Reviewerly/exHarmony huggingface dataset:
Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment

Aiming at the problems of security, high repetition rate, and many restrictions of multirobot coverage path planning (MCPP) in an unknown environment, Deep Q-Network (DQN) is selected as a part of the method in this paper after considering its powerful approximation ability to the optimal action value function. Then, a deduction method and some environments handling methods are proposed to improve the performance of the decision-making stage. The deduction method assumes the movement direction of each robot and counts the reward value obtained by the robots in this way and then determines the actual movement directions combined with DQN. For these reasons, the whole algorithm is divided into two parts: offline training and online decision-making. Online decision-making relies on the sliding-view method and probability statistics to deal with the nonstandard size and unknown environments and the deduction method to improve the efficiency of coverage. Simulation results show that the performance of the proposed online method is close to that of the offline algorithm which needs long time optimization, and the proposed method is more stable as well. Some performance defects of current MCPP methods in an unknown environment are ameliorated in this study.
Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs

Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.
High-Speed Privacy Amplification Algorithm Using Cellular Automate in Quantum Key Distribution

Privacy amplification is an important step in the post-processing of quantum communication, which plays an indispensable role in the security of quantum key distribution systems. In this paper, we propose a Cellular Automata-based privacy amplification algorithm, which improves the speed of key distribution. The proposed algorithm is characterized by block iteration to generate secure key of arbitrary length. The core of the algorithm in this paper is to use the property that Cellular Automata can generate multiple new associated random sequences at the same time to carry out bit operations for multiple negotiation keys in the meantime and calculate in turn, so as to quickly realize the compression of negotiation keys. By analyzing the final key, the proposed algorithm has the advantages of fast key generation speed and high real-time performance. At the same time, the results of the NIST randomness test and avalanche test show that the algorithm has good randomness performance.
Towards publishing directed social network data with <i>k</i>‐degree anonymization

Summary Anonymization is a practical solution for preserving user's identity privacy before data publishing. There are various anonymity techniques can be applied to maintain data utility of micro‐data and social networks, however these methods lead to a high runtime or low anonymous graph utility. In this article, an efficient, utility‐preserving approach has been proposed to reduce anonymization runtime as well as the amount of information loss incurred by graph anonymization. We craft our anonymization algorithm by combining greedy partition‐based aggregating with multi‐dimensional sorting as main heuristic tools. The proposed algorithm generates a partial order of the vertices so that the vertex at top rank and another vertex at bottom rank can never be aggregated in the same group, the runtime is reduced. Greedy partition‐based aggregating is employed to create ‐anonymous clusters which minimizing information loss. Experimental results on real‐world datasets show the proposed method has good performance and is superior to the existing methods.
Obtaining Parallel Sentences in Low-Resource Language Pairs with Minimal Supervision

Machine translation relies on parallel sentences, the number of which is an important factor affecting the performance of machine translation systems, especially in low-resource languages. Recent advances in learning cross-lingual word representations from nonparallel data by machine learning make a new possibility for obtaining bilingual sentences with minimal supervision in low-resource languages. In this paper, we introduce a novel methodology to obtain parallel sentences via only a small-size bilingual seed lexicon about hundreds of entries. We first obtain bilingual semantic by establishing cross-lingual mapping in monolingual languages via a seed lexicon. Then, we construct a deep learning classifier to extract bilingual parallel sentences. We demonstrate the effectiveness of our methodology by harvesting Uyghur-Chinese parallel sentences and constructing a machine translation system. The experiments indicate that our method can obtain large and high-accuracy bilingual parallel sentences in low-resource language pairs.
Higher-Order MDS Codes

An improved Singleton-type upper bound is presented for the list decoding radius of linear codes, in terms of the code parameters [n, k, d] and the list size L. L-MDS codes are then defined as codes that attain this bound (under a slightly stronger notion of list decodability), with 1 -MDS codes corresponding to ordinary linear MDS codes. Several properties of such codes are presented, one of which is that the 2-MDS property is preserved under duality. Finally, explicit constructions for 2-MDS codes are presented through generalized Reed-Solomon (GRS) codes.
The platypus of the quantum channel zoo

