Reviewerly/exHarmony huggingface dataset:
A federated learning attack method based on edge collaboration via cloud

Abstract Federated learning (FL) is widely used in edge‐cloud collaborative training due to its distributed architecture and privacy‐preserving properties without sharing local data. FLTrust, the most state‐of‐the‐art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high‐dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method.
Multi-axis control of a qubit in the presence of unknown non-Markovian quantum noise

In this paper, we consider the problem of open-loop control of a qubit that is coupled to an unknown fully quantum non-Markovian noise (either bosonic or fermionic). A graybox model that is empirically obtained from measurement data is employed to approximately represent the unknown quantum noise. The estimated model is then used to calculate the open-loop control pulses under constraints on the pulse amplitude and timing. For the control pulse optimization, we explore the use of gradient descent and genetic optimization methods. We consider the effect of finite sampling on estimating expectation values of observables and show results for single- and multi-axis control of a qubit.
Strategies for Fault-Tolerant Tightly-Coupled HPC Workloads Running on Low-Budget Spot Cloud Infrastructures

Cloud providers can rent their spare computing capacity at substantial discounts, reclaiming it whenever there is a more profitable higher-priority request - a business model well known as spot infrastructure market. Users can attain significant cloud investment savings using spot machines, however with the caveat of increasing software complexity, given the fault tolerance requirements of this environment. Improvements in virtualization and network technology, combined with the development of key new software tools, may allow the HPC community to effectively take advantage of cheap cloud resources, cutting expensive maintenance costs. This study aims to evaluate the viability of budget-constrained cloud environments for tightly-coupled MPI applications, exploring both spot and traditional low-budget infrastructures from real public cloud platforms. We propose and evaluate two different fault tolerance strategies tailored for unreliable spot cloud environments: system-level rollback restart with Berkeley Labs Checkpoint/Restart (BLCR) and in-memory rollback restart with User-Level Failure Mitigation (ULFM). We also propose a provider-agnostic empirical method for testing and predicting MPI workloads execution times and cloud infrastructure costs. A detailed cost analysis and performance benchmark of a case-study application is provided, with data gathered from experiments with both spot and persistent machines from AWS and Vultr Cloud, respectively. Our results show that: (i) adequate cluster sizing plays an important role in the overall job execution performance and cost-effectiveness, regardless of the type of selected instances; (ii) fault tolerance strategies based on BLCR may have worse performance than ULFM, but still be costeffective considering software migration costs; (iii) the use of spot infrastructure does not guarantee costs savings depending on the chosen machine flavors and discounts, as experiments with persistent low-budget options attained better cost-effectiveness in some conditions.
Classification of Legal Documents in Portuguese Language Based on Summarization

Legal document classification in Portuguese language is a research area highly benefited by computational intelligence techniques as the availability of better processing with the easiness of digital text recording of juridic processes. Different techniques have been explored to achieve reliable results in real-world conditions; however, the most suitable configuration of methods remains to be an open problem. This study proposes a model consisting of four stages: preprocessing, extractive summarization using page rank algorithm, feature extraction with bag-of-words, and classification with Support Vector Classifier. Testing sessions were conducted using three versions of the model as a mean for comparison and evaluation. The first one was a basic classifier without preprocessing nor summarization stages, the second included preprocessing but not summarization, and the third one was an implementation of the complete proposed model. All three were evaluated using a separated set of examples falling into six different labeled categories and their performance was recorded calculating weighted average precision, recall, F1-score and accuracy values. The best performance obtained was the one presented by the proposed model with precision, recall and F-1 score values of 96% each, which represents a 2% improvement for all of them in comparison to the first version and a 1 % improvement for precision and recall in comparison to the second version. Specially F1-score pointing to the most balanced performance, the proposed model outperformed the versions of it itself excluding some stages, allowing to infer that preprocessing and extractive summarization have positive impacts in the text classification task for Portuguese-written legal documents.
Predictive Analytics of First Blood and Match Outcome in Dota 2

Data analytics in games like Dota 2 can generate various educational and economic benefits. We survey several methods for predicting match outcome and first blood in Dota 2, based solely on pre-match team character selections.
Distance Metric Recommendation for k-Means Clustering: A Meta-Learning Approach

The choice of distance metric impacts the clustering quality of centroid-based algorithms, such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means. Theoretical attempts to select the optimal metric entail deep domain knowledge, while experimental approaches are resource-intensive. This paper presents a meta-learning approach to automatically recommend a distance metric for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means clus-tering that optimizes the Davies-Bouldin score. Three distance measures were considered: Chebyshev, Euclidean, and Manhattan. General, statistical, information-theoretic, structural, and complexity meta-features were extracted, and random forest was used to construct the meta-learning model; borderline SMOTE was applied to address class imbalance. The model registered an accuracy of 70.59%. Employing Shapley additive explanations, it was found that the mean of the sparsity of the attributes has the highest meta-feature importance. Feeding only the top 25 most important meta-features increased the accuracy to 71.57%. The main contribution of this paper is twofold: the construction of a meta-learning model for distance metric recommendation and a fine-grained analysis of the importance and effects of the meta-features on the model's output. The code and dataset are available at https://github.com/memgonzales/meta-learning-clustering.
To Start Automatic Commentary of Soccer Game with Mixed Spatial and Temporal Attention

