Reviewerly/exHarmony huggingface dataset:
Toward Using Package Centrality Trend to Identify Packages in Decline

Due to their increasing complexity, today's software systems are frequently built by leveraging reusable code in the form of libraries and packages. Software ecosystems (e.g., npm) are the primary enablers of this code reuse, providing developers with a platform to share their own and use others' code. These ecosystems evolve rapidly: developers add new packages every day to solve new problems or provide alternative solutions, causing obsolete packages to decline in their importance to the community. Developers should avoid depending on packages in decline, as these packages are reused less over time and may become less frequently maintained. However, current popularity metrics (e.g., Stars, and Downloads) are not fit to provide this information to developers because their semantics do not aptly capture shifts in the community interest. In this paper, we propose a scalable approach that uses the package's centrality in the ecosystem to identify packages in decline. We evaluate our approach with the npm ecosystem and show that the trends of centrality over time can correctly distinguish packages in decline with an ROC-AUC of 0.9. The approach can capture 87% of the packages in decline, on average 18 months before the trend is shown in currently used package popularity metrics. We implement this approach in a tool that can be used to augment the npms metrics and help developers avoid packages in decline when reusing packages from npm.
EfficientTDNN: Efficient Architecture Search for Speaker Recognition

Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have shown their remarkable capability in learning speaker embedding. However, they meanwhile bring a huge computational cost in storage size, processing, and memory. Discovering the specialized CNN that meets a specific constraint requires a substantial effort of human experts. Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken language processing tasks such as speaker recognition. In this paper, we propose EfficientTDNN, an efficient architecture search framework consisting of a TDNN-based supernet and a TDNN-NAS algorithm. The proposed supernet introduces temporal convolution of different ranges of the receptive field and feature aggregation of various resolutions from different layers to TDNN. On top of it, the TDNN-NAS algorithm quickly searches for the desired TDNN architecture via weight-sharing subnets, which surprisingly reduces computation while handling the vast number of devices with various resources requirements. Experimental results on the VoxCeleb dataset show the proposed EfficientTDNN enables approximate $10^{13}$ architectures concerning depth, kernel, and width. Considering different computation constraints, it achieves a 2.20% equal error rate (EER) with 204M multiply-accumulate operations (MACs), 1.41% EER with 571M MACs as well as 0.94% EER with 1.45G MACs. Comprehensive investigations suggest that the trained supernet generalizes subnets not sampled during training and obtains a favorable trade-off between accuracy and efficiency.
Remote sensing and faithful quantum teleportation through non-localized qubits

One of the most important applications of quantum physics is quantum teleportation, the possibility to transfer quantum states over arbitrary distances. In this paper, we address the idea of remote sensing in a teleportation scenario with topological qubits more robust against noise. We also investigate the enhancement of quantum teleportation through non-local characteristics of the topological qubits. In particular, we show that how this nonlocal property, helps us to achieve near-perfect quantum teleportation even with mixed quantum states. Considering the limitations imposed by decoherence and the subsequent mixedness of the resource state, we find that our results may solve important challenges in realizing faithful teleportation over long distances.
Quantum Fourier transform on photonic qubits using cavity QED

The authors propose the implementation of a photonic quantum Fourier transform protocol on a cavity QED setup involving a single atom-cavity system, which requires no active feedforward control. The authors analyze the performance of the protocol and show that the proposed methods could work at the tens-of-photons level.
Generating Multiscale Maps From Satellite Images via Series Generative Adversarial Networks

Considering the success of generative adversarial networks (GANs) for image-to-image translation, researchers have attempted to translate satellite images to maps (si2map) through GAN for cartography. However, these studies involved limited scales, which hinders multiscale map creation. By extending their method, high-resolution satellite images can be trivially translated to multiscale maps through scale-wise si2map generators trained for certain scales. However, this strategy has two theoretical limitations. First, inconsistency between high-resolution satellite images and object generalization on multiscale maps (SI-M inconsistency) increasingly complicates the extraction of geographical information from satellite images for generators with decreasing scale. Second, as si2map translation is cross-domain, generators incur high computation costs to transform the pixel distribution on satellite images to that on maps. Thus, we designed a series strategy of generators for multiscale si2map translation to address these limitations. In this strategy, high-resolution satellite images are inputted to an si2map generator to output large-scale maps, which are translated to multiscale maps through series multiscale map generators. The series strategy avoids SI-M inconsistency as high-resolution satellite images are only translated to large-scale maps and transforms cross-domain translation to approximately intradomain translation when generating multiscale maps. Our experimental results showed better quality multiscale map generation with the series strategy, as shown by average increases of 11.69%, 53.78%, 55.42%, and 72.34% in the structural similarity index (SSIM), edge structural similarity index (ESSI), intersection over union (road), and intersection over union (water) for data from Mexico City and Tokyo at zoom levels 17–13.
Energy levels estimation on a quantum computer by evolution of a physical quantity

