Reviewerly/exHarmony huggingface dataset:
On the Logical Error Rate of Sparse Quantum Codes

The quantum paradigm presents a phenomenon known as degeneracy that can potentially improve the performance of quantum error correcting codes.However, the effects of this mechanism are sometimes ignored when evaluating the performance of sparse quantum codes and the logical error rate is not always correctly reported.In this paper, we discuss previously existing methods to compute the logical error rate and we present an efficient coset-based method inspired by classical coding strategies to estimate degenerate errors and distinguish them from logical errors.Additionally, we show that the proposed method presents a computational advantage for the family of Calderbank-Shor-Steane (CSS) codes.We use this method to prove that degenerate errors are frequent in a specific family of sparse quantum codes, which stresses the importance of accurately reporting their performance.Our results also reveal that the modified decoding strategies proposed in the literature are an important tool to improve the performance of sparse quantum codes.
A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning

Based on the concept of letting training organizations only exchange their partial gradients instead of the proprietary datasets owned by them, federated learning has become a promising approach for organizations to train deep learning models collaboratively. However, conventional federated learning based on a centralized parameter server is susceptible to “recovery” attacks, in which the original data can be recovered if the attacker can collect enough gradients from the organizations. To solve the problem, we first propose a blockchain-based decentralized model training architecture for federated learning, which is more robust than the centralized architecture. Based on this architecture, we develop a joint efficiency and randomness aware gradient aggregation approach. Our real-world experiments show that our design is not affected by a single point of failure. Moreover, it can increase the model accuracy of the participating organization, while mitigating the data privacy disclosure risk and improving the gradient aggregation performance.
Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization

Recent years have seen the advancement of data-driven paradigms in population-based and evolutionary optimization. This reflects on one hand the mere abundance of available data, but on the other hand also progresses in the refinement of previously available machine learning methods. Surprisingly, deep pattern recognition methods emerging from the studies of neural networks have only been sparingly applied. This comes unexpected, as the complex data generated by evolutionary search algorithms can be considered tedious and intractable for manual analysis with mere practical intuitions. In this work, we therefore explore opportunities to employ deep networks to directly learn problem characteristics of continuous optimization problems. Particularly, with data obtained during initial runs of an optimization algorithm. We find that a graph neural network, trained upon a graph representation of continuous search spaces, shows in comparison to more traditional approaches higher validation accuracy and retrieves characteristics within the latent space which are better at distinguishing different continuous optimization problems. We hope that our study can pave the way towards new approaches which allow us to learn problem-dependent algorithm components and recall these from predictions of inputs generated during the run-time of an optimization algorithm.
A New Head Pose Estimation Method Using Vision Transformer Model

In this paper, a self-attention-based Vision Transformer (VIT) method is introduced into estimate human head pose parameters. Firstly, the head pose image is divided into 32X32 patches, each image patch is regarded as a word, and the whole image is treated as a paragraph composed of n words by the VIT. Image recognition can be regarded as the semantic recognition of this paragraph. Next, we redesign the regression VIT to estimate the parameters. Then we select Head Pose Database as the training and validation dataset. The VIT is trained on the enhanced and normalized dataset. Finally, the trained VIT is used to regress the head pose parameters on testing samples. Experimental results show that VIT has high accuracy and good generalization ability for head pose estimation.
Think About Boundary: Fusing Multi-level Boundary Information for Landmark Heatmap Regression

Although current face alignment algorithms have obtained pretty good performances at predicting the location of facial landmarks, huge challenges remain for faces with severe occlusion and large pose variations, etc. On the contrary, semantic location of facial boundary is more likely to be reserved and estimated on these scenes. Therefore, we study a two-stage but end-to-end approach for exploring the relationship between the facial boundary and landmarks to get boundary-aware landmark predictions, which consists of two modules: the self-calibrated boundary estimation (SCBE) module and the boundary-aware landmark transform (BALT) module. In the SCBE module, we modify the stem layers and employ intermediate supervision to help generate high-quality facial boundary heatmaps. Boundary-aware features inherited from the SCBE module are integrated into the BALT module in a multi-scale fusion framework to better model the transformation from boundary to landmark heatmap. Experimental results conducted on the challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature. Code will be available soon.
Network Traffic Anomaly Detection Method Based on CAE and LSTM

