Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Image Quality Assessment: A Survey

Image quality assessment (IQA) is the problem of measuring the perceptual quality of images, which is crucial for many image-related applications. It is a difficult task due to the coupling of various degradation and the scarcity of annotations. To facilitate a better understanding of IQA, we survey the recent advances in deep learning based IQA methods, which have demonstrated remarkable performance and innovation in this field. We classify the IQA methods into two main groups: reference-based and reference-free methods. Reference-based methods compare query images with reference images, while reference-free methods do not. We further subdivide reference-based methods into full-reference and reduced-reference methods, depending on the amount of information they need from the reference images, and reference-free methods into single-input, pair-input, and multimodal-input methods, according to the form of input they use. The advantages and limitations of each category are analyzed and some representative examples of state-of-the-art methods are provided. We conclude our paper by highlighting some of the future directions and open challenges in deep learning based IQA.
A Universal Random Coding Ensemble for Sample-Wise Lossy Compression

We propose a universal ensemble for the random selection of rate–distortion codes which is asymptotically optimal in a sample-wise sense. According to this ensemble, each reproduction vector, x^, is selected independently at random under the probability distribution that is proportional to 2−LZ(x^), where LZ(x^) is the code length of x^ pertaining to the 1978 version of the Lempel–Ziv (LZ) algorithm. We show that, with high probability, the resulting codebook gives rise to an asymptotically optimal variable-rate lossy compression scheme under an arbitrary distortion measure, in the sense that a matching converse theorem also holds. According to the converse theorem, even if the decoder knew the ℓ-th order type of source vector in advance (ℓ being a large but fixed positive integer), the performance of the above-mentioned code could not have been improved essentially for the vast majority of codewords pertaining to source vectors in the same type. Finally, we present a discussion of our results, which includes among other things, a clear indication that our coding scheme outperforms the one that selects the reproduction vector with the shortest LZ code length among all vectors that are within the allowed distortion from the source vector.
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment

Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local details or are computationally expensive. What's worse, they fail to leverage the heterogeneous concepts in data. In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings. For disentangled conceptualization, we divide the coarse feature into multiple latent factors related to semantic concepts. For set-to-set alignment, where a set of visual concepts correspond to a set of textual concepts, we propose an adaptive pooling method to aggregate semantic concepts to address the partial matching. In particular, since we encode concepts independently in only a few dimensions, DiCoSA is superior at efficiency and granularity, ensuring fine-grained interactions using a similar computational complexity as coarse-grained alignment. Extensive experiments on five datasets, including MSR-VTT, LSMDC, MSVD, ActivityNet, and DiDeMo, demonstrate that our method outperforms the existing state-of-the-art methods.
A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG.
Combating Dark Web Terrorism: Strategies for Disruption and Prevention

Abstract: Dark web platforms have emerged as a significant concern in the context of global security, with the potential to facilitate and amplify terrorism activities. This paper presents an analysis of the evolving landscape of dark web terrorism and proposes strategies for combating this emerging threat. The study begins by examining the characteristics and functionalities of the dark web that make it attractive to terrorist organizations, including anonymity, encrypted communications, and illicit marketplaces. It further explores the methods employed by terrorists to exploit the dark web for recruitment, propaganda dissemination, and coordination of attacks. To counteract this growing menace, a comprehensive framework for combating dark web terrorism is introduced. The proposed strategies encompass various dimensions, such as technological advancements, international collaboration, legislative measures, and law enforcement efforts. Technological approaches focus on enhancing digital surveillance capabilities, developing advanced data analytics tools, and improving cybersecurity measures to disrupt terrorist networks operating in the dark web. International cooperation is emphasized to foster information sharing, intelligence coordination, and joint operations among nations to counter the cross-border nature of dark web terrorism. By implementing these multifaceted strategies, governments, law enforcement agencies, and technology providers can collectively mitigate the threats posed by dark web terrorism
Effects of social interaction on virtual reality cybersickness

