Reviewerly/exHarmony huggingface dataset:
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding

This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-query paired data for training, but also easily fail into the dataset distribution bias. To alleviate these limitations, we introduce a novel Equivariant Consistency Regulation Learning (ECRL) framework to learn more discriminative query-related frame-wise representations for each video, in a self-supervised manner. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted under various video-level transformations. Concretely, we first design a series of spatio-temporal augmentations on both foreground and background video segments to generate a set of synthetic video samples. In particular, we devise a self-refine module to enhance the completeness and smoothness of the augmented video. Then, we present a novel self-supervised consistency loss (SSCL) applied on the original and augmented videos to capture their invariant query-related semantic by minimizing the KL-divergence between the sequence similarity of two videos and a prior Gaussian distribution of timestamp distance. At last, a shared grounding head is introduced to predict the transform-equivariant query-guided segment boundaries for both the original and augmented videos. Extensive experiments on three challenging datasets (ActivityNet, TACoS, and Charades-STA) demonstrate both effectiveness and efficiency of our proposed ECRL framework.
Protecting Privacy in Digital Records: The Potential of Privacy Enhancing Technologies

With increased concerns about data protection and privacy over the past several years, and concomitant introduction of regulations restricting access to personally information (PI), archivists in many jurisdictions now must undertake ‘sensitivity reviews’ of archival documents to determine if they can make those documents accessible to researchers. Such reviews are onerous, given increasing volume of records, and complex, due to how difficult it can be for archivists to identify whether records contain personal information (PI) under the provisions of various laws. Despite research into the application of tools and techniques to automate sensitivity reviews, effective solutions remain elusive. Not yet explored as a solution to the challenge of enabling access to archival holdings subject to privacy restrictions is the application of privacy enhancing technologies (PETs) - a class of emerging technologies that rest on the assumption that a body of documents is confidential or private and must remain so. While seemingly being counter-intuitive to apply PETs to making archives more accessible, we argue that PETs could provide an opportunity to protect PI in archival holdings whilst still enabling research on those holdings. In this paper, to lay a foundation for archival experimentation with use of PETs, we contribute an overview of these technologies based on a scoping review and discuss possible use cases and future research directions.
Transformer-Based Visual Object Tracking with Global Feature Enhancement

With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features of the template and the search regions to capture the global information of the object. However, studies have found that the feature information fused by cross-attention does not pay enough attention to the object region. In order to enhance cross-attention for the object region, an enhanced cross-attention (ECA) module is proposed for global feature enhancement. By calculating the average attention score for each position in the fused feature sequence and assigning higher weights to the positions with higher attention scores, the proposed ECA module can improve the feature information in the object region and further enhance the matching accuracy. In addition, to reduce the computational complexity of self-attention, orthogonal random features are introduced to implement a fast attention operation. This decomposes the attention matrix into the product of a random non-linear function between the original query and key. This module can reduce the spatial complexity and improve the inference speed by avoiding the explicit construction of a quadratic attention matrix. Finally, a tracking method named GFETrack is proposed, which comprises a Siamese backbone network and an enhanced attention mechanism. Experimental results show that the proposed GFETrack achieves competitive results on four challenging datasets.
Influence of gravitational waves on quantum multibody states

Based on the freely-falling Unruh-Dewitt model, we study the influence of gravitational waves on the quantum multibody states, i.e., the twin-Fock (TF) state and the mixture of Dicke states. The amount of entanglement of quantum many-body states decreases first and then increases with increasing frequency of gravitational waves. In particular, for some fixed frequencies of gravitational waves, entanglement will increase with the increasing amplitude of gravitational waves, which is different from the usual thought of gravity-induced decoherence and could provide a novel understanding for the quantum property of gravitational waves.
A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows-Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.
Biased Gottesman-Kitaev-Preskill repetition code