We watch soccer games because of the excitement, the skill of players, the brand name of soccer teams, etc. However, contribution of the commentary is also indispensable. At this stage of our technology development, it is good to have automatic commentary to be provided by our computer. This paper is on the production of soccer commentary automatically making use of hi-tech and deep learning. There are many aspects on the production. However, we just concentrate on the production of key commentaries. We make use of spatial-temporal representation with 2 stages of operations to design our transformer network. This involves Temporal-Grouped Attention that dynamically separates and groups all channels together for the advantage of extracting temporal domain features, Local-Global Mixed Attention that enforces the model to find relationship between local and global feature representations in one attention structure, and Selective Feature Aggregation to intelligently select the final weights between the two attention networks. Our results provides key commentaries in line with other state-of-the-art approaches but with high potential to be further developed to real-time commentary system.
Nuclei Classification in ER-IHC Stained Histopathology Images using Deep Learning Models

Breast cancer treatment is highly dependent on the carcinoma stage, which was obtained by evaluating the pathological slides and the estrogen receptor status. The Allred score has been manually calculated by the pathologists to represent the percentage and intensity of tumor nuclei. The task can be automated by enabling digital pathology, by classifying the nuclei using learning-based method. We present here a comprehensive analysis of 32 pretrained deep learning models from DenseN et, EfficientN et, InceptionResN et, Inception, ResN et, MobileNet, NasNet, VGG and Xception. The aim of this exper-iment is to identify the best pre-trained model for classifying the negative, weak, moderate and strong nuclei taken from 44 whole slide images of estrogen receptor immunohistochemistry stained histopathology. The highest test accuracy is achieved by DenseNet169 with the measure of 94.91 %. This study will be a basis for the future development of more complex deep learning models with cascading or any combination of the tested models.
FiBHA: Fixed Budget Hybrid CNN Accelerator

Seeking the “sweet spot” in the accuracy-efficiency trade-off is increasing the heterogeneity of state-of-the-art Convolutional Neural Networks (CNNs). Such CNN models exhibit heterogeneity at two levels: intra- and inter-layer-type. Generic accelerators do not capture these levels of heterogeneity. Consequently, researchers have proposed model-specific accelerators with dedicated modules or engines. The proposed accelerators belong to two categories at the two ends of the design spectrum. In the first category, the accelerators contain a minimal number of dedicated engines such that all the layers of one type (e.g. depthwise convolutions) are handled by one engine. In the second, they have one dedicated engine per layer. While the first category addresses the inter-layer-type heterogeneity, it cannot capture the heterogeneity among layers of the same type. The second category is resource-demanding. In this paper, we propose a hybrid architecture that combines design concepts from both categories in a way that captures more heterogeneity than the first category and is more resource-efficient than the second. To derive a hybrid accelerator given a fixed resource budget, we propose a heuristic that splits the CNN and the available resources between the components of the hybrid architecture. The proposed architecture is implemented and evaluated using high-level synthesis (HLS) on an FPGA. For a fixed hardware budget, the hybrid accelerator achieves up to 1.7x and 4.1x of the throughput achieved by state-of-the-art accelerators of the two categories.
Recognition of geological legends on a geological profile via an improved deep learning method with augmented data using transfer learning strategies

Accurately and efficiently legend recognition for geological maps aims to not only helps users quickly obtain the name and meaning of symbols, but also understand the geological profile and establishing a connection between the profile and the contextual text. Detection and recognition entail high complexity and uncertainty because of the diversity of geological legend types and the randomness of legend distribution; thus, automatically recognizing dynamic legends for geological profiles is challenging. Deep learning (DL) algorithms have a strong ability to recognize high-level features in mineral exploration data and have been widely employed for the recognition of multiple image data. In this study, we developed a geological legends identification model, DT-SE-ResNet50, optimized by SENet and pre-trained by ImageNet to form a new model, SE-ResNet50. The SE-ResNet50 model was trained with the constructed database to obtain a new weight. The SE-ResNet50 model with the new weight was then trained by transfer learning with a self-built database to create the DT-SE-ResNet50 model for the identification of geological legends. We also propose an automatic workflow for generating datasets by data enhancement for training geological legend models to deal with the difficulty of lacking of large amount of samples. We present the results of a wide-ranging evaluation of the performance of the proposed method using the constructed geological profile legend dataset. These results show that the proposed method can significantly outperform previous studies, as well as previous methods based on supervised deep learning.
Decentralized Identity Authentication with Auditability and Privacy