A key objective of quantum information theory is to understand quantum channels and their capacities. Here we study a remarkably simple, low-dimensional, single-parameter family of quantum channels with exotic quantum information-theoretic features. We focus on the simplest example from this family, a qutrit-to-qutrit channel intuitively obtained by hybridizing together a simple degradable channel with a completely useless qubit channel. Such hybridizing makes this channel’s capacities behave in a variety of interesting ways. For instance, the private and classical capacity of this channel coincide and can be explicitly calculated, even though the channel lies outside any previous class with calculable capacities. Moreover, the quantum capacity of the channel can be computed explicitly, given a clear and compelling conjecture is true. This "spin alignment conjecture", which may be of independent interest, is proved in certain special cases and backed numerically in certain other cases. Finally, we generalize the qutrit channel; the resulting channels and their capacities display similarly rich behavior. Our companion paper [22] demonstrates superadditivity when transmitting quantum information jointly across our qutrit channel used with a variety of assisting channels, in a manner unknown before.
Generalized Probability Density Function Estimation via Convex Optimization

A longstanding problem in statistics pertains to the estimation of probability density functions of continuous random variables from a finite set of their samples. In this paper, we propose a new parametric probability density function estimator based on convex programming. Our formulation decomposes the unknown distribution as a Gaussian penalty function plus an error function, which is then expanded by multi-scale wavelet functions (specifically frames) such as B-Spline and Mexican Hat wavelets. To recover the wavelet coefficients in the error function, a convex quadratic program is formulated which takes into account the positivity of the probability density function-through a linear constraint. The proposed decomposition model is shown to facilitate an accurate estimation of the probability density functions of interest.
Encrypted Network Traffic Classification in SDN using Self-supervised Learning

Network traffic classification has a huge application in software-defined networking (SDN) where we talk about more control over the network traffic. With the increase of encrypted protocols in the network, the problem of traffic classification has become extremely challenging. Many researchers have proposed different techniques to do traffic classification. This demo paper presents an application of our proposed method for traffic classification in an SDN environment. The proposed method leverages one of the self-supervised learning approaches, an emerging field of deep learning, to classify network traffic. This paper shows that the proposed method can outperform the corresponding supervised approach by $\sim 2$% in terms of accuracy using data collected from an SDN testbed. Furthermore, an SDN application is developed to show that the trained model is able to classify real-time traffic.
c<sup>3</sup>-Locally Testable Codes from Lossless Expanders

A locally testable code (LTC) is an error correcting code with a property tester. The tester tests if a word is a codeword by reading constant random bits and rejects the word with probability proportional to the distance from the word to the closest codeword. An important open question until recently is whether there exist c <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -LTCs which are LTCs with a constant rate, constant relative distance, and constant locality. In this work, we construct a new LTC family using 1-sided lossless expanders and balanced products.
Byzantine Attack Identification in Distributed Matrix Multiplication via locally testable codes

Coded computing has proved its efficiency in handling a straggler issue in distributed computing framework. However, in a coded distributed computing framework, there may exist Byzantine workers who send the wrong computation results to a master to contaminate the overall computation output. Therefore, it is essential to identify Byzantine workers from their computation results in coded computing. In this paper, we consider Byzantine attack identification problem in coded computing for distributed matrix multiplication tasks. We propose locally testable codes which facilitate the efficient Byzantine attack identification, and suggest a hierarchical group testing method for Byzantine attack identification. We show that our scheme requires smaller number of tests than the conventional group testing methods for the existing coded computing schemes.
Norm-trace-lifted codes over binary fields

In this paper, we introduce norm-trace-lifted codes over binary fields, which are codes with locality and high availability based on the norm-trace curve over the field ${\mathbb{F}_{{2^r}}}$. While they are inspired by Hermitian-lifted codes, norm-trace-lifted codes are easier to define and provide some potential advantages in terms of locality, meaning the number of symbols required to recover another, or alphabet size.
An Information-Theoretic Characterization of Pufferfish Privacy

Pufferfish privacy (PP) is an appealing generalization of differential privacy (DP), that offers flexibility in specifying sensitive information and integrating domain knowledge into the privacy definition. Inspired by the illuminating equivalent formulation of DP in terms of mutual information proposed by Cuff and Yu [1], this work explores PP through the lens of information theory. We provide an equivalent information-theoretic formulation of PP as the conditional mutual information between the mechanism and the secret, given the public information. This formulation lends well for an information-theoretic analysis, and we use it to prove convexity, composability, and post-processing properties for PP mechanisms. We also leverage our formulation to derive noise levels for the Gaussian PP mechanisms. The obtained mechanisms are applicable under relaxed assumptions and provide improved noise levels in some regimes, compared to existing approaches,
Run Length Limited de Bruijn Sequences for Quantum Communications

