Reviewerly/exHarmony huggingface dataset:
Towards Human Performance on Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) solutions are attracting increased interest in the field of computer vision. These solutions provide an intuitive and powerful tool to retrieve images in large-scale image databases. In this paper, we conduct a comprehensive study of classic triplet CNN training pipelines within the SBIR context. We study the impact of embeddings normalization, model sharing, margin selection, batch size, hard mining selection and the evolution of the number of hard triplets during training to propose several avenues for improvement. We also propose dropout column, an adaptation of dropout for triplet network and similar pipelines. In addition, we also introduce a novel approach to build state-of-the-art SBIR solutions that can be used with low power systems. The whole study is conducted using The Sketchy Database, a large-scale SBIR database. We carry out a series of experiments and show that adopting a few simple modifications enhances significantly existing SBIR pipelines (faster training & higher accuracy). Our study enables us to propose an enhanced pipeline that outperforms previous state-of-the-art on the Sketchy Database by a significant margin (a recall of 53.92% compared to 46.2% at k = 1) and reaches almost human performance (54.27%) on a large-scale benchmark.
Few-shot Object Detection as a Semi-supervised Learning Problem

This paper addresses the issue of dealing with few-shot learning settings in which different classes are annotated on different datasets. Each part of the data has exhaustive annotations for only one or a small set of classes, but not for others used in training. It is likely, that unannotated samples of a class exist, potentially impacting the gradient as negative samples. Because of this fact, we argue that few-shot learning is essentially a semi-supervised learning problem. We analyze how approaches from semi-supervised learning can be applied. In particular, the use of soft-sampling to weight the gradient based on overlap of detections and ground truth, and creating missing annotations using a preliminary detector are studied. The use of soft-sampling provides small but consistent improvements, at much lower computational effort than predicting additional annotations.
StyleGAN-based CLIP-guided Image Shape Manipulation

In this paper, we propose a text-guided image manipulation method which focuses on editing shape attribute using text description. We combine an image generation model, StyleGAN2, and image-text matching model, CLIP, and we have achieved the goal of image shape attribute manipulation by modifying the parameters of the pretrained StyleGAN2 generator. Qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of the proposed method.
Improving Nearest Neighbor Indexing by Multitask Learning

In the task of approximate nearest neighbor search, the conventional lookup-table indexing calculates the distances (or similarities) between the query and codewords, and then re-ranks the data points associated with the nearest (or the most similar) codewords. To address the codeword quantization loss problem exhibited in the conventional method, the probability-based indexing leverages the data distribution among codewords learned by neural networks to locate the nearest neighbor [8]. In this paper, we present a multitasking model to improve the probability-based indexing method. The model is formulated by two objectives of NN distribution probabilities and data retrieval quantity. The NN distribution probabilities are an estimation to determine the possible codewords where the nearest neighbor may be associated. The candidate retrieval quantity specifies the prediction for the least number of codewords to be re-ranked for capturing the nearest neighbor. The proposed model is then trained by minimizing triplet loss, probability loss, and quantity loss. By learning these tasks in parallel, we find the predictions for both data distribution probability and data retrieval quantity are more accurate, so that search accuracy and computation efficiency can be improved together. We experiment on two billion-scale benchmark datasets to evaluate the proposed method and compare with several approximate nearest neighbor search methods, and the results demonstrate the outperformance of the proposed method.
Scaling up stochastic gradient descent for non-convex optimisation

Abstract Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arising when using both shared and distributed memory. Typically, the former is bounded by limited computation resources and bandwidth whereas the latter suffers from communication overheads. We propose a unified distributed and parallel implementation of SGD (named DPSGD) that relies on both asynchronous distribution and lock-free parallelism. By combining two strategies into a unified framework, DPSGD is able to strike a better trade-off between local computation and communication. The convergence properties of DPSGD are studied for non-convex problems such as those arising in statistical modelling and machine learning. Our theoretical analysis shows that DPSGD leads to speed-up with respect to the number of cores and number of workers while guaranteeing an asymptotic convergence rate of $$O(1/\sqrt{T})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msqrt><mml:mi>T</mml:mi></mml:msqrt><mml:mo>)</mml:mo></mml:mrow></mml:math> given that the number of cores is bounded by $$T^{1/4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>T</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math> and the number of workers is bounded by $$T^{1/2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>T</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> where T is the number of iterations. The potential gains that can be achieved by DPSGD are demonstrated empirically on a stochastic variational inference problem (Latent Dirichlet Allocation) and on a deep reinforcement learning (DRL) problem (advantage actor critic - A2C) resulting in two algorithms: DPSVI and HSA2C. Empirical results validate our theoretical findings. Comparative studies are conducted to show the performance of the proposed DPSGD against the state-of-the-art DRL algorithms.
Trading-off privacy and utility: the Washington State experience assessing the performance of a public health digital exposure notification system for coronavirus disease 2019

Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State's experience utilizing these constructs to design data-driven evaluation approaches.We conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs.We identified 8 useful performance indicators within reach, engagement, and effectiveness constructs.We use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure.Continued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.
The algebraic structure of additive codes over 𝔽2𝔽8

In this paper, we investigate the algebraic structure of [Formula: see text]-additive codes, where [Formula: see text] and [Formula: see text] are nonnegative integers, [Formula: see text] (respectively, [Formula: see text]) denotes the finite field of order 2 (respectively, [Formula: see text]). We first give the generator polynomials of additive cyclic codes over [Formula: see text] and then the generator polynomials of additive cyclic codes over [Formula: see text] is also given. In addition, we introduce a linear map [Formula: see text], and study its properties. What’s more, the dual of additive cyclic codes over [Formula: see text] are investigated as well. And we get that the duals of any additive cyclic codes over [Formula: see text] are also additive cyclic codes. Finally, separable [Formula: see text]-additive cyclic codes are investigated.
Making Technical Debt Visible Using Hybrid Sankey Diagrams: An Industrial Case Study

Context: Technical debt (TD) is a challenge for companies who develop software on which their critical operations depend. To properly manage TD, it is necessary to make it visible to the different stakeholders involved to support informed decisions. Objective: To validate a TD visualization approach based on hybrid Sankey diagrams that makes the TD visible by showing (a) technical and business aspects, and (b) the flow of value and TD impacts. This approach regards visualizations as boundary objects. Method: We performed a multi-case study in a large multi-industry state-owned company. The objective was to validate the effectiveness of such visualizations and to explore their possible uses in TD management. We first used a retrospective case study on a TD decision-making scenario and, later, visualization usage scenarios using focus groups to evaluate its usefulness. Results: The results suggest that the proposed approach: (a) provides a structured process for systematic TD visualization to help the decision-making process; (b) enables the communication at knowledge boundaries between stakeholders to make informed decisions; (c) uses flow representations that are important for assessing the impact in multiple functional areas; and (d) enables documentation and reuse. Conclusion: The study results suggest that TD decision-making events can benefit from using our TD visualizations based on hybrid Sankey diagrams as boundary objects to portray the impact of TD in business, services, and technical aspects.
#buildbanksbetter: Central Bank Digital Currencies (CBDCs), Public Banks and Money’s Potential as a Non-Scarce Medium of Communication and a Source of Local Self-Determination

This paper argues that public banking at the local and community level is the most effective monetary technology for restoring the powers of finance to the people. Public banking is not a new idea and it’s enjoying newfound popularity alongside the rise of modern monetary theory (MMT) and the emergence of a new generation of financially aware and digitally enabled researchers, “conspiracy theorists” and activists. So while these days cryptocurrencies are trumpeted as the cutting edge of alternative monetary technologies, I suggest that public banking is the money technology best able to respond to the democratic desires of today’s citizens. I conclude with a discussion of the development of Central Bank Digital Currencies (CBDCs) and the ways they are being used to undermine local, community-led self-determination by further centralizing monetary control in the centrally controlled system of central banks whose mandate is to serve private interests.
Load Balancing Algorithms for Hadoop Cluster in Unbalanced Environment

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time.
Development of Interactive English Teaching Online Platform Based on Collaborative Filtering Algorithm

In order to better promote the development of Interactive English teaching, we develop the existing online teaching platform on the basis of an improved collaborative filtering recommendation algorithm and construct an online English teaching platform that can meet the needs of teaching interaction. With the help of the “hierarchical learning” mode, an interactive teaching model in the form of self-study is constructed to provide users with the most suitable English learning resources. A new method is used to search the nearest neighbor set intelligently for the target user as a supplement to its similarity. This algorithm can be used well in interactive English teaching mode. On this basis, the system module of this study mainly includes a communication page, course evaluation, collaborative editing, and resource sharing. This study also investigates the actual use of students. The results show that the interactive English online teaching platform can meet most of the students’ needs. In this study, the interactive English online teaching platform developed based on a collaborative filtering algorithm has a significant effect, which enhances the interactivity of teaching, improves the quality of English teaching, and increases the breadth of teaching.
Knowledge base question answering based on regularization and feature fusion

Knowledge base based question and answer method has achieved remarkable success by deep learning. However, the method research process remains challenging due to the difficulty of capturing the global information of the question, the inconsistency of the outputs in training and prediction, and the low recognition accuracy of the current topic entity detection. Here, we propose an improved model TRBAM based on BAMnet to efficiently and effectively solve above problems. TRBAM is improved by Transformer and R-dropout. In the problem feature extraction layer, we use BiLSTM and Transformer to extract features from the problem respectively, and fuses the two extracted problem features to obtain a new problem representation, so that the model can more fully capture semantic information in the problem; especially, we improve the generalizability by R-dropout. We improve the subject entity detection model entnet by R-dropout to improve the accuracy in recognizing the best subject entities. The experimental results show that the improved model TRBAM has some performance improvement compared with the BAMnet model, the improved entnet model has effectively improved the recognition accuracy of the best subject entities, and the overall method of this paper has some advantages compared with other methods.
Image interpolation