Cybersickness is a well-known issue that creates barriers to the broad usage of virtual reality (VR) in real-life scenarios. It is essential to explore the factors influencing cybersickness and intervention strategies to alleviate cybersickness. In this study, social interaction is considered a potentially promising approach to addressing cybersickness. A total of 30 participants were recruited to join both social and solitary experimental tasks in VR experiments. The subjective and objective experiences in relation to cybersickness in VR were collected. The results indicated that social interaction could mitigate the sense of cybersickness. Significant differences were observed in verbally rated cybersickness, physiological measurements, and machine learning classifications. Random forest models were constructed to classify the severity of VR cybersickness and identify the feature importance in detecting cybersickness. This study is the first to explore the role of social interaction in VR cybersickness and raises new possibilities for researchers and designers to address this issue.
Distributed Data Consistency Algorithm in Database Based on Language Recognition

With the development and popularization of computer technology, people's dependence on computers is becoming stronger and their requirements for computers are also increasing.Traditional independent computer systems can no longer meet people's needs in terms of functionality and performance.Distributed systems are systems where multiple computer systems are connected through a network and are becoming increasingly common.In the field of databases, distributed database systems have become an important research field.Distributed database systems are becoming increasingly popular because they meet the needs of current information system applications and meet the popular management ideas and methods in today's business organizations.At the same time, the industry has also begun to actively research and develop distributed database systems.In distributed databases, data redundancy is achieved by copying the same data block to multiple nodes for application and security reasons.If the database platform of a distributed system is not necessarily the same on all nodes, the problem to be solved is how to maintain data consistency with minimal duplication throughout the entire database system.
Detection of Entangled States Supported by Reinforcement Learning

Discrimination of entangled states is an important element of quantum-enhanced metrology. This typically requires low-noise detection technology. Such a challenge can be circumvented by introducing nonlinear readout process. Traditionally, this is realized by reversing the very dynamics that generates the entangled state, which requires a full control over the system evolution. In this Letter, we present nonlinear readout of highly entangled states by employing reinforcement learning to manipulate the spin-mixing dynamics in a spin-1 atomic condensate. The reinforcement learning found results in driving the system toward an unstable fixed point, whereby the (to be sensed) phase perturbation is amplified by the subsequent spin-mixing dynamics. Working with a condensate of 10 900 ^{87}Rb atoms, we achieve a metrological gain of 6.97_{-1.38}^{+1.30} dB beyond the classical precision limit. Our work will open up new possibilities in unlocking the full potential of entanglement caused quantum enhancement in experiments.
Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360° panoramas under upright-camera assumption. Recent work has demonstrated the merit of deep-learning for end-to-end direct relative pose regression in 360° panorama pairs [11]. To exploit the benefits of multi-view logic in a learning-based framework, we introduce Graph-CoVis, which non-trivially extends CoVisPose [11] from relative two-view to global multi-view spherical camera pose estimation. Graph-CoVis is a novel Graph Neural Network based architecture that jointly learns the co-visible structure and global motion in an end-to-end and fully-supervised approach. Using the ZInD [4] dataset, which features real homes presenting wide-baselines, occlusion, and limited visual overlap, we show that our model performs competitively to state-of-the-art approaches.
Complete spectral characterization of biphotons by simultaneously determining their frequency sum and difference in a single quantum interferometer

We propose theoretically a quantum interferometer in which the NOON state interferometer is combined with the Hong-Ou-Mandel interferometer. With this interferometer, the temporal interference patterns associated with biphoton frequency sum and difference can be shown in different parts of a single interferogram. It is thus possible to simultaneously obtain the spectral correlation information of biphotons in both frequency sum and difference by taking the Fourier transform from such an interferogram. This provides a method for complete spectral characterization of an arbitrary two-photon state with exchange symmetry and might be useful in quantum Fourier-transform spectroscopy where direct spectral measurement is difficult. Furthermore, as it can realize the measurement of time intervals on three scales at the same time, we expect that it can provide a method in quantum metrology. Finally, we discuss another potential application of such an interferometer in the generation and characterization of high-dimensional and phase-controlled frequency entanglement.
T2V2T: Text-to-Video-to-Text Fusion for Text-to-Video Retrieval

