Reviewerly/exHarmony huggingface dataset:
Approaching the Quantum Limit of sub-Diffraction Incoherent Barcode Reading

We developed a quantum-inspired adaptive algorithm using spatial-mode-demultiplexing measurements to read incoherently illuminated barcodes at large distances. In simulations, our scheme accurately discriminates barcodes in 20x less time than direct imaging at a standoff of 1 km.
Radix‐16 CSA‐based low‐latency non‐Montgomery modular multiplier

The long-precision modular multiplier is usually performed by Montgomery algorithm with bit shifts, yielding a result in the form of A · B · 2 − n mod M . In fact, with the precomputation of δ · 2 n mod M proposed by others, it is able to compute classic modular multiplication A · B mod M rather than Montgomery modular multiplication A · B · N − 1 mod M . In this work, a multi-bit CSA-based long-precision modular multiplier is proposed for hardware implementation. It includes two carry save adders and one RAM access to perform modular multiplications. After every modular multiplication, a division is applied to reduce the result down to [ 0 , M ) . Hardware implementation results show that a 1024-bit modular multiplication can be completed in 2.39 μs on the XC5V FPGA platform, costing 5793 slices, 8 DSPs and 36 BRAMs, which is a promising candidate to compute incontinuous classic modular multiplications.
Back to the Future? The Digital Services Act and regulating online platforms built on community-led moderation.

The Digital Services Act (DSA) is the most profound update of the EU’s online legislation since 2000, when the e-Commerce Directive was adopted. On the one hand, the European Commission’s proposal wrestles with this long legacy of conceptualizing what online intermediation is about. On the other, it tackles technological developments and current approaches to capitalizing on user-generated content. This article follows the evolution of the EU legislator’s approaches to conceptualizing online intermediation’s distinctive features. It investigates the adequacy of responses crafted to address illegal content and its availability from the perspective of user freedom. The following assessment focuses on services that have adopted user-led content moderation as their model of promoting freedom to receive and impart information and access to free knowledge. Due diligence provisions aimed at increasing transparency, especially those shaping the terms and conditions of online platforms and involving notice and action mechanism, provide key examples.
PROJECT CULTURE OF TEAM STRATEGIES

Modern forms of design activities that contribute to the development of professional design are considered in the article. The principles of designers’ teamwork, which are similar in structure but differ in content, are compared. In one case, this is teamwork; in the other, groups of like-minded people. It has been proved that colleagues unite by common values, meanings, and ideological attitudes to form a picture of the future and formulate a global vision of professional strategies, design principles, methods, and attitudes to improve the quality of life. By joint intellectual and spiritual efforts, like-minded people form the vector of modern design activity. The professional elite acts as the driving force behind the evolutionary transformation of contemporary design, reveals the potential of conscious design and advanced design thinking, determines the place of design in the future professions, and sets moral standards of behaviour. The types of project design thinking (from engineering, creative – to artistic and resource one) are presented as the results of the individual creativity of team members. The continuous development of certain areas of design thinking and the acquisition of competencies based on individual capabilities provides designers with long-term professional development, regardless of external conditions. Moreover, an honest attitude towards oneself and one’s abilities promotes a deep understanding of the profession, which allows the development of new types of design that respond to deep human needs. In conclusion, self-knowledge, gained in a collective creative act, promotes the development of individual abilities and leads to intellectual and spiritual co-evolution.
High-accuracy real-time omnidirectional 3D scanning and inspection system

Three-dimensional (3D) imaging technology has been widely applied in various fields, such as intelligent manufacturing, online inspection, reverse engineering, cultural relic protection, etc. In this work, we present a high-accuracy real-time omnidirectional 3D scanning and inspection system based on fringe projection profilometry. Firstly, a multi-camera system based on geometric constraints is constructed to perform stereo phase unfolding without additional auxiliary projection images to ensure high-accuracy 3D data acquisition in real time. Then, we propose a rapid 3D point cloud registration approach combining simultaneous localization and mapping (SLAM) with iterative closest point (ICP) techniques to achieve alignment of point cloud slices with accuracy of up to 100 microns. Finally, a cycle-positioning-based registration scheme is developed to allow for real-time 360 degree 3D surface defect inspection. The experimental results show that our system is capable of real-time omnidirectional 3D modelling and real-time 360&deg; defect detection.
Parallel construction of module networks