Abstract This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the feature extraction capability, and combined with the long short-term memory network to extract temporal features, the effective features extracted in this paper are more comprehensive and less losses compared to the models used in other researches. A comparison of the loss values in the training of CAE (Convolutional Auto-Encoders) and SAE (Stacked Auto-Encoders) in the experiments shows that the loss values of CAE are about one-tenth lower than those of SAE, and the method consisting of Multi-CAE and LSTM for the USTC- TFC2016 dataset was trained with accuracy values up to 99.98%, and the precision, recall and f1-score parameters were also above 99%, outperforming other studies.
An Effective Multi-Task Two-Stage Network with the Cross-Scale Training Strategy for Multi-Scale Image Super Resolution

Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources as well as time-consuming training and testing. What is more, the per-pixel loss measured by L2 and the Peak Signal-to-Noise Ratio do not correlate well with human perception of image quality, since L2 simply does not capture the intricate characteristics of human visual systems. To address these issues, we propose an effective two-stage hourglass network with multi-task co-optimization, which enables the entire network to focus on training and testing time and inherent image patterns such as local luminance, contrast, structure and data distribution. Moreover, to avoid overwhelming memory overheads, our model is capable of performing real-time single image multi-scale super-resolution, so it is memory-friendly, meaning that memory space is utilized efficiently. In addition, in order to best use the underlying structure and perception of image quality and the intermediate estimates during the inference process, we introduce a cross-scale training strategy with 2×, 3× and 4× image super-resolution. This effective multi-task two-stage network with the cross-scale strategy for multi-scale image super-resolution is named EMTCM. Quantitative and qualitative experiment results show that the proposed EMTCM network outperforms state-of-the-art methods in recovering high-quality images.
Categorical representation learning: morphism is all you need

Abstract We provide a construction for categorical representation learning and introduce the foundations of ‘ categorifier ’. The central theme in representation learning is the idea of everything to vector. Every object in a dataset <?CDATA $\mathcal{S}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="script">S</mml:mi> </mml:mrow> </mml:math> can be represented as a vector in <?CDATA $\mathbb{R}^n$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> </mml:math> by an encoding map <?CDATA $E: \mathcal{O}bj(\mathcal{S})\to\mathbb{R}^n$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>E</mml:mi> <mml:mo>:</mml:mo> <mml:mrow> <mml:mi mathvariant="script">O</mml:mi> </mml:mrow> <mml:mi>b</mml:mi> <mml:mi>j</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi mathvariant="script">S</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">→</mml:mo> <mml:msup> <mml:mrow> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> </mml:math> . More importantly, every morphism can be represented as a matrix <?CDATA $E: \mathcal{H}om(\mathcal{S})\to\mathbb{R}^{n}_{n}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>E</mml:mi> <mml:mo>:</mml:mo> <mml:mrow> <mml:mi mathvariant="script">H</mml:mi> </mml:mrow> <mml:mi>o</mml:mi> <mml:mi>m</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi mathvariant="script">S</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">→</mml:mo> <mml:msubsup> <mml:mrow> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> . The encoding map E is generally modeled by a deep neural network . The goal of representation learning is to design appropriate tasks on the dataset to train the encoding map (assuming that an encoding is optimal if it universally optimizes the performance on various tasks). However, the latter is still a set-theoretic approach. The goal of the current article is to promote the representation learning to a new level via a category-theoretic approach. As a proof of concept, we provide an example of a text translator equipped with our technology, showing that our categorical learning model outperforms the current deep learning models by 17 times. The content of the current article is part of a US provisional patent application filed by QGNai, Inc.
Exploring Tablet Interfaces for Product Appearance Authoring in Spatial Augmented Reality

