Reviewerly/exHarmony huggingface dataset:
Sequential Gradient Coding For Straggler Mitigation

In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient technique that uses principles of error-correcting codes to distribute gradient computation in the presence of stragglers. In this paper, we consider the distributed computation of a sequence of gradients $\{g(1),g(2),\ldots,g(J)\}$, where processing of each gradient $g(t)$ starts in round-$t$ and finishes by round-$(t+T)$. Here $T\geq 0$ denotes a delay parameter. For the GC scheme, coding is only across computing nodes and this results in a solution where $T=0$. On the other hand, having $T>0$ allows for designing schemes which exploit the temporal dimension as well. In this work, we propose two schemes that demonstrate improved performance compared to GC. Our first scheme combines GC with selective repetition of previously unfinished tasks and achieves improved straggler mitigation. In our second scheme, which constitutes our main contribution, we apply GC to a subset of the tasks and repetition for the remainder of the tasks. We then multiplex these two classes of tasks across workers and rounds in an adaptive manner, based on past straggler patterns. Using theoretical analysis, we demonstrate that our second scheme achieves significant reduction in the computational load. In our experiments, we study a practical setting of concurrently training multiple neural networks over an AWS Lambda cluster involving 256 worker nodes, where our framework naturally applies. We demonstrate that the latter scheme can yield a 16\% improvement in runtime over the baseline GC scheme, in the presence of naturally occurring, non-simulated stragglers.
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation

In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training self-supervised depth estimation. We argue that the semantics of a coherent group of pixels in 3D space is self-contained and invariant to the contexts in which they appear. We group coherent, semantically related pixels into coherent depth regions given their estimated depth and use copy-paste to synthetically vary their contexts. In this way, cross-context correspondences are built in contrastive learning and a context-invariant representation is learned. For unsupervised semantic segmentation of urban scenes, our method surpasses the previous state-of-the-art baseline by +7.14% in mIoU on Cityscapes and +6.65% on KITTI. For fine-tuning on Cityscapes and KITTI segmentation, our method is competitive with existing models, yet, we do not need to pre-train on ImageNet or COCO, and we are also more computationally efficient. Our code is available on https://github.com/LeungTsang/CPCDR
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data

Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. In this paper, we present a deep learning-based method - GRAph Convolutional nEtwork feature Selector (GRACES) - to select important features for HDLSS data. We demonstrate empirical evidence that GRACES outperforms other feature selection methods on both synthetic and real-world datasets.
Online Movie Ticket Booking System

The development of an online movie ticket booking system for a movie theatre is the goal of this project. The reservation of tickets will be automated with this program. Any user will be able to access a movie theatre's website provided by this movie ticket reservation system There are three logins: admin login, theatre login and user login. The administrator adds or deletes the movies, the theatre operator adds or deletes the show times for a movie, the user books a ticket for a specific movie. The user confirms their choice of seats at the cinema. Once the seats are confirmed, a voice message is issued. At any time, the user can use this website to buy tickets online.
Human being in a world without alternatives: active or passive anthropocentrism

The article discusses the need for Man to develop such a way of being in the World that would exclude the possibility of worldview and practical, anthropocentrism. It is noted that the Western way of being was initially anthropocentric. At the same time, anthropocentrism here appears in two modes as active and as passive. It is emphasized that the eastern way of being is ontologically acceptable, but it does not orient Man to be a co-worker of cosmogenesis. It is concluded that the desired way of being a person in the World can be grown from the synthesis of what is represented in the eastern and western ways of human being.
Frequent Itemset Mining using QUBO

In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
FeynMG: a FeynRules extension for scalar-tensor theories of gravity

The ability to represent perturbative expansions of interacting quantum field theories in terms of simple diagrammatic rules has revolutionized calculations in particle physics (and elsewhere). Moreover, these rules are readily automated, a process that has catalysed the rise of symbolic algebra packages. However, in the case of extended theories of gravity, such as scalar-tensor theories, it is necessary to precondition the Lagrangian to apply this automation or, at the very least, to take advantage of existing software pipelines. We present a Mathematica code FeynMG, which works in conjunction with the well-known package FeynRules, to do just that: FeynMG takes as inputs the FeynRules model file for a non-gravitational theory and a user-supplied gravitational Lagrangian. FeynMG provides functionality that inserts the minimal gravitational couplings of the degrees of freedom specified in the model file, determines the couplings of the additional tensor and scalar degrees of freedom (the metric and the scalar field from the gravitational sector), and preconditions the resulting Lagrangian so that it can be passed to FeynRules, either directly or by outputting an updated FeynRules model file. The Feynman rules can then be determined and output through FeynRules, using existing universal output formats and interfaces to other analysis packages.
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both texts and images. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.
BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV (Birds-Eye-View) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map queries sequence. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-locator is capable to estimate the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052m, 0.135m and 0.251$^\circ$ in lateral, longitudinal translation and heading angle degree.
Robotic Arm Generative Painting Through Real-time Analysis of Music Performance