Video-language transformers for text-to-video retrieval typically consist of a video encoder, a text encoder, and a joint encoder. The joint encoder can be categorized into 1) self-attention-based fusion and 2) unidirectional fusion based on cross-attention. The former approach performs self-attention on the concatenation of video and text embeddings. Although it allows complete interaction between text and video, the length of the input sequences makes it computationally intensive. Instead, unidirectional fusion employs rather efficient cross-attention to fuse video embeddings into text embeddings while ignoring text-to-video interaction. The text-to-video fusion is not well explored because of the information imbalance between text and video, which makes it difficult to determine which video patches can be used as queries in cross-attention. i.e., a text embedding corresponds to one or more patch embeddings, while a video patch embedding may not correspond to any text embeddings. In order to address this challenge, we devise a Bypass cross-attention (Bypass CA) which prevents matching between irrelevant video and text embedding pairs in the cross-attention. Using Bypass CA, we propose a novel bidirectional interaction approach, Text-to-Video-to-Text (T2V2T) fusion. The proposed T2V2T uses two unidirectional fusions with opposite directions, i.e., text-to-video fusion followed by video-to-text fusion. As a result, the proposed T2V2T fusion yields state-of-the-art results on MSR-VTT, DiDeMo, and ActivityNet Captions.
Quantum error correction from complexity in Brownian SYK

A bstract We study the robustness of quantum error correction in a one-parameter ensemble of codes generated by the Brownian SYK model, where the parameter quantifies the encoding complexity. The robustness of error correction by a quantum code is upper bounded by the “mutual purity” of a certain entangled state between the code subspace and environment in the isometric extension of the error channel, where the mutual purity of a density matrix ρ AB is the difference $$ {\mathcal{F}}_{\rho}\left(A:B\right)\equiv \textrm{Tr}\ {\rho}_{AB}^2-\textrm{Tr}\ {\rho}_A^2\ \textrm{Tr}\ {\rho}_B^2 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>ρ</mml:mi> </mml:msub> <mml:mfenced> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>:</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> </mml:mfenced> <mml:mo>≡</mml:mo> <mml:mi>Tr</mml:mi> <mml:mspace /> <mml:msubsup> <mml:mi>ρ</mml:mi> <mml:mi>AB</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> <mml:mo>−</mml:mo> <mml:mi>Tr</mml:mi> <mml:mspace /> <mml:msubsup> <mml:mi>ρ</mml:mi> <mml:mi>A</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> <mml:mspace /> <mml:mi>Tr</mml:mi> <mml:mspace /> <mml:msubsup> <mml:mi>ρ</mml:mi> <mml:mi>B</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> . We show that when the encoding complexity is small, the mutual purity is O (1) for the erasure of a small number of qubits (i.e., the encoding is fragile). However, this quantity decays exponentially, becoming O (1/ N ) for O (log N ) encoding complexity. Further, at polynomial encoding complexity, the mutual purity saturates to a plateau of O ( e − N ). We also find a hierarchy of complexity scales associated to a tower of subleading contributions to the mutual purity that quantitatively, but not qualitatively, adjust our error correction bound as encoding complexity increases. In the AdS/CFT context, our results suggest that any portion of the entanglement wedge of a general boundary subregion A with sufficiently high encoding complexity is robustly protected against low-rank errors acting on A with no prior access to the encoding map. From the bulk point of view, we expect such bulk degrees of freedom to be causally inaccessible from the region A despite being encoded in it.
Software Evolution of Next.js and Angular

Software evolution is the process of updating a software from it's initial phase incorporating updates taken from the users.E-type software takes feedback and requirements from the users and updates the software accordingly.In the 70's, Lehman proposed some laws of software evolution, which are still considered as the standards of measuring software evolution.Now-a-days, E-type softwares are getting very popular as users now use softwares by giving feedback.We are measuring Lehman's laws of software evolution for two E-type softwares, Next.js and Angular.By downloading the source codes from github repositories, we analysed the source codes and evaluated if the softwares follow Lehman's Laws of Software Evolution or not.This analysis completes the research which aims at measuring if the softwares evolve by maintaining Lehman's Laws.
A Proposal of Medical Information Management System Based on Consortium Blockchain

Data mining and analysis have produced a more comprehensive healthcare ecosystem due to the application of digital healthcare systems, such as mobile health, telehealth and so on. However, this inevitably raised the issue of data trust and security, which requires a more secure healthcare data sharing scheme to solve it. This paper utilized the consortium blockchain to build a decentralized mutual trust platform based on AWS cloud service, considering that information is transparent and difficult to alter in the blockchain. Additionally, the platform integrated access control lists (ACL) and elliptic curve digital signature algorithm (ECDSA) to ensure that the medical data sharing across organizations could be done securely without disclosing too much personal information. Ultimately, we demonstrated that the proposed system is workable without the forging of smart contracts by experimental observation.
Hi Doctor Strange! Play it again, and discover the Metaverse : Perceptions of the Metaverse among gamers