We show that the time dependence of mean value of a physical quantity is related with the transition energies of a quantum system. In the case when the operator of a physical quantity anticommutes with the Hamiltonian of a system, studies of the evolution of its mean value allow determining the energy levels of the system. On the basis of the result, we propose a method for determining energy levels of physical systems on a quantum computer. The method opens a possibility to achieve quantum supremacy in solving the problem of finding minimal or maximal energy of Ising model with spatially anisotropic interaction using multi-qubit quantum computers. We apply the method for spin systems (spin in magnetic field, spin chain, Ising model on squared lattice) and realize it on IBM's quantum computers.
CRM-Based E-Business Design (Customer Relationship Management) Case Study : Shoe Washing Service Company S-Neat-Kers

S-Neat-Kers is a shoe-washing service firm with troubles. S-Neat-Kers is a freshly created SME with problems. The issue at hand is how to acquire new potential clients while still retaining old ones. It is hoped that by creating an e-Business system based on CRM, the company's challenges would be solved. It begins with an analysis based on the CRM phase, followed by the design and development of e-Business applications utilising UML tools. The system is implemented when the application's design and development are completed. Based on the implementation findings, an implementation study was conducted to assess the design's applicability for the company's challenges. As a result, it can be inferred that the application's capabilities can assist businesses in gaining new consumers and increasing sales, as well as processing customer data that can be utilised to retain existing customers, thanks to data that is now held by the business.
Measurement Crosstalk Errors in Cloud-Based Quantum Computing

Quantum technologies available currently contain noise in general, often dubbed noisy intermediate-scale quantum (NISQ) systems. We here present the verification of noise in measurement readout errors in cloud-based quantum computing services, IBMQ and Rigetti, by directly performing quantum detector tomography, and show that there exist measurement crosstalk errors. We provide the characterization and the quantification of noise in a quantum measurement of multiple qubits. We remark that entanglement is found as a source of crosstalk errors in a measurement of three qubits.
Kramers–Kronig relations and precision limits in quantum phase estimation

The ultimate precision in any measurement is dictated by the physical process implementing the observation. The methods of quantum metrology have now succeeded in establishing bounds on the achievable precision for phase measurements over noisy channels. In particular, they demonstrate how the Heisenberg scaling of the precision can not be attained in these conditions. Here we discuss how the ultimate bound in presence of loss has a physical motivation in the Kramers-Kronig relations and we show how they link the precision on the phase estimation to that on the loss parameter.
Deterministic and Universal Quantum Squeezing Gate with a Teleportation‐Like Protocol

Squeezing transformation as an essential component, gives rise to the possibility to perform various tasks of quantum information processing. However, the reported squeezing gate with best performance so far is a conditional realization at the expense of low success probability, while the performance of the deterministic ones is currently circumscribed by the limited squeezing degree of the non-classical ancilla. To address this issue, we develop and demonstrate a new scheme of deterministic and universal quantum squeezing gate with the property of non-local operation attributed to the teleportation-like protocol. We demonstrate high fidelity squeezing operation, even when the level of target squeezing being up to 10 dB, where a squeezed state with non-classical noise reduction of 6.5 dB is directly observed. Moreover, we perform a high-fidelity complex operation including a Fourier transformation and a phase squeezing gate, exploring the potential of implementing the complex task of quantum information processing with the presented functional unit. Our method can be applied to distributed quantum processor, the creation of exotic non-classical states, and quantum error correction.
Isolation without taxation: near-zero-cost transitions for WebAssembly and SFI

Software sandboxing or software-based fault isolation (SFI) is a lightweight approach to building secure systems out of untrusted components. Mozilla, for example, uses SFI to harden the Firefox browser by sandboxing third-party libraries, and companies like Fastly and Cloudflare use SFI to safely co-locate untrusted tenants on their edge clouds. While there have been significant efforts to optimize and verify SFI enforcement, context switching in SFI systems remains largely unexplored: almost all SFI systems use heavyweight transitions that are not only error-prone but incur significant performance overhead from saving, clearing, and restoring registers when context switching. We identify a set of zero-cost conditions that characterize when sandboxed code has sufficient structured to guarantee security via lightweight zero-cost transitions (simple function calls). We modify the Lucet Wasm compiler and its runtime to use zero-cost transitions, eliminating the undue performance tax on systems that rely on Lucet for sandboxing (e.g., we speed up image and font rendering in Firefox by up to 29.7% and 10% respectively). To remove the Lucet compiler and its correct implementation of the Wasm specification from the trusted computing base, we (1) develop a static binary verifier , VeriZero, which (in seconds) checks that binaries produced by Lucet satisfy our zero-cost conditions, and (2) prove the soundness of VeriZero by developing a logical relation that captures when a compiled Wasm function is semantically well-behaved with respect to our zero-cost conditions. Finally, we show that our model is useful beyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our zero-cost conditions; our prototype performs on-par with the state-of-the-art Native Client SFI system.
Effect of Different Environmental Factors on Performance of Solar Panel