This paper describes a prototype audio-visual performance of a Ufactory Uarm Swift and a live musician. In this setting, the robotic arm was used as an AI agent to create a visual representation of a musical work in real-time. An A4 white canvas was gradually filled with a mixture of black, blue, red and yellow paints across the span of approximately eight minutes. The musician, performing on an acoustic violin, fitted with a custom built audio interface, performed multiple versions of an improvisatory work developed specifically for the prototype performance. The following sections discuss our technical approach to programming and implementing the Ufactory Uarm Swift as a painting arm, reflections of the musical process and propose future directions for this project.
Research on system integration architecture of industrial robot design platform

With the development and progress of the times, China’s scientific and technological strength has gradually improved. The communication between robots and humans is getting closer and closer, and humans have endowed robots with emotions and actions through repeated research and experiments. The research of robots has also attracted the attention of many experts and scholars in the fields of psychology and computer science. These scholars and experts combine computer science with psychological science and cognitive science to try to create a human-computer interaction environment with emotional feedback. Efforts to achieve harmonious and natural human-computer interaction. The research content of this paper is guided by artificial psychology theory and affective computing, and the technology of constructing service robot interaction platform is studied in detail. The following issues related to service robots are analyzed and discussed: control architecture, emotion expression, emotion modeling, robotic application systems, etc. It also lays a solid theoretical foundation for the establishment of a harmonious man-machine.
Pemisahan Objek Sel Tumpang Tindih pada Citra Pap Smear dengan Metode Deep learning dan Watershed

The object observed in the Pap Smear image is Cervical Cancer which forms overlapping cells. This cancer must be observed early because it is a disease that causes the death of thousands of women worldwide every year. The death rate from this disease is the fourth highest among women. One way to be aware of this disease is to do an early check on the Pap Smear test image. This cell separation process uses the image segmentation method. This method is one of the important steps to be able to identify existing cell objects. This study proposes a segmentation method to separate 2 overlapping cells in the RepomedUNM dataset. The dataset is engineered in the manufacture of synthetic Pap Smear images. The segmentation method proposed is a Deep Learning-based method so that it can identify 2 overlapping cells in one area. The level of accuracy of the test with an average score of Intersection over Union (IoU) is 0.9003. And the results of segmentation with Deep Learning can be divided into all areas using the Watershed segmentation method. So that this research becomes a reference in the early identification of Cervical Cancer.
Impact on the Quality of Interpersonal Relationships by Proximity using the Ventriloquism Effect in a Virtual Environment

This paper discusses more effective interaction between a user immersed in a virtual environment (VE) and a salesperson avatar. Utilizing the superiority of visual information over auditory information, we implemented an interpersonal situation in which only a sound image invaded the user’s personal space (PS) using a virtual salesperson avatar. We investigated the impressions that 16 participants had of the avatar. The experimental results showed that when the sound image invaded the PS, the correlation coefficient between the distance from the user to the sound image normalized by the interpersonal distance of each participant and the rapport for service quality was -0.24 (p<0.05). This indicates that in the range of the ventriloquism effect, the avatar’s sound image intruding into the PS and approaching the user leads to an improvement in rapport. The application of the technique proposed in this paper, in which the position of the visual image and the sound image diverge, is expected to improve the value cocreation of the interpersonal service experience in VEs such as virtual stores, which have been increasing in recent years.
Parametric Synthesis of Quantum Circuits for Training Perceptron Neural Networks

This work contains the analysis of results received after running synthesized quantum circuits for training perceptron neural networks. The training is performed by creating a Grover’s algorithm with a custom oracle function. The concept of synthesizing quantum circuits was showcased in the process of generating training circuits for three perceptron topologies, which were designed to test the accuracy of the synthesis process. The test circuits serve to prove that the proposed synthesis approach could be scaled to utilize more complex quantum computing systems and to solve more practical tasks. IBM’s 100-qubit cloud quantum simulator was used as the debugging environment. Quantum circuits for described algorithms are generated by the "Naginata" quantum synthesizer, its source code is published and further documented on GitHub along with the code for the provided example algorithms. The article describes the processes behind the algorithm for synthesizing quantum circuits that perform the training process of single-layer perceptrons by finding their weights by filtering all possible input values through a predefined accuracy criterion. Since quantum computing is still in its early development phase, quantum circuits are created mainly by manual placement of logic elements. Implementing quantum algorithms, especially more use-case specific ones, directly on the quantum circuit level could lead to the circuit easily becoming too complex for human comprehension. Quantum Circuit Synthesizer "Naginata" was created to simplify the development and debugging process of quantum algorithms, by adding better clarity to their development process. In our case, better clarity for the development process is achieved by composing functions for commonly used operations performed in the implemented quantum algorithm. The programmer could now implement the quantum algorithm as a set of functions, instead of manually creating a circuit from single logic elements. After this, the synthesizer would handle the task of creating the data for placing logic elements on the circuit. This enables an opportunity of implementing quantum algorithms with higher-level commands. In the scope of this work, parametrically generated generic blocks for frequently used operations such as: the adder, multiplier and digital comparator were created and utilized to form the training circuits. The test results, proved that with the help of the proposed quantum synthesizer, these compositions could be used efficiently as building blocks for implementing quantum algorithms. And by visually comparing sizes of both code and circuit representations of the synthesized circuits, to the code examples used to synthesize these circuits, it is determined that the proposed approach for implementing quantum circuits greatly simplifies the processes of development and debugging a quantum algorithm.
Dual-wavelength interferometry based on dispersion