Module networks (MoNets) are a parameter-sharing specialization of Bayesian networks that are used for reasoning about multi-dimensional entities with concerted interactions between groups of variables. Construction of MoNets is compute-intensive, with sequential methods requiring months for learning networks with a few thousand variables. In this paper, we present the first scalable distributed-memory parallel solution for constructing MoNets by parallelizing Lemon-Tree, a widely used sequential software. We demonstrate the scalability of our parallel method on a key application of MoNets - the construction of genome-scale gene regulatory networks. Using 4096 cores, our parallel implementation constructs regulatory networks for 5, 716 and 18, 373 genes of two model organisms in 24 minutes and 4.2 hours, compared to an estimated 49 and 1561 days using Lemon-Tree for generating exactly the same networks, respectively. Our method is application-agnostic and broadly applicable to the learning of high-dimensional MoNets for any of its wide array of applications.
Sensitivity Analysis of the Square Frequency Response of IIR Digital Filters in Equivalent Direct Form

This work was performed within the framework of a new approach of design of IIR digital filters (DF) with a finite word length (FWL) developed by the authors. In the developed approach, the poles and the zeros of the IIR DF are computed considering the FWL already at the stage of functional synthesis. The results obtained at the functional stage of synthesis will not distort on the stage of structural synthesis. This considers the algebraic-numerical nature of the zeros and poles of recursive filters with coefficients having a finite bit depth investigated by the authors. Since at the stage of constructing the DF block diagram, the zeros and poles have already been determined, it may seem that the calculation of the sensitivity of specific structures does not make sense. However, the sensitivity calculations implemented in this article are necessary to implement the developed approach. The fact is that analytical expressions that allow functional synthesis taking into account FWL are obtained only for zeros and poles, which are algebraic numbers of the second degree. Therefore, for the algebraic numbers of higher degree we must apply the indirect approach. In this case, the calculation of zeros and poles is replaced by the calculation of the coefficients of the equivalent direct form of the DF with the corresponding bit depth. To reduce the complexity of solving the functional synthesis problem, effective algorithms for calculating the above coefficients, developed within the framework of the approach being implemented, require an analysis of the module of the normalized absolute sensitivity of the DF equivalent direct form. This paper is devoted to this task.
Image Enhancement for Railway Inspections Based on CycleGAN under the Retinex Theory

Under the “safe, green, efficient and intelligent” requirements of modern railway construction, it's important and challenging to improve the automated and intelligent railway inspection system, which is an essential part of building domestic transportation infrastructure. Furthermore, the enhancement of images collected in low-light environments is an indispensable prerequisite for the railway inspection system to carry out intelligent analysis. Therefore, a low-light image enhancement means inspired by the Retinex theory based on CycleGAN is advanced. Specifically, a residual neural network is designed to decompose input images. Meanwhile, the unsupervised learning model CycleGAN is selected to adjust brightness maps. In addition, the BM3D algorithm is used to reduce the noise in reflection images. Eventually, extensive experiments on multiple data sets show that the proposed method can better restore image details, obtain more comfortable visual effects and more natural colors. Moreover, the enhancement results are better than the majority of classic methods.
A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA

In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combination model based on SVR and ARIMA is proposed. The main difference in comparison with other works is the use of an interactive Particle Swarm Optimization (PSO) to increase the prediction performance. To the experimental setup, six well-known datasets of the literature is used. The performance is assessed by the metrics Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show the proposed system attains better outcomes when compared to the other tested techniques, for most of the used data.
Calibration of the astigmatism errors induced by misalignment of quadri-wave lateral shearing interferometer