Users acceptance of innovative product appearance authoring tools based on Spatial Augmented Reality (SAR) is still limited due to their perception of a high technology complexity and a low performance/functionality of the current interaction systems. The integration of SAR technologies in professional design activities is still marginal, though many studies in this field have already proved their potential as supporting tools. To overcome this barrier, efficient means for interacting with the digital images projected onto the surfaces of real objects are essential. The aim of the current study is to respond to this demand by proposing and validating three UI configurations displayed by an unique and portable device embedded with a touch screen. These interface layouts, designed to cooperate with the output of the SAR system and to not affect the well-known benefits of its augmented environment, provide different types of visual feedback to the user by duplicating, extending or hiding the information already displayed by the projected mock-up. The experimental study reported here, performed with a panel of 41 subjects, revealed that accuracy, efficiency and perceived usability of the proposed solutions are comparable with each other and in comparison to standard desktop setups commonly used for design activities. According to these findings, the research simultaneously demonstrates (i) the high performances achieved by the touch device when coupled with a SAR system during the execution of authoring tasks, (ii) the capability of the projected mock-up to behave as an actual three-dimensional display for the real time rendering of the product appearance and (iii) the possibility to freely select - according to the users preference, the design task or the type of product - one of the three UI configurations without affecting the quality of the result.
Anomalous Cognition in the Context of Time: Does the Viewer Describe a Probabilistic Future?

In this process-oriented study, we examined the influence of the time dimension on Psi effects in two experimental conditions (present vs. future). For data collection, selected viewers with experience in the remote viewing method gathered information about targets that were distant in space (the present) and time (the future). The present condition was composed of binary truth statements consisting of two possible options related to current world knowledge. The future condition consisted of two options that were not yet determined at the time of viewing, but depended on the outcome of future mixed martial arts fights. According to the associative remote viewing (ARV) method, the binary outcomes of the present and future options were each associated with a photo, which had to be described by the viewers. An independent judge analyzed the viewers’ qualitative reports through binary correspondence ratings amounting to a hit (1) or no hit (0) per trial. Independently of the time condition, a Psi effect could be observed. The hit rates of the judge (0.88 and 0.62 for the present and future, respectively) were significantly higher than the expected value (0.5) under the null hypothesis (present: p &lt; 0.001, ESP = 0.73; future: p = 0.027, ESF = 0.22; binomial distribution). In addition, the hit rates in the two time conditions differed significantly from each other (χ2 = 9.01; df = 1, p &lt; 0.003). The results confirm the hypothesis that Psi is not completely independent of the time dimension and that the hit rate is influenced by a priori target probabilities. With regard to the Informational Psi (IΨ) theory, we will discuss the implications of a probabilistic future for the understanding of Psi effects.
Analysis of Driving Factors of Innovation and Entrepreneurship Based on Time Series Analysis

In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis, so that entrepreneurial traits influence entrepreneurial propensity through entrepreneurial attitude. The test results show that entrepreneurial attitude can better explain the influence of entrepreneurial traits on entrepreneurial propensity. In addition, this paper constructs an analysis model of driving factors for innovation and entrepreneurship, obtains experimental data through questionnaire survey methods, and conducts experimental research in combination with mathematical statistics. From the statistical results, it can be seen that the innovative and entrepreneurial driving factor analysis model based on time series analysis proposed in this paper is effective.
A Two-Level Semi-supervised Clustering Technique for News Articles

The web and social media are overcrowded with news pieces in terms of amount and diversity. Document clustering is a useful technique that is widely used in organizing and managing data into smaller groups. One of the factors influencing the quality of clustering is the way documents are represented. Some traditional methods of document representation depend on word frequencies and create sparse and large-sized document vectors. These methods cannot preserve proximity information between documents. In addition, neural network-based methods that preserve proximity information suffer from poor interpretability. Conceptual text representation methods have overcome the shortcomings of previous methods, but semi-supervised text clustering does not currently use concept-based document representation. This paper presents a two-level semi-supervised text clustering method that uses labeled and unlabeled data simultaneously to achieve higher clustering quality. In the first level, documents are represented based on the concepts extracted from the raw corpus. Second, the semi-supervised clustering process applies unlabeled data to capture the overall structure of the clusters and a small amount of labeled data to adjust the center of the clusters. Experiments on the Reuters-21578 data collection show that the proposed model is superior to other semi-supervised approaches in both text classification and text clustering.
CMSS: Use Low-power IoT Cameras to Monitor Store Shelves