Interferometry is commonly used for an optical element surface accurate test. But the testing dynamic range is affected by the ambiguity of 2π. To solve this problem, dual-wavelength interferometry testing method has been proposed. The current dual-wavelength interference system usually uses two different wavelength monochromatic lights to work time-sharing and obtain their interference patterns respectively, which makes the system complex and measurement time-consuming. In this paper, we put forward a dual-wavelength testing method based on Michelson interference system. It enables simple and efficient extraction of the phase distribution of the tested optical element surface to be realized in a sub-millimeter scale dynamic range with a nanometer accuracy. A sodium lamp has two different wavelengths, 589 nm and 589.6 nm, it is selected as the light of our interference measurement system, so the equivalent wavelength is 0.579 mm. A dispersion element is adopted to make the interference patterns which correspond to 589 nm and 589.6 nm can be separated. Furthermore, in order to eliminate the influence of background light intensity on the interference patterns processing, we do Fourier transform for the patterns recorded by CCD to extract the spectral component related to the tested phase. And then, an inverse Fourier transform for this component is done to obtain the phase distribution. Finally, the tested optical element surface can be obtained from the phase distribution. Simulations have been done to validate the feasibility of the method. The test error of the surface profile is 0.252 nmRMS. The simulations prove that this method can guarantee high accuracy and expand the detection range.
Optimising First-Class Pattern Matching

Pattern matching is a high-level notation for programs to analyse the shape of data, and can be optimised to efficient low-level instructions. The Stratego language uses first-class pattern matching, a powerful form of pattern matching that traditional optimisation techniques do not apply to directly.
Conditioning factors in the remote learning context in higher education

With the epidemiological setting produced by the COVID-19 pandemic, information technologies have increased in importance in education and have become even more significant, forcing unanticipated and fundamental adjustments in work organization. To guarantee that students continue to study, these developments in educational institutions have led to the growing usage of remote learning. This study investigates the factors that either support or constrain students' perceptions of their perceived efficacy and skill development in a remote learning environment during the second COVID-19 lockdown, in Portugal. Between March 1 and March 15, 2021, 258 students (higher school) who were enrolled in remote learning responded to a web-based cross-sectional survey. The results of a regression analysis revealed several conditioning factors for this learning mode that could impact on students' efficacy and soft skills development: technology issues, learning conditions (e.g., autonomy, study materials) and demotivation were all significant factors. Given the current and future generations of students, the results of this study provide clues for managers and teachers of higher education establishments to develop digital strategies to provide innovative, attractive, and motivating learning environments.
Analyzing Database Security and A Study of Ownership Protection using Watermarking Algorithm

This paper is an analysis of database security and a study of ownership protection using the watermarking algorithm. Information is a crucial component of a database, and customers rely on service providers to have a solid system in place to guard against malicious attacks and data breaches. To boost the database security level, a few different types of approaches are used. The watermarking algorithm is one of those approaches. Digital watermarking is an important sub-field of information concealment technology research. It is a technology that incorporates identifying data into digital works. To protect digital product copyright, the basic idea is to embed confidential information in digital products such as images, audio, and video to achieve data fusion. The current digital watermarking technology has made significant progress in the field of multimedia information (such as images, audio, and video). It is resistant to frequent database attacks.
Fault Location of Secondary Equipment in Smart Substation Based on Transformer

Aiming at the problems that there are many alarm signals of secondary equipment in smart substation, and misjudgment of fault equipment because the unbalanced number of fault samples may lead to insufficient learning of neural network, a fault location method for secondary equipment in smart substation based on Transformer is proposed. Firstly, an alarm signal set is formed by using the alarm signal when secondary equipment fails. Secondly, using Transformer network, fault location model of secondary equipment deep neural network is established, and the process of secondary equipment fault location is given. Finally, taking a typical 220kV line interval as an example, the validity and accuracy of secondary equipment fault location model based on Transformer are verified. Compared with fault location models based on recurrent neural network and long short-term memory, the method proposed in this paper can more quickly and accurately locate main secondary equipment in smart substation.