Error calibration is one of the most important factors to realize the quadri-wave lateral shearing interferometer (QWLSI) with high accuracy. The misalignment errors of QWLSI, such as the tilt of grating and the tilt of charge coupled device (CCD), will affect the measurement accuracy. The astigmatism errors induced by the tilt of grating and CCD during the alignment process of QWLSI, which are neglected in previous studies, are analyzed and presented in analytical expressions in this paper. Firstly, the additional phase difference in X and Y directions induced by the tilt of grating and CCD are analyzed using the optical wave interference theory. Representing the phase difference in the two directions and the test wavefront with the combinations of Zernike polynomials respectively, we further obtain the analytical expressions between the Zernike coefficients of the phase difference and the Zernike coefficients of the test wavefront, according to the wavefront reconstruction theory. Then the analytical expressions for the measurement errors induced by the tilt of grating and CCD, which are mainly astigmatism, can be obtained. The analytical results show that the misalignment induced astigmatism errors are inversely proportional to the shearing ratio and proportional to the tilt angle of grating and CCD. The alignment experiment of a home-made QWLSI under null test condition is conducted to verify the correctness of the theoretical analysis. With different shearing ratios for the QWLSI, the astigmatism errors, which are induced by the tilt of grating in experimental results, are consistent with the theoretical analysis results. This paper can provide technical support for the alignment of QWLSI with small shearing ratio and high precision.
Experimental Analysis of Line Detection Based on Image Segmentation

With the rapid development of computer technology, digital image analysis is increasingly becoming an important application in many fields such as face detection and, therefore, can be widely applied to various industries. However, while this technology aims to make people's lives more convenient, it still contains few issues about the precision of object detection. We proposed an experimental analysis based on image segmentation to tackle this issue, which can be used to explore the correlation between the precision of object detection and the adjusted threshold values. We use a multi-paradigm programming language MATLAB to extract the features of images. The process involves image denoising, sampling and quantization, segmentation, and compression. The experimental results demonstrated that the more sampling points surrounded around the shape of the objects represent, the better the line detection. Furthermore, we note that as the number of sample points decreases, the boundary of each pixel becomes clearer, which negatively affects the spatial resolution of the line detection image.
Education Informatics (EI) For Effective Student Progression In B-Schools: A Framework

Informatics as a term evolved in the 50s but the field of informatics is gaining attention in the recent past. There have been several initiatives taken in the direction of healthcare informatics and nursing but education informatics has not been much developed so far. Education informatics is an emerging field today. B-Schools today face challenges in the efficient functioning and organization of volumes of information that are generated but used sparingly. This paper explores the different dimensions for using, organizing, and converting data generated at B-Schools for sustainable functioning and enhanced student progression.
Analysis on the Strategies of Information Technology to Improve Autonomous Learning in Higher Vocational English Teaching

In the environment of continuous deepening of information technology, higher vocational English classroom has also poured into different teaching modes, to a certain extent, it has played a role in promoting students’ autonomous learning ability, teaching efficiency and teaching quality. Higher vocational English teaching must innovate and break through the traditional self, emphasize the effective linkage of content inside and outside the teaching process, using information multimedia platforms to stimulate students’ subjective initiative in English learning. For higher vocational students, having a strong autonomous language learning ability is the basis for survival and development in their future careers. This paper mainly analyzes the definition and characteristics of information teaching, the importance and necessity of autonomous learning of higher vocational students, and explain the feasibility of cultivating students’ autonomous learning ability through information technology. Meanwhile, not only the effective guidance of teachers and the perseverance of students are needed, but also the schools need to provide abundant educational resources and information technology to enhance students’ independent learning and individualized learning capabilities.
A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion

This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorithms although with some improvements, still exist big imbalances between local exploration and macro development or between route (solution) diversity and faster convergence. In this paper, a greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric Brownian motion, is proposed, based on our earlier theoretical breakthroughs on G-Brownian motion and through a series of mathematical derivations or proofs; furthermore, in order for suitable for the hybrid processor architecture of the scheduling management server, the algorithm is designed in parallel with deep fusion of coarse-grained and master-slave models. A large number of experimental results are given. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with lower improvement factors for small inconsistent instances.
Source Task Selection in Time Series via Performance Prediction