In this paper, we propose a method of using low-power small cameras to detect shelf status. For shopping malls, especially large shopping malls, when to replenish the shelves is very important. Whether they can replenish them immediately or not is directly related to their turnover. Now most shopping malls use manual inspection mode, that is, a waiter is responsible for certain shelves. They will replenish shelves in time when they are need. But this method wastes too much manpower. In our scheme, we use low-power IoT C ameras to M onitor S tore S helves (CMSS). Firstly, we put a specific photo on the back of the shelf and put the camera in front of the shelf to face the photo. When the goods on the shelf are emptied, the camera recognizes the graphics of the photos posted on the shelf in advance, and will send a message to inform the manager that replenishment is needed. For image recognition, we use an ultra low-power FPGA chip and run a convolutional neural networks(CNN) model on it to achieve. The chips and cameras we use are low power consumption. Their power consumption is milliwatts level, which is also the lowest power consumption device we know of running CNN. The power consumption of the entire system is less than 6 mW, and the price is about 22 dollars. Our experimental results show that on a shelf with a width of 40cm, the maximum lateral distance that the system can recognize is about 40cm. Compared with the existing solutions, our solution has the advantages of lower cost, low power consumption and easy maintenance.
CERES: Container-Based Elastic Resource Management System for Mixed Workloads

It is common to deploy multiple workloads in one cluster to achieve high resource utilization, which tends to bring more resource contentions and performance interferences. If the allocable resources cannot satisfy the resource requirements of a task, the task should wait for resources, significantly increasing its scheduling latency. The inappropriate resource requirements may make a running task become a swollen task or a straggler task, which makes many allocated resources underutilized or the task be processed slowly. Therefore, how to guarantee the QoS of various services in the mixed workload deployment cluster is a challenge. Existing solutions preempt the resources from batch jobs to guarantee the resource requirements of latency-sensitive tasks without taking into account the underutilized resources in swollen tasks, which inevitably compromises the performance of batch jobs. Thus, we try to meet the resource requirements of newly incoming latency-sensitive tasks and straggler tasks with the underutilized resources instead of directly preempting the resources of batch jobs.
Optimized Evacuation Plan and Decision Support System Development with Agent-Based Modelling and GIS Analysis for Tsunami Events in Pandeglang, Banten, Indonesia

Sambuaga, R.D. and Lee, H.S., 2021. Optimized evacuation plan and decision support system development with agent-based modelling and GIS analysis for tsunami events in Pandeglang, Banten, Indonesia. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 509–513. Coconut Creek (Florida), ISSN 0749-0208. On December 22, 2018, a tsunami caused by the eruption of the volcanic mountain and partial collapse of the Mt. Anak Krakatoa (The child of Krakatoa) volcano occurred in the Sunda Strait (a strait between Java and Sumatera island), Indonesia. The tsunami in the Sunda Strait resulted in 430 casualties (January 7th, 2019). The aim of this study is to provide optimum evacuation shelters by reducing evacuation time and casualty number caused by tsunami events. Early evacuation is the most significant measure in reducing casualties in tsunami disaster. Panimbang sub-district is chosen as a study area in this research, which it has the highest casualties according to Pandeglang Regional Disaster Management Agency (BPBD Pandeglang). Digital Elevation Model (DEM), information dataset (road network, hospital, worship facilities, schools, population distribution, etc.), and casualties' information are used in the analysis. This study conducts two parts; shelter analysis and agent-based modeling. The service area evaluation of proposed shelter is conducted using network analyst tool in ArcGIS. The important variable in the shelter analysis is the nearest accessibility based on the road network from residential area to proposed shelters. Public facilities such as mosque, school, health center, etc. are used as proposed shelters in the analysis. Ages group, walking speed, travel mode (walk or driving car), etc. are used as parameters and scenarios in the agent-based model. It affects total evacuee in this model.