Continuous-variable quantum computing architectures based upon the Gottesman-Kitaev-Preskill (GKP) encoding have emerged as a promising candidate because one can achieve fault tolerance with a probabilistic supply of GKP states and Gaussian operations. Furthermore, by generalizing to rectangular-lattice GKP states, a bias can be introduced and exploited through concatenation with qubit codes that show improved performance under biasing. However, these codes (such as the XZZX surface code) still require weight-four stabilizer measurements and have complex decoding requirements to overcome. In this work, we study the code-capacity behavior of a rectangular-lattice GKP encoding concatenated with a repetition code under an isotropic Gaussian displacement channel. We find a numerical threshold of $\ensuremath{\sigma}=0.599$ for the noise's standard deviation, which outperforms the biased GKP planar surface code with a trade-off of increased biasing at the GKP level. This is all achieved with only weight-two stabilizer operators and simple decoding at the qubit level. Furthermore, with moderate levels of bias (aspect ratio $\ensuremath{\le}2.4$) and nine or fewer data modes, significant reductions in logical error rates can still be achieved for $\ensuremath{\sigma}\ensuremath{\le}0.3$, opening the possibility of using biased GKP repetition codes as a simple low-level qubit encoding for further concatenation.
Error compensation for form errors of plate beamsplitter in near optical coaxial phase measuring deflectometry with reflection error model

The near optical coaxial phase measuring deflectometry (NCPMD) is one of the phase measuring deflectometry (PDM) techniques which is typically used for specular surface form measurement. The NCPMD utilizing a plate beamsplitter to folding the optical axis of display screen to make it close to the optical axis of the imaging system which makes the system more compact and has significantly reduced volume compared with the traditional PMD configuration. The NCPDM can achieve compact configuration, light weight, and reduce measurement error caused by structure shadows of the off-axis configuration of traditional PDM. However, the plate beamsplitter will lead measurement errors to the NCPMD system due to the beamsplitter will inevitably inherited certain form errors on the two surfaces during manufacturing process. In this paper, a reflection error model of the NCPMD system is proposed, and the measurement error caused by the reflection effect of the plate beamsplitter is determined by considering the influence of the unevenness of the upper and lower surfaces of the plate beamsplitter. Simulation studies show that the proposed reflection model can accurately determine the measurement errors caused by the form errors of the beamsplitter, which can be effectively used for subsequent error compensation.
Do US State Breach Notification Laws Decrease Firm Data Breaches?

Abstract From 2003 to 2018, all 50 states and the District of Columbia enacted breach notification laws (BNLs) mandating that firms suffering data breaches provide timely notification to affected persons and others about breach incidents and mitigation responses. BNLs were supposed to decrease data breaches and develop a market for data privacy where firms could strike their preferred balance between data security quality and cost. We find no systemic evidence for either supposition. Results from two-way difference-in-difference analyses indicate no decrease in data breach incident counts or magnitudes after BNLs are enacted. Results also indicate no longer-term decrease in data misuse after breaches. These non-effects appear to be precisely estimated nulls that persist for different firms, time-periods, data-breach types, and BNL types. Apparently inconsistent notification standards and inadequate information dissemination to the public may explain BNL ineffectiveness. An alternative federal regime may address these shortcomings and let a national BNL achieve goals state BNLs have apparently failed to meet.
AN INTRO TO $\delta_d$-FUZZY GRAPHS

Graph is a easy way to represent the real life situation. Graph is a combination of Points and Lines. In network analysis, the degree of a point plays a prominent role in Graph Theory. The degree of a point is the number of connections it has with the other points in the point set. Among the degrees of all the points in graph $G^*$, the minimum value is denoted by $\delta(G^*)$. In this article, a new abstraction of fuzzy graph is initiated by combining the parameters, degree of a point and minimum degree of the graph and termed it is as $\delta_d$-fuzzy graphs. Order and Size on $\delta_d$-fuzzy graphs were studied and Handshaking Lemma were explained with illustration. Idea on $\delta_d$-regular fuzzy graph were interpreted using the theorems. Also operations on graphs such as union, intersection, complement, cartesian product, Tensor Product, Corona are extended for $\delta_d$-fuzzy graphs.
Using citation networks to evaluate the impact of text length on keyword extraction

