Reviewerly/exHarmony huggingface dataset:
Ebooks in Academic Libraries

The number of ebooks continues to grow in academic libraries. Ebooks have many advantages that make them attractive as a library resource. However, ebooks also have restrictions. These include use and sharing restrictions, license restrictions, and technological barriers. Some students also prefer print books for specific tasks. The following articles examine many aspects of ebooks in academic libraries, including student preference, overall ebook use, and the advantages and disadvantages of various ebook acquisition models.
High-accuracy calibration of cameras without depth of field and target size limitations

During camera calibration, targets need to be placed in the depth of field of the lens to ensure clear imaging, and they should take up proper proportions in the image. These requirements cause difficulty in many calibration scenarios, such as those involving large-field-of-view, shallow-depth-of-field, or online operation cameras. In view of the above-mentioned problems, this study proposes a high-accuracy camera calibration method, which can overcome the influence of image blur and noise and is not limited by depth of field and target size. First, a high-accuracy light-spot small target is placed closely in front of the camera, so that the target image can take up a large proportion in the whole image. In case of defocus blur, the adaptive multi-scale method is used to extract feature point coordinates at first to ensure accuracy, and the location variance of each feature point is estimated concurrently. Finally, the high-accuracy intrinsic and extrinsic parameters of the camera under test are obtained by nonlinear optimization where re-projection errors are normalized by location variances according to the Gauss-Markov theorem. Simulation and physical experiments validate the effectiveness of the proposed method.
Dynamic Node Embeddings From Edge Streams

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accurately modeling the temporal information, most embedding methods ignore it entirely or approximate the temporal network using a sequence of static snapshot graphs. In this work, we propose using the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal walks</i> for learning dynamic embeddings from temporal networks. Temporal walks capture the temporally valid interactions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , flow of information, spread of disease) in the dynamic network in a lossless fashion. Based on the notion of temporal walks, we describe a general class of embeddings called continuous-time dynamic network embeddings (CTDNEs) that completely avoid the issues and problems that arise when approximating the temporal network as a sequence of static snapshot graphs. Unlike previous work, CTDNEs learn dynamic node embeddings directly from the temporal network at the finest temporal granularity and thus use only temporally valid information. As such CTDNEs naturally support online learning of the node embeddings in a streaming real-time fashion. Finally, the experiments demonstrate the effectiveness of this class of embedding methods that leverage temporal walks as it achieves an average gain in AUC of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$11.9\%$</tex-math></inline-formula> across all methods and graphs.
Building an expert system for printer forensics: A new printer identification model based on niching genetic algorithm

Abstract Inside digital forensic science, expert systems are utilized to clarify suspicions where normally one or more human experts would need to be consulted. Expert systems‐based printer identification is provided with the objective of distinguishing the printer that produced a suspicious or questioned document. The arising problem is that the extraction of many features of the printed document for printer forensics sometimes increases the time and decreases the classification accuracy as many of the printed document descriptors may emanate to be recurring and non‐valuable. Therefore, the distinct combinatorial collection of features (knowledge base) will demand to be acquired in order to preserve the essence of operative features' fusion to accomplish the maximum accuracy. This paper presents a bio‐inspired expert system for printer forensics that integrates both texture features conveyed from the grey level co‐occurrence matrix of the printed letter ‘WOO’ and niching genetic search to select the good enough reduced feature set. This combination intends to realize high classification precision relies on a trivial collection of discriminative descriptors. Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions based on adjusting the crossover ratio and occurrence of mutation of each individual and employs the slope of the individuals to choose their mutation value. For categorization, the scheme exploits k‐nearest neighbours ( KNN ) to distinguish the brand of the printer for its simplicity. Results confirm that the suggested approach has high classification accuracy and needs less computation time.
Dynamic Reciprocal Authentication Protocol for Mobile Cloud Computing

