha256 and 6.5-round ChaCha128 with time complexities 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">244.85</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">121.40</sup> respectively.
FS<sup>3</sup><sub>change</sub>: A Scalable Method for Change Pattern Mining

Mining change patterns can give unique understanding on the evolution of dynamically changing systems like social relation graphs, weblinks, hardware descriptions and models. A more recent focus is source code change pattern mining that may qualitatively justify expected or uncover unexpected patterns. These patterns then offer a basis, e.g., for program language evolution or auto-completion support. We present a change pattern mining method that greatly expands the limits of input data and pattern complexity, over existing methods. We propose scalability solutions on conceptual and algorithmic level, thereby evolving the state-of-the-art sampling-based frequent subgraph mining method FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , resulting in 75% reduction in memory consumption and a speedup of 6500 for a large scale dataset. Patterns can have 100,000s of occurrences for which manual review is impossible and may lead to misinterpretation. We propose the novel content track approach for interactively exploring pattern contents in context, based on marginal distributions. We evaluate our approach by mining 1,000 open source projects contributing a total of 558 million changes and 2 billion contextual connections among them, thereby, demonstrating its scalability. A manual interpretation of 19 patterns shows sensible mined patterns allowing to deduct implications for language design and demonstrating the soundness of the approach.
Interactive Two-Stream Network Across Modalities for Deepfake Detection

As face forgery techniques have become more mature, the proliferation of deepfakes may threaten the security of human society. Although existing deepfake detection methods achieve good performance for in-dataset evaluation, it remains to be improved in the generalization ability, where the representation of the imperceptible artifacts plays a significant role. In this paper, we propose an Interactive Two-Stream Network (ITSNet) to explore the discriminant inconsistency representation from the perspective of cross-modality. In particular, the patch-wise Decomposable Discrete Cosine Transform (DDCT) is adopted to extract fine-grained high-frequency clues, and information from different modalities communicates with each other via a designed interaction module. To perceive the temporal inconsistency, we first develop a Short-term Embedding Module (SEM) to refine subtle local inconsistency representation between adjacent frames, and then a Long-term Embedding Module (LEM) is designed to further refine the erratic temporal inconsistency representation from the long-range perspective. Extensive experimental results conducted on three public datasets show that ITSNet outperforms the state-of-the-art methods both in terms of in-dataset and cross-dataset evaluations.
FakePoI: A Large-Scale Fake Person of Interest Video Detection Benchmark and a Strong Baseline

Deepfake technique can synthesize realistic images, audios, and videos, facilitating the thriving of entertainment, education, healthcare, and other industries. However, its abuse may pose potential threats to personal privacy, social stability, and even national security. Therefore, the development of deepfake detection methods is attracting more and more attention. Existing works mainly focus on the detection of common videos for entertainment purposes. In contrast, fake videos maliciously synthesized for Person of Interest (PoI, i.e., who is in an authoritative position and has broadly public influences) are much more harmful to society because of celebrity endorsement. However, there is no particular benchmark for driving related research in the community. Motivated by this observation, we present the first large-scale benchmark dataset, named FakePoI, to enable the research on fake PoI detection. It contains numerous fake videos of important people from all walks of life, e.g., police chiefs, city mayors, famous artists, and well-known Internet bloggers. In summary, our FakePoI includes 11092 synthesized videos where only a few clips rather than the entire are fake. Previous fake detection algorithms deteriorate heavily or even fail on our FakePoI due to two main challenges. On the one hand, the rich diversity of our fake videos makes it pretty difficult to find universally applicable patterns for detection. On the other hand, the high credibility contributed by the presence of real frames easily confuses a common detector. To tackle these challenges, we present an amplifier framework, highlighting the feature gap between real and generated video frames. Specifically, we present a quadruplet loss to narrow the distance of all real PoIs and meanwhile push away each real and fake PoI in embedding space. We implement our framework and conduct extensive experiments on the proposed benchmark. The quantitative results demonstrate that our approach outperforms existing methods significantly, setting a strong baseline on FakePoI. The qualitative analysis also shows its superiority. We will release our dataset and code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cslltian/deepfake-detection</uri> to encourage future research on this valuable area.
A Survey of pet Action Recognition with action Recommendation based on HAR

Human Action Recognition (HAR) is a challenging task, the aim of HAR is to detect the person performing the action in an unknown video sequence, to determine the duration of the action and to identify the type of action. In sports, the main idea of HAR is to monitor the performance of players, i.e. to detect players, track their movements, identify the actions performed, compare various actions, compare the performance of different types and skills, or perform automatic statistical analysis, an overview of HAR research focuses on various approaches performed on publicly available datasets, including actions for everyday activities. This paper raises the idea of whether pet action recognition can be linked to HAR. When pet action can be recognised, the corresponding data can be used to target recommendations for the health and diet of the pet. pet and human movement recognition are closely related and this paper highlights and summarises HAR as a contribution to future research.
SparseMult: A Tensor Decomposition model based on Sparse Relation Matrix

Knowledge graphs (KGs) provide rich knowledge for lots of downstream tasks, such as recommendation system and question answering. However, KGs suffer from an incompleteness issue, namely, lots of relations between entities are missing. Link prediction, also known as knowledge graph completion (KGC), aims to predict missing relationships between entities. The models based on tensor decomposition, such as Rescal and DistMult, are one of the most effective methods to solve the link prediction task. However, previous Rescal method lack the ability to scale to large KGs due to the large amount of parameters. DistMult simplifies Rescal using diagonal matrices to represent relations, while it suffers from the limitation of dealing with antisymmetric relations. To address these problems, in this paper, we propose a novel tensor decomposition model based on sparse relation matrix, which is named as SparseMult. We conduct extensive experiments on link prediction task and experimental results show that our SparseMult model outperforms most of the state-of-the-art methods.
MTPL-G2T: Graph-to-Text Generation Task Based on Mixed Template Prompt Learning

The Graph-to-Text(G2T) generation tasks are mainly done by pre-training and fine-tuning currently, but the drawback of fine-tuning is that it changes all parameters of the pre-trained model. In this paper, we aim to accomplish the text generation task through prompt learning so that no or a small number of model parameters can be changed. Also, we analyze the impact of three different prompt templates on the generation results. The results show that when the pre-trained language model is large (e.g., T5), prompt learning is competitive with finetuning, but the number of parameters that need to be modified for prompt learning is much smaller than for fine-tuning; meanwhile, compared with text templates and soft templates, using mixed prompt templates can make the model converge faster.
Behavior Trees as executable representation of Milner Calculus notations

The efforts that go into setting up stable, efficient, fault-tolerant Multi-Agent Systems are twofold: Modelling and designing agent interactions with both environment and other agents; and transfering the model into an actual executable implementation of the system. For formal modeling, notations in process calculi, such as Milner’s Calculus for Communicating Systems (CCS), have shown to be helpful, as they allow for formal model checking of the system before implementation. Typically, translating from such notations to an executable counterpart comes with pitfalls, as constraints and limitations set by the chosen programming language or software framework make a one-to-one mapping from the formal expression to executable code difficult. In this paper, we for this investigate the similarities between formal expressions in CCS, and executable behavior trees as agent programs. Our study confirms that a semantic preserving mapping between both exist, and we demonstrate how to exploit these similarities to transfer CCS expressions to semantically equivalent, executable agent programs.