The identification of key concepts within unstructured data is of paramount importance in practical applications. Despite the abundance of proposed methods for extracting primary topics, only a few works investigated the influence of text length on the performance of keyword extraction (KE) methods. Specifically, many studies lean on abstracts and titles for content extraction from papers, leaving it uncertain whether leveraging the complete content of papers can yield consistent results. Hence, in this study, we employ a network-based approach to evaluate the concordance between keywords extracted from abstracts and those from the entire papers. Community detection methods are utilized to identify interconnected papers in citation networks. Subsequently, paper clusters are formed to identify salient terms within each cluster, employing a methodology akin to the term frequency-inverse document frequency (tf-idf) approach. Once each cluster has been endowed with its distinctive set of key terms, these selected terms are employed to serve as representative keywords at the paper level. The top-ranked words at the cluster level, which also appear in the abstract, are chosen as keywords for the paper. Our findings indicate that although various community detection methods used in KE yield similar levels of accuracy. Notably, text clustering approaches outperform all citation-based methods, while all approaches yield relatively low accuracy values. We also identified a lack of concordance between keywords extracted from the abstracts and those extracted from the corresponding full-text source. Considering that citations and text clustering yield distinct outcomes, combining them in hybrid approaches could offer improved performance.
Statistical physics of learning in high-dimensional chaotic systems

Abstract In many complex systems, elementary units live in a chaotic environment and need to adapt their strategies to perform a task by extracting information from the environment and controlling the feedback loop on it. One of the main examples of systems of this kind is provided by recurrent neural networks. In this case, recurrent connections between neurons drive chaotic behavior, and when learning takes place, the response of the system to a perturbation should also take into account its feedback on the dynamics of the network itself. In this work, we consider an abstract model of a high-dimensional chaotic system as a paradigmatic model and study its dynamics. We study the model under two particular settings: Hebbian driving and FORCE training. In the first case, we show that Hebbian driving can be used to tune the level of chaos in the dynamics, and this reproduces some results recently obtained in the study of more biologically realistic models of recurrent neural networks. In the latter case, we show that the dynamical system can be trained to reproduce simple periodic functions. To do this, we consider the FORCE algorithm—originally developed to train recurrent neural networks—and adapt it to our high-dimensional chaotic system. We show that this algorithm drives the dynamics close to an asymptotic attractor the larger the training time. All our results are valid in the thermodynamic limit due to an exact analysis of the dynamics through dynamical mean field theory.
Parallel learning by multitasking neural networks

Abstract Parallel learning , namely the simultaneous learning of multiple patterns, constitutes a modern challenge for neural networks. While this cannot be accomplished by standard Hebbian associative neural networks, in this paper we show how the multitasking Hebbian network (a variation on the theme of the Hopfield model, working on sparse datasets) is naturally able to perform this complex task. We focus on systems processing in parallel a finite (up to logarithmic growth in the size of the network) number of patterns, mirroring the low-storage setting of standard associative neural networks. When patterns to be reconstructed are mildly diluted, the network handles them hierarchically, distributing the amplitudes of their signals as power laws w.r.t. the pattern information content (hierarchical regime), while, for strong dilution, the signals pertaining to all the patterns are simultaneously raised with the same strength (parallel regime). Further, we prove that the training protocol (either supervised or unsupervised) neither alters the multitasking performances nor changes the thresholds for learning. We also highlight (analytically and by Monte Carlo simulations) that a standard cost function (i.e. the Hamiltonian) used in statistical mechanics exhibits the same minima as a standard loss function (i.e. the sum of squared errors) used in machine learning.
Multi-view learning-based heterogeneous network representation learning

Network representation learning is an important tool for extracting latent features from heterogeneous networks to enhance downstream analysis tasks. However, for heterogeneous networks in the era of big data, their heterogeneity, unseen network noises, latent structural and semantic features, and the coupling degree between the two features are increasing. These unignorable changes lead to the difficulties of extracting latent features and the demand for further optimization of existing embedding models. In this paper, an unsupervised multi-view learning based heterogeneous network representation model is proposed, called MVHNR. Firstly, to simplify the difficulty of feature learning, a novel view generation strategy is designed to divide the heterogeneous network into multiple sub-views that contain only one semantic feature and its related structural features. Then, to improve the accuracy and robustness of feature learning with network noises, a novel adjacency matrix strategy is constructed for each view, and a novel adversarial learning strategy is used to both learn the local semantic and structural features in each view. Finally, to aggregate the local features of all views, a multi-headed attention network is used to generate a global feature vector of each node. Extensive experimental results on three tasks and five public networks show the advantages of our MVHNR model.
MERN Stack Based User Authentication Technique for Evernote Application