New, past, or, in development, are common attributes given to the Metaverse concept. Possibly it is all of that, but the most important is how we perceive it. Our lives are being influenced and transformed by technology which has a huge impact on our social, physical, and virtual representations. Regarding what many authors say about this concept we may question: how do real people represent the Metaverse? The main objective of this study is to analyze how Portuguese adults think and feel about this concept. To achieve this goal, two focus groups were conducted, with a total of 8 participants - Portuguese adults, all of them digital gamers. The results showed us expected and unexpected findings that probably rely on the (still in development) definition of what is and what will be the Metaverse. The results will offer us a deep understanding of which concepts can be more relevant for our research, as items and dimensions to be considered in a Ph.D. research in development.
Anchor free based Siamese network tracker with transformer for RGB-T tracking

Abstract In recent years, many RGB-THERMAL tracking methods have been proposed to meet the needs of single object tracking under different conditions. However, these trackers are based on ANCHOR-BASED algorithms and feature cross-correlation operations, making it difficult to improve the success rate of target tracking. We propose a siamAFTS tracking network, which is based on ANCHOR-FREE and utilizes a fully convolutional training network with a Transformer module, suitable for RGB-THERMAL target tracking. This model addresses the issue of low success rate in current mainstream algorithms. We also incorporate channel and channel spatial attention modules into the network to reduce background interference on predicted bounding boxes. Unlike current ANCHOR-BASED trackers such as MANET, DAPNet, SGT, and ADNet, the proposed framework eliminates the use of anchor points, avoiding the challenges of anchor hyperparameter tuning and reducing human intervention. Through repeated experiments on three datasets, we ultimately demonstrate the improved success rate of target tracking achieved by our proposed tracking network.
Ethical implications for teaching students to hack to combat cybercrime and money laundering

Purpose Malicious hackers are increasingly evolving with technology by developing advanced tools to infiltrate. They are looking at micro laundering via sites like PayPal or using job advertising sites, to avoid exposure. Micro laundering makes it possible to launder a large amount of money in small amounts through thousands of electronic transactions. Therefore, the purpose of this paper is to examine whether the ethical hacking pedagogy is both a feasible and effective approach to prepare information security professionals of the future to combat black hat hacking and other forms of unethical conduct in the cyberspace. Design/methodology/approach The paper will specifically explore the ethics and implications of teaching students how to hack. It examines the strengths and limitations of the ethical hacking pedagogy. The discussion will then form the basis for exploring whether ethical hacking pedagogy is logical and justifiable. Findings The research has examined whether the ethical hacking pedagogy is an initiative-taking and effective approach to preparing information security professionals. Teaching students to hack is the only feasible approach to preparing future cybersecurity professionals because such training will allow them to master technical skills necessary for penetration testing. Originality/value A dominant theme that emerged from the research is the inability to evaluate students’ intention and provide oversight after their graduation. Thus, professional networks and peer groups will play an instrumental role in sustaining students in an environment that fosters ethical conduct.
Relation extraction in Chinese using attention-based bidirectional long short-term memory networks

Relation extraction is an important topic in information extraction, as it is used to create large-scale knowledge graphs for a variety of downstream applications. Its goal is to find and extract semantic links between entity pairs in natural language sentences. Deep learning has substantially advanced neural relation extraction, allowing for the autonomous learning of semantic features. We offer an effective Chinese relation extraction model that uses bidirectional LSTM (Bi-LSTM) and an attention mechanism to extract crucial semantic information from phrases without relying on domain knowledge from lexical resources or language systems in this study. The attention mechanism included into the Bi-LSTM network allows for automatic focus on key words. Two benchmark datasets were used to create and test our models: Chinese SanWen and FinRE. The experimental results show that the SanWen dataset model outperforms the FinRE dataset model, with area under the receiver operating characteristic curve values of 0.70 and 0.50, respectively. The models trained on the SanWen and FinRE datasets achieve values of 0.44 and 0.19, respectively, for the area under the precision-recall curve. In addition, the results of repeated modeling experiments indicated that our proposed method was robust and reproducible.