A combination of mobile and cloud computing delivers many advantages such as mobility, resources, and accessibility through seamless data transmission via the Internet anywhere at any time. However, data transmission through vulnerable channels poses security threats such as man-in-the-middle, playback, impersonation, and asynchronization attacks. To address these threats, we define an explicit security model that can precisely measure the practical capabilities of an adversary. A systematic methodology consisting of 16 evaluation criteria is used for comparative evaluation, thereby leading other approaches to be evaluated through a common scale. Finally, we propose a dynamic reciprocal authentication protocol to secure data transmission in mobile cloud computing (MCC). In particular, our proposed protocol develops a secure reciprocal authentication method, which is free of Diffie-Hellman limitations, and has immunity against basic or sophisticated known attacks. The protocol utilizes multifactor authentication of usernames, passwords, and a one-time password (OTP). The OTP is automatically generated and regularly updated for every connection. The proposed protocol is implemented and tested using Java to demonstrate its efficiency in authenticating communications and securing data transmitted in the MCC environment. Results of the evaluation process indicate that compared with the existing works, the proposed protocol possesses obvious capabilities in security and in communication and computation costs.
Approximate Nearest Neighbor Search and Lightweight Dense Vector Reranking in Multi-Stage Retrieval Architectures

In the context of a multi-stage retrieval architecture, we explore candidate generation based on approximate nearest neighbor (ANN) search and lightweight reranking based on dense vector representations. These results serve as input to slower but more accurate rerankers such as those based on transformers. Our goal is to characterize the effectiveness-efficiency tradeoff space in this context. We find that, on sentence-length segments of text, ANN techniques coupled with dense vector reranking dominate approaches based on inverted indexes, and thus our proposed design should be preferred. For paragraph-length segments, ANN-based and index-based techniques share the Pareto frontier, which means that the choice of alternatives depends on the desired operating point.
3D shape measurement of objects with uncooperative surface by projection of aperiodic thermal patterns in simulation and experiment

3D shape measurement systems based on diffuse reflection of projected structured light are widely used. Unfortunately, this measurement principle does not work for uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. Recently, it was shown that 3D reconstruction of an uncooperative object can be performed by a two-step process. In the first step, the object absorbs a projected thermal pattern. In the second step, after energy conversion, the object surface reemits a diffused thermal pattern according to Planck’s law. To achieve high 3D result qualities in short measurement times, projection parameters such as irradiance pattern and irradiation period must be optimized depending on optical and thermal material properties, e.g., complex spectral refractive index, thermal conductivity, specific heat capacity, or emissivity. Therefore, we have developed a simulation tool to describe the entire measurement process beginning with the projection unit, followed by the interaction of the irradiation with the measurement object, reemission of thermal radiation and recording unit, and finally the 3D reconstruction. In this contribution, we present our simulation tool, verify it with measurement results, and apply it to investigations of the influence of projection and material parameters on the 3D result quality.
Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching

Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, cluster heterogeneity is pervasive in computing infrastructure, and is a fundamental characteristic of low-cost transient resources (such as EC2 spot instances). In this paper, we develop a dynamic batching technique for distributed data-parallel training that adjusts the mini-batch sizes on each worker based on its resource availability and throughput. Our mini-batch controller seeks to equalize iteration times on all workers, and facilitates training on clusters comprised of servers with different amounts of CPU and GPU resources. This variable mini-batch technique uses proportional control and ideas from PID controllers to find stable mini-batch sizes. Our empirical evaluation shows that dynamic batching can reduce model training times by more than $ 4\times$ on heterogeneous clusters.
Building Digital Archive through Collaborative UX Research

When digital archives are increasingly becoming a space for knowledge production, construction, consumption, maintenance, dissemination, and amplification, the necessary questions to be interrogated are: Who has access to write, participate, and construct pasts, memories, and collective public imaginations through these digital domains? How can the ones who have access to engage in epistemological performances through digital archives make these domains a space to deconstruct essentialized identity categories forced upon the Other by the (hetero)patriarchal and/or imperial-colonial Self? And how can participatory design frameworks contribute to that? Triggered by these questions, my critical digital archival research project attempts at finding theories-praxes to make dialogic rooms in digital archives to build a relationship with historically, structurally, and/or systematically minoritized communities and their distinctive, transitory, plural, and conjunctural narratives. This paper introduces my ongoing doctoral project of the critical digital archive where I am documenting and theorizing a digital archive of my street photography in Nepal that I am building through participatory design frameworks with South Asian communities. In this paper, I offer a look at how the complexities and obstacles faced during UX research can enable technical communicators to rupture the essentialist foundation of identity constructions of Non-Western communities. My paper aims to communicate the necessity of conducting UX research through participatory-design frameworks not only to find answers to design technology but to see a way of completely subverting epistemic violence of colonial discourses through the “failure” to find answers.
Privacy-Preserving OpenID Connect