The de Bruijn based timing and synchronization (dBTS) system has been proposed and studied recently for some channels require reliable synchronization, such as quantum communication. To avoid a long period of no-pulse in the dBTS system, we propose to study the run length limited de Bruijn sequences which not only are run length limited sequences but also can be used to locate the location of any sub-string. Such subjects are expected to have various applications and they also present some interesting theoretical questions in combinatorics, algorithms and coding theory.In this paper, we are the first to provide an explicit formula of the maximal length of the run length limited de Bruijn sequences. Besides that, using Lyndon words, we present an efficient construction of a run length limited de Bruijn sequence with the maximal length. Furthermore, we also provide a sub-linear decoding algorithm which can locate the position of an arbitrary sub-string.
Computational Code-Based Privacy in Coded Federated Learning

We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their data with the faster ones and do not participate in the learning process. The proposed scheme employs code-based cryptography to ensure computational privacy of the private data, i.e., no device with bounded computational power can obtain information about the other devices’ data in feasible time. For a scenario with 25 devices, the proposed scheme achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively, for an accuracy of 95% on the MNIST dataset compared with conventional mini-batch FL.
Automatic Liver Segmentation Method from CT Images Based on Improved 3D U-Net

Surgery is the best way to treat liver cancer, automatic and precise segmentation of the liver from the patient's abdominal CT image plays an important role in guiding the surgical procedure. It is difficult to segment the liver, not only because of the similar grayscale values and the low contrast between the liver and surrounding organs, but also the quite different shape of the liver in CT images between different individuals. To address the above problems, an automatic liver segmentation network based on multi-scale feature fusion (MSFF-Net) was proposed. The model was based on the basic network structure of 3D U-Net. At first, the residual dilated convolution module was used to expand the receptive field and obtain multi-scale information. In addition, the residual attention module was used to fully fuse the multi-scale features in the upper and lower networks to improve the network's attention to the detailed information of the target region. Finally, a hybrid loss function was designed for the data label imbalance problem, which was combined with deep supervision to improve the segmentation effect. The ablation study was conducted on the public datasets LiTS 2017 and 3DIRCADb. The model has performed on the LiTS 2017 dataset, Dice per Case and Dice Global metrics are 0.963 and 0.964 with an increase of 3.4% and 3.1% higher compared with the baseline network respectively, and the remaining auxiliary evaluation metrics were also relatively high. The Dice per Case and Dice Global evaluation metrics on the 3DIRCADb dataset were 0.961 and 0.961 with an increase of 3.3% and 3.1% higher compared with the baseline network respectively, which further demonstrates the effectiveness and generalization ability of the proposed method.
Speech Recognition Using Double Data Augmentation Strategy

Nowadays, deep learning-based voice recognition technology has achieved incredible advancements, significantly increasing the accuracy of speech recognition. However, this type of technology requires a huge amount of training data to prevent the model from becoming overfit. To address this issue, this article suggests a dual data augmentation strategy for voice recognition. To begin, the data set is enhanced using the vocal tract length perturbation (VTLP) technique, and then the data set is enhanced again using noise perturbation technology based on genetic algorithms. To accomplish the objective of speech recognition, the DeepSpeech2 model is trained as the acoustic model. The experimental results indicate that the proposed strategy reduces the character error rate (CER) by 3.61 percent when compared to the baseline and by about 0.92 percent when compared to vtlp.
Automatic Detection of Chinese Generated Essayss Based on Pre-trained BERT

The text generator based on the pre-trained language model has powerful generation ability., and essays generator based on pre-trained language model produces essays of high quality. Essays generators may be misused., for example by making some changes on the basis of the generated document., a essays of acceptable quality can be obtained. Corresponding efficient detection methods need to be developed. First., we built a essays text generator based on GPT-2 with training data., and then developed a generative essays detector with the pretrained language model BERT using the generated data and real data. Experiments show that the detector is 88% accurate for random sampling and 92% accurate for top-k sampling. Therefore., the detector based on the pretrained language model performs well and is an effective essays generation detector.
Research on Relation Extraction of Fusion Entity Enhancement and Shortest Dependency Path based on BERT