Deep Learning has shown high performance in different domains, however, they need large computational resources and training data sets. Transfer learning offers an alternative to reduce both aspects but requires an effective way for selecting relevant pre-trained models for the current task. In this work, we propose a meta-learning formulation using the entropies of the feature maps produced by a pre-trained model for predicting its performance in a new target task via a regression model. Our method is tested in the time-series domain, where it obtains a better top-1 precision and a better position of a useful source task than state-of-the-art methods in 85 datasets from the UCR archive.
Cross-modal Retrieval of Archives based on Principal Affinity Representation

The development of information technology has resulted in an exponential increase of archive information. Using cross-modal retrieval can achieve mutual retrieval of data like image and text. Aside from the former progresses, it is still challenging to mine both inter-modal connection and the intrinsic semantic associations of cross-modal data. In this paper, we propose a method to achieve an accurate and effective cross-modal retrieval. It uniformly represents heterogeneous data through the principal affinity representation algorithm based on a hybrid kernel function. To improve the accuracy of retrieval, we first employ an adaptive nearest neighbor search method to dynamically decide the retrieval radius. The search method is then combined with the existing tree structure-based retrieval algorithm to find the nearest neighbor points efficiently. The experimental results show our algorithms have a certain improvement in efficiency and accuracy of cross-modal retrieval.
CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems

Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendation, which may hurt users' or providers' satisfaction in recommender systems as well as the interests of the platforms. The tutorial focuses on the foundations and algorithms for fairness in recommendation. It also presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking. The tutorial will introduce the taxonomies of current fairness definitions and evaluation metrics for fairness concerns. We will introduce previous works about fairness in recommendation and also put forward future fairness research directions. The tutorial aims at introducing and communicating fairness in recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Sand Scope

In the artwork, the topic of life space has been discussed. Instead of physical space, mental space of human being was considered. People usually focus on themselves to solve various life tasks, and the scales of their mental space influence how they realize the world. The artwork tried to arouse people to aware the connection between mental space and life space. The Sand Scope introduces a microcosm of the world, for comparing the scale of mental space with the scale of the microcosm, from the relative scales between the microcosm and the whole world. From the new perspective, the Sand Scope reminds people to escape from the routine of their daily lives, for rethinking meaning of life. Multimedia input contains gray image analysis to form the stamps with portraits of current audiences and past participants, color image subtraction to compose texture of the mountain drawing with wearing cloth information on the painting, and a buffer with timer to capture and replay ambient sounds continuously in a delayed time, along with color images with blending effect in the period. In the interactive installation, an improvisational painting in the form of a Chinese brush painting with stamps from connoisseurs was exhibited. The generation of stamps from the audiences on the painting also indicates that they are parts of the microcosm. The microcosm was constructed from the elements of the inhabitants who live in the real world in physical aspect, and the awareness of meaning of life implies harmony between nature and humanity on the Zen painting in mental aspect.
Learning Through Play; a Study Investigating How Effective Video Games Can Be Regarding Keyboard Education at a Beginner Level

This paper describes the development of a set of video games designed to reduce the high drop-off rates associated with learning to play the keyboard by gamifying rote tasks that require monotonous practice. By defining our own understanding of what musicianship is and creating a custom framework for assessment through the use of existing curriculums and learning applications, we have chosen specific areas which require the most rote learning, are critical to developing motor skills and to building an understanding of music; these include learning and practicing musical scales, keeping in time with tempo and the basics of hand coordination and fingering styles. We developed solutions which offer a new way for learners to practice in an engaging and entertaining way with the aim to reduce the drop-off rates and lower the barrier for entry to learning keyboard. Developing games requires an iterative process of development, testing, isolating key issues and solving them through further development. Therefore, through a pilot study (using observations, screen recordings and semi-structured interviews as data collection methods), we have observed that whilst this novel method of learning and practicing using video games is positively accepted by learners and teachers alike, the games themselves and the process of validation requires refinement in order to truly gauge each game relating to engagement, motivation and educational benefit. This paper describes the findings of this pilot study regarding the improvements and changes of each developed game as well how to improve future user studies.