OpenID Connect is the most widely used Internet protocol for delegated authentication today. It provides single sign-on functionality for users who use their account with an identity provider to authenticate to different services, called relying parties. Unfortunately OpenID Connect is not privacy-friendly: the identity provider learns with each use which relying party the user logs in to. This necessitates a high degree of trust in the identity provider, and is especially problematic when the relying parties' identity reveals sensitive information. We present two extensions to OpenID Connect that address this privacy concern. We first present a simple extension that prevents the identity provider from learning to which relying parties its users log in, and we further extend this solution to also prevent colluding relying parties from tracking users. We give formal security proofs for both standard OpenID Connect and our extensions using the Tamarin security protocol verification tool.
Blockchain-oriented Requirements Engineering: A Framework

This paper presents a PhD research plan that focuses on solving existing problems in requirements engineering, by means of a novel blockchain-enabled framework. Requirements engineering in general and requirements traceability in particular are characterized by a variety of distributed stakeholders and heterogeneity of tools. These characteristics introduce integration, communication, coordination and trust issues and impede the accurate and trustworthy traceability of requirements. Thereby, this paper proposes blockchain technology for the trustworthy management and traceability of requirements throughout the software development life cycle.
An artificial intelligence based construction and application of english multimodal online reading mode

English reading plays an important role in promoting oral English and comprehensive English ability. At present, the traditional online reading mode is less effective. In order to change the shortcomings of traditional education, this article builds on the artificial intelligence algorithm and combines the spoken language spectrum algorithm to build the system. Moreover, this article combines with the actual needs to put forward endpoint detection and judgment criteria based on spectral entropy information, establishes a mathematical model of knowledge forgetting, and obtains an intelligent memory algorithm to guide students in personalized learning. In order to verify the effect of the model, this article takes the students in the experimental class and the control class as the experimental objects and compares the spoken pronunciation of the students and the comprehensive English scores of the students after the experiment. The research results show that the artificial intelligence-based English multimodal online reading mode platform constructed in this article has certain effects and can effectively improve students’ English scores.
Temporal Hierarchical Dictionary Guided Decoding for Online Gesture Segmentation and Recognition

Online segmentation and recognition of skeleton- based gestures are challenging. Compared with offline cases, the inference of online settings can only rely on the current few frames and always completes before whole temporal movements are performed. However, incompletely performed gestures are ambiguous and their early recognition is easy to fall into local optimum. In this work, we address the problem with a temporal hierarchical dictionary to guide the hidden Markov model (HMM) decoding procedure. The intuition is that, gestures are ambiguous with high uncertainty at early performing phases, and only become discriminate after certain phases. This uncertainty naturally can be measured by entropy. Thus, we propose a measurement called “relative entropy map” (REM) to encode this temporal context to guide HMM decoding. Furthermore, we introduce a progressive learning strategy with which neural networks could learn a robust recognition of HMM states in an iterative manner. The performance of our method is intensively evaluated on three challenging databases and achieves state-of-the-art results. Our method shows the abilities of both extracting the discriminate connotations and reducing large redundancy in the HMM transition process. It is verified that our framework can achieve online recognition of continuous gesture streams even when they are halfway performed.
Quantum Energetics Volume 1: Theory of Superunification

[This work is dedicated] to the quantum theory of Superunification written in 1996-2000 and published in English in England (2010) and India (2011). The Theory of Superunification (SU) is the Unified Field Theory (UFT) or the Theory of Everything (TE), which physicists around the world are trying unsuccessfully to create, ignoring the fact that the Superunification theory was created 20 years ago. The theory of Superunification is a theory of quantum gravity that logically completes Einstein's general theory of relativity (GR). The theory of Superunification is the first quantum theory of relativity (QR). The theory of Superunification is based on the discovery of a new universal particle the quantum of space-time (quanton), as a particle of dark matter. Quanton was discovered by Vladimir Leonov in 1996. The dynamic interferometer Leonov in 2020 was the first to detect the field of quantons as dark matter particles. The theory of Superunification has been confirmed experimentally. This is a sensational scientific breakthrough that brings fundamental physics out of its deepest crisis. The elastic dark matter tissue is presented by us as a four-dimensional quantized space-time consisting of quantons. This is the global static electromagnetic field of the Universe, which is the carrier of the superstrong electromagnetic interaction (SEI) - the fifth fundamental force (Superforce). Electromagnetism and gravitation are manifestations of the SEI field perturbation. Electromagnetism is the result of the electromagnetic polarization of quantized space-time. Gravity is the result of spherical deformation of the elastic dark matter tissue. The mass of particle (body) is like a nodule inside the elastic dark matter tissue in the form of a lump (cluster) of its deformation energy. Gravitational well appears around the body's mass inside the elastic tissue of dark matter as a result of its spherical deformation. Gravitational well is the physical basis of gravity and the principle of relativity. In this case, the gravitational field is described by the deformation vector D inside the elastic dark matter tissue as a gradient of the quantum density (p) of the medium D = grad (p). The force of gravity F is the gradient F = gradW of the dark energy W. The accelerated run of galaxies from the center of the Universe under the action of the force F = gradW is possible only for an inhomogeneous spherically deformed Universe.
Unsupervised Image Registration for Video SAR