Deep learning models rely on single word features and location features of text to achieve good results in text relation extraction tasks. However, previous studies have failed to make full use of semantic information contained in sentence dependency syntax trees, and data sparseness and noise propagation still affect classification models. The BERT(Bidirectional Encoder Representations from Transformers) pretrained language model provides a better representation of natural language processing tasks. And entity enhancement methods have been proved to be effective in relation extraction tasks. Therefore, this paper proposes a combination of the shortest dependency path and entity-enhanced BERT pre-training language model for model construction to reduce the impact of noise terms on the classification model and obtain more semantically expressive feature representation. The algorithm is tested on SemEval-2010 Task 8 English relation extraction dataset, and the F1 value of the final experiment can reach 0. 881.
Recognition and detection of ports and ships based on image application and non local feature enhancement

Remote sensing image target detection has a wide application demand in the civil field. Using remote sensing image to supervise port facilities, ships and other targets is of great significance. With the rapid development of deep learning, target detection has made remarkable commercial application achievements in the general field. The target detection method of remote sensing image originates from the target detection method in the general field. Combined with the characteristics of remote sensing image, the application of deep learning target detection method in the field of remote sensing image is the main method of target detection in recent years. Firstly, the coarse-grained remote sensing target detection is gradually enhanced from the coarse-grained feature of RC fast to the fine-grained feature of remote sensing. Then, the non local feature enhancement module is used to obtain the features after non local feature enhancement. Finally, the scale of the features enhanced by nonlocal features is adjusted in turn, and the results are used as output. The result extracts the features of the horizontal candidate box area, learns the geometric features of rboi, and roughly adjusts the bbox to obtain the rotating candidate box, so as to make up for the lack of correction samples and reduce the impact of the reduction of the number of positive samples on the performance of the detector. Use the above methods to design and develop the software platform. It realizes the monitoring and display of port ships, silos and other objects.
Complexity bounded classification of fish-eye distorted objects with micro-controllers

Since 2019, tiny machine learning has imposed itself everywhere as an innovative technology trend deployed at the edge and has been pervasive in many IoT applications. One interesting, addressed by this work, is related to the welfare of the laboratory animals that could be preserved by acquiring and classifying some image data to monitor object’s presence in their cages. For example mice and rats activities such as drinking and eating can be indicators of their state of health. In that context, $\mu$BottleNet has been developed to classify the presence, or the absence of the water bottle, while $\mu$FoodNet to classify the level of the food into the feeder. Those neural networks (NNs) achieved 99.8% accuracy. At our best knowledge, no datasets were available to support this study and therefore we built three image datasets to train the neural networks on both Bottle and Food objects. From image capture to the inferences execution, the tasks have been carried out by STM32L4 (ultra-low-power that consumes 120 $\mu$A/MHz) and STM32H7 micro controller units (MCUs). Both NNs have been designed to fit into constrained MCU resources. Special attention has been given to the on-chip memory occupation to ensure the RAM footprint was 39.44 KBytes. To evaluate and test on the field the performances of these models (and against to the MobileNetV2 one), a graphical user interface (GUI), has been developed, capable of rendering, the validation and test results of inference runs on the MCUs. K-fold cross validation has been run and the resulting performances of the two NNs were compared to the MobileNetV2 confirming that the $\mu$BottleNet and the $\mu$FoodNet reached and exceeded the accuracy achieved by a more complex NN architecture.
Students’ Perceptions of Android-Based Interactive Multimedia in Learning Biology

Multimedia that you can interact with is an important part of learning. Multimedia can be used as a learning tool to improve the quality of learning. Learning media can be used to try out different ways to teach and learn. Interactive media made for Android helps students learn more. Using this material makes a big difference in how well students learn. The goal of this study is to find out how students feel about using interactive multimedia based on Android in Biology classes at Senior High Schools after COVID-19. This study was done with 60 students from Mts Ar Royan Aek Nabara Labuhanbatu. They all filled out a questionnaire. Students were sent a questionnaire to gather information, and the results were looked at. According to the perception findings, 67 percent of students have used interactive multimedia based on Android, 69 percent of teachers use audiovisual tools to teach ideas, and 78 percent of respondents prefer to use interactive multimedia. This shows that using Android-based interactive multimedia apps could improve the quality and effectiveness of biology education during the COVID-19 epidemic.