The MERN (MongoDB, Express.js, React, Node.js) stack gives a solid foundation for building web applications, and executing client affirmation may be a imperative point of numerous applications, checking the Evernote application. Inside the setting of Evernote, a MERN stack- based client confirmation procedure incorporates utilizing MongoDB as the database to store client qualifications securely. Express.js, a backend framework for Node.js, is utilized to create a exit API that handles client enrollment, login, and affirmation. The confirmation handle incorporates creating and endorsing JSON Web Tokens (JWT), confirming secure communication between the client (React front conclusion) and the server. React is utilized for building the client interface, enabling a steady and responsive association for clients affiliation with Evernote. The Node.js server, fueled by Express.js, manages client sessions, favors tokens, and communicates with the MongoDB database to confirm clients and authorize get to to their Evernote data. In this MERN stack-based affirmation strategy for Evernote, the integration of these developments ensures a reliable ,modifible and capable course of action. Clients can securely enroll, log in, and get to their Evernote accounts with certainty, knowing that their affirmation data is taken care of with industry-standard security sharpens. The combination of MongoDB for data capacity, Express.js for server-side method of reasoning, React for a lively client interface, and Node.js for server runtime shapes a competent and cohesive designing for executing client confirmation inside the Evernote application, progressing both security and client inclusion. KeyWords: MERN Stack, Authentication, Evernote, Nodemailer, JsonWebToken, BcryptJS, Axios
Introduction to Architecture and Technological Advancements of Education 4.0 in the 21st Century

In the 21st century, Education 4.0 has transformed the field of architecture and revolutionized educational practices. Education 4.0, characterized by the integration of emerging technologies, offers new possibilities for architectural education and design processes. This chapter provides an overview of the key concepts and advancements associated with Education 4.0 in the context of architecture. This chapter explores the impact of technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and building information modeling on architectural education. This chapter highlights the benefits of these technologies, including enhanced visualization, immersive experiences, collaborative learning, and data-driven design. It also discusses the issues and challenges associated with implementing Education 4.0 in architectural education, such as the need for faculty training, technological infrastructure, and ethical implications. This work discusses the importance of recent technological advancements and preparing architecture students for the smart era.
Evaluating Group Recommender Systems

In the previous chapters, we have learned how to design group recommender systems but did not explicitly discuss how to evaluate them. The evaluation techniques for group recommender systems are often the same or similar to those that are used for single user recommenders. We show how to apply these techniques on the basis of examples and introduce evaluation approaches that are specifically useful in group recommendation scenarios.
Algorithms for Group Recommendation

In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.
A Novel Approach Based on Service Level Agreement (SLA) for Evaluating the Web Service Quality

Web service technology has gained a more important role in developing distributed applications and systems on the Internet. The rapid growth of published web services makes their discovery more and more difficult. Nowadays, most web service providers sign Services Level Agreement (SLA) contracts with their clients in order to guarantee the offered functionality of their services. This paper proposes an approach to monitor the Quality of Services (QoS) in web service according to Service Level Objectives (SLO) in SLA. Monitoring procedures are introduced to check variations in the pre-agreed metric values of SLAs. Then, the deviation between the actual quality and the acceptable quality level can be identified and analyzed. Finally, the weaknesses of the web service practices can be discovered and solved.
A Proposed Model for Risk Management in Companies with Multiple Projects

Recently, the importance of risk management has increased in the field of project management, especially software projects. The importance of risk management is because it has direct effects on the success of the project. Therefore, software organizations face a major challenge in achieving effective and efficient risk management. Therefore, measuring risks in software development is a key activity to be undertaken in risk management. Risk measurement is also one of the important topics that need more effort, especially in a work environment with multiple software projects. A multi-project environment involves multiple projects sharing the same available resources. This research aims to conduct a literature review to discover patterns or trends in software development to measure project risks and to reveal aspects of the measurement process, risk indicators, and various metrics for software projects. This research also aims to present a proposed framework for assessing risks in a multi-project work environment with shared resources for different levels in organizations with different capabilities.