Existing approaches for SAR image registration focus on the global transformation correction between SAR images.However, there are often local deformations between images.Due to the time-changing viewpoint of video SAR, the images suffer a lot from local deformations, which can result in false alarms in moving target detection.This article presents an unsupervised image registration approach for the use of video SAR moving target detection, which has good registration performance and acceptable processing efficiency.The designed unsupervised learning-based framework is a cascade of two convolutional neural networks.The first network directly predicts the parameters of the rigid transformation between the reference and unregistered images, and recovers the global transformation between them.Then, the second network uses the reference image and the registered image from the first network as input and then predicts a displacement field.After that, we put a limitation on the predicted displacement field to prevent moving target shadows from being aligned.Finally, the displacement field with limitation is used to compensate local deformations between the two images.Processing results of real video SAR images have shown good performance of the proposed approach with convincing generation ability.
A Technical Overview of Cortex-M55 and Ethos-U55: Arm’s Most Capable Processors for Endpoint AI

This article consists only of a collection of slides from the author's conference presentation.
Productivity, Turnover, and Team Stability of Agile Teams in Open-Source Software Projects

Productivity in software development has been studied for a long time and is still a topic of interest. Many factors, ranging from team size to music listened by developers, have been studied regarding their effect on productivity. Surprisingly, little is known about how the dynamics of open-source projects that use agile practices are related to the productivity of the developer teams. Our study aims to close this gap by analyzing the productivity of open-source projects using measures that are popular in the context of agile software development. To do this, we study data from seven open-source projects and calculated both the individual velocity of developers and focus factor of teams per iteration. First, we applied statistical process control to identify iterations with out-of-control velocity and focus factor values. Then, we studied these iterations regarding four context factors that partly characterize the dynamics of open-source projects, i.e., iteration length, turnover of developers who left, turnover of new developers, and team stability index. Our results suggest that high team stability and low turnover are strongly associated with iterations showing high velocity.
Understanding when spatial transformer networks do not support invariance, and what to do about it

Spatial transformer networks (STNs) were designed to enable convolutional neural networks (CNNs) to learn invariance to image transformations. STNs were originally proposed to transform CNN feature maps as well as input images. This enables the use of more complex features when predicting transformation parameters. However, since STNs perform a purely spatial transformation, they do not, in the general case, have the ability to align the feature maps of a transformed image with those of its original. STNs are therefore unable to support invariance when transforming CNN feature maps. We present a simple proof for this and study the practical implications, showing that this inability is coupled with decreased classification accuracy. We therefore investigate alternative STN architectures that make use of complex features. We find that while deeper localization networks are difficult to train, localization networks that share parameters with the classification network remain stable as they grow deeper, which allows for higher classification accuracy on difficult datasets. Finally, we explore the interaction between localization network complexity and iterative image alignment.
Single Image De-Hazing via Multiscale Wavelet Decomposition and Estimation with Fractional Gradient-Anisotropic Diffusion Fusion

This paper presents algorithms based on fractional multiscale gradient fusion and multilevel wavelet decomposition for underwater and hazy image enhancement. The algorithms utilize partial differential equation (PDE)-generated low- and high-frequency images fused via gradient domain and anisotropic diffusion. Furthermore, wavelet multi-level decomposition, estimation and adjustment of detail and approximation coefficients are employed in improving local and global enhancement. Solutions to halo effect are also developed using compressive bilateral filters or other nonlinear/nonlocal means filter. Ultimately, experimental comparisons indicate that the proposed methods surpass or are comparable to several algorithms from the literature.