Solar power is future of our planet due to the depletion of non-renewable sources of energy. The renewable sources of energy are responsible for 80% of world’s power and we all are directly dependent on non-renewable source which will only last for 1 or 2 decades. Due to increase in power demands new development in renewable sources of energy are going on due to which on commercial level solar cell achieved an efficiency of 15-20% and improving day by day. Some factors affects the performance of solar panel and decreases its output while some factors improves the efficiency of solar cell and improves its performance. These factors includes temperature of solar panel, humidity, and wind velocity, light intensity, altitude and air pressure along with many other factors. In this paper these factors are discussed along with their impact on solar cell performance.
Monogamy and Polygamy Relations of Quantum Correlations for Multipartite Systems

We study the monogamy and polygamy inequalities of quantum correlations in arbitrary dimensional multipartite quantum systems. We first derive the monogamy inequality of the α th $\left (0\leq \alpha \leq \frac {r}{2}, r\geq 2\right )$ power of concurrence for any 2 ⊗ 2 ⊗ 2n− 2 tripartite states and generalize it to the n-qubit quantum states. In addition to concurrence, we show that the monogamy relations are satisfied by other quantum correlation measures such as entanglement of formation. Moreover, the polygamy inequality of the β th (β ≤ 0) power of concurrence and the β th (β ≥ s, 0 ≤ s ≤ 1) power of the negativity are presented for 2 ⊗ 2 ⊗ 2n− 2. We then obtain the polygamy inequalities of quantum correlations for multipartite states. Finally, our results are shown to be tighter than previous studies using detailed examples.
Roslingifier: Semi-Automated Storytelling for Animated Scatterplots

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948–2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time—such as countries and continents with their demographic data—into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been studied before. In this article, we aim to define a design space for this new genre—data presentation—and provide a semi-automated authoring tool for helping presenters create quality presentations. From an in-depth analysis of video clips of presentations using interactive visualizations, we derive three specific techniques to achieve this: natural language narratives, visual effects that highlight events, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of identifying and clustering significant movements, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. From two user studies, we show that Roslingifier allows users to effectively create engaging data stories and the system features help both presenters and viewers find diverse insights.
Deep ensembling for perceptual image quality assessment

Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicate the perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database.
Analisis Kesulitan Membaca Permulaan Siswa Kelas 1 Sekolah Dasar

This study aims to explain the difficulty of reading at the beginning of grade 1 at SDN 022 Pulau Baru Kopah. This type of research is descriptive qualitative research. The subjects of this study were class 1A teachers and class 1A students. Data collection techniques used are interviews, observation and documentation. The data analysis technique uses the Milles and Huberman model with stages, data reduction, data presentation, and drawing conclusions. The results showed that 47.6% of students who had difficulty reading the beginning were, (1) students did not recognize letters, (2) could not read syllables, (3) could not read word for word, (4) could not read diphthongs, clusters and diagraphs, (5) cannot read consonants, (6) cannot read vowels, (7) series, (8) wrong prephasers, (9) do not know the meaning of words. The solution given by the teacher to overcome the difficulty of early reading is to provide additional hours, pay more attention to students who have difficulty reading the beginning and teach students to recognize letters with creative techniques such as singing, students are asked to write sentences and aloud and so on.
HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher’s public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments.
Feeling Meter

Abstract This article explores the nature of metrical knowledge underlying Justin London's many-meters hypothesis. It argues that meter is a form of culturally situated kinesthetic knowledge, or a knowledge of what it feels like to move to music in a particular way. This reframing of meter focuses on its technical and bodily dimensions, which links the analysis of meter with issues of social inclusion and exclusion, of learning and unlearning, and of habit and novelty. The article illustrates the implications of this approach with examples from recent progressive metal, where musicians manipulate the backbeat to present listeners with affordances for the enactment of different forms of time.
A Secure Visual Secret Sharing Scheme with Authentication Based on QR Code

With the rise of the Internet of Things (IoT), various devices in life and industry are closely linked. Because of its high payload, stable error correction capability, and convenience in reading and writing, Quick Response (QR) code has been widely researched in IoT. However, the security of privacy data in IoT is also a very important issue. At the same time, because IoT is developing towards low-power devices in order to be applied to more fields, the technology protecting the security of private needs to have the characteristics of low computational complexity. Visual Secret Sharing (VSS), with its features of safety and low computational cost, can fully meet the requirements of communication security in IoT. Therefore, a VSS scheme with QR code (VSS-QR) was proposed and has been applied to some extent. In VSS-QR, the secret is shared into a series of shares. These shares are usually common QR codes, which cannot cause the attention of the attacker. However, if there is dishonesty among participants, the secret cannot be recovered, which will lead to VSS-QR cannot be widely used due to its inadequate security. In this paper, we propose a visual secret sharing scheme with authentication based on QR code (VSSA-QR). Both the reconstructed secret QR code and shares can be verified whether they are forged by attackers. The abovementioned operations conveniently are performed on low-power QR scanning devices. Not only does the proposed scheme prevent some dishonest participants or attackers from cheating, but also prevent all participants from conspiring. In addition, the payload is the QR code itself, which is higher than other schemes. Theoretical analysis and experiments prove that the proposed scheme is effective.