Decentralized identity (DID) systems aim to give users full control over their identities by using completely decentralized technologies, such as blockchain or distributed ledgers, as identity providers. However, when user credentials are compromised, it is impossible in existing DID systems for the users to detect credential misuse. In this paper, we propose new DID authentication protocols with two properties: auditability and privacy. The former enables the detection of malicious authentication events, while the latter prevents an adversary from linking an authentication event to the corresponding user and service provider. We present two protocols that achieve auditability with varying privacy and performance guarantees. The first protocol has high performance, but it reveals information about the user. The second protocol achieves full privacy, but it incurs a higher performance overhead. We present a formal security analysis of our privacy-preserving protocols by using the Tamarin prover. We implemented them and evaluated their performance with a permissioned blockchain deployed over the Amazon AWS and a local cloud infrastructure. The results demonstrate that the first protocol is able to support realistic authentication workloads, while the second is nearly practical.
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations

This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in the context of topic models. The data consists of observed multinomial counts of p words across n independent documents. In topic models, the p×n expected word frequency matrix is assumed to be factorized as a p×K word-topic matrix A and a K×n topic-document matrix T. Since columns of both matrices represent conditional probabilities belonging to probability simplices, columns of A are viewed as p-dimensional mixture components that are common to all documents while columns of T are viewed as the K-dimensional mixture weights that are document specific and are allowed to be sparse. The main interest is to provide sharp, finite sample, ℓ1-norm convergence rates for estimators of the mixture weights T when A is either known or unknown. For known A, we suggest MLE estimation of T. Our nonstandard analysis of the MLE not only establishes its ℓ1 convergence rate, but also reveals a remarkable property: the MLE, with no extra regularization, can be exactly sparse and contain the true zero pattern of T. We further show that the MLE is both minimax optimal and adaptive to the unknown sparsity in a large class of sparse topic distributions. When A is unknown, we estimate T by optimizing the likelihood function corresponding to a plug in, generic, estimator Aˆ of A. For any estimator Aˆ that satisfies carefully detailed conditions for proximity to A, we show that the resulting estimator of T retains the properties established for the MLE. Our theoretical results allow the ambient dimensions K and p to grow with the sample sizes. Our main application is to the estimation of 1-Wasserstein distances between document generating distributions. We propose, estimate and analyze new 1-Wasserstein distances between alternative probabilistic document representations, at the word and topic level, respectively. We derive finite sample bounds on the estimated proposed 1-Wasserstein distances. For word level document-distances, we provide contrast with existing rates on the 1-Wasserstein distance between standard empirical frequency estimates. The effectiveness of the proposed 1-Wasserstein distances is illustrated by an analysis of an IMDB movie reviews data set. Finally, our theoretical results are supported by extensive simulation studies.
Diagnosis of heart failure from imbalance datasets using multi-level classification

The incidence of heart failure is continuing to rise, and the mortality rate is high. Chest X-ray (CXR) has irreplaceable advantages in diagnosing heart failure, such as fast, low risk, and cheap. However, excessive CXR images place a huge burden on physicians and create data imbalance problems. Traditional methods, such as random under-sampling, are used to solve the problems. However, the under-sampling method can destroy the integrity of the data distribution. So, it is necessary to have a method that can address data imbalance problems and assist overburdened healthcare systems. This study establishes an automatic heart failure diagnosis system based on deep learning from imbalance datasets. To address the data imbalance problem based on the publicly available CheXpert dataset, this study proposes a method combining under-sampling and instance selection to ensure the integrity of the data distribution. To help physicians better treat heart failure, this study proposes an end-to-end multi-level classification method to diagnose the specific causes of heart failure. On the testing set, our method improves the average accuracy by 3.78% compared to the traditional random under-sampling method, and the accuracy of our end-to-end multi-class classification experiment is 84.44%. The heart failure automated diagnostic system is more efficient and accurate in diagnosing heart failure compared to state-of-the-art methods.
Perceptual Image Hashing Based on Canny Operator and Tensor for Copy-Move Forgery Detection

Abstract Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What’s more, we can directly locate the forgery areas from the hashing for copy-move forged images.
The Innovative Development of Chinese Vocational Education from the Perspective of the Metaverse

The metaverse is an online digital space parallel to the real world, and a ternary-world digital society in which people participate in the virtual reality as a digital identity. It is characterized by immersion, interaction, co-construction and civilization. The metaverse has a broad future to apply to education. The educational metaverse has such characteristics as the deep integration of virtuality and reality, the comprehensive cooperation between students and technology, and the comprehensive connection between schools and society. With the rapid development of various intelligent technologies and the Internet, it provides a strong support for the modernization of vocational education. The educational metaverse constructs rich and realistic scenes similar to the real world, which can provide diverse educational resources, personalized teaching methods, intelligent evaluation methods, and integrated cooperation platforms. It will enhance the adaptability of China's vocational education and improve the cultivation of technical and skilled talents.
Text prediction recurrent neural networks using long short-term memory-dropout

&lt;span lang="EN-US"&gt;Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem "&lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;La Ciudad y los perros&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt;" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.&lt;/span&gt;
Achieving Human Parity on Visual Question Answering

The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper introduces a novel hierarchical integration of vision and language AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding) , which leads to similar or even slightly better results than a human being does on VQA. A hierarchical framework is designed to tackle the practical problems of VQA in a cascade manner including: (1) diverse visual semantics learning for comprehensive image content understanding; (2) enhanced multi-modal pre-training with modality adaptive attention; and (3) a knowledge-guided model integration with three specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.