In signal processing, interpolation, as one of the fundamental operations, is a significant step when up sampling images. Traditional linear interpolation methods include the nearest neighbor method, the bilinear method, and the bicubic method. Non-linear interpolation methods include wavelets method and methods based on edge detection. This paper compares the major three linear interpolation methods and two non-linear interpolation methods in details. By utilizing both the python code, the Canny method is found to provide the best performance when up sampling images.
Research on the application of cloud computing big data processing technology in smart grid construction

With the development of society, the power system is becoming larger and larger, which makes the analysis and processing of power data more and more complex. It is necessary to introduce modern data technology to make the power system develop rapidly in the new era and better serve people's production and life. From the perspective of cloud computing technology, this paper analyzes the smart grid big data processing in detail.
Research on smoke-free university construction management system based on cloud service

In order to improve health awareness and consciousness of tobacco control, the introduction of cloud service platform system design ideas, put forward the smoke-free university construction management system based on cloud services research ideas. Through the construction of three layers of cloud service construction and management mode of application layer, platform layer and facility layer, relying on SpringBoot, MyBatis, LFW, OpenCV and other technologies of server and VUE 2.x and other technologies of WEB side, the effective identification and data feedback of smoking behavior of teachers and students are realized, so as to realize the construction and management of smoke-free universities under cloud service. After testing, it is found that the designed system can realize the identification and application of synchronization segment, has practical application technology value, and provides cloud power for the construction of smoke-free campus universities.
Psychological adaptation level optimization to attain the goal of sustainable education

The article aimed to study the psychological adaptability of university students. Along with that, the influence of sports activities on enhancing the psychology of students is also examined. The study also analyzes the impact of music on the psychoogy of students. A two-factor model is used for analyzing the level of college students' mental health. In this study, data was collected from 555 first-year undergraduate university students through an online questionnaire survey. Levels of both positive and negative psychological factors among students are examined. The findings indicate that students who participate in sports appear to have higher levels of positive psychological characteristics than students who do not exhibit any interest in participating in sports. Additionally, students that participate in sports have lower levels of negative psychological effects than nonparticipants. As a result, the study concluded that sports and other physical activities have a positive effect on the psychology of university students. The advantages of physical activities and sports in enhancing the mental and emotional health of students are highlighted in this study. To improve students' psychology and attain better academic results, educational institutions should provide students with various opportunities for sports.
Preschool Cyber Security Management System Based on Intelligent Agents

As information and communication technologies create an ever-increasing complexity in interconnected systems and devices, cybersecurity and privacy issues are constantly at the fore, highlighting the need to strengthen the protection and resilience of these systems against the ever-evolving threats of modern cyberspace. This particular work, taking into account that preschool children now have significant needs to ensure their digital identity and, in general, their protection from their contacts with the internet, aspires to provide an understandable and practical guide to strengthen the security of information systems and information from both public and private school agencies. Specifically, a preschool cybersecurity management system based on intelligent agents is proposed. Using sophisticated, intelligent techniques, it aims to improve the ability of preschools to resist modern threats adequately, respond to cyber-attack incidents with the least possible impact, and protect their critical systems, services offered, and the personal data they hold and process. The system intends to link and control distributed systems that currently exist, as well as to solve issues that are beyond the knowledge and skills of a single agent. This novel research idea has never been offered in the relevant literature, and we think it has the potential to advance the state of the art in cybersecurity significantly.
Design of Hotel Human Interaction System Based on Deep Learning

This paper designs a hotel human-computer interaction (HCI) system which is based on deep learning. The whole system mainly includes face detection, speech synthesis, and speech recognition. Real time face detection is realized by transplanting the Opencv library into the Android system and combining that with the AdaBoost algorithm. Furthermore, the local speech recognition is realized through the special chip for speech recognition hbr740, while the local speech synthesis is realized through the special chip for speech synthesis syn6288. Subsequently, the massive speech resources are obtained through the network connection into the iFLYTEK’s open platform, which can realize online speech recognition, semantic understanding, and speech synthesis. In the data flow phase, we transmit data to the lower computer through the serial port of tiny4412 in order to realize the motion control of the lower computer. This is achieved through the SQLite database for system voice interaction and motion control, which is built through the jar package of the Litepal and POI. Finally, a hybrid voice interaction system is designed combining the local voice interaction with the online voice interaction. Through numerical simulation, the suggested system is tested to verify the feasibility of the hybrid voice interaction scheme. We observed that when the network is in good condition, the speech recognition rate of the whole system reaches as high as 94.67%; while without the network, the speech recognition rate can still reaches up to 84.67%. The attained outcomes demonstrate the superiority of